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arXiv 2021年

作者： Wang, Fusen Sang, Jun Wu, Zhongyuan Liu, Qi Sang, Nong Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China

The existing crowd counting methods usually adopted attention mechanism to tackle background noise, or applied multi-level features or multi-scales context fusion to tackle scale variation. However, these ap- proaches deal with these two problems separately. In this paper, we propose an Hybrid Attention Network (HAN) by employing Progressive Embedding Scale-context (PES) information, which enables the network to simultaneously suppress noise and adapt head scale variation. We build the hybrid attention mechanism through paralleling spatial attention and channel attention module, which makes the network to focus more on the human head area and reduce the interference of background objects. Besides, we embed certain scale-context to the hybrid attention along the spatial and channel dimensions for alleviating these count- ing errors caused by the variation of perspective and head scale. Finally, we propose a progressive learning strategy through cascading multiple hybrid attention modules with embedding diαerent scale-context, which can gradually integrate diαerent scale-context information into the current feature map from global to local. Ablation experiments provides that the network architecture can gradually learn multi-scale features and suppress background noise. Extensive experiments demonstrate that HANet obtain state-of-the-art counting performance on four mainstream datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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O2iJoin： An Efficient Index-Based Algorithm for Overlap Interval Join

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Journal of Computer Science & technology 2018年第5期33卷 1023-1038页

作者： Ji-Zhou Luo Sheng-Fei Shi Guang Yang Hong-Zhi Wang Jian-Zhong Li School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Guangdong Key Laboratory of Popular High Performance Computers Key Laboratory of Service Computing and Application Shenzhen 518000 China

Time intervals are often associated with tuples to represent their valid time in temporal relations, where overlap join is crucial for various kinds of queries. Many existing overlap join algorithms use indices based on tree structures such as quad-tree, B＋-tree and interval tree. These algorithms usually have high CPU cost since deep path traversals are unavoidable, which makes them not so competitive as data-partition or plane-sweep based algorithms. This paper proposes an efficient overlap join algorithm based on a new two-layer flat index named as Overlap Interval Inverted Index （i.e., O2i Index）. It uses an array to record the end points of intervals and approximates the nesting structures of intervals via two functions in the first layer, and the second layer uses inverted lists to trace all intervals satisfying the approximated nesting structures. With the help of the new index, the join algorithm only visits the must-be-scanned lists and skips all others. Analyses and experiments on both real and synthetic datasets show that the proposed algorithm is as competitive as the state-of-the-art algorithms.

关键词： overlap interval join temporal relation overlap inverted index join algorithm

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Anomaly Detection of Vehicle CAN Network Based on Message Content 2nd

Anomaly Detection of Vehicle CAN Network Based on Message Co...

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2nd EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2019

作者： Mo, Xiuliang Chen, Pengyuan Wang, Jianing Wang, Chundong Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China Sichuan University ChengduSichuan610207 China

ISBN: (纸本)9783030213725

With the rapid advance of intelligent vehicles, auxiliary driving and automatic driving have been paid more attention to. While vehicle security has become increasingly prominent, which is seriously related to the property and personal safety. The attacker can send abnormal information to the controller through internal CAN bus. Because of the particularity of the vehicle CAN network information communication protocol, the encryption authentication technology cannot effectively solve the safety problem of the vehicle network. In the paper, a novel anomaly detection method based on CAN packet content is proposed. The scheme is effective in preventing in-vehicle ECU attacks caused by malicious modifications. Statistical thinking is adopted to analyze the characteristics of normal message content. Then a confidence interval based on normal features is defined for detecting abnormal network messages. Its detection performance has been demonstrated through experiments carried out on real CAN traffic gathered from an unmodified licensed vehicle. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Anomaly detection

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SAMamba: Adaptive state space modeling with hierarchical vision for infrared small target detection

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Information Fusion 2025年 124卷

作者： Wenhao Xu Shuchen Zheng Changwei Wang Zherui Zhang Chuan Ren Rongtao Xu Shibiao Xu School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing 100876 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100190 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250014 China School of Software Microelectronics Peking University Beijing 100084 China

