检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Hou, Luyang Yan, Jun Wu, Yuankai Wang, Chun Qiu, Tie School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications 10 Xitucheng Rd Haidian District Beijing100876 China Institute for Information Systems Engineering Concordia University 1455 De Maisonneuve Blvd. W. MontréalQCH3G 1M8 Canada College of Computer Science Sichuan University No. 29 Wangjiang Road Wuhou District Sichuan Chengdu61006 China School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin300350 China

Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities. Empowered by the Internet-of-Things (IoT) and Artificial Intelligence (AI), EI aims to unlock peer-to-peer energy trading and sharing among prosumers, who can adeptly switch roles between providers and consumers in localized energy markets with rooftop photovoltaic panels, vehicle-to-everything technologies, packetized energy management, etc. The integration of prosumers in EI, however, will encounter many challenges in modelling, analyzing, and designing an efficient, economic, and social-optimal platform for energy sharing, calling for advanced AI/IoT-based solutions to resource optimization, information exchange, and interaction protocols in the context of the share economy. In this study, we aim to introduce a recently emerged paradigm, Machina Economicus, to investigate the economic rationality in modelling, analysis, and optimization of AI/IoT-based EI prosumer behaviors. The new paradigm, built upon the theory of machine learning and mechanism design, will offer new angles to investigate the selfishness of AI through a game-theoretic perspective, revealing potential competition and collaborations resulting from the self-adaptive learning and decision-making capacity. This study will focus on how the introduction of AI will reshape prosumer behaviors on the EI, and how this paradigm will reveal new research questions and directions when AI meets the share economy. With an extensive case analysis in the literature, we will also shed light on potential solutions for advancements of AI in future smart cities. Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

NFCS: An Efficient Neural Framework for Cold Start Recommendation

NFCS: An Efficient Neural Framework for Cold Start Recommend...

引用

International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Wang Zhou Laixiang Qiu Meijun Duan Amin Ul Haq School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9781665413657

In this article we have illustrated an efficient and effective neural framework referred to as NFCS for cold start recommendation. In this neural network, the average ratings will be as the input, and the missing ratings will be regarded as the dropout rate for the neural network. Therefore, in the output layer ratings will be reconstructed, which could provide an high performance solution for cold start problem. Experimental results also demonstrate that NFCS could outperform state-of-the-art PMF and IRCD-ICS over real life datasets.

关键词： Neural networks Information processing Media

来源：评论

学校读者我要写书评

暂无评论

Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition

arXiv

引用

arXiv 2022年

作者： Han, Mengya Zhan, Yibing Luo, Yong Du, Bo Hu, Han Wen, Yonggang Tao, Dacheng The National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory Wuhan China *** China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning paradigm by comparing the query representation with class representations to predict the category of query instance. However, current metric-based methods generally treat all instances equally and consequently often obtain biased class representation, considering not all instances are equally significant when summarizing the instance-level representations for the class-level representation. For example, some instances may contain unrepresentative information, such as too much background and information of unrelated concepts, which skew the results. To address the above issues, we propose a novel metric-based meta-learning framework termed instance-adaptive class representation learning network (ICRL-Net) for few-shot visual recognition. Specifically, we develop an adaptive instance revaluing network with the capability to address the biased representation issue when generating the class representation, by learning and assigning adaptive weights for different instances according to their relative significance in the support set of corresponding class. Additionally, we design an improved bilinear instance representation and incorporate two novel structural losses, i.e., intra-class instance clustering loss and inter-class representation distinguishing loss, to further regulate the instance revaluation process and refine the class representation. We conduct extensive experiments on four commonly adopted few-shot benchmarks: miniImageNet, tieredImageNet, CIFAR-FS, and FC100 datasets. The experimental results compared with the state-of-the-art approaches demonstrate the superiority of our ICRL-Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Computation Pre-Offloading for MEC-Enabled Vehicular Networks via Trajectory Prediction

Computation Pre-Offloading for MEC-Enabled Vehicular Network...

引用

Smart World Congress (SWC), IEEE

作者： Ting Zhang Bo Yang Zhiwen Yu Xuelin Cao George C. Alexandropoulos Yan Zhang Chau Yuen School of Software Northwestern Polytechnical University Xi’an Shaanxi China School of Computer Science Northwestern Polytechnical University Xi’an Shaanxi China School of Cyber Engineering Xidian University Xi’an Shaanxi China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Department of Informatics University of Oslo Oslo Norway School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Task offloading is of paramount importance to efficiently orchestrate vehicular wireless networks, necessitating the availability of information regarding the current network status and computational resources. However, due to the mobility of the vehicles and the limited computational resources for performing task offloading in near-real-time, such schemes may require high latency, thus, become even infeasible. To address this issue, in this paper, we present a Trajectory Prediction-based Pre-offloading Decision (TPPD) algorithm for analyzing the historical trajectories of vehicles to predict their future coordinates, thereby allowing for computational resource allocation in advance. We first utilize the Long Short-Term Memory (LSTM) network model to predict each vehicle’s movement trajectory. Then, based on the task requirements and the predicted trajectories, we devise a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) that enables the edge server to minimize task processing delay, while ensuring effective utilization of the available computational resources. Our simulation results verify the effectiveness of the proposed approach, showcasing that, as compared with traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.

