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Is information normalization helpful in online communication? Evidence from online healthcare consultation Internet Res. (IF 5.9) Pub Date : 2024-07-19 Xuan Wang, Tao Huang, Wenping Zhang, Qingfeng Zeng, Xin Sun
Purpose This study aims to investigate the role of information normalization in online healthcare consultation, a typical complex human-to-human communication requiring both effectiveness and efficiency. The globalization and digitization trend calls for high-quality information, and normalization is considered an effective method for improving information quality. Meanwhile, some researchers argued
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Gamification as a panacea to workplace cyberloafing: an application of self-determination and social bonding theories Internet Res. (IF 5.9) Pub Date : 2024-07-17 K.S. Nivedhitha, Gayathri Giri, Palvi Pasricha
Purpose Gamification has been constantly demonstrated as an effective mechanism for employee engagement. However, little is known about how gamification reduces cyberloafing and the mechanism by which it affects cyberloafing in the workplace. This study draws inspiration from self-determination and social bonding theories to explain how game dynamics, namely, personalised challenges, social interactivity
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A brief review on quantum computing based drug design WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-17 Poulami Das, Avishek Ray, Siddhartha Bhattacharyya, Jan Platos, Vaclav Snasel, Leo Mrsic, Tingwen Huang, Ivan Zelinka
Design and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre‐existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug
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A systematic review and research recommendations on artificial intelligence for automated cervical cancer detection WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-16 Smith K. Khare, Victoria Blanes‐Vidal, Berit Bargum Booth, Lone Kjeld Petersen, Esmaeil S. Nadimi
Early diagnosis of abnormal cervical cells enhances the chance of prompt treatment for cervical cancer (CrC). Artificial intelligence (AI)‐assisted decision support systems for detecting abnormal cervical cells are developed because manual identification needs trained healthcare professionals, and can be difficult, time‐consuming, and error‐prone. The purpose of this study is to present a comprehensive
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Machine learning for pest detection and infestation prediction: A comprehensive review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-15 Mamta Mittal, Vedika Gupta, Mohammad Aamash, Tejas Upadhyay
Pests pose a major danger to a variety of industries, including agriculture, public health, and ecosystems. Fast and precise pest detection, as well as the ability to predict infestations, are required for effective pest management tactics. This paper provides a comprehensive literature review on this subject to provide an overview of the state of research on pest detection and infestation prediction
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ABDP: Accurate Billing on Differentially Private Data Reporting for Smart Grids IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-15 Jialing He, Ning Wang, Tao Xiang, Yiqiao Wei, Zijian Zhang, Meng Li, Liehuang Zhu
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Is Stubborn Mining Severe in Imperfect GHOST Bitcoin-like Blockchains? Quantitative Analysis IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-15 Haoran Zhu, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić, Lei Han, Zhi Chen
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CEO social media celebrity status and credit rating assessment Internet Res. (IF 5.9) Pub Date : 2024-07-15 Yue Fang, Xin Bao, Baiqing Sun, Raymond Yiu Keung Lau
Purpose This paper aims to investigate the effect of CEO social media celebrity status on credit ratings and to determine whether potential threats on the CEO celebrity status negatively moderate the above association. Design/methodology/approach The authors collected tweets for 874 CEOs from 513 unique S&P 1500 firms. A panel data analysis was conducted on a panel with 4,235 observations from 2009
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Onset of a conceptual outline map to get a hold on the jungle of cluster analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-12 Iven Van Mechelen, Christian Hennig, Henk A. L. Kiers
The domain of cluster analysis is a meeting point for a very rich multidisciplinary encounter, with cluster‐analytic methods being studied and developed in discrete mathematics, numerical analysis, statistics, data analysis, data science, and computer science (including machine learning, data mining, and knowledge discovery), to name but a few. The other side of the coin, however, is that the domain
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Fuzzy Deduplication: Color-Aware Deduplication for Multi-Media Data IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-12 Zehui Tang, Shengke Zeng, Song Han, Yawen Feng, Tao Li, Mingxing He
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Diner: Interpretable Anomaly Detection for Seasonal Time Series in Web Services IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-11 Yuhan Jing, Jingyu Wang, Ji Qi, Qi Qi, Bo He, Zirui Zhuang, Naixing Wu, Jianxin Liao