Infrared small target detection (ISTD) is vital for long-range surveillance systems, particularly in military defense, maritime monitoring, and early warning applications. Despite its strategic importance, ISTD remains challenging due to two fundamental limitations: targets typically occupy less than 0.15% of the image area and exhibit low distinguishability from complex backgrounds. While recent advances in deep learning have shown promise, existing methods struggle with information loss during downsampling and inefficient modeling of global context. This paper presents SAMamba, a novel framework that synergistically integrates SAM2’s hierarchical feature learning with Mamba’s selective sequence modeling to address these challenges. Our key innovations include: (1) Feature Selection Adapter (FS-Adapter) that enables efficient domain adaptation from natural to infrared imagery by employing a dual-stage selection mechanism, which includes token-level selection via a learnable task embedding and channel-wise refinement through adaptive transformations; (2) Cross-Channel State-Space Interaction (CSI) module that achieves efficient global context modeling through selective state space modeling with linear complexity; and (3) Detail-Preserving Contextual Fusion (DPCF) module that adaptively combines multi-scale features through learnable fusion strategies, utilizing a gating mechanism to balance contributions from high-resolution and low-resolution features. SAMamba effectively addresses the core challenges of ISTD by bridging the domain gap, maintaining fine-grained target details, and efficiently modeling long-range dependencies. Extensive experiments on NUAA-SIRST, IRSTD-1k and NUDT-SIRST datasets demonstrate that SAMamba significantly outperforms state-of-the-art methods, particularly in challenging scenarios with heterogeneous backgrounds and varying target scales. Code is available at https://***/zhengshuchen/SAMamba .

关键词： Feature fusion Infrared small object detection Segment Anything Model Vision Mamba

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Mining Robust Frequent Items in Data Streams

Mining Robust Frequent Items in Data Streams

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Rui Xia Haipeng Dai Zhanchao Du Meng Li Alex X. Liu Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing CHINA Institute of Manned Space System Engineering China Academy of Space Technology Beijing CHINA Shanghai key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai CHINA

ISBN: (数字)9781728169811

ISBN: (纸本)9781728169828

This paper studies the problem of robust frequent items mining in data streams that generalizes the traditional frequent items mining by considering the noise of datasets. That is, different items may correspond to the same entity because of noise; examples include different images of the same object and fluctuated data in the same setting measured by sensors. Our objective is to identify those items that correspond to the same entity and have an aggregated frequency exceeding a given threshold, which named as robust frequent items. To the best of our knowledge, there is no existing works on mining robust frequent items in a data stream. In this paper, we first propose a scheme by applying sampling and spatial partition to address the problem in low dimensional spaces. Furthermore, we extend the above algorithmic framework to high dimensional spaces by incorporating the locality sensitive hashing scheme to deal with the approximate nearest neighbor problem. We conduct evaluations using synthetic datasets and compare our scheme with two prior adapted schemes. Our results demonstrate that the efficiency of our algorithms outperforms the adaptive Space Saving by 14.8% and 9.8% in terms of precision and recall, respectively.

关键词： Noise measurement Robustness Data mining Partitioning algorithms Approximation algorithms Two dimensional displays Sensors

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Human Motion Recognition by Three-view Kinect Sensors in Virtual Basketball Training

Human Motion Recognition by Three-view Kinect Sensors in Vir...

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IEEE Region 10 International Conference TENCON

作者： Baoqi Yao Hui Gao Xin Su Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728184555

ISBN: (纸本)9781728184562

In recent years, human action recognition has received a considerable amount of research attention because of it's potential in a variety of applications, such as video surveillance, human-computer interaction, and virtual reality (VR). However, many researches on human action recognition performed in single-camera or double-camera system, which achieve reduced performance due to vulnerability to partial occlusion and miss-recognition of back. Some works on human action recognition use multiple cameras but are too complex for practical application. In this paper, we propose a new human action recognition system using triple Kinect sensors for VR application. Particularly, we design a mark detection method to determine the front of user and fusion skeleton data in real time. Features are extracted from three-dimensional (3D) skeleton data sequences, and divided into five parts according to body parts. A classification model based on the part-aware long short-term memory networks is proposed to recognize human motion. Finally, we demonstrate the system with a virtual reality basketball application and the results of experiment validate the feasibility of the proposed system.

