Updated on July 2, 2024

THE 10TH ANNUALInternational Conference on
Smart Computing (SmartComp)
Osaka, Japan
29th June - 2nd July 2024
https://smartcomp.w.waseda.jp/

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THE 10TH ANNUALInternational Conference on
Smart Computing (SmartComp)
General Co-Chairs
Hayato Yamana Waseda University, Japan
Franca Delmastro IIT-CNR, Italy

Technical Program Committee Co-Chairs
Dario Bruneo Univ. of Messina, Italy
Dirk Pesch Univ. College Cork, Ireland

Conference Sponsors

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Welcome Message
The tenth IEEE International Conference on Smart Computing (SmartComp 2024),
sponsored by the IEEE Computer Society, will be held in-person in Osaka, Japan. It
continues the tradition of the previous editions in presenting high-quality research andtechnology in smart and connected computing.
This year, the conference program covers four days of stimulating content andpresentations. This includes a first day with five thematic workshops and a common
keynote, and three days of the main conference program with six research paper sessions
and one industry paper session. The main conference program also includes two keynote
talks, one panel, a poster and demo session, a PhD forum, as well as four excellent
tutorials.
IEEE SmartComp 2024 received 81 full manuscript submissions and 4 submissions to theIndustry track. From these, we accepted 26 papers for the main track and 2 papers for the
Industry track. The industry track was organized by Kyoungsook Kim (AIST, Japan) and
Eduard Martin (Telefonica Research, Spain).
The organization of such a complex event requires a major effort, and we wish to express
our sincere appreciation to the Organizing Committee members for their excellent work,and to the Steering Committee, including Prof. Giuseppe Anastasi, Prof. Jiannong Cao, andProf. Sajal Das, for providing their guidance to ensure that SmartComp 2024 satisfies thehigh-quality standards of this conference series.
We are excited to present the three keynote talks: the first one is entitled “Toward
Enhancing Digital Resiliency” by Dr. Masugi Inoue (National Institute of Information andCommunications Technology (NICT), Japan); the second one by Prof. Niki Trigoni (Universityof Oxford, UK) entitled “Cyber Physical Systems in healthcare, transport and emergency
response”, and the last one by Prof. Paul Lukowicz (DKFI and University of Kaiserslautern,
Germany) entitled “25 years later: Delivering the promise of wearable computing withgenerative AI”.
All of them are distinguished experts with a long history of outstanding contributions to thecommunity.
Furthermore, organized by Satoko Itaya (NICT, Japan) and Stephan Sigg (Aalto University,
Finland), SmartComp 2024 features a highly interesting panel entitled “Generative, Creative,
Cooperative - The transformative element of AI for Smart Computing”.
The main conference is complemented by a Poster and Demo session organized by Yuki
Matsuda (NAIST, Japan) and Francesco Betti Sorbelli (University of Perugia, Italy), and a PhD
forum organized by Shameek Bhattacharjee (Western Michigan University, USA) and
Debashri Roy (University of Texas at Arlington, USA).
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Our special thanks go to the workshop co-chairs Jin Nakazawa (Keio University, Japan) andEirini Eleni Tsiropoulou (University of New Mexico, USA) for their outstanding work insetting up a very interesting program with five workshops covering several hot topics insmart computing research areas, including big data, IoT Security, artificial intelligence,
smart agriculture, smart cities and services, and digital and mobile health.
We thank the Tutorial co-chairs, Keiichi Yasumoto (NAIST, Japan) and Meiyi Ma (Vanderbilt
University, USA), for their outstanding contribution to the organization of four strong
hands-on interactive tutorials on the following topics: The Internet of Bio-Nano Things-Smart
Computing in the Human Body from Stefan Fischer (University of Lübeck), Contactless
Physiological Health Sensing: Challenges, Solutions & Opportunities from Nirmalya Roy(University of Maryland Baltimore County), Advancing Smart Computing: A Comprehensive
Tutorial to 3D Point Clouds from Tatsuya Amano (Osaka University) and Hamada Rizk (Osaka
University, Tanta University), and Science of Cyber Physical Security in Smart Living
Applications from Sajal K. Das (Missouri University of Science and Technology) and Shameek
Bhattacharjee (Western Michigan University).
We also wish to thank all the other members of the Organizing Committee who contributed
to making the 10th edition of SmartComp a big success. Specifically, Hirozumi Yamaguchi(Osaka University, Japan), Manato Fujimoto (Osaka Metropolitan University, Japan), andHiroki Kudo (Kyoto Tachibana University, Japan) provided invaluable help in localorganization and registration. Masato Oguchi (Ochanomizu University) and Maciej
Zawodniok (Missouri University of Science & Technology, USA) with their great experiencein Finance matters have been of tremendous support. Sunyanan Choochotkaew (IBMResearch, JAPAN) and Cheng-Hsin Hsu (National Tsinghua University, Taiwan) managed the
publication of the conference proceedings in a timely manner, coordinating with chairs ofspecific events. Ruixiao Li (Waseda University, JAPAN) and Takuya Suzuki (Waseda
University) designed the webpage and provided constant and timely updates to it. Takuro
Yonezawa (Nagoya University, JAPAN) and Ella Peltonen (University of Oulu, Finland) as
publicity and social media co-chairs, widely and effectively publicized the call for papers
and the conference program. We thank all the student volunteers who helped us
orchestrate the individual sessions of the conference.
We are extremely grateful towards the National Science Foundation for supporting US
based students to travel to the conference through 12 student travel grants.
A special thanks to IEEE Computer Society and Missouri S&T supported by NSF (forUS-based student travel), and Tateisi Science and Technology Foundation for co-sponsoring
the event.
We are excited to present an excellent program and wish all attendees of SmartComp 2024
a very enjoyable and professionally fruitful experience. We look forward to meeting you in
Osaka.

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May 18th , 2024

Franca Delmastro

Hayato Yamana

IIT-CNR, Italy

Waseda University, Japan
General Co-Chairs

Dario Bruneo

Dirk Pesch

University of Messina, Italy

University College Cork, Ireland
TPC co-chairs

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Table of Content
Welcome Message...................................................................................................................2
Table of Content......................................................................................................................5
General Information............................................................................................................... 7
Wireless Internet Connection......................................................................................................7
Poster and Demos........................................................................................................................ 7
Conference Banquet.....................................................................................................................7
Conference Reception............................................................................................................. 7
About Osaka............................................................................................................................ 7
Conference Maps & Directions.............................................................................................. 8
Conference Venue.........................................................................................................................8
Meeting Floor - 12F................................................................................................................11
Meeting Floor - 11F..................................................................................................................... 12
Meeting Floor - 10F............................................................................................................... 13
Meeting Floor - 8F................................................................................................................. 14
Banquet (JULY 1, 2024)........................................................................................................ 15
Reception (JUNE 29, 2024)...................................................................................................18
Conference Organization Committee...................................................................................... 20
Organization Committee........................................................................................................ 20
Technical Program Committee...............................................................................................21
Program Agenda....................................................................................................................25
Saturday, June 29th.................................................................................................................... 25
Sunday, June 30th....................................................................................................................... 27
Monday, July 1st.......................................................................................................................... 29
Tuesday, July 2nd........................................................................................................................ 31
Keynote Presentations......................................................................................................... 33
Invited Speaker.......................................................................................................................... 38
Tutorial........................................................................................................................................40
Panel Session............................................................................................................................ 46
Workshops................................................................................................................................. 49
8th International Workshop on Big Data and IoT Security in Smart Computing (BITS2024)............................................................................................................................................. 49
10th Workshop on Sensors and Smart Cities (SSC)............................................................... 51
3rd International Workshop on Smart Agriculture for the Environmental Emergency(SmartAgr 2024).......................................................................................................................... 53
9th IEEE Workshop on Smart Service Systems (SmartSys)................................................... 55
1st International Workshop on Digital and Mobile Smart Health Systems
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6(DM-SmartHealth 2024)............................................................................................................. 56
Main Technical Sessions.......................................................................................................57
Industry Session........................................................................................................................73
PhD Forum............................................................................................................................. 74
Posters | Demos.......................................................................................................................76
Posters...................................................................................................................................76
Demos................................................................................................................................... 76
Local Restaurants & Attractions......................................................................................... 78

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General Information
Wireless Internet Connection
TBA

Poster and DemosThe poster and demo session will be held in the Foyer next to the conference hall locatedon the 12th floor between 16:45 – 18:00 on Sunday, June 30th (see map on page 8). The
presenters are required to display their poster by noon on Sunday, June 30th

Conference Banquet
The conference banquet will be held from 18:30 to 21:00 on the evening of July 1st at theLandmark Square Osaka (1-1 Osaka-Castle, Chuo-ku, Osaka-city) (6 Km from the conference
center). It takes around 45 min from the conference venue by train and walk. You can findmore detailed information on page 14.

Conference Reception
The reception, Japanese boat course, will be held from 17:45 to 20:00 on the evening ofJune 29th. Please gather at the Hachikenya Boat Dock no later than 17:40 (1-1 Tenmabashi
Kyomachi, Chuo-ku, Osaka-city) (3.5 Km from the conference center). It takes 20 to 25 minfrom the conference venue by train and walk. You can find more detailed information on
page 15.
Note: As the reception is a boat cruise, the number of people allowed on board is limited.
Please make sure your name has been added to the name list at the registration desk.

About Osaka
OSAKA INFOInternational Conference on Smart Computing 2024 Osaka Japan8

Conference Maps & Directions
Conference Venue
OSAKA INTERNATIONAL CONVENTION CENTER
5-3-51, Nakanoshima Kita-ku, Osaka 530-0005 JAPAN
Tel：+81-06-4803-5555
Link: https://www-gco-co-jp/© Osaka Convention & Tourism Bureau
Find more details about terms of use: https://osaka-info.jp/en/photo_library/

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AccessBy public transportation, you can check the link below.
https://www.gco.co.jp/visitor/access/
The following guide for your attendance starting from Osaka Station, involving multiple
transfers and walking to reach the Osaka International Convention Center:
1. Start at Osaka Station: Begin at Osaka Station, one of the major transportation hubs
in the city.
2. Walk to Nishi-Umeda Station: Instead of taking a train, walk to Nishi-Umeda Station.
This walk takes about 5 minutes as the stations are quite close to each other within
the bustling Umeda district.
3. From Nishi-Umeda Station to Higobashi Station: At Nishi-Umeda, board the
Yotsubashi Line, marked in blue, towards Suminoekoen. Travel to Higobashi Station,
which is just one stop away and takes about 2 minutes.
4. Transfer at Higobashi Station to Watanabebashi Station: Once at Higobashi Station,
stay on the Yotsubashi Line and head towards Watanabebashi Station, the very next
stop. The journey is brief, typically lasting only 1-2 minutes.
5. Switch to the Keihan Nakanoshima Line at Watanabebashi Station: At
Watanabebashi, transfer to the Keihan Nakanoshima Line. Take a train towards
Temmabashi and get off at Nakanoshima Station, which is also a short ride of about
2 minutes.
6. Walk to Osaka International Convention Center from Nakanoshima Station: After
exiting Nakanoshima Station, it’s a 5-minute walk to the Osaka International
Convention Center. You can walk west directly along the road or choose a morescenic route through Nakanoshima Park.

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Meeting Floor - 12F

International Conference on Smart Computing 2024 Osaka Japan

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Meeting Floor - 11F

International Conference on Smart Computing 2024 Osaka Japan

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Meeting Floor - 10F

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Meeting Floor - 8F

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Banquet (JULY 1, 2024)
The conference banquet will be held on the evening of July 1st at "The LANDMARK SQUAREOSAKA," a stately historical building featuring classical red bricks in Romanesque style. The
venue is located in Osaka Castle Park, a vast urban park in the heart of Osaka. From thebanquet hall, guests can enjoy views of Osaka Castle, one of the city's most iconic symbols.
Visitors can spend a fantastic evening admiring the majestic castle tower, which is rich inhistory.
Formoredetails,
pleasevisit
thefollowing
website:https://www.landmark-osaka.com/

Route to Banquet Venue:
Take a train from Keihan Nakanoshima Station, which is a four-minute walk from theconference venue, and get off at Keihan Tenmabashi Station, which is four stops away.
After that, take Exit #14 of Tenmabashi Station and walk to the banquet venue, which willtake about 23 minutes on foot.

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Exterior view of Banquet Venue

Banquet Area (LOTUS)International Conference on Smart Computing 2024 Osaka Japan

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The LANDMARK SQUARE OSAKA Logo

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Reception (JUNE 29, 2024)
The reception will be held on the evening of June 29th. Hachikenyahama Pier, the starting
point of the reception, is known as a prosperous landing place for the 30-koku boat, whichconnected
Kyoto
and
Osaka
duringtheEdoperiod(reference:https://osaka-info.jp/spot/hachikenya-boat-dock/). For this reception, we will offer a
two-hour Japanese boat tour of the Okawa River, which will take you to Hachikenyahama,
Temmabashi, the Osaka Mint Bureau, Sakuranomiya Bridge, and Kema Lock Gate. Enjoy
the cityscape and the extraordinary elegance of the boat ride.

Route to Starting Point of Reception:
Take a train from Keihan Nakanoshima Station, which is a four-minute walk from theconference venue, and get off at Keihan Tenmabashi Station, which is four stops away.
After that, take Exit #17 or #18 of Tenmabashi Station and walk to the reception venue,
which will take about 1 minute on foot.

