Fwd: ACM Computing Surveys Volume 55 Issue 5, June 2023 is now available

[Ernesto Cuadros-Vargas]

Estimados,

     Personalmente creo que una valiosa fuente para ponerse al día en cualquier área de computación es buscar en esta publicación de ACM Computing Surveys.

Esta publicación es de junio de 2023.

Saludos

er

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From: ACM Digital Library <tocse...@acm.org>
Date: Sun, Dec 4, 2022 at 3:02 AM
Subject: ACM Computing Surveys Volume 55 Issue 5, June 2023 is now available
To: <ecua...@spc.org.pe>

	New Issue available
	ACM Computing Surveys, Volume 55, Issue 5, (June 2023)   is now available online.
	Table of Contents
	This new issue contains the following articles:
	Mitigating Bias in Algorithmic Systems—A Fish-eye View Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner Tal, Alan Hartman and Tsvi Kuflik Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders—including developers, end users, and third-parties—there is a need to understand the landscape of the sources of bias, and the solutions being proposed to address them, from a broad, cross-domain perspective. This survey provides a “fish-eye view,” examining approaches across four areas of research. The literature describes three steps toward a comprehensive treatment—bias detection, fairness management, and explainability management—and underscores the need to work from within the system as well as from the perspective of stakeholders in the broader context. Pages:  1–37 |   DOI:  10.1145/3527152
	A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues Jiang Xiao, Huichuwu Li, Minrui Wu, Hai Jin, M. Jamal Deen and Jiannong Cao In the last decade, many studies have significantly pushed the limits of wireless device-free human sensing (WDHS) technology and facilitated various applications, ranging from activity identification to vital sign monitoring. This survey presents a novel taxonomy that classifies the state-of-the-art WDHS systems into 11 categories according to their sensing task type and motion granularity. In particular, existing WDHS systems involve three primary sensing task types. The first type, behavior recognition, is a classification problem of recognizing predefined meaningful behaviors. The second type is movement tracking, monitoring the quantitative values of behavior states integrating with spatiotemporal information. The third type, user identification, leverages the unique features in behaviors to identify who performs the movements. The selected papers in each sensing task type can be further divided into sub-categories according to their motion granularity. Recent advances reveal that WDHS systems within a particular granularity follow similar challenges and design principles. For example, fine-grained hand recognition systems target extracting subtle motion-induced signal changes from the noisy signal responses, and their sensing areas are limited to a relatively small range. Coarse-grained activity identification systems need to overcome the interference of other moving objects within the room-level sensing range. A novel research framework is proposed to help to summarize WDHS systems from methodology, evaluation performance, and design goals. Finally, we conclude with several open issues and present the future research directions from the perspectives of data collection, sensing methodology, performance evaluation, and application scenario. Pages:  1–35 |   DOI:  10.1145/3530682
	Cross-Technology Communication for the Internet of Things: A Survey Yuan He, Xiuzhen Guo, Xiaolong Zheng, Zihao Yu, Jia Zhang, Haotian Jiang, Xin Na and Jiacheng Zhang The ever-developing Internet of Things (IoT) brings the prosperity of wireless sensing and control applications. In many scenarios, different wireless technologies coexist in the shared frequency medium as well as the physical space. Such wireless coexistence may lead to serious cross-technology interference (CTI) problems, e.g., channel competition, signal collision, and throughput degradation. Compared with traditional methods like interference avoidance, tolerance, and concurrency mechanism, direct and timely information exchange among heterogeneous devices is therefore a fundamental requirement to ensure the usability, inter-operability, and reliability of the IoT. Under this circumstance, Cross-Technology Communication (CTC) technique thus becomes a hot topic in both academic and industrial fields, which aims at directly exchanging data among heterogeneous devices that follow different standards. This paper comprehensively summarizes the CTC techniques and reveals that the key challenge for CTC lies in the heterogeneity of IoT devices, including the incompatibility of technical standards and the asymmetry of connection capability. Based on the above finding, we present a taxonomy of the existing CTC works (packet-level CTCs and physical-level CTCs) and compare the existing CTC techniques in terms of throughput, reliability, hardware modification, and concurrency. Pages:  1–29 |   DOI:  10.1145/3530049
	Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges Yoshitomo Matsubara, Marco Levorato and Francesco Restuccia Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others. However, continuously executing the entire DNN on mobile devices can quickly deplete their battery. Although task offloading to cloud/edge servers may decrease the mobile device’s computational burden, erratic patterns in channel quality, network, and edge server load can lead to a significant delay in task execution. Recently, approaches based on split computing (SC) have been proposed, where the DNN is split into a head and a tail model, executed respectively on the mobile device and on the edge server. Ultimately, this may reduce bandwidth usage as well as energy consumption. Another approach, called early exiting (EE), trains models to embed multiple “exits” earlier in the architecture, each providing increasingly higher target accuracy. Therefore, the tradeoff between accuracy and delay can be tuned according to the current conditions or application demands. In this article, we provide a comprehensive survey of the state of the art in SC and EE strategies by presenting a comparison of the most relevant approaches. We conclude the article by providing a set of compelling research challenges. Pages:  1–30 |   DOI:  10.1145/3527155
	Software-Based Dialogue Systems: Survey, Taxonomy, and Challenges Quim Motger, Xavier Franch and Jordi Marco The use of natural language interfaces in the field of human-computer interaction (HCI) is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks (RNNs), the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview of the current state of research on conversational agents covering all research perspectives involved is missing. Motivated by this context, this article reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces. Pages:  1–42 |   DOI:  10.1145/3527450
	Explainable Deep Reinforcement Learning: State of the Art and Challenges George A. Vouros Interpretability, explainability, and transparency are key issues to introducing artificial intelligence methods in many critical domains. This is important due to ethical concerns and trust issues strongly connected to reliability, robustness, auditability, and fairness, and has important consequences toward keeping the human in the loop in high levels of automation, especially in critical cases for decision making, where both (human and the machine) play important roles. Although the research community has given much attention to explainability of closed (or black) prediction boxes, there are tremendous needs for explainability of closed-box methods that support agents to act autonomously in the real world. Reinforcement learning methods, and especially their deep versions, are such closed-box methods. In this article, we aim to provide a review of state-of-the-art methods for explainable deep reinforcement learning methods, taking also into account the needs of human operators—that is, of those who make the actual and critical decisions in solving real-world problems. We provide a formal specification of the deep reinforcement learning explainability problems, and we identify the necessary components of a general explainable reinforcement learning framework. Based on these, we provide a comprehensive review of state-of-the-art methods, categorizing them into classes according to the paradigm they follow, the interpretable models they use, and the surface representation of explanations provided. The article concludes by identifying open questions and important challenges. Pages:  1–39 |   DOI:  10.1145/3527448
	Survey on the Objectives of Recommender Systems: Measures, Solutions, Evaluation Methodology, and New Perspectives Bushra Alhijawi, Arafat Awajan and Salam Fraihat Recently, recommender systems have played an increasingly important role in a wide variety of commercial applications to help users find favourite products. Research in the recommender system field has traditionally focused on the accuracy of predictions and the relevance of recommendations. However, other recommendation quality measures may have a significant impact on the overall performance of a recommender system and the satisfaction of users. Hence, researchers’ attention in this field has recently shifted to include other recommender system objectives. This article aims to provide a comprehensive review of recent research efforts on recommender systems based on the objectives achieved: relevance, diversity, novelty, coverage, and serendipity. In addition, the definitions and measures associated with these objectives are reviewed. Furthermore, the article surveys the evaluation methodology used to measure the impact of the main challenges on performance and the new applications of the recommender system. Finally, new perspectives, open issues, and future directions are provided to develop the field. Pages:  1–38 |   DOI:  10.1145/3527449
