Reviewerly/exHarmony huggingface dataset:
A systematic survey on security and privacy issues of medicine supply chain: Taxonomy, framework, and research challenges

Authors:
raw_author_name: Jigna J. Hathaliya
raw_affiliation_string: Department of Computer Science and Engineering Institute of Technology, Nirma University Ahmedabad Gujarat India
raw_author_name: Sudeep Tanwar
raw_affiliation_string: Department of Computer Science and Engineering Institute of Technology, Nirma University Ahmedabad Gujarat India

Abstract: Abstract Several decades ago, the medicine supply chain (MSC) transferred the medicines from the manufacturer to the end‐consumer and kept all records in a manual register. The manual intermediary management of MSC and medicine data often leads to issues like unauthorized third parties participating in the process and illegally tempering medicine data. As a result of this medicine temperament, end users get counterfeit medicine that poses severe consequences for patients' health. Over time, manual data management and intermediaries transform into digital platforms that track, manage, and exchange data in real‐time. Real‐time data exchange opens attackers up to target MSCs, access medicine data illegally, and modify medicine conditions, locations, and specifications. With the objective of this, the proposed survey identifies security and privacy issues and discusses security solutions. This security solution involves various data security and privacy frameworks such as micro‐segmentation, zero trust model, and many other software‐based security solutions. Moreover, The proposed survey explores radio frequency identification for medicine tracking in which each intermediary transforms the medicine location over the internet. In contrast, the Internet of Things is used to exchange medicine temperature conditions in real‐time. Furthermore, cybersecurity‐based solutions help protect against malicious threats, and blockchain keeps data private and temper‐proof. Artificial intelligence provides machine learning and deep learning models for analyzing large amounts of data to generate insights from the MSC data. Therefore, this survey addresses the various security and privacy issues and provides solutions that help researchers carry out work in this domain.
Board 353: On ACCESS Program Support for Students’ Academic Successin the Cybersecurity Field

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

Abstract: Abstract The goal of the NSF S-STEM funded program "Attracting and Cultivating Cybersecurity Experts and Scholars through Scholarships" (ACCESS) is to increase the cybersecurity-related degree completion of high-achieving undergraduate students with demonstrated financial need and thus help address the tremendous unmet need for highly skilled cybersecurity experts. The ACCESS program has successfully awarded scholarships to three cohorts of students and has achieved its goal to increase the annual enrollment of students in the B.S. and Area of Emphasis in Cybersecurity at West Virginia University. Specifically, the enrollment has more than doubled in only two years (from 2020 to 2022). Furthermore, the ACCESS scholarship recipients have greater diversity than their peers enrolled in the cybersecurity field at West Virginia University. Over the last three years, the ACCESS program provided numerous co-curricular activities and student support services and has strengthen its partnerships with employers from the public and private sectors. Students' feedback, which was provided via anonymous surveys and focus groups discussions conducted by an external evaluation team, was overwhelmingly positive and highlighted numerous benefits of the ACCESS program to students' education and future professional careers.
A new frontier in dashboard design: Evaluating an innovative meta‐modelling approach through expert insights

Authors:
raw_author_name: Andrea Vázquez‐Ingelmo
raw_affiliation_string: GRIAL Research Group, Computer Science Department, University of Salamanca, Salamanca, Spain
raw_author_name: Alicia García‐Holgado
raw_affiliation_string: GRIAL Research Group, Computer Science Department, University of Salamanca, Salamanca, Spain
raw_author_name: Francisco José García‐Peñalvo
raw_affiliation_string: GRIAL Research Group, Computer Science Department, University of Salamanca, Salamanca, Spain
raw_author_name: Roberto Therón
raw_affiliation_string: VisUSAL, GRIAL Research Group, Computer Science Department, University of Salamanca, Salamanca, Spain
raw_author_name: Ricardo Colomo‐Palacios
raw_affiliation_string: Polytechnic University of Madrid, Madrid, Spain

