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Machine Learning in Metaverse Security: Current Solutions and Future Challenges ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-28 Yazan Otoum, Navya Gottimukkala, Neeraj Kumar, Amiya Nayak
The Metaverse, positioned as the next frontier of the internet, has the ambition to forge a virtual shared realm characterized by immersion, hyper spatiotemporal dynamics, and self-sustainability. Recent technological strides in AI, Extended Reality (XR), 6G, and blockchain propel the Metaverse closer to realization, gradually transforming it from science fiction into an imminent reality. Nevertheless
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FairSNA: Algorithmic Fairness in Social Network Analysis ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-27 Akrati Saxena, George Fletcher, Mykola Pechenizkiy
In recent years, designing fairness-aware methods has received much attention in various domains, including machine learning, natural language processing, and information retrieval. However, in social network analysis (SNA), designing fairness-aware methods for various research problems by considering structural bias and inequalities of large-scale social networks has not received much attention. In
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Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep and Exploratory Works ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-27 Pengyuan Lu, Lin Zhang, Mengyu Liu, Kaustubh Sridhar, Oleg Sokolsky, Fanxin Kong, Insup Lee
Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However, like any other computer-based systems, malicious attacks evolve mutually, driving CPS to undesirable physical states and potentially causing catastrophes. Although the current state-of-the-art is well aware of this issue, the majority of researchers have not focused on CPS recovery, the procedure we defined as restoring
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How many FIDO protocols are needed? Analysing the technology, security and compliance ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-27 Anna Angelogianni, Ilias Politis, Christos Xenakis
To overcome the security vulnerabilities caused by weak passwords, thus bridge the gap between user friendly interfaces and advanced security features, the Fast IDentity Online (FIDO) alliance defined a number of authentication protocols. The existing literature leverages all versions of the FIDO protocols, without indicating the reasons behind the choice of each individual FIDO protocol (i.e., U2F
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From conventional to programmable matter systems: A review of design, materials, and technologies ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-26 Ahmed Amine Chafik, Jaafar Gaber, Souad Tayane, Mohamed Ennaji, Julien Bourgeois, Tarek El-Ghazawi
Programmable matter represents a system of elements whose interactions can be programmed for a certain behavior to emerge (e.g. color, shape) upon suitable commands (e.g., instruction, stimuli) by altering its physical characteristics. Even though its appellation may refer to a morphable physical material, programmable matter has been represented through several approaches from different perspectives
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Identifying Authorship in Malicious Binaries: Features, Challenges & Datasets ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-26 Jason Gray, Daniele Sgandurra, Lorenzo Cavallaro, Jorge Blasco
Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to disassemble binaries to obtain authorship-related features. We perform a systematic analysis of works in the area of malware authorship attribution. We identify key findings, some shortcomings of current approaches and explore
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Semantic data integration and querying: a survey and challenges ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-23 Maroua Masmoudi, Sana Ben Abdallah Ben Lamine, Mohamed Hedi Karray, Bernard Archimede, Hajer Baazaoui Zghal
Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused
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A Survey on the Densest Subgraph Problem and Its Variants ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-22 Tommaso Lanciano, Atsushi Miyauchi, Adriano Fazzone, Francesco Bonchi
The Densest Subgraph Problem requires to find, in a given graph, a subset of vertices whose induced subgraph maximizes a measure of density. The problem has received a great deal of attention in the algorithmic literature over the last five decades, with many variants proposed and many applications built on top of this basic definition. Recent years have witnessed a revival of research interest in
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A Survey on Software Vulnerability Exploitability Assessment ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-20 Sarah Elder, Rayhanur Rahman, Gage Fringer, Kunal Kapoor, Laurie Williams
Knowing the exploitability and severity of software vulnerabilities helps practitioners prioritize vulnerability mitigation efforts. Researchers have proposed and evaluated many different exploitability assessment methods. The goal of this research is to assist practitioners and researchers in understanding existing methods for assessing vulnerability exploitability through a survey of exploitability
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Rearrangement Distance Problems: An updated survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-20 Andre Rodrigues Oliveira, Klairton Lima Brito, Alexsandro Oliveira Alexandrino, Gabriel Siqueira, Ulisses Dias, Zanoni Dias
One of the challenges in the Comparative Genomics field is to infer how close two organisms are based on the similarities and differences between their genetic materials. Recent advances in DNA sequencing have made complete genomes increasingly available. That said, several new algorithms trying to infer the distance between two organisms based on genome rearrangements have been proposed in the literature
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A Taxonomy and Survey on Grid-based Routing Protocols Designed for Wireless Sensor Networks ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-20 Shubhra Jain, Rahul Kumar Verma
Minimization of energy consumption is the main attention of researchers while developing a routing protocol for wireless sensor networks (WSNs), since sensor nodes are equipped with limited power supply. Virtual topology is an integral part of routing, and grid-based routing protocols are very popular due to their simplified and efficient virtual grid topology construction. Although a list of surveys
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Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-19 Bing Zhang, Xuyang Zhao, Jiangtian Nie, Jianhang Tang, Yuling Chen, Yang Zhang, Dusit Niyato
