Computer Science Review最新文献_第8页

A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges 利用自然语言处理检测精神障碍的调查：文献综述、趋势与挑战

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-06-22 DOI: 10.1016/j.cosrev.2024.100654

Arturo Montejo-Ráez , M. Dolores Molina-González , Salud María Jiménez-Zafra , Miguel Ángel García-Cumbreras , Luis Joaquín García-López

{"title":"A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges","authors":"Arturo Montejo-Ráez , M. Dolores Molina-González , Salud María Jiménez-Zafra , Miguel Ángel García-Cumbreras , Luis Joaquín García-López","doi":"10.1016/j.cosrev.2024.100654","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100654","url":null,"abstract":"<div><p>For years, the scientific community has researched monitoring approaches for the detection of certain mental disorders and risky behaviors, like depression, eating disorders, gambling, and suicidal ideation among others, in order to activate prevention or mitigation strategies and, in severe cases, clinical treatment. Natural Language Processing is one of the most active disciplines dealing with the automatic detection of mental disorders. This paper offers a comprehensive and extensive review of research works on Natural Language Processing applied to the identification of some mental disorders. To this end, we have identified from a literature review, which are the main types of features used to represent the texts, the machine learning algorithms that are preferred or the most targeted social media platforms, among other aspects. Besides, the paper reports on scientific forums and projects focused on the automatic detection of these problems over the most popular social networks. Thus, this compilation provides a broad view of the matter, summarizing main strategies, and significant findings, but, also, recognizing some of the weaknesses in the research works published so far, serving as clues for future research.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100654"},"PeriodicalIF":13.3,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013724000388/pdfft?md5=1aa9d3d86e8e2a92377e4b8afd982458&pid=1-s2.0-S1574013724000388-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141444644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adding relevance to rigor: Assessing the contributions of SLRs in Software Engineering through Citation Context Analysis 为严谨性添加相关性：通过引文上下文分析评估软件工程中 SLR 的贡献

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-18 DOI: 10.1016/j.cosrev.2024.100649

Oscar Díaz , Marcela Genero , Jeremías P. Contell , Mario Piattini

{"title":"Adding relevance to rigor: Assessing the contributions of SLRs in Software Engineering through Citation Context Analysis","authors":"Oscar Díaz , Marcela Genero , Jeremías P. Contell , Mario Piattini","doi":"10.1016/j.cosrev.2024.100649","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100649","url":null,"abstract":"<div><p>Research in Software Engineering greatly benefits from Systematic Literature Reviews (SLRs), in view of the citations they receive. While there has been a focus on improving the quality of SLRs in terms of the process, it remains unclear if this emphasis on rigor has also led to an increase in relevance. This study introduces Citation Context Analysis for SLRs as a method to go beyond simple citation counting by examining the reasons behind citations. To achieve this, we propose the Resonance Scheme, which characterizes how referring papers use SLRs based on the outputs that SLRs can provide, either backward-oriented (such as synthesis or aggregating evidence) or forward-oriented (such as theory building or identifying research gaps). A proof-of-concept demonstrates that most referring papers appreciate SLRs for their synthesis efforts, while only a small number refer to forward-oriented outputs. This approach is expected to be useful for three stakeholders. First, SLR producers can use the scheme to capture the contributions of their SLRs. Second, SLR consumers, such as Ph.D. students looking for research gaps, can easily identify the contributions of interest. Third, SLR reviewers can use the scheme as a tool to assess the contributions that merit SLR publication.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100649"},"PeriodicalIF":12.9,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141423843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive review on transformer network for natural and medical image analysis 用于自然和医学图像分析的变压器网络综述

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-14 DOI: 10.1016/j.cosrev.2024.100648

Ramkumar Thirunavukarasu , Evans Kotei

{"title":"A comprehensive review on transformer network for natural and medical image analysis","authors":"Ramkumar Thirunavukarasu , Evans Kotei","doi":"10.1016/j.cosrev.2024.100648","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100648","url":null,"abstract":"<div><p>The Transformer network is the main application area for natural language processing. It has gained traction lately and exhibits potential in the field of computer vision. This cutting-edge method has proven to offer a significant impact on image analysis, a crucial area of computer vision. The transformer's outstanding performance in vision computing places it as an alternative to the convolutional neural network for vision tasks. Transformers have taken center stage in the field of natural language processing. Despite the outstanding performance of transformer networks in natural image processing, their implementation in medical image analysis is gradually gaining roots. This study focuses on the transformer application in natural and medical image analysis. The first part of the study provides an overview of the core concepts of the attention mechanism built into transformers for long-range feature extraction. The study again highlights the various transformer architectures proposed for natural and medical image tasks such as segmentation, classification, image registration and diagnosis. Finally, the paper presents limitations identified in proposed transformer networks for natural and medical image processing. It also highlights prospective study opportunities for further research to better the computer vision domain, especially medical image analysis. This study offers knowledge to scholars and researchers studying computer vision applications as they focus on creating innovative transformer network-based solutions.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100648"},"PeriodicalIF":12.9,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Auto-scaling mechanisms in serverless computing: A comprehensive review 无服务器计算中的自动扩展机制：全面回顾