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Appearance of the Japanese-style Boat (Note: The type of boat may change on the
day of the event)Inside the Japanese-style Boat

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Conference Organization Committee
Organization Committee
General Co-Chairs
Hayato Yamana, Waseda University, Japan
Franca Delmastro, IIT-CNR, Italy

Technical Program Committee Co-Chairs
Dario Bruneo, University of Messina, Italy
Dirk Pesch, University College Cork, Ireland

Finance Co-Chairs
Masato Oguchi, Ochanomizu University, Japan
Maciej Zawodniok, Missouri University of Science and Technology, United States

Local-Organization and Registration Co-Chairs
Hirozumi Yamaguchi, Osaka University, Japan
Manato Fujimoto, Osaka Metropolitan University, Japan
Hiroki Kudo, Kyoto Tachibana University, Japan

Workshop Chairs
Jin Nakazawa, Keio University, Japan
Eirini Eleni Tsiropoulou, University of New Mexico, United States

Tutorial Co-Chairs
Keiichi Yasumoto, Nara Institute of Science and Technology, Japan
Meiyi Ma, Vanderbilt University, United States

Panel Co-Chairs
Satoko Itaya, National Institute of Information and Communications Technology, Japan
Stephan Sigg, Aalto University, Finland

Industry Track Chair
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Kyoungsook Kim, National Institute of Advanced Industrial Science and Technology, Japan
Eduard Marin, Telefonica Research, Spain

Publicity and Social Media Co-Chairs
Takuro Yonezawa, Nagoya University, Japan
Ella Peltonen, University of Oulu, Finland

Publication Chair
Sunyanan Choochotkaew, IBM Research, Japan
Cheng-Hsin Hsu, National Tsing Hua University, Taiwan

Web Chair
Ruixiao Li, Waseda University, Japan
Takuya Suzuki, Waseda University, Japan

Poster and Demo Chairs
Yuki Matsuda, Nara Institute of Science and Technology, Japan
Francesco Betti Sorbelli, University of Perugia, Italy

PhD Forum Chairs
Shameek Bhattacharjee, Western Michigan University, United States
Debashri Roy, University of Texas at Arlington, United States

Steering Committee Chairs
Giuseppe Anastasi, University of Pisa, Italy
Jiannong Cao, Hong Kong Polytechnic University, Hong Kong
Sajal K. Das, Missouri University of Science and Technology, United States

Technical Program Committee
Main Research Track
Amrita Anam, Amazon
Christian Becker, Universität Stuttgart
Paolo Bellavista, University of Bologna
Francesco Betti Sorbelli, University of Perugia
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Shameek Bhattacharjee, Western Michigan University
Shuvra Bhattacharyya, University of Maryland
Chiara Boldrini, IIT-CNR
Georgios Bouloukakis, Telecom SudParis
Tristan Braud, The Hong Kong University of Science and Technology
Claudio Cicconetti, IIT-CNR
Gabriele Civitarese, University of Milan
Alexandre da Silva Veith, Nokia Bell Labs
Luca Davoli, University of Parma
Fabrizio De Vita, University of Messina
Jyotirmoy Deshmukh, University of Southern California
Ivana Dusparic, Trinity College Dublin
Schahram Dustdar, Vienna University of Technology
Salma Elmalaki, University of California, Irvine
Stefan Fischer, University of Lübeck
Huber Flores, University of Tartu
Nikolaos Georgantas, INRIAPaul Grace, Aston University
Estrid He, RMIT
Salil Kanhere, UNSW Sydney
Ulf Kulau, Hamburg University of Technology
Stephen Lee, University of Pittsburgh
Zhi Liu, The University of Electro-Communications
Lauri Lovén, University of Oulu
Takuya Maekawa, Osaka University
Gaia Maselli, Sapienza University of Rome
Fidan Mehmeti, Technical University of Munich
Jin Nakazawa, Keio University
Kien Nguyen, Chiba University

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Le Nguyen Ngu Nguyen, University of Oulu
Petteri Nurmi, University of Helsinki
Ella Peltonen, University of Oulu
Antonio Puliafito, University of Messina
Susan Rea, Nimbus Research Centre
Francesco Restuccia, Northeastern University
Francesca Righetti, University of Pisa
Nirmalya Roy, University of Maryland Baltimore County
Gregor Schiele, University of Duisburg-Essen
Francisco Silva, UFMA
Simone Silvestri, University of Kentucky
Eirini Eleni Tsiropoulou, University of New Mexico
Carlo Vallati, University of Pisa
Hirozumi Yamaguchi, Osaka University
Keiichi Yasumoto, Nara Institute of Science and Technology

Industry Track
Steven Liang, University of Calgary
Suryadipta Majumdar, Concordia University
Luis Munoz, Telefonica Research
Yoshihiro Osakabe, Hitachi Ltd.
Martin Saerbeck, TUV SUD
Eric Simmon, National Institute of Standards and Technology
Gürkan Solmaz, NEC Laboratories Europe
Dimitra Tsigkari, Telefonica Research

Poster/Demo Track
Hyuckjin Choi, Kyushu University
Federico Corò, University of Padova
Yu Enokibori, Nagoya University
Sajjad Ghobadi, University of Perugia
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Teruhiro Mizumoto, Osaka University
Yugo Nakamura, Kyushu University
Ayumi Ohnishi, Kobe University
Lorenzo Palazzetti, University of Florence
Ko Watanabe, University of Kaiserslautern
Yasue Kishino, NTT Communication Science Laboratories

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Program Agenda
Mentoring Session

Saturday, June 29th
08:00 - 08:45

Venue will open & Breakfast(Provided) /Registration

801 & 802 8F

08:45 - 09:45

Common WS Keynote 1: Toward Enhancing Digital Conf. Hall 12F
Resiliency
Dr. Masugi Inoue

09:45 - 10:15

Break

10:15 - 12:15

WorkshopSSC

804 8F

SmartAgr

Conf. Hall 12F

SmartSys

805 8F

DM-SmartHealth803 8FBITS

806 8F

12:15 - 13:15

Lunch

801 & 802 8F

13:15 - 14:15

WorkshopSSC

804 8F

SmartAgr

Conf. Hall 12F

SmartSys

805 8F

Mentoring Session (Closed Session)

806 8F

14:15 - 14:45

Coffee Break801 & 802 8F

14:45 - 16:45

Tutorial 1: The Internet of Bio-Nano Things -Smart 803 8F
Computing in the Human Body
Prof. Stefan Fischer
Tutorial 2: Contactless Physiological Health Conf. Hall 12F
Sensing: Challenges, Solutions & Opportunities
Prof. Nirmalya Roy and Ph.D. candidate Zahid Hasan

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16:45 - 17:45

Move to Reception Site

17:45 - 20:00

Reception (Japanese Boat Course) * Eligibility must
be confirmed at the registration deskInternational Conference on Smart Computing 2024 Osaka Japan

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Sunday, June 30th
08:00 - 08:45

Venue will open & Breakfast(Provided) /Registration

Foyer 12F

08:45 - 10:00

Opening & Keynote 2: Cyber Physical Systems in Conf. Hall 12F
Healthcare, Transport and Emergency Response
Prof. Niki Trigoni

10:00 - 10:15

Break

10:15 - 12:15

Session 1 Smart Computing for Smart Cities

Conf. Hall 12F

POSCA: Path Optimization for Solar Cover Amelioration in Urban Air
Mobility
Debjyoti Sengupta, Anurag Satpathy and Sajal K. Das
A Graph Neural Network Framework for Imbalanced Bus Ridership
Forecasting
Samir Amitkumar Gupta, Agrima Khanna, Jose Talusan, Anwar Said, Dan
Freudberg, Ayan Mukhopadhyay and Abhishek Dubey
iFair: Achieving Fairness in the Allocation of Scarce Resources for
Senior Health Care
Modeste M Kenne, Prasanna Date, Ronald Eguchi, Zhenghui Hu, Julie
Rousseau and Nalini Venkatasubramanian
Towards Enhanced Urban Management: Introducing A Model for
Autonomic Smart City Management
Elham Okhovat and Michael A. Bauer
A method for city-wide PoI-level congestion prediction via assimilationof actual and simulation-based PoI congestion data
Haruka Sakagami, Osamu Yamada, Yuki Matsuda, Hirohiko Suwa andKeiichi Yasumoto
12:15 - 13:15

Lunch

Foyer 12F

13:15 - 15:15

Session 2 Smart Computing Applications

Conf. Hall 12F

RATTLE: Train Identification through Audio Fingerprinting
Leonardo Ciabattini, Luca Sciullo, Alfonso Esposito, Ivan Zyrianoff andMarco Di Felice
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity
Recognition Models
Luca Arrotta, Claudio Bettini, Gabriele Civitarese and Michele Fiori

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AMPHI: Adaptive Mission-Aware Microservices Provisioning in
Heterogeneous IoT Settings
Yuqiao Li, Fangqi Liu, Cheng-Hsin Hsu, Nalini VenkatasubramanianDistributed Radiance Fields for Edge Video Compression and Metaverse
Integration in Autonomous Driving
Eugen Šlapak and Matúš Dopiriak, Mohammad Al Faruque, Juraj Gazda
and Marco Levorato
Sharing the Edge: System Status Aware Object Recognition Task
Offloading
Chenyang Wang, Owen Eicher and Qi Han
15:15 - 15:45

Coffee BreakFoyer 12F

15:45 - 16:45

Industry Session

Conf. Hall 12F

16:45 - 18:00

Poster/Demo Session + PhD Forum Poster

Foyer 12F

18:00 - 19:00

Town Hall Meeting
Conf. Hall 12F
InvitedSpeaker:
TowardHigh-Assurance
Learning-Enabled Cyber-Physical Systems
Prof.Insup Lee, PRECISE Center,Department of Computerand Information Science, University of Pennsylvania

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Monday, July 1st
08:00 - 08:45

Venue will open & Breakfast(Provided) /Registration

08:45 - 09:45

Keynote 3: 25 years later: Delivering the promise Conf. Hall 12F
of wearable computing with generative AI
Prof. Paul Lukowicz

09:45 - 10:15

Break

10:15 - 12:15

Session 3 Smart
optimization

resource

management

Foyer 12F

and Conf. Hall 12F

Enhancing UAV Operational Efficiency through Cloud Computing andAutopilot System Integration
Luca D’Agati, Francesco Longo, Giovanni Merlino, Antonio Puliafito andGiuseppe Tricomi
Water-COLOR: Water-COnservation using a Learning-based Optimized
Recommender
Guang Xue Zhang, David L. Feldman, Yiming Lin, Sharad Mehrotra andNalini Venkatasubramanian, Thayer Drew and Kim Sentovich and Owen
VeranthFrom Calls to Scales: Harnessing Smartphone for Daily Mass
Measurement
Hamada Rizk, Merna Hesham and Moustafa Youssef
Cost-based Modeling and Optimization of Secure Matrix Multiplicationin the Cloud
Richard Hernandez, Kemal Akkaya and Soamar Homsi
Optimizing Risk-averse Human-AI Hybrid Teams
Andrew S Fuchs, Andrea Passarella and Marco Conti
12:15 - 13:15

Lunch

13:15 - 15:15

Tutorial 3: Advancing Smart Computing:Comprehensive Tutorial to 3D Point Clouds
Asst. Prof. Tatsuya Amano, Assoc. Prof. Hamada Rizk

Foyer 12F
A 1101 & 1102 11F

Tutorial 4: Science of Cyber Physical Security in Conf. Hall 12F
Smart Living Applications
Prof. Sajal K. Das, Asst. Prof. Shameek Bhattacharjee
15:15 - 15:45

Coffee BreakFoyer 12F

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15:45 - 17:05

Session 4 Best paper candidates

Conf. Hall 12F

An MCS Navigation System Based on Road Surface Quality for Bicycle
RidersFederico Montori, Rocco Pastore, Luca Sciullo and Luciano Bononi andLuca Bedogni
Clustering-Enhanced Reinforcement Learning for Adaptive Offloading inResource-Constrained Devices
Khoa Anh Tran, Nguyen Do Van, Minh-Son Dao and Koji Zettsu
PREDEA: Predicted-RoI based Resource-Efficient Object Detection with
Aggregation
Yoshikazu Watanabe, Seiya Shibata and Takashi Takenaka
TASR: A Novel Trust-Aware Stackelberg Routing Algorithm to Mitigate
Traffic Congestion
Doris E M Brown, Venkata Sriram Siddhardh Nadendla and Sajal K. Das
17:05 - 18:30

Move to Banquet Site

18:30 - 21:00

BanquetThe Landmark
Square Osaka

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Tuesday, July 2nd
08:00 - 08:45

Venue will open & Breakfast(Provided) /Registration

Foyer 12F

08:45 - 10:15

Panel: Generative, Creative, Cooperative – Thetransformative element of AI for Smart Computing
Prof. Takashi Takenaka, Prof. Qi Han, Prof. Sozo Inoueand Prof. Gregor Schiele

Conf. Hall 12F

10:15 - 10:45

Break

10:45 - 12:15

Session 5 Federated Learning for Networked
Systems

Conf. Hall 12F

DP-MTFL: Differentially Private Multi-Tier Federated Learning for IoT
applications
Ramin Soleimani and Dirk Pesch
FMLFS: A federated multi-label feature selection based on information
theory in IoT environment
Afsaneh Mahanipour and Hana Khamfroush
Towards Opportunistic Federated Learning Using Independent
Subnetwork Training
Victor Romero II, Tomokazu Matsui, Yuki Matsuda, Hirohiko Suwa andKeiichi Yasumoto
12:15 - 13:15

Lunch

Foyer 12F

13:15 - 15:00

Session 6 Smart Computing for Security andAnomaly Detection

Conf. Hall 12F

Explainable Deep Learning Models for Dynamic and Online Malware
Classification
Quincy Card, Daniel Simpson, Kshitiz Aryal, Maanak Gupta and Sheik R
Islam
Leveraging Homeostatic Plasticity to Enable Anomaly Detection in
Spiking Neural Networks
Rawan M. A. Nawaiseh, Fabrizio De Vita, Enrico Catalfamo and Dario
BruneoOn the Role of Re-Descending M-estimators in Resilient Anomaly
Detection for Smart Living CPS
Sahar Abedzadeh and Shameek Bhattacharjee

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A Domain-Specific Tool for the Creation of Machine Learning Models
with Imbalanced Datasets
Dmitrii Fomin, Philippe Lalanda and Denis Morand
15:00 - 15:30

