检索结果-内蒙古大学图书馆

Decoding Musical Neural Activity in Patients With Disorders of Consciousness Through Self-Supervised Contrastive Domain Generalization

引用

IEEE Transactions on Affective Computing 2025年第2期16卷 726-743页

作者： Cai, Honghua Pan, Jiahui Xiao, Qiuyi Jin, Jiarui Li, Yuanqing Xie, Qiuyou South China Normal University School of Software Foshan528225 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southern Medical University Zhujiang Hospital Guangzhou510260 China South China University of Technology Center for Brain-Computer Interfaces and Brain Information Processing Guangzhou510640 China Pazhou Lab Guangzhou510330 China

Identifying the brain responses of patients with disorders of consciousness (DOCs), which include comas, vegetative states (VSs, also called unresponsive wakefulness syndrome (UWS)) and minimally conscious states (MCSs), based on electroencephalography (EEG) has important clinical diagnosis implications. However, due to impaired motor and cognitive abilities, patients with DOCs may not be able to express their feelings and their brain responses to different stimuli, making it difficult to correctly label data. EEG classification algorithms trained with these data cannot make reliable classifications and predictions for clinical diagnosis purposes. To identify the brain responses produced for different types of stimuli in patients with DOCs, we proposed a self-supervised contrastive domain generalization framework (SSCDG) for cross-subject EEG classification. The model was first trained with healthy-subject EEG data induced by different stimuli to learn their corresponding unsupervised representations. Then, we used these representations to train a classifier to predict the emotional states of patients with DOCs under the corresponding stimuli. SSCDG was first evaluated on the SEED dataset, and it achieved an accuracy of 87.6%, which was 1.1% higher than that of the state-of-the-art (SOTA) approaches. Moreover, the SSCDG method was utilized to categorize EEG data acquired from seventeen DOC patients, including eleven in a UWS state and six in an MCS state, with seven patients demonstrating notable accuracy in three-class EEG classification tasks. The SSCDG results indicated that the seven patients with DOCs may have shown classifiable EEG responses to the presented stimuli. © 2010-2012 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Enhancing road safety with DL vision: Do driver distraction alerts hold the key?

引用

Neural Computing and Applications 2025年 1-21页

作者： Ali, Luqman Swavaf, Muhammad Alnajjar, Fady Zou, Zhao Mohan Ambali Parambil, Medha Mubin, Omar AlJassmi, Hamad Emirates Center for Mobility Research UAE University Abu Dhabi Al Ain United Arab Emirates Department of Civil and Environmental Engineering College of Engineering UAE University Abu Dhabi Al Ain United Arab Emirates Department of Computer Science and Software Engineering College of IT UAE University Abu Dhabi Al Ain United Arab Emirates School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW Australia

Driver distraction is a significant contributor to road accidents and traffic safety challenges. This study evaluates the performance of various YOLO models trained on a combination of custom and publicly available datasets, with their performance assessed using metrics such as mAP@50, precision, recall, F1-score, inference time, FPS, and model parameters. The aim is to determine the best trade-off between accuracy, speed, and computational efficiency, providing insights into the practical applicability of these models for distracted driving detection. Among the models, YOLOv8l achieves the highest accuracy with a mAP@50 of 0.921, precision of 0.911, recall of 0.895, and an F1-score of 0.899, making it suitable for scenarios prioritizing detection precision. YOLOv5m balances accuracy and efficiency, achieving a mAP@50 of 0.917 and an F1-score of 0.896, while maintaining moderate inference times. For real-time applications, YOLOv5n and YOLOv8n offer the fastest speeds, with inference times of 0.9 ms and FPS of 1111, at the cost of reduced accuracy. YOLOv8m provides a balance between performance and speed, achieving a mAP@50 of 0.915 and F1-score of 0.898, with an inference time of 3.4 ms and FPS of 294. A post-simulation survey reveals that participants find the visual warning system effective in mitigating driver distraction and enhancing perceived road safety. By integrating simulation data and user feedback, this study highlights the potential of advanced deep learning models like YOLO in improving driver assistance systems and promoting safer driving practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

Exploiting the sequential nature of genomic data for improved analysis and identification

引用

computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

来源：评论

学校读者我要写书评

暂无评论

Joint Energy and Computation Workload Management for Geo-distributed Data Centers

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

An Efficient Autonomous Maze Solving Robot based on Microcontroller by Avoiding Obstacles Using a Decision-Making Algorithm

An Efficient Autonomous Maze Solving Robot based on Microcon...

