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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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Using Model-Based Systems engineering Tools in software Development 19

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

引用

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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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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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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Neural Networks and Deep Learning: Innovations and Applications in Soft computing 7

Neural Networks and Deep Learning: Innovations and Applicati...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Abdulkadar, Jahagirdar Imran Mohan, Chinnem Rama Bharati, P. Vijaya Palit, Shamik Rao, Gunda Srinivasa Mudholkar, Pankaj Savitribai Phule Pune University India Narayana Engineering College Department of Computer Science and Engineering Andhra Pradesh Nellore524004 India Vignans Institute of Engineering for Women Department of Cse Andhra Pradesh Visakhapatnam530049 India University of Stirling Department of Computing Science and Software Engineering RAK Campus United Arab Emirates Mallareddy University School of Management and Commerce Kompally500100 India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India

ISBN: (纸本)9798350350067

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing. © 2024 IEEE.

关键词： Adversarial machine learning

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A hierarchical software architecture for "smart firefighting" platform

A hierarchical software architecture for "smart firefighting...

引用

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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Accelerating software Development with Artificial Intelligence

Accelerating Software Development with Artificial Intelligen...

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2024 international conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Anupriya Jain, Paras Goel, Lipika Sharma, Ramesh Chander Jasola, Sanjay Ali, Amjad School of Computing Graphic Era Hill University UK Dehradun India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh Sehore India Sridevi Women's Engineering College Department of Cse Hyderabad India Graphic Era Hill University Department of Computer Science and Engineering UK Dehradun India Graphic Era Deemed University Department of Management Dehradun India

ISBN: (纸本)9798350379716

software development is experiencing a significant transformation driven by Artificial Intelligence. The demand for high-quality software is ever-growing. Traditional software development can be time-consuming, often bottlenecked by repetitive tasks and the need to write vast amounts of code. Artificial Intelligence (AI) offers a revolutionary approach to code creation. This study dives into the exciting world of the AI-powered code era, exploring how gears like Open AI's GPT can automate various degrees of the software program improvement procedure. The study reveals how AI can extensively pace things by comparing AI-generated code to standard manual coding and other computerized methods. The research emphasizes the need for a considerate approach, thinking about the strengths and weaknesses of different AI algorithms, and highlights AI's immense potential to reshape the entire field of software program engineering. The research concludes by exploring the broader implications, demanding situations, and thrilling future guidelines for AI to make software development even more efficient. © 2024 IEEE.

关键词： software design

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