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26th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining

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Australasian Journal of engineering Education 2019年第1期24卷 4-13页

作者： Moscoso-Zea, Oswaldo Saa, Pablo Luján-Mora, Sergio Faculty of Engineering Sciences Universidad Tecnológica Equinoccial Quito Ecuador Department of Software and Computing Systems University of Alicante Alicante Spain

Nowadays, researchers analyse student data to predict the graduation rate by looking at the characteristics of students enrolled and to take corrective actions at an early stage or improve the admission process. Educational data mining (EDM) is an emerging field that can support the implementation of changes in the management of higher education institutions. EDM analyses educational data using the development and the application of data mining (DM) methods and algorithms to information stored in academic data repositories. The purpose of this paper is to review which methods and algorithms of DM can be used in the analysis of educational data to improve decision-making. Furthermore, it evaluates these algorithms using a dataset composed of student data in the computer science school of a private university. The core of the analysis is to discover trends and patterns of study in the graduation rate indicator. Finally, it compares these methods and algorithms and suggests which has the best precision in certain scenarios. Our analyses suggest that random trees had better precision but had limitations due to the difficulty of interpretation while the J48 algorithm had better possibilities of interpretation of results in the visualisation of the classification of data and only had slightly inferior performance. © 2019, © 2019 Engineers Australia.

关键词： Data warehouses

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A systematic literature review of blockchain cyber security

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Digital Communications and Networks 2020年第2期6卷 147-156页

作者： Paul J.Taylor Tooska Dargahi Ali Dehghantanha Reza M.Parizi Kim-Kwang Raymond Choo School of Computing Science&EngineeringUniversity of SalfordManchesterUK Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Software Engineering and Game Development Kennesaw State UniversityMariettaGA30060USA Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,trustless,peer-to-peer *** research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security *** findings show that the Internet of Things(IoT)lends itself well to novel blockchain applications,as do networks and machine visualization,public-key cryptography,web applications,certification schemes and the secure storage of Personally Identifiable Information(PII).This timely systematic review also sheds light on future directions of research,education and practices in the blockchain and cyber security space,such as security of blockchain in IoT,security of blockchain for AI data,and sidechain security.

关键词： Blockchain Smart contracts Cyber security Distributed ledger technology IoT Cryptocurrency Bitcoin

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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Use of augmented reality for social communication skills in children and adolescents with autism spectrum disorder (ASD): A systematic review 6

Use of augmented reality for social communication skills in ...

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6th IEEE International Conference on engineering, Technologies and Applied Sciences, ICETAS 2019

作者： Khowaja, Kamran Al-Thani, Dena Banire, Bilikis Salim, Siti Salwah Shah, Asadullah Information Computing Technology Hamad Bin Khalifa University Doha Qatar University of Malaya Department of Software Engineering Kuala Lumpur Malaysia International Islamic University Malaysia Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9781728140827

The purpose of this review is to provide a systematic analysis of studies investigating the use of augmented reality (AR) to improve the social communications skills of individuals (children and adults) diagnosed with autism spectrum disorder (ASD). This review synthesizes the AR technology, research design, data collection methods, settings, and intervention outcomes. Across the studies, the effect of using AR towards the learning of individuals was positive. However, given the wide variety of skills targeted in the shortlisted studies, and the heterogeneity of the participants, a summative conclusion regarding the effectiveness of AR for teaching social communication skills to an individual with ASD based on the existing literature is not possible. Future research addressing this area as well as the relative effectiveness of AR among more participants, different technologies supporting AR, its intervention, and the evaluation in the classroom environment is warranted. © 2019 IEEE.

关键词： Augmented reality

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks 18

Machine Learning-based Energy-Aware Offloading in Edge-Cloud...

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18th International Conference on Mobile systems and Pervasive computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Ismail, Leila Materwala, Huned Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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Anytime diagnosis for reconfiguration

arXiv

引用

arXiv 2021年

作者： Felfernig, Alexander Walter, Rouven Galindo, José A. Benavides, David Erdeniz, Seda Polat Atas, Müslüm Reiterer, Stefan Applied Software Engineering Group Institute for Software Technology TU Graz Austria Symbolic Computation Group WSI Informatics Universität Tübingen Germany Computer Languages and Systems Department University of Sevilla Spain SelectionArts Austria

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling

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Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain

arXiv

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arXiv 2024年

作者： Luttermann, Malte Baake, Edgar Bouchagiar, Juljan Gebel, Benjamin Grüning, Philipp Manikwadura, Dilini Schollemann, Franziska Teifke, Elisa Rostalski, Philipp Möller, Ralf Institute of Information Systems University of Lübeck Germany Institute of Telematics University of Lübeck Germany Institute for Software Engineering and Programming Languages University of Lübeck Germany Department of Infectious Diseases and Microbiology University Hospital Schleswig-Holstein Campus Lübeck Germany Institute for Neuro- and Bioinformatics University of Lübeck Germany Institute for Molecular Medicine University of Lübeck Germany Institute for Electrical Engineering in Medicine University of Lübeck Germany franziska.schollemann Department of Anesthesiology and Intensive Care University Hospital Schleswig-Holstein Campus Lübeck Germany

Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients. © 2024, CC BY.

关键词： Patient treatment

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A Reinforcement Learning Based Approach to Identify Resource Bottlenecks for Multiple Services Interactions in Cloud computing Environments 16th

A Reinforcement Learning Based Approach to Identify Resource...

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16th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Xu, Lingxiao Xu, Minxian Semmes, Richard Li, Hui Mu, Hong Gui, Shuangquan Tian, Wenhong Wu, Kui Buyya, Rajkumar School of Software and Information Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Co. Ltd. Chengdu China Department of Computer Science University of Victoria Victoria Canada CLOUDS Lab School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9783030675394

Cloud service providers are provisioning resources including a variety of virtual machine instances to support customers that migrate their services to the cloud. From the customers’ perspective, selecting the appropriate amount of resources is tightly coupled with performance and cost. By identifying the potential resource bottlenecks in the early stage of the service deployment process, resource planning can be significantly optimized. However, due to the unpredictable workloads and heterogeneous resources, it is difficult to identify resource bottlenecks that can degrade system performance. To support system non-functional requirements (NFR) in a better manner, we propose a reinforcement learning based approach to support the NFR management of system concerning the multiple services interactions scenario by identifying the potential resource bottleneck and optimizing the demanded resources. The proposed approach can predict the resource bottleneck for multiple services interactions, e.g. bottleneck in CPU or overloads in specific service, and provide guidance for resource planning. We modeled and simulated the proposed approach using an extended version of the CloudSim toolkit. Comprehensive evaluations with realistic use case from Siemens Digital Industries software’s MindSphere Solution on AliCloud show that our proposed approach can achieve high accuracy in terms of performance metrics, such as response time, queries per second (QPS), and resource usage. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Reinforcement learning

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