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Why do people customize avatars in the metaverse? Curiosity and SOR model perspective Internet Res. (IF 5.9) Pub Date : 2024-11-20 Suhyoung Ahn, Byoungho Ellie Jin, Hyesim Seo
Purpose The metaverse, a virtual space where one can build and explore with others using avatars, is drawing global interest. Then questions arise: What drives consumers to customize their avatars and purchase virtual items in the metaverse? Who customizes and purchases virtual items more than others? To find the answers, this study tested a research model that explains why consumers customize their
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Gameful systems for corporate sustainability: systematic review, conceptual framework and research agenda on gamification and sustainable employee behavior in companies Internet Res. (IF 5.9) Pub Date : 2024-11-21 Jeanine Kirchner-Krath, Samanthi Dijkstra-Silva, Benedikt Morschheuser, Harald F.O. von Korflesch
Purpose Given the urgency of corporate engagement in sustainable development, companies seek ways to involve their employees in sustainability efforts. In this regard, gamified systems have gained attention as a novel tool to promote sustainable employee behavior. However, as the research field matures, researchers and practitioners are confronted with a scattered academic landscape that makes it difficult
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Can digital transformation alleviate corporate fraud? Evidence from China Internet Res. (IF 5.9) Pub Date : 2024-11-21 Duo Shang, Dongliang Yuan, Xinmei Wu, Dehui Li
Purpose The aim of this paper is to explore the relationship between digital transformation and corporate fraud. Design/methodology/approach This paper uses panel data of Chinese listed corporations from 2010 to 2021 and captures digital transformation from the perspectives of awareness and investment by extracting related content from annual reports. Our work investigates whether and how digital transformation
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Low-cost Data Offloading Strategy with Deep Reinforcement Learning for Smart Healthcare System IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-19 Qiang He, Zheng Feng, Zhixue Chen, Tianhang Nan, Kexin Li, Huiming Shen, Keping Yu, Xingwei Wang
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“I am sorry for judging you”: conceptualizing sentiment reversal among followers in case of falsely alleged social media influencer transgression Internet Res. (IF 5.9) Pub Date : 2024-11-19 Ishaan Sengupta, Kokil Jain, Arpan Kumar Kar, Isha Sharma
Purpose Influencer transgressions can disappoint their followers. However, there is a lack of clarity about the effects of a false allegation on an influencer–follower relationship. Drawing from cognitive dissonance and moral reasoning theory, the current study aims to examine how this relationship is shaped across three time periods (before the allegation is leveled, after the allegation is leveled
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Private and Secure Distributed Deep Learning: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-16 Corinne Allaart, Saba Amiri, Henri Bal, Adam Belloum, Leon Gommans, Aart van Halteren, Sander Klous
Traditionally, deep learning practitioners would bring data into a central repository for model training and inference. Recent developments in distributed learning, such as federated learning and deep learning as a service (DLaaS) do not require centralized data and instead push computing to where the distributed datasets reside. These decentralized training schemes, however, introduce additional security
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From attraction to monetization: How do knowledge influencers trigger users’ willingness to subscribe to knowledge products? Internet Res. (IF 5.9) Pub Date : 2024-11-15 Xiaoyu Chen, Alton Y.K. Chua
Purpose This study examines the phenomenon of “knowledge influencers,” individuals who convey expert information to non-expert audiences and attract users to subscribe to their self-created knowledge products. It seeks to address two research questions: (1) What are the antecedents that promote perceived attractiveness of knowledge influencers? and (2) How does perceived attractiveness of knowledge
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Backdoor Attacks and Defenses Targeting Multi-Domain AI Models: A Comprehensive Review ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-15 Shaobo Zhang, Yimeng Pan, Qin Liu, Zheng Yan, Kim-Kwang Raymond Choo, Guojun Wang
Since the emergence of security concerns in artificial intelligence (AI), there has been significant attention devoted to the examination of backdoor attacks. Attackers can utilize backdoor attacks to manipulate model predictions, leading to significant potential harm. However, current research on backdoor attacks and defenses in both theoretical and practical fields still has many shortcomings. To
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Do CEOs matter? Divergent impact of CEO power on digital and non-digital innovation J. Strategic Inf. Syst. (IF 8.7) Pub Date : 2024-11-15 Inmyung Choi, Min-Seok Pang
Digital innovation is ubiquitous across a wide range of industries, blurring the boundary between traditional and technology industries. An increasing number of firms in traditional industries such as manufacturing, retail, or service now regard themselves as technology companies. In this study, drawing on corporate governance literature, we develop a theoretical framework for the relationship between