Coffee BreakFoyer 12F

15:30 - 16:45

PhD Forum

1008 10F

17:00 -

Adjourn

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Keynote Presentations
Keynote 1: Toward Enhancing Digital Resiliency
Chair: Hayato YAMANA, Waseda University, Japan
Time: June 29th, 8:45 - 9:45
Location: Conf. Hall (12F)
Presenter: Dr. Masugi Inoue, Director General of Resilient ICT Research Center, National
Institute of Information and Communications Technology (NICT), Japan
Abstract:
Thirteen years have passed since the Great East Japan Earthquake. During that time, efforts
have been made to strengthen the information and telecommunications infrastructure andresearch and development for this purpose. However, the Noto Peninsula earthquake that
occurred on January 1 this year once again recognized the vulnerability of theinfrastructure, as in the previous earthquake, although the emergency recovery responseof the telecommunications environment has improved from 13 years ago. Connected cars,
drones, HAPS, and satellite constellations are essential to the layering of information andcommunication resilience, but the existing infrastructure that is the foundation of thosesystems has multiple vulnerabilities. After reviewing them and research trends in resilient
information and communication networks, this presentation will introduce the researchand development being undertaken at NICT. NerveNet, a regional digital andtelecommunications infrastructure with enhanced resilience, has recently begun to be usedas both a normal and emergency network in Shirahama Town, Wakayama Prefecture, andNobeoka City, Miyazaki Prefecture. A network based on the “Die-Hard Network” concept,
which is capable of carrying and delivering data in a bucket relay fashion even in the eventof a complete loss of communication, is in use in Konan City, Kochi Prefecture, andresearch and development is still ongoing for systems that can be used by the Self-Defense
Forces, police, fire departments, and other agencies that are in actual operation at the timeof disaster. The presentation will consider how ICT can improve the resilience of society.
Biography:
Dr. Masugi Inoue, Director General of Resilient ICT ResearchCenter, National Institute of Information and Communications
Technology (NICT), Japan
Masugi Inoue graduated from Kyoto University in 1992, received
his Dr. Eng. degree from The University of Tokyo in 1997, andjoined the Communications Research Laboratory (CRL) of theInternational Conference on Smart Computing 2024 Osaka Japan

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Ministry of Posts and Telecom, Tokyo, Japan, which is now NICT. He was involved in the
research and development of the world fastest WLAN in MM-wave bands,
common-signaling MIRAI architecture for heterogeneous networks in the 4G era, ID-locator
split architecture called HIMALIS for future Internet, a resilient information andcommunications platform system called NerveNet, etc. He also worked for international
cooperation through the operation of Joint Research Programs such as ASEAN IVO,
Japan-US called JUNO and Japan-EU under HORIZON 2020. He has been in his current
position since April 2021. He was a visiting researcher at Polytechnic University, New York,in 2000 – 2001. He received the Best Paper Award from IPSJ in 2006 and 2007, the Young
Scientists’ Prize in 2007 and the Prize for Science and Technology in 2019 from the Ministerof Education, Culture, Sports, Science and Technology, the ITU-AJ Accomplishment Award in
2020, etc. He has been serving as Director of Finance of IEICE (a member of the board) andpreviously served as Chairman of the Technical Committee on Mobile Network andApplications, Director of General Affairs, Director of Finance and so on in the IEICE
Communications Society. He is a fellow of IEICE and a member of IEEE.

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Keynote 2: Cyber Physical Systems in Healthcare, Transport and Emergency Response
Chair: Dirk Pesch, University College Cork, Ireland
Time: June 30st, 8:45 - 10:00
Location: Conf. Hall (12F)
Presenter: Prof. Niki Trigoni (University of Oxford, UK)
Abstract:
Cyber physical systems have widespread application in a multitude of smart city
applications. In this talk, I will present our experiences and lessons learnt from cyber
physical systems in three different settings: healthcare, transport, and emergency
response. I will present some of the key challenges faced by sensing, scene understanding
and coordination algorithms, including lack of sensing infrastructure, sparsity of training
data and dynamic changes in sensing quality and connectivity. I will then present recent
research directions that we have pursued to address these challenges including
multi-modal sensing, cross modality training and domain adaptation techniques.
Biography:
Prof. Niki Trigoni (University of Oxford, UK)
Niki Trigoni is Professor at the Oxford Department of Computer
Science, heading the Cyber Physical Systems Group. Her interests
lie in the tight integration of sensing and machine intelligence forcontext inference, control and human-machine interaction. She
has applied her work to a number of application scenarios,
including mobile autonomy, asset monitoring, and localisation
systems for emergency situations, as well as workforce safety andefficiency. Trigoni has founded and served from 2014-2019 as Director of the Centre forDoctoral Training on Autonomous and Intelligent Machines and Systems. Driven by her
passion for research translation, in 2015, she founded Navenio Ltd, a deep tech Oxford
spinout on infrastructure free indoor positioning, and a 2020 KPMB Best British Tech
Pioneer. In 2020, she won the CTO of the Year award at the UK’s Women in IT Awards,
demonstrating impact from translating positioning tech to improve efficiency in thehealthcare sector.

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Keynote 3: 25 years later: Delivering the promise of wearable computing with generative AI
Chair: Franca Delmastro, IIT-CNR, Italy
Time: July 1st, 8:45 - 9:45
Location: Conf. Hall (12F)
Presenter: Prof. Paul Lukowicz (DFKI and University of Kaiserslautern, Germany)
Abstract:
For nearly three decades now Wearable Computing has been promoted as the next step inthe evolution of personal devices. The vision builds on a combination of advanced context
awareness, unobtrusive multimodal input and head mounted displays to provide “any time
any place” access to digital information and a seamless fusion of the digital and physicalworlds. While with smart watches, sensor enabled earbuds and even rings individual
wearable devices have become common, the broader vision of ubiquitous intelligent
assistance acting at the interface of the physical and the digital world to continually support
human actions and interactions in all situations is still far from reality. Despite many
attempts head mounted displays (HMD), the hallmark of wearables, have never caught on.
In the talk I will argue that the above is less due to hardware limitations, including thelimitations of current HMDs,than to the limited ability of current systems to adequate
recognize and adapt to complex dynamic, real world situations. Context recognition
systems are trained to recognize predefined lists of narrowly defined aspects of theenvironment, rather then being able to comprehensively interpret the situation around theuser. Assistive functionalities tend to be limited to static sets of actions “hard wired” to suchnarrowly defined situations.
Emerging generative AI systems in particular multimodal foundational models, promise to
be able to change that. Vision enabled LLMs are already able to provide rich descriptions ofcomplex real world scenes. From such descriptions they can also generate flexible actions
plans taking into account user profiles and processing advanced multimodal input (speech
gesture etc). This will finally enable the original wearable vision of ubiquitous always on
assistants and the fusion of the digital and physical world in users’s every day life.
Biography:
Prof. Paul Lukowicz (DKFI and University of Kaiserslautern,
Germany)
Prof. Dr. Paul Lukowicz is a Full Professor of AI at the RPTU in
Kaiserslautern, Germany and at the same time Scientific Director at
DFKI Kaiserslautern, where he heads the Embedded Intelligencegroup. His research focuses on context-aware ubiquitous andwearable systems, quantum computing, and human-AI interaction,
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including sensing, machine learning, system architectures, large-scale-to-edge systems, andapplications. He is the Coordinator of the HumanE AI-Net, a large networking project withmore than 50 European partners and acts as Editor for various scientific publications. He
has served on more than 50 program committees (including TPC Chair) at high-quality
international conferences of all the main conferences within his research area.

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Invited Speaker
Toward High-Assurance Learning-Enabled Cyber-Physical Systems
Chair: Sajal K. Das, Missouri University of Science and Technology, USA
Time: June 30th, 18:00-18:30
Location: Conf. Hall (12F)
Presenter: Prof.Insup Lee, PRECISE Center,Department of Computer and Information
Science, University of Pennsylvania
Abstract:
Learning-Enabled Cyber-Physical Systems are becoming increasingly essential to society.
Many advances have been made in the last decade in constructing autonomous
Cyber-Physical Systems (CPS), as evidenced by the proliferation of unmanned systems inair, ground, and sea. These advances have been driven by innovations in several areas,such as computing platform technologies, control theory, design methods and tools,
machine learning, modeling, and simulation technologies, among others. In particular,
machine learning provides a potentially revolutionary way of extracting functionalities
needed for higher-level autonomy. Unfortunately, our current lack of understanding ofwhen and how machine learning works makes it challenging to provide guarantees forlearning-enable components in safety-critical systems. Despite this limitation, given theimpressive experimental results of machine learning, researchers have quickly
incorporated learning in perception-action loops, even in driverless cars and aerial vehicles,
where the safety requirements are very high. This has resulted in unreliable behavior andpublic failures (e.g., Tesla and drone crashes, Uber running a red light) that may lead to aloss of trust in autonomy.
There are many challenges in developing autonomous Learning-Enabled CPS that are safe
and secure. This talk will present assurance problems, challenges, and techniques. They
include safety verification of closed-loop systems with neural network components,
confidence estimation and composition at runtime, assumption monitoring and checking of
CPS, Out-of-Distribution (OOD) detection and adversarial digital and physical attack
detection, and CPS checkpointing and recovery.
Biography:
Prof.Insup Lee, PRECISE Center,Department of Computer and Information Science,
University of Pennsylvania
Insup Lee is Cecilia Fitler Moore Professor of Computer and Information Science andDirector of PRECISE Center since 2008 at the University of Pennsylvania. He also holds a
International Conference on Smart Computing 2024 Osaka Japan39
secondary appointment in the Department of Electrical and Systems Engineering. His
research interests include cyber-physical systems (CPS), real-time systems, embedded
systems, high-confidence medical device systems, formal methods and tools, run-timeverification, and adversarial learning. The theme of his research
activities has been to assure and improve the correctness, safety,
and timeliness of life-critical embedded systems. His papers
received the nine best conference paper awards. Recently, he has
been working in Internet of Medical Things, security of cyber
physical systems, and safe autonomy.
He has served on many program committees, chaired many
international conferences and workshops, and served on various
steering and advisory committees of technical societies. He is
founding co-Editor-in-Chief of ACM Transactions on Computing for
Healthcare (HEALTH, 2018) and was founding co-Editor-in-Chief of
KIISE Journal of Computing Science and Engineering (JCSE, 2007). He has also served on theeditorial boards on the several scientific journals, including Journal of ACM, ACM
Transactions on Cyber-Physical Systems, IEEE Transactions on Computers, Formal Methods
in System Design, and Real-Time Systems Journal. He was founding Co-Director of Penn
Health Tech (2017-2020). He was Chair of ACM Special Interest Group on Embedded
Systems (SIGBED, 2015-2019) and Chair of IEEE TC on Real-Time Systems (TCRTS,
2003-2004). He was a member of Technical Advisory Group (TAG) of President's Council ofAdvisors on Science and Technology (PCAST) Networking and Information Technology(2006-2007). He was a member of the National Research Council’s committee on 21stCentury Cyber-Physical Systems Education (2014-2015). He received IEEE TC-RTS
Outstanding Technical Achievement and Leadership Award in 2008. He received an
appreciation award from Ministry of Science, IT and Future Planning, South Korea in 2013.
His work received the Runtime Verification (RV) Test-of-Time award in 2019. He received
ACM SIGBED Distinguished Leadership Award in 2022 and IEEE Technical Committee on
Cyber-Physical Systems (TCCPS) Distinguished Leadership Award in 2023. He is ACM fellow,
IEEE fellow and AAAS fellow.

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Tutorial
Tutorial 1: The Internet of Bio-Nano Things -Smart Computing in the Human Body
Time: June 29th, 14:45 – 16:45
Location: 803 (8F)
Presenter: Prof. Stefan Fischer, the University of Lübeck, Germany
Abstract:
The Internet of Bio-Nano Things (IoBNT) is an innovative field of research located at theintersection of nanotechnology, biotechnology and information and communication
technologies. It aims to enable the seamless integration of biological and nanoscale
systems into the Internet in order to develop advanced biomedical applications,environmental monitoring sensors and energy-efficient networks. At the core of IoBNT arebiocompatible nanodevices that can function in living organisms to monitor or modify
specific biological processes in real time. These devices communicate with each other andwith the Internet to collect, process and transmit data, opening up entirely new possibilities
for health monitoring, disease control, environmental protection and many other areas. By
merging biology and nanotechnology, IoBNT promises to push the boundaries of what is
technically possible while improving the efficiency and sustainability of technologicalsolutions.
This tutorial first introduces the basics of IoBNT. It explains the basic requirements andexisting technologies and looks at a number of example applications, primarily medical
applications. In the main part, DNA-based nanonetworks are presented as a promising
implementation technology. Before providing an outlook, further concepts are addressed,
which focus in particular on gateways between communication inside and outside thebody.
Biography:
Prof. Stefan Fischer, the University of Lübeck, Germany
Stefan Fischer is a full professor in Computer Science at the
University of Lübeck, Germany, and the director of the Institute forTelematics. He got his doctoral degree in Computer Science fromthe University of Mannheim, Germany, in 1996, respectively. He
held positions at the International University in Germany as an
assistant professor and at the Technical University of
Braunschweig as an associate professor, until he joined Lübeck
University in 2004. His research interest is focused on network andInternational Conference on Smart Computing 2024 Osaka Japan41
distributed system structures such as Internet of Things and nano communications inthese fields. He has (co-)authored more than 200 scientific books and articles.

