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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271-280 订阅

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SiJesi: Large Language Model Chatbot with Augmented Retrieval Approach Generation and Prompt engineering

SiJesi: Large Language Model Chatbot with Augmented Retrieva...

引用

2024 international conference on Computer engineering, Network and Intelligent multimedia, CENIM 2024

作者： Rosari, Happy Sandhiyadini Girinoto Novita Yasa, Ray Qomariasih, Nurul Anindya Wijayanti, Rheva Politeknik Siber dan Sandi Negara Crypto Software Engineering Bogor Indonesia

ISBN: (纸本)9798350368802

This research designs a chatbot based on Large Language Models (LLM) for cyber incident consultation, utilizing the Retrieval Augmented Generation (RAG) approach, integrated with GPT and prompt engineering techniques. The goal is to improve public cybersecurity awareness, especially in areas with low awareness, by enhancing community consultation services through automation. The chatbot addresses security concerns by examining measures against prompt injection attacks to ensure reliability. The research follows the Waterfall System Development Life Cycle methodology. Results show that LLM chatbots using RAG and prompt engineering generate more accurate responses compared to human-based answers and GPT 3.5, achieving a top score of 35.26% in A/B testing. Additionally, the chatbot received a high satisfaction score of 86.47% in User Acceptance Testing. These findings are expected to contribute to increased cybersecurity awareness and the development of innovative, secure chatbot technology for public services. © 2024 IEEE.

关键词： Life cycle

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Hybrid Deep Learning Architectures for Histological Image Segmentation 6

Hybrid Deep Learning Architectures for Histological Image Se...

引用

6th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Hasan, Md Jahid Ahmad, Wan Siti Halimatul Munirah Wan Ahmad Fauzi, Mohammad Faizal Abas, Fazly Salleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Center for Visual Computing Faculty of Computing and InformaticsFaculty of Engineering Cyberjaya Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350344349

In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (HandE) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied. © 2024 IEEE.

关键词： Image segmentation

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AI based Frame Interpolation Using OneVPL 15

AI based Frame Interpolation Using OneVPL

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Madhumathi, R. Chandra Kumar, N. Vigneshwaran, S. Vignesh, P. Anbumani, P. Sri Ramakrishna Engineering College Department. of Computer Science and Engineering Tamil Nadu Coimbatore India Visual Collaboration Technologies Bangalore India

ISBN: (纸本)9798350370249

The increasing popularity of multimedia content, particularly videos, has highlighted the significance of compression methods like HEVC and H.264, which effectively reduce file sizes without compromising quality. However, in the encoding and decoding process, hardware malfunctions or other issues may lead to frame loss, affecting frame rates and overall quality. To mitigate this problem, interpolation techniques are utilized to insert frames and maintain smooth playback. This method employs Artificial Intelligence (AI), specifically a trained Frame Interpolation for Large Motion (FILM) model, to generate intermediate frames by analyzing pairs of input images. This technique ensures seamless motion and enhances video quality despite frame loss during encoding and decoding. This work develops a pipeline using the FILM model and hardware acceleration to generate smooth videos. Testing demonstrates a 4x improvement in visual quality over raw input, setting a new standard for video interpolation and processing in multimedia applications. © 2024 IEEE.

关键词： Decoding

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GRADESTYLE: GitHub-Integrated and Automated Assessment of Java Code Style 23

GRADESTYLE: GitHub-Integrated and Automated Assessment of Ja...

引用

IEEE/ACM 45th international conference on software engineering - software engineering Education and Training (ICSE-SEET)

作者： Iddon, Callum Giacaman, Nasser Terragni, Valerio Univ Auckland Auckland New Zealand

ISBN: (纸本)9798350322590

Every programming language has its own style conventions and best practices, which help developers to write readable and maintainable code. Learning code style is an essential skill that every professional software engineer should master. As such, students should develop good habits for code style early on, when they start learning how to program. Unfortunately, manually assessing students' code with timely and detailed feedback is often infeasible, and professional static analysis tools are unsuitable for educational contexts. This paper presents GRADESTYLE, a tool for automatically assessing the code style of Java assignments. GRADESTYLE automatically checks for violations of some of the most important Google Java Style conventions, and Java best practices. Students receive a report with a code style mark, a list of violations, and their source code locations. GRADESTYLE nicely integrates with GitHub and GitHub Classroom, and can be configured to provide continuous feedback every time a student pushes new code. We adopted our tool in a second-year software engineering programming course with 327 students and observed consistent improvements in the code style of their assignments.

