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Deceived by Immersion: A Systematic Analysis of Deceptive Design in Extended Reality ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Hilda Hadan, Lydia Choong, Leah Zhang-Kennedy, Lennart E. Nacke
The well-established deceptive design literature has focused on conventional user interfaces. With the rise of extended reality (XR), understanding deceptive design’s unique manifestations in this immersive domain is crucial. However, existing research lacks a full, cross-disciplinary analysis that analyzes how XR technologies enable new forms of deceptive design. Our study reviews the literature on
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Value implications of followers in social marketplaces: insights into ego network structures Internet Res. (IF 5.9) Pub Date : 2024-05-17 Shan Wang, Fang Wang
Purpose In social marketplaces, follower ego networks are integral social capital assets for online sellers. While previous research has underscored the positive impact of the follower number on seller performance, little attention has been given to the structure of follower networks and their value implications. This research investigates two structural properties of follower networks—network centralization
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Exploring the dual routes in influencing sales and adoption in augmented reality retailing: a mixed approach of SEM and FsQCA Internet Res. (IF 5.9) Pub Date : 2024-05-15 Xiaoyu Xu, Qingdan Jia, Syed Muhammad Usman Tayyab
Purpose This study investigates augmented reality (AR) retailing and attempts to develop a profound understanding of consumer decision-making processes in AR-enabled e-retailing. Design/methodology/approach The study is grounded in rich informational cues and information processing mechanisms by incorporating the elaboration likelihood model (ELM) and trust transfer theory. This study employs a mixed
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A Systematic Literature Review of Novelty Detection in Data Streams: Challenges and Opportunities ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Jean-Gabriel Gaudreault, Paula Branco
Novelty detection in data streams is the task of detecting concepts that were not known prior, in streams of data. Many machine learning algorithms have been proposed to detect these novelties, as well as integrate them. This study provides a systematic literature review of the state of novelty detection in data streams, including its advancement in recent years, its main challenges and solutions,
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A Survey on Automatic Generation of Figurative Language: From Rule-based Systems to Large Language Models ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Huiyuan Lai, Malvina Nissim
Figurative language generation (FLG) is the task of reformulating a given text to include a desired figure of speech, such as a hyperbole, a simile, and several others, while still being faithful to the original context. This is a fundamental, yet challenging task in Natural Language Processing (NLP), which has recently received increased attention due to the promising performance brought by pre-trained
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Integration of Sensing, Communication, and Computing for Metaverse: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Xiaojie Wang, Qi Guo, Zhaolong Ning, Lei Guo, Guoyin Wang, Xinbo Gao, Yan Zhang
The metaverse is an Artificial Intelligence (AI)-generated virtual world, in which people can game, work, learn, and socialize. The realization of metaverse not only requires a large amount of computing resources to realize the rendering of the virtual world, but also requires communication resources to realize real-time transmission of massive data to ensure a good user experience. The metaverse is
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Neuromorphic Perception and Navigation for Mobile Robots: A Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Alvaro Novo, Francisco Lobon, Hector Garcia de Marina, Samuel Romero, Francisco Barranco
With the fast and unstoppable evolution of robotics and artificial intelligence, effective autonomous navigation in real-world scenarios has become one of the most pressing challenges in the literature. However, demanding requirements, such as real-time operation, energy and computational efficiency, robustness, and reliability, make most current solutions unsuitable for real-world challenges. Thus
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Artificial Intelligence for Web 3.0: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Meng Shen, Zhehui Tan, Dusit Niyato, Yuzhi Liu, Jiawen Kang, Zehui Xiong, Liehuang Zhu, Wei Wang, Xuemin (Sherman) Shen
Web 3.0 is the next generation of the Internet built on decentralized technologies such as blockchain and cryptography. It is born to solve the problems faced by the previous generation of the Internet such as imbalanced distribution of interests, monopoly of platform resources, and leakage of personal privacy. In this survey, we discuss the latest development status of Web 3.0 and the application
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Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Max Sponner, Bernd Waschneck, Akash Kumar
