Reviewerly/exHarmony huggingface dataset:
A multi-view heterogeneous knowledge learning method for personalized POI recommendation

Authors:
raw_author_name: Chenzhong Bin
raw_affiliation_string: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
raw_author_name: Wenqiang Liu
raw_affiliation_string: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
raw_author_name: Hantao Ding
raw_affiliation_string: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
raw_author_name: Yimin Wen
raw_affiliation_string: Guilin Tourism University, Guilin, China; School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

Abstract: Existing POI recommendation methods often fail to capture the fine-grained preferences of users and face the challenge of modeling multiple relationships. Moreover, knowledge graph-based recommendation methods are limited in storing dynamic user trajectories, making them unsuitable for POI recommendation scenarios. In this paper, we propose a Multi-View Heterogeneous Knowledge learning model that utilizes techniques for heterogeneous knowledge representation learning and multi-view context modeling. Our model comprehensively models user preferences and the relationships between users and POIs by utilizing information from users’ visiting sequences and POI attributes knowledge graph. Specifically, we design a heterogeneous knowledge embedding method to learn the representation of users and POIs using POI attribute knowledge and users’ visiting sequences. Additionally, we constructed a user trajectory similarity graph and a POI attribute similarity graph to explore potential relations between users and between POIs. The former measures the similarity of user behaviors based on user visit sequences, and the latter quantifies the similarity between different POIs through a novel feature mapping method. Finally, we propose a multi-view hybrid learning method that combines unsupervised and supervised learning paradigms to model complex relationships, improving the overall recommendation performance. Extensive experiments on real-world datasets validate the effectiveness of our method.
Semiquantum proxy blind signature based on quantum teleportation

Authors:
raw_author_name: Xiao TAN
raw_author_name: Tian-Yu YE

Abstract: <p indent="0mm">In this paper, we propose a novel semiquantum proxy blind signature scheme using quantum teleportation based on <italic>χ</italic> states, where the original message owner, proxy signer, and third party are quantum participants with complete quantum capabilities, while the original signer and signature verifier are semiquantum participants with limited quantum capabilities. Our protocol not only has complete blindness, unforgeability, and nonrepudiation, but it also resists the attack behavior of an eavesdropper. Because of the limited quantum capabilities of the original signer and signature verifier, our protocol does not require them to prepare and measure the quantum superposition and quantum entangled states when used in practical applications. Hence, our protocol can reduce the burden of quantum state preparation and measurement for the original signer and signature verifier.
Development of a smart home suitability indicator and indicative self-assessment platform for the disabled facilities grants (DFGs)

Authors:
raw_author_name: Saheed Ajayi
raw_author_name: Muhammad Azeem Abbas

Abstract: Purpose Delay in housing adaptation is a major problem, especially in assessing if homes are suitable for the occupants and in determining if the occupants are qualified for the Disabled Facilities Grant (DFG). This paper describes the development of two self-administered intelligent integrated assessment tools from the DFG Adapt-ABLE system: (1) The Home Suitability Assessment Platform, which is a preventive mechanism that allows assessment of the suitability of homes based on occupants’ mobility status and (2) an indicative assessment platform that determines if the applicants are qualified for the DFG to prevent lengthy delays. Design/methodology/approach The adopted method aligned with a development study approach: a grounded literature review, a severity measurement approach, two stakeholder engagement workshops, four brainstorming sessions and four focus group exercises. The system development relied on Entity–Relationship Diagram (ERD) technique for data structures and database systems design. It uses DFG context sensitivity with alignment with DFG guidance, interlinkages and interoperability between the assessment tools and other platforms of the integrated Adapt-ABLE system. Findings The assessment tools are client-level outcomes related to accessibility, usability and activity based on the assessment process. The home suitability platform shows the percentage of the suitability of a home with assessment results that suggest appropriate action plans based on individual mobility status. The indicative assessment combines the function of referral, allocation, assessment and test of resources into an integrated platform. This enables timely assessment, decision-making and case-escalation by Occupational Therapists based on needs criteria and the eligibility threshold. Originality/value These assessment tools are useful for understanding occupants’ perception of their physical housing environment in terms of accessibility, suitability and usability based on basic activities of daily living and their mobility status. The indicative self-assessment tool will substantially cut down the application journey. The developed tools have been recommended for use in the CSJ Disability Commission report and the UK government Guidance on DFGs for local authorities in England.
Deep survival analysis model for incident clearance time prediction

