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A Survey on Cyber-Resilience Approaches for Cyber-Physical Systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-16 Mariana Segovia-Ferreira, Jose Rubio-Hernan, Ana Rosa Cavalli, Joaquin Garcia-Alfaro
Concerns for the resilience of Cyber-Physical Systems (CPS) in critical infrastructure are growing. CPS integrate sensing, computation, control, and networking into physical objects and mission-critical services, connecting traditional infrastructure to internet technologies. While this integration increases service efficiency, it has to face the possibility of new threats posed by the new functionalities
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How review content, sentiment and helpfulness votes jointly affect trust of reviews and attitude Internet Res. (IF 5.9) Pub Date : 2024-03-18 Jing Li, Xin Xu, Eric W.T. Ngai
Purpose We investigate the joint impacts of three trust cues – content, sentiment and helpfulness votes – of online product reviews on the trust of reviews and attitude toward the product/service reviewed. Design/methodology/approach We performed three studies to test our research model, presenting participants with scenarios involving product reviews and prior users' helpful and unhelpful votes across
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Mobile Near-Infrared Sensing - A Systematic Review on Devices, Data, Modeling and Applications ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-16 Weiwei Jiang, Jorge Goncalves, Vassilis Kostakos
Mobile near-infrared sensing is becoming an increasingly important method in many research and industrial areas. To help consolidate progress in this area, we use the PRISMA guidelines to conduct a systematic review of mobile near-infrared sensing, including 1) existing prototypes and commercial products; 2) data collection techniques; 3) machine learning methods; 4) relevant application areas. Our
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Local Interpretations for Explainable Natural Language Processing: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-15 Siwen Luo, Hamish Ivison, Soyeon Caren Han, Josiah Poon
As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models. This work investigates various methods to improve the interpretability of deep neural networks for Natural Language Processing (NLP) tasks, including machine translation
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The role of para-social relationship in live streaming virtual gift purchase: a two-stage SEM-neural network analysis Internet Res. (IF 5.9) Pub Date : 2024-03-14 Fangfang Hou, Boying Li, Zhengzhi Guan, Alain Yee Loong Chong, Chee Wei Phang
Purpose Despite the burgeoning popularity of virtual gifting in live streaming, research lacks an in-depth understanding of the drivers behind this behavior. Using para-social relationship (PSR), this study aims to capture viewers’ lively social feelings toward the streamer as the key factor leading to the purchase behavior of virtual gifts. It also aims to establish a theoretical link between PSR
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Extended Reality (XR) Toward Building Immersive Solutions: The Key to Unlocking Industry 4.0 ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-14 A’aeshah Alhakamy
When developing XR applications for Industry 4.0, it is important to consider the integration of visual displays, hardware components, and multimodal interaction techniques that are compatible with the entire system. The potential use of multimodal interactions in industrial applications has been recognized as a significant factor in enhancing humans’ ability to perform tasks and make informed decisions
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Intel TDX Demystified: A Top-Down Approach ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-14 Pau-Chen Cheng, Wojciech Ozga, Enriquillo Valdez, Salman Ahmed, Zhongshu Gu, Hani Jamjoom, Hubertus Franke, James Bottomley
Intel Trust Domain Extensions (TDX) is an architectural extension in the 4th Generation Intel Xeon Scalable Processor that supports confidential computing. TDX allows the deployment of virtual machines in the Secure-Arbitration Mode (SEAM) with encrypted CPU state and memory, integrity protection, and remote attestation. TDX aims to enforce hardware-assisted isolation for virtual machines and minimize
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Warm-Starting and Quantum Computing: A Systematic Mapping Study ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-13 Felix Truger, Johanna Barzen, Marvin Bechtold, Martin Beisel, Frank Leymann, Alexander Mandl, Vladimir Yussupov
Due to low numbers of qubits and their error-proneness, Noisy Intermediate-Scale Quantum (NISQ) computers impose constraints on the size of quantum algorithms they can successfully execute. State-of-the-art research introduces various techniques addressing these limitations by utilizing known or inexpensively generated approximations, solutions, or models as a starting point to approach a task instead
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Drug–Drug Interaction Relation Extraction Based on Deep Learning: A Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-13 Mingliang Dou, Jijun Tang, Prayag Tiwari, Yijie Ding, Fei Guo
Drug–drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects of medicine and patient safety, and has a significant impact on public health. Therefore, using deep learning technology to extract DDI from scientific literature has become a valuable research direction to researchers. In
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On Trust Recommendations in the Social Internet of Things – A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-13 Marius Becherer, Omar Khadeer Hussain, Yu Zhang, Frank den Hartog, Elizabeth Chang
The novel paradigm Social Internet of Things (SIoT) improves the network navigability, identifies suitable service providers, and addresses scalability concerns. Ensuring trustworthy collaborations among devices is a key aspect in SIoT and can be realized through trust recommendations. However, the outcome of trust recommendations depends on multiple factors related to the context-dependent nature
