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arXiv 2020年

作者： Yin, Heng Zhang, Hengwei Wang, Jindong Dou, Ruiyu State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China

Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam Iterative Fast Gradient Method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models. Copyright © 2020, The Authors. All rights reserved.

关键词： Image recognition

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Event detection and evolution in multi-lingual social streams

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Frontiers of Computer Science 2020年第5期14卷 213-227页

作者： Yaopeng Liu Hao Peng Jianxin Li Yangqiu Song Xiong Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Department of Computer Science and Engineering HKUSTHong Kong99907China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029China

Real-life events are emerging and evolving in social and news *** methods have succeeded in capturing designed features of monolingual events,but lack of interpretability and multi-lingual *** this end,we propose a multi-lingual event mining model,namely MLEM,to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English,Chinese,French,German,Russian and ***,we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec *** propose an 8-tuple to describe event for correlation analysis and evolution graph *** evaluate the MLEM model using a massive human-generated dataset containing real world *** results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.

关键词： event detection event evolution stream processing multi-lingual anomaly detection

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Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony 4th

Potential Indicator for Continuous Emotion Arousal by Dyna...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Pan, Guandong Wu, Zhaobang Yang, Yaqian Wang, Xin Liu, Longzhao Zheng, Zhiming Tang, Shaoting School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China

ISBN: (纸本)9789819640003

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is challenging. Inspired by inter-subject correlation (ISC) utilized in neuroscience, this study introduces a novel Electroencephalography (EEG) based ISC methodology that leverages a single-electrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitable for classifying emotions-first-order difference (FD) an differential entropy (DE). Secondly, through the use of overall correlation analysis, we demonstrate the heterogeneous synchronized performance of electrodes. This performance aligns with neural emotion patterns established in prior studies, thus validating the effectiveness of our approach. Thirdly, by employing a sliding window correlation technique, we showcase the significant consistency of dynamic ISCs across various features or key electrodes in each analyzed film clip. Our findings indicate the method’s reliability in capturing consistent, dynamic shared neural synchrony among individuals, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal. The implications of this research are significant for advancements in affective computing and the broader neuroscience field, suggesting a streamlined and effective tool for emotion analysis in real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography (EEG) Emotion Annotation Inter-subject Correlation

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Deity: Finding Deep Rooted Bugs in JavaScript Engines 19

Deity: Finding Deep Rooted Bugs in JavaScript Engines

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Lin, Hongyang Zhu, Junhu Peng, Jianshan Zhu, DIxia State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450002 China

ISBN: (纸本)9781728105352

Fuzzing [1] is a well-known technique which was employed to provide unexpected or random data as input to JavaScript engines in hopes of finding a security vulnerability. For effective fuzzing, the input must be both syntactically correct and uncommonly randomized for exceptions such as crashes, failing built-in code assertions, or potential memory leaks. In this work, we introduced system Deity which managed to resolve the conflict with innovative AST(Abstract Syntax Tree) [2] based tree mutation and generating methods. It leverages a high-level structural representation of intermediate process JavaScript code. Our evaluation demonstrates the effectiveness of Deity. For large-scale JavaScript engines (njs, mjs, Javascript-Core, ChakraCore, Espruino, Jerryscript) fuzzing, our results significantly show that Deity can improve code coverage and finding more deep rooted bugs (i.e., 35 new bugs, among which we discovered 21 new vulnerabilities with 3 CVEs assigned) over Superion and CodeAlchemist. © 2019 IEEE.

关键词： Engines

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Network Topology Overlapping Group Detection Based on h-Core Pruning

Network Topology Overlapping Group Detection Based on h-Core...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Qi Zhang Yan Liu Yuheng Cai Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781665447300

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently.

关键词： Network topology Signal processing algorithms Interference Signal processing Load management Topology Noise measurement

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A Novel Method for Detection of ECG with Deep Learning

A Novel Method for Detection of ECG with Deep Learning

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International Conference on Computer and Communications (ICCC)

作者： Haiyan Wang Yanjie Zhou Bing Zhou Zongmin Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Simulation Experiment Centre Zhengzhou University of Aeronautics Collaborative Innovation Centre for Internet Healthcare Zhengzhou University Zhengzhou China School of Management Engineering Zhengzhou University Zhengzhou China Collaborative Innovation Centre for Internet Healthcare Zhengzhou University Zhengzhou China School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665409513

With the rapid development of machine learning and deep learning, ECG intelligent detection models lean more heavily on the labeled data. However, the development of ECG annotation cannot satisfy the development of an intelligent detection model because of its high complexity and heavy workload. Developing a model with low dependence on labeled data becomes the core for solving the ECG intelligent detection. To overcome this limitation, this paper proposes a deep active learning (DAL) model for ECG intelligent detection, which combines high-performance deep learning (DL) with active learning (AL) that is less dependent on labeled data. Experimental results show that the proposed method can achieve high performance with the support of a small amount of labeled data. The proposed method could be adopted for data annotation, which achieves a high-quality data annotation and avoids useless annotation.

