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10th Annual Conference of Advanced computer Architecture, ACA 2014

作者： Pei, Songwen Kim, Myoung-Seo Gaudiot, Jean-Luc Xiong, Naixue Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai 200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Department of Electrical Engineering and Computer Science University of California Irvine CA 92697 United States School of Computer Science Colorado Technical University Springs CO 80907 United States

ISBN: (纸本)9783662444900

Future heterogeneous systems will integrate CPUs and GPUs on a single chip to achieve high computing performance as well as high throughput. In general, it would discard the current discrete pattern and will build a uniformed shared memory system avoiding explicit data movement among CPUs and GPUs connected by high throughput NoC. We propose a scalable cache coherence solution Fusion Coherence for Heterogeneous Kilo-core System Architecture by integrating CPUs and GPUs on a single chip to mitigate the coherence bandwidth side effects of GPU memory requests as well as overhead of copying data among memories of CPUs and GPUs. The Fusion Coherence coalesces L3 data cache of CPUs and GPUs based on a uniformed physical memory, further integrates a region directory and cuckoo directory into two levels of cache coherence directory without modifying cache coherence protocol. According to the experimental results with a subset of Rodina benchmarks, it is effective to decrease the overhead of data transfer and get an average execution speedup by 2.4x. The highest speedup is approximate to 4x for data-intensive applications. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Program processors

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PIM-FFL: Evaluating Participation Index Metrics for Fairness Enhancement in Federated Learning 4

PIM-FFL: Evaluating Participation Index Metrics for Fairness...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Batool, Hina Xu, Jiuyun Ur Rehman, Ateeq Kaur, Gurjot Bharany, Salil College of Computer Science and Technology China University of Petroleum China School of Computing Gachon University Seongnam13120 Korea Republic of Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331540364

Federated learning (FL) is a technique that allows multiple devices to train a machine learning model without sharing their raw data, this preserves user privacy. However, traditional FL often suffers from problems like nodes failing to participate in training due to resource limitations. This negatively impacts the model's performance and fairness, making it harder for all devices to benefit equally. Ensuring fairness in FL is challenging due to the significant differences in clients' data quality, data size, and contribution levels. A notable issue arises when prime contributing clients do not receive fair rewards while underperforming still benefits. This disparity leads to an inequity causing complexity among contributors. As a result, clients may feel unappreciated, leading to a decrease in their interest in participating which ultimately compromises the complete reliability and effectiveness of the system. To address these challenges, a client clustering method is presented based on their participation index, which ensures that prime participants receive better model updates rather than underperformers which makes the system more reliable. Additionally, the Double Deep Q-Networks (DDQN) method is employed, as it determines the best actions through learning from past experiences and updating Q-values, which dynamically update participation count and reduce the model biases. Our experiments show that this clustering method substantially enhances fairness in Federated Learning, fostering a more efficient and cooperative learning environment that outperforms traditional methods like FedAvg and FedProx. © 2024 IEEE.

关键词： Federated learning

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Resting-state EEG in the Vestibular Region Can Predict Motion Sickness Induced by a Motion-Simulated in-car VR Platform

Resting-state EEG in the Vestibular Region Can Predict Motio...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Li, Gang Wang, Yu-Kai McGill, Mark Pohlmann, Katharina Brewster, Stephen Pollick, Frank School of Psychology and Neuroscience University of Glasgow Glasgow United Kingdom School of Computer Science University of Technology Sydney Sydney Australia School of Computing Science University of Glasgow Glasgow United Kingdom Kite Research Institute University of Toronto Toronto Canada

ISBN: (纸本)9781665430654

Monitoring in-car VR motion sickness (VRMS) by neurophysiological signals is a formidable challenge due to unavoidable motion artifacts caused by the moving vehicle and necessary physical movements by the user to interact with the VR environment. Therefore, this paper for the first time investigates if resting-state neurophysiological features and self-reports of stress levels collected prior to exposure to a motion-simulated in-car VRMS induction platform could predict final motion sickness ratings. Our results of linear regression modeling show that the traditional EEG power spectrum was the only resting-state feature set that could predict in-car VRMS ratings. Further, the best regression result was achieved by beta power spectrum in the left parietal area with adjusted R2=22.6% versus 11.6% in the right. This result not only confirmed the left parietal involvement in motion sickness susceptibility observed in a previous resting-state fMRI study, but also advanced that methodology to mobile neurotechnologies, represented by mobile EEG, referenced by other types of resting-state features. Together, this study may offer a new mobile neurotechnology-based approach to predict passengers' VRMS levels before they start to use VR apps in a moving vehicle. © 2023 IEEE.

