Reviewerly/exHarmony huggingface dataset:
Educating the Next Generation of Cybersecurity Experts

Authors:
raw_author_name: Katerina Goseva-Popstojanova
raw_affiliation_string: West Virginia University

Abstract: Abstract The overall goal of the NSF Division of Undergraduate Education (DUE) S-STEM funded "Attracting and Cultivating Cybersecurity Experts and Scholars through Scholarships" (ACCESS) program is to increase Cybersecurity-related STEM degree completion of low-income, high-achieving undergraduate students with demonstrated financial need and to generate knowledge about academic success, retention, persistence, graduation, and career pathways of these students to improve the education of future STEM workers. Specifically, ACCESS aims to contribute towards addressing the tremendous governmental and industry need for highly skilled cybersecurity experts. Program objectives include: (1) increasing annual enrollment of students in the B.S. in Computer and Information Sciences programs with specialization in Cybersecurity; (2) enhancing curricular and extra-curricular student support services and activities for students; (3) strengthening the partnerships with computer and information technology employers; and (4) investigating the impact of the curricular and co-curricular activities on student success. While significant research has been done relative to student success, retention, and persistence to graduation in STEM fields, cybersecurity is a new field of study and factors affecting student recruitment, academic success, retention, persistence to graduation within this field are not known. In year 1, students were recruited, applications were evaluated, and scholarships were awarded to nine academically talented students, beginning fall 2020. Of these students, four are female (one is from an underrepresented minority population) and five are male (three are from underrepresented minority populations). The students engage in a set of co-curriculum activities, including participation in: outreach activities; technical and career development seminars; a cybersecurity-focused student organization; and potentially, cybersecurity undergraduate research and summer internship opportunities. The paper and poster describe the background of the ACCESS program, recruitment and selection of ACCESS scholarship recipients, project activities, and challenges presented by the COVID-19 pandemic.
Upskilling to Meet Cloud Talent Needs

Authors:
raw_author_name: Lawrence Eric Meyer
raw_affiliation_string: Miami Dade College
raw_author_name: Elodie Billionniere
raw_affiliation_string: Miami Dade College

Abstract: Abstract Miami Dade College, based on demands in the workforce, has focused on creating emerging technology education, particularly Cloud technology. Business Cloud migration has accelerated over the last several years, and companies around the world are investing in their future with the cloud. With the increased demand for cloud-skilled professionals the last four years, we launched a cloud literacy initiative to meet cloud talent needs. This initiative aims to provide our students in the computing/IT fields with the knowledge, abilities and skills needed to accelerate their cloud-related learning. With the support of NSF ATE, we collaborated with Amazon Web Services (AWS) to create a new pathway for the next generation of cloud computing professionals. The course sequence was designed in conjunction with an AWS Educate team assisting in the design of course sequencing and degree plans to leverage their educational experience in teaching cloud technologies. The core curriculum designed for the cloud literacy initiative leveraged an existing pathway for an associate degree in networking technology, and then partially pulled classes from the design of the bachelor’s in information systems technology degree. The classes identified, used current offerings across our programs and included a focus on the supporting infrastructure of cloud systems: Databases, Linux OS, and Networking. With these three foundational classes that were cloudified, three cloud-focused courses based on industry certifications were developed: Cloud Essentials for AWS Cloud Practitioner and Cloud Infrastructure and Services for AWS Solutions Architect certifications with a capstone class completing the academic pathway. This new curriculum includes pedagogical changes to utilize project-based learning by incorporating resources and learning from multiple sources to best mimic real-world application, data, and design attributes. In addition to the associate degree, a college credit certificate in cloud computing was created to strengthen (re-)entering students in the workforce and dual enrollment students with credentials and employability skills by using high impact educational practices. Our cloud curriculum incorporates project-based learning approach, a real-world experience using the cloud technology. This poster shares strategies and pedagogical tools for teaching a cloud-focused curriculum for broader impact and student success.
Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