关键词： computing education code style programming courses automated marking GitHub Java programming language

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Using Model-Based Systems engineering Tools in software Development 19

Using Model-Based Systems Engineering Tools in Software Deve...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Kolesnikova, Kateryna Lukianov, Dmytro Khikmetov, Askar Alpysbayev, Kaisar Mukhamedyeva, Ardak Dauletbek, Yergali International IT University 34/1 Manas str. Almaty Kazakhstan GIGACloud Bortnikova Aveny Kyiv499A Ukraine

The article discusses the application of model-based systems engineering in software development. MBSE offers a new model-based approach to viewing complex systems, allowing data and information to be effectively shared across different product life cycle stages. The paper examines the model-based systems engineering methodology, its benefits, and its impact on requirements, design, analysis, and verification within the product life cycle. The study highlights the relevance of using model-based systems engineering to modern systems engineering practices, especially in the context of software engineering. The study results demonstrate the benefits of model-based systems engineering and its ability to optimize development processes, improve product quality, and reduce delivery times. The scientific novelty of the article lies in the application and research of the model-based systems engineering in the context of software development, which represents a significant contribution to the development of modern systems engineering practices and development technologies. © 2024 The Authors. Published by Elsevier B.V.

关键词： Application programs

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Abstract Interpretation for PLONKish Circuits 34

Abstract Interpretation for PLONKish Circuits

引用

34th Annual international conference on Collaborative Advances in software and computing, CASCON 2024

作者： Soureshjani, Fatemeh Heidari Gorzny, Jan Polytechnique Montréal Computer Engineering and Software Engineering Department Canada Zircuit Canada

ISBN: (纸本)9798331504830

Zero-knowledge proof systems are becoming powerful tools in domains like blockchain networks. In this work, we improve abstract interpretation methods to sanity check PLONKish arithmetizations of computation, such as those used in the Halo2 zero-knowledge proof system. Our work aims to improve the accuracy of checks for unused gates, assigned but unconstrained values, and possibly under-constrained circuits, all of which may indicate a bug in the circuits. We show how copy constraints and arbitrary lookup functions can be used in abstract interpretation based analysis of these circuits. We are motivated to revisit this topic as these circuits, through their use in general purpose blockchain networks, may be responsible for the security of millions of dollars in cryptocurrency. © 2024 IEEE.

关键词： Blockchain

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Recommend Source Codes Considering Designing Patterns using Machine Learning Algorithm

Recommend Source Codes Considering Designing Patterns using ...

引用

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

作者： Thennakoon, A.G.D.S. Wijeratne, P.M.A.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Computing and Information System Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Software Engineering Belihuloya Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375688

As a result of the fact that design patterns offer templates that are necessary for addressing common problems and improving the communication, productivity, and maintainability of projects consistently, design patterns are of the highest importance in the process of developing software. Machine learning (ML) techniques, such as Support Vector Machine (SVM) and Decision Trees (DT), can be utilized to simplify the process of recognizing software design patterns in source code to educate machines on how to deal with complex data, unsupervised learning is used to recognize patterns, and supervised learning is used to map inputs to outputs. The research was carried out to employ ML to produce predictions about design patterns that are present in code. Initially, the procedure begins with the gathering of datasets from professionals in the field of software engineering. These datasets were eventually classified into three categories: Abstract Factory, Factory Method, and Singleton. Three different ML models and one deep learning model were applied to make predictions. K-Nearest Neighbors (KNN), SVM, DT and Long Short-Term Memory (LSTM) respectively. For the goal of creating such predictions, SVM which has been demonstrated to correctly recognize design patterns with an impressive 98.67% accuracy, is the suitable algorithm to use. The degree of cosine similarity among the input code snippets and the group code snippets is used to decide which 5 code suggestions are the next most relevant. © 2024 IEEE.

关键词： Support vector machines

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Utilizing Resource Estimation for the Development of Quantum computing Applications 5

Utilizing Resource Estimation for the Development of Quantum...

