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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Devansh Mehra A Sri Vamsi Prabhu Kavin Department of Data Science and Business Systems School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

CBDC–as-a-problem looks like a mania of interest from central banks and financial institutions all over the world. It may describe CBDC technology and CBDC technology development, the positive aspects of CBDC and its future perspectives as follows: The CBDC is linked to the existing banking systems in the past which was based on the past findings of the blockchain, DLT, etc., that possessed the qualities the previous ones did not have – security, trust, and transparency. Innovation, design and CBDC safety that falls below this paper are discussed in this section below. The paper proceeds to show several paths that central banks are taking to CBDC development given the regulation and changing dynamic of the financial world. However, the studies do not end there; information on the potential of CBDC for financial stability, monetary policy formulation trajectories, transaction privacy, and the role of banks is included. Given that in field of finance the CBDCs are becoming more and more important, this research paper shall ensure to provide consistent background to policymakers, financial institutions and society at large on CBDC and its structural implications. This guarantees that everyone is in the know so it will be possible for them to come up with compliant plans and decisions informed by the knowledge in this changing landscape that simply does not stop evolving.

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CoKV: Optimizing KV Cache Allocation via Cooperative Game

arXiv

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arXiv 2025年

作者： Sun, Qiheng Zhang, Hongwei Xia, Haocheng Zhang, Jiayao Liu, Jinfei Ren, Kui The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data China Security China Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

Large language models (LLMs) have achieved remarkable success on various aspects of human life. However, one of the major challenges in deploying these models is the substantial memory consumption required to store key-value pairs (KV), which imposes significant resource demands. Recent research has focused on KV cache budget allocation, with several approaches proposing head-level budget distribution by evaluating the importance of individual attention heads. These methods, however, assess the importance of heads independently, overlooking their cooperative contributions within the model, which may result in a deviation from their true impact on model performance. In light of this limitation, we propose CoKV, a novel method that models the cooperation between heads in model inference as a cooperative game. By evaluating the contribution of each head within the cooperative game, CoKV can allocate the cache budget more effectively. Extensive experiments show that CoKV achieves state-of-the-art performance on the LongBench benchmark using LLama-3-8B-Instruct and Mistral-7B models. Code is provided in https://***/nawei1010/CoKV. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution 39

Alleviating Performance Disparity in Adversarial Spatiotempo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bai, Songran Ji, Yuheng Liu, Yue Zhang, Xingwei Zheng, Xiaolong Zeng, Daniel Dajun Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Institute of Data Science School of Computing National University of Singapore Singapore

ISBN: (纸本)157735897X

Spatiotemporal Graph Learning (SGL) under Zero-Inflated Distribution (ZID) is crucial for urban risk management tasks, including crime prediction and traffic accident profiling. However, SGL models are vulnerable to adversarial attacks, compromising their practical utility. While adversarial training (AT) has been widely used to bolster model robustness, our study finds that traditional AT exacerbates performance disparities between majority and minority classes under ZID, potentially leading to irreparable losses due to underreporting critical risk events. In this paper, we first demonstrate the smaller top-k gradients and lower separability of minority class are key factors contributing to this disparity. To address these issues, we propose MinGRE, a framework for Minority Class Gradients and Representations Enhancement. MinGRE employs a multi-dimensional attention mechanism to reweight spatiotemporal gradients, minimizing the gradient distribution discrepancies across classes. Additionally, we introduce an uncertainty-guided contrastive loss to improve the inter-class separability and intra-class compactness of minority representations with higher uncertainty. Extensive experiments demonstrate that the MinGRE framework not only significantly reduces the performance disparity across classes but also achieves enhanced robustness compared to existing baselines. These findings underscore the potential of our method in fostering the development of more equitable and robust models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Risk management

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Blaming Humans and Machines: What Shapes People’s Reactions to Algorithmic Harm

arXiv

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arXiv 2023年

作者： Lima, Gabriel Grgić-Hlača, Nina Cha, Meeyoung School of Computing KAIST & Data Science Group IBS Korea Republic of Max Planck Institute for Software Systems Max Planck Institute for Research on Collective Goods Germany Data Science Group IBS & School of Computing KAIST Korea Republic of

Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. Building upon research suggesting that people blame AI systems, we investigated how several factors influence people’s reactive attitudes towards machines, designers, and users. The results of three studies (N = 1,153) indicate differences in how blame is attributed to these actors. Whether AI systems were explainable did not impact blame directed at them, their developers, and their users. Considerations about fairness and harmfulness increased blame towards designers and users but had little to no effect on judgments of AI systems. Instead, what determined people’s reactive attitudes towards machines was whether people thought blaming them would be a suitable response to algorithmic harm. We discuss implications, such as how future decisions about including AI systems in the social and moral spheres will shape laypeople’s reactions to AI-caused harm. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Performance Evaluation of Optical Wireless Communication System in Fog Weather Conditions 2

Performance Evaluation of Optical Wireless Communication Sys...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Venusamy, Kanagaraj Kannadhasan, S. Prakash, E. Priyadarsini, K. Study World College of Engineering Department of Electronics and Communication Engineering Chennai Coimbatore India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India School of Computing Srm Institute of Science & Technology Department of Data Science and Business Systems Chennai Kattankulathur India

ISBN: (纸本)9798350346640

Free space optical communication being an emerging technology, has Fog as a primary challenge on its way which makes fog modelling necessary to manage wireless networks. In this paper, an analytical study was made on the performance of five mathematical fog models employing the NRZ-OOK modulation scheme for transmission and a Si PIN photodiode as the photo receptor. This article focuses on numerous performance specifications including Signal to noise ratio [SNR], Bit error rate [BER] and channel capacity all against visibility under fog for propagations wavelengths of 850 nm and 1550 nm and simulations were carried out in MATLAB. The results indicate that the wavelength of 1550nm as more advantageous option over the others for FSO wireless links and an investigation on different models and their characteristics define their suitability for implementation. © 2023 IEEE.

关键词： Visibility

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Edge Learning-Based Efficient data Imputation of Water Quality 7th

Edge Learning-Based Efficient Data Imputation of Water Quali...

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7th International Conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Wang, Yongsheng Chen, Zhen Liu, Limin Gao, Jing Liu, Guangwen Xu, Zhiwei School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100000 China

ISBN: (纸本)9789819927296

Water quality monitoring is critical for developing effective surface water management strategies, whereas data preprocessing is the foundation for monitoring water quality. Water quality monitoring usually suffers from abnormal and missing values in the collected data of surface water quality, and thus an efficient data imputation method for water quality data is highly desired. To handle these challenges, we propose an edge learning-based data imputation method. By using VAE as the generator of WGAN-LP, the performance of value imputation of water quality is generalized and improved. In addition, we demonstrate the performance of the proposed method on two real-world datasets, collected from Wuliangsu Lake and Taihu Lake in Inner Mongolia. Compared with state of the art, the enhanced imputation accuarcy of water quality is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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OL4TeX: Adaptive Online Learning for Text Classification under Distribution Shifts

OL4TeX: Adaptive Online Learning for Text Classification und...

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IEEE International Conference on Big data

作者： Min-Seon Kim Ling Liu Hyuk-Yoon Kwon Graduate School of Data Science Seoul National University of Science and Technology Seoul Korea College of Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study presents an adaptive online learning method for text classification under distribution shifts. We formulate a typical neural network-based text classification model as multiple logical modules. By leveraging the characteristics of the modules, we introduce three novel indicators to effectively measure the degree of dynamic distribution shifts without evaluating the model. To enhance online learning, we tactically trade off between learning efficiency and accuracy based on distribution shifts measured in real time. To the best of our knowledge, this is the first effort to adapt the model to the preference of learning efficiency or accuracy for online text classification. Extensive experiments on real-world streaming text datasets show that our method outperforms the best static update strategy and state-of-the-art online text classification models. Our code and data are available at https://***/bigbases/online-learning-text.

关键词： Learning systems Adaptation models Accuracy Codes Text categorization Predictive models Big data Time measurement Real-time systems

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Execution of Convolution Coding Method for FPGA in Industrial Automation using VERILOG HDL 7

Execution of Convolution Coding Method for FPGA in Industria...