关键词： Heuristic algorithms Wireless networks Prediction algorithms Trajectory Delays Computational efficiency Resource management Servers Vehicle dynamics Long short term memory

来源：评论

学校读者我要写书评

暂无评论

GPT-Based Automated Induction: Vulnerability Detection in Medical software

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Enhanced Answer Selection in CQA Using Multi-Dimensional Features Combination

引用

Tsinghua science and Technology 2019年第3期24卷 346-359页

作者： Hongjie Fan Zhiyi Ma Hongqiang Li Dongsheng Wang Junfei Liu the School of Electronics Engineering and Computer Science Peking University the School of Software and Microelectronics Peking University he School of Software and Microelectronics Peking University National Engineering Research Center for Software Engineering Peking University

Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question *** of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects,and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multidimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks:(a) classifying answers as good, bad, or potentially associated with a question, and(b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.

关键词： community question answering information retrieval multi-dimensional features extraction similarity computation

来源：评论

学校读者我要写书评

暂无评论

Characterizing Architecture Related Posts and Their Usefulness in Stack Overflow

arXiv

引用

arXiv 2023年

作者： de Dieu, Musengamana Jean Liang, Peng Shahin, Mojtaba Khan, Arif Ali School of Computer Science Wuhan University Wuhan430072 China School of Computing Technologies RMIT University Melbourne3000 Australia M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland

Context: Stack Overflow (SO) has won the intention from software engineers (e.g., architects) to learn, practice, and utilize development knowledge, such as Architectural Knowledge (AK). But little is known about AK communicated in SO, which is a type of high-level but important knowledge in development. Objective: This study aims to investigate the AK in SO posts in terms of their categories and characteristics as well as their usefulness from the point of view of SO users. Method: We conducted an exploratory study by qualitatively analyzing a statistically representative sample of 968 Architecture Related Posts (ARPs) from SO. Results: The main findings are: (1) architecture related questions can be classified into 9 core categories, in which "architecture configuration" is the most common category, followed by the "architecture decision" category, and (2) architecture related questions that provide clear descriptions together with architectural diagrams increase their likelihood of getting more than one answer, while poorly structured architecture questions tend to only get one answer. Conclusions: Our findings suggest that future research can focus on enabling automated approaches and tools that could facilitate the search and (re)use of AK in SO. SO users can refer to our proposed guidelines to compose architecture related questions with the likelihood of getting more responses in SO. © 2023, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine

Development of a Model for Discriminating Electroencephalogr...

引用

International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Akifumi Tanabe Kyosuke Nakatani Kento Harachi Yusuke Yamamoto Ayumi Muramatsu Hajime Nagahara Noriko Takemura Shinji Shimojo Daisuke Furihata Yuko Mizuno-Matsumoto Kazue Kamae-Shigenobu Kazuhiro Shinosaki Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Department of Medical Engineering Aino University Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Software and Information Technology Aomori University Aomori Japan Cyber media center Osaka University Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients.

关键词： Support vector machines Electrodes Analytical models Accuracy Machine learning Brain modeling Electroencephalography Vectors Spectral analysis

来源：评论

学校读者我要写书评

暂无评论

A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

来源：评论

学校读者我要写书评

暂无评论

APADGCN: Adaptive Partial Attention Diffusion Graph Convolutional Network for Traffic Flow Forecasting 4th

APADGCN: Adaptive Partial Attention Diffusion Graph Convolut...

引用

4th International Conference on Spatial Data and Intelligence, SpatialDI 2023

作者： Zhang, Bowen Li, Bohan Wei, Jinzhan Wen, Hao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China School of Electronic Information and Automation Guilin University of Aerospace Technology Guilin541010 China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9783031329098

Traffic flow forecasting is a core task of urban governance and plays a vital role in the development of ITS. Because of the complexity and uncertainty of traffic patterns, it is of great challenge to capture spatial-temporal correlations. Recent researches mainly focus on the pre-defined adjacency matrix based on prior knowledge as the basis of spatial-temporal correlation modeling, but the fixed graph structure cannot adequately describe the dependency between traffic sensors. To tackle this issue, a novel deep learning model framework is proposed in this paper: Adaptive Partial Attention Diffusion Graph Convolutional Network(APADGCN), which consists of three main parts: 1) the Multi-Component module that divides the historical traffic flow into recent, daily-periodic, and weekly-periodic, to capture the traffic patterns of different periodic;2) the spatial correlation modeling which can dynamically capture node relationships and model spatial dependency, and enhance the aggregation ability of low-order information;3) the temporal correlation modeling which models long-term time dependencies using convolution and gating. The final result is obtained by the weighted fusion of the results of the multi-components. We compared our APADGCN with various baseline models in the four real datasets from the Caltrans Performance Measurement System (PeMS). The experimental results show that the prediction accuracy of APADGCN is better than that of the baseline models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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：