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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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ConnectedUNets++: Mass Segmentation from Whole Mammographic Images 17th

ConnectedUNets++: Mass Segmentation from Whole Mammographic...

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17th international symposium on Visual computing, ISVC 2022

作者： Sarker, Prithul Sarker, Sushmita Bebis, George Tavakkoli, Alireza Department of Computer Science and Engineering University of Nevada Reno United States

ISBN: (纸本)9783031207129

Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge. In this context, UNet is one of the most advanced medical image segmentation models, with promising results in mammography. Despite its excellent overall performance in segmenting multimodal medical images, the traditional U-Net structure appears to be inadequate in various ways. There are certain U-Net design modifications, such as MultiResUNet, Connected-UNets and AU-Net, that have improved overall performance in areas where the conventional U-Net architecture appears to be deficient. Following the success of UNet and its variants, we have presented two enhanced versions of the Connected-UNets architecture: ConnectedUNets+ and ConnectedUNets++. In ConnectedUNets+, we have replaced the simple skip connections of Connected-UNets architecture with residual skip connections, while in ConnectedUNets++, we have modified the encoder decoder structure along with employing residual skip connections. We have evaluated our proposed architectures on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Hyperdimensional computing vs. Neural Networks: Comparing architecture and Learning Process

Hyperdimensional Computing vs. Neural Networks: Comparing Ar...

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IEEE international symposium on Quality Electronic Design

作者： Dongning Ma Cong Hao Xun Jiao Villanova University Georgia Institute of Technology

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

Hyperdimensional computing (HDC) has obtained abundant attention as an emerging non von Neumann computing paradigm. Inspired by the way human brain functions, HDC leverages high dimensional patterns to perform learning tasks. Compared to neural networks, HDC has shown advantages such as energy efficiency and smaller model size, but sub-par learning capabilities in sophisticated applications. Recently, researchers have observed when combined with neural network components, HDC can achieve better performance than conventional HDC models. This motivates us to explore the deeper insights behind theoretical foundations of HDC, particularly the connection and differences with neural networks. In this paper, we make a comparative study between HDC and neural network to provide a different angle where HDC can be derived from an extremely compact neural network trained upfront. Experimental results show such neural network-derived HDC model can achieve up to 21% and 5% accuracy increase from conventional and learning-based HDC models respectively. This paper aims to provide more insights and shed lights on future directions for researches on this popular emerging learning scheme.

关键词： Training Analytical models Computational modeling computer architecture Brain modeling Energy efficiency Task analysis

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Thermal-aware Energy Efficient Task Scheduling Framework

Thermal-aware Energy Efficient Task Scheduling Framework

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IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Jian Nong Jia Chen Yinqing Wang Wei Qin Xi He Faculty of Innovation Engineering-School of Computer Science and Engineering Macau University of Science and Technology Macau China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China High Performance Computing Lab Wuzhou University Wuzhou China

Large energy consumption in data centers has become a challenging problem with the emergence of cloud computing and large scale data centers. In this paper, we present an architectural framework for thermal-aware resource management while considering energy efficiency. The framework consists of a layered architecture and integrates a set of easy-to-use client tools and a thermal-aware task management middleware to schedule tasks based on thermal conditions within a cluster and among different data centers. As part of this paper we focus on the development of a thermal-aware task scheduling component for a single data center. This component is fundamental to our future activities, while considering to balance the temperature distribution in a single data center, thus implicitly minimizing energy cost in data centers.

关键词： Data centers Temperature distribution Schedules Processor scheduling Thermal management Ubiquitous computing Energy efficiency

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A Regression Model to Secure Cloud Datacenter Against DDoS Attack

A Regression Model to Secure Cloud Datacenter Against DDoS A...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Hamad Al-Thani Jarah Al-Otaibi Saad Al-Homaidan Shafi'i M. Abdulhamid Nadin Rana Abdullah H. Al-Ghushami Department of Information Technology Community College of Qatar Lusail Qatar Department of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Cloud computing is a major area for conventional IT executions since it offers insignificant expense and admittance to “pay-as-you-go” registering capacities and concerning requests by clients. The cloud offers systems, stages, and software through cloud climate models. There are numerous security-related issues in this domain. A distributed denial of service (DDoS) attack can be named a “ tsunami ” due to its devastating effect. DDoS attacks are a ceaseless danger to present-day organizations, causing serious business fiascos, client issues, and monetary misfortunes. This study utilizes a machine learning strategy named the Logistic Regression model to give security to servers working in a cloud climate against DDoS. This assists with giving security and is valuable in updating the strong advancement of associations. WEKA tool is used to develop a regression model. The data set used is NSL-KDD. The model helped categorize traffic as normal or anomaly with high accuracy and precision.