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7th International Conference on computing Methodologies and Communication, ICCMC 2023

作者： Venusamy, Kanagaraj Kannadhasan, S. Vimalesh, A. Chandramohan, P. Shanmugam, M. Priyadarsini, K. Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communication Engineering Study World College of Engineering Coimbatore Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science & Technology Kattankulathur Chennai India

ISBN: (纸本)9781665464086

In this paper, the performance study of the FPGA-based encoder is delivered and received with little errors given the large number of applications that employ digital communication to store and retrieve data recently. Forward Error Correction units a wireless communication system region that is essential. Several types of encryption methods have been measured, and the error is completely different. Convolution code writing approach, an honest technique used to rectify mistakes during the data transfer. Convolutional encoder area unit compelled to provide the output in the kind of register style with specified positions in the register. The areas in which the region is entirely arranged are likewise shown as arrows. The position of the registers provides probable state transitions wherein example, L is the register's length. Using these facts, the best way to execute of data transmission in digital communication channels is to correct errors in codes. FPGA device utilized in convolutional encoder's hardware implementation. Vivado and XILINX ISE are usedfor performance analysis. © 2023 IEEE.

关键词： Convolution

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Movie Reviews using Sentiment Analysis with Natural Language API

Movie Reviews using Sentiment Analysis with Natural Language...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： Athish V P Rajeswari D Sree Nandha S S Dept. of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

Sentiment Analysis is the process of analyzing sentiment of a piece of text by identifying polarity or writer’s attitude towards any topic or product such as positive, neutral or negative. This paper uses sentiment analysis on movie reviews in twitter data, which is the most dominant public social media and its data is accessible using their own Twitter API. Words and misspellings make sentiment analysis for twitter data, harder due to the existence of slang words and bad spelling. This work uses Hadoop for the various features it provides over the API. The proposed work uses the knowledge base approach using lexica analyzer for sentiment analysis of movie reviews. The assigned score for movies is 100. The proposed work get all the positive, negative and neutral comments to judge "how good" or "how bad" the movie is.

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High efficient net gain and low noise figure based vertical cavity semiconductor optical amplifiers for wavelength division multiplexing applications

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Journal of Optical Communications 2025年第2期46卷 247-256页

作者： Prabu, Ramachandran Thandaiah Balasubramanian, Arunsundar Balakrishnan, Nithyasundari Jebanazer, Jeneetha Kandasamy, Mohana Sundaram Govindaswamy, Nishanthi Mahmoud, Rashida Maher Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Tamilnadu Chennai India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai93104 India Department of ECE S.A. Engineering College Tamilnadu Chennai India Department of ECE Panimalar Engineering College Tamilnadu Chennai India Department of CSE R.M.D. Engineering College Kavarapettai Tamilnadu Chennai India Department of CSE R.M.K. Engineering College Kavarapettai Tamilnadu Chennai India Sohag Institute of Technology Sohag Egypt

This paper has demonstrated the high efficient net gain and low noise figure based vertical cavity semiconductor light amplifiers for wavelength division multiplexing applications. Previous study on the chip reflective gain variations versus SOA current under temperature effects is clarified. We have transferred light semiconductor amplifiers for wavelength multiplexing schemes applications. Amplifier output power is demonstrated with bias current and amplifier active layer region length variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The amplifier output noise variations are clarified against the bias current and amplifier active layer region width variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The output OSNR variations are studied clearly and deeply against the bias current and amplifier active layer region length variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The signal loss is demonstrated versus both active layer width/length and temperature based optimum 30 % gallium core dopant ratio and 28 % arsenide cladding dopant ratio at optimum input signal power of 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. SOA amplifier output power can be enhance with the management of both bias current and input signal power and the reduction of active amplifier length. The amplifier output noise can be enhance with the management of both bias current and input signal power and the reduction of active amplifier width. Output OSNR system can be improved with the management of both bias current and input signal power and the reduction of active amplifier length. The SOA amplifier gain can be improved with the management of both bias current and input signal power and the reduction of active amplifier length. The SOA amplifie

关键词： Noise figure

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