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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 85768 条记录，以下是11-20 订阅

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Pre-training and Adverse Audio Samples for data-Efficient Wake Word Detection 26th

Pre-training and Adverse Audio Samples for Data-Efficient Wa...

引用

26th international conference on Speech and Computer

作者： Torralbo, Manuel Mendez, Ariane Agirre, Maia Del Pozo, Arantza Vicomtech Fdn Basque Res & Technol Alliance BRTA Mikeletegi 57 Donostia San Sebastian 20009 Spain

ISBN: (纸本)9783031779602;9783031779619

This work investigates the impact of pre-training and the use of adverse audio samples on both the data efficiency and performance of end-to-end neural Wake Word Detection systems. Alongside intensive data augmentation, the proposed methodology involves pre-training Keyword Spotting models, followed by fine-tuning to recognize specific wake words by leveraging their foundational capabilities. The study also examines the inclusion of adverse audio samples resembling the target wake word. Experiments evaluate various state-of-the-art architectures to assess the effects of model size, amount of training data, model pre-training, and the incorporation of adverse audio samples on system performance. Results demonstrate that pre-training improves performance, with fine-tuned models consistently outperforming those trained from scratch, especially with limited data. Additionally, training with adverse samples resembling the wake word also enhances results by reducing false acceptance rates. These findings provide valuable insights for developing data-efficient Wake Word Detection systems.

关键词： Wake word detection Pre-training Adverse audio samples data-efficient

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FPGA-Based Near-data Processing Emulation Platform for System-Level Evaluation of DIMM Memory systems

FPGA-Based Near-Data Processing Emulation Platform for Syste...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Minkyu Kim, Kyungho Lee, Eunchong Kim, Aeri Lee, Sang-Seol Korea Electronics Technology Institute Korea Republic of

ISBN: (纸本)9798331510756

As applications like recommendation systems and large language models (LLMs) emerge, the demand for higher memory capacity and bandwidth is rapidly increasing. This has led to a growing overhead associated with moving data between memory and compute units in these memory-intensive applications. To address the data movement overhead, near-data processing (NDP) has gained attention as a promising solution by enabling computation closer to the memory. However, exploring and evaluating NDP architectures face significant challenges. Many prior studies rely on simulation-based approaches, which are often limited by constrained environments and long simulation times. To overcome these limitations, this paper presents an FPGA-based NDP emulation platform designed for the study of NDP architectures in LRDIMM memory systems. The proposed platform emulates the Register Clock Driver (RCD) and data Buffer (DB) components of LRDIMM, providing a flexible environment to easily integrate and test NDP architectures. Through the implementation of a simple NDP structure, we demonstrate the effectiveness of this emulation platform for system-level NDP evaluation. © 2025 ieee.

关键词： Memory architecture

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Osmotic Computing Based Secure Resource Orchestration Scheme for Vehicular Communication 17

Osmotic Computing Based Secure Resource Orchestration Scheme...

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Garg, Deepanshu Angurala, Mohit Bali, Rasmeet S. Kumar, Neeraj Chandigarh University Computer Science & Engineering Department India Guru Nanak Dev University Computer Science Department India Thapar University Patiala Computer Science & Engineering Department India

ISBN: (纸本)9798331531195

The advancement in technology leads to provide an efficient communication among vehicles to offload resource-intensive tasks for transportation-based services. However, it may cause issue related to efficient secure resource utilization among vehicular nodes. The key reason behind these considerations are the high mobility of vehicles and their high computational requirements for task execution. To solve these issues, an osmotic computing based vehicular communication scheme has been proposed. The primary objective of the scheme is to provide a communication model which can perform effective secure resource management among static or dynamic vehicular nodes through Osmotic Computing. By utilizing the key components of osmotic computing such as osmotic resource manager and node manager, numerous resources have been shared with the vehicles in an effective way. The performance of the proposed scheme has been evaluated using simulation. Results obtained show that the proposed scheme outperform the existing schemes in terms of various performance evaluation parameters. © 2025 ieee.

关键词： data privacy

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Streamlining Kubernetes Deployments through GitOps Methodologies

Streamlining Kubernetes Deployments through GitOps Methodolo...

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2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Kurrewar, Samiksha Dhomane, Sejal Dahake, Aditya Yadav, Ram Kumar Wyawahare, Nikhil Morris, Nekita Chavhan Ghrce IoT and Cyber Security Department of Data Science Nagpur India Manipal University Department of Data Science and Engineering Jaipur India

ISBN: (纸本)9798331529833

A clear process for automating the deployment of apps on Kubernetes using GitOps concepts is shown, with an emphasis on tools like Helm and Argo CD. The method demonstrates how to improve the effectiveness and reliability of application delivery by utilizing techniques like infrastructure- as-code, YAML configuration files, and CI/CD pipelines through GitHub Actions. The entities can accomplish reliable and repeatable deployments while developing teamwork among development teams by considering Git as the primary source of information. Applications remain in their original state with the support of Argo CD's continuous delivery, and monitoring tools provide data that is insightful on resource use and performance. By optimizing the management of cloud-native apps, our research helps people remain effective in the face of rapidly evolving technology. All things considered, the results demonstrate how applying GitOps techniques can streamline deployment procedures and improve application management in Kubernetes systems. © 2025 ieee.

关键词： Information management

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Synergistic Approach to Enhancing Financial Transparency and Fraud Prevention With Blockchain and data science

Synergistic Approach to Enhancing Financial Transparency and...