Tutorial 2: Contactless Physiological Health Sensing: Challenges, Solutions & Opportunities
Time: June 29th, 14:45 – 16:45
Location: Conf. Hall (12F)
Presenter: Prof. Nirmalya Roy, University of Maryland Baltimore County, USA
Ph.D. candidate Zahid Hasan, University of Maryland Baltimore County, USA
Abstract:
Contactless health monitoring techniques, such as Remote Photoplethysmography (rPPG)
and video-based Respiratory Rate (RR) estimation have emerged as promising methods
utilizing regular camera sensors for capturing vital signs like Heart Rate (HR) andRespiratory Rate (RR). These approaches offer cost-effective, widely applicable, and safe
solutions for regular and long-term health monitoring. However, the primary challenge lies
in extracting accurate vital signals from the captured videos due to their low signal-to-noise
ratios.
In this tutorial, we aim to provide a comprehensive background on rPPG and video-basedheart rate estimation, covering signal acquisition and physics-inspired estimation
principles. We will discuss signal-processing approaches and their limitations. We will delve
into DL-based estimation systems, addressing the challenge of inherent aleatoric
uncertainty (irreducible uncertainty from various stochastic factors like sensor variations
and inter-subject differences) in the ground truth data annotation that hinders thedevelopment of generalized deep learning-based rPPG estimation system. To reinforce arobust DL model addressing these inherent uncertainties in rPPG data streams, we will
discuss three novel deep-learning approaches – a multi-task learning method, aself-supervised learning method, and a generative adversarial network-based architecture
for rPPG estimation. Furthermore, we will demonstrate a real-time heart rate estimation
system, RhythmEdge, using a low-cost camera sensor and describe the rPPG-specificpruning techniques to reduce rPPG model size for efficient edge implementation. We willconclude the tutorial with existing research gaps and potential directions in contactlessrPPG and respiratory rate estimation research.
Biography:
Prof. Nirmalya Roy, University of Maryland Baltimore County, USA
Dr. Nirmalya Roy is a Professor in the Information Systems department and the director of
the Mobile, Pervasive and Sensor Computing Lab at the University of Maryland, Baltimore
County. He is also the associate director of the Center for Real-time Distributed Sensing
International Conference on Smart Computing 2024 Osaka Japan42
and Autonomy (CARDS) at UMBC. His current research interests
include use-inspired AI/ML and human-centric data science withapplications to smart health, cyber-physical systems, IoT, robotics
and autonomy. He received his B.E., M.S. and Ph.D. degrees in
Computer Science and Engineering from Jadavpur University in
2001, and the University of Texas at Arlington in 2004 and 2008,
respectively. More information about his research can be found athttps://mpsc.umbc.edu/.

Ph.D. candidate Zahid Hasan, University of Maryland Baltimore
County, USA
Mr. Zahid Hasan is a Ph.D. candidate under the supervision of Dr.
Nirmalya Roy in the Information Systems department at the
University of Maryland, Baltimore County. His research interests
include video information retrieval and contactless physiologicalhealth monitoring using camera sensors. As the student lead of
this project, he has pioneered the development of deep learningmodels for rPPG estimation, addressing the challenge of
uncertainty associated with ground truth data annotation. More information about his
research can be found at https://mxahan.github.io/digital-cv/.

Tutorial 3: Advancing Smart Computing: A Comprehensive Tutorial to 3D Point Clouds
Time: July 1st, 13:15 - 15:15
Location: 1101 & 1102 (11F)
Presenter: Asst. Prof. Tatsuya Amano, Osaka University, JAPAN
Assoc. Prof. Hamada Rizk, Osaka University, JAPAN and Tanta University, Egypt
Abstract:
3D point clouds from LiDAR sensors have emerged as a powerful representation forunderstanding and interacting with the surrounding environment in smart computing
systems. This tutorial aims to provide an overview of fundamental techniques,considerations, and applications of 3D point cloud recognition, with a focus on both deeplearning and non-deep learning approaches. We will begin by introducing the uniquecharacteristics and challenges of point cloud processing, highlighting the differences fromtraditional image-based approaches. Through the example of PointNet, a pioneering deeplearning model for point cloud recognition, we will discuss the specific considerations andbest practices for handling point cloud data. Recognizing the limitations of deep learning inInternational Conference on Smart Computing 2024 Osaka Japan43
resource-constrained and mobile environments, we will also explore alternative statisticaland probabilistic techniques, such as Fisher Vector-based approaches, which enableefficient and lightweight point cloud processing and context recognition. These techniques
are particularly relevant for smart computing applications that require real-time processing
on edge devices and in Internet of Things scenarios.
The tutorial will also showcase various applications of point cloud recognition in smart
computing contexts. We will showcase a case study of Osaka University smart campus,
where we have deployed a large-scale pedestrian tracking system using a network ofapproximately 70 LiDAR sensors. Additionally, we will discuss the use of point clouds forremote space sharing in extended reality (XR) applications, demonstrating how point cloudrecognition enables immersive and interactive experiences.We will also present a casestudy of our LiDAR mobile device (hitonavi-μ).
Biography:
Asst. Prof. Tatsuya Amano, Osaka University, JAPANTatsuya Amano is an Assistant Professor at Osaka University,
Japan. He received M.E. Degree and Ph.D. in Information andComputer Science from Osaka University in 2018 and 2021,
respectively. He was a young research fellow (DC1) of the Japan
Society for the Promotion of Science (JSPS) from2018 to 2021. His
research interests include spatial computing, smart city, andmobile computing.Since 2023, he has been an PRESTO Researcher,Japan Science and Technology Agency (JST). He is a member of IEEE, IPSJ (Information
Processing Society of Japan), and IEICE (Institute of Electronics, Information andCommunication Engineers).
Assoc. Prof. Hamada Rizk, Osaka University, JAPAN and Tanta
University, Egypt
Hamada Rizk (Associate Professor, IEEE Senior Member) received
the M.E. and Ph.D. degrees in computer science and engineeringfrom Tanta University and E-JUST in 2016 and 2020, respectively.
He is with Osaka University, Japan and Tanta University, Egypt. He
has been working in mobile and pervasive computing, spatialintelligence, and AI research areas. He has been involved in several
projects funded by many academic and industrial organizations such as NTRA Egypt, Uber,
USA, ASTEP JST, Kakenhi JSPS, NVIDIA, Japan, etc. He has authored several publications in
top journals and conferences and holds a number of patents. Hamada is the recipient of
the silver medal in the 2019th ACM SigSpatial competition held in Chicago and washonored as an outstanding young researcher by the HLF foundation, Germany (2019), andby Google (2019 & 2020), among other awards.

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Tutorial 4: Science of Cyber Physical Security in Smart Living Applications
Time: July 1st, 13:15 - 15:15
Location: Conf. Hall (12F)
Presenter: Prof. Sajal K. Das, Missouri University of Science and Technology, USA
Asst. Prof. Shameek Bhattacharjee, Western Michigan University, USA
Abstract:
The vision behind community-scale smart living applications is to use sensor-actuator
devices, the so-called Internet of Things (IoT), to generate sensing data that provide
situational awareness of the physical world to improve the quality of human life at the city
scale. Examples applications include smart transportation, customer and distribution layersof the smart grid metering, smart water networks, etc. The effects of threats suchcyber-attacks, device/network faults and malfunctions, unsafe events, typically manifest
themselves as anomalies that need to be promptly detected. However, there are uniquechallenges in anomaly detection for smart living : (1) behavioral randomness of humans
creates dynamic spatiotemporal variations in data patterns making it difficult to learn the
profile of benign behavior leading to unusable false alarm frequencies; (2) High
non-linearity, non-IID data, random evolving patterns, cause traditional anomaly
detection/learning methods to lose detection sensitivity; (3) smart living sensing data often
have privacy and individual device profiling concerns; (4) unlabeled threats present whilelearning benign profile.
To address such challenges, we present a unified framework to detect various threats in alight-weight, timely, and privacy preserving manner. The tutorial covers the following
topics: (1) decentralized graph representations of the wide area CPS; (2) anomaly detection
metric learning; (3) resilient machine learning for finding detection thresholds; (4)bio-inspired classification methods to identify individual devices affected by the threats.
We show key results achieved by the overarching framework in smart grid, smart
transportation, smart water networks, etc.
Biography:
Prof. Sajal K. Das, Missouri University of Science and Technology,
USA
Sajal K. Das is a Curators’ Distinguished Professor of Computer
Science and Daniel St. Clair Endowed Chair at Missouri Universityof Science and Technology, USA. His interdisciplinary researchexpertise includes machine learning, data science, security ofcyber-physical systems, IoT, smart environments, UAVs wirelessInternational Conference on Smart Computing 2024 Osaka Japan45
and sensor networks, mobile and pervasive computing, edge/ and cloud computing. He has
made fundamental contributions to these areas, published more than 600 papers in highquality journals and conference proceedings, coauthored 4 books, and 5 US patents. A
recipient of 12 Best Paper Awards at prestigious IEEE and ACM conferences, he received
the IEEE Computer Society’s Technical Achievement Award for pioneering contributions to
sensor networks, and University of Missouri System President’s Award for Sustained Career
Excellence. His h-index is 99 with more than 41,800 citations. He is the founding
Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing journal, and Associate Editorof IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Mobile
Computing, IEEE Transactions on Sustainable Computing, ACM/IEEE Transactions on
Networking, and ACM Transactions on Sensor Networks. He is a Distinguished Alumnus of
the Indian Institute of Science, Bangalore, and a Fellow of the IEEE, National Academy
Inventors (NAI), Asia-Pacific Artificial Intelligence Association (AAIA).

Asst. Prof. Shameek Bhattacharjee, Western Michigan University,
USA
Dr. SHAMEEK BHATTACHARJEE is an assistant professor ofComputer Science at Western Michigan University, Kalamazoo,USA. His research interests include the theory of anomaly
detection, explainable artificial intelligence and data science forcyber security. He is a recipient of IEEE PIMRC best paper award
and Top 3 paper award in IEEE/ACM ICCPS.

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Panel Session
Generative, Creative, Cooperative - The Transformative Element of AI for Smart Computing
Moderator: Stephan Sigg (Aalto University, Finland)
Time: July 2nd, 8:45 – 10:15
Location: Conf.Hall (12F)
Panelists: Takashi Takenaka (NEC, Japan), Qi Han (Colorado School of Mines, USA), Sozo
Inoue (Kyushu Institute of Technology, Japan), and Gregor Schiele (Universität
Duisburg-Essen, Germany)
Biography:
Dr. Takashi Takenaka (NEC)Takashi Takenaka received his M.E. and Ph.D. degrees from Osaka
University in 1997 and 2000, respectively. He joined NEC
Corporation in 2000 and is currently a director of Green AI
Research Group, Secure System Platform Laboratories, NEC
Corporation. He was a visiting scholar at the University ofCalifornia, Irvine, from 2009 to 2010. His current research interests
include the acceleration of AI/ML, system-level design
methodology, high-level synthesis, formal verification, and stream processing. He is amember of IEEE, IEICE, and IPSJ.
Prof. Qi Han (Colorado School of Mines)
Dr. Qi Han is Professor in the Department of Computer Science at
the Colorado School of Mines. She founded and currently directs
the Pervasive Computing Systems (PeCS) research group(http://pecs.mines.edu). Her broad research interests lie in theareas of wireless and mobile systems with specific focuses onmobile sensing, crowdsourcing, Internet of Things, networked
robotic systems, mobile augmented reality, and cyber-physicalsystems. She has been active in interdisciplinary research where
she applies her expertise to a variety of applications in the domains of smart cities,
intelligent transportation, underground safety, environmental monitoring, and space
exploration. Her research has been mainly funded by the National Science Foundation(NSF), National Aeronautics and Space Administration (NASA), and Army Research Labs
(ARL). She has led multiple interdisciplinary research projects. Dr. Han holds a Ph.D.
degree from the Donald Bren School of Information and Computer Sciences at theInternational Conference on Smart Computing 2024 Osaka Japan47
University of California, Irvine.
She has served on a number of technical programcommittees for international conferences and held several workshop or conference
program chair positions. She is an ACM Distinguished Speaker, an ACM senior member,and an IEEE senior member.
Prof. Sozo Inoue (Kyushu Institute of Technology)
Sozo INOUE is a full professor in National Kyushu Institute ofTechnology, Japan, and the Chief Technical Officer of AUTOCARE
LLC. His research interests include human activity recognition withsmart
phones,andhealthcare
applicationof
web/pervasive/ubiquitous systems. Currently he is working on
applictions of activity recognition and health forecasting fornursing care and medicine to hospitals and more than 50 nursing
facilities using smartphones and IoT. Inoue has a Ph.D ofEngineering from Kyushu University in 2003. After completion of his degree, he wasappointed as an assistant professor in the Faculty of Information Science and ElectricalEngineering at the Kyushu University, Japan. He then moved to the Research Department at
the Kyushu University Library in 2006. Since 2009, he has been appointed as an associate
professor in the Faculty of Engineering at Kyushu Institute of Technology, Japan, and moved
to Graduate School of Life Science and Systems Engineering at Kyushu Institute ofTechnology in 2018, and appointed as a full professor from 2020 and a head of Care XDXCenter in Kyushu Institute of Technology from 2022. Meanwhile, he was a guest professor
in Kyushu University, a visiting professor at Karlsruhe Institute of Technology, Germany, in
2014, a special researcher at Institute of Systems, Information Technologies andNanotechnologies (ISIT) during 2015-2016, and a guest professor at University of Los Andesin Colombia in 2019. He was a technical advisor of Team AIBOD Co. Ltd during 2017-2019,
and a guest researcher at RIKEN Center for Advanced Intelligence Project (AIP) during
2017-2019. He was a director during 2020-2021 and is a senior member of the Information
Processing Society of Japan (IPSJ), a member of the IEEE Computer Society, the ACM, theInstitute of Electronics, Information and Communication Engineers (IEICE), the Japan
Society for Fuzzy Theory and Intelligent Informatics, the Japan Association for Medical
Informatics (JAMI), and the Database Society of Japan (DBSJ).
Dr. Gregor Schiele (Universität Duisburg-Essen, Germany)
Dr. Gregor Schiele is professor for embedded systems and leads
since november 2014 the Intelligent Embedded Systems group at
the University Duisburg-Essen at the campus Duisburg. Before that
he was working from 2012 to 2014 at the Insight Centre for Data
Analytics and the Digital Enterprise Research Institute (DERI) as well
as at the National University of Ireland, Galway. From 2006 to 2012he was working at the department of Prof. Dr. Christian Becker at
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the University Mannheim. He wrote his doctorate 2007 at the University Stuttgart at the
department of Prof. Dr. Kurt Rothermel.
The core of his current research interest consist of adaptive embedded systems as well as
the Internet of Things. He has great interest in how embedded systems can be operated
with as little maintenance as possible, in a cheap and reliable way over a long operation
time of several years to decades by using three-dimensional environmental models andmethods of artificial intelligence.
Prof. Stephan Sigg (Aalto University)
Stephan Sigg is an Associate Professor at Aalto University in the
Department of Communications and Networking. With abackground in the design, analysis and optimisation of algorithms
for distributed and ubiquitous systems, he focuses on sensing
systems for environmental perception and Usable(perception-based) Security. Especially, his work covers proactive
computing, distributed adaptive beamforming, context-based
secure key generation and device-free passive activity recognition. Stephan is an editorial
board member of the Proceedings of the ACM on Interactive, Mobile, Wearable andUbiquitous Technologies (IMWUT), the Elsevier Journal on Computer Communications andhas been a guest editor for the Springer Personal and Ubiquitous Computing Systems
Journal. He has served on the organizing and technical committees numerous prestigious
conferences including IEEE PerCom, ACM Ubicomp, IEEE ICDCS.

