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

共 66536 条记录，以下是171-180 订阅

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Overview of Current Challenges in Multi-architecture software engineering and a Vision for the Future 12th

Overview of Current Challenges in Multi-architecture Softwar...

引用

12th international conference on Big Data Analytics, BDA 2024

作者： Sowiński, Piotr Lacalle, Ignacio Vaño, Rafael Palau, Carlos E. Ganzha, Maria Paprzycki, Marcin Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland Communications Department Universitat Politècnica de València Valencia46022 Spain

ISBN: (纸本)9783031861925

The landscape of computing technologies is changing rapidly, straining existing software engineering practices and tools. The growing need to produce and maintain increasingly complex multi-architecture applications makes it crucial to effectively accelerate and automate software engineering processes. At the same time, artificial intelligence (AI) tools are expected to work hand-in-hand with human developers. Therefore, it becomes critical to model the software accurately, so that the AI and humans can share a common understanding of the problem. In this contribution, firstly, an in-depth overview of these interconnected challenges faced by modern software engineering is presented. Secondly, to tackle them, a novel architecture based on the emerging WebAssembly technology and the latest advancements in neuro-symbolic AI, autonomy, and knowledge graphs is proposed. The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins, which model the software throughout all stages of its lifecycle. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user. The concept takes a leap beyond the current software engineering approaches, addressing some of the most urgent issues in the field. Finally, the efforts towards realizing the proposed approach, as well as future research directions, are summarized. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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FBI-Net: Frequency Band Integration Network for Infrared Small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xin, Biqiao Li, Qiang Mao, Qianchen Wang, Jinbao Wang, Bingshu School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches. © 2025 IEEE.

关键词： Feature Fusion Infrared Small Target Segmentation Semantic Injection Wavelet Transformation

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Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests 24

Automatic Adjustment of HPA Parameters and Attack Prevention...

引用

9th international conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Chan, Huah Yong Zhou, Hanlin Ni, Jingfei Wu, Mengchun Deng, Qing School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Xiamen Institute of Software Technology Xiamen China Department of Manzhouli Customs Manzhouli China College of Information Science and Engineering Jimei University Xiamen China

ISBN: (纸本)9798400717161

In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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Proceedings - 2024 international conference on engineering and computing, ICECT 2024

Proceedings - 2024 International Conference on Engineering a...

引用

2024 international conference on engineering and computing, ICECT 2024

ISBN: (纸本)9798350349719

The proceedings contain 73 papers. The topics discussed include: leveraging ensemble learning for dry beans classification;portable edge computing system analysis for real-time detection and classification of wheat yellow rust infection types using embedded AI;NanoCNN: a parameters efficient network for traffic sign recognition;a heuristic multimedia verticals aggregated search approach and user behavioral analysis;intelligent diagnosis of gasoline engine faults using acoustic features;modeling and control of boost converter: switched, averaged, and state-space comparison;harnessing long short term memory networks for stock market forecasts;enhancing misogyny detection in bilingual texts using FastText and explainable AI;and detection of ophthalmic disorders through deep feature fusion.

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Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live Streaming 25

Palantir: Towards Efficient Super Resolution for Ultra-high-...

引用

16th ACM multimedia Systems conference, MMSys 2025

作者： Jin, Xinqi Zhu, Zhui Sun, Xikai Dang, Fan Liu, Jiangchuan Xu, Jingao Liu, Kebin Chen, Xinlei Liu, Yunhao School of Software & BNRist Tsinghua University Beijing China Department of Automation & BNRist Tsinghua University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Computing Science Simon Fraser University Burnaby Canada Computer Science Department Carnegie Mellon University Pittsburgh United States Global Innovation Exchange Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798400714672

Neural enhancement through super-resolution (SR) deep neural networks (DNNs) opens up new possibilities for ultra-high-definition (UHD) live streaming. Yet, the heavy SR DNN inference overhead leads to severe deployment challenges. To reduce the overhead, existing systems propose to apply DNN-based SR only on carefully selected anchor frames while upscaling non-anchor frames via the lightweight reusing-based SR approach. However, frame-level scheduling is coarse-grained and fails to deliver optimal efficiency. In this work, we propose Palantír, the first neural-enhanced UHD live streaming system with fine-grained patch-level *** the core of Palantír is its SR video quality estimation strategy which guides the low-delay selection of the most beneficial anchor patches. Although existing systems propose estimation strategies for anchor frame selection, these strategies heavily rely on empirical insights that cannot be transferred to our context, making fine-grained scheduling a challenging problem that requires a fundamentally new solution. Facing the challenge, we follow the first-principles approach and derive a directed acyclic graph (DAG) model to address the problem. The model can also be generalized to various settings due to its first-principles nature. Compared to the state-of-the-art real-time frame-level scheduling strategy for live streaming, Palantír reduces the anchor size by 80.1% at most and 38.4% on average without compromising the quality gain. Furthermore, Palantír incurs a scheduling latency accounting for only 0.6-3.9% of the end-to-end latency requirement for UHD live streaming. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep neural networks

