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检索条件"任意字段=International Conference on Multimedia, Software Engineering and Computing"

共 66549 条记录，以下是381-390 订阅

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Video Chat using WebRTC with In-built Sign Language Detection 3

Video Chat using WebRTC with In-built Sign Language Detectio...

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3rd international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Sendhan, A. Saran, J.U. Vidhya Sagar, P.V. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350306989

Many research efforts into video conferencing technology have focused on audio-only interactions. Text chat, on the other hand, has many uses and is often used in conjunction with the audio channel in multimedia conferencing systems because of the many benefits it offers to users in a variety of contexts. Video chat on the other hand has been providing participants to participate not only through voice but also through visual clues, creating a richer and more immersive communication experience. With the development of technology, video chat has transformed how people cooperate and communicate across distances by becoming more accessible and user-friendly. This study details a prototype program that uses video chat with in-built sign language identification to help the hearing-impaired and the speech-impaired. The prototype is constructed from WebRTC to provide realtime communication for the application, on top of which the object detection model from Tensor Flow is integrated to provide hand sign detection and recognition for the user. We focus our testing on the bandwidth and sign language detection needs of customers in order to provide improved support for video chat. We then talk about how this evaluation's findings can inform the evolution of video chatting software. © 2023 IEEE.

关键词： Object detection

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Design and implementation of the experimental platform based on cloud computing 24

Design and implementation of the experimental platform based...

引用

5th international conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Qin, Yu Department of Information Technology Guangdong Teachers' College of Foreign Language and Arts Guangdong Guangzhou China

ISBN: (纸本)9798400718007

In the teaching of cloud computing and big data related majors in colleges and universities, in order to let learners master the relevant knowledge in addition to theoretical teaching, it is also very important to allow students to carry out experiment by themselves. However, because the experimental environment of cloud computing requires huge computer hardware and software resources, it is usually difficult for universities to afford such high costs. Based on vSphere cloud computing platform, through the comprehensive use of python language, powershell script, html5, css language, this paper designed and implemented a cloud computing experimental platform, integrating multiple servers and disk array, forming an efficient cloud computing experimental platform. Using limited resources, it can provide an experimental environment for dozens of learners at the same time, and guide learners to complete the experiment. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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Identifying factors of Migration Intention from the Sri Lankan software engineering Industry: An Analysis using the Theory of Planned Behavior

Identifying factors of Migration Intention from the Sri Lank...

引用

2024 international Research conference on Smart computing and Systems engineering, SCSE 2024

作者： Dodanwala, Thushan Asanka, Dinesh Rajapakse, Chathura Withanaarachchi, Amila University of Kelaniya Department of Industrial Management Sri Lanka

ISBN: (纸本)9798350375688

The increasing level of expertise in software engineering in Sri Lanka is a crucial moment for its Information Technology and Business Process Management (IT/BPM) industry. Nevertheless, the obstacle of migration, particularly brain drain, is a substantial barrier to the expansion of the local software engineering sector. This study utilizes the Theory of Planned Behavior (TPB) as a conceptual framework to investigate the cognitive processes that affect migratory tendencies in the software engineering sector in Sri Lanka. This study utilizes SmartPLS software and the PLS-SEM technique to examine and evaluate the relationships between crucial elements that influence the intention to migrate within the software engineering industry in Sri Lanka. Twenty-seven influential elements were initially identified through a thorough literature review and expert opinions. These factors were then categorized into five primary groupings that are in line with the TPB, along with a set of moderator factors. The data analysis was conducted in two distinct stages, where direct relationships and moderator relationships were examined independently. Political and economic concerns, as well as job satisfaction, were used as moderators. Results indicate that all variables significantly impact the intention to migrate at a 0.05 significance level. Nevertheless, subjective norms had a distinct impact on migration intentions only among individuals aged 30-35. Unexpectedly, perceived behavioral control exhibited a negative relationship with migration intentions, which contradicts the Theory of Planned Behavior framework. Furthermore, both job satisfaction and perceived behavioral variables exclusively moderated attitudes. Ultimately, this research provides insight into the complex connection between several factors that affect migration patterns in the software engineering industry in Sri Lanka. It also offers practical implications for industry stakeholders and policymakers. © 2024 IEEE.

关键词： Job satisfaction

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An Automated Model of software Requirement engineering Using GPT-3.5

An Automated Model of Software Requirement Engineering Using...