关键词： cognitive load EEG in-car VR motion sickness linear regression stress level

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A haptic device-based reproduction system of active finger movement and its evaluation using sensory evoked potentials 15

A haptic device-based reproduction system of active finger m...

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15th Biomedical Engineering International Conference, BMEiCON 2023

作者： Ozawa, Yusuke Yoshimura, Natsue Uehara, Kazumasa Seki, Kazuhiko Tokyo Institute of Technology School of Engineering Yokohama Japan Tokyo Institute of Technology School of Computing Yokohama Japan Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan National Center of Neurology and Psychiatry Department of Neurophysiology Kodaira Japan

ISBN: (纸本)9798350345247

To develop an experimental system that could be used for physiological experiments on voluntary finger movements in humans, we developed an experimental system that can realize passive finger movements by reproducing recorded users' own active movement trajectories using a haptic device called SPIDAR-GCC. The efficacy of the system was verified through high agreement of the finger trajectories between active and passive movements and no muscle activations during the passive movements. We further confirmed that high signal-to-noise ratio sensory evoked potentials (SEPs) derived from median nerve stimulation of the wrist was observed by electrical stimulation exceeding motor threshold without muscle fatigue throughout the experiment. As N20/P25 component of SEPs from 20 participants showed amplitude changes according to the movement conditions, these results suggested that the proposed experimental system has the capability to be utilized as a physiological experimental system for elucidating the neural control mechanisms of voluntary movements. © 2023 IEEE.

关键词： Cell proliferation

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Cabinet Tree: an orthogonal enclosure approach to visualizing and exploring big data

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Journal of Big Data 2015年第1期2卷 1-18页

作者： Yang, Yalong Zhang, Kang Wang, Jianrong Nguyen, Quang Vinh School of Computer Science and Technology Tianjin University 92 Weijin Road Tianjin300072 China School of Computer Software Tianjin University 92 Weijin Road Tianjin300072 China Caulfield School of Information Technology Monash University 900 Dandenong Road Caulfield East Melbourne3145 Australia Department of Computer Science The University of Texas at Dallas 800 West Campbell Road RichardsonTX75080-3021 United States MARCS Institute and School of Computing Eng. and Maths University of Western Sydney 179 Locked Bag South Penrith DC Sydney2751 Australia

Treemaps are well-known for visualizing hierarchical data. Most related approaches have been focused on layout algorithms and paid little attention to other display properties and interactions. Furthermore, the structural information in conventional Treemaps is too implicit for viewers to perceive. This paper presents Cabinet Tree, an approach that: i) draws branches explicitly to show relational structures, ii) adapts a space-optimized layout for leaves and maximizes the space utilization, iii) uses coloring and labeling strategies to clearly reveal patterns and contrast different attributes intuitively. We also apply the continuous node selection and detail window techniques to support user interaction with different levels of the hierarchies. Our quantitative evaluations demonstrate that Cabinet Tree achieves good scalability for increased resolutions and big datasets. © 2015, Yang et al.

关键词： Enclosures

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A Holistic Client Selection Scheme in Federated Mobile CrowdSensing Based on Reverse Auction 25

A Holistic Client Selection Scheme in Federated Mobile Crowd...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zheng, Zhaohua Qin, Zhaobin Li, Deshun Li, Keqiu Xu, Guangquan College of Intelligence and Computing Tianjin University Tianjin300350 China School of CyberSpace Security School of Cryptology Hainan University Haikou570228 China School of Computer Science and Technology Hainan University Haikou570228 China

ISBN: (纸本)9781665405270

Federated Mobile CrowdSensing is applied to collect massive sensory data and exploits the computing power of mobile devices brought by their embedded specialized computing engines (e.g., Neural Engine in iPhone) to train machine learning (ML) models. However, the heterogeneity of mobile devices includes significant differences in the size and quality of datasets, different computing power, and some unreliable clients using unreliable data for training. The heterogeneity of mobile devices reduces FL's performance. Therefore, selecting high-quality clients for Federated learning (FL) is vital. This study proposes a client selection scheme based on the reverse auction. First, each client's training time is predicted, the total FL time threshold is optimized, and the reputation value is calculated based on the historical performance of each client. Then, each client's current computing power and dataset size are converted into an efficiency value. Finally, the selection value of each client is calculated based on the efficiency value and reputation value. The results of the experiments show that our scheme can select high-quality clients. Compared with FedRep, our scheme can reduce training time by 91.5%. Compared with FedEff, our scheme can reduce communication rounds by 87.5%. In the same communication rounds (5000), our scheme has higher accuracy than RandomFL, and the average accuracy is improved by about 4.4%. © 2022 IEEE.