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Automated AI Tool for Log File Analysis 6

Automated AI Tool for Log File Analysis

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Lohar, Prerna Baraskar, Trupti Dept.of Computer Engineering & Technology Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (纸本)9798331522667

Log file analysis has a critical role in monitoring and maintaining software systems, yet the manual inspection of logs becomes increasingly impractical with the growing volume of data. This survey paper explores recent advancements in automated log file analysis, with a particular focus on the integration of AI techniques, which includes ML models and NLP. The study identifies key challenges in traditional methods, such as the need for human interpretation, difficulty in detecting new errors, and issues in backtracking within continuous data streams. Moreover, we examine state-of-the-art AI approaches, like LLaMA 2, to streamline log analysis by automating error detection, summarization, and anomaly identification. Research deficiencies are identified, notably the necessity for advanced methodologies to manage variety of log formats, automated model optimization, and ongoing learning processes. This comprehensive review endeavors to provide a thorough examination of the current landscape, encompassing perspectives on potential outcomes and prospective trajectories in the domain of AI-enhanced log file analysis. In addition to providing insights into prospective solutions and future directions in AI-driven log file analysis, this study attempts to give an in-depth analysis of the current situation. © 2025 IEEE.

关键词： Economic and social effects

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Implementing RDMA over Soft-RoCE with Basic eBPF Monitoring: A Cost-Effective Approach for High-Performance Data Communication

Implementing RDMA over Soft-RoCE with Basic eBPF Monitoring:...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kagal, Suresh Narasimha Murthy, M.S. Mutalikdesai, Tejas Yugal Kishore, V. Tejaswini, T.V. Bms Institute of Technology and Management Dept. of Information Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331538538

RDMA is a technology that enables extremely fast communication between different systems by allowing these systems to transfer data without using the CPU and operating system. This results in the rapid transfer of data and reduces latency in data transfer. RDMA, brings more efficiency in moving the data between systems, improving general performance and responsiveness in many applications as CPU is not involved in the data transfer. Traditional RDMA solutions usually require special and costly hardware, such as RoCE adapters. This can make them less accessible for companies with lesser budgets. Soft-RoCE is a software-based solution that brings RDMA capabilities to standard Ethernet hardware. In this way, we can replicate the benefits that RDMA offers without the expensive equipment, hence making it possible in environments where it would not have been feasible. Also, an innovative monitoring framework using eBPF is introduced, which offers comprehensive real-time intelligence to RDMA performance by decreasing the latency and throughput. © 2025 IEEE.

关键词： Budget control

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Leveraging a Microservice Architecture, Access Control and Interoperability Patterns to Manage Privacy-Related User Consents 22nd

Leveraging a Microservice Architecture, Access Control and I...

引用

22nd international conference on Service Oriented computing

作者： Lamari, Selena Benblidia, Nadjia Tibermacine, Chouki Urtado, Christelle Vauttier, Sylvain Univ Montpellier CNRS LIRMM Montpellier France Blida 1 Univ Fac Sci LRDSI Lab Blida Algeria Univ Bretagne Sud IRISA UMR CNRS 6074 F-56000 Vannes France Univ Montpellier EuroMov Digital Hlth Mot Montpellier France IMT Mines Ales Ales France

ISBN: (纸本)9789819608072;9789819608089

Consent management is of paramount importance when processing personal data. It is now prescribed as mandatory by regulations such as the GDPR. This article presents a micro-service architecture providing a protocol for access control including authentication, authorization management, and externalized consent management based on the ABAC (Attribute-Based Access Control) and the side-car architecture patterns. The experimentation on a case example validates that its integration is light and non intrusive for existing applications.

关键词： software engineering Privacy GDPR regulation Personal data Consent management Microservice architecture Interoperability

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Scheduling Tasks in the Cloud environment by using evolutionary optimization technique 5

Scheduling Tasks in the Cloud environment by using evolution...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Mahapatra, Liza Rout, Minakhi Tripathy, Sarita Deemed to be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

Among the many software technologies around the world,there is no parallel to cloud computing(CC).Businesses use cloud computing for hosting *** cloud computing, tasks are distributed across a group of resources which when executed, help reduce time needed to finish them *** optimization refers to the process of minimizing the time needed to accomplish tasks. These group of tasks are present in a domain related to computers. In CC, the goal of this optimization is reducing the total time needed to execute the group of tasks when distributing and controlling virtual machines or resources. This paper aims to obtain optimization in scheduling tasks in CC by utilizing genetic *** methods whose basis are natural selection and genetics are called Genetic algorithms. They combine the idea of "survival of the fittest"in string-based representations with a mix of structured and random information exchange, forming a search approach that includes a level of human-like creativity..After the application of genetic algorithms to task scheduling problem, the minimum fitness per generation is seen in the graph after two generation of stability. © 2025 IEEE.

关键词： Genetic algorithms

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Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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