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-13 DOI: 10.1016/j.cosrev.2024.100650

Mohammad Tari , Mostafa Ghobaei-Arani , Jafar Pouramini , Mohsen Ghorbian

{"title":"Auto-scaling mechanisms in serverless computing: A comprehensive review","authors":"Mohammad Tari , Mostafa Ghobaei-Arani , Jafar Pouramini , Mohsen Ghorbian","doi":"10.1016/j.cosrev.2024.100650","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100650","url":null,"abstract":"<div><p>The auto-scaling feature is fundamental to serverless computing, and it automatically allows applications to scale as needed. Hence, this allows applications to be configured to adapt to current traffic and demands and acquire resources as necessary without the need to manage servers directly. Auto-scaling is an important principle in developing serverless applications that is considered and increasingly recognized by academia and industry. Despite the strong interest in auto-scaling in serverless computing in the scientific and industrial community, no clear, comprehensive, and systematic investigation has been conducted. As part of the study of automatic scaling in serverless computing, key strategies and</p><p>approaches are investigated during the lifecycle of cloud applications. This research examines three key approaches to automatically scaling serverless computing applications in the taxonomy presented. These approaches include machine learning (ML)-based, frameworks-based, and models-based. Additionally, we provide an overview of key performance metrics essential to the auto-scaling process of cloud applications and discuss the requirements. It discusses key concepts and limitations of serverless computing approaches, challenges, future directions, and research opportunities.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100650"},"PeriodicalIF":12.9,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Backbones-review: Feature extractor networks for deep learning and deep reinforcement learning approaches in computer vision Backbones-review：计算机视觉中深度学习和深度强化学习方法的特征提取器网络

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-07 DOI: 10.1016/j.cosrev.2024.100645

Omar Elharrouss , Younes Akbari , Noor Almadeed , Somaya Al-Maadeed

{"title":"Backbones-review: Feature extractor networks for deep learning and deep reinforcement learning approaches in computer vision","authors":"Omar Elharrouss , Younes Akbari , Noor Almadeed , Somaya Al-Maadeed","doi":"10.1016/j.cosrev.2024.100645","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100645","url":null,"abstract":"<div><p>To understand the real world using various types of data, Artificial Intelligence (AI) is the most used technique nowadays. While finding the pattern within the analyzed data represents the main task. This is performed by extracting representative features step, which is proceeded using the statistical algorithms or using some specific filters. However, the selection of useful features from large-scale data represented a crucial challenge. Now, with the development of convolution neural networks (CNNs), feature extraction operation has become more automatic and easier. CNNs allow to work on large-scale size of data, as well as cover different scenarios for a specific task. For computer vision tasks, convolutional networks are used to extract features and also for the other parts of a deep learning model. The selection of a suitable network for feature extraction or the other parts of a DL model is not random work. So, the implementation of such a model can be related to the target task as well as its computational complexity. Many networks have been proposed and become famous networks used for any DL models in any AI task. These networks are exploited for feature extraction or at the beginning of any DL model which is named backbones. A backbone is a known network trained and demonstrates its effectiveness. In this paper, an overview of the existing backbones, e.g. VGGs, ResNets, DenseNet, etc, is given with a detailed description. Also, a couple of computer vision tasks are discussed by providing a review of each task regarding the backbones used. In addition, a comparison in terms of performance is also provided, based on the backbone used for each task.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100645"},"PeriodicalIF":12.9,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chaos Game Optimization: A comprehensive study of its variants, applications, and future directions 混沌博弈优化：对其变体、应用和未来方向的全面研究

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-07 DOI: 10.1016/j.cosrev.2024.100647

Raja Oueslati , Ghaith Manita , Amit Chhabra , Ouajdi Korbaa

{"title":"Chaos Game Optimization: A comprehensive study of its variants, applications, and future directions","authors":"Raja Oueslati , Ghaith Manita , Amit Chhabra , Ouajdi Korbaa","doi":"10.1016/j.cosrev.2024.100647","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100647","url":null,"abstract":"<div><p>Chaos Game Optimization Algorithm (CGO) is a novel advancement in metaheuristic optimization inspired by chaos theory. It addresses complex optimization problems in dynamical systems, exhibiting unique behaviours such as fractals and self-organized patterns. CGO’s design exemplifies adaptability and robustness, making it a significant tool for tackling intricate optimization scenarios. This study presents a comprehensive and updated overview of CGO, exploring the various variants and adaptations that have been published in numerous research studies since its introduction in 2020, with 4% in book chapters, 7% in international conference proceedings, and 89% in prestigious international journals. CGO variants covered in this paper include 4% binary, 22% for multi-objective and modification and 52% for hybridization variants. Moreover, the applications of CGO, demonstrate its efficacy and flexibility across different domains with 32% in energy, 28% in engineering, 11% in IoT and machine learning, 6% in truss structures, 4% in big data, 2% in medical imaging, in security, in electronic, and in microarray technology. Furthermore, we discuss the future directions of CGO, hypothesizing its potential advancements and broader implications in optimization theory and practice. The primary objectives of this survey paper are to provide a comprehensive overview of CGO, highlighting its innovative approach, discussing its variants and their usage in different sectors, and the burgeoning interest it has sparked in metaheuristic algorithms. As a result, this manuscript is expected to offer valuable insights for engineers, professionals across different sectors, and academic researchers.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100647"},"PeriodicalIF":12.9,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DDoS attacks & defense mechanisms in SDN-enabled cloud: Taxonomy, review and research challenges 支持 SDN 的云中的 DDoS 攻击和防御机制：分类、回顾与研究挑战