引用

2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Kabir, Md Farhad Gharami, Arun Kumar Rahman, Ashequr Rahman, Md Masudur Hossain, Iffat Sania Hossain, Zakir Marshall School of Business University of Southern California Los Angeles United States College of Business Westcliff University IrvineCA United States College of Technology and Engineering Westcliff University IrvineCA United States Martin V. Smith School of Business and Economics California State University Channel Island Camarillo CA United States College of Engineering and Computer Science California State University NorthridgeCA United States

ISBN: (纸本)9798331513320

In recent years, the fusion of data science and Blockchain technology has been described as revolutionary in the field of finance. The features native to Blockchain, including decentralization, transparency and immutability, are essential within the context of preventing financial fraud and maintaining the integrity of the transactions. At the same time, data science tools such as machine learning, anomaly detection and predictive analytics are essential in detecting fraudulent activity and estimating risk over time. In this paper, we consider the complementary of applications between data science and Blockchain technology, examining how some strengths can be combined to facilitate enhanced creation of trustworthy, effective and efficient financial structures. We examine the role of Blockchain in providing enhanced accountability in the conduct of financial transactions and that of data science in providing timely detection of fraud, supported by relevant case studies. Additionally, we deal with the problems and ethical aspects of trying to use such integrated systems, giving recommendations for further development of these systems in order to improve their performance in the modern financial systems. © 2025 ieee.

关键词： Decentralized finance

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An Efficient Graphics data Retrieval Method Based on data Conversion Schemes

An Efficient Graphics Data Retrieval Method Based on Data Co...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Baek, Nakhoon School of Computer Science and Engineering Kyungpook National University Daegu41566 Korea Republic of Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of Data-Driven Intelligent Mobility ICT Research Center Kyungpook National University Daegu41566 Korea Republic of

ISBN: (纸本)9798331510756

Modern computer graphics systems usually manage a set of internal data to store control parameters and all kind of graphics data. The behind problem on these graphics system data is that they are frequently updated and retrieved with variety of data types, even they are initially stored as specific data types. To support these updates and retrievals with various data types, modern graphics systems typically use data type conversion features, and even in the worst cases, they should provide all combinations of internally stored data types and newly provided or retrieved data types. In this paper, we show how to efficiently provide all the required combinations of data type conversions, with motivations, analysis, and overall designs. We also show typical type conversion scenarios, with specific API function examples. Our design shows that it can improve the internal architecture and the internal efficiency of modern computer graphics systems. In its implementation, we use C++std:: map container for fast lookup of the best-suitable conversion function. This overall scheme was integrated in our existing systems, and shows its feasibility and correctness. © 2025 ieee.

关键词： data assimilation

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Dynamic Algorithmic Configuration for Enhanced Malware Detection 3

Dynamic Algorithmic Configuration for Enhanced Malware Detec...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ramya, S. Periyasamy, M. Ramprasanth, P.S. Srinivasa Aravindh, S. Mohan kumar, B. Department of Artificial Intelligence And Data Science M Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

As edge-based decisions continue to grow in demand, organizations are increasingly seeking user data, raising privacy concerns. Federated Learning (FL) addresses this problem but is typically dependent on computationally intensive Neural Network (NN) models. This work introduces a novel lightweight approach by integrating Support Vector Machines (SVMs) into FL frameworks. Optimized methodology SVM with the compatibility of SGD is used where the meta-trained controller dynamically adapts the learning rate to the improvement of models. The proposed method shows equal performance to those based on the NN-based FL methods but drastically reduces the cost of computation for being applicable to resource-constrained environments. © 2025 ieee.

关键词： Federated learning

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Text Recognition in Persian Documents Using Self-Supervised Learning 6

Text Recognition in Persian Documents Using Self-Supervised ...

引用

6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Babaei, Atefeh Dehghani, Alireza Yazdi, Mehran Jamshidi, Ali Rasouli, Mahdi Shiraz University School of Electrical and Computer Engineering Shiraz Iran CeADAR Ireland's Centre For AI Dublin Ireland Université Grenoble Alpes School of Computer Science and Mathematics Grenoble France

ISBN: (纸本)9798331506520

Text is one of humankind's most significant inventions essential for communication and collaboration in modern society. Extracting text from images, especially for languages with cursive and connected scripts like Persian, presents unique challenges due to issues such as font types, word structures, and the scarcity of labeled data. This paper addresses these challenges by implementing deep learning techniques, specifically self-supervised learning with convolutional and recurrent neural networks (CNNs and RNNs). Our approach successfully mitigates the data scarcity problem, achieving an accuracy of 92.9% on the prepared dataset. © 2025 ieee.

关键词： Self-supervised learning

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A High-Speed Compression and Secure Authentication Framework for data Sharing Cloud-Enabled Big data Environments

A High-Speed Compression and Secure Authentication Framework...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Abirami, I. Selvi, S. Lalitha, R. Julian, Anitha Rmk Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509828

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment. © 2025 ieee.

关键词： data integrity

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Towards Efficient Deep Hashing Retrieval: Condensing Your data via Feature-Embedding Matching

Towards Efficient Deep Hashing Retrieval: Condensing Your Da...

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

作者： Feng, Tao Zhang, Jie Liu, Huashan Wang, Zhijie Pang, Shengyuan College of Information Science and Technology Donghua University Shanghai China Department of Computer Science ETH Zurich Zurich Switzerland College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing models has become more expensive due to complex optimizations and large datasets. Coreset selection and dataset Condensation lower overall training costs by reducing the volume of training data without significantly compromising model accuracy for classification task. In this paper, we explore the effect of mainstream dataset condensation methods for deep hashing retrieval and propose IEM (Information-intensive feature-Embedding Matching), which is centered on distribution matching and incorporates model and data augmentation techniques to further enhance the feature of hashing space. Extensive experiments demonstrate the superior performance and efficiency of our approach. © 2025 ieee.

关键词： dataset Condensation Deep Hashing Retrieval

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