Authors:
raw_author_name: Quanwang Wu
raw_affiliation_string: College of Computer Science, Chongqing University,Chongqing,China,400044
raw_author_name: Jie Zeng
raw_affiliation_string: Chongqing University,National Experimental Teaching Demonstration Center,Chongqing,China,400044
raw_author_name: Junhao Wen
raw_affiliation_string: College of Big Data and Software Engineering, Chongqing University,Chongqing,China,400044
raw_author_name: Fengji Luo
raw_affiliation_string: School of Civil Engineering, The University of Sydney,Sydney,Australia,2006

Abstract: When deploying workflows in cloud environments, the use of Spot Instances (SIs) is intriguing as they are much cheaper than on-demand ones. However, SIs are volatile and may be revoked at any time, which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling techniques. Although some scheduling methods for SIs have been proposed, most of them are no more applicable to the latest SIs, as they have evolved by eliminating bidding and simplifying the pricing model. This study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile SIs in cloud environments. Based on Monte Carlo simulation and list scheduling, a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this problem. With the Monte Carlo simulation framework, MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling, and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection criteria. Experimental results show that the performance of MCLS is more competitive compared with traditional algorithms.
MCARS-CC: A Salable Multicontext-Aware Recommender System

Authors:
raw_author_name: Dina Nawara
raw_affiliation_string: Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
raw_author_name: Rasha Kashef
raw_affiliation_string: Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada

Abstract: Context-aware recommendation systems (CARSs) leverage contextual information, e.g., time, location, or mood, to generate more personalized recommendations with high accuracy; however, existing CARSs fall short in: 1) handling the high sparsity of data; 2) designing scalable solutions in real time; and 3) providing more personalized solutions with the current limited static contexts. This article proposes a multi-CARS based on consensus clustering (MCARS-CC) to solve these challenges. The item-based contextual information is acquired using explicit static and inferred contexts by applying sentiment analysis to the users’ reviews. The proposed model is experimented using contextual prefiltering and postfiltering techniques applied to two benchmark datasets, Yelp and TripAdvisor. The model is evaluated using mean absolute error (MAE), root-mean-squared error (RMSE), response time, precision, recall, and F-measure. The experimental results show that the proposed MCARS-CC model outperforms other baseline techniques using the accuracy and error-based metrics. Incorporating hypergraph partitioning algorithm (HGPA) could improve the MAE and RMSE by 25.96% and 8.94% (Yelp), respectively. Also, HGPA led to an 18.47% and 15.94% improvement ratio in terms of MAE and RMSE (TripAdvisor), respectively.
An efficient and secure certificateless aggregate signature scheme

Authors:
raw_author_name: Ran Xu
raw_affiliation_string: School of Computer Science, Shaanxi Normal University, Xi’an, China; State Key Laboratory of Cryptology, P.O. Box 5159, Beijing, China
raw_author_name: Yanwei Zhou
raw_affiliation_string: School of Computer Science, Shaanxi Normal University, Xi’an, China; State Key Laboratory of Cryptology, P.O. Box 5159, Beijing, China
raw_author_name: Qiliang Yang
raw_affiliation_string: Shanghai Computer Software Technology Development Center, Shanghai 201203, China
raw_author_name: Kunwei Yang
raw_affiliation_string: School of Computer Science, Shaanxi Normal University, Xi’an, China
raw_author_name: Yu Han
raw_affiliation_string: School of Computer Science, Shaanxi Normal University, Xi’an, China
raw_author_name: Bo Yang
raw_affiliation_string: School of Computer Science, Shaanxi Normal University, Xi’an, China
raw_author_name: Zhe Xia
raw_affiliation_string: School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

Abstract: In recent years, the Internet of Things (IoT) has been developing rapidly and popularizing the world. Smart home is one of its important applications, the devices and household appliances are connected together through the network to provide intelligent services. However, malicious attacks often occur during the transmission of data information, resulting in data leakage or data changes. Therefore, it is necessary to ensure the integrity and availability of data. Based on this, we propose a new pairing-free certificateless aggregate signature (CLAS) scheme, which achieves efficient authentication of multiple messages and improves the verification rate. In addition, we demonstrate its security based on the hardness of the elliptic curve discrete logarithm problem in the random oracle model. Comparison with other CLAS schemes proves that our scheme has better security and higher efficiency through performance analysis.
Multilingual Character Recognition Dataset for Moroccan Official Documents

