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MemSort: In-Memory Sorting Architecture

MemSort: In-Memory Sorting Architecture

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Rui Liu Xiaoyu Zhang Xinyu Wang Feng Min Zhejian Luo Xiaoming Chen Yinhe Han Minghua Tang School of Materials Science and Engineering Xiangtan University Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Sciences

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Sorting is one of the most fundamental operations in computer programming and used in countless algorithms. The performance of traditional von Neumann computers running sorting is limited by the bandwidth between memories and processors. computing-in-memory (CiM) is a promising technology which has the potential to solve the “memory wall” bottleneck. CiM is suitable for data-intensive applications, and it is ideal for accelerating large-scale data sorting. In this paper, we propose a novel in-memory sorting accelerator, named MemSort, based on a proposed in-memory comparison array design based on emerging non-volatile devices. MemSort supports three sort operations including counting sort, merging sort, and the combination of counting sort and merging sort. We build a performance model for the combination sort which enables flexible allocation of resources under given constraints to meet the requirements of various applications for sorting. The evaluation results show that MemSort shows significant performance improvement and energy efficiency at both the system level and application level when processing large-scale data sorting. Compared with the CPU implementation, MemSort achieves energy savings of 19.69-72.75x and speedups of 24.48-38.58 x with the same power constraint. MemSort's throughput is at least 4.86 x higher than that of the recent FPGA-based sorting accelerator FANS. MemSort exhibits more than 11 x throughput and 4.03 x area efficiency, compared with the recent CiM - based sorting accelerator, RIME.

关键词： Fans Merging Energy conservation Bandwidth Throughput Energy efficiency Central Processing Unit Arrays Time complexity Sorting

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Deploying Hybrid MAELM Approach for Human Emotion Detection Through Speech and Facial Expressions 3

Deploying Hybrid MAELM Approach for Human Emotion Detection ...

引用

3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Senthil Kumar, K. Rukmani Devi, S. Ranjan, Nidhi Rath, Gitika Indira, G. Nishant, Neerav SRM Institute of Science and Technology School of Computing Department of Networking and Communications College of Engineering and Technology Chennai India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India Computer Engineering Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai India Marri Laxman Reddy Institute of Technology and Management Department of CSE Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350343632

Emotion recognition is one of the most fascinating emerging fields of research. It's useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots' ability to see and communicate with one another. Human emotions can be recognized through both visual and audible cues. Facial expressions are one of the most accurate indicators of a person's emotional state. Data preprocessing, feature extraction, and model training are the first three steps of the proposed methodology. Image resizing, a median filter, noise removal, and histogram equalization are all components of preprocessing. Gaussian mixture models and the gray level co-occurrence matrix are being used for feature extraction. After the extraction of features, models with those features are trained using MA-ELM. The proposed method outperforms PCA and ELM, two of the most popular alternatives. © 2023 IEEE.

关键词： Gaussian Mixture Models(GMM) Gray Level Co-Occurrence Matrix (GLCM) Principal Component Analysis (PCA)

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ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension 29

ET5: A Novel End-to-end Framework for Conversational Machine...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Zhang, Xiao Huang, Heyan Chi, Zewen Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China

Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning;(2) span extraction if required by the above decision;(3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Decision making

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Cultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning 34

Cultivating Expertise: Unravelling Type 2 Diabetes Associati...

引用

34th Australasian Conference on Information Systems, ACIS 2023

作者： Omar, Adel Beydoun, Ghassan Win, Khin Shukla, Nagesh Jelinek, Herbert F. Elias, Hector School of Systems Management and Leadership University of Technology Sydney Sydney Australia School of Computing and Information Technology Wollongong University Wollongong Australia Department of Business Strategy & Innovation Griffith University Griffith Australia Department of Biomedical Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Wollongong University College Wollongong University Wollongong Australia

Type 2 diabetes is a chronic disease that is caused by a combination of genetic and environmental factors, with social determinants playing a significant role. These social determinants include but not limited to factors such as education level, occupation, family history, ethnicity and place of residence etc. The prevalence of Type 2 diabetes (T2D) necessitates inventive management strategies. This study explores the potential of utilizing Ripple Down Rules (RDR) in comparison with Machine Learning (ML) approaches for the incremental development of a Knowledge-Based System (KBS). The aim is to create a robust Decision Support System (DSS) for effective T2D management, focusing on the dynamic influence of evolving social determinants. The research outcomes substantiate the viability of this approach. The KBS, fortified by RDRs, demonstrated notable performance metrics. Specifically, the system achieved an impressive accuracy rate of 90.2%, accompanied by a specificity of 96.9%. The sensitivity, crucial for identifying potential T2D cases, reached 73.6%, indicating the system's proficiency in recognizing diverse instances. Copyright © 2023 Omar, Beydoun, Win, Shukla, Jelinek and Elias.

关键词： Decision support systems

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Analysis and Treatment of Missing Data in Happiness Score Using Machine Learning 21

Analysis and Treatment of Missing Data in Happiness Score Us...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Touati, Wassim El Touati, Yamen Hadj-Alouane, Nejib Ben Jemai, Abderrazek Polytechnic School of Tunisia Carthage University SERCOM Laboratory Tunis Tunisia Northern Border University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia ENIT University of Tunis EI Manar OASIS Laboratory Tunisia American University in Dubai Electrical Engineering and Computer Engineering Department Dubai United Arab Emirates

ISBN: (纸本)9798350378078

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible. © 2024 IEEE.

关键词： Adversarial machine learning

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Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models 41

Task Groupings Regularization: Data-Free Meta-Learning with ...

