-
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
-
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,
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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.
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
Controllable Data Generation by Deep Learning: A Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 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
-
Warm-Starting and Quantum Computing: A Systematic Mapping Study ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 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
-
Pre-Trained Language Models for Text Generation: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 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 article, we provide a survey on the
-
DevOps Metrics and KPIs: A Multivocal Literature Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Ricardo Amaro, Rúben Pereira, Miguel Mira da Silva
Context: Information Technology organizations are aiming to implement DevOps capabilities to fulfill market, customer, and internal needs. While many are successful with DevOps implementation, others still have difficulty measuring DevOps success in their organization. As a result, the effectiveness of assessing DevOps remains erratic. This emphasizes the need to withstand management in measuring the
-
Local Interpretations for Explainable Natural Language Processing: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 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
-
A Deep Dive into Robot Vision - An Integrative Systematic Literature Review Methodologies and Research Endeavor Practices ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 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 remarkable pace to move towards diversified advancement. This research considers the most topical, recent, related and state-of-the-art research reviews that revolve around Robot vision, and shapes the research into Systematic Literature
-
Intelligent Edge-powered Data Reduction: A Systematic Literature Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Laércio Pioli, Douglas D. J. de Macedo, Daniel G. Costa, Mario A. R. Dantas
The development of the Internet of Things (IoT) paradigm and its significant spread as an affordable data source has brought many challenges when pursuing efficient data collection, distribution, and storage. Since such hierarchical logical architecture can be inefficient and costly in many cases, Data Reduction (DR) solutions have arisen to allow data preprocessing before actual transmission. To increase
-
Extended Reality (XR) Toward Building Immersive Solutions: The Key to Unlocking Industry 4.0 ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 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
-
Intel TDX Demystified: A Top-Down Approach ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 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 at enforcing hardware-assisted isolation for virtual machines and minimize
-
Deep Multimodal Data Fusion ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 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
-
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 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
-
Financial Sentiment Analysis: Techniques and Applications ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 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
-
Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 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
-
Deep Learning for Iris Recognition: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Kien Nguyen, Hugo Proença, Fernando Alonso-Fernandez
In this survey, we provide a comprehensive review of more than 200 articles, 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
-
Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 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 Nuclear Industry
-
Towards Hybrid-Optimization Video Coding ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Shuai Huo, Dong Liu, Haotian Zhang, Li Li, Siwei Ma, Feng Wu, Wen Gao
Video coding that pursues the highest compression efficiency is the art of computing for rate-distortion optimization. The optimization has been approached in different ways, exemplified by two typical frameworks: block-based hybrid video coding and end-to-end learned video coding. The block-based hybrid framework encompasses more and more coding modes that are available at the decoder side; an encoder
-
Contactless Diseases Diagnoses Using Wireless Communication Sensing: Methods and Challenges Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 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 has 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
-
Optimizing with Attractor: A Tutorial ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Weiqi Li
This tutorial presents a novel search system—the Attractor-Based Search System (ABSS)—that can solve the Traveling Salesman Problem very 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
-
Tutorial on Matching-based Causal Analysis of Human Behaviors Using Smartphone Sensor Data ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 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 article 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
-
Mix-Zones as an Effective Privacy Enhancing Technique in Mobile and Vehicular Ad-hoc Networks ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-22 Nirupama Ravi, C. M. Krishna, Israel Koren
Intelligent Transportation Systems (ITS) promise significant increases in throughput and reductions in trip delay. ITS makes extensive use of Connected and Autonomous Vehicles (CAV) frequently broadcasting location, speed, and intention information. However, with such extensive communication comes the risk to privacy. Preserving privacy while still exchanging vehicle state information has been recognized
-
Qualitative Approaches to Voice UX ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-20 Katie Seaborn, Jacqueline Urakami, Peter Pennefather, Norihisa P. Miyake
Voice is a natural mode of expression offered by modern computer-based systems. Qualitative perspectives on voice-based user experiences (voice UX) offer rich descriptions of complex interactions that numbers alone cannot fully represent. We conducted a systematic review of the literature on qualitative approaches to voice UX, capturing the nature of this body of work in a systematic map and offering
-
Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-20 James Halvorsen, Clemente Izurieta, Haipeng Cai, Assefaw H. Gebremedhin
Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense, alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to further distinguish between benign and malicious behaviors, but it comes with several challenges, including lack of quality training data and high false positive rates. Generative Machine Learning Models (GMLMs) can help overcome
-
A Survey on Resilience in Information Sharing on Networks: Taxonomy and Applied Techniques ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-20 Agnaldo de Souza Batista, Aldri L. dos Santos
Information sharing is vital in any communication network environment to enable network operating services take decisions based on the information collected by several deployed computing devices. The various networks that compose cyberspace, as Internet-of-Things (IoT) ecosystems, have significantly increased the need to constantly share information, which is often subject to disturbances. In this
-
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-18 Jiajun Wu, Fan Dong, Henry Leung, Zhuangdi Zhu, Jiayu Zhou, Steve Drew
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is a natural solution for massive user-owned devices in edge computing with distributed and private training data. FL methods based on FedAvg typically follow a naive star topology,
-
A challenge-based survey of e-recruitment recommendation systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-18 Yoosof Mashayekhi, Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie
E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation
-
Systems Interoperability Types: A Tertiary Study ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-15 Rita S. P. Maciel, Pedro H. Valle, Kécia S. Santos, Elisa Y. Nakagawa
Interoperability has been a focus of attention over at least four decades, with the emergence of several interoperability types (or levels), diverse models, frameworks, and solutions, also as a result of a continuous effort from different domains. The current heterogeneity in technologies such as blockchain, IoT and new application domains such as Industry 4.0 brings not only new interaction possibilities