关键词： Skeleton Sensors Cameras Sensor systems Feature extraction Face recognition Shoulder

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EDOA: an efficient delay optimization approach for mixed-polarity Reed-Muller logic circuits under the unit delay model

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Frontiers of Computer Science 2019年第5期13卷 1102-1115页

作者： Zhenxue HE Limin XIAO Fei GU Li RUAN Zhisheng HUO Mingzhe LI Mingfa ZHU Longbing ZHANG Rui LIU Xiang WANG College of Information Science and Technology Hebei Agricultural University Baoding 071001 China State Key Laboratory of Software Development Environment Beihang University Beijing 100083 China School of Computer Science and Engineering Beihang University Beijing 100083 China School of Electronic and Information Engineering Beihang University Beijing 100083 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100083 China

Delay optimization has recently attracted signif-icant attention. However, few studies have focused on the delay optimization of mixed-polarity Reed-Muller (MPRM) logic circuits. In this paper, we propose an efficient delay op-timization approach (EDOA) for MPRM logic circuits under the unit delay model, which can derive an optimal MPRM logic circuit with minimum delay. First, the simplest MPRM expression with the fewest number of product terms is ob-tained using a novel Reed-Muller expression simplification approach (RMESA) considering don't-care terms. Second, a minimum delay decomposition approach based on a Huffman tree construction algorithm is utilized on the simplest MPRM expression. Experimental results on MCNC benchmark cir-cuits demonstrate that compared to the Berkeley SIS 1.2 and ABC, the EDOA can significantly reduce delay for most cir-cuits. Furthermore, for a few circuits, while reducing delay, the EDOA incurs an area penalty.

关键词： delay optimization mixed-polarity Reed-Muller logic circuits unit delay model don't-care terms,

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RNC: Reliable network property classifier based on graph embedding 20

RNC: Reliable network property classifier based on graph emb...

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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

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BTI Aging Monitoring based on SRAM Start-up Behavior

BTI Aging Monitoring based on SRAM Start-up Behavior

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Asian Test Symposium (ATS)

作者： Shengyu Duan Peng Wang Gaole Sai School of Computer Engineering and Science Shanghai University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.

关键词： Degradation Monitoring Logic circuits Transistors SRAM cells Aging system-on-chip

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A Framework Analysis of the 6G Vertical Ecosystem Based on Stakeholder Theory 24

A Framework Analysis of the 6G Vertical Ecosystem Based on S...

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Proceedings of the 2024 10th International Conference on Communication and Information Processing

作者： Fengshuai Du Jiao Zhang Xinyue Yan Tianjian Yang Haiyan Yan Jiayin Qi Enzo Bivona Suya Xiao Xing Shi Department of Political Sciences and International Relations (DEMS) University of Palermo Palermo Sicily Italy and Huangpu Research School of Guangzhou University Guangzhou University Guanghzou Guangdong China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Fudan University Shanghai China Beijing University of Posts and Telecommunications Beijing China Shanghai University of International Business and Economics Shanghai China Cyberspace Institute of Advanced Technology Guangzhou Guangdong China and Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Department of Political Sciences and International Relations (DEMS) University of Palermo Palermo Sicily Italy International Business School Shandong Jiaotong University Weihai Shandong China

ISBN: (纸本)9798400717444

This research provides a framework analysis of the 6G vertical ecosystem's development through stakeholder theory, identifying three phases: introduction, development, and maturity, and discussing the evolution from a simple value chain to a complex ecosystem driven by industry demands and technology advancements, focusing on the challenges and drivers including technological innovation, economic viability, regulatory needs, environmental sustainability, security, and societal impact.

关键词： 6G

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