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2024 ASU international conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Yeow, Jie Sh'ng Rana, Muhammad Ehsan Abdul Majid, Nur Amira School of Computing Asia Pacific University of Technology and Innovation Malaysia

ISBN: (纸本)9798350372229

While the potential of AI in software development is undeniable, integrating advanced models like GPT-3.5 into its core processes like requirements engineering remains largely unexplored. This research investigates the effectiveness of GPT-3.5 in automating key tasks within software requirements engineering. The primary objective is to comprehensively explore the capabilities, limitations, and potential applications of GPT-3.5 in software requirements engineering. Subsequently, the research undergoes thorough analysis and evaluation to gather insights into the strengths and limitations of GPT-3.5 in the requirement-gathering process. The research concludes by identifying the limitations and putting forth recommendations for future research endeavours aimed at integrating GPT-3.5 into software requirement engineering processes. While GPT-3.5 demonstrates proficiency in aspects like creative prototyping and question generation, limitations in areas like domain understanding and context awareness become evident. By outlining these limitations, the authors offer concrete recommendations for future research focusing on the seamless integration of GPT-3.5 and similar models into the broader framework of software requirements engineering. © 2024 IEEE.

关键词： Artificial Intelligence (AI) GPT 3.5 Natural Language Processing (NLP) software Requirement engineering software Requirement-gathering

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AI-Driven software Reuse: Optimization and Comparative Performance Analysis 2

AI-Driven Software Reuse: Optimization and Comparative Perfo...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aditya Pai, H. Chandrappa, S. Guru Prasad, M.S. Thakare, Atul Christa, Sharon Kumar, Pratik MIT School of Computing MIT Art Design and Technology University Pune India Bengaluru India Dehradun India Dehradun India

ISBN: (纸本)9798331530389

software reuse improves productivity and ensures that there is less time delivered to the market by enabling the proper usage of reusable code from the software repositories. The study provides the collaboration between the AI techniques, which includes Convolution Neural Networks (CNN), Random Forest (RF), and Support Vector Machines (SVM), to predict software components' reusability. The comparative analysis shows the effectiveness of different AI-driven algorithms, where CNN has shown the highest accuracy of 92%, thus reducing the human effort in finding the reusable modules. The Case studies in e-commerce and health care show the benefit of using AI-driven reuse of software;this means that the development time is reduced by 25%, and the maintenance cost is decreased by 20%. The paper highlights how different AI algorithm integration with software engineering has some limitations. The findings show that AI has greatly revolutionized software reuse. Thus bringing innovation and efficiency in software engineering. © 2025 IEEE.

关键词： Computer software reusability

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A Standard Baseline for software Defect Prediction: Using Machine Learning and Explainable AI 47

A Standard Baseline for Software Defect Prediction: Using Ma...

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47th IEEE-Computer-Society Annual international conference on Computers, software, and Applications (COMPSAC)

作者： Bommi, Nitin Sai Negi, Atul Univ Hyderabad Sch Comp & Informat Sci Hyderabad India

ISBN: (纸本)9798350326970

The utility of ubiquitous computing systems drives large-scale software development with millions of lines of code (LOC). As there are vast code sets, it also increases the possibility of coding errors since it is difficult for even highly trained software engineers to write flawless code. Such flawed software can lead to severe issues once deployed. McCabe and Halstead proposed feature extractors for source code to define software quality. Based on the static features proposed by McCabe and Halstead, we run a series of feature engineering techniques and different machine learning models to detect code defects and use explainable algorithms to assess the prediction quality. We report different processing pipeline combinations to detect defects and compare the approaches. We conclude the paper with comments on the nature of the dataset and establish a baseline for further research.

关键词： Machine Learning Explainable AI software Defect Prediction software engineering Dataset Imbalance

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Research on the Development and Application of Offline Simulation software for Electric Vehicle Active Sound Design 3

Research on the Development and Application of Offline Simul...

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3rd international conference on Cloud computing, Big Data Application and software engineering, CBASE 2024

作者： Du, Songze Qian, Yushu Xie, Liping Lin, Zhien Huang, Tao Guangzhou511434 China Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan430070 China College of Mechanical: Engineering & Automation Fuzhou University Fuzhou350108 China

ISBN: (纸本)9798331506582

In recent years, the rise of electric vehicles (EVs) has brought Active Sound Design (ASD) to the forefront as a crucial methodology for enhancing both acoustic quality and the overall driving experience. To address the challenges associated with sound quality design in electric vehicles, an offline simulation software for Active Sound Design (ASD) systems has been developed using the MATLAB platform in this paper, where the vehicle dynamics model and three distinct sound synthesis algorithms have been incorporated into the software, and the formulation and modification of ASD system control strategies can be implemented. The simulations of vehicle operating conditions can be performed, the acoustic performance of the ASD system can be evaluated, and control strategies can be modified expeditiously based on the software developed in this paper. Moreover, the software supports integration with a Digital Signal Processing (DSP) hardware platform, facilitating a seamless closed-loop from sound design to real vehicle application, which contribute to substantially enhance the efficiency of ASD system development. The practical application effect of the software developed are further demonstrated on the electric SUV model, and the results show that the sound of the real vehicle test is the same as that of the software design, which proves its effectiveness and practicability in the active sound design of the vehicle. © 2024 IEEE.