引用

2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Zulfiqar, Muhammad Imran Abdullah, Irum Akram, Aasma Mehmood, Shahid Fareeha Ali, Asad Nanjing University of Science & Technology School of Computer Science & Engineering Nanjing China Lahore College for Women University Department of Computer Science Jhang Pakistan Lahore College for Women University Department of Software Engineering Lahore Pakistan Higher Education Department Department of Computer Science Punjab Faisalabad Pakistan Air University Department of Business Administration Multan Pakistan

ISBN: (纸本)9798350315974

The task of maze solving has a rich historical background and continues to hold great significance in the realms of Robotics and Artificial Intelligence. The recent advancements in robotics technology aim to develop self-driving robots capable of autonomous operation, with the goal of streamlining our daily tasks. This emphasizes the focus of most research endeavors on augmenting sensor capabilities and refining algorithms to create robots that are adaptable and *** this paper, we present a contemporary approach to robotics that centers on the creation of self-navigating autonomous robots proficient in maze-solving while avoiding obstacles. This innovative research distinguishes itself from conventional line-following robots that merely adhere to predetermined paths. Instead, our proposed methodology enables the robot to navigate the maze independently, optimizing its path effectively. To achieve this, the robot employed in our study incorporates an ultrasonic sensor and operates on an open-source platform featuring an ATmega328P-based microcontroller *** core of this research lies in the development of a decision-making algorithm, which can be stored within the robot's programmable microcontroller, enabling it to tackle maze-solving tasks. The implications of this research span various applications, such as material handling, warehouse management, pipeline inspection, and bomb disposal. Compared to conventional line-following robots, our method provides superior results in terms of precision, recall, F1-score, time required to solve mazes, and practicality. © 2023 IEEE.

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

引用

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

来源：评论

学校读者我要写书评

暂无评论

Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests 24

Automatic Adjustment of HPA Parameters and Attack Prevention...

引用

9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Chan, Huah Yong Zhou, Hanlin Ni, Jingfei Wu, Mengchun Deng, Qing School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Xiamen Institute of Software Technology Xiamen China Department of Manzhouli Customs Manzhouli China College of Information Science and Engineering Jimei University Xiamen China

ISBN: (纸本)9798400717161

In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

A verifiable and privacy-preserving framework for federated recommendation system

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4273-4287页

作者： Gao, Fei Zhang, Hanlin Lin, Jie Xu, Hansong Kong, Fanyu Yang, Guoqiang College of Computer Science and Technology Qingdao University Qingdao266071 China School of Electronic and Information Engineering Xian Jiaotong University Xian710049 China School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai200240 China School of Software Shandong University Jinan250100 China Sansec Technology Co. Ltd. Jinan250101 China

The data such as features involved in recommendation systems often contain private information that can cause serious security problems if leaked to other participants in the system. At present, Federated Learning (FL) combined with encryption technology is a popular privacy preserving technology. However, the distributed computing of FL threatens the credibility of calculation results. Incorrect calculation results in the recommendation system can reduce the accuracy of the recommendation. In this paper, we design a verifiable and privacy-preserving framework for the federated recommendation system (VePriRec) to ensure the privacy of data and verifiability of calculation results. For three components involved in the system, we design three privacy-preserving protocols, including a secure similarity network construction protocol, a secure gradient descent protocol and a secure aggregation protocol. We conduct experiments on real-world datasets, the results demonstrate the effectiveness and efficiency of VePriRec. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Feature Reduction-based Intrusion Detection (FRID) in Satellite-Terrestrial Integrated Networks 5

Feature Reduction-based Intrusion Detection (FRID) in Satell...

引用

5th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2023

作者： Yao, Lin Tian, Ziyuan Deng, Jing Wu, Guowei Li, Li DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China School of Software Dalian University of Technology Dalian China The Department of Computer Science UNC Greensboro UNC NC Greensboro27412 United States Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798350394160

Satellite-Terrestrial Integrated Networks (STINs) integrate satellite networks, the Internet, and mobile wireless networks and are able to provide powerful services to users. In STIN, satellite gateways play an important role of connecting space-based networks to terrestrial-based networks, but can be under frequent attacks such as Distributed Denial of Services (DDoS) attack. Therefore, intrusion detections become essential in STINs. In this work, we propose a feature reduction-based intrusion detection (FRID) technique with the help from deep reinforcement learning. In FRID, only features that are highly distinguishable, robust, and independent are selected for the later stages. Experimental results show the accuracy, recall, FAR, and F1 scores comparing FRID with several state-of-the-art schemes and demonstrate its superiority. ©2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Mind-Body TaoRelax: Relieving Stress Through Immersive Virtual Reality Relaxation Training in a Taoist Atmosphere

Mind-Body TaoRelax: Relieving Stress Through Immersive Virtu...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Hao Yan Ding Ding Zhuying Li College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Virtual reality technology is providing a new method for psychologi-cal relaxation training, but most systems focus primarily on medi-tation as a singular relaxation method. We propose a novel virtual reality relaxation system called TaoRelax. Building upon traditional Taoist culture, the system integrates both meditation and progressive muscle relaxation training. It combines electromyography (EMG) as a means of feedback and assessment during training, providing users with a dual experience of relaxation for both body and mind. Our preliminary results indicate that after training in our system, participants experienced a significant reduction in psychological stress and a notable improvement in meditation abilities.

关键词： Training Three-dimensional displays Conferences Psychology Virtual reality Human factors User interfaces

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：