检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Dong, Li Peng, Yubo Jiang, Feibo Wang, Kezhi Yang, Kun School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China School of Intelligent Software and Engineering Nanjing University Suzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Department of Computer Science Brunel University London United Kingdom

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Single spin qubit geometric gate in a silicon quantum dot

arXiv

引用

arXiv 2023年

作者： Ma, Rong-Long Li, Ao-Ran Wang, Chu Kong, Zhen-Zhen Liao, Wei-Zhu Ni, Ming Zhu, Sheng-Kai Chu, Ning Zhang, Cheng-Xian Liu, Di Cao, Gang Wang, Gui-Lei Li, Hai-Ou Guo, Guo-Ping CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Anhui Hefei230026 China Integrated Circuit Advanced Process R&D Center Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China School of Physical Science and Technology Guangxi University Nanning530004 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Beijing Superstring Academy of Memory Technology Beijing100176 China Origin Quantum Computing Company Limited Anhui Hefei230026 China

Preserving qubit coherence and maintaining high-fidelity qubit control under complex noise environment is an enduring challenge for scalable quantum computing. Here we demonstrate an addressable fault-tolerant single spin qubit with an average control fidelity of 99.12 % via randomized benchmarking on a silicon quantum dot device with an integrated micromagnet. Its dephasing time T2∗ is 1.025 µs, and can be enlarged to 264 µs by using the Hahn echo technique, reflecting strong low-frequency noise in our system. To break through the noise limitation, we introduce geometric quantum computing to obtain high control fidelity by exploiting its noise-resilient feature. However, the control fidelities of the geometric quantum gates are lower than 99 %. According to our simulation, the noise-resilient feature of geometric quantum gates is masked by the heating effect. With further optimization to alleviate the heating effect, geometric quantum computing can be a potential approach to reproducibly achieving high-fidelity qubit control in a complex noise environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Self-supervised representation learning for Bayesian quantum architecture search

引用

Physical Review A 2025年第3期111卷 032403-032403页

作者： Zhimin He Hongxiang Chen Yan Zhou Haozhen Situ Yongyao Li Lvzhou Li School of Electronic and Information Engineering Foshan University Foshan 528000 China School of Computer Science and Artificial Intelligence Foshan University Foshan 528000 China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology Foshan University Foshan 528000 China College of Mathematics and Informatics South China Agricultural University Guangzhou 510642 China School of Physics and Optoelectronic Engineering Foshan University Foshan 528225 China Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China

Quantum architecture search (QAS) has shown significant promise in automating quantum circuit design for variational quantum algorithms (VQAs). However, a central challenge remains in the high computational cost of evaluating the ground-truth performance of numerous circuit architectures during the search process. This challenge is further complicated by factors such as inefficient gradient computation, the barren plateau phenomenon, and frequent data exchange between classical and quantum systems within VQAs, all of which restrict the scalability of QAS for increasingly complex VQAs. In this study, we propose an enhanced QAS framework that integrates a performance predictor trained through self-supervised learning with Bayesian optimization to overcome these computational barriers. To address the challenges of circuit performance evaluation, we develop a graph-isomorphism-network-based predictor pretrained using expressibility prediction, an intrinsic measure of a circuit's capacity to explore the Hilbert space uniformly. This pretraining process creates a structured and smooth latent space, significantly improving the predictor's accuracy and generalizability with minimal labeled data. Furthermore, Bayesian optimization is integrated into the QAS framework to refine the predictor iteratively, concentrating the search on regions containing high-performance circuits and thus accelerating the search process. Simulation results for VQAs involving the variational quantum eigensolver and quantum approximate optimization algorithm demonstrate that the proposed framework achieves faster convergence to optimal circuits and substantially reduces computational costs compared to existing QAS methods.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Improving the Undersampling Technique by Optimizing the Termination Condition for Software Defect Prediction

SSRN

引用

SSRN 2023年

作者： Feng, Shuo Keung, Jacky Zhang, Peichang Xiao, Yan Yu, Xiao School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Computer Science City University of Hong Kong Kowloon Hong Kong College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Computing National University of Singapore 117417 Singapore School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