引用

2024 international conference on Quantum computing and engineering

作者： Quetschlich, Nils Soeken, Mathias Murali, Prakash Wille, Robert Tech Univ Munich Chair Design Automat Munich Germany Microsoft Quantum Zurich Switzerland Univ Cambridge Cambridge England Software Competence Ctr Hagenberg GmbH SCCH Hagenberg Austria

ISBN: (纸本)9798331541378

Quantum computing has made considerable progress in recent years in both software and hardware. But to unlock the power of quantum computers in solving problems that cannot be efficiently solved classically, quantum computing at scale is necessary. Unfortunately, quantum simulators suffer from their exponential complexity and, at the same time, the currently available quantum computing hardware is still rather limited (even if roadmaps make intriguing promises). Hence, in order to evaluate quantum computing applications, end-users are still frequently restricted to toy-size problem instances (which additionally often do not take error correction into account). This substantially hinders the development and assessment of real-world quantum computing applications. In this work, we demonstrate how to utilize Resource Estimation to improve this situation. We show how the current workflow (relying on simulation and/or execution) can be complemented with an estimation step, allowing that end-users (1) actually can consider real-world problem instances already today (also considering error correction schemes and correspondingly required hardware resources), (2) can start exploring possible optimizations of those instances across the entire design space, and (3) can incorporate hypotheses of hardware development trends to derive more informed and, thus, better design space parameters. Overall, this enables end-users already today to check out the promises of possible future quantum computing applications, even if the corresponding hardware to execute them is not available yet.

关键词： Quantum computers

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Knowledge Management Using Cloud computing in Higher Educational Institutions 4

Knowledge Management Using Cloud Computing in Higher Educati...

引用

4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sharma, Ruchi Lakhera, Girish Graphic Era Deemed to Be University Department of Management Studies Dehradun India

ISBN: (纸本)9798350364729

"Knowledge"is a vital resource for organisation that should be gathered, safeguarded, & disseminated. It should be used to inform decisions. "Cloud-based computing"and 'Knowledge Management' solutions help organizations manage their information and boost productivity by fusing traditional organizational methods with cutting-edge technology. This study specifically looks at cloud-based software services to explore how organizations use ubiquitous computing to adopt innovative software development and delivery approaches. The study's findings show that factors like complexity, compatibility, relative advantage, security, privacy, and trust, as well as reputation and KM practices (knowledge accessibility, storage, application, and sharing), have a significant and positive impact on whether cloud-based software services are adopted. 'Coordination and communication' problems are the cornerstone of winning remote 'development and innovation' practices. The conclusion also lend credence to the idea that the effects of cloud services and knowledge management practices can be attenuated by spatial (cultural) diversity. © 2024 IEEE.

关键词： Cloud platforms

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A hierarchical software architecture for "smart firefighting" platform

A hierarchical software architecture for "smart firefighting...

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2024 international conference on Computer Network Security and software engineering, CNSSE 2024

作者： Zhang, Zixiang Slam, Nady Di, Zhengqiang Zhu, Yu Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Lanzhou China Lanzhou Fire Brigade Lanzhou China Anshan Dexin Technology Co. Anshan China

ISBN: (纸本)9781510680234

To enhance the fire service system, many countries have conducted extensive research on "Smart Firefighting" platform architecture and achieved significant results. However, there are still numerous challenges that persist in the field. To address the challenges related to storage, computing, security, and scalability of large-scale data in the "Smart Firefighting" domain, we have conducted an in-depth study and comparison of existing "Smart Firefighting" platform architectures. Based on the analysis, we propose a novel architectural for the "Smart Firefighting" platform, which combines the strengths of traditional hierarchical architecture with the Cloud-Edge-End architectural, this integration effectively preserves the key features of both architectural styles, and the proposed fusion model is able to satisfy the computation and storage of large amounts of firefighting data while guaranteeing security. This architectural model can serve as a reference for the construction of highly available, high-performance, and more secure and stable "Smart Firefighting" platforms and holds significant importance in accelerating the development and maintenance of "Smart Firefighting" to ensure long-term social stability and security. © 2024 SPIE.

关键词： Cloud computing architecture

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