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Unveiling factors and contingencies influencing exhaustion in professional esports players: evidence from China Internet Res. (IF 5.9) Pub Date : 2024-11-15 Gordon Liu, Yue Meng-Lewis, Weiyue Wang, Yupei Zhao
Purpose The rapid growth of professional esports has highlighted the lack of a universally recognised governing body to standardise operations and competition rules. This absence presents many challenges. A key concern is the well-being of professional esports players (e-pro-players), who often suffer from exhaustion. This study aims to examine the factors contributing to exhaustion among e-pro-players
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Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-14 Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek, Peter Schneider-Kamp
Sharing data with third parties is essential for advancing science, but it is becoming more and more difficult with the rise of data protection regulations, ethical restrictions, and growing fear of misuse. Fully synthetic data, which transcends anonymisation, may be the key to unlocking valuable untapped insights stored away in secured data vaults. This review examines current synthetic data generation
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Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-14 Zhihong Liu, Xin Xu, Peng Qiao, DongSheng Li
Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuously, handling the training process and reducing the time consumption using parallel and distributed computing is becoming an urgent and essential desire
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Democratizing Container Live Migration for Enhanced Future Networks - A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-14 Wissem Soussi, Gürkan Gür, Burkhard Stiller
Emerging cloud-centric networks span from edge clouds to large-scale datacenters with shared infrastructure among multiple tenants and applications with high availability, isolation, fault tolerance, security, and energy efficiency demands. Live migration (LiMi) plays an increasingly critical role in these environments by enabling seamless application mobility covering the edge-to-cloud continuum and
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Membership Inference Attacks and Defenses in Federated Learning: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-14 Li Bai, Haibo Hu, Qingqing Ye, Haoyang Li, Leixia Wang, Jianliang Xu
Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devices to collaboratively build a high-quality model without requiring direct access to the raw training data. However, despite only sharing model updates, federated learning still faces several privacy vulnerabilities. One
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Value co-creation in live streaming through tourism scenes: a contextual marketing perspective Internet Res. (IF 5.9) Pub Date : 2024-11-12 Jun Yu, Chaowu Xie, Songshan Huang
Purpose This study aims to identify a value co-creation framework for live streaming through tourism scenes (LStTS). It also clarifies the value attributes of LStTS and makes an empirical test. Design/methodology/approach The study used a mixed-method approach. In Study 1, a total of 12,216 pieces of viewers’ comments and ten web news reports were coded and analyzed employing a grounded theory approach
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Understanding the influence of communication visibility in preventing knowledge sabotage: a knowledge power perspective Internet Res. (IF 5.9) Pub Date : 2024-11-13 Junli Wang, Ling Yuan, Zhihong Tan
Purpose This study explores the potential impact of enterprise social media (ESM) communication visibility on knowledge sabotage to reduce knowledge sabotage within organizations. Design/methodology/approach We collected data from 389 Chinese employees across three stages and used hierarchical regression analysis and the bootstrap method to test our hypotheses. Findings Communication visibility negatively
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Personalization in educational gamification: Learners with different trait competitiveness benefit differently from rankings on leaderboards Comput. Educ. (IF 8.9) Pub Date : 2024-11-12 Jing Wang, Shaoying Gong, Yang Cao, Xiaorong Guo, Peiyan Peng
Leaderboards are among the most prevalent game elements and are widely used in educational gamification. However, most research has primarily compared learning scenarios using leaderboards with those not using leaderboards, ignoring the role of specific components of leaderboards such as rankings. Given that learners’ perceptions of leaderboards depend on their rankings, this study investigated how
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A knowledge-centric model for government-orchestrated digital transformation among the microbusiness sector J. Strategic Inf. Syst. (IF 8.7) Pub Date : 2024-11-11 Anuragini Shirish, Shirish C. Srivastava, Niki Panteli, John O’Shanahan
Most prior public sector digital transformation (DT) research has examined the role of digitalization in improving either the internal operational efficiency of the government or the quality of government service delivery to external stakeholders such as citizens and businesses. Although policy-driven digitalization of specific sectors is key for promoting public value, government’s role in orchestrating
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Efficient Hierarchical Federated Services for Heterogeneous Mobile Edge IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-11 Shengyuan Liang, Qimei Cui, Xueqing Huang, Borui Zhao, Yanzhao Hou, Xiaofeng Tao