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Workshops
8th International Workshop on Big Data and IoT Security in Smart Computing(BITS 2024)
Invited Talk 1: Security and Privacy Challenges in Digital Realities: A Focus on VR/AR Devices
in Smart Computing
Time: 10:20-11:00 (30 min + 10 min QA)
Location: 806 (8F)
Presenter: Prof. Akira Kanaoka (Toho University, Japan)
Abstract:
The rapid advancements in VR/AR technology are creating new dimensions in the realm ofdigital realities. While traditional mobile devices like smartphones and tablets continue tobe used, it is the dedicated VR/AR head-mounted displays (HMDs) with integrated
computing capabilities that deserve significant attention. These specialized VR/AR devices
are equipped with a variety of sensors, making them key components in the emerging
metaverse and other digital reality spaces.
This talk will explore how the well-established concepts of Big Data and IoT security withinsmart computing are being redefined and applied to these VR/AR ecosystems. As VR/AR
devices become more widespread, the security and privacy concerns associated with their
sensor-rich environments and data-intensive operations become paramount. This session
will examine the current state of security and privacy technologies in digital realities,
highlighting the unique challenges and opportunities they present.
Biography:
Prof. Akira Kanaoka (Toho University, Japan)
Akira Kanaoka received his Ph.D. in Engineering from the Universityof Tsukuba in 2004. His career includes positions as a researcher at
SECOM Co., Ltd., a researcher and assistant professor at the
University of Tsukuba, and an assitant professor at Toho University
before assuming his current position. He is engaged in research on
usable security with a focus on human factors and the practicalapplication of advanced cryptography. He has been a driving force
in the field of usable security research in Japan, having the launchof the Usable Security Workshop (UWS) and served as the editor-in-chief for the special
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issue on usable security in the IPSJ Journal. He received a number of awards including theBest Paper Award of the 3rd International Conference on HCI for Cybersecurity, Privacy andTrust (HCI-CPT), the Best Paper Award of the 10th Asia Joint Conference on Information
Security (AsiaJCIS 2015). He served as a Program Co-Chair of IWSEC 2020, AsiaJCIS 2017,2018, 2019.

Main Session
Chair: TBA
Time: June 29th, 11:00 – 11:25
Location: 806 (8F)
Protecting Cross-camera Person Re-identification Data with Image Differential Privacy
Lucas Maris (Nara Institute of Science and Technology, Japan), Yuki Matsuda (Okayama
University and RIKEN AIP, Japan), Keiichi Yasumoto (Nara Institute of Science and Technology
and RIKEN AIP, Japan)

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10th Workshop on Sensors and Smart Cities (SSC)
Main Session
Chair: TBA
Time: June 29th, 10:15 – 12:15, 13:15 - 14:15
Location: 804 (8F)

Crowd Flow Prediction from Mobile Traces Through Time Series PoI Stay Counts
Osamu Yamada (Nara Institute of Science and Technology, Japan); Yuki Matsuda (Okayama
University, Nara Institute of Science and Technology, and RIKEN Center for Advanced Intelligence
Project AIP, Japan); Hirohiko Suwa (Nara Institute of Science and Technology, and RIKEN Center
for Advanced Intelligence Project AIP, Japan); Keiichi Yasumoto (Nara Institute of Science andTechnology, and RIKEN Center for Advanced Intelligence Project AIP, Japan)

Comparison of Commercial Pedometer Applications: A Rigorous Approach
Alessio Terzi, Federico Montori (University of Bologna, Italy); Lorenzo Gigli (University of Bologna,
Italy); Luca Bedogni (University of Modena and Reggio Emilia, Italy); Marco Di Felice (University of
Bologna, Italy); Luciano Bononi (University of Bologna, Italy)

Bibliometric Mining of Research Trends for Smart Cities
Lars G. Lundberg (Blekinge Institute of Technology, Sweden)

Paving the way for an Urban Intelligence OpenStack-based Architecture
Giuseppe Tricomi (National Interuniversity Consortium for Informatics, University of Messina,
and National Research Council (CNR), Italy); Luca D'Agati, Francesco Longo, Giovanni Merlino,
and Antonio Puliafito (National Interuniversity Consortium for Informatics, and University ofMessina, Italy); Stefano Silvestri (National Research Council (CNR), Italy)

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A Machine Learning-Based Temperature Control and Security Protection for Smart
Buildings
Mostafa Zaman, and Maher Al Islam (Virginia Commonwealth University, USA); Nasibeh Zohrabi(Pennsylvania State University Brandywine, USA); Sherif Abdelwahed (Virginia Commonwealth
University, USA)International Conference on Smart Computing 2024 Osaka Japan

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3rd International Workshop on Smart Agriculture for the Environmental
Emergency (SmartAgr 2024)
Welcome from the Chairs
Chair: Gabriele Cecchetti (Scuola Superiore Sant'Anna, Italy)
Time: June 29th, 10:15 - 10:20
Location: Conf. Hall (12F)

Session 1: Exploring Artificial Intelligence Applications in Smart Agriculture
Time: June 29th, 10:20 - 11:00

Automated Visual Quality Detection for Tilapia Using MobilenetV2 Convolutional Neural
Network
Israel F Breta, Karl Adriane D.C. Catalan, Sev Kristian M Constantino, Ralph Adrian R Mones andEdward D Bustillos (Adamson University, Philippines)

Early Identification of Oil Palm Health based on UAV images using Feature-Based Machine
Learning
Chang Yi Lee, Lee Choo Tay, Weng Chun Tan, Weng Kin Lai (Tunku Abdul Rahman University ofManagement and Technology, Malaysia), Sheng Siang Lee (Aonic, Malaysia)

Session 2: Advanced ICT for Smart Agriculture
Time: June 29th, 11:00 - 12:00

A Smart Sensor-based Watering Automation System for Nursery Plants
Gabriele Cecchetti and Anna Lina Ruscelli (Scuola Superiore Sant'Anna, Italy)

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Boosting Farm Efficiency: An Ant Colony Optimization Approach to Smart Agriculture
Lai Weng Kin and Lee Choo Tay (Tunku Abdul Rahman University of Management andTechnology, Malaysia)

Session 3: Sustainable Agriculture
Time: June 29th, 13:15 - 14:00

Farm households food security status automation through supervised learning approach: alook at agroecological farms
Nikiema Théodore (Institut de Mathématiques et de Sciences Physiques, Benin); Pamela G. Katic(University of Greenwich, United Kingdom (Great Britain)); Eugène C. Ezin (Institut de Formation
et de Recherche en Informatique (IFRI), Benin); Sylvain Kpenavoun Chogou (University of Abomey
Calavi, Benin)

Categorizing farms to promote agroecology: a supervised learning approach
Nikiema Théodore (Institut de Mathématiques et de Sciences Physiques, Benin); Eugène C. Ezin
(Institut de Formation et de Recherche en Informatique (IFRI), Benin); Sylvain Kpenavoun Chogou(University of Abomey Calavi, Benin)

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9th IEEE Workshop on Smart Service Systems (SmartSys)
Main Session
Chair: TBA
Time: June 29th, 10:15 – 12:15, 13:15 - 14:15
Location: 805 (8F)

AcouDL: Context-Aware Daily Activity Recognition from Natural Acoustic Signals
Avijoy Chakma (Bowie State University, USA); Anirban Das (NIIT University, India); Abu Zaher Md
Faridee (Amazon, USA); Suchetana Chakraborty (IIT Jodhpur, India); Sandip Chakraborty (IIT
Kharagpur, India); Nirmalya Roy (UMBC, USA)

AI-Based Kinematic Analysis for Track Athletes
Mostafa Habibi and Mehrdad Nourani (The University of Texas at Dalla, USA); Mohammad
Nourani (California Polytechnic State University, USA)

Acoustic Camera-based Anomaly Detection for Wind Turbines
Ming-Lun Lee (National Yang Ming Chiao Tung University, Taiwan); Yuan-Heng Sun (National
Yang Ming Chiao Tung University, and Industrial Technology Research Institute, Taiwan); Ya-Chu
Chi (National Yang Ming Chiao Tung University, Taiwan); Yan-Ann Chen (Yuan Ze University,
Taiwan); Yao-Long Tsai (Industrial Technology Research Institute, Taiwan); Yu-Chee Tseng(National Yang Ming Chiao Tung University, National Yang Ming Chiao Tung University, andAcademia Sinica, Taiwan)

Detecting Distress Changes Using Multimodal Data During Interaction with A Smart Speaker
Chingyuan Lin (Nara Institute of Science and Technology, Japan); Yuki Matsuda (Okayama
University and Nara Institute of Science and Technology, Japan); Hirohiko Suwa, and Keiichi
Yasumoto (Nara Institute of Science and Technology, Japan)

DIME: Distributed Inference Model Estimation for Minimizing Profiled Latency
Robert Viramontes, Azadeh Davoodi (University of Wisconsin - Madison, USA)International Conference on Smart Computing 2024 Osaka Japan

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1st International Workshop on Digital and Mobile Smart Health Systems(DM-SmartHealth 2024)
Main Session
Chair: TBA
Time: June 29th, 10:15 – 12:15
Location: 803 (8F)

Requirements Analysis for Responsible Explainable AI for Pediatric Sleep Apnea Diagnosis
Marta Quemada López, Thomas Plageman, Vera H. Goebel, Jonathan Adams and David Hui,
Britt Øverland, Harriet Akre and Lars Andre Strøm Arnesen (University of Oslo, Norway)

Federated Learning for Sleep Detection Problems
Guilherme Borges (University of Coimbra, Coimbra, Portugal and Federal InstituteSul-rio-grandense (IFSUL) Rio Grande Sul, Brazil); Julio Cesar Santos dos Anjos (Federal Universityof Ceará Fortaleza, Brazil); Jorge Sá Silva (University of Coimbra Coimbra, Portugal)

A Numerical Comparison of Deeply Quantized Models for sEMG Hand Gesture Classification
on Constrained Devices
Emanuele Giuseppe Siani and Laura Scigliano, Dario Bruneo, Fabrizio De Vita, Valeria Tomaselli
and Danilo Pietro Pau (University of Messina, Italy)

Insights on the development of PRACTICE, a research-oriented healthcare platform
Dragan Ahmetovic, Alessio Angileri, Sara Arcudi, Claudio Bettini, Gabriele Civitarese, Marco
Colussi, Andrea Giachi, Roberta Gualtierotti, Sergio Mascetti, Matteo Manzoni, Flora Peyvandi,
Aiman Solyman and Addolorata Truma (Università degli Studi di Milano, Milan, Italy and
Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy)

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Main Technical Sessions
Session 1: Smart Computing for Smart Cities
Chair: Stefan Sigg, Aalto University, Finland
Time: June 30th, 10:15 – 12:15
Location: Conference Hall (12F)

POSCA: Path Optimization for Solar Cover Amelioration in Urban Air Mobility
Debjyoti Sengupta, Anurag Satpathy and Sajal K. Das (Missouri University ofTechnology, USA)

Science and

Abstract: Urban Air Mobility (UAM) encompasses both piloted and autonomous aerial
vehicles, spanning from small unmanned aerial vehicles (UAVs) like drones topassenger-carrying personal air vehicles (PAVs), to revolutionize smart transportation incongested urban areas. This emerging paradigm is anticipated to offer disruptive solutions
to the mobility challenges in congested cities. In this context, a pivotal concern centers on
the sustainability of transitioning to this mode of transportation, especially with the focus
on incorporating clean technology into developing innovative solutions from the groundup. Recent studies highlight that a significant portion of the total energy consumption in
UAM can be attributed to the flight operations of the aircraft. To address this challenge, thispaper introduces a framework POSCA aimed at meeting the energy requirements of UAM
flights. It delves into a complex and dynamic route-planning problem. It introduces a novel
concept called the Phototropic Index, calculated by considering the traversal distance andsolar coverage along the route. To solve the path planning problem, we propose twosolutions, S-POSCA and D-POSCA, catering to static and dynamic setups. Simulation results
confirm an average increase of 8.81% in static conditions and 10.64% in the dynamic
condition for the cumulative Global Horizontal Irradiance (GHI) compared to the baseline
approaches.