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How emotions affect the outcomes of information overload: information avoidance or information consumption? Internet Res. (IF 5.9) Pub Date : 2024-07-11 Xusen Cheng, Shuang Zhang, Bo Yang
Purpose Information overload has become ubiquitous during a public health emergency. The research purpose is to examine the role of mixed emotions in the influence of perceived information overload on individuals’ information avoidance intention and the state of fear of missing out. Design/methodology/approach A mixed-methods approach was used in this study: a qualitative study of 182 semi-structured
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Navigating elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions Comput. Educ. (IF 8.9) Pub Date : 2024-07-09 Tzu-Yu Tai, Howard Hao-Jan Chen
Generative artificial intelligence (GAI) and automatic speech recognition (ASR) have ushered in promising tools for foreign language learning, notably GAI chatbots. This study investigated the impact of GAI chatbots on elementary school English as a foreign language (EFL) learners' speaking skills, focusing on two interaction configurations—individual and paired. Eighty-five elementary school EFL learners
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Incentive Mechanism for Resource Trading in Video Analytic Services Using Reinforcement Learning IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-05 Nan He, Song Yang, Fan Li, Liehuang Zhu, Lifeng Sun, Xu Chen, Xiaoming Fu
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ZKWASM: A ZKSNARK WASM Emulator IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-05 Sinka Gao, Guoqiang Li, Hongfei Fu
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Machine learning applied to tourism: A systematic review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-04 José Carlos Sancho Núñez, Juan A. Gómez‐Pulido, Rafael Robina Ramírez
The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper
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Investigating behavioral and cognitive patterns among high-performers and low-performers in Co-viewing video lectures Comput. Educ. (IF 8.9) Pub Date : 2024-07-03 Zhongling Pi, Yuan Yang, Xin Zhao, Qiuyi Guo, Xiying Li
Co-viewing video lectures, where students watch lectures simultaneously with one or more remote peers and engage in interpersonal communication on learning tasks online, is becoming increasingly popular. However, there is a dearth of studies examining students' behavioral and cognitive patterns during the co-viewing process. This study employed eye-tracking and screen recording methods to examine undergraduate
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Digital Twins-Empowered Secure Network Slice Access and Isolation for Consumer Healthcare Applications IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-03 Basudeb Bera, Ashok Kumar Das, Biplab Sikdar
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SpotDAG: An RL-based algorithm for DAG workflow scheduling in heterogeneous cloud environments IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-03 Liduo Lin, Li Pan, Shijun Liu
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PIAS: Privacy-Preserving Incentive Announcement System based on Blockchain for Internet of Vehicles IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-03 Yonghua Zhan, Yang Yang, Hongju Cheng, Xiangyang Luo, Zhangshuang Guan, Robert H. Deng
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FlowValve+: Multi-queue Packet Scheduling Framework on SoC-based SmartNICs IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-03 Shaoke Xi, Fuliang Li, Lingxiang Hu, Xingwei Wang, Kui Ren
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A Blockchain-based Fish Supply Chain Framework for Maintaining Fish Quality and Authenticity IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-03 Shereen Ismail, Muhammad Nouman, Hassan Reza, Fartash Vasefi, Hossein Kashani Zadeh
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Detecting Evolving Fraudulent Behavior in Online Payment Services: Open-Category and Concept-Drift IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-03 Hangyu Zhu, Cheng Wang, Songyao Chai
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SECaaS-based Partially Observable Defense Model for IIoT Against Advanced Persistent Threats IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-03 Zikai Zhang, Chuntao Ding, Yidong Li, Jinhui Yu, Jingy Li
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Privacy-Preserving Multi-Modality-Based Computer-Aided Diagnosis Processing of Liver Diseases IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-03 Yi Zhuang, Nan Jiang, Bi Chen, Lei Chen
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Profiling students’ learning engagement in MOOC discussions to identify learning achievement: An automated configurational approach Comput. Educ. (IF 8.9) Pub Date : 2024-07-02 Zhi Liu, Qianhui Tang, Fan Ouyang, Taotao Long, Sannyuya Liu
In the Massive Online Open Course (MOOC) forum, learning engagement encompasses three fundamental dimensions—cognitive, emotional, and behavioral engagement—that intricately interact to jointly influence students' learning achievements. However, the interplay between multiple engagement dimensions and their correlations with learning achievement remain understudied, particularly across different academic