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Multi-granularity Weighted Federated Learning for Heterogeneous Edge Computing IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-11 Yunfeng Zhao, Chao Qiu, Shangxuan Cai, Zhicheng Liu, Yu Wang, Xiaofei Wang, Qinghua Hu
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A Novel Knowledge Search Structure for Android Malware Detection IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-11 Huijuan Zhu, Mengzhen Xia, Liangmin Wang, Zhicheng Xu, Victor S. Sheng
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A Reinforcement Learning based Framework for Holistic Energy Optimization of Sustainable Cloud Data Centers IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-11 Daming Zhao, Jiantao Zhou, Jidong Zhai, Keqin Li
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ADSS: An Available-but-invisible Data Service Scheme for Fine-grained Usage Control IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-11 Hao Wang, Jun Wang, Chunpeng Ge, Yuhang Li, Lu Zhou, Zhe Liu, Weibin Wu, Mingsheng Cao
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MADRL-based Order Dispatching in MoD Systems with Bipartite Graph Splitting IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-11 Shuxin Ge, Xiaobo Zhou, Tie Qiu
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Delay-prioritized and Reliable Task Scheduling with Long-term Load Balancing in Computing Power Networks IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-11 Renchao Xie, Li Feng, Qinqin Tang, Tao Huang, Zehui Xiong, Tianjiao Chen, Ran Zhang
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Reducing interpretative ambiguity in an educational environment with ChatGPT Comput. Educ. (IF 8.9) Pub Date : 2024-11-10 Francisco Garcia-Varela, Zvi Bekerman, Miguel Nussbaum, Marcelo Mendoza, Joaquin Montero
The study posits that both concrete and abstract words are crucial for effective communication, particularly in educational contexts where the interplay between these forms of language intersects with linguistic, cognitive, and social stratification theories. A key challenge is balancing the efficiency of abstract language in conveying complex concepts with the accessibility of concrete language, which
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The OPAD-perception framework: measuring perceptions of online personalized advertising Internet Res. (IF 5.9) Pub Date : 2024-11-11 Lijie Guo, Daricia Wilkinson, Moses Namara, Karishma Patil, Bart P. Knijnenburg
Purpose The paper aims to develop and validate an instrument to measure users’ perceptions of online personalized advertising. Design/methodology/approach First, we identified 12 different aspects of online personalized advertisement and formulated candidate items through a literature review. A card sorting study and expert review were conducted to generate the initial scale items. We then conducted
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Unpacking the association between social media use and support for unlawful behaviors in protests: a study in Hong Kong Internet Res. (IF 5.9) Pub Date : 2024-11-08 Chuanli Xia, Fei Shen
Purpose Existing research has shown the role of social media in facilitating general protest participation. However, there is a noticeable gap in understanding the dynamics related to explicitly unlawful behaviors during protests, which have become increasingly prominent in recent times. Drawing upon the communication mediation model (O-S-O-R model), this study proposes a moderated mediation model
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A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-08 Xiaojie Wang, Zhonghui Zhao, Ling Yi, Zhaolong Ning, Lei Guo, F. Richard Yu, Song Guo
The increasing popularity of Unmanned Aerial Vehicle (UAV) swarms is attributed to their ability to generate substantial returns for various industries at a low cost. Additionally, in the future landscape of wireless networks, UAV swarms can serve as airborne base stations, alleviating the scarcity of communication resources. However, UAV swarm networks are vulnerable to various security threats that
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Dependency-Aware Task Offloading based on Application Hit Ratio IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-08 Junna Zhang, Xinxin Wang, Peiyan Yuan, Hai Dong, Pengcheng Zhang, Zahir Tari
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Orchestration of Services in Smart Manufacturing through Automated Synthesis IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-08 Flavia Monti, Luciana Silo, Marco Favorito, Giuseppe De Giacomo, Francesco Leotta, Massimo Mecella
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Exploiting Stragglers in Distributed Computing Systems with Task Grouping IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-08 Tharindu Adikari, Haider Al-Lawati, Jason Lam, Zhenhua Hu, Stark C. Draper
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Security and Privacy on Generative Data in AIGC: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-07 Tao Wang, Yushu Zhang, Shuren Qi, Ruoyu Zhao, Xia Zhihua, Jian Weng
The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media
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Open-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-06 Sabrina Sicari, Jesus F. Cevallos M., Alessandra Rizzardi, Alberto Coen-Porisini
This survey summarizes the most recent methods for building and assessing helpful, honest, and harmless neural language models, considering small, medium, and large-size models. Pointers to open-source resources that help to align pre-trained models are given, including methods that use parameter-efficient techniques, specialized prompting frameworks, adapter modules, case-specific knowledge injection
