检索结果-内蒙古大学图书馆

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Xingtong Liu Meng Shen Laizhong Cui Ke Ye Jizhe Jia Guangchun Yue School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science Beijing Institute of Technology Beijing China

Malware traffic is constantly evolving and remains destructive. The detection and classification of malware traffic is crucial for maintaining cyberspace security. Only by swiftly and accurately detecting and classifying malware traffic can user privacy and cyberspace security be effectively *** this paper, we propose FewFine, an approach for few-shot malware traffic classification based on transfer learning. We initially pre-train a detection model and two classification models with substantial quantity of malware and application traffic samples. For classifying new types of malware traffic accurately and promptly, we utilize transfer learning based on fine-tuning strategy and freeze several blocks in the pre-trained model. Utilizing prior knowledge from the pre-trained models, we leverage few samples of novel classes to perform accurate malware detection and classification. We execute extensive experiments on publicly available datasets to evaluate the effectiveness of FewFine. In model pre-training, with considerable number of samples, the accuracy of malware detection and classification can reach 0.99. The pre-trained models are saved for fine-tuning. When detecting and classifying novel malware traffic, FewFine can achieve the accuracy of 0.95 leveraging only 10 samples per class through fine-tuning the pre-trained model. It outperforms methods under comparison in terms of accuracy and efficiency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring Depth-based Perception Conflicts in Virtual Reality through Error-Related Potentials

Exploring Depth-based Perception Conflicts in Virtual Realit...

引用

IEEE Annual International Symposium Virtual Reality

作者： Haolin Gao Kang Yue Songyue Yang Yu Liu Mei Guo Yue Liu Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Human Computer Interactions Institute of Software Chinese Academy of Sciences Beijing China

Virtual Reality (VR) offers a valuable platform for real-life skills training. However, previous research has indicated that human’s perception of depth in VR differs from that of the real world. Such perceptual conflicts can impact immersion and the learning of skills, thus attracting widespread attention. Various methods have been proposed to enhance users’ depth perception, yet the underlying mechanisms of depth perception conflicts still require further research. In this paper, we used Error-Related Potentials (ErrPs) from electroencephalography (EEG) data to investigate the differences in participants’ perceptions at varying depths within the near-field. We designed a within-subjects experiment to successfully introduce depth perception conflicts. From participants exposed to three distinct depths, we collected questionnaire results, performance data, and EEG data. Our findings showed that EEG can effectively detect depth perception conflicts and, following each conflict, participants’ behavioral patterns showed significant changes. In situations with shallower depths, participants exhibited stronger responses to the designed conflicts. This increased sensitivity correlates with their accuracy in depth estimation. This study represents a novel approach to depth perception in VR using ErrPs, setting the stage for further use of physiological signals to measure the granularity of depth perception in VR/AR environments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dual Antenna-Based Line Crossing Detection with UHF RFID

引用

Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Chen, Xingyu Wang, Xuan Xun, Kai Wang, Xia Liu, Jia Zhao, Zhihong Chen, Lijun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Engineering Jinling Institute of Technology Nanjing China Principal of Suzhou City College Suzhou China

Line crossing detection is to check whether people or objects go across a given barrier line, which is quite common and important in our daily life, such as the electronic article surveillance (EAS) checkpoint in a retail store or the finish line in track and field. Although existing solutions to line crossing detection have achieved great advancement, they do not function well when multiple objects or people cross the line at the same time. In this paper, we propose a new radio frequency identification (RFID)-based solution called RF-Line to line crossing detection, especially for multiobject scenarios. The biggest challenge is that the RFID reader's coverage zone is invisible and irregular;we cannot roughly take the time when a tag is seen by the reader for the first time as the time when line crossing occurs. In RF-Line, we deploy two antennas opposite to each other and collect the RF phase profiles of two antennas at the same time. By a series of geometric transformations and mathematical derivations, we find that summing up the two phase profiles will get a new phase curve, in which the inflection point of the curve is the time of line crossing. In addition, we address the problem of turning back or long stay on the barrier line. We implement RF-Line with commodity RFID systems. Extensive experiments show that RF-Line can achieve accurate line crossing detection with a small error of 6.1 cm, with no need for any system calibration or complicated deployment. © 2023 Xingyu Chen et al.