关键词： Efficiency

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Enhancing Student Outcomes with LSTM-CNN and Data Analytics in Higher Education

Enhancing Student Outcomes with LSTM-CNN and Data Analytics ...

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Sangeetha, S.K.B. Maheswari, S. Rajendran, Sukumar Mathivanan, Sandeep Kumar Balusamy, Balamurugan Verma, Meenakshi SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India VIT Bhopal University School of Computing Science Engineering and Artificial Intelligence Madhya Pradesh India Galgotias University School of Computer Science and Engineering Greater Noida203201 India India Bengaluru Campus Pune India

ISBN: (纸本)9798350390049

With a focus on the use of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) approaches to predict students' academic performance, the study provides an extensive review of the literature on the application of artificial neural networks (ANNs) and machine learning (ML) techniques. The study highlights the possible advantages of implementing cutting-edge technology innovations like analytics and data mining in learning environments. The analysis reveals that the reviewed literature primarily focuses on higher education. The results show that LSTM-CNN and data mining methodologies are consistently integrated, suggesting a trend in using these combined approaches to evaluate academic progress. No observable patterns were found in spite of the variability in the input variables, which was dictated by the study's context and the availability of data. It is acknowledged, nevertheless, that there is a dearth of hard data regarding the effective application of these strategies for raising student achievement and outcomes. The study emphasizes how critical it is to close the theoretical and practical divides regarding the limited applicability of LSTM-only CNN models in practical educational contexts. It highlights the need for more research to address this discrepancy and suggests putting more effort into creating and putting into practice strategies that directly improve student performance and help schools achieve their goals. © 2024 IEEE.

关键词： Convolutional neural networks

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Development of Portable Air Quality Index (AQI) and Emergency Vehicles Preemption Prototype Based on Internet of Mobile Things (IoMT) 20

Development of Portable Air Quality Index (AQI) and Emergenc...

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3rd International Conference on Information science and System, ICISS 2020

作者： Anjum, Shaik Shabana Noor, Rafidah Md Ahmedy, Ismail Khamis, Norazlina Binti Anisi, Mohammad Hossein Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

ISBN: (纸本)9781450377256

The technological advancements of the Internet of Things (IoT) in the recent past have facilitated immense progress towards mitigation of environmental pollution through smart transportation systems and solutions. In particular, communication to the commuters about the traffic ahead or occurrences of congestion has been envisioned to play a major role in outsmarting traffic through mobile applications giving rise to the emergence of the Internet of Mobile Things (IoMT). However, the existing mobile applications that serve as traffic reporting solutions still face major issues such as fixed route suggestions, longer delays during busy hours or emergencies, inefficient prompting of road accidents and heavy traffic en route to a particular destination. This research aims at providing solutions for notifying the commuters with updates on the traffic based upon the Air Quality Index (AQI) of the routes towards the destination and also about the approach of emergency vehicles. The cross-platform mobile application in this way enables the user to opt for a route with good air quality so that the more congested routes are avoided thereby mitigating the air pollution induced by road traffic. The experimental testing and validation of the proposed methodology are applied for areas belonging to Greater Kuala Lumpur. The various timings divided according to peak and non-peak hours are experimentally tested for analyzing the parameters of traffic usage and pattern through the mobile application. The outcome of the experiments has showed that when traffic flow is modelled and governed through vehicular emissions and concentrations of air pollutants, nearly 75% of the congested traffic is reduced thereby, giving rise to pollution-free environment as well as mitigation of urban heat island (UHI) effect that is formed through vehicular heat generation and difference in temperatures. On the other hand, the approach of emergency vehicles also prompts the commuters to avoid panic. © 20

关键词： Traffic congestion

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EgpuIP: An Embedded GPU Accelerated Library for Image Processing 24

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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24th IEEE International Conference on High Performance computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Luhan Jia, Haipeng Zhang, Yunquan Li, Kun Wei, Cunyang Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research China

ISBN: (纸本)9798350319934

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm;2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm;3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm;4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to 19×88×, and 3× speedup respectively. © 2022 IEEE.

关键词： Graphics processing unit

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