A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting
Samir Amitkumar Gupta, Agrima Khanna, Jose Talusan and Anwar Said (Vanderbilt University,USA); Dan Freudberg (WeGo Public Transit, USA); Ayan Mukhopadhyay and Abhishek Dubey(Vanderbilt University, USA)
Abstract: Public transit systems are critical in lowering carbon emissions and reducing
urban congestion for environmental sustainability. However, overcrowding has adverse
effects on the quality of service, passenger experience, and overall efficiency of public
transit causing a decline in the usage of public transit systems. Therefore, it is crucial toInternational Conference on Smart Computing 2024 Osaka Japan

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identify and forecast potential windows of overcrowding to enhance passenger experienceand encourage higher ridership. Predicting ridership is a complex task, due to the inherent
noise of collected data and the sparsity of overcrowding events. Existing studies in
predicting public transit ridership consider only a static depiction of bus networks.We
address these issues by first applying a data processing pipeline that cleans noisy data andengineers several features for training. Then, we address sparsity by converting the
network to a dynamic graph and using a graph convolutional network, incorporating
temporal, spatial, and auto-regressive features, to learn generalizable patterns for eachroute. Finally, since conventional loss functions like categorical cross-entropy have
limitations in addressing class imbalance inherent in ridership data, our proposed
approach uses focal loss to refine the prediction focus on less frequent yet critical
overcrowding instances. Our experiments, using real-world data from our partner agency,
show that the proposed approach outperforms existing state-of-the-art baselines in termsof accuracy and robustness.

iFair: Achieving Fairness in the Allocation of Scarce Resources for Senior Health Care
Modeste M Kenne (University of California Irvine, USA); Prasanna Date (Oak Ridge National
Laboratory, USA); Ronald Eguchi and Zhenghui Hu (ImageCat, Inc., USA); Julie Rousseau(University of California Irvine School of Medicine, USA); Nalini Venkatasubramanian (Universityof California, Irvine, USA)
Abstract: Efficient resource allocation is crucial in many domains, particularly in seniorcare, where assigning resources to older adults must consider uncertainties associatedwith vulnerable populations. In collaboration with Senior Health Facilities (SHFs) anddomain experts, this paper presents iFair, a novel framework designed to assist
decision-makers in equitably allocating scarce resources to older adults. iFair was
prototyped in the context of ongoing work on a data exchange platform, CAREDEX, used forenhancing older adults' resilience during disasters. A key novelty of iFair focuses on aligning
resident preferences with resources in urgent situations, expediting care, and enhancing
task efficiency. We integrate static and dynamic environmental data, including facility
layouts and sensor data, with detailed resident profiles to cater to the individual needs andpreferences of residents. While our framework primarily focuses on allocation within
facilities, it also extends to a regional scale to support the planning and transfer of seniors
to mutual aid facilities. Our experiments adapt data from a real SHF to emulate resource
allocation in an emergency fire evacuation setting, and highlights the delicate balance thatdecision-makers can achieve between efficiency and fairness.

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Towards Enhanced Urban Management: Introducing A Model for Autonomic Smart City
Management
Elham Okhovat and Michael A. Bauer (University of Western Ontario, Canada)
Abstract: A smart city leverages Internet of Things (IoT) to enhance citizens' lives. Real-time
management of massive data is a challenge. Smart cities are complex and interconnected,
requiring interdisciplinary collaboration for urban planning, IoT, and monitoring. This paper
proposes a novel Model for Autonomic Smart City Management, aimed at comprehensive
management of diverse urban aspects, including environmental conditions andmulti-layered infrastructure. Emphasizing continuous monitoring, data analysis, andreal-time resource assessment, the system employs the MAPE-K approach for automateddata processing, with the aim of reducing the need for human intervention. A novel aspectof the model is that it leverages and integrates existing IoT and monitoring platforms. The
model introduces algorithms and policies with the aim of automating management
activities and operations. The primary goal is to enhance urban efficiency, sustainability,
and residents' quality of life while minimizing manual efforts and ensuring long-term costsavings.

A method for city-wide PoI-level congestion prediction via assimilation of actual andsimulation-based PoI congestion data
Haruka Sakagami, Osamu Yamada, Yuki Matsuda, Hirohiko Suwa and Keiichi Yasumoto (NaraInstitute of Science and Technology, Japan)
Abstract: Regulating human flow is essential to reducing congestion in areas where people
gather. A digital twin that realistically simulates human flow helps for thispurpose.Regulating human flow is essential to reducing congestion in areas where people
gather. A digital twin that realistically simulates human flow helps for this purpose.To
realize a realistic human flow simulation mechanism, it is essential to take into accountpeople's attributes.However, existing simulation methods use only location-specificinformation to predict people's behavior, thus do not reflect the routines that appear inpeople's actual lives. In this paper, we propose a human flow simulation using synthetic
population data that help extract the attributes of people living in a target area. In the
proposed method, we simulate the movement of people with each attribute like office
workers, students, etc. every 15 minutes using the synthetic population data and the hourlytransition probability matrix between PoIs (Points of Interest) by computing the transition
probability matrix from hourly PoI-level congestion (people count) in the target area using
people trajectory data included in the point-type fluid population data commerciallyavailable and applying a Markov chain to the congestion. The proposed simulation
mechanism is based on the data assimilation of the actual PoI congestion vector (howmany people were staying in each PoI) obtained from the point-type fluid population data
International Conference on Smart Computing 2024 Osaka Japan60
and the virtual PoI congestion vector generated from the prediction of people's movement
using the attribute information in the synthetic population data at regular time intervals.
The data are assimilated at regular intervals to obtain highly accurate PoI-level congestion
forecasts. The results of the mobility simulation for office workers showed that the
maximum cosine similarity with the actual PoI congestion was 0.96 after 12 hours evenwhen the actual PoI congestion vector is known only for a part of the area (one mesh).

Session 2: Smart Computing Applications
Chair: Nirmalya Roy, University of Maryland Baltimore County, USA
Time: June 30th, 13:15 – 15:15
Location: Conference Hall (12F)RATTLE: Train Identification through Audio Fingerprinting
Leonardo Ciabattini, Luca Sciullo, Alfonso Esposito, Ivan Zyrianoff and Marco Di Felice (Universityof Bologna, Italy)
Abstract: Train model identification can enhance the structural monitoring of railway
infrastructures by providing contextual information about train passages. Whileapproaches relying on timetables are impractical due to delays, camera-based solutions
present challenges related to deployment costs and privacy concerns. In this paper, we
propose RATTLE, a self-contained framework for train tracking and identification based on
audio signal fingerprinting. We have developed a prototype IoT system tailored for train
tracking and ground truth assessment, enabling the acquisition of a real-world dataset
spanning four months of measurements. Then, we conducted a comparative analysis ofseveral traditional Machine Learning (ML) and Deep Learning (DL) algorithms for audio
features classification, MEL spectrogram classification, and image classification (serving as
baselines). Our findings highlight that MEL-trained CNN algorithms achieve high accuracy(97%) comparable to the best video-based DL solution, while substantially reducing modelsize. Furthermore, we explored the potential for migrating the classification task to theedge through quantisation techniques.

ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models
Luca Arrotta (University of Milan, Italy); Claudio Bettini (Università degli Studi di Milano, Italy);Gabriele Civitarese and Michele Fiori (University of Milan, Italy)
Abstract: Context-aware Human Activity Recognition (HAR) is a hot research area in mobile
computing, and the most effective solutions in the literature are based on supervised deepInternational Conference on Smart Computing 2024 Osaka Japan

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learning models. However, the actual deployment of these systems is limited by the
scarcity of labeled data that is required for training. Neuro-Symbolic AI (NeSy) provides aninteresting research direction to mitigate this issue, by infusing common-sense knowledge
about human activities and the contexts in which they can be performed into HAR deeplearning classifiers. Existing NeSy methods for context-aware HAR rely on knowledge
encoded in logic-based models (e.g., ontologies) whose design, implementation, andmaintenance to capture new activities and contexts require significant human engineering
efforts, technical knowledge, and domain expertise. Recent works show that pre-trained
Large Language Models (LLMs) effectively encode common-sense knowledge about humanactivities. In this work, we propose ContextGPT: a novel prompt engineering approach toretrieve from LLMs common-sense knowledge about the relationship between humanactivities and the context in which they are performed. Unlike ontologies, ContextGPT
requires limited human effort and expertise. An extensive evaluation carried out on two
public datasets shows how a NeSy model obtained by infusing common-sense knowledge
from ContextGPT is effective in data scarcity scenarios, leading to similar (and sometimes
better) recognition rates than logic-based approaches with a fraction of the effort.

AMPHI: Adaptive Mission-Aware Microservices Provisioning in Heterogeneous IoT Settings
Yuqiao Li (University of California, Irvine, USA); Fangqi Liu (University of California at Irvine, USA);
Cheng-Hsin Hsu (National Tsing Hua University, Taiwan); Nalini Venkatasubramanian (Universityof California, Irvine, USA)
Abstract: In mission-critical IoT environments, where data from diverse data sources(stationary and mobile) must be analyzed rapidly, ensuring reliable service provisioning forthe execution of analytics is critical. Under dynamic conditions and changing infrastructure
capabilities, adaptive methods are required to handle surges in data volumes and executecomplex services for IoT applications. In this paper, we propose AMPHI - an adaptive
microservice provisioning framework that handles various mission-aware workflows in
combined stationary-mobile IoT environments to enable flexible deployment ofcontainerized microservices. AMPHI exploits the variability in cost and performance among
operators that implement similar functionalities but with distinct cost/quality/timetradeoffs, and employs apriori and on-the-fly techniques for intelligent selection,
placement, and sharing of operators across a hybrid set of devices (stationary sensors,drones, and rovers) to maximize Quality of Service (QoS) and resource efficiency underdynamic situations. We formulate the microservice provisioning and instantiation as an(NP-hard) optimization problem and design efficient heuristic approaches to deploy
microservices by utilizing application and system context. AMPHI is evaluated in thecontext of smart firefighting with high-rise fires utilizing building sensors and autonomous
aerial mobile units. Through real-world testbeds and extensive simulations, we show how
AMPHI allows flexible and cost-effective execution of dynamically changing IoT workflows.
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Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in
Autonomous Driving
Eugen Šlapak and Matúš Dopiriak (Technical University of Košice, Slovakia); Mohammad Al
Faruque (UC Irvine, USA); Juraj Gazda (Technical University of Kosice, Slovakia); Marco Levorato(University of California, Irvine, USA)
Abstract: The metaverse is a virtual space that combines physical and digital elements,
creating immersive and connected digital worlds. For autonomous mobility, it enables new
possibilities with edge computing and digital twins (DTs) that offer virtual prototyping,
prediction, and more. DTs can be created with 3D scene reconstruction methods that
capture the real world's geometry, appearance, and dynamics. However, sending data forreal-time DT updates in the metaverse, such as camera images and videos from connectedautonomous vehicles (CAVs) to edge servers, can increase network congestion, costs, andlatency, affecting metaverse services. Herein, a new method is proposed based on
distributed radiance fields (RFs), multiaccess edge computing (MEC) network for video
compression and metaverse DT updates. RF-based encoder and decoder are used to create
and restore representations of camera images. The method is evaluated on a dataset ofcamera images from the CARLA simulator. Data savings of up to 80% were achieved for
H.264 I-frame - P-frame pairs by using RFs instead of I-frames, while maintaining high peaksignal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) qualitative
metrics for the reconstructed images. Possible uses and challenges for the metaverse andautonomous mobility are also discussed.

Sharing the Edge: System Status Aware Object Recognition Task Offloading
Chenyang Wang, Owen Eicher and Qi Han (Colorado School of Mines, USA)
Abstract: The combination of object recognition capability with Unmanned Aerial Vehicles(UAVs) and Unmanned Ground Vehicles (UGVs) benefits applications like remotesurveillance, search and rescue, and infrastructural monitoring. Offloading deeplearning-based object recognition models from UAVs to UGVs can address energy andcomputational constraints on UAVs. However, when multiple UAVs are in contact with one
single UGV, how to provide timely offloading decisions considering various system status
information such as dynamic network conditions and remaining energy levels on UAVs
remains under-explored. This paper presents our work in this area. Our online offloading
decision engine, running on the UAV's onboard computer, dynamically determines
offloading decisions and encoding bitrates considering fluctuating network conditions. In
addition, our task scheduler running on the UGV prioritizes tasks according to the systemstatuses of each UAV. We conducted extensive evaluations of our approach in both lab andInternational Conference on Smart Computing 2024 Osaka Japan63
field settings. Experiments show that our system reduces the end-to-end system latency by
up to 88% compared to a previous study that reduces both offload data size and local
computation.

Session 3: Smart resource management and optimization
Chair: Keiichi Yasumoto, NAIST, Japan
Time: July 1st, 10:15 – 12:15
Location: Conference Hall (12F)

Enhancing UAV Operational Efficiency through Cloud Computing and Autopilot System
Integration
Luca D’Agati, Francesco Longo, Giovanni Merlino and Antonio Puliafito (University of Messina,
Italy); Giuseppe Tricomi (Institute for High Performance Computing and Networking of ICAR, Italy& University of Messina, Italy)
Abstract: This paper presents a groundbreaking study on the integration of cloudcomputing with UAV autopilot systems, aiming to significantly enhance operational
efficiency, scalability, and autonomy in unmanned aerial vehicle (UAV) operations. Byleveraging the synergies between the Internet of Things (IoT), cloud computing, andsophisticated UAV autopilot technologies, we introduce a novel architectural framework
designed to overcome existing challenges in UAV operations, such as advanced mission
planning, real-time data analytics, and dynamic resource management. The presented
approach emphasizes the integration of the MAVLink protocol and the Robot Operating
System (ROS) with the PX4 autopilot system, marking a crucial step towards achieving
autonomous UAV operations characterized by enhanced safety and reduced mission
execution times. Empirical assessments, supported by detailed operational scenario
analyses, demonstrate the effectiveness of this integration, revealing improvements in UAV
performance metrics. The outcomes highlight the potential of cloud computing integration
with UAV operational paradigms, paving the way for further exploration in autonomous
systems and cloud-assisted IoT applications.