关键词： Retail stores

来源：评论

学校读者我要写书评

暂无评论

Searching for an Accurate Robot Calibration via Improved Levenberg–Marquardt and Radial Basis Function System

引用

Journal of Field Robotics 2025年

作者： Li, Zhibin Deng, Xun Chen, Tinghui Yang, Yuhang Chen, Linlin Yang, Xiwen Hu, Zhenzhen Hu, Lun Hu, Pengwei Li, Shuai Luo, Xin Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences Urumqi China School of Software Engineering Chengdu University of Information Technology Chengdu China Dazhou Key Laboratory of Government Data Security Sichuan University of Arts and Science Dazhou China School of Software Engineering Xinjiang University Urumqi China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland VTT-Technical Research Centre of Finland Oulu Finland College of Computer and Information Science Southwest University Chongqing China

Robots are frequently utilized in manufacturing, aviation, and other industries, which enhance industrial production efficiency and quality. Specifically, robots perform high-precision tasks like welding, assembly and material handling, which reduce the intensity of manual labor in factories. In the logistics field, robots automatically sort and deliver goods, thereby speeding up supply chain operations. However, the prolonged operation of robots suffers from a decline in positioning accuracy, which makes them unable to satisfy task requirements. To address this challenging issue, this study designs an efficient calibration system integrating the Levenberg–Marquardt algorithm with fuzzy proportion integration differentiation controller and radial basis function neural network. The innovations of this method include: (1) integrating the fuzzy proportion integration differentiation controller into the updating rules of Levenberg–Marquardt algorithm, which further enhances the identification performance of kinematic errors;(2) adopting the radial basis function neural network to handle the robot dynamic errors, which addresses the complexity of dynamic error sources. Extensively experimental robot positioning points are gathered on an HSR JR680 robot, and then experimental validations are conducted by using the designed calibration system. The experiments indicate that the developed algorithm outperforms these existing advanced algorithms. © 2025 Wiley Periodicals LLC.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

CHEMFUZZ: Large Language Models-Assisted Fuzzing for Quantum Chemistry software Bug Detection

CHEMFUZZ: Large Language Models-Assisted Fuzzing for Quantum...

引用

IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Feng Qiu Pu Ji Baojian Hua Yang Wang School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China Computer Science Department The Johns Hopkins University USA

Quantum chemistry software implements the first principle quantum computation and is indispensable in both scientific research and chemical industries. Any bugs in such software will lead to serious consequences, thus defeating its trustworthiness and reliability. However, bug detection techniques for such software have not been fully investigated. In this paper, to fill this gap, we propose a novel approach to fuzz quantum chemistry software with the aid of Large Language Models (LLMs). Our basic idea is utilize LLMs to mutate and generate syntactic and semantic valid input files from seed inputs, by proving valuable domain-specific knowledge of chemistry. With this basic idea, we have designed and implemented CHEMFuzz, a fully automatic fuzzing framework to fuzz quantum chemistry software for bugs. Our evaluation of CHEMFUZZ leverages popular LLMs including GPT3.5, Claude-2, and Bart as test oracles to generate parameters to mutate inputs and analyze computation results. CHEMFUZZ detected 40 unique bugs, which have been classified and reported to developers, with a code coverage of 17.4%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation Driven by Large Models in Future Semantic-Aware Networks

arXiv

引用

arXiv 2025年

作者： Zhang, Haijun Ni, Jiaxin Wu, Zijun Liu, Xiangnan Leung, V.C.M. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing10083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden College of Computer Science and Software Engineering Shenzhen University Shenzhen51806 China VancouverBCV6T 1Z4 Canada

Large model has emerged as a key enabler for the popularity of future networked intelligent applications. However, the surge of data traffic brought by intelligent applications puts pressure on the resource utilization and energy consumption of the future networks. With efficient content understanding capabilities, semantic communication holds significant potential for reducing data transmission in intelligent applications. In this article, resource allocation driven by large models in semantic-aware networks is investigated. Specifically, a semantic-aware communication network architecture based on scene graph models and multimodal pre-trained models is designed to achieve efficient data transmission. On the basis of the proposed network architecture, an intelligent resource allocation scheme in semantic-aware network is proposed to further enhance resource utilization efficiency. In the resource allocation scheme, the semantic transmission quality is adopted as an evaluation metric and the impact of wireless channel fading on semantic transmission is analyzed. To maximize the semantic transmission quality for multiple users, a diffusion model-based decision-making scheme is designed to address the power allocation problem in semantic-aware networks. Simulation results demonstrate that the proposed large-model-driven network architecture and resource allocation scheme achieve high-quality semantic transmission. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Web

来源：评论

学校读者我要写书评

暂无评论

Traffic Aware Power Saving Communication Assisted By Double-Faced Active RIS

Traffic Aware Power Saving Communication Assisted By Double-...