The class imbalance problem significantly hinders the ability of the software defect prediction (SDP) models to distinguish defective (minority class) software instances from non-defective (majority class) ones. Recent studies on the data resampling technique concluded that Random UnderSampling (RUS) performs better than several complex oversampling techniques at alleviating this problem. However, RUS blindly removes majority class instances, leading to significant information loss. These studies also pointed out that the conventional termination condition of the data resampling technique may be inappropriate (terminating the data resampling technique when the number of instances for both the minority and majority classes are the same).In fact, the undersampling technique is like a recommender system or a web search engine to recommend majority class instances to SDP models. Therefore, we propose the Learning-To-Rank Undersampling technique (LTRUS). The novelty of our work is lying in two aspects: 1) We regard the undersampling process as a learning-to-rank task. We optimize a linear model to rank majority class instances and removes these instances from the bottom of the rank to alleviate the class imbalance problem. 2) We propose two termination conditions for the undersampling technique different from the conventional termination *** significantly outperforms RUS, the complexity-based oversampling technique, SMOTUNED, and Borderline-SMOTE in terms of F-measure, AUC, and MCC by 6.5%, 6.5%, and 13.1% on average under the conventional termination condition. Furthermore, LTRUS under the two termination conditions we propose perform similarly to each other and both outperform LTRUS and all the other baselines under the conventional termination condition. The experimental results show the effectiveness of LTRUS and indicate that the conventional termination condition for the data resampling technique is improper. © 2023, The Authors. All rights reserved.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models

arXiv

引用

arXiv 2024年

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Fireclay

来源：评论

学校读者我要写书评

暂无评论

Exploring reliable edge-cloud computing for service latency optimization in sustainable cyber-physical systems

Exploring reliable edge-cloud computing for service latency ...

引用

作者： Cao, Kun Wei, Tongquan Chen, Mingsong Li, Keqin Weng, Jian Tan, Wuzheng College of Information Science and Technology/College of Cyber Security Jinan University Guangzhou China School of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Department of Computer Science State University of New York New PaltzNY United States

In recent years, the advance in information technology has promoted a wide span of emerging cyber-physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality-of-experience to terminal users. Edge-cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency-aware edge-cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge-cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two-stage approach composed of static and dynamic service latency optimization. At static stage, Monte-Carlo simulation with integer-linear-programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup-adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions. © 2021 John Wiley & Sons, Ltd.

关键词： Cyber Physical System

来源：评论

学校读者我要写书评

暂无评论

Design of a Mobile Augmented Reality-based Indoor Navigation System 4

Design of a Mobile Augmented Reality-based Indoor Navigation...

引用

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Ng, Xin Hui Lim, Woan Ning Sunway University Research Centre for Human-Machine Collaboration Department of Computing and Information Systems School of Science and Technology Malaysia

ISBN: (纸本)9781728190907

GPS-based navigation technology has been widely used in most of the commercial navigation applications nowadays. However, its usage in indoor navigation is not as effective as when it is used at outdoor environment. Much research and developments of indoor navigation technology involve additional hardware installation which usually incur high setup cost. In this paper, research and comparisons were done to determine the appropriate techniques of indoor positioning, pathfinding, and route guidance for an indoor navigation method. The aim of this project is to present a simple and cost- effective indoor navigation system. The proposed system uses the existing built-in sensors embedded in most of the mobile devices to detect the user location, integrates with AR technology to provide user an immersive navigation experience. In this project, an indoor navigation mobile application was developed and tested. The development demonstrates the usage of IndoorAtlas which enables indoor positioning through technology fusion to detect user's position and obtain the route to destination, and ARCore to display AR guidance using the computed route. Surveys were carried out to gauge the efficiency of the method and to gather the feedback from the participants. The architecture of the method and the demonstration of the application is presented. © 2020 IEEE.