Water-COLOR: Water-COnservation using a Learning-based Optimized Recommender
Guang Xue Zhang and David L. Feldman (University of California, Irvine, USA); Yiming Lin(University of California, Berkeley, USA); Sharad Mehrotra and Nalini Venkatasubramanian(University of California, Irvine, USA); Thayer Drew and Kim Sentovich (Rachio Inc., USA); Owen
Veranth (Analytiks Inc., USA)International Conference on Smart Computing 2024 Osaka Japan

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Abstract: Efficient water use, particularly in the realm of irrigation, has emerged as a
critical concern in regions suffering from persistent drought, such as California and Florida.
With the advent of smart irrigation controllers encouraged by environmental policies, a
new paradigm of water management is gaining traction. Among these, the Rachio smart
controller has garnered significant attention. However, without direct feedback or actual
water usage data, optimizing these irrigation systems for enhanced efficiency remains
challenging. This paper introduces Water-COLOR, a novel recommendation system
integrated within the Rachio smart controller's framework to address this challenge. The
system leverages similar landscape profiles to suggest irrigation schedules that are bothwater-efficient and user-preferable. By analyzing manual user interactions with thecontroller, Water-COLOR infers user satisfaction, which, along with estimated water usage,
informs the adaptation of irrigation plans. The system eschews the need for additional
sensors, thereby reducing infrastructure requirements. Our evaluation demonstrates
consistent performance across diverse climatic regions and indicates that the system's
recommendations could significantly contribute to water conservation efforts. The results
not only showcase the potential of Water-COLOR to enhance the efficiency of existingsmart irrigation systems but also open avenues for deploying real-time, data-driven
environmental solutions.

From Calls to Scales: Harnessing Smartphone for Daily Mass Measurement
Hamada Rizk (Osaka University, Japan); Merna Hesham (AUC, Egypt); Moustafa Youssef(American University in Cairo, Egypt)
Abstract: Mobile technologies, particularly smartphones, have become integral to human
life, influencing nearly every aspect of daily activities. Their ubiquity and advanced
capabilities have led to them being a solution to a wide range of problems. Recent research
has focused on leveraging the existing sensors in smartphones to supplant more costly andless accessible dedicated hardware. In this vein, we introduce MobiScale, a novel systemdesigned to measure the weight of light objects using the built-in accelerometer andvibration motor of smartphones. This system utilizes advanced feature extraction
techniques to effectively capture the temporal and spectral characteristics of the weightdata. The core of MobiScale is its CNN-LSTM architecture, which processes these features
to estimate weights accurately. The implementation of various regularization techniques
has been vital in enhancing the system’s ability to generalize, thereby improving its
performance. Tested across multiple devices and under various conditions, MobiScale has
shown consistent and precise performance, achieving a mean square error of just 12
grams in weight estimation, underscoring its potential as a versatile and reliable tool forweight measurement using smartphone technology.

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Cost-based Modeling and Optimization of Secure Matrix Multiplication in the Cloud
Richard Hernandez and Kemal Akkaya (Florida International University, USA); Soamar Homsi (Air
Force Research Laboratory – Information Directorate (AFRL/RI), USA)
Abstract: Machine Learning (ML) applications are prominent in many fields due to theirability to derive insights and automate processes. However, the need for sensitive data in
ML operations raises significant privacy concerns when outsourcing to the cloud. Multiparty
computation (MPC) is a promising method for privacy-preserving ML. However, it has asignificant computational overhead, mainly due to the required Matrix Multiplications(MM). This paper improves the secure MM performance by adopting and integrating theStrassen algorithm in the MPC protocols. The challenge is tuning the different cloudinfrastructure parameters to maximize the Strassen algorithm's benefits. To address thischallenge, we formulated a multiobjective optimization problem. The goal is to improve theMM's response time while minimizing the cloud resource costs and potential security
losses, given a successful cyberattack on the cloud. We then developed several solutions to
identify the best parameters (e.g., matrix dimension, number of MPC nodes, among others)
to optimize the trade-off among different optimization goals. Our implementation over
SPDZ shows that we can significantly reduce resource costs and minimize potential security
loss with respect to the naive MM over MPC approach.

Optimizing Risk-averse Human-AI Hybrid Teams
Andrew S Fuchs (University of Pisa & National Research Council (CNR), Italy); Andrea Passarella
and Marco Conti (IIT-CNR, Italy)
Abstract: We anticipate increased instances of humans and AI systems working together inwhat we refer to as a hybrid team. The increase in collaboration is expected as AI systems
gain proficiency and their adoption becomes more widespread. However, their behavior is
not error-free, making hybrid teams a very suitable solution. As such, we consider methods
for improving performance for these teams of humans and AI systems. For hybrid teams,
we will refer to both the humans and AI systems as agents. To improve team performanceover that seen for agents operating individually, we propose a manager which learns,
through a standard Reinforcement Learning scheme, how to best delegate, over time, theresponsibility of taking a decision to any of the agents. We further guide the manager's
learning so they also minimize how many changes in delegation are made as a result of
undesirable team behavior. We demonstrate the optimality of our manager's performancein several grid environments which include failure states which terminate an episode andshould be avoided. We perform our experiments with teams of agents with varying degreesof acceptable risk, in the form of proximity to a failure state, and measure the manager's
ability to make effective delegation decisions with respect to its own risk-based constraints,
then compare these to the optimal decisions. Our results show our manager canInternational Conference on Smart Computing 2024 Osaka Japan

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successfully learn desirable delegations which result in team paths near/exactly optimal
with respect to path length and number of delegations.

Session 4: Best Paper Candidates
Chair: Dario Bruneo, University of Messina, Italy
Time: July 1st, 13:45 – 15:30
Location: Conference Hall (12F)​An MCS Navigation System Based on Road Surface Quality for Bicycle Riders
Federico Montori, Rocco Pastore, Luca Sciullo and Luciano Bononi (University of Bologna, Italy);
Luca Bedogni (University of Modena and Reggio Emilia, Italy)
Abstract: Road surface quality is a major concern for bicycle riders and plays an importantrole in the mobility infrastructure. In the era where smarter cities aim to increase thewell-being of citizens and the efficiency of infrastructures, navigation systems relying on
Mobile Crowdsensing (MCS) are mostly designed for car drivers, and account for roadtraffic conditions. To cover the gap, in this paper, we propose a full architectural pipeline of
an MCS-based navigation system for bicycle riders that accounts for the road surface
quality. The MCS paradigm leverages the sensor data produced by the personal devices ofparticipating citizens to describe phenomena of common interest. Our system classifies
road segments using inertial sensor data gathered by users, using a combination ofsupervised and unsupervised methods, as human labeling in this context is impractical andtoo subjective. We prove the efficacy of our method in a controlled environment, and thenwe implement and deploy the full system in a real city, finally reporting on its results.

Clustering-Enhanced
Reinforcement
Resource-Constrained Devices

Learning

for

Adaptive

OffloadinginKhoa Anh Tran (National Institute of Information and Communications Technology, Japan);Nguyen Do Van (NICT, Japan); Minh-Son Dao (National Institute of Information andCommunication Technology, Japan); Koji Zettsu (NICT, Japan)
Abstract: The Federated Edge Artificial Intelligence (Edge AI) deploys AI applications on
Internet-of-Things (IoT) devices, addressing data privacy concerns in the real world. To
achieve effective Federated Learning (FL), three challenges must be addressed: i) limited
computing power on devices, ii) non-uniform impacts on devices, and iii) adaptability tochanging network conditions. This study introduces a new algorithm called Adaptive
Offloading Point (AOP), designed to accelerate local training on constrained devices. It
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decomposes deep neural network (DNN) layer blocks, enabling training on both client andserver sides. The novelty of the proposed method lies in using a reinforcement
learning-based Gaussian mixture model (GMM) clustering to dynamically determine DNN
layer offloading, addressing nullability, computation uniformity, training time, and network
bandwidth variation issues. Experimental results on real devices, using vision transformer(ViT) models with an identification image dataset, show that AOP's training time issignificantly faster than that of previous baseline methods.

PREDEA: Predicted-RoI based Resource-Efficient Object Detection with Aggregation
Yoshikazu Watanabe, Seiya Shibata and Takashi Takenaka (NEC Corporation, Japan)
Abstract: In recent years, there has been a growing need to perform object detection evenat the edge. Since the edge environment has tight physical constraints, resource efficiency
is a key challenge to executing object detection at high throughput. This paper proposes
PREDEA, a predicted-RoI (Region of Interest) based Resource-Efficient object detection with
RoI aggregation. PREDEA prepares expected RoIs for the incoming subsequent frames
before the arrival using a lightweight predictor, and it generates aggregated images bycombining the cropped images for the RoIs on the reception of the next frames. Object
detection inference is performed on the aggregated images, which contain less backgroundregions than the original frames. Our evaluation with the MOT17 dataset confirmed that
the PREDEA reduced the computation resources needed for object detection inference byhalf while keeping degradation in accuracy to almost zero.

TASR: A Novel Trust-Aware Stackelberg Routing Algorithm to Mitigate Traffic Congestion
Doris E M Brown, Venkata Sriram Siddhardh Nadendla and Sajal K. Das (Missouri University of
Science and Technology, USA)
Abstract: Stackelberg routing platforms (SRP) reduce congestion in one-shot traffic
networks by proposing optimal route recommendations to selfish travelers. Traditionally,
Stackelberg routing is cast as a partial control problem where a fraction of traveler flow
complies with route recommendations, while the remaining respond as selfish travelers. In
this paper, a novel Stackelberg routing framework is formulated where the agents exhibit
probabilistic compliance by accepting SRP's route recommendations with a trust
probability. A greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) is
proposed for SRP to compute unique path recommendations to each traveler flow with aunique demand. Simulation experiments are designed with random travel demands withdiverse trust values on real road networks such as Sioux Falls, Chicago Sketch, and Sydney
networks for both single-commodity and multi-commodity flows. The performance of TASR
is compared with state-of-the-art Stackelberg routing methods in terms of traffic

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congestion and trust dynamics over repeated interaction between the SRP and thetravelers. Results show that TASR improves network congestion without causing asignificant reduction in trust towards the SRP, when compared to most well-known
Stackelberg routing strategies.

Session 5: Federated Learning for Networked Systems
Chair: Antonio Puliafito, University of Messina, Italy
Time: July 2nd, 11:00 – 12:15
Location: Conference Hall (12F)

DP-MTFL: Differentially Private Multi-Tier Federated Learning for IoT applications
Ramin Soleimani (University College Cork & SFI Centre for Research Training in Advanced
Networks for Sustainable Societies, Ireland); Dirk Pesch (University College Cork, Ireland)
Abstract: Differentially Private Federated Learning (DP-FL) is a privacy-preserving machinelearning paradigm. Building on a standard DP-FL approach, we introduce and implement a
novel Differentially Private Multi-Tier Federated Learning approach specifically tailored for
IoT applications, specifically short-term load forecasting. Our method integrates a Sampled
Gaussian Mechanism for differential privacy with a hierarchical federated learning
approach. By enabling local servers to participate in learning with other local federations
while adhering to approximate differential privacy with respect to the global server, ourproposed method mitigates privacy risks associated with the failure parameter delta within
the approximate differential privacy context. Our approach fosters reduced information
sharing with less trusted entities, such as global servers. We specifically study the optimal
number of local rounds on global model convergence. In the evaluation of our method, we
utilise an energy consumption dataset from the UK Power Networks Low Carbon London
project. Our results show that our approach achieves privacy preserving objectives whileobtaining the optimal number of local rounds that minimise the prediction error. Our
research contributes to advancing privacy-preserving federated learning methods,
particularly in the context of IoT applications.

FMLFS: A federated multi-label feature selection based on information theory in IoTenvironment
Afsaneh Mahanipour (University of Kentucky, USA); Hana Khamfroush (University of Kentucky,
USA)

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Abstract: In certain emerging applications such as health monitoring wearable and trafficmonitoring systems, Internet-of-Things (IoT) devices generate or collect a huge amount ofmulti-label datasets. Within these datasets, each instance is linked to a set of labels. The
presence of noisy, redundant, or irrelevant features in these datasets, along with the curseof dimensionality, poses challenges for multi-label classifiers. Feature selection (FS) proves
to be an effective strategy in enhancing classifier performance and addressing thesechallenges. Yet, there is currently no existing distributed multi-label FS method
documented in the literature that is suitable for distributed multi-label datasets within IoTenvironments. This paper introduces FMLFS, the first federated multi-label feature
selection method. Here, mutual information between features and labels serves as therelevancy metric, while the correlation distance between features, derived from mutual
information and joint entropy, is utilized as the redundancy measure. Following
aggregation of these metrics on the edge server and employing Pareto-based bi-objective
and crowding distance strategies, the sorted features are subsequently sent back to the IoT
devices. The proposed method is evaluated through two scenarios: 1) transmitting
reduced-size datasets to the edge server for centralized classifier usage, and 2) employing
federated learning with reduced-size datasets. Evaluation across three metrics performance, time complexity, and communication cost - demonstrates that FMLFS
outperforms five other comparable methods in the literature and provides a good trade-off
on three real-world datasets.

Towards Opportunistic Federated Learning Using Independent Subnetwork Training
Victor Romero II (Nara Institute of Science and Technology, Japan & University of the Philippines,
Philippines); Tomokazu Matsui, Yuki Matsuda, Hirohiko Suwa and Keiichi Yasumoto (NaraInstitute of Science and Technology, Japan)
Abstract: Enabling federated learning in opportunistic networks unlocks the potential formachine learning in challenging environments like disaster zones and remote regions.
However, the divergent models induced by dynamic node encounters, combined withcomplete parameter overlap in model-homogeneous training lead to catastrophic
interference, which disrupts training progress. Furthermore, when whole models must betransmitted, nodes with shorter contact duration are limited from participating in the
training process. To address these challenges, we propose a different approach for training
neural networks in opportunistic settings that leverages independent subnetworks andsequential training. We partition the original neural network into non-overlapping
subnetworks and assign each to a unique node. These subnetworks are then trained andexchanged repeatedly during node encounters, exposing them further to diverse datasets.
As a consequence, we achieve parallel and conflict-free progress while minimizing
participation costs. Our experiments demonstrate that continuous training andsubnetwork accumulation foster the development of a more robust model. Moreover, byInternational Conference on Smart Computing 2024 Osaka Japan

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utilizing pre-trained backbones as feature extractors, we achieve a test accuracy of 75.06%
on MEDIC's disaster damage severity assessment task, demonstrating that the approach
can be adopted in resource-constrained and dynamic scenarios in the real world.