关键词： MATLAB

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Generating Multimodal Metaphorical Features for Meme Understanding 24

Generating Multimodal Metaphorical Features for Meme Underst...

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32nd ACM international conference on multimedia, MM 2024

作者： Xu, Bo Zheng, Junzhe He, Jiayuan Sun, Yuxuan Lin, Hongfei Zhao, Liang Xia, Feng School of Software Key Laboratory for Ubiquitous Network and Service Software of Liaoning Dalian University of Technology Liaoning Dalian China School of Computing Technologies Royal Melbourne Institute of Technology MelbourneVIC Australia School of Computer Science and Technology Dalian University of Technology Liaoning Dalian China School of Computing Technologies RMIT University MelbourneVIC Australia

ISBN: (纸本)9798400706868

Understanding a meme is a challenging task, due to the metaphorical information contained in the meme that requires intricate interpretation to grasp its intended meaning fully. In previous works, attempts have been made to facilitate computational understanding of memes through introducing human-annotated metaphors as extra input features into machine learning models. However, these approaches mainly focus on formulating linguistic representation of a metaphor (extracted from the texts appearing in memes), while ignoring the connection between the metaphor and corresponding visual features (e.g., objects in meme images). In this paper, we argue that a more comprehensive understanding of memes can only be achieved through a joint modelling of both visual and linguistic features of memes. To this end, we propose an approach to generate Multimodal Metaphorical feature for Meme Classification, named MMMC. MMMC derives visual characteristics from linguistic attributes of metaphorical concepts, which more effectively convey the underlying metaphorical concept, leveraging a text-conditioned generative adversarial network. The linguistic and visual features are then integrated into a set of multimodal metaphorical features for classification purpose. We perform extensive experiments on a benchmark metaphorical meme dataset, MET-Meme. Experimental results show that MMMC significantly outperforms existing baselines on the task of emotion classification and intention detection. Our code and dataset are available at https://***/liaolianfoka/MMMC. © 2024 ACM.

关键词： Generative adversarial networks

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2024 2nd international conference on software engineering and Information Technology, ICoSEIT 2024

2024 2nd International Conference on Software Engineering an...

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2nd international conference on software engineering and Information Technology, ICoSEIT 2024

ISBN: (纸本)9798350317503

The proceedings contain 60 papers. The topics discussed include: mobile payment authentication using QR code based on visual cryptography scheme;a classroom usage monitoring system with image detection for student attendance;tapping into the interactive online classroom: the use of multimedia in e-learning;disrupting higher education: a comparative study of synchronous lecturing and self-paced learning in higher education;course recommendation on online learning platforms using collaborative filtering and content-based filtering with implicit feedback;Delone-Mclean and technology acceptance model in investors use of automated order online trading;exploring COVID-19 vital signs using Bayesian network causal graph;stunting classification analysis for toddlers in Bojongsoang: a data-driven approach;analyzing the factors influencing the use of digital banking services among generation Z;and performance of traditional and dense vector information retrieval models in code search.

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Deepfake Detection Using Deep Learning 10

Deepfake Detection Using Deep Learning

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Prakash Raj, S. Pravin, D. Sabareeswaran, G. Sanjith, R.K. Gomathi, B. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore641202 India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

In recent decades, rapid advancements in AI, machine learning, and deep learning have yielded new methods and tools for manipulating multimedia. While this technology has primarily found applications in legitimate fields such as entertainment and education, there have been instances of malicious users exploiting it for unlawful or nefarious purposes. For instance, individuals have used these techniques to create highly realistic fake videos, images, or audio recordings, with the intention of spreading misinformation, propaganda, inciting political discord, promoting hate, or even engaging in harassment and blackmail. These manipulated and hyper-realistic media have gained notoriety as 'Deepfakes' in recent times. The literature has documented various approaches to address the challenges posed by Deepfakes. © 2024 IEEE.

关键词： Adversarial machine learning

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