关键词： Deep learning Machine learning algorithms Annotations Electrocardiography Rhythm Data models Complexity theory

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge computing Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal state update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Body Shape Recognition and Prototype Construction Based on LightGBM Algorithm

Body Shape Recognition and Prototype Construction Based on L...

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作者： Lanmin WANG Hongmin WANG Li LIU Hao LIU School of Textile Science and Engineering Tiangong University Institute of Smart Wearable Electronic Textiles Tiangong University State Key Laboratory of Mathematical Engineering and Advanced Computing School of Fashion Art and Engineering Beijing Institute of Fashion Technology

The regional characteristics of middle-aged women in Xinjiang are obvious,and the phenomenon of incompatible clothing is *** order to avoid the information loss of principal component analysis or factor analysis during body type classification,this study uses the LightGBM algorithm to establish a body type recognition model based on the results of K-Means clustering,and compares it with random forest and linear regression recognition *** is found that the LightGBM model has the best body type recognition *** is *** correlation analysis and regression analysis,regression equations between variables are *** on the new cultural prototype,Liu Ruipu women's clothing prototype,and Donghua prototype,a clothing prototype suitable for middle-aged women in Xinjiang is *** virtual fitting evaluation and actual fitting,the experimental prototypes established based on the Donghua prototype and the Liu Ruipu prototype can meet the needs of middle-aged women for the comfort and aesthetics of *** experimental prototype established by the Donghua prototype has the best effect.

关键词： Middle-aged women K-Means clustering body shape recognition LightGBM algorithm clothing prototype

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Joint Partial Offloading and Resource Allocation for Distributed Multi-access Edge computing Enabled Vehicular Network

TechRxiv

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TechRxiv 2022年

作者： Ma, Guifu Hu, Manjiang Wang, Xiaowei Wu, Di Li, Haoran Bian, Yougang Zhu, Konglin The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Hunan University Wuxi214072 China The College of Design Hunan University Changsha410082 China The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha410082 China The College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In the foreseeable Intelligent Transportation System (ITS), Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge computing (MEC) helps overcome this limitation effectively. In this paper, we investigate the vehicle and edge server collaborative computation offloading problem by jointly optimizing the partial offloading ratio and resource allocation ratio. Different from existing two-level centralized solution methods, we give a new perspective to deal with multi-variable non-convex optimization problem in a distributed manner. That is, by analyzing the feasibility conditions of the problem, we separate the coupling between variables and derive the bounds of partial offloading ratio and resource allocation ratio based on the constraints. We further separate the coupling variables among ICVs and transform the original problem into an equivalent form and decompose it into a two-level convex problem, where each sub-problem satisfies the task offloading requirements. We then develop two low-complexity distributed algorithms to obtain the optimal solution. Finally, extensive simulations demonstrate that our proposed algorithms can converge faster and significantly outperform the baseline schemes in terms of system cost. © 2022, CC BY.

关键词： Resource allocation

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Ultra-Fast Mini License Plate Recognition System Based-on Vision Processing Unit 20

Ultra-Fast Mini License Plate Recognition System Based-on Vi...

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Proceedings of the 2020 2nd International Conference on Big-data Service and Intelligent Computation

作者： Junhui Wang Shuangyin Ren Jiezhong He Xiaolan Ji Diqing Huang College of Computer National University of Defense Technology and State Key Laboratory of Mathematical Engineering and Advanced Computing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Academy of Military Science College of Computer National University of Defense Technology

ISBN: (纸本)9781450388399

As more embedded environments need license plate recognition systems, how to recognize car plates with high speed/accuracy and low energy has become an important and challenging problem. In this paper, we propose a ultra-Fast miNi (FaNi) license plate recognition (LPR) system. The FaNi system are divided into one training sub-system and one inference sub-system. The former are used to get some offline features; then, the latter is deployed online to recognize license numbers with nearly real-time speed. The inference system is comprised of the vision processing unit (VPU) and the display unit. These two parts are both implemented with hardware logic. Experiments show that the FaNi system can obtain high accuracy and high speed with low resource cost.

关键词： VPU Plate Recognition System FPGA

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