Abstract: Abstract Context Data visualizations and dashboards are essential in disseminating information to broad audiences. However, designing these instruments is complex due to the multitude of involved factors. Theoretical frameworks are therefore critical to guide the design and implementation of data visualizations. Objective This article presents the approach used to pinpoint key features that impact the creation of information visualizations, organize these features using meta‐modelling, and validate the final artefact through expert judgement. Method Meta‐modelling facilitates the distillation of characteristics and functionalities critical for the development of information systems. Utilizing this method, a dashboard meta‐model was constructed in preceding research, yielding a framework of abstract components and connections to delineate specific products. This meta‐model serves as a robust tool for the automated generation of information visualizations and dashboards. Nonetheless, to confirm its quality and efficacy, it underwent a thorough quality assessment and expert evaluation to identify and address potential shortcomings. Results The validation process resulted in positive ratings and constructive feedback to enhance the meta‐model's present iteration. Experts mainly highlighted the meta‐model's complexity and the clarity of certain entities, noting potential confusion for non‐experts. Conclusions While the meta‐model is confirmed to be a coherent and pertinent tool, it requires additional clarifications to simplify its complexity. Addressing this, graphical interfaces have been developed to enable users to interact with the dashboard meta‐model—and to create dashboard instances—through straightforward interactions.
A Systematic Survey of Multilingual Speech Transcription and Translation

Authors:
raw_author_name: None Prof. Vinay Raj
raw_affiliation_string: Global Academy of Technology, Bangalore, Karnataka, India

Abstract: This paper presents an innovative initiative dedicated to revolutionizing multilingual communication by leveraging state-of-the-art technologies such as Artificial Intelligence (AI), NLP, or natural language processing and Machine Learning. With a primary focus on Indian languages, the research aims to develop an advanced system capable of seamlessly transcribing speech across diverse linguistic landscapes. Through the incorporation of cutting-edge algorithms and parallel processing techniques, the proposed system facilitates real-time transcription and translation of multiple languages concurrently. Rigorous experimentation and analysis demonstrate the efficiency of the developed framework in breaking down language barriers and fostering inclusive communication. Furthermore, the paper emphasizes the cultural significance of this technology in promoting global connectivity and celebrating linguistic diversity. Ultimately, this research underscores the transformative potential of technology in facilitating cross-cultural understanding and enabling meaningful interactions within a multilingual society
Board 363: Promoting the Dispositional Dimension of Competency in Undergraduate Computing Programs

Authors:
raw_author_name: Bonnie MacKellar
raw_affiliation_string: St John's University
raw_author_name: Natalie Kiesler
raw_affiliation_string: DIPF | Leibniz Institute for Research and Information in Education
raw_author_name: Rajendra Raj
raw_affiliation_string: Rochester Institute of Technology
raw_author_name: Mihaela Sabin
raw_affiliation_string: University of New Hampshire
raw_author_name: Renee McCauley
raw_affiliation_string: College of Charleston
raw_author_name: Amruth Kumar
raw_affiliation_string: Ramapo College of New Jersey

Abstract: Abstract The Computing Curricula 2020 (CC2020) report, issued by the ACM and IEEE Computer Society, identified knowledge, skills, and dispositions as the three main components of competency for undergraduate programs in computer engineering, computer science, cybersecurity, information systems, information technology, and software engineering, as well as data science. As earlier generations of curricular guidelines in computing have described knowledge and skills to some extent, the notion of dispositions is relatively new to computing. Dispositions are cultivable behaviors, such as adaptability, meticulousness, and self-directedness, that are desirable in the workplace. Multiple employer surveys and interviews confirm that dispositions are as crucial for success in the workplace as the knowledge and skills students develop in their academic programs of study. As such, the CC2020 report describes eleven dispositions that are expected of competent computing graduates. These are distinct and separate from the technical knowledge and disciplinary skills of computing and engineering. Dispositions are also distinct from baseline or cross-disciplinary skills, such as critical thinking, problem-solving, teamwork, and communication. In contrast, dispositions are inherently human characteristics that describe individual qualities and behavioral patterns that lead to professional success. Dispositions are learnable, not necessarily teachable. This work-in-progress paper motivates dispositions within computing disciplines and presents the background of this approach. It also discusses the use of reflection exercises and vignettes in understanding, promoting, and fostering behavioral patterns that undergraduate computing students identify as related to dispositions they experience in the course. Preliminary data and results from the study are also presented.
Multimedia Messaging for Social Change: Assessing the Impact of Multimedia Integration in Development Communication Campaigns

Authors:
raw_author_name: Uduak Udoudom
raw_affiliation_string: Heritage Polytechnic