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Business Analytics in Customer Lifetime Value: An Overview Analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-11-06 Onur Dogan, Abdulkadir Hiziroglu, Ali Pisirgen, Omer Faruk Seymen
In customer‐oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied
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“Storytelling and educational robotics: A scoping review (2004–2024)” Comput. Educ. (IF 8.9) Pub Date : 2024-11-05 Maria Palioura, Theodosios Sapounidis
Storytelling has been used for years in educational practice and Educational Robotics is a rapidly growing field worldwide. Accordingly, researchers have attempted to combine Storytelling and Robotics in education. However, no systematic record exists on this combination. Therefore, we conducted a scoping review of 82 papers out of 5272 articles published in 5 Databases in the last 20 years to map
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Advancing a Practical Inquiry Model with multi-perspective role-playing to foster critical thinking behavior in e-book reading Comput. Educ. (IF 8.9) Pub Date : 2024-11-05 Gloria Yi-Ming Kao, Hui-Chin Yeh, Shih-Wen Su, Xin-Zhi Chiang, Chuen-Tsai Sun
In the digital age, where media proliferation challenges traditional reading habits, this study investigated the impact of digital platforms on critical thinking (CT) and reading practices. Some conventional e-books may not sufficiently encourage reflective thinking or foster CT skills due to their linear nature and lack of engaging elements. Employing the Practical Inquiry Model (PIM) within the Community
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KA2SE: Key-Aggregation Authorized Searchable Encryption Scheme for Data Sharing in Wireless Sensor Networks IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-04 Haijiang Wang, Jianting Ning, Wei Wu, Chao Lin, Kai Zhang
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A Survey on Emerging Trends and Applications of 5G and 6G to Healthcare Environments ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-02 Shamsher Ullah, Jianqiang Li, Jie Chen, IKRAM ALI, Salabat Khan, Abdul Ahad, Farhan Ullah, Victor Leung
A delay, interruption, or failure in the wireless connection has a significant impact on the performance of wirelessly connected medical equipment. Researchers presented the fastest technological innovations and industrial changes to address these problems and improve the applications of information and communication technology. The development of the 6G communication infrastructure was greatly aided
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Fog Computing Technology Research: A Retrospective Overview and Bibliometric Analysis ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-02 Paola Vinueza-Naranjo, Janneth Chicaiza, Ruben Rumipamba-Zambrano
Researchers’ interest in Fog Computing and its application in different sectors has been increasing since the last decade. To discover the emerging trends inherent to this architecture, we analyzed the scientific literature indexed in Scopus through a bibliometric study. Exposing trends in areas of development will allow researchers to understand the changes and evolution over time. For analysis purposes
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Evaluation Methodologies in Software Protection Research ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-02 Bjorn De Sutter, Sebastian Schrittwieser, Bart Coppens, Patrick Kochberger
Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains
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Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-11-02 Marion Bartl, Abhishek Mandal, Susan Leavy, Suzanne Little
Taking an interdisciplinary approach to surveying issues around gender bias in textual and visual AI, we present literature on gender bias detection and mitigation in NLP, CV, as well as combined visual-linguistic models. We identify conceptual parallels between these strands of research as well as how methodologies were adapted cross-disciplinary from NLP to CV. We also find that there is a growing
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Knowledge Graph for Solubility Big Data: Construction and Applications WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-11-01 Xiao Haiyang, Yan Ruomei, Wu Yan, Guan Lixin, Li Mengshan
Dissolution refers to the process in which solvent molecules and solute molecules attract and combine with each other. The extensive solubility data generated from the dissolution of various compounds under different conditions, is distributed across structured or semi‐structured formats in various media, such as text, web pages, tables, images, and databases. These data exhibit multi‐source and unstructured
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RESTLess: Enhancing State-of-The-Art REST API Fuzzing With LLMs in Cloud Service Computing IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-01 Tao Zheng, Jiang Shao, Jinqiao Dai, Shuyu Jiang, Xingshu Chen, Changxiang Shen
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FedUP: Bridging Fairness and Efficiency in Cross-Silo Federated Learning IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-01 Haibo Liu, Jianfeng Lu, Xiong Wang, Chen Wang, Riheng Jia, Minglu Li
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Cost-Aware Dispersed Resource Probing and Offloading At the Edge: A User-Centric Online Layered Learning Approach IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-11-01 Tao Ouyang, Xu Chen, Liekang Zeng, Zhi Zhou
