Computer Networks & Communications最新文献

Development of a Monitoring System for the Management of Medical Devices 医疗器械管理监控系统的开发

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130407

Kazuto Kakutani, Nobuhiro Ito, Kosuke Shima, Shintaro Oyama, T. Otsuka

引用次数: 0

LEON: Light Weight Edge Detection Network 轻量级边缘检测网络

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130402

N. Akbari, A. Baniasadi

{"title":"LEON: Light Weight Edge Detection Network","authors":"N. Akbari, A. Baniasadi","doi":"10.5121/csit.2023.130402","DOIUrl":"https://doi.org/10.5121/csit.2023.130402","url":null,"abstract":"Deep Convolutional Neural Networks (CNNs) have achieved human-level performance in edge detection. However, there have not been enough studies on how to efficiently utilize the parameters of the neural network in edge detection applications. Therefore, the associated memory and energy costs remain high. In this paper, inspired by Depthwise Separable Convolutions and deformable convolutional networks (Deformable-ConvNet), we aim to address current inefficiencies in edge detection applications. To this end, we propose a new architecture, which we refer to as Lightweight Edge Detection Network (LEON ). The proposed approach is designed to integrate the advantages of the deformable unit and DepthWise Separable convolutions architecture to create a lightweight backbone employed for efficient feature extraction. As we show, we achieve state-of-the-art accuracy while significantly reducing the complexity by carefully choosing proper components for edge detection purposes. Our results on BSDS500 and NYUDv2 demonstrate that LEON outperforms the current lightweight edge detectors while requiring only 500k parameters. It is worth mentioning that we train the network from scratch without using pre- trained weights.","PeriodicalId":159989,"journal":{"name":"Computer Networks & Communications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125993426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Desktop Application to Help Speakers Switch Slides by using AI and Voice Recognition 通过使用人工智能和语音识别来帮助演讲者切换幻灯片的桌面应用程序

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130412

Yixin Liang, Marisabel Chang

{"title":"A Desktop Application to Help Speakers Switch Slides by using AI and Voice Recognition","authors":"Yixin Liang, Marisabel Chang","doi":"10.5121/csit.2023.130412","DOIUrl":"https://doi.org/10.5121/csit.2023.130412","url":null,"abstract":"Presentation is a skill that everyone has, and it is very commonly seen in companies, schools, conferences, etc [1]. And the purpose of a slide is to give the audience a better understanding of the topic and to add ideas that they forgot to mention [2]. It also adds visual support to the speaker's discussion. Usually the presenter held a slide remote or just used their computer to control the slide pace while presenting. However, the slide remote can often be unstable due to battery switching. Even those who do not have a slide remote are unable to ensure a smooth presentation because they need to constantly switch back and forth on the computer screen with the mouse, which not only makes the speaker more nervous but also likely to skip the slide. Slidecontroller uses existing AI technology, voice recognition, as a medium to allow users to enter the transition word used to switch slides [3]. For example, when the user enters \"Now I am going to talk about'' when this word is spoken the Slidecontroller will receive the voice and match the speaker's turn to the next slide. The user can be creative with the keyword selection that best fits their presentation vibe. Or the user could use the Slidecontroller default option which controls the slide by simply saying “Next” to go to the next slide, “Previous” to go to the previous slide, and “Thank you” to stop the App to prevent from catching a similar keyword that accidentally switches the slide [4].","PeriodicalId":159989,"journal":{"name":"Computer Networks & Communications","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125104437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Machine Learning/Deep Learning Hybrid for Augmenting Teacher-LED Online Dance Education 机器学习/深度学习的混合，以增强教师主导的在线舞蹈教育

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130403

Catherine Hung

引用次数: 1

Lexical Features of Medicine Product Warnings in the Philippines 菲律宾药品警示的词汇特征

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130411

Shielanie Soriano Dacumos

引用次数: 0

Eye-tracking in Association with Phishing Cyber Attacks: a Comprehensive Literature Review 眼动追踪与网络钓鱼攻击:综合文献综述