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DevOps Metrics and KPIs: A Multivocal Literature Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-13 Ricardo Amaro, Rúben Pereira, Miguel Mira da Silva
Context: Information Technology (IT) organizations are aiming to implement DevOps capabilities in order to fulfill market, customers and internal needs. While many are successful with DevOps implementation, others still have difficulty to measure DevOps success in their organization. As a result, the effectiveness of assessing DevOps remains erratic. This emphasizes the need to withstand management
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Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-13 Jiahang Cao, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang
Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this article, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we
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Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-13 Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo, Matteo Magnini, Andrea Omicini
In this article, we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities—symbolic knowledge extraction (SKE) and symbolic knowledge injection (SKI)—from and into sub-symbolic predictors. We consider as symbolic any language being intelligible and interpretable for both humans and computers. Accordingly, we propose general meta-models for both
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Achieving Privacy-preserving Trajectory Query in Geospatial Information Systems with Outsourced Cloud IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-13 Qinglei Kong, Songnian Zhang, Rongxing Lu, Haiyong Bao, Bo Chen, Shiwu Xu
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FRLN: Federated Residual Ladder Network for Data-Protected QoS Prediction IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-13 Guobing Zou, Wenzhuo Yu, Shengxiang Hu, Yanglan Gan, Bofeng Zhang, Yixin Chen
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Boosting Dynamic Decentralized Federated Learning by Diversifying Model Sources IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-13 Dongyuan Su, Yipeng Zhou, Laizhong Cui, Song Guo
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A comprehensive review and new taxonomy on superpixel segmentation ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-12 Isabela Borlido Barcelos, Felipe de Castro Belém, Leonardo de Melo João, Zenilton K. G. do Patrocínio Jr., Alexandre Xavier Falcão, Silvio Jamil Ferzoli Guimarães
Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works among
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Incentive-Aware Resource Allocation for Multiple Model Owners in Federated Learning IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-12 Feng-Yang Chen, Li-Hsing Yen
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Attribute-hiding fuzzy encryption for privacy-preserving data evaluation IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-12 Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia
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DPFLA: Defending Private Federated Learning Against Poisoning Attacks IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-12 Xia Feng, Wenhao Cheng, Chunjie Cao, Liangmin Wang, Victor S. Sheng
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Fission Spectral Clustering Strategy For UAV Swarm Networks IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-12 Gepeng Zhu, Haipeng Yao, Tianle Mai, Zunliang Wang, Di Wu, Song Guo
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E2MS: An Efficient and Economical Microservice Migration Strategy for Smart Manufacturing IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-12 Yuxiang Liu, Bo Yang, Xiaoyuan Ren, Qi Liu, Sicheng Liu, Xinping Guan
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Dynamic Task Offloading and Resource Allocation for NOMA-aided Mobile Edge Computing: An Energy Efficient Design IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-12 Ying Chen, Jiajie Xu, Yuan Wu, Jie Gao, Lian Zhao
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POP-FL: Towards Efficient Federated Learning on Edge Using Parallel Over-Parameterization IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-12 Xingjian Lu, Haikun Zheng, Wenyan Liu, Yuhui Jiang, Hongyue Wu
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StraAlgin: automated strategic alignment of services IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-03-12 Ahmed Saeed Alsayed, Hoa Khanh Dam, Aditya Ghose, Chau Nguyen
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A Survey of Cutting-edge Multimodal Sentiment Analysis ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-11 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
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Controllable Data Generation by Deep Learning: A Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-09 Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Zhaohui Qin, Liang Zhao
Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation
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Pre-trained Language Models for Text Generation: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-07 Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
Text Generation aims to produce plausible and readable text in human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (PLMs). Text generation based on PLMs is viewed as a promising approach in both academia and industry. In this paper, we provide a survey on the utilization
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Resilient Machine Learning: Advancement, Barriers and Opportunities in the Nuclear Industry ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-05 Anita Khadka, Saurav Sthapit, Gregory Epiphaniou, Carsten Maple