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Corporate social and digital responsibility in esports Internet Res. (IF 5.9) Pub Date : 2024-11-01 Dimitrios Kolyperas, Christos Anagnostopoulos, Ismini Pavlopoulou, Argyro Elisavet Manoli, Simon Chadwick
Purpose The esports industry has experienced a dynamic growth. In this context, a significant evolution in the logic of corporate social responsibility (CSR) can be observed, particularly in the digital sphere. By extending Carroll’s three-dimensional model to include corporate digital responsibility (CDR), this paper addresses a key research question: How does CSR evolve and develop in the dynamic
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Application‐Based Review of Soft Computational Methods to Enhance Industrial Practices Abetted by the Patent Landscape Analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-31 S. Tamilselvan, G. Dhanalakshmi, D. Balaji, L. Rajeshkumar
Soft computing is a collective methodology that touches all engineering and technology fields owing to its easiness in solving various problems while comparing the conventional methods. Many analytical methods are taken over by this soft computing technique and resolve it accurately and the soft computing has given a paradigm shift. The flexibility in soft computing results in swift knowledge acquisition
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Flexible Computing: A New Framework for Improving Resource Allocation and Scheduling in Elastic Computing IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-10-31 Weipeng Cao, Jiongjiong Gu, Zhong Ming, Zhiyuan Cai, Yuzhao Wang, Changping Ji, Zhijiao Xiao, Yuhong Feng, Ye Liu, Liang-Jie Zhang
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Multi-objective Deep Reinforcement Learning for Function Offloading in Serverless Edge Computing IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-10-31 Yaning Yang, Xiao Du, Yutong Ye, Jiepin Ding, Ting Wang, Mingsong Chen, Keqin Li
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DeFiGuard: A Price Manipulation Detection Service in DeFi Using Graph Neural Networks IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-10-31 Dabao Wang, Bang Wu, Xingliang Yuan, Lei Wu, Yajin Zhou, Helei Cui
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Light Heterogeneous Hypergraph Contrastive Learning Based Service Recommendation for Mashup Creation IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-10-31 Mingdong Tang, Jiajin Mai, Fenfang Xie, Zibin Zheng
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Efficient Public-key Searchable Encryption Scheme from PSI with Scalable Proxy Servers IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-10-31 Xiangqian Kong, Lanxiang Chen, Yizhao Zhu, Yi Mu
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Blockchain-Enabled HeartCare Framework for Cardiovascular Disease Diagnosis in Devices with Constrained Resources IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-10-31 Bidyut Bikash Borah, Khushboo Das, Geetartha Sarma, Soumik Roy, Dhruba Kumar Bhattacharyya
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No More Data Silos: Unified Microservice Failure Diagnosis with Temporal Knowledge Graph IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-10-31 Shenglin Zhang, Yongxin Zhao, Sibo Xia, Shirui Wei, Yongqian Sun, Chenyu Zhao, Shiyu Ma, Junhua Kuang, Bolin Zhu, Lemeng Pan, Yicheng Guo, Dan Pei
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Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges ACM Comput. Surv. (IF 23.8) Pub Date : 2024-10-30 Junhao Dong, Junxi Chen, Xiaohua Xie, Jianhuang Lai, Hao Chen
Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medical diagnosis systems. In this exposition
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Blockchain for sustainable consumption: an affordance and consumer value-based view Internet Res. (IF 5.9) Pub Date : 2024-10-30 Maryam Hina, Najmul Islam, Amandeep Dhir
Purpose There is little empirical evidence on how blockchain affordances may encourage consumers to make sustainable choices. Thus, this paper examines how blockchain affordances affect consumers’ sustainable consumption. Design/methodology/approach We focus on three blockchain affordances: transparency, traceability, and immutability in this paper. By integrating the affordance lens and theory of
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Development and techniques in learner model in adaptive e-learning system: A systematic review Comput. Educ. (IF 8.9) Pub Date : 2024-10-29 Xiyu Wang, Yukiko Maeda, Hua-Hua Chang
Adaptive e-learning systems (AeLS), which emerged in the late 1990s, offer an alternative to the 'one-size-fits-all' approach by addressing the demand for individualized learning experiences. These systems typically consist of five elements, including a domain model, a media space, an adaptation model, a user interface, and a learner model. Despite the increasing academic interest in this topic and
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A process model for design-oriented machine learning research in information systems J. Strategic Inf. Syst. (IF 8.7) Pub Date : 2024-10-29 Hamed Zolbanin, Benoit Aubert
This paper proposes a process model for design-oriented machine learning (DS-ML) research in the area of information systems (IS). As DS-ML studies become more prevalent in addressing complex business and societal challenges, there is a need for a standardized framework to conduct, communicate, and evaluate such research. We integrate elements from the design science research (DSR) process model, action