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41st International Conference on Machine Learning, ICML 2024

作者： Wei, Yongxian Hu, Zixuan Shen, Li Wang, Zhenyi Li, Yu Yuan, Chun Tao, Dacheng Tsinghua Shenzhen International Graduate School Tsinghua University China College of Computing & Data Science Nanyang Technological University Singapore School of Cyber Science and Technology Sun Yatsen University China University of Maryland College Park United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen tasks. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. In this paper, we empirically and theoretically identify and analyze the model heterogeneity in DFML. We find that model heterogeneity introduces a heterogeneity-homogeneity trade-off, where homogeneous models reduce task conflicts but also increase the overfitting risk. Balancing this trade-off is crucial for learning shared representations across tasks. Based on our findings, we propose Task Groupings Regularization, a novel approach that benefits from model heterogeneity by grouping and aligning conflicting tasks. Specifically, we embed pre-trained models into a task space to compute dissimilarity, and group heterogeneous models together based on this measure. Then, we introduce implicit gradient regularization within each group to mitigate potential conflicts. By encouraging a gradient direction suitable for all tasks, the meta-model captures shared representations that generalize across tasks. Comprehensive experiments showcase the superiority of our approach in multiple benchmarks, effectively tackling the model heterogeneity in challenging multi-domain and multi-architecture scenarios. Copyright 2024 by the author(s)

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Military Text Triplet Extraction Model Combining ERNIE and Conditional Normalization 3

Military Text Triplet Extraction Model Combining ERNIE and C...

引用

3rd International Conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Guo, Dongliang Wang, Licai Liu, Zhonglin Du, Tianfeng North China Institute of Computing Technology Smart Plat Rd Department Beijing China School of Mangement And Engineering Capital University of Economics And Business Beijing China

ISBN: (纸本)9781665479974

There are a large number of information resources that can be analyzed and mined in the existing military information system. Extracting the key knowledge from these resources is of great significance for knowledge response and reasoning. Aiming at the problems of error propagation and relationship overlap in samples in the process of military text triplet extraction, this paper proposes a triplet extraction model combining Ernie and conditional normalization, which introduces Ernie knowledge enhanced pre training extraction model, and uses 'semi pointer semi Annotation' structure and conditional layer norm to make the model have better expression ability. The experimental results show that on the manually labeled military news data set, the extraction effect of the model is significantly better than the traditional pipeline extraction model and the joint extraction model based on a single pre training model, and the F1 value reaches 81.23%. © 2022 IEEE.

关键词： Extraction

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Can Skincare Products Impact mmWave Exposure Effects on Skin?

Can Skincare Products Impact mmWave Exposure Effects on Skin...

引用

IEEE World Forum on Internet of Things (WF-IoT)

作者： Negin Foroughimehr Paul R. Stoddart Ali Yavari 6G Research and Innovation Laboratory School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

In preparation for the implementation of 5 G spectrum in the millimeter wave (mmWave) region across different countries, understanding its potential health impacts is crucial. This study investigates the protein denaturation of chicken skin under exposure to radiofrequency (RF) radiation at 26 gigahertz ($\mathbf{G H z}$), applying various products such as sunscreen and moisturiser. Samples underwent RF exposure within an anechoic chamber. Raman spectroscopy was used to analyze the samples, focusing on protein-related peaks. The data were compared to a reference sample, revealing a slight change in the amide III peak pattern. Further investigation with different products and a range of ingredients is needed to thoroughly explore this phenomenon.

关键词： Proteins Anechoic chambers Raman scattering Focusing Skin Internet of Things Millimeter wave communication Radiofrequency exposure

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Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Con...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Huarong Shan, Jinjun Li, Xing Yu, Hongnian School of Internet of Things Engineering Jiangnan University Wuxi214122 China York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada School of Electrical Engineering and Intelligentization Dong-guan University of Technology Dongguan523000 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9781665410205

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches. © 2024 IEEE.

关键词： Sliding mode control

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Blockchain-Enabled Energy Trading in Microgrids for Sustainable computing Infrastructure

Blockchain-Enabled Energy Trading in Microgrids for Sustaina...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： M. L. Sworna Kokila Sunitha R P. Kalyanakumar J. Relin Francis Raj R. Santhana Krishnan S. Gopikumar Department of Computing Technologies Faculty of Engineering and Technology School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Rajarajeswari College of Engineering Bangalore India Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Civil Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This research investigates Blockchain-enabled energy trading within microgrids to enhance sustainable computing infrastructure. A novel algorithm, GridCoin, is proposed and evaluated against existing methodologies including Optimal Power Flow (OPF), Microgrid Energy Management System (MEMS), and Hierarchical Control Algorithms (HCA). The study employs simulation metrics such as Transaction Throughput, Energy Trading Efficiency, and Transaction Latency to compare the performance of these algorithms. GridCoin demonstrates superior capabilities in optimizing energy trading operations, achieving higher transaction throughput, improved efficiency in energy utilization, and reduced transaction latency. These results highlight the potential of Blockchain technology to revolutionize energy markets by providing secure, transparent, and decentralized transaction mechanisms. Future research directions include enhancing optimization techniques, integrating renewable energy sources, addressing scalability and security challenges, conducting real-world deployments, and analyzing economic and policy implications. This study contributes to advancing sustainable computing infrastructure through innovative Blockchain-based solutions.

关键词： Micromechanical devices Renewable energy sources System performance Microgrids Green computing Throughput Blockchains Energy management systems Optimization Load flow

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