Existing surveys and reviews on Influential Node Ranking Methods (INRMs) have primarily focused on technical details, neglecting thorough research on verifying the actual influence of these nodes in a network. This oversight may result in erroneous rankings. In this survey, we address this gap by conducting an extensive analysis of 82 primary studies related to INRMs based on the epidemic model over
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Privacy Preservation of Electronic Health Records in the Modern Era: A Systematic Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-03-19 Raza Nowrozy, Khandakar Ahmed, A.S.M. Kayes, Hua Wang, Timothy R. McIntosh
Building a secure and privacy-preserving health data sharing framework is a topic of great interest in the healthcare sector, but its success is subject to ensuring the privacy of user data. We clarified the definitions of privacy, confidentiality and security (PCS) because these three terms have been used interchangeably in the literature. We found that researchers and developers must address the
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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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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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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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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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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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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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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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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 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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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
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Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-17 Jasmina Gajcin, Ivana Dusparic
While AI algorithms have shown remarkable success in various fields, their lack of transparency hinders their application to real-life tasks. Although explanations targeted at non-experts are necessary for user trust and human-AI collaboration, the majority of explanation methods for AI are focused on developers and expert users. Counterfactual explanations are local explanations that offer users advice
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Fuzzers for Stateful Systems: Survey and Research Directions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-17 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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Social Network Analysis: A Survey on Process, Tools, and Application ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-17 Shashank Sheshar Singh, Samya Muhuri, Shivansh Mishra, Divya Srivastava, Harish Kumar Shakya, Neeraj Kumar
Due to the explosive rise of online social networks, social network analysis (SNA) has emerged as a significant academic field in recent years. Understanding and examining social relationships in networks through network analysis opens up numerous research avenues in sociology, literature, media, biology, computer science, sports, and more. Therefore, certain studies review and discuss some research
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Contactless Diseases Diagnoses using Wireless Communication Sensing: Methods and Challenges Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-16 Najah Abed Abu Ali, Mubashir Rehman, Shahid Mumtaz, Muhammad Bilal Khan, Mohammad Hayajneh, Farman Ullah, Raza Ali Shah
Respiratory illness diagnosis and continuous monitoring are becoming popular as sensitive markers of chronic diseases. This interest motivated the increased development of respiratory illness diagnosis by exploiting wireless communication as a sensing system. Several methods for diagnosing a respiratory illness are based on multiple sensors and techniques. Depending on whether the device embeds the
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Distributed Graph Neural Network Training: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-16 Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin, Yawen Li, Xupeng Miao, Wentao Zhang, Bin Cui, Lei Chen
Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources
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Adversarial Robustness of Neural Networks From the Perspective of Lipschitz Calculus: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-15 Monty-Maximilian Zühlke, Daniel Kudenko
We survey the adversarial robustness of neural networks from the perspective of Lipschitz calculus in a unifying fashion by expressing models, attacks and safety guarantees, that is, a notion of measurable trustworthiness, in a mathematical language. After an intuitive motivation, we discuss algorithms to estimate a network’s Lipschitz constant, Lipschitz regularisation techniques, robustness guarantees
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Optimizing with Attractor: A Tutorial ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-15 Weiqi Li
This tutorial presents a novel search system—the Attractor-Based Search System (ABSS)—that can solve the Traveling Salesman Problem much efficiently with optimality guarantee. From the perspective of dynamical systems, a heuristic local search algorithm for an NP-complete combinatorial problem is a discrete dynamical system. In a local search system, an attractor drives the search trajectories into
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Recent trends in Robotic Prosthetics: Neuroprosthetics, Soft Actuators and Control Strategies ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-15 Jyothish K. J., Subhankar Mishra
The field of robotics is a quickly evolving feat of technology that accepts contributions from various genres of science. Neuroscience, Physiology, Chemistry, Material science, Computer science, and the wide umbrella of mechatronics have all simultaneously contributed to many innovations in the prosthetic applications of robotics. This review begins with a discussion of the scope of the term robotic
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A Tutorial on Matching-based Causal Analysis of Human Behaviors Using Smartphone Sensor Data ACM Comput. Surv. (IF 16.6) Pub Date : 2024-02-14 Gyuwon Jung, Sangjun Park, Eun-Yeol Ma, Heeyoung Kim, Uichin Lee
Smartphones can unobtrusively capture human behavior and contextual data such as user interaction and mobility. Thus far, smartphone sensor data have primarily been used to gain behavioral insights through correlation analysis. This paper provides a tutorial on the causal analysis of human behavior using smartphone sensor data by reviewing well-known matching methods. The key steps of the causal inference