Authors:
raw_author_name: Eui-Jin Kim
raw_affiliation_string: Department of Transportation System Engineering, Ajou University, Suwon, Republic of Korea

Abstract: Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers "duration dependence" of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.
Knowledge graph‐driven data processing for business intelligence

Authors:
raw_author_name: Lipika Dey
raw_affiliation_string: Ashoka University Delhi India

Abstract: Abstract With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre‐training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use‐cases from various domains. This article is categorized under: Algorithmic Development &gt; Text Mining Application Areas &gt; Business and Industry
Benchmarking YOLOv5 models for improved human detection in search and rescue missions

Authors:
raw_author_name: Namat Bachir
raw_affiliation_string: Electrical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain, 15551, United Arab Emirates
raw_author_name: Qurban Ali Memon
raw_affiliation_string: Electrical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain, 15551, United Arab Emirates

Abstract: Drone or unmanned aerial vehicle (UAV) technology has undergone significant changes. The technology allows a UAV to carry out a wide range of tasks with an increasing level of sophistication, since drones can cover a large area with cameras. Meanwhile, the increasing number of computer vision applications utilizing deep learning provides a unique insight into such applications. The primary target in UAV-based detection applications is humans, yet aerial recordings are not included in the massive datasets used to train object detectors, which makes it necessary to gather the model data from such platforms. You only look once (YOLO) version 4, RetinaNet, faster region-based convolutional neural network (R–CNN), and cascade R–CNN are several well-known detectors that have been studied in the past using a variety of datasets to replicate rescue scenes. Here, we used the search and rescue (SAR) dataset to train the you only look once version 5 (YOLOv5) algorithm to validate its speed, accuracy, and low false detection rate. In comparison to YOLOv4 and R–CNN, the highest mean average accuracy of 96.9% is obtained by YOLOv5. For comparison, experimental findings utilizing the SAR and the human rescue imaging database on land (HERIDAL) datasets are presented. The results show that the YOLOv5-based approach is the most successful human detection model for SAR missions.
Contextualised dictionary literacy, information literacy and information behaviour in the e-environment

Authors:
raw_author_name: Theo J.D. Bothma
raw_affiliation_string: Department of Information Science, University of Pretoria, Pretoria, South Africa
raw_author_name: Ina Fourie
raw_affiliation_string: Department of Information Science, University of Pretoria, Pretoria, South Africa

Abstract: Purpose Needs for information literacy, disparities in society, bridging digital divides, richness of information sources in electronic (e-)environments and the value of dictionaries have often been propagated. To improve information sources and information literacy training, information behaviour must be understood (i.e. all information activities). This paper conceptualises new opportunities for information sources (e.g. electronic dictionaries) to all society sectors, dictionary literacy and research lenses such as lexicography to supplement information literacy and behaviour research. Design/methodology/approach A scoping review of information literacy and behaviour, lexicography and dictionary literature grounds the conceptualisation of dictionary literacy, its alignment with information literacy, information activities and information behaviour and lexicography as additional research lens. Findings Research lenses must acknowledge dictionary use in e-environments, information activities and skills, meanings of information and dictionary literacy, the value of e-dictionaries, alignment with information behaviour research that guides the development of information sources and interdisciplinary research from, e.g. lexicography – thus contextualisation. Research limitations/implications Research implications – information behaviour and information literacy research can be enriched by lexicography as research lens. Further conceptualisation could align information behaviour, information literacy and dictionary literacy. Practical implications Dictionary training, aligned with information literacy training, can be informed by this paper. Social implications The value of dictionary literacy for all sectors of societies can be improved. Originality/value Large bodies of literature on information behaviour and lexicography individually do not cover combined insights from both.
Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