The widespread adoption and success of Machine Learning (ML) technologies depend on thorough testing of the resilience and robustness to adversarial attacks. The testing should focus on both the model and the data. It is necessary to build robust and resilient systems to withstand disruptions and remain functional despite the action of adversaries, specifically in the security-sensitive industry like
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Scenario-Based Adaptations of Differential Privacy: A Technical Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-05 Ying Zhao, Jia Tina Du, Jinjun Chen
Differential Privacy has been a de facto privacy standard in defining privacy and handling privacy preservation. It has received great success in scenarios of local data privacy and statistical dataset privacy. As a primitive definition, standard differential privacy has been adapted to a wide range of practical scenarios. In this work, we summarize differential privacy adaptations in specific scenarios
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Deep Learning for Iris Recognition: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-05 Kien Nguyen, Hugo Proença, Fernando Alonso-Fernandez
ABSTRACT In this survey, we provide a comprehensive review of more than 200 papers, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques
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A Survey on Content Retrieval on the Decentralised Web ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-04 Navin V. Keizer, Onur Ascigil, Michał Król, Dirk Kutscher, George Pavlou
The control, governance, and management of the web have become increasingly centralised, resulting in security, privacy, and censorship concerns. Decentralised initiatives have emerged to address these issues, beginning with decentralised file systems. These systems have gained popularity, with major platforms serving millions of content requests daily. Complementing the file systems are decentralised
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Academic procrastination, incentivized and self-selected spaced practice, and quiz performance in an online programming problem system: An intensive longitudinal investigation Comput. Educ. (IF 12.0) Pub Date : 2024-03-05 Yingbin Zhang, Luc Paquette, Xiaoyong Hu
Time management is crucial for college students' academic success and learning of computer programming. Yet the changes of time management behaviors and their associations with learning outcomes are underexplored in online learning of programming. To address the gap, this study employed an intensive longitudinal approach to examine undergraduates’ time management behaviors in an online programming
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Learning analytics and the Universal Design for Learning (UDL): A clustering approach Comput. Educ. (IF 12.0) Pub Date : 2024-03-02 Marvin Roski, Ratan Sebastian, Ralph Ewerth, Anett Hoppe, Andreas Nehring
In the context of inclusive education, Universal Design for Learning (UDL) is a framework used worldwide to create learning opportunities accessible to all learners. While much research focused on the design and students' perceptions of UDL-based learning settings, studies on students’ usage patterns in UDL-guided elements, particularly in digital environments, are still scarce. Therefore, we analyze
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The impact of school support for professional development on teachers' adoption of student-centered pedagogy, students’ cognitive learning and abilities: A three-level analysis Comput. Educ. (IF 12.0) Pub Date : 2024-03-02 Siu-Cheung Kong, Yi-Qing Wang
Student-centered pedagogy (SCP) is highly considered for its potential to facilitate cognitive learning in Computational Thinking (CT) education. However, there is a noticeable gap in understanding its influence on students' cognitive development from a multilevel perspective. This study delves into cognitive learning theories and aims to bridge the existing gap by introducing a three-level conceptual
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A Deep Dive into Robot Vision - An integrative Systematic Literature Review Methodologies and Research Endeavor Practices ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-01 Saima Sultana, Muhammad Mansoor Alam, Mazliham Mohd Su’ud, Jawahir Che Mustapha, Mukesh Prasad
Novel technological swarm and industry 4.0 mold the recent Robot vision research into innovative discovery. To enhance technological paradigm Deep Learning offers magical pace to get into diversified advancement. This research considers most topical, recent, related and state of the art research review revolves around Robot vision, shapes the research into Systematic Literature Survey – SLR. The SLR
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Running in circles: A systematic review of reviews on technological pedagogical content knowledge (TPACK) Comput. Educ. (IF 12.0) Pub Date : 2024-03-01 Mirjam Schmid, Eliana Brianza, Sog Yee Mok, Dominik Petko
Extensive research exists on the Technological Pedagogical Content Knowledge (TPACK) model and has led to a substantial number of systematic reviews and meta-analyses. These publications vary greatly in their focus and provide overviews of specific aspects of TPACK research. This paper aims to consolidate these insights and investigate the following research questions: What do systematic literature
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Application of cluster analysis to identify different reader groups through their engagement with a digital reading supplement Comput. Educ. (IF 12.0) Pub Date : 2024-02-29 Yawen Ma, Kate Cain, Anastasia Ushakova
The focus of this study is the identification of reader profiles that differ in performance and progression in an educational literacy app. A total of 19,830 students in Grade 2 from 347 Elementary schools located in 30 different districts in the United States played the app from 2020 to 2021. Our aim was to identify unique groups of readers using an unsupervised statistical learning technique - cluster