Adaptive optimization methods for deep learning adjust the inference task to the current circumstances at runtime to improve the resource footprint while maintaining the model’s performance. These methods are essential for the widespread adoption of deep learning, as they offer a way to reduce the resource footprint of the inference task while also having access to additional information about the
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A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Juncheng Li, Zehua Pei, Wenjie Li, Guangwei Gao, Longguang Wang, Yingqian Wang, Tieyong Zeng
Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their design targets. Specifically, we first introduce the problem definition
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UAV-Assisted IoT Applications, QoS Requirements and Challenges with Future Research Directions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-14 Muhammad Adil, Houbing Song, Mian Ahmad Jan, Muhammad Khurram Khan, Xiangjian He, Ahmed Farouk, Zhanpeng Jin
Unmanned Aerial Vehicle (UAV)-assisted Internet of Things application communication is an emerging concept that effectuates the foreknowledge of innovative technologies. With the accelerated advancements in IoT applications, the importance of this technology became more impactful and persistent. Moreover, this technology has demonstrated useful contributions across various domains, ranging from general
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Problem resolution with business analytics: a task-technology fit perspective Internet Res. (IF 5.9) Pub Date : 2024-05-10 Givemore Muchenje, Marko Seppänen, Hongxiu Li
Purpose The study explores the extent to which business analytics can address business problems using the task-technology fit theory. Design/methodology/approach The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes
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Recent Advances for Aerial Object Detection: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-13 jiaxu leng, Yongming Ye, Mengjingcheng MO, Chenqiang Gao, Ji Gan, Bin Xiao, Xinbo Gao
Aerial object detection, as object detection in aerial images captured from an overhead perspective, has been widely applied in urban management, industrial inspection, and other aspects. However, the performance of existing aerial object detection algorithms is hindered by variations in object scales and orientations attributed to the aerial perspective. This survey presents a comprehensive review
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How do NPOs’ topics and moral foundations in gun-related issues influence public engagement on Twitter? Internet Res. (IF 5.9) Pub Date : 2024-05-14 Yafei Zhang, Li Chen, Ming Xie
Purpose Drawing on the moral foundations theory (MFT), we examine what nonprofit organizations (NPOs) discuss and how NPOs engage in gun-related issues on Twitter. Specifically, we explore latent topics and embedded moral values (i.e. care, fairness, loyalty, authority, and sanctity) in NPOs’ tweets and investigate the effects of the latent topics and moral values on invoking public engagement. De
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Lightweight Deep Learning for Resource-Constrained Environments: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-11 Hou-I Liu, Marco Galindo, Hongxia Xie, Lai-Kuan Wong, Hong-Han Shuai, Yung-Hui Li, Wen-Huang Cheng
Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources.
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Multi-Task Learning in Natural Language Processing: An Overview ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-11 Shijie Chen, Yu Zhang, Qiang Yang
Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. In recent years, Multi-Task Learning (MTL), which can leverage useful information of related tasks to achieve simultaneous performance improvement on these tasks
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A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-11 Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares
In the field of Sequential Decision Making (SDM), two paradigms have historically vied for supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of reconciliation, this paper reviews AP, RL and hybrid methods (e.g., novel learn to plan techniques) for solving Sequential Decision Processes (SDPs), focusing on their knowledge representation: symbolic, subsymbolic or a combination
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Creativity and Machine Learning: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-11 Giorgio Franceschelli, Mirco Musolesi
There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, key machine learning techniques (including generative deep learning), and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current
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A review of explainable fashion compatibility modeling methods ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-11 Karolina Selwon, Julian Szyma?ski
The paper reviews methods used in the fashion compatibility recommendation domain. We select methods based on reproducibility, explainability, and novelty aspects and then organize them chronologically and thematically. We presented general characteristics of publicly available datasets that are related to the fashion compatibility recommendation task. Finally, we analyzed the representation bias of
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Synthetic Data for Deep Learning in Computer Vision & Medical Imaging: A Means to Reduce Data Bias ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-09 Anthony Paproki, Olivier Salvado, Clinton Fookes