	Generative Adversarial Networks for Face Generation: A Survey Amina Kammoun, Rim Slama, Hedi Tabia, Tarek Ouni and Mohmed Abid Recently, generative adversarial networks (GANs) have progressed enormously, which makes them able to learn complex data distributions in particular faces. More and more efficient GAN architectures have been designed and proposed to learn the different variations of faces, such as cross pose, age, expression, and style. These GAN-based approaches need to be reviewed, discussed, and categorized in terms of architectures, applications, and metrics. Several reviews that focus on the use and advances of GAN in general have been proposed. However, to the best of our knowledge, the GAN models applied to the face, which we call facial GANs, have never been addressed. In this article, we review facial GANs and their different applications. We mainly focus on architectures, problems, and performance evaluation with respect to each application and used datasets. More precisely, we review the progress of architectures and discuss the contributions and limits of each. Then, we expose the encountered problems of facial GANs and propose solutions to handle them. Additionally, as GAN evaluation has become a notable current defiance, we investigate the state-of-the-art quantitative and qualitative evaluation metrics and their applications. We conclude this work with a discussion on the face generation challenges and propose open research issues. Pages:  1–37 |   DOI:  10.1145/3527850
	A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf and Isabel Valera Machine learning is increasingly used to inform decision making in sensitive situations where decisions have consequential effects on individuals’ lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role in the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavorably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions toward which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness. Pages:  1–29 |   DOI:  10.1145/3527848
	The Role of Generative Adversarial Network in Medical Image Analysis: An In-depth Survey Manal Alamir and Manal Alghamdi A generative adversarial network (GAN) is one of the most significant research directions in the field of artificial intelligence, and its superior data generation capability has garnered wide attention. In this article, we discuss the recent advancements in GANs, particularly in the medical field. First, the different medical imaging modalities and the principal theory of GANs were analyzed and summarized, after which, the evaluation metrics and training issues were determined. Third, the extension models of GANs were classified and introduced one-by-one. Fourth, the applications of GAN in medical images including cross-modality, augmentation, detection, classification, and reconstruction were illustrated. Finally, the problems we needed to resolve and future directions were discussed. The objective of this review is to provide a comprehensive overview of the GAN, simplify the GAN’s basics, and present the most successful applications in different scenarios. Pages:  1–36 |   DOI:  10.1145/3527849
	Graph Neural Networks in Recommender Systems: A Survey Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie and Bin Cui With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS. Pages:  1–37 |   DOI:  10.1145/3535101
	A Leap among Quantum Computing and Quantum Neural Networks: A Survey Fabio Valerio Massoli, Lucia Vadicamo, Giuseppe Amato and Fabrizio Falchi In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community’s interest since the late ’80s. In such a context, we propose our contribution. First, we introduce basic concepts related to quantum computations, and then we explain the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms. Finally, we gather, compare, and analyze the current state-of-the-art concerning Quantum Perceptrons and Quantum Neural Networks implementations. Pages:  1–37 |   DOI:  10.1145/3529756
	On the Edge of the Deployment: A Survey on Multi-access Edge Computing Pedro Cruz, Nadjib Achir and Aline Carneiro Viana Multi-Access Edge Computing (MEC) attracts much attention from the scientific community due to its scientific, technical, and commercial implications. In particular, the European Telecommunications Standards Institute (ETSI) standard convergence consolidates the discussions around MEC. Still, the existing MEC practical initiatives are incomplete in their majority, hardening or invalidating their effective deployment. To fill this gap, it is essential to understand a series of experimental prototypes, implementations, and deployments. The early implementations can reveal the potential, the limitations, the related technologies, and the development tools for MEC adoption. In this context, this work first brings a discussion on existing MEC initiatives regarding the use cases they target and their vision (i.e., whether they are more network-related or more distributed systems). Second, we survey MEC practical initiatives according to their strategies, including the ETSI MEC standard. Besides, we compare the strategies according to related limitations, impact, and deployment efforts. We also survey the existing tools making MEC systems a reality. Finally, we give hints to issues yet to be addressed in practice. By bringing a better comprehension of MEC initiatives, we believe this survey will help researchers and developers design their own MEC systems or improve and simplify the usability of existing ones. Pages:  1–34 |   DOI:  10.1145/3529758