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Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-28 Karen Carrasco, Lenin Tomalá, Eileen Ramírez Meza, Doris Meza Bolaños, Washington Ramírez Montalvan
The problem arises from the lack of sufficient and comprehensive information about the necessary computer techniques. These techniques are crucial for developing information systems that assist doctors in diagnosing breast cancer, especially those related to positron emission tomography and computed tomography (PET/CT). Despite global efforts in breast cancer prevention and control, the scarcity of
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Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-27 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 paper surveys the current state of the art
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Enhancing undergraduates’ engagement in a learning community by including their voices in the technological and instructional design Comput. Educ. (IF 12.0) Pub Date : 2024-02-28 Wangda Zhu, Gaoxia Zhu, Ying Hua
Over the past decades, Social Networking Tools (SNT) have been applied in educational settings to support students' engagement in learning communities. Previous studies suggested the positive effects of including students' voices in technological and instructional design. However, educators usually cannot revise the features of SNT as they like, which may limit the possibility of enhancing students'
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IMPACTS Homeostasis Trust Management System: Optimizing Trust in Human-AI Teams ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-26 Ming Hou, Simon Banbury, Brad Cain, Scott Fang, Hannah Willoughby, Liam Foley, Edward Tunstel, Imre J. Rudas
Artificial Intelligence (AI) is becoming more ubiquitous throughout our lives. As our reliance on this technology increases, ensuring human operators maintain an adequate level of trust is integral to their safe and effective operations. To facilitate the appropriate level of operator trust in AI, a mechanism to continuously evaluate and calibrate human-AI trust is required. Such a Trust Management
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Financial Sentiment Analysis: Techniques and Applications ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-26 Kelvin Du, Frank Xing, Rui Mao, Erik Cambria
Financial Sentiment Analysis (FSA) is an important domain application of sentiment analysis that has gained increasing attention in the past decade. FSA research falls into two main streams. The first stream focuses on defining tasks and developing techniques for FSA, and its main objective is to improve the performances of various FSA tasks by advancing methods and using/curating human-annotated datasets
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SoK: Security in Real-Time Systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-26 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 paper, 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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Deep Multimodal Data Fusion ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-24 Fei Zhao, Chengcui Zhang, Baocheng Geng
Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data (e.g., images, texts, or data collected from different sensors), feature engineering (e.g., extraction, combination/fusion), and decision-making (e.g., majority vote). As architectures become more and more sophisticated, multimodal neural networks can integrate feature extraction, feature fusion, and decision-making
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Security for Machine Learning-based Software Systems: A Survey of Threats, Practices, and Challenges ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Huaming Chen, M. Ali Babar
The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision and video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, how to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge, for which the insufficient consideration
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Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-24 Jianping Yao, Son N. Tran, Saurabh Garg, Samantha Sawyer
Deep learning (DL) plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of DL within this research domain, there remains a notable absence of an empirical study to offer insightful comparisons due to the employment of varied datasets
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A self-determination theory approach to teacher digital competence development Comput. Educ. (IF 12.0) Pub Date : 2024-02-24 Thomas K.F. Chiu, Garry Falloon, Yanjie Song, Vincent W.L. Wong, Li Zhao, Murod Ismailov
Teacher Digital Competence (TDC) framework guides policy revision and professional development, empowering teachers for future classrooms by technologies such as artificial intelligence (AI) and metaverse. Falloon (2020) expanded the TPACK framework to include personal-ethic and personal-professional competencies, addressing ethical, safe, and productive functioning in diverse, digital environments
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A Survey on Cyber Resilience: Key Strategies, Research Challenges, and Future Directions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Saleh Mohamed AlHidaifi, Muhammad Rizwan Asghar, Imran Shafique Ansari
Cyber resilience has become a major concern for both academia and industry due to the increasing number of data breaches caused by the expanding attack surface of existing IT infrastructure. Cyber resilience refers to an organisation’s ability to prepare for, absorb, recover from, and adapt to adverse effects typically caused by cyber-attacks that affect business operations. In this survey, we aim
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Survey on Recommender Systems for Biomedical Items in Life and Health Sciences ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Matilde Pato, Márcia Barros, Francisco M. Couto