Authors:
raw_author_name: Michele Caselle
raw_affiliation_string: Department of Physics, University of Turin and INFN, Turin, Via Pietro Giuria 1, I-10125, Turin, Italy
raw_author_name: Elia Cellini
raw_affiliation_string: Department of Physics, University of Turin and INFN, Turin, Via Pietro Giuria 1, I-10125, Turin, Italy
raw_author_name: Alessandro Nada
raw_affiliation_string: Department of Physics, University of Turin and INFN, Turin, Via Pietro Giuria 1, I-10125, Turin, Italy

Abstract: A bstract Effective String Theory (EST) represents a powerful non-perturbative approach to describe confinement in Yang-Mills theory that models the confining flux tube as a thin vibrating string. EST calculations are usually performed using the zeta-function regularization: however there are situations (for instance the study of the shape of the flux tube or of the higher order corrections beyond the Nambu-Goto EST) which involve observables that are too complex to be addressed in this way. In this paper we propose a numerical approach based on recent advances in machine learning methods to circumvent this problem. Using as a laboratory the Nambu-Goto string, we show that by using a new class of deep generative models called Continuous Normalizing Flows it is possible to obtain reliable numerical estimates of EST predictions.
More Than Meets the Eye: Misconduct and Decoupling Against Blockchain for Supply Chain Transparency

Authors:
raw_author_name: Leonardo Marques
raw_affiliation_string: Supply Chain Management, Audencia Business School, Nantes, France

Abstract: The fashion industry has consistently ranked high in terms of its association with modern slavery. While scandals like the Rana Plaza incident have focused the world's attention on Asia, in Brazil, over 35,000 people have been rescued from conditions analogous to slavery in the past 15 years. Outsourcing and the underlying social structures that blur the implementation of outsourcing are central to this problem. Fashion supply chains are highly fragmented and labour intensive, exhibiting power asymmetry and informality. Although supply chain research has focused on how focal firms can control or improve supplier practices, this study examines focal firm misconduct and decoupling. Our research presents an intervention aimed at developing a blockchain solution to create a census of fashion working conditions in Brazil. The project included two non-governmental organisations, two fashion brands and their suppliers, the fashion retail association, a blockchain start-up and the research team (18 organisations). We contribute to both theory and practice, revealing that the blockchain's potential to ease transaction costs is outweighed by governance costs related to third-party supervision. We show that focal firms justify unfair purchasing practices via victimisation and diverting attention from themselves to consumers (‘unwilling to pay’) and suppliers (‘not behaving as expected’), unveiling how these drivers of organisational misconduct lead to supplier decoupling (means–end) and focal firm decoupling (policy–practice). Such underlying social structures sustain inaction or, at best, advance focal firms’ visibility of their supply chains while offering no true transparency to the broader society.
Real-time anomaly detection of short-term traffic disruptions in urban areas through adaptive isolation forest

Authors:
raw_author_name: Jingqin Gao
raw_affiliation_string: Department of Civil and Urban Engineering, C2SMARTER Center, Tandon School of Engineering, New York University, Brooklyn, NY, USA
raw_author_name: Kaan Ozbay
raw_affiliation_string: Department of Civil and Urban Engineering, C2SMARTER Center, Tandon School of Engineering, New York University, Brooklyn, NY, USA

Abstract: The escalating congestion impacts of short-term traffic disruptions, such as double parking or short-duration work zones, are gaining increased attention. This study introduces an enhanced isolation forest-based unsupervised anomaly detection algorithm to detect short-term traffic disruptions in urban areas. To enable the detection in real-time, a sliding window approach is introduced to allow the streaming data to be read and processed according to the window size. An adaptive threshold that automatically adjusts itself to overcome the limitations of the miss rate on local anomalies is shown to further enhance the model predictions. The proposed algorithm is empirically validated on four study sites in Manhattan, New York City. The experimental results demonstrate that the proposed unsupervised algorithm can effectively detect different types of traffic anomalies including accidents, work zones/road closures, traffic jams, double parking events and police activities. On all sites, the average detection rate is 81.1% for traffic jams and 89.6% for police activities, respectively. For three out of the four sites, the detection rate ranges from 71.4% to 100% for accidents, work zones and double parking. An optimizer using high-pass filter is also presented to further improve off-line detection. The primary advantages of this proposed computationally-efficient method are that its only required data input is travel time information and that it does not need labeled data for training, which make it highly deployable for real-time operational applications and can be easily adopted by other cities.
A control scheme for interval type‐2 T‐S fuzzy systems at constrained bit rates: A coding and decoding scheme