关键词： Cloud computing Smart cities Denial-of-service attack Tsunami Software Security Servers computer crime Robots Meteorology

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Optimizing Average AoI with Directional Charging for Wireless-Powered Network Edge 31

Optimizing Average AoI with Directional Charging for Wireles...

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31st IEEE/ACM international symposium on Quality of Service, IWQoS 2023

作者： Chen, Quan Guo, Song Xu, Wenchao Li, Jing Shi, Tuo Cai, Zhipeng Gao, Hong School of Computers Guangdong University of Technology Guangzhou China Hong Kong Polytechnic University Department of Computing Hong Kong College of Intelligence and Computing Tianjin University Tianjin China Georgia State University Department of Computer Science Atlanta United States School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (纸本)9798350399738

Age of Information, which emerged as a new performance metric to quantify the data freshness, has drawn increasing interests recently. At wireless-powered network edge, the source nodes can only transmit their updates after being charged ready, which means the system AoI is not only determined by the update transmission strategies, but also the charging strategies. However, the existing works either only focused on the point of scheduling of update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average $A$ oI optimization problem at wireless-powered network edge without such limitations in this paper. Firstly, the lower bound of the weighted sum of average $A$ oI of the whole network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time allocation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed, which can obtain a periodical charging schedule including a charging period $T$ and the charging orientation assigned to each time slot within $T$, while the average AoI is bounded. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI. © 2023 IEEE.

关键词： Scheduling algorithms

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Clinical application of remote fetal heart rate monitoring system for AI early warning1 25

Clinical application of remote fetal heart rate monitoring s...

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25th IEEE international Conferences on high performance computing and Communications, 9th international Conference on Data Science and Systems, 21st IEEE international Conference on Smart City and 9th IEEE international Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhuang, Chunyu Chen, Xiaojing Lin, Chunlan Han, Yan Zhang, Qingmei Du, Qiongliang Duan, Yucong Chen, Jiacheng Haikou Maternal and Child Health Care Hospital Nursing Department Hainan Haikou570203 China Haikou Maternal and Child Health Care Hospital Neonatology Hainan Haikou570203 China Haikou Maternal and Child Health Care Hospital Pediatric Intensive Care Unit Hainan Haikou570203 China School of Computer Science and Technology Hainan University Hainan Haikou570203 China Hainan General Hospital Department of Hepatobiliary and Pancreatic Surgery Hainan Haikou570311 China

ISBN: (纸本)9798350330014

To compare the safety, effectiveness and effect of the AI early warning system and the traditional fetal monitoring system. Methods: Establish and deploy AI remote fetal monitoring system;From January 1, 2022 to December 31, 2022, 440 pregnant women in the third trimester of pregnancy admitted to the Maternal and Child Health Hospital of Haikou City and meeting the admission criteria were selected and randomly assigned to the artificial intelligence early warning remote fetal visit group 220 cases and the traditional group 220 cases. In the AI group, remote fetal monitoring system was used for examination and treatment, while in the traditional group, pregnant women went to the hospital for examination and delivery according to the usual standard procedures. The effectiveness and safety of the AI-early warning impromptu fetal heart test system were observed, and the rates of perinatal adverse outcomes (low birth weight, perinatal death, premature delivery, neonatal asphyxia, cord blood oxygen saturation and fetal distress) of pregnant women in the two groups were compared. Results: In the test of artificial intelligence early warning fetal monitoring system, the AI group detected 332 normal fetal cardiogram cases of pregnant women, 206 suspected pathological changes in 18 cases;In the traditional group, 421 cases of normal fetal cardiogram were detected, 117 cases of suspected pathological changes were detected in 16 cases. The sensitivity and specificity of normal pregnant women (true positive was normal group, false negative was suspicious + lesion) were 81.55% and 96.98% respectively. The sensitivity and specificity of pregnant women with abnormal fetal monitoring map were 100% and 82%. The low birth weight rate was 6/220 in the AI group and 13/220 in the control group, with a total value of 2.695 (p value 0. 101). Perinatal death was 0 in both groups. The rate of preterm infants in AI group was 7/220, compared with 15/220 in control group, and peason2 was 3.062,

关键词： Obstetrics

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Frequent and high Utility Itemsets Mining Based on Bi-Objective Evolutionary Algorithm with An Improved Mutation strategy

Frequent and High Utility Itemsets Mining Based on Bi-Object...