Deep-learning (DL) performs well in computer-vision and medical-imaging automated decision-making applications. A bottleneck of DL stems from the large amount of labelled data required to train accurate models that generalise well. Data scarcity and imbalance are common problems in imaging applications that can lead DL models towards biased decision making. A solution to this problem is synthetic data
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Natural Language Reasoning, A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-09 Fei Yu, Hongbo Zhang, Prayag Tiwari, Benyou Wang
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct definition for natural language reasoning in NLP, based on both philosophy and NLP scenarios, discuss what types of tasks require reasoning, and introduce a taxonomy of reasoning. Practically, we conduct a comprehensive
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Efficient and Privacy-Enhanced Federated Learning Based on Parameter Degradation IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-10 Wenling Li, Ping Yu, Yanan Cheng, Jianen Yan, Zhaoxin Zhang
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An Efficient Algorithm for Microservice Placement in Cloud-Edge Collaborative Computing Environment IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-10 Xiang He, Hanchuan Xu, Xiaofei Xu, Yin Chen, Zhongjie Wang
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Loss Aware Federated Learning for Service Migration in Multimodal E-Health Services IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-10 Himanshu Singh, Ajay Pratap, Ram Narayan Yadav, Debasis Das
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PPNNI: Privacy-Preserving Neural Network Inference against Adversarial Example Attack IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-10 Guanghui He, Yanli Ren, Gang He, Guorui Feng, Xinpeng Zhang
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Communication-Efficient Federated Learning with Adaptive Aggregation for Heterogeneous Client-Edge-Cloud Network IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-10 Long Luo, Chi Zhang, Hongfang Yu, Gang Sun, Shouxi Luo, Schahram Dustdar
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Long-Term Proof-of-Contribution: an Incentivized Consensus Algorithm for Blockchain-enabled Federated Learning IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-10 Yao Zhao, Youyang Qu, Yong Xiang, Feifei Chen, Longxiang Gao
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MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-10 Dianhai Yu, Liang Shen, Hongxiang Hao, Weibao Gong, Huachao Wu, Jiang Bian, Lirong Dai, Haoyi Xiong
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Cross-Edge Orchestration of Serverless Functions with Probabilistic Caching IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-10 Chen Chen, Manuel Herrera, Ge Zheng, Liqiao Xia, Zhengyang Ling, Jiangtao Wang
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Interactive Question Answering Systems: Literature Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-08 Giovanni Maria Biancofiore, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci
Question-answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their queries by presenting their questions in natural language. Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and
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Horizontal Federated Recommender System: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-08 Lingyun Wang, Hanlin Zhou, Yinwei Bao, Xiaoran Yan, Guojiang Shen, Xiangjie Kong
Due to underlying privacy-sensitive information in user-item interaction data, the risk of privacy leakage exists in the centralized-training recommender system (RecSys). To this issue, federated learning, a privacy-oriented distributed computing paradigm, is introduced and promotes the crossing field “Federated Recommender System (FedRec).” Regarding data distribution characteristics, there are horizontal
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Computational Politeness in Natural Language Processing: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-08 Priyanshu Priya, Mauajama Firdaus, Asif Ekbal
Computational approach to politeness is the task of automatically predicting and/or generating politeness in text. This is a pivotal task for conversational analysis, given the ubiquity and challenges of politeness in interactions. The computational approach to politeness has witnessed great interest from the conversational analysis community. This article is a compilation of past works in computational
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Exploring Blockchain Technology through a Modular Lens: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-08 Minghui Xu, Yihao Guo, Chunchi Liu, Qin Hu, Dongxiao Yu, Zehui Xiong, Dusit (Tao) Niyato, Xiuzhen Cheng
Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration
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NLOS Identification and Mitigation for Time-based Indoor Localization Systems: Survey and Future Research Directions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-07 Raphael Elikplim Nkrow, Bruno Silva, Dutliff Boshoff, Gerhard Hancke, Mikael Gidlund, Adnan Abu-Mahfouz