	A Survey on Data-driven Software Vulnerability Assessment and Prioritization Triet H. M. Le, Huaming Chen and M. Ali Babar Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment and prioritization help practitioners devise optimal SV mitigation plans based on various SV characteristics. The surges in SV data sources and data-driven techniques such as Machine Learning and Deep Learning have taken SV assessment and prioritization to the next level. Our survey provides a taxonomy of the past research efforts and highlights the best practices for data-driven SV assessment and prioritization. We also discuss the current limitations and propose potential solutions to address such issues. Pages:  1–39 |   DOI:  10.1145/3529757
	Data Mining on Smartphones: An Introduction and Survey Darren Yates and Md Zahidul Islam Data mining is the science of extracting information or “knowledge” from data. It is a task commonly executed on cloud computing resources, personal computers and laptops. However, what about smartphones? Despite the fact that these ubiquitous mobile devices now offer levels of hardware and performance approaching that of laptops, locally executed model-training using data mining methods on smartphones is still notably rare. On-device model-training offers a number of advantages. It largely mitigates issues of data security and privacy, since no data is required to leave the device. It also ensures a self-contained, fully portable data mining solution requiring no cloud computing or network resources and able to operate in any location. In this article, we focus on the intersection of smartphones and data mining. We investigate the growth in smartphone performance, survey smartphone usage models in previous research, and look at recent developments in locally executed data mining on smartphones. Pages:  1–38 |   DOI:  10.1145/3529753
	Multi-document Summarization via Deep Learning Techniques: A Survey Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang and Quan Z. Sheng Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our survey, the first of its kind, systematically overviews the recent deep-learning-based MDS models. We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state of the art. We highlight the differences between various objective functions that are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting field. Pages:  1–37 |   DOI:  10.1145/3529754
	On the Explainability of Natural Language Processing Deep Models Julia El Zini and Mariette Awad Despite their success, deep networks are used as black-box models with outputs that are not easily explainable during the learning and the prediction phases. This lack of interpretability is significantly limiting the adoption of such models in domains where decisions are critical such as the medical and legal fields. Recently, researchers have been interested in developing methods that help explain individual decisions and decipher the hidden representations of machine learning models in general and deep networks specifically. While there has been a recent explosion of work on Explainable Artificial Intelligence (ExAI) on deep models that operate on imagery and tabular data, textual datasets present new challenges to the ExAI community. Such challenges can be attributed to the lack of input structure in textual data, the use of word embeddings that add to the opacity of the models and the difficulty of the visualization of the inner workings of deep models when they are trained on textual data.Lately, methods have been developed to address the aforementioned challenges and present satisfactory explanations on Natural Language Processing (NLP) models. However, such methods are yet to be studied in a comprehensive framework where common challenges are properly stated and rigorous evaluation practices and metrics are proposed.Motivated to democratize ExAI methods in the NLP field, we present in this work a survey that studies model-agnostic as well as model-specific explainability methods on NLP models. Such methods can either develop inherently interpretable NLP models or operate on pre-trained models in a post hoc manner. We make this distinction and we further decompose the methods into three categories according to what they explain: (1) word embeddings (input level), (2) inner workings of NLP models (processing level), and (3) models’ decisions (output level). We also detail the different evaluation approaches interpretability methods in the NLP field. Finally, we present a case-study on the well-known neural machine translation in an appendix, and we propose promising future research directions for ExAI in the NLP field. Pages:  1–31 |   DOI:  10.1145/3529755