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international symposium on Distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Chongyang Li School of Artificial Intelligence & Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9781665454636

Frequent and high utility itemsets mining (FHUIM) is one of the important tasks in pattern mining. In order to solve the exponential search space and parameter setting problems that traditional HUIM algorithms encountered, the task of FHUIM was reformulated as a bi-objective problem that can be solved by multi-objective evolutionary algorithms (MOEAs). However, the search efficiency of the MOEAs may become lower when the total distinct items, the number of transactions, and the average length of transactions in the database are larger. To further improve the efficiency of MOEAs for FHUIM, we proposed FHUIM based on bi-objective evolutionary algorithm with an improved mutation strategy (FHUIM-BOEA-IMS). In FHUIM-BOEA-IMS, an improved mutation strategy is proposed to make the items with higher support and utility more likely to be saved in population, by which the FHUIs are more likely to be searched. The results on four popular datasets show that the proposed FHUIM-BOEA-IMS has better performance than the compared baseline in the task of FHUIM in terms of the convergence and final solutions.

关键词： Itemsets Databases Sociology Evolutionary computation Search problems Task analysis statistics

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Prediction of high Heating Value Using ANN Technique Aftermath Natural Disasters

Prediction of High Heating Value Using ANN Technique Afterma...

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ACS/IEEE international Conference on computer Systems and Applications

作者： Disha Thakur Sanjay Kumar Bhisham Sharma Imed Ben Dhaou Department of Civil Engineering UIT Himachal Pradesh University Shimla India Department of Electrical Engineering UIT Himachal Pradesh University Shimla India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Natural disasters, particularly earthquakes and floods, can generate substantial waste, presenting challenges for waste management and energy recovery. This study explores the application of machine learning, specifically Artificial Neural Networks (ANN), to optimize Waste-to-Energy (WTE) conversion processes for disaster-generated Municipal Solid Waste (MSW). Using data from Sundernagar City, India, we constructed and evaluated multiple ANN models (collectively named ANNSNAGAR) to estimate high Heating Value, HHV, based on ultimate/elemental analysis of MSW. The models were rigorously assessed using statistical error metrics. Our best-performing network, with a 9-50-1 architecture (ANNMS), achieved a mean absolute percentage error (MAPE) of 1.47%, demonstrating high predictive accuracy.

关键词： Waste management Heating systems Waste materials Accuracy Disasters Urban areas Artificial neural networks Machine learning Predictive models Data models

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Mental Wear and Tear: An Exploratory study on Mental Fatigue in Video Games Using the Example of League of Legends 1

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21st international Conference on Entertainment computing (ICEC)

作者： Bikas, Ioannis Pfau, Johannes Daenekas, Bastian Malaka, Rainer Univ Bremen Bibliothekstr 1 D-28359 Bremen Germany Univ Calif Santa Cruz 1156 High St Santa Cruz CA 95064 USA

ISBN: (数字)9783031202124

ISBN: (纸本)9783031202117;9783031202124

Playing video games has become a major factor of spending leisure time. As competitive and e-sports games grow in popularity and size, demands of complexity, time investment and cognitive strain to acquire proficiency and keep up with competitors grow with them. Fundamental psychological work stresses the detrimental impact of sinking extensive amounts of time into tasks onto mental health and capabilities to perform in those tasks. However, the effects of prolonged task execution without adequate rest in self-imposed leisure environments (e.g. video games) are largely under-investigated. We therefore seek to investigate these effects as well as their related consequences in the highly competitive scene of e-sports games. Prolonged task execution in tasks that are primarily of a cognitive nature is usually not associated with physical strain or physical fatigue. We therefore primarily investigate effects on cognitive capabilities, like the onset of mental fatigue, as well as mental well-being. Over the course of seven weeks, we monitored the playing routines of League of Legends players and tracked measures of mental fatigue before and after sessions. Together with qualitative remarks, explanations and attitudes towards playing, time investment and skill acquisition, we gathered player reports showing significantly deteriorated moods and dispositions to playing as well as high indications of mental fatigue based on significant occurrences of related symptoms. Additionally, we analyzed the matches played by the participants during the study period and found a gaussian-like distribution of performance relative to the number of successively played matches. We discuss associated problems and propose ideas for methods facilitating more time-efficient alternatives of video game skill acquisition.

关键词： Mental fatigue Video games E-sports Game survey

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Enhancing Micro-Expression Recognition with Broadbent Attention Mechanism: A high-performance Approach for Emotion Detection

Enhancing Micro-Expression Recognition with Broadbent Attent...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Imran Ashraf Tanzila Kehkashan Muhammad Zaman Adnan Akhunzada Hafiz Khizer bin Talib Yanlong Cao Faculty of Computer Science University of Lahore Lahore Pakistan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Data and Cybersecurity University of Doha for Science and Technology Doha Qatar School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis.

关键词： Emotion recognition Analytical models Accuracy Attention mechanisms Smart cities Feature extraction Robustness Convolutional neural networks Security Testing

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