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Feasibility of adaptive teaching with technology: Which implementation conditions matter? Comput. Educ. (IF 8.9) Pub Date : 2024-07-02 Leonie Sibley, Andreas Lachner, Christine Plicht, Armin Fabian, Iris Backfisch, Katharina Scheiter, Thorsten Bohl
Adaptive teaching is regarded to address students' heterogeneity in schools and to individually support their learning. Technology may help to teach adaptively. However, it is still unclear whether realizing adaptive teaching with technology is a feasible teaching practice in real-world classrooms. More importantly, it is an open question which boundary conditions constrain the feasibility of adaptive
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The smell of paper or the shine of a screen? Students’ reading comprehension, text processing, and attitudes when reading on paper and screen Comput. Educ. (IF 8.9) Pub Date : 2024-07-01 Ragnhild Engdal Jensen, Astrid Roe, Marte Blikstad-Balas
With the increasing prevalence of digital devices such as smartphones, tablets and e-readers, more and more reading is happening in digital formats – also in classrooms across the world. The present study focuses on lower secondary school students and their reading comprehension and attitudes toward reading on paper and screens. The students read a selection of texts and answered questions from the
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Mitigating Backdoor Attacks in Pre-trained Encoders via Self-supervised Knowledge Distillation IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-07-01 Rongfang Bie, Jinxiu Jiang, Hongcheng Xie, Yu Guo, Yinbin Miao, Xiaohua Jia
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Seeking social support on social media: a coping perspective Internet Res. (IF 5.9) Pub Date : 2024-06-28 Adela Chen, Kristina Lemmer
Purpose This paper aims to examine the strength characteristics of a stressful event (i.e. novelty, disruption, and criticality) as factors that drive people’s social media use for seeking different types of supportive resources (i.e. emotional, appraisal, informational, and instrumental support) to facilitate emotion-focused and problem-focused coping. We further assess the impact of different types
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The role of omnichannel integration and digital value in building brand trust: a customer psychological perception perspective Internet Res. (IF 5.9) Pub Date : 2024-06-25 Zhihui Yang, Dongbin Hu, Xiaohong Chen
Purpose In the dynamic landscape of the digital economy, companies are increasingly adopting omnichannel integration strategies to enhance customer experiences. However, the interplay between this strategy and digitalisation in fostering brand trust remains uncharted. Drawing on the social exchange and psychological reactance theories, this study ventures into unexplored territory by examining the
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Web Service Recommendation via Combining Topic-aware Heterogeneous Graph Representation and Interactive Semantic Enhancement IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-06-24 Buqing Cao, Qian Peng, Xiang Xie, Zhenlian Peng, Jianxun Liu, Zibin Zheng
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Resource Allocation in Blockchain Integration of UAV-Enabled MEC Networks: A Stackelberg Differential Game Approach IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-06-24 Die Wang, Yunjian Jia, Liang Liang, Kaoru Ota, Mianxiong Dong
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A Comprehensive Survey on Biclustering-based Collaborative Filtering ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Miguel G. Silva, Sara C. Madeira, Rui Henriques
Collaborative Filtering (CF) is achieving a plateau of high popularity. Still, recommendation success is challenged by the diversity of user preferences, structural sparsity of user-item ratings, and inherent subjectivity of rating scales. The increasing user base and item dimensionality of e-commerce and e-entertainment platforms creates opportunities, while further raising generalization and scalability
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A Challenge-based Survey of E-recruitment Recommendation Systems ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Yoosof Mashayekhi, Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie
E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation
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A Survey on the Applications of Semi-supervised Learning to Cyber-security ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Paul Kiyambu Mvula, Paula Branco, Guy-Vincent Jourdan, Herna Lydia Viktor
Machine Learning’s widespread application owes to its ability to develop accurate and scalable models. In cyber-security, where labeled data is scarce, Semi-Supervised Learning (SSL) emerges as a potential solution. SSL excels at tasks challenging traditional supervised and unsupervised algorithms by leveraging limited labeled data alongside abundant unlabeled data. This article presents a comprehensive
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Systems Interoperability Types: A Tertiary Study ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Rita Suzana Pitangueira Maciel, Pedro Henrique Dias Valle, Kécia Souza Santos, Elisa Yumi Nakagawa