引用

IEEE International Conference on Communications (ICC)

作者： Yuyan Zhou Yang Liu Qingqing Wu Qingjiang Shi Jun Zhao School of Information and Communication Engineering Dalian University of Technology Department of Electronic Engineering Shanghai Jiao Tong University School of Software Engineering Tongji University and Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Nanyang Technological University

Despite its high energy and hardware efficiency, some defects of the reconfigurable intelligence surface (RIS) technology have come to be realized, including the severe fading loss and restricted-to-half-space coverage. This paper proposes a novel double-faced-active (DFA)-RIS structure to overcome these defects. Besides, we utilize this novel DFA-RIS to improve power saving of the communication system. Unlike traditional power saving literature, we aim at fulfilling queueing stability and long-term power minimization in a downlink system assisted by the DFA-RIS, with a realistic data arriving process taken into consideration. Enlightened by Lyapunov control theory, we propose an online optimization strategy that adaptively adjusts DFA-RIS configuration. Each online problem can be efficiently solved by leveraging alternative directional method of multipliers (ADMM) method. Numerical results demonstrate the effectiveness of our proposed Lyapunov-guided strategy and DFA-RIS’ superiority over the classical passive RIS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Light Gradient Residual Encoder-Decoder Network for Multimodal Image Fusion

A Light Gradient Residual Encoder-Decoder Network for Multim...

引用

Intelligent Computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Muhammad Ishfaq Hussain Zubia Naz Linh Van Ma Jeonghwan Gwak Moongu Jeon School of Electrical Engineering and Computer Sciene Gwangju Institute of Science and Technology (GIST) South Korea Large-scale AI Research Group Korea Institute of Science and Technology Information (KISTI) South Korea Department of Software Korea National University of Transportation South Korea

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

Image fusion combines the complementary traits of source images into a single output, enhancing both human visual observation and machine vision perception. The existing fusion algorithms typically prioritize visual enhancement, often overlooking the real-time needs for critical surveillance applications. To address these real-time deployment needs, we present a compact fusion network for combining infrared and visible image representations, named Light-weight Fusion (LightFusion). This network employs incremental semantic integration and scene recognition accuracy constraints by incorporating three different bands of images (IR, RGB, and Grayscale) to fuse the data. Our approach includes a sparse semantic perception branch that captures critical semantic features, which are then integrated into the fusion network through a semantic injection module. This ensures that high-level vision tasks are adequately addressed. The scene fidelity path ensures that fusion features preserve all details required to reconstruct the original images. The importance and applicability of the proposed network are enhanced by employing an extra input in the form of a grayscale image, obtained by converting the RGB image for improved contrast, along with prominent target masks to enhance the visual quality of the fusion results. Our extensive analysis shows that the lightweight LightFusion network outperforms existing methods in both visual quality and semantic integrity, even under challenging conditions. The source code will be released at https://***/MI-HussainiLightFusion.

关键词： Visualization Image recognition Surveillance Source coding Semantics Gray-scale Real-time systems Robustness Image reconstruction Image fusion

来源：评论

学校读者我要写书评

暂无评论

Correction to: Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

引用

Neural Processing Letters 2022年第1期55卷 33-34页

作者： Baskar, M. Renuka Devi, R. Ramkumar, J. Kalyanasundaram, P. Suchithra, M. Amutha, B. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram India Department of Software Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram India Department of Wireless Sensor Networks Institute of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

来源：评论

学校读者我要写书评

暂无评论

WFTNet: Exploiting Global and Local Periodicity in Long-Term Time Series Forecasting

WFTNet: Exploiting Global and Local Periodicity in Long-Term...

引用

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

作者： Peiyuan Liu Beiliang Wu Naiqi Li Tao Dai Fengmao Lei Jigang Bao Yong Jiang Shu-Tao Xia College of Computer Science and Software Engineering Shenzhen University Tsinghua Shenzhen International Graduate School Tsinghua University WeBank Institute of Financial Technology Shenzhen University Ping An Bank Co. Ltd.

Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourier transform, which cannot capture fine-grained and local frequency structure. In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. WFTNet utilizes both Fourier and wavelet transforms to extract comprehensive temporal-frequency information from the signal, where Fourier transform captures the global periodic patterns and wavelet transform captures the local ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to adaptively balance the importance of global and local frequency patterns. Extensive experiments on various time series datasets show that WFTNet consistently outperforms other state-of-the-art baseline. Code is available at https://***/Hank0626/WFTNet.

关键词：

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：