关键词： augmented reality indoor navigation indoor positioning

来源：评论

学校读者我要写书评

暂无评论

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

arXiv

引用

arXiv 2023年

作者： Wen, Haomin Lin, Youfang Wu, Lixia Mao, Xiaowei Cai, Tianyue Hou, Yunfeng Guo, Shengnan Liang, Yuxuan Jin, Guangyin Zhao, Yiji Zimmermann, Roger Ye, Jieping Wan, Huaiyu The Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing101318 China The School of Computing National University of Singapore Singapore College of Systems Engineering National University of Defense Technology Changsha China The School of Information Science and Engineering Yunnan University Kunming650091 China The Cainiao Network Hangzhou China The Alibaba Group Hangzhou China INTR Thrust and DSA Thrust The Hong Kong University of Science and Technology Guangzhou China

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction;(ii) model architecture, which encompasses sequence-based and graph-based models;and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field. © 2023, CC BY.

关键词： Taxonomies

来源：评论

学校读者我要写书评

暂无评论

A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecasting in Cloud Data Center

A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecas...

引用

International Conference on Wavelet Active Media technology and information Processing (ICWAMTIP)

作者： Habte Lejebo Leka Zhang Fengli Ayantu Tesfaye Kenea Abebe Tamrat Tegene Peter Atandoh Negalign Wake Hundera School of Information and Software Engineering University of Electronic Science and Technology of China China School of Electrical Engineering and Computing Adama Science and Technology University Ethiopia School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781665413657

It is vital to precisely forecast the workload of Virtual Machines (VMs) to achieve efficient cloud resources management and reduce power consumption. In this research study, a deep learning-based hybrid strategy for VM workload prediction is proposed. To create an accurate prediction, the suggested prediction model integrated a convolutional neural network (CNN) architecture and a long-short-term memory (LSTM) neural network. The CNN component is used to elicit complex distinctive attributes of the VM workload data, while the LSTM component models temporal information and predict the future VM workload. Experimental results on real-world dataset have shown that the proposed CNN-LSTM model is effective on VM workload prediction, and when compared to frequently used workload prediction models, the proposed approach enhances workload forecasting performance.

关键词： Cloud computing Power demand Computational modeling Neural networks Predictive models Data models Virtual machining

来源：评论

学校读者我要写书评

暂无评论

Integrating Higher-Order Features for Structural Role Discovery 15th

Integrating Higher-Order Features for Structural Role Disco...

引用

15th EAI International Conference on Mobile Multimedia Communications, MobiMedia 2022

作者： Tian, Qiang Zhang, Wang Jiao, Pengfei Zhong, Kai Wu, Nannan Pan, Lin School of Computer and Information Engineering Tianjin Normal University Tianjin300384 China College of Intelligence and Computing Tianjin University Tianjin300350 China School of Cyberspace Hangzhou Dianzi University Hangzhou310018 China The Joint College of SWJTU-Leeds Southwest-Jiaotong University Chengdu610097 China Shenzhen Research Institute Tianjin University Shenzhen518063 China School of Marine Science and Technology Tianjin University Tianjin300350 China

ISBN: (纸本)9783031239014

The role of node is able to denote its function and effect in the network, and can represent personal identity or status in real-world complex systems. It is defined on the local connective patterns and structural similarities. Compared to the community detection, the task of role discovery is independent to the node proximity which is generally related to the distance of density in the network. It is more likely to be determined by structural similarity, and the structural node representations have achieved great success in this field. Some existing methods focus on the local structural features to generate role-oriented node representations, but they consider too much on local structures and fail to learn multi-aspects representations of structural roles. More specifically, the local, global, and higher-order structures can denote different type of roles, and there are varying dependencies between them, leading to the difficulty to effectively integrate them. Thus, we propose a novel model HORD to integrate higher-order features into structural role discovery, aiming to learn multi-aspects structural node representations of roles. We leverage higher-order and local features and utilize the unified graph neural network (GNN) framework to organically combine them to generate structural node representations. We conduct extensive experiments on several real-world networks and the results demonstrate that our model is better than state-of-the art methods. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Graph neural networks

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：