	A Systematic Survey on Android API Usage for Data-driven Analytics with Smartphones Hansoo Lee, Joonyoung Park and Uichin Lee Recent industrial and academic research has focused on data-driven analytics with smartphones by collecting user interaction, context, and device systems data through Application Programming Interfaces (APIs) and sensors. The Android operating system provides various APIs to collect such mobile usage and sensor data for third-party developers. Usage Statistics API (US API) and Accessibility Service API (AS API) are representative Android APIs for collecting app usage data and are used for various research purposes, as they can collect fine-grained interaction data (e.g., app usage history and user interaction type). Furthermore, other sensor APIs help to collect a user’s context and device state data, along with AS/US APIs. This review investigates mobile usage and sensor data-driven research using AS/US APIs by categorizing the research purposes and the data types. In this article, the surveyed studies are classified as follows: five themes and 21 subthemes and a four-layer hierarchical data classification structure. This allows us to identify a data usage trend and derive insight into data collection according to research purposes. Several limitations and future research directions of mobile usage and sensor data-driven analytics research are discussed, including the impact of changes in the Android API versions on research, the privacy and data quality issues, and the mitigation of reproducibility risks with standardized data typology. Pages:  1–38 |   DOI:  10.1145/3530814
	Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats Zhiyan Chen, Jinxin Liu, Yu Shen, Murat Simsek, Burak Kantarci, Hussein T. Mouftah and Petar Djukic Despite its technological benefits, the Internet of Things (IoT) has cyber weaknesses due to vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. An Advanced Persistent Threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys that fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth bridging the state of the art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research. Pages:  1–37 |   DOI:  10.1145/3530812
	Pushing the Level of Abstraction of Digital System Design: A Survey on How to Program FPGAs Emanuele Del Sozzo, Davide Conficconi, Alberto Zeni, Mirko Salaris, Donatella Sciuto and Marco D. Santambrogio Field Programmable Gate Arrays (FPGAs) are spatial architectures with a heterogeneous reconfigurable fabric. They are state-of-the-art for prototyping, telecommunications, embedded, and an emerging alternative for cloud-scale acceleration. However, FPGA adoption found limitations in their programmability and required knowledge. Therefore, researchers focused on FPGA abstractions and automation tools. Here, we survey three leading digital design abstractions: Hardware Description Languages (HDLs), High-Level Synthesis (HLS) tools, and Domain-Specific Languagess (DSLs). We review these abstraction solutions, provide a timeline, and propose a taxonomy for each abstraction trend: programming models for HDLs; Intellectual Property (IP)-based or System-based toolchains for HLS; application, architecture, and infrastructure domains for DSLs. Pages:  1–48 |   DOI:  10.1145/3532989
	A Survey on Cyber Situation-awareness Systems: Framework, Techniques, and Insights Hooman Alavizadeh, Julian Jang-Jaccard, Simon Yusuf Enoch, Harith Al-Sahaf, Ian Welch, Seyit A. Camtepe and Dan Dongseong Kim Cyberspace is full of uncertainty in terms of advanced and sophisticated cyber threats that are equipped with novel approaches to learn the system and propagate themselves, such as AI-powered threats. To debilitate these types of threats, a modern and intelligent Cyber Situation Awareness (SA) system needs to be developed that has the ability of monitoring and capturing various types of threats, analyzing, and devising a plan to avoid further attacks. This article provides a comprehensive study on the current state-of-the-art in the cyber SA to discuss the following aspects of SA: key design principles, framework, classifications, data collection, analysis of the techniques, and evaluation methods. Last, we highlight misconceptions, insights, and limitations of this study and suggest some future work directions to address the limitations. Pages:  1–37 |   DOI:  10.1145/3530809
	File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements Trivikram Muralidharan, Aviad Cohen, Noa Gerson and Nir Nissim With the growing sophistication of malware, the need to devise improved malware detection schemes is crucial. The packing of executable files, which is one of the most common techniques for code protection, has been repurposed for code obfuscation by malware authors as a means of evading malware detectors (mainly static analysis-based detectors). This paper provides statistics on the use of packers based on an extensive analysis of 24,000 PE files (both malicious and benign files) for the past 10 years, which allowed us to observe trends in packing use during that time and showed that packing is still widely used in malware. This paper then surveys 23 methods proposed in academic research for the detection and classification of packed portable executable (PE) files and highlights various trends in malware packing. The paper highlights the differences between the methods and their abilities to detect and identify various aspects of packing. A taxonomy is presented, classifying the methods as static, dynamic, and hybrid analysis-based methods. The paper also sheds light on the increasing role of machine learning methods in the development of modern packing detection methods. We analyzed and mapped the different packing methods and identified which of them can be countered by the detection methods surveyed in this paper. Pages:  1–45 |   DOI:  10.1145/3530810
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