Session 6: Smart Computing for Security and Anomaly Detection
Chair: Hana Khamfroush, University of Kentucky, USA
Time: July 2nd, 13:15 – 15:00
Location: Conference Hall (12F)

Explainable Deep Learning Models for Dynamic and Online Malware Classification
Quincy Card, Daniel Simpson, Kshitiz Aryal and Maanak Gupta (Tennessee Technological
University, USA); Sheik R Islam (Rutgers University, USA)
Abstract: In recent years, there has been a significant surge in malware attacks,
necessitating more advanced preventive measures and remedial strategies. While several
successful AI-based malware classification approaches exist-categorized into static,
dynamic, or online analysis-most successful AI models lack easily interpretable decisions
and explanations for their processes. Our paper aims to delve into explainable malware
classification across various execution environments (such as dynamic and online),thoroughly analyzing their respective strengths, weaknesses, and commonalities. To
evaluate our approach, we train Feed Forward Neural Networks (FFNN) and Convolutional
Neural Networks (CNN) to classify malware based on features obtained from dynamic andonline analysis environments. The feature attribution for malware classification is
performed by explainability tools, SHAP, LIME and Permutation Importance. We perform adetailed evaluation of the calculated global and local explanations from the experiments,
discuss limitations and, ultimately, offer recommendations for achieving a balanced
approach.

Leveraging Homeostatic Plasticity to Enable Anomaly Detection in Spiking Neural Networks
Rawan M. A. Nawaiseh, Fabrizio De Vita and Enrico Catalfamo (University of Messina, Italy);
Dario Bruneo (Universita di Messina, Italy)
Abstract: Anomaly detection systems are crucial for identifying deviations from normal
behavior in various domains, ranging from cybersecurity to industrial system monitoring.
Traditional approaches often rely on predefined rules or supervised learning algorithms,
which may struggle to adapt to complex and evolving patterns. As a result, deep learningtechniques have been employed to address these challenges, but they usually requireInternational Conference on Smart Computing 2024 Osaka Japan

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significant computational resources and memory for training and inference. On thecontrary, Spiking Neural Networks (SNNs), inspired by the brain neural architecture, excel
in effectively capturing complex temporal patterns present in real-world data throughunsupervised learning, potentially leading to more robust performance while also
conserving memory and reducing energy consumption. In this paper, we employ the
homeostatic plasticity characteristic that governs the synapses behaviour in SNN to
implement an anomaly detection system designed for processing raw vibration data.
Initially, we generate spike trains to represent the input data. These are then fed into a
Balanced Spiking Neural Network (BSNN) to detect suspicious anomalies. The proposed
SNN learning algorithm dynamically adjusts synaptic weights based on the precise timing of
spikes between neurons, and introduces a combination of Spike Timing Dependent
Plasticity (STDP) and reverse STDP techniques. Experimental results conducted on a publicavailable vibration dataset demonstrate the effectiveness of the proposed approach inaccurately identifying anomalies while maintaining low false positive rates.

On the Role of Re-Descending M-estimators in Resilient Anomaly Detection for Smart
Living CPS
Sahar Abedzadeh and Shameek Bhattacharjee (Western Michigan University, USA)
Abstract: Anomaly-based attack detection methods that rely on learning the benign profile
of operation are commonly used for identifying data falsification attacks and faults incyber-physical systems. However, most works do not assume the presence of attacks (ordata errors) while training the anomaly detectors- and their impact on eventual attack
detection performance during the test set. Some robust learning methods also
overcompensate mitigation which leads to increased false positive in the absence of
attacks/threats during training. To achieve this balance, this paper proposes a framework
to enhance the robustness of previous anomaly detection frameworks in smart living
applications, by introducing three profound design changes for threshold learning of timeseries anomaly detectors:(1) Tukey biweight loss function instead of square loss function (2)adding quantile weights to regression errors to Tukey Loss (3) modifying the definition of
empirical cost function from MSE to the harmonic mean of quantile weighted Tukey losses.
We show that these changes mitigate performance degradation in anomaly detectors
caused by poisoning attacks during training- while is simultaneously able to prevent false
alarms in the absence of training set attacks. We evaluate our work using a proof ofconcept that uses state-of-the-art anomaly detection in smart living CPS that detects false
data injection in smart metering.

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A Domain-Specific Tool for the Creation of Machine Learning Models with Imbalanced
Datasets
Dmitrii Fomin (Université Grenoble Alpes, France); Philippe Lalanda (Grenoble University,
France); Denis Morand (Schneider Electric, France)
Abstract: In this paper, we introduce a domain-specific tool designed to streamline thedevelopment of deep learning models for industrial applications, particularly those facing
the challenges posed by imbalanced datasets. The advent of pervasive computing has
allowed the creation of new smart services based on machine learning, yet the complexityof effectively harnessing data from IoT devices remains a challenge. The datacharacteristics ranging from noise and scarcity to a lack of labels and imbalance - present
significant obstacles for their utilization. A focal point of this research is the issue of classimbalance, which significantly impedes the achievement of accurate predictive models,
especially in critical applications like anomaly detection. Our paper presents a solution
through the development of a specialized tool that integrates state-of-the-art techniquesand metrics specifically designed to deal with class imbalance. This tool also adapts tovarious constraints set forth by domain experts, thereby facilitating the creation of
optimized models suited to specific industrial needs. To demonstrate its practicality andeffectiveness, we apply our tool in a case study focusing on the detection of motor
malfunctions in the context of Industry 4.0, in collaboration with Schneider Electric.

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Industry Session
Chair: Kyoungsoon Kim, AIST, Japan
Time: June 30th, 15:45 – 16:45
Location: Conf. Hall (12F)

Threshold Estimation-assisted Unsupervised Patch-wise Model for Industrial Inspection of
Anomaly
Yang Chen, Peiyue Yuan, Yanyu Wang and Chai Kiat Kiat Yeo (Nanyang Technological University,
Singapore); David Aik-Aun Khoo, Minhoe Hur and Keng Teck Ma (Hyundai Motor Group
Innovation Center in Singapore, Singapore)

OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization
Jose Talusan and Rishav Sen (Vanderbilt University, USA); Ava Pettet, Aaron Kandel, Yoshinori
Suzue and Liam Pedersen (Nissan Advanced Technology Center Silicon Valley, USA); Ayan
Mukhopadhyay and Abhishek Dubey (Vanderbilt University, USA)International Conference on Smart Computing 2024 Osaka Japan

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PhD Forum
Time: June 30th, 16:45 – 18:15, (Poster: June 30th, 16:45 – 18:00)
Location: 1008 (10F), (Poster: Foyer (12F))

MalFormer 001: Transformer-based Multimodal Fused Attention Malware Detector
Pradip Kunwar (Tennessee Technological University, USA)

Sustainable Route Planning and Efficient Computation Offloading in Urban Air Mobility
Debjyoti Sengupta (Missouri University of Science and Technology, USA)

Learning at the Time of Disasters
Victor Romero II (Nara Institute of Science and Technology, Japan & University of the Philippines,
Philippines)

Trust-Aware Routing of Human Drivers to Mitigate Traffic Congestion
Doris E M Brown (Missouri University of Science and Technology, USA)

Deep Neural Networks at the Edge
Robert Viramontes (University of Wisconsin Madison, USA)

Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in
Autonomous Driving
Matúš Dopiriak (Technical University of Košice, Slovakia)

Drug Solubility And Subcategory Prediction Using SMILES Strings
Sarwan Ali (Georgia State University, USA)International Conference on Smart Computing 2024 Osaka Japan

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Towards Efficient Urban Mobility: Leveraging GNN and MTL for Demand Forecasting
Samir Amitkumar Gupta (Vanderbilt University, USA)International Conference on Smart Computing 2024 Osaka Japan

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Posters | Demos
Time: June 30th, 16:45 – 18:00
Location: Foyer (12F)

Posters
An Implementation of Private Function Evaluation Using FHE and TEE for Smart Computing
Systems
Ruixiao Li, Ryutaro Onishi and Hayato Yamana (Waseda University, Japan)

Solving Sequential Competitive Facility Location Challenges: Using Parallel Genetic
Algorithms
Sadan Kulturel-Konak and Abdullah Konak (Penn State Berks, USA); Lawrence Snyder (Lehigh
University, USA)

Reinforcement Learning based Matching for Parallel Computation Offloading in Dynamic
Fog Computing Networks
Tran Hoa and Dong Seong Kim (Kumoh National Institute of Technology, Korea)

OpenCyberCity Testbed’s Recent Progress in Smart City Management
Mostafa Zaman, Ahmed Malik, Maher Al Islam and Courtney Van (Virginia Commonwealth
University, USA); Alyssa Pollard and Brittany Davis (Virginia State University, USA); Sherif
Abdelwahed(Virginia Commonwealth University, USA); Nasibeh Zohrabi (Pennsylvania State
University, USA)Demos
A Table-top Interface for Real-time Coaching in Abacus Learning
Yuki Matsuda (Okayama University)

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Enhanced Pedestrian Detection Model Transfer-Trained on YOLOv8 Using DenseFused RGB
and FIR Images
Yoshihara Arata, Ismail Arai, Arata Endo, Kazutoshi Fujikawa and Masatoshi Kakiuchi (NaraInstitute of Science and Technology)

A Demonstration of Voice-Interactive AI Agents for Vehicles Utilizing Multiple LLMs
Toru Furusawa, Masatoshi Saitoh (Toyota Motor Corporation)

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Local Restaurants & Attractions
Osaka Castle
URL: https://www.osakacastle.net/
Location: 1-1 Osakajo, Chuo-ku, Osaka 540-0002
The symbol of Osaka. From the top floor of Osaka Castle, you can have a panoramic view ofOsaka City.

Tsutenkaku
URL: https://www.tsutenkaku.co.jp/
Location: 1-18-6 Ebisu Higashi, Naniwa-ku, Osaka CityOne of the landmark towers in Osaka. There's a 'Billiken' statue at the observatory;
touching its feet is said to bring good luck.

Osaka City Central Public Hall
URL: https://osaka-chuokokaido.jp/
Location: 1-1-27 Nakanoshima, Kita-ku, Osaka 530-0005
Nationally designated important cultural property. One of the symbols of Osaka
Nakanoshima in the neo-renaissance style.

Bank of Japan Osaka Branch Old Building
URL: https://www3.boj.or.jp/osaka/guide/tour-index.html
Location: 2-1-45 Nakanoshima, Kita-ku, Osaka 530-8660
Authentic Western-style architecture of brick and stone. Tours of the building interior areavailable.

Umeda Sky Building/Floating Garden Observatory
URL: https://www.skybldg.co.jp/en/
Location: 1-1-88 Oyodonaka, Kita-ku, Osaka-shi, Osaka Prefecture
One of the symbolic buildings of Osaka with 360-degree open-air rooftop observation floor.
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Osaka Nakanoshima Museum of Art
URL: https://nakka-art.jp/en/
Location: 4-3-1 Nakanoshima, Kita-ku, Osaka
The collection comprises over 6,000, focusing on representative art and design pieces fromJapan and abroad.

National Museum of Art, Osaka
URL: https://www.nmao.go.jp/en/
Location: 4-2-56 Nakanoshima, Kita-ku, Osaka
An exceptionally rare, fully underground art museum that houses around 8,200 collections.

Universal Studios Japan
URL: https://www.usj.co.jp/web/en/us
Location: 2-1-33 Sakurajima, Konohana-ku, Osaka 554-0031
One of Osaka's representative theme parks. You can experience the worlds of The
Wizarding World of Harry Potter™, Minions, Super Mario, and others.

Kaiyukan
URL: https://www.kaiyukan.com/language/eng/
Location: 1-1-10 Kaigandori, Minato-ku, Osaka 552-0022
A prominent aquarium in Osaka, which houses and displays about 620 species and 30,000
creatures.

Kuromon Market
URL: https://kuromon.com/en/
Location: 2-4-1 Nihonbashi, Chuo-ku, Osaka 542-0073
In the market, where wholesale and retail combined amount to about 150 stores, it is very
popular with tourists.

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Sakebo Uoman Yodobashi
URL: https://www.cfs-japan.com/brands/uoman/shops/umeda/en/
Location: 8F Yodobashi Umeda, 1-1 Ofuka-cho, Kita-ku, Osaka-shi, Osaka 530-0011
You can enjoy dishes such as fresh sashimi platters and sea bream rice cooked in a
Shigaraki clay pot.

Osaka Botejyu
URL: https://osaka-botejyu.com/en/
Location: 3-7-20 Namba, Chuo-ku, Osaka 542-0076
The Osaka Botejyu main store is a popular, long-established restaurant for okonomiyaki,
one of Osaka's soul foods.

Kushikatsu Daruma
URL: https://www.kushikatu-daruma.com/
Location: 10F LUCUA 1100, 3-1-3 Umeda, Kita-ku, Osaka-shi, Osaka
Kushikatsu Daruma is a popular restaurant that serves kushikatsu, one of Osaka's soul
foods. You can experience the unique Osaka culture of "no double-dipping" the sauce.

sumile OSAKA
URL: https://love-central.jp/sumile/
Location: 2-1-18 Nishitenma, Kita-ku, Osaka City
This restaurant is a casual Italian restaurant based on the concept of Seafood & Grill.

Created by Web Chair, Ruixiao Li and Takuya Suzuki, Waseda University

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International Conference on Smart Computing 2024 Osaka Japan