Interoperability has been a focus of attention over at least four decades, with the emergence of several interoperability types (or levels), diverse models, frameworks, and solutions, also as a result of a continuous effort from different domains. The current heterogeneity in technologies such as blockchain, IoT and new application domains such as Industry 4.0 brings not only new interaction possibilities
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Domain Adaptation and Generalization of Functional Medical Data: A Systematic Survey of Brain Data ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Gita Sarafraz, Armin Behnamnia, Mehran Hosseinzadeh, Ali Balapour, Amin Meghrazi, Hamid R. Rabiee
Despite the excellent capabilities of machine learning algorithms, their performance deteriorates when the distribution of test data differs from the distribution of training data. In medical data research, this problem is exacerbated by its connection to human health, expensive equipment, and meticulous setups. Consequently, achieving domain generalizations and domain adaptations under distribution
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Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research Challenges ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Paulo Souza, Tiago Ferreto, Rodrigo Calheiros
The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the Internet Technology community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant
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Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 James Halvorsen, Clemente Izurieta, Haipeng Cai, Assefaw Gebremedhin
Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense, alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to further distinguish between benign and malicious behaviors, but it comes with several challenges, including lack of quality training data and high false-positive rates. Generative Machine Learning Models (GMLMs) can help overcome
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Blockchained Federated Learning for Internet of Things: A Comprehensive Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Yanna Jiang, Baihe Ma, Xu Wang, Guangsheng Yu, Ping Yu, Zhe Wang, Wei Ni, Ren Ping Liu
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models
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A Review on the emerging technology of TinyML ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Vasileios Tsoukas, Anargyros Gkogkidis, Eleni Boumpa, Athanasios Kakarountas
Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to democratize AI by making it available to more sectors and contribute to the digital revolution of intelligent devices. In this work, a classification
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Security, Privacy, and Decentralized Trust Management in VANETs: A Review of Current Research and Future Directions ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Mishri AlMarshoud, Mehmet Sabir Kiraz, Ali H. Al-Bayatti
Vehicular Ad Hoc Networks (VANETs) are powerful platforms for vehicular data services and applications. The increasing number of vehicles has made the vehicular network diverse, dynamic, and large-scale, making it difficult to meet the 5G network’s demanding requirements. Decentralized systems are interesting and provide attractive services because they are publicly available (transparency), have an
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From Detection to Application: Recent Advances in Understanding Scientific Tables and Figures ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Jiani Huang, Haihua Chen, Fengchang Yu, Wei Lu
Tables and figures are usually used to present information in a structured and visual way in scientific documents. Understanding the tables and figures in scientific documents is significant for a series of downstream tasks, such as academic search, scientific knowledge graphs, and so on. Existing studies mainly focus on detecting figures and tables from scientific documents, interpreting their semantics
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Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Jiajun Wu, Fan Dong, Henry Leung, Zhuangdi Zhu, Jiayu Zhou, Steve Drew
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is a natural solution for massive user-owned devices in edge computing with distributed and private training data. FL methods based on FedAvg typically follow a naive star topology,
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A Review on the Impact of Data Representation on Model Explainability ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Mostafa Haghir Chehreghani
In recent years, advanced machine learning and artificial intelligence techniques have gained popularity due to their ability to solve problems across various domains with high performance and quality. However, these techniques are often so complex that they fail to provide simple and understandable explanations for the outputs they generate. To address this issue, the field of explainable artificial
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Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image
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A Survey of Graph Neural Networks for Social Recommender Systems ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-22 Kartik Sharma, Yeon-Chang Lee, Sivagami Nambi, Aditya Salian, Shlok Shah, Sang-Wook Kim, Srijan Kumar
Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users’ tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention
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Object-Centric Learning with Capsule Networks: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-21 Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett, Georgios Leontidis, Mubarak Shah
Capsule networks emerged as a promising alternative to convolutional neural networks for learning object-centric representations. The idea is to explicitly model part-whole hierarchies by using groups of neurons called capsules to encode visual entities, then learn the relationships between these entities dynamically from data. However, a major hurdle for capsule network research has been the lack
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Swift and Accurate Mobility-Aware QoS Forecasting for Mobile Edge Environments IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-06-21 Huiying Jin, Pengcheng Zhang, Hai Dong, Athman Bouguettaya, Albert Y. Zomaya
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EDCOMA: Enabling Efficient Double Compressed Auditing for Blockchain-Based Decentralized Storage IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-06-21 Haiyang Yu, Yurun Chen, Zhen Yang, Yuwen Chen, Shui Yu
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Graph Neural Network Aided Deep Reinforcement Learning for Microservice Deployment in Cooperative Edge Computing IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-06-21 Shuangwu Chen, Qifeng Yuan, Jiangming Li, Huasen He, Sen Li, Xiaofeng Jiang, Jian Yang
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SEHGN: Semantic-Enhanced Heterogeneous Graph Network for Web API Recommendation IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2024-06-21 Xuanye Wang, Meng Xi, Ying Li, Xiaohua Pan, Yangyang Wu, Shuiguang Deng, Jianwei Yin
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A survey of 3D Space Path-Planning Methods and Algorithms ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-20 Hakimeh mazaheri, salman goli, ali nourollah
Due to their agility, cost-effectiveness, and high maneuverability, Unmanned Aerial Vehicles (UAVs) have attracted considerable attention from researchers and investors alike. Path planning is one of the practical subsets of motion planning for UAVs. It prevents collisions and ensures complete coverage of an area. This study provides a structured review of applicable algorithms and coverage path planning
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Platform control and multi-realized platform benefits: a meta-analysis Internet Res. (IF 5.9) Pub Date : 2024-06-20 Nicholas Roberts, Inchan Kim, Kishen Iyengar, Jennifer Pullin
Purpose Platform owners need to encourage yet control complementors in ways that generate benefits. Retaining too much control can restrict innovation and knowledge flows; giving up too much control can lead to poor quality and platform instability. Studies provide mixed findings that make it difficult to draw generalizable conclusions. We aim to provide a more accurate understanding of the link between
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Does corporate digitalization improve disclosure quality? Internet Res. (IF 5.9) Pub Date : 2024-06-21 Mingzhi Hu, Yinxin Su, Xiaofen Yu
Purpose This study investigates the potential association between corporate digitization and disclosure quality, and how this relationship is moderated by non-state ownership and institutional environment. Design/methodology/approach Drawing on signaling theory and factors that affect disclosure quality, the authors developed a framework to study how corporate digitization is associated with disclosure
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AI-Based Affective Music Generation Systems: A Review of Methods and Challenges ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-17 Adyasha Dash, Kathleen Agres
Music is a powerful medium for altering the emotional state of the listener. In recent years, with significant advancements in computing capabilities, artificial intelligence-based (AI-based) approaches have become popular for creating affective music generation (AMG) systems. Entertainment, healthcare, and sensor-integrated interactive system design are a few of the areas in which AI-based affective
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Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and Solutions ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-17 Lamyanba Laishram, Muhammad Shaheryar, Jong Taek Lee, Soon Ki Jung
Abstract Recent advancements in face recognition (FR) technology in surveillance systems make it possible to monitor a person as they move around. FR gathers a lot of information depending on the quantity and data sources. The most severe privacy concern with FR technology is its use to identify people in real-time public monitoring applications or via an aggregation of datasets without their consent
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Secure UAV (Drone) and the Great Promise of AI ACM Comput. Surv. (IF 23.8) Pub Date : 2024-06-17 Behrouz Zolfaghari, Mostafa Abbasmollaei, Fahimeh Hajizadeh, Naoto Yanai, Khodakhast Bibak
UAVs have found their applications in numerous applications from recreational activities to business in addition to military and strategic fields. However, research on UAVs is not going on as quickly as the technology. Especially, when it comes to the security of these devices, the academia is lagging behind the industry. This gap motivates our work in this paper as a stepping stone for future research