Authors:
raw_author_name: Ali Benaissa
raw_affiliation_string: Data Science and Competitive Intelligence Team (DSCI), ENSAH, Abdelmalek Essaadi University (UAE), Tetouan, Morocco; Finance and Governance of Organizations team, Governance and Performance of Organizations laboratory, The National School of Management, Abdelmalek Essaadi University, Tangier, Morocco
raw_author_name: Abdelkhalak Bahri
raw_affiliation_string: Data Science and Competitive Intelligence Team (DSCI), ENSAH, Abdelmalek Essaadi University (UAE), Tetouan, Morocco
raw_author_name: Ahmad El Allaoui
raw_affiliation_string: Decisional Computing and Systems Modelling Team, Engineering Sciences and Techniques Laboratory, Faculty of Sciences and Techniques Errachidia, Moulay Ismail University of Meknes, Morocco

Abstract: This article focuses on the construction of a dataset for multilingual character recognition in Moroccan official documents. The dataset covers languages such as Arabic, French, and Tamazight and are built programmatically to ensure data diversity. It consists of sub-datasets such as Uppercase alphabet (26 classes), Lowercase alphabet (26 classes), Digits (9 classes), Arabic (28 classes), Tifinagh letters (33 classes), Symbols (14 classes), and French special characters (16 classes). The dataset construction process involves collecting representative fonts and generating multiple character images using a Python script, presenting a comprehensive variety essential for robust recognition models. Moreover, this dataset contributes to the digitization of these diverse official documents and archival papers, essential for preserving cultural heritage and enabling advanced text recognition technologies. The need for this work arises from the advancements in character recognition techniques and the significance of large-scale annotated datasets. The proposed dataset contributes to the development of robust character recognition models for practical applications.
A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS

Authors:
raw_author_name: Arif Mudi Priyatno
raw_affiliation_string: Universitas Negeri Malang
raw_author_name: Triyanna Widiyaningtyas
raw_affiliation_string: Universitas Negeri Malang

Abstract: Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results.
Highly invariant recognition memory spaces for real-world objects revealed with signal-detection analysis

Authors:
raw_author_name: Igor Utochkin

Abstract: Recognition memory is an essential form of memory retrieval, allowing us to determine whether a thing or episode we experience in the present has been experienced in the past. Recognition typically involves decisions on whether stimuli look "old" or "new". These decisions can be difficult, especially when the new stimuli are similar to the old ones (e.g., as in police lineup identification). Although the role of similarity between old (targets) and new (foils) in recognition is obvious, it is often hard to predict how confusable particular targets and foils would be and how much of their confusability comes from their similarity as opposed to insufficient memory. Here, we show that, even when stimuli are various real-world objects whose subjective similarity depends on numerous non-defined factors and varies across people, they form highly consistent recognition memory spaces. That is, proportions of correct (hits) and incorrect recognitions (false alarms) produced by certain target-foil combinations are distributed similarly across different participants. Moreover, using the discriminability index d' from signal-detection theory as a distance metric, we found that the recognition spaces recovered from simple two-alternative choice tests (2-AFC) correctly predicted performance in more complex 4-AFC tests not only for hits but differentiated false alarms for each foil. We also found that the decreased encoding time reduced all absolute d' distances (presumably due to lower memory strength) but preserved the relative distances. Therefore, we found evidence for highly consistent signal-detection recognition memory spaces of real-world objects invariant to memory task complexity.
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges

Authors:
raw_author_name: Giorgio Franceschelli
raw_affiliation_string: a:1:{s:5:"en_US"; s:21:"University of Bologna"; }
raw_author_name: Mirco Musolesi
raw_affiliation_string: University College London; University of Bologna

Abstract: Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has emerged as a very successful paradigm for a variety of machine learning tasks. In this survey, we discuss the state of the art, opportunities and open research questions in applying RL to generative AI. In particular, we will discuss three types of applications, namely, RL as an alternative way for generation without specified objectives; as a way for generating outputs while concurrently maximizing an objective function; and, finally, as a way of embedding desired characteristics, which cannot be easily captured by means of an objective function, into the generative process. We conclude the survey with an in-depth discussion of the opportunities and challenges in this fascinating emerging area.
Quantum Error Mitigated Classical Shadows

