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International Conference on Computer Supported Cooperative Work in Design

作者： Ji Zhang Peng Li Weiyi Huang Lei Nie Haizhou Bao Qin Liu School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China School of Cyber Science and Engineering Wuhan University China

Task assignment is a key challenge in mobile crowd-sensing because of the varying capabilities of crowd users. Location-based task assignment schemes require users to upload their location to an untrusted platform, which raises many privacy concerns. However, stronger privacy preservation may lead to lower system utility. It is challenging to maximize system utility under privacy preservation for users. In this paper, we propose a privacy-preserving task assignment for heterogeneous users (PTAH) problem in mobile crowdsensing. Specifically, we divide users into two groups: private users with location privacy requirements and public users without location privacy requirements. We first design a privacy-preserving mechanism to obfuscate the actual location of private users. Then we construct a relationship graph based on the locations between users and tasks. We prove that the PTAH problem is an NP-hard problem, so to maximize the system utility, we propose an approximation algorithm based on the greedy algorithm. Then we propose a multi-thread cooperative simulated annealing algorithm to search for a better approximate solution. Finally, we conducted simulations based on the widely-used real-world Roma dataset. The results show that our proposed algorithm consistently outperforms other baseline algorithms.

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Towards DDIs Identification by Knowledge Graph with BiRW and Back Aggregation

Towards DDIs Identification by Knowledge Graph with BiRW and...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhuang Yin Xiaoli Lin Xiaolong Zhang Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Effective identification of potential drug-drug interactions (DDIs) can prevent adverse effects caused by DDIs to a certain extent. This paper proposes the new hybrid method for predicting DDIs, which is called BiRW-KGBAN that combines bidirectional random walk and back aggregation based on knowledge graph. BiRW-KGBAN first constructs two knowledge graph KG-DrugBank and KG-KEGG that integrate data related to drugs. Then, an improved sampling method based on bidirectional random walk (BiRW) is used to sample the neighborhood information of drugs, including directly connected entities, related semantic relations and potential information. In addition, the path with drug as the center node is also extracted. Finally, two improved back aggregation model KGBAN1 and KGBAN2 are used to obtain the final embedded representation of the drugs. The experiments show that, compared with other existing methods, our method could obtain the higher-order topological information and the deeper potential neighborhood information of drugs, and has great improvements in DDIs prediction. The case study also shows that the proposed method has the potential for actual DDIs prediction.

关键词： Drugs Correlation Biological system modeling Semantics Medical treatment Sampling methods Bioinformatics

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Adjacency-hopping de Bruijn Sequences for Non-repetitive Coding

arXiv

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arXiv 2023年

作者： Chen, Bin Liang, Zhenglin Wu, Shiqian The Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430072 China

A special type of cyclic sequences named adjacency-hopping de Bruijn sequences is introduced in this paper. It is theoretically proved the existence of such sequences, and the number of such sequences is derived. These sequences guarantee that all neighboring codes are different while retaining the uniqueness of subsequences, which is a significant characteristic of original de Bruijn sequences in coding and matching. At last, the adjacency-hopping de Bruijn sequences are applied to structured light coding, and a color fringe pattern coded by such a sequence is presented. In summary, the proposed sequences demonstrate significant advantages in structured light coding by virtue of the uniqueness of subsequences and the adjacency-hopping characteristic, and show potential for extension to other fields with similar requirements of non-repetitive coding and efficient matching. Copyright © 2023, The Authors. All rights reserved.

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Combined Representation Learning for Uncertain Knowledge Graphs

Combined Representation Learning for Uncertain Knowledge Gra...

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Mechatronics, IoT and Industrial Informatics (ICMIII), International Conference on

作者： Rui Qiao Xiaolei Zhong Yang Fu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China Dianchi College Kunming China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386646

Uncertain knowledge graphs store the relations between entities in the real world in the form of weighted triples, where the weights indicate the confidence scores of the triples to be true. However, there is limited research on the inference of uncertain knowledge graphs, especially in the integration of symbolic and numerical methods. Rules are explicit knowledge. Encoding the rules directly can provide more knowledge graph semantic information and can make full use of the interpretability and accuracy of logical rules. Unfortunately, none of the existing methods directly encode rules for uncertain knowledge graphs, but only use rules to calculate the confidence of unseen triples in training data. In this paper, we propose a new reasoning model CRLUKG for uncertain knowledge graphs, that combines the advantages of symbolic and numerical reasoning. We directly encode rules mined from the knowledge graph as one of the inputs of the CRLUKG model. Compositional representation learning with triple and rule embedding is implemented for optimizing objective function for triples and relations pairs. Experimental results demonstrate that our proposed method enhances the accuracy of reasoning, and furthermore, CRLUKG exhibits superior performance in comparison to baseline models when handling multi-task scenarios.

关键词： Representation learning Accuracy Large language models Semantics Training data Knowledge graphs Tail

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AIDetectVul: Software Vulnerability Detection Method Based on Feature Fusion of Pre-trained Models

AIDetectVul: Software Vulnerability Detection Method Based o...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Shiying Xue Lin Li Tao Li Haodong Chen Jiapan Li Yangqing Qin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real time Industrial Systems Wuhan University of Science and Technology Wuhan China Wuhan Jiaoyun Huizhi Information Technology Co. Ltd. Wuhan China Guotou Securities Co. Ltd. Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Data-driven deep learning models are constrained by the scale and diversity of training data, making them vulnerable to data bias. While large language models (LLMs) exhibit superior generalization in vulnerability detection, their low inference efficiency and high computational costs hinder practical deployment in industrial settings. To address these limitations, we propose AIDetectVul, a novel vulnerability detection framework leveraging feature fusion from pre-trained models. Our approach concurrently utilizes encoder-only and decoder-only architectures to extract complementary code embeddings, with feature fusion enhancing semantic diversity. These enriched representations are then processed by a Transformer model, where the self-attention mechanism effectively captures long-range code dependencies, ultimately improving both detection accuracy and generalization capability. Comprehensive evaluations on proprietary enterprise datasets and open-source