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-04 DOI: 10.1016/j.cosrev.2024.100644

Jasmeen Kaur Chahal , Abhinav Bhandari , Sunny Behal

{"title":"DDoS attacks & defense mechanisms in SDN-enabled cloud: Taxonomy, review and research challenges","authors":"Jasmeen Kaur Chahal , Abhinav Bhandari , Sunny Behal","doi":"10.1016/j.cosrev.2024.100644","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100644","url":null,"abstract":"<div><p>Software-defined Networking (SDN) is a transformative approach for addressing the limitations of legacy networks due to decoupling of control planes from data planes. It offers increased programmability and flexibility for designing of cloud-based data centers. SDN-Enabled cloud data centers help in managing the huge traffic very effectively and efficiently. However, the security of SDN-Enabled Cloud data centers against different attacks is a key concern for cloud security professionals. Distributed Denial of Service Attacks have emerged as one of the most devastating attacks that constantly worried the entire cloud security research community. To prelude this, it is pertinent to fundamentally focus on classification of these attacks and their defence strategies in an effective way which has been the basis of this research paper. The aim of this paper is to formulate and conceptualize the taxonomies of DDoS attacks and its Défense mechanisms. Improved taxonomy of DDoS attacks highlights the various vulnerable points of vulnerability in SDN-enabled cloud architecture. Additionally, a taxonomy of defence mechanisms offers an extensive survey of recent techniques for detecting and mitigating DDoS attacks in the SDN-enabled cloud environment. Finally, we discuss the open research issues and challenges for the cloud security research community for carrying out future research and investigation.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100644"},"PeriodicalIF":12.9,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141243866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning with the generative models for recommender systems: A survey 用于推荐系统的生成模型深度学习：调查

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-04 DOI: 10.1016/j.cosrev.2024.100646

Ravi Nahta , Ganpat Singh Chauhan , Yogesh Kumar Meena , Dinesh Gopalani

引用次数: 0

More than a framework: Sketching out technical enablers for natural language-based source code generation 不仅仅是一个框架：勾勒基于自然语言的源代码生成的技术手段

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-05-25 DOI: 10.1016/j.cosrev.2024.100637

Chen Yang, Yan Liu, Changqing Yin

{"title":"More than a framework: Sketching out technical enablers for natural language-based source code generation","authors":"Chen Yang, Yan Liu, Changqing Yin","doi":"10.1016/j.cosrev.2024.100637","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100637","url":null,"abstract":"<div><p>Natural Language-based Source Code Generation (NLSCG) holds the promise to revolutionize the way how software is developed by means of facilitating a collection of intelligent technical enablers, based on sustained improvements on the natural language to source code pipelines and continuous adoption of new coding paradigms. In recent years, a large variety of NLSCG technical solutions have been proposed, and quite exciting experimental results have been reported. Meanwhile, current researches and initiative application projects in this area reflect a large diversity of NLSCG contexts and of major technical enablers. Such heterogeneity, fragmentation, and vagueness of the NLSCG technical landscape are currently frustrating the full realization of the NLSCG research and application vision. Players in this field could not find systematic guidelines on how to effectively address the ”known unknowns” and how to simply spot the ”unknown unknowns”, which eventually hinder the turning of NLSCG solutions into further research enhancements or production applications. Understanding the context, boundaries, capabilities, and integrations of NLSCG enablers is considered as one of the key drivers for the more practical application of NLSCG models. In this paper, we analyze in detail the natural language to source code pipelines and the evolvement of source code generation tasks, by considering both the problem context and technological aspects. A foresight reference framework for NLSCG is proposed to help handle the source code generation tasks with proper intelligent models. We review the present-day NLSCG technical landscape, as well as the core technical enablers along the source code generation pipelines. Relevant experiments are conducted to validate the role of representative models across different technical enablers on typical datasets, and we finally highlight the contribution of different enablers to code generation capabilities.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100637"},"PeriodicalIF":12.9,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141095944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive review on applications of Raspberry Pi 树莓派应用综述

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-05-01 DOI: 10.1016/j.cosrev.2024.100636

Sudha Ellison Mathe , Hari Kishan Kondaveeti , Suseela Vappangi , Sunny Dayal Vanambathina , Nandeesh Kumar Kumaravelu

引用次数: 0