One hurdle to accurate indoor localization using time-based networks is the presence of Non-Line-Of-Sight (NLOS) and multipath signals, affecting the accuracy of ranging in indoor environments. NLOS identification and mitigation have been studied over the years and applied to different time-based networks, with most works considering NLOS links with WiFi and UWB channels. In this paper, we discuss
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Survey on Redundancy Based-Fault tolerance methods for Processors and Hardware accelerators - Trends in Quantum Computing, Heterogeneous Systems and Reliability ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-06 Shashikiran Venkatesha, Ranjani Parthasarathi
Rapid progress in the CMOS technology for the past 25 years has increased the vulnerability of processors towards faults. Subsequently, focus of computer architects shifted towards designing fault-tolerance methods for processor architectures. Concurrently, chip designers encountered high order challenges for designing fault tolerant processor architectures. For processor cores, redundancy-based fault
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Sharenting in China: perspectives from mothers and adolescents Internet Res. (IF 5.9) Pub Date : 2024-05-07 Lin Zhu, Yan Wang, Yanhong Chen
Purpose Mothers sharing images and information on social media about their children is a contemporary cultural norm. While the practice has been heavily discussed in popular media, there is a lack of empirical research examining the phenomenon from the perspectives of parents and adolescent children in China. The current study aims to find out whether or not mothers and their children engage in discussions
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Impact of pre-knowledge and engagement in robot-supported collaborative learning through using the ICAPB model Comput. Educ. (IF 12.0) Pub Date : 2024-05-06 Jia-Hua Zhao, Qi-Fan Yang, Li-Wen Lian, Xian-Yong Wu
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Meta-learning approaches for few-shot learning: A survey of recent advances ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-03 Hassan Gharoun, Fereshteh Momenifar, Fang Chen, Amir Gandomi
Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first
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Multi-objective evolutionary search for optimal Robotic Process Automation architectures IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-02 Geeta Mahala, Renuka Sindhgatta, Hoa Khanh Dam, Aditya Ghose
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2D-SAZD: A Novel 2D Coded Distributed Computing Framework for Matrix-Matrix Multiplication IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-02 Mingjun Dai, Zelong Zhang, Ziying Zheng, Zhonghao Zhang, Xiaohui Lin, Hui Wang
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A Survey on Privacy of Personal and Non-Personal Data in B5G/6G Networks ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-01 Chamara Sandeepa, Bartlomiej Siniarski, Nicolas Kourtellis, Shen Wang, Madhusanka Liyanage
The upcoming Beyond 5G (B5G) and 6G networks are expected to provide enhanced capabilities such as ultra-high data rates, dense connectivity, and high scalability. It opens many possibilities for a new generation of services driven by Artificial Intelligence (AI) and billions of interconnected smart devices. However, with this expected massive upgrade, the privacy of people, organisations, and states
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A Systematic Literature Review on Reasons and Approaches for Accurate Effort Estimations in Agile ACM Comput. Surv. (IF 16.6) Pub Date : 2024-05-01 Jirat Pasuksmit, Patanamon Thongtanunam, Shanika Karunasekera
Background: Accurate effort estimation is crucial for planning in Agile iterative development. Agile estimation generally relies on consensus-based methods like planning poker, which require less time and information than other formal methods (e.g., COSMIC) but are prone to inaccuracies. Understanding the common reasons for inaccurate estimations and how proposed approaches can assist practitioners
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Social influence and the choice of product upgrades: evidence from virtual product adoption in online games Internet Res. (IF 5.9) Pub Date : 2024-05-01 Qing Huang, Xiaoling Li, Dianwen Wang
Purpose Previous studies on social influence and virtual product adoption have mainly taken users’ purchase behavior as a dichotomous variable (i.e. purchasing or not). Given the prevalence of competing versions (basic vs upgraded) of a virtual product in online communities, this paper investigated the differences in the effect of social influence on users’ adoption of basic and upgraded choices of
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Efficient and Privacy-preserving Outsourcing of Gradient Boosting Decision Tree Inference IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-01 Shuai Yuan, Hongwei Li, Xinyuan Qian, Meng Hao, Yixiao Zhai, Guowen Xu
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Resource-aware Cyber Deception for Microservice-based Applications IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-01 Marco Zambianco, Claudio Facchinetti, Roberto Doriguzzi-Corin, Domenico Siracusa
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AISM: An Adaptable Image Steganography Model with User Customization IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-05-01 Bobiao Guo, Ping Ping, Siqi Zhou, Olano Teah Bloh, Feng Xu, Xiaofeng Zhou