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130406

Noon Hussein

{"title":"Eye-tracking in Association with Phishing Cyber Attacks: a Comprehensive Literature Review","authors":"Noon Hussein","doi":"10.5121/csit.2023.130406","DOIUrl":"https://doi.org/10.5121/csit.2023.130406","url":null,"abstract":"As of 2021, it has been reported that around 90% of data breaches occur on ac- count of phishing, while about 83% of organizations experienced phishing attacks [1]. Phishing can be defined as the cybercrime in which a target is contacted through e-mail, telephone or text message by someone impersonating a legitimate institution [2]. Through psychological manipulation, the threat actor attempts to deceive users into providing sensitive information, thereby causing financial and intellectual property losses, reputational damages, and operational activity disruption. In this light, this paper presents a comprehensive review of eyetracking in association with phishing cyberattacks. To determine their impact on phishing detection accuracy, this work reviews 20 empirical studies which measure eye-tracking metrics with respect to different Areas of Interest (AOIs). The described experiments aim to produce simple cognitive user reactions, examine concentration, perception and trust in the system; all in which determine the level of susceptibility to deception and manipulation. Results suggest that longer gaze durations on AOIs, characterized by higher attention control, are strongly correlated with detection accuracy. Eye-tracking behavior also shows that technical background, domain knowledge, experience, training, and risk perception con- tribute to mitigating these attacks. Meanwhile, Time to First Fixation (TTFF), entry time and entry sequence data yielded inconclusive results regarding the impact on susceptibility to phishing attacks. The results aid in designing user-friendly URLs, visual browsing aids, and embedded and automated authentication systems. Most importantly, these findings can be used to establish user awareness through the development of training programs. be used to establish user awareness through the development of training programs.","PeriodicalId":159989,"journal":{"name":"Computer Networks & Communications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121643709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Smart Plantmoisture Level Determination System to Determine if the Plant Needs to be Watered or not by using Machine Learning 智能植物水分水平测定系统，通过机器学习来确定植物是否需要浇水

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130410

Ruohan Zhang, Yaotian Zhang, Yu Sun

引用次数: 0

Measuring Performance of Web Protocol with Updated Transport Layer Techniques for Faster Web Browsing 使用更新的传输层技术测量Web协议的性能以实现更快的Web浏览

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130401

Ziaul Hossain, G. Fairhurst

引用次数: 0

A Belief Revision Mechanism with Trust Reasoning based on Extended Reciprocal Logic for Multi-Agent Systems 基于扩展互反逻辑的多智能体系统信任推理信念修正机制

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130413

S. Basit, Y. Goto

引用次数: 0

Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets 用于Covid-19推文情感分析的机器学习聊天机器人

Computer Networks & Communications Pub Date : 2023-02-25 DOI: 10.5121/csit.2023.130404

Suha Khalil Assayed, K. Shaalan, M. Alkhatib, Safwan Maghaydah

{"title":"Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets","authors":"Suha Khalil Assayed, K. Shaalan, M. Alkhatib, Safwan Maghaydah","doi":"10.5121/csit.2023.130404","DOIUrl":"https://doi.org/10.5121/csit.2023.130404","url":null,"abstract":"The various types of social media were increased rapidly, as people’s need to share knowledge between others. In fact, there are various types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter remains one of the most popular social application that people use for sharing their emotional states. However, this has increased particularly during the COVID-19 pandemic. In this paper, we proposed a chatbot for evaluating the sentiment analysis by using machine learning algorithms. The authors used a dataset of tweets from Kaggle’s website, and that includes 41157 tweets that are related to the COVID-19. These tweets were classified and labelled to four categories: Extremely positive, positive, neutral, negative, and extremely negative. In this study, we applied Machine Learning algorithms, Support Vector Machines (SVM), and the Naïve Bayes (NB) algorithms and accordingly, we compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, even though Naïve Bayes perform poorly with low accuracy, but it trained the data faster comparing to SVM.","PeriodicalId":159989,"journal":{"name":"Computer Networks & Communications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129834353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computer Networks &amp; Communications最新文献

Computer Networks & Communications最新文献