Authors:
raw_author_name: Hamza Jnane
raw_affiliation_string: Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom; Quantum Motion, 9 Sterling Way, London N7 9HJ, United Kingdom
raw_author_name: Jonathan Steinberg
raw_affiliation_string: Naturwissenschaftlich–Technische Fakultät, Universität Siegen, Siegen 57068, Germany; State Key Laboratory for Mesoscopic Physics, School of Physics and Frontiers Science Center for Nano-Optoelectronics, Peking University, Beijing 100871, China
raw_author_name: Zhenyu Cai
raw_affiliation_string: Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom; Quantum Motion, 9 Sterling Way, London N7 9HJ, United Kingdom
raw_author_name: H. Chau Nguyen
raw_affiliation_string: Naturwissenschaftlich–Technische Fakultät, Universität Siegen, Siegen 57068, Germany
raw_author_name: Bálint Koczor
raw_affiliation_string: Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, United Kingdom; Quantum Motion, 9 Sterling Way, London N7 9HJ, United Kingdom

Abstract: The rich toolbox of quantum error-mitigation strategies is synergized with powerful classical shadows for the purpose of estimating properties of an ideal quantum state, aiding in the development of near-term and early fault-tolerant quantum computers.
Transforming Healthcare Data Management: A Blockchain-Based Cloud EHR System for Enhanced Security and Interoperability

Authors:
raw_author_name: Agariadne Dwinggo Samala
raw_affiliation_string: Faculty of Engineering, Universitas Negeri Padang, West Sumatera, Indonesia
raw_author_name: Soha Rawas
raw_affiliation_string: Faculty of Science, Department of Mathematics and Computer Science, Beirut Arab University, Beirut, Lebanon

Abstract: The adoption of cloud-based electronic health record (EHR) systems and blockchain technology in healthcare is gaining attention for enhancing data security and interoperability. This research focuses on designing and implementing a blockchain-based cloud EHR system. It explores selecting suitable blockchain technology, cloud infrastructure, and data management methods to ensure patient data confidentiality, integrity, and availability. The architecture and components of the system, including the blockchain network, cloud storage layer, and user interface, are thoroughly discussed. A pilot study evaluates the system’s feasibility and performance, showcasing improved data protection, sharing, and management compared to traditional EHR systems. The potential benefits, drawbacks, and barriers to adoption of a blockchain-based cloud EHR system are examined. This research provides valuable insights and recommendations for healthcare institutions considering the implementation of such systems, addressing the challenges, and offering guidance for successful adoption.
Dynamic Fit Index Cutoffs for Treating Likert Items as Continuous

Authors:
raw_author_name: Daniel McNeish

Abstract: Recent reviews report that about 80% of empirical factor analyses are applied to Likert-type responses and that it is exceedingly common to treat Likert-type item responses as continuous. However, traditional model fit index cutoffs like RMSEA ≤ .06 or CFI ≥ .95 were derived to have 90+% sensitivity to misspecification with continuous responses. A disconnect therefore emerges whereby traditional methodological guidelines assume continuous responses whereas empirical data often contain Likert-type responses. We provide an illustrative simulation study to show that this disconnect is not innocuous—sensitivity of traditional cutoffs to misspecification is close to 100% with continuous responses but can fall considerably if 5-point Likert responses are treated as continuous in some conditions. In other conditions, the reverse may occur, and traditional cutoffs may be too strict. Generally, applying traditional cutoffs to Likert-type responses can adversely impact conclusions about fit adequacy. This paper aims to address this prevalent issue by extending the dynamic fit index (DFI) framework to accommodate Likert-type responses. DFI is a simulation-based method that was initially intended to address changes in cutoff sensitivity to misspecification due to model characteristics (e.g., number of items, strength of loadings). Here, we propose extending DFI so that it also accounts for data characteristics (e.g., number of Likert scale points, response distribution) . Two simulations are included to demonstrate that – with 5-point Likert-type responses – the proposed method (a) improves upon traditional cutoffs, (b) improves upon DFI cutoffs based on multivariate normality, and (c) consistently maintains 90+% sensitivity to misspecification.