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A Review on the emerging technology of TinyML ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-30 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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A Survey of Graph Neural Networks for Social Recommender Systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-29 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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A Review on the Impact of Data Representation on Model Explainability ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-29 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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How do small-to-medium-sized e-commerce businesses stay competitive? Evidence on the critical roles of IT capability, innovation and multihoming Internet Res. (IF 5.9) Pub Date : 2024-04-30 Qin Weng, Danping Wang, Stephen De Lurgio II, Sebastian Schuetz
Purpose Small-to-medium-sized enterprises (SMEs) in e-commerce often invest in information technology (IT) to stay competitive. However, whether and how IT capability (ITC) translates into financial performance requires further research. This paper examines the role of ITC in enabling value proposition innovation (VPI) as an important mechanism that improves financial performance for Chinese e-commerce
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Multi-Tree Genetic Programming Hyper-Heuristic for Dynamic Flexible Workflow Scheduling in Multi-Clouds IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-29 Zaixing Sun, Yi Mei, Fangfang Zhang, Hejiao Huang, Chonglin Gu, Mengjie Zhang
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SiaDFP: A Disk Failure Prediction Framework Based on Siamese Neural Network in Large-Scale Data Center IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-29 Xiaoyu Fang, Wenbai Guan, Jiawen Li, Chenhan Cao, Bin Xia
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ELXGB: An Efficient and Privacy-Preserving XGBoost for Vertical Federated Learning IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-29 Wei Xu, Hui Zhu, Yandong Zheng, Fengwei Wang, Jiaqi Zhao, Zhe Liu, Hui Li
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Latency-Aware Container Scheduling in Edge Cluster Upgrades: A Deep Reinforcement Learning Approach IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-29 Hanshuai Cui, Zhiqing Tang, Jiong Lou, Weijia Jia, Wei Zhao
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Trusted Sharing of Computing Power Resources: Benefit-Driven Heterogeneous Network Service Provision Mechanism IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-29 Meiling Dai, Shaoyong Guo, Song Guo, Sujie Shao, Xuesong Qiu
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A Meta-Study of Software-Change Intentions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Jacob Krüger, Yi Li, Kirill Lossev, Chenguang Zhu, Marsha Chechik, Thorsten Berger, Julia Rubin
Every software system undergoes changes, for example, to add new features, fix bugs, or refactor code. The importance of understanding software changes has been widely recognized, resulting in various techniques and studies, for instance, on change-impact analysis or classifying developers’ activities. Since changes are triggered by developers’ intentions—something they plan or want to change in the
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Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan, Geoffrey I. Webb, Germain Forestier, Mahsa Salehi
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This article surveys the current state of the
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SoK: Security in Real-Time Systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Monowar Hasan, Ashish Kashinath, Chien-Ying Chen, Sibin Mohan
Security is an increasing concern for real-time systems (RTS). Over the last decade or so, researchers have demonstrated attacks and defenses aimed at such systems. In this article, we identify, classify and measure the effectiveness of the security research in this domain. We provide a high-level summary [identification] and a taxonomy [classification] of this existing body of work. Furthermore, we
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Fuzzers for Stateful Systems: Survey and Research Directions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Cristian Daniele, Seyed Behnam Andarzian, Erik Poll
Fuzzing is a very effective testing methodology to find bugs. In a nutshell, a fuzzer sends many slightly malformed messages to the software under test, hoping for crashes or incorrect system behaviour. The methodology is relatively simple, although applications that keep internal states are challenging to fuzz. The research community has responded to this challenge by developing fuzzers tailored to
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A Survey of Cutting-edge Multimodal Sentiment Analysis ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Upendra Singh, Kumar Abhishek, Hiteshwar Kumar Azad
The rapid growth of the internet has reached the fourth generation, i.e., web 4.0, which supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk management, healthcare, businesses, websites, data mining, e-learning, psychology, and many more. Sentiment analysis is a powerful tool for governments, businesses, and researchers to analyse users’ emotions and mental states