Authors:
raw_author_name: Sunjie Zhang
raw_affiliation_string: Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China
raw_author_name: Ying Yu
raw_affiliation_string: Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China

Abstract: Summary This study focuses on a class of interval type‐2 T‐S fuzzy systems under bit rate constraint(BRC), which can adapt to complex systems more flexibly than the traditional interval type‐1 T‐S fuzzy model. In order to guarantee the security of data transmission, and considering that the channel is affected by the bit rate during transmission, this study introduces a codec scheme in the interval type‐2 T‐S fuzzy system and applies it to the channel from the sensor to the controller. With the support of the codec scheme, this study explores the sufficient conditions to satisfy the data recoverability, and based on the satisfaction of these conditions, the system is proposed to satisfy the sufficient conditions for input‐to‐state stability (ISS) and the controller gain. Through the in‐depth analysis in this paper, this study proposes a control scheme that can fit complex systems more accurately and also confirms the importance of considering bit rate constraints in communication systems. Finally, the paper is validated by simulation with two specific examples, which fully demonstrate the superiority and effectiveness of the proposed control scheme.
Artificial intelligence in voice assistants: User benefits explored

Authors:
raw_author_name: Minseong Kim
raw_affiliation_string: Department of Management &amp; Marketing, College of Business Louisiana State University Shreveport Shreveport Louisiana USA
raw_author_name: Jihye Kim
raw_affiliation_string: Department of Integrated Strategic Communication, College of Communication and Information University of Kentucky Lexington Kentucky USA

Abstract: Abstract This study demonstrates that consumers' perceptions of voice assistant device (VAD) elements promote psychological ownership and interface flow experience, resulting in satisfaction with, and loyalty toward VADs. To that end, this study collected VAD users in the United States and conducted structural equation modeling to empirically examine the relationships among the variables. The empirical findings indicated that perceived artificiality and intelligence had significant influences on psychological ownership and flow experience. In addition, the empirical results demonstrated that psychological ownership resulted in flow experience with VADs, significantly affecting customer satisfaction and loyalty toward VADs. Finally, customer satisfaction had a significant impact on loyalty. Based on the empirical findings, this study provided meaningful implications for marketing scholars and practitioners.
Women’s cellphone access and ownership in rural Uganda: implications for self-care interventions

Authors:
raw_author_name: Amy Booth
raw_affiliation_string: Women's Health Research Institute, Vancouver, Canada

Abstract: Abstract Background The World Health Organization (WHO) call for cervical cancer elimination includes increasing global cervical screening coverage. HPV-based self-collection (HPV-SC) is a promising screening model for low- and middle-income countries (LMICs), and while digital technology, such as cellphones, can be used to streamline HPV-SC, there is limited data on digital technology penetration in LMICs. Determining women’s cellphone access is critical to understanding the feasibility of using cellphones to support HPV-SC. Methods This study is a secondary analysis of a larger clinical trial. Participants of a cluster-randomized trial comparing HPV-SC models in Uganda completed a survey, including questions about demographics, cellphone access/ownership, prior cervical cancer screening (CCS), and willingness to receive CCS information by text. A logistic regression model was used to determine adjusted rates of cellphone ownership using survey variables as factors. Results Of 2019 participants, 76.1% owned a cellphone. In non-cellphone owners ( n = 483), 82.4% had daily cellphone access and 7.3% had no access. Compared to non-cellphone owners, cellphone owners were significantly older, more educated, closer to major health centers, more likely to have prior CCS, and more willing to receive a CCS text. In the logistic regression model, the aforementioned variables were all significantly associated with the odds of owning a cellphone. Conclusions As health care systems consider adopting HPV-SC, it is imperative to understand digital technology penetration. The majority of participants were cellphone owners and were willing to receive CCS information by text; however, significant socioeconomic and demographic differences remain between cellphone owners and non-owners. Further investigation is needed to understand whether HPV-SC using cellphones is feasible in similar settings. Trial registration ISRCTN, 12767014 . ClinicalTrials.gov, NCT04000503 .