The generation of biomedical data is of such magnitude that its retrieval and analysis have posed several challenges. A survey of recommender system (RS) approaches in biomedical fields is provided in this analysis, along with a discussion of existing challenges related to large-scale biomedical information retrieval systems. We collect original studies, identify entities and models, and discuss how
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Completeness, Recall, and Negation in Open-world Knowledge Bases: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian Suchanek
General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from web sources and are thus far from complete. This poses challenges for the consumption as well as the curation of their content. While several surveys target the problem of completing incomplete KBs, the first problem is arguably to know whether and where the KB is incomplete
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Security and Privacy Issues in Deep Reinforcement Learning: Threats and Countermeasures ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Kanghua Mo, Peigen Ye, Xiaojun Ren, Shaowei Wang, Wenjun Li, Jin Li
Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI), where agents interact with environments to learn policies for solving complex tasks. In recent years, DRL has achieved remarkable breakthroughs in various tasks, including video games, robotic control, quantitative trading, and autonomous driving. Despite its accomplishments, security and privacy-related issues
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Mouse Dynamics Behavioral Biometrics: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Simon Khan, Charles Devlen, Michael Manno, Daqing Hou
Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical
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The Art of Cybercrime Community Research ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Jack Hughes, Sergio Pastrana, Alice Hutchings, Sadia Afroz, Sagar Samtani, Weifeng Li, Ericsson Santana Marin
In the last decade, cybercrime has risen considerably. One key factor is the proliferation of online cybercrime communities, where actors trade products and services, and also learn from each other. Accordingly, understanding the operation and behavior of these communities is of great interest, and they have been explored across multiple disciplines with different, often quite novel, approaches. This
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Machine Learning for Refining Knowledge Graphs: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Budhitama Subagdja, D. Shanthoshigaa, Zhaoxia Wang, Ah-Hwee Tan
Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in KGs. With the growing popularity of KG in various domains, many techniques involving machine learning have been applied, but there is no survey dedicated to machine learning-based KG refinement yet. Based on a novel framework following the KG refinement process
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Model-based Trustworthiness Evaluation of Autonomous Cyber-Physical Production Systems: A Systematic Mapping Study ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-23 Maryam Zahid, Alessio Bucaioni, Francesco Flammini
The fourth industrial revolution, i.e., Industry 4.0, is associated with Cyber-Physical Systems (CPS), which are entities integrating hardware (e.g., smart sensors and actuators connected through the Industrial Internet of Things) together with control and analytics software used to drive and support decisions at several levels. The latest developments in Artificial Intelligence (AI) and Machine Learning
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A Survey on Haptic Feedback through Sensory Illusions in Interactive Systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-20 Marco Kurzweg, Yannick Weiss, Marc O. Ernst, Albrecht Schmidt, Katrin Wolf
A growing body of work in human-computer interaction (HCI), particularly work on haptic feedback and haptic displays, relies on sensory illusions, which is a phenomenon investigated in perception research. However, an overview of which illusions are prevalent in HCI for generating haptic feedback in computing systems and which remain underrepresented, as well as the rationales and possible undiscovered
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Service quality in cloud gaming: instrument development and validation Internet Res. (IF 5.9) Pub Date : 2024-02-20 Winston T. Su, Zach W.Y. Lee, Xinming He, Tommy K.H. Chan
Purpose The global market for cloud gaming is growing rapidly. How gamers evaluate the service quality of this emerging form of cloud service has become a critical issue for both researchers and practitioners. Building on the literature on service quality and software as a service, this study develops and validates a gamer-centric measurement instrument for cloud gaming service quality. Design/methodology/approach
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A meta-analysis of antecedents and consequences of trust in the sharing economy Internet Res. (IF 5.9) Pub Date : 2024-02-21 Jiang Jiang, Eldon Y. Li, Li Tang
Purpose Trust plays a crucial role in overcoming uncertainty and reducing risks. Uncovering the trust mechanism in the sharing economy may enable sharing platforms to design more effective marketing strategies. However, existing studies have inconsistent conclusions on the trust mechanism in the sharing economy. Therefore, this study aims to investigate the antecedents and consequences of different
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Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-20 Na Li, Rui Zhou, Bharath Krishna, Ashirbad Pradhan, Hyowon Lee, Jiayuan He, Ning Jiang
Muscle fatigue represents a complex physiological and psychological phenomenon that impairs physical performance and increases the risks of injury. It is important to continuously monitor fatigue levels for early detection and management of fatigue. The detection and classification of muscle fatigue also provide important information in human-computer interactions (HMI), sports injuries and performance