Abstract: This research explores the transformative potential of multimedia messaging in development communication campaigns, with a focus on its impact on social change. As technology continues to advance, multimedia integration, encompassing elements such as text, images, audio, and video, has become a powerful tool for conveying messages and influencing behaviour. This study aims to assess the effectiveness and implications of incorporating multimedia into development communication initiatives. The paper examines various multimedia formats utilized in social change campaigns and assesses their influence on target audiences. By reviewing existing literature and case studies the study aims to identify patterns, challenges, and success factors associated with multimedia messaging. Additionally, attention is given to the role of digital platforms and social media in amplifying the reach and engagement of multimedia campaigns. Key considerations include the cognitive and emotional impact of multimedia messages, audience receptivity, and the potential for behaviour change. The research also investigates ethical considerations related to multimedia communication in the context of social change, addressing issues such as cultural sensitivity, inclusivity, and the potential for unintended consequences. Findings from this research will contribute valuable insights to the field of development communication, guiding practitioners, and researchers in optimizing the design and implementation of multimedia messaging strategies for effective social change. The overarching goal is to enhance our understanding of the dynamic interplay between multimedia communication and its ability to drive positive societal transformations.
Remote sensing image feature matching via graph classification with local motion consistency

Authors:
raw_author_name: Xiujing Gao
raw_affiliation_string: Fujian Provincial Key Laboratory of Marine Smart Equipment, Fuzhou, People’s Republic of China; Institute of Smart Marine and Engineering, Fujian University of Technology, Fuzhou, People’s Republic of China
raw_author_name: Zhihong Li
raw_affiliation_string: School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, People’s Republic of China

Abstract: Feature matching is a classic challenge in the computer vision field. In this paper, we propose an innovative graph classification method based on neighborhood motion consistency to eliminate erroneous matches. Specifically, we transform the coordinates of feature matching points into vectors on a unified scale. For a given match, we construct a graph centered around the match and incorporating neighboring matches. Node attributes are designed to represent the similarity between the vector of the central node and those of its neighbors. To facilitate this, we develop a lightweight graph attention neural network dedicated to graph property classification, thereby predicting the accuracy of the match under consideration. To effectively train the model, we employ a random cropping strategy to generate a plethora of diverse graphs for classifier training. We evaluate our method on datasets encompassing translational remote sensing data, rotational and scaled remote sensing imagery produced via random cropping, and nonrigid fisheye datasets. Our algorithm demonstrates superior performance to current state-of-the-art methods.
A comparative study for the assessment of marker-less mixed reality applications for the operator training

Authors:
raw_author_name: Agnese Brunzini
raw_affiliation_string: Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Ancona, Italy
raw_author_name: Marianna Ciccarelli
raw_affiliation_string: Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Ancona, Italy
raw_author_name: Mikhailo Sartini
raw_affiliation_string: Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Ancona, Italy
raw_author_name: Alessandra Papetti
raw_affiliation_string: Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Ancona, Italy
raw_author_name: Michele Germani
raw_affiliation_string: Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Ancona, Italy

Abstract: With the rising concept of Industry 5.0, the worker's needs and well-being are placed at the center of the production process. The use of extended reality can be exploited to provide the operator with interactive training, allowing independent, step-by-step learning in the real environment, with instructional digital contents, superimposed over the machinery in the workplace. This kind of training promotes the operator's skills, and consequently empowers the industrial competitiveness. The aim of this paper is to assess and compare the effectiveness of two different marker-less mixed reality (MR) applications to train the operator in the wire harness activities over a tractor's drivetrain. An application based on Azure Spatial Anchors by Microsoft and another one based on the CAD model of the tractor's drivetrain have been developed and tested through the comparison with the traditional paper-based instructions. On-field trials have been accomplished with 10 end-users. User experience, usability, perceived workload, and training effectiveness have been assessed through specific questionnaires and by analyzing user performances. Even if the Anchors-based app showed better outcomes in terms of effectiveness and acceptability, both MR applications resulted in good usability. Operators' willingness of using this technology as a standard tool for training and support emerged.
A copula formulation for multivariate latent Markov models

Authors:
raw_author_name: Alfonso Russo
raw_affiliation_string: Department of Economics and Finance, Tor Vergata University of Rome, Via Columbia, 2, 00133, Rome, Italy
raw_author_name: Alessio Farcomeni
raw_affiliation_string: Department of Economics and Finance, Tor Vergata University of Rome, Via Columbia, 2, 00133, Rome, Italy

Abstract: Abstract We specify a general formulation for multivariate latent Markov models for panel data, where outcomes are possibly of mixed-type (categorical, discrete, continuous). Conditionally on a time-varying discrete latent variable and covariates, the joint distribution of outcomes simultaneously observed is expressed through a parametric copula. We therefore do not make any conditional independence assumption. The observed likelihood is maximized by means of an expectation–maximization algorithm. In a simulation study, we argue how modeling the residual contemporary dependence might be crucial in order to avoid bias in the parameter estimates. We illustrate through an original application to assessment of poverty through direct and indirect indicators in a cohort of Italian households.
Empowering complex-valued data classification with the variational quantum classifier

Authors:
raw_author_name: Yan Li
raw_affiliation_string: Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, United States

Abstract: The evolution of quantum computers has encouraged research into how to handle tasks with significant computation demands in the past few years. Due to the unique advantages of quantum parallelism and entanglement, various types of quantum machine learning (QML) methods, especially variational quantum classifiers (VQCs), have attracted the attention of many researchers and have been developed and evaluated in numerous scenarios. Nevertheless, most of the research on VQCs is still in its early stages. For instance, as a consequence of the mathematical constraints imposed by the properties of quantum states, the majority of research has not fully taken into account the impact of data formats on the performance of VQCs. In this paper, considering a significant number of data in the real world exist in the form of complex numbers, i.e., phasor data in power systems and the result of Fourier transform on image processing, we develop two categories of data encoding methods, including coupling data encoding and splitting data encoding. This paper features the coupling data encoding method to encode complex-valued data in a way of amplitude encoding. By leveraging the property of quantum states living in a complex Hilbert space, the complex-valued data is embedded into the amplitude of quantum states to comprehensively characterize complex-valued information. Optimizers will be utilized to iteratively tune a parameterized ansatz, with the aim of minimizing the value of loss functions defined with respect to the specific classification task. In addition, distinct factors in VQCs have been explored in detail to investigate the performance of VQCs, including data encoding methods, loss functions, and optimizers. The experimental result shows that the proposed data encoding method outperforms other typical encoding methods on a given classification task. Moreover, different loss functions are tested, and the capability of finding the minimum value is evaluated for gradient-free and gradient-based optimizers, which provides valuable insights and guidelines for practical implementations.
A Graph Contrastive Learning Model Based on Structural and Semantic View for HIN Recommendation

Authors:
raw_author_name: Ruowang Yu
raw_affiliation_string: Ningbo University, 818 Fenghua Road, Ningbo, 315211, China
raw_author_name: Yihong Dong
raw_affiliation_string: Ningbo University, 818 Fenghua Road, Ningbo, 315211, China
raw_author_name: Jiangbo Qian
raw_affiliation_string: Ningbo University, 818 Fenghua Road, Ningbo, 315211, China

Abstract: Abstract With the rapid growth of information in the Internet era, people are in great need of recommendation methods to filter information. At present, recommendation methods which based on heterogeneous information network (HIN) have attracted wide attention. Recently, HIN-based recommendation methods need to be modeled from two aspects: node structural association and semantic association. To this end, we propose a graph contrastive learning model based on structural and semantic view for HIN recommendation (GCL-SS). GCL-SS utilizes U-I interactive view to obtain node structural embeddings, and utilizes U-I semantic view to obtain node semantic embeddings. Based on these two kinds of embeddings, we establish a self-supervised contrastive learning mechanism to effectively integrate structural information and semantic information of user (item) nodes in HIN, and finally learn a more discriminative user (item) embedding. In addition, in order to strengthen the semantic association between nodes, we innovatively utilize time sequence encoder (TSE), such as LSTM, to encode semantic homogeneous network decomposed by HIN in U-I semantic view. At last, based on the user and item embeddings, we adopt bilinear decoder to model the potential association between user and item, so as to realize rating prediction of user to item. The experimental results on three real datasets confirm that our GCL-SS model performs better than state-of-the-art recommendation methods in rating prediction task. In addition, the results of four ablation experiments indicate that our GCL-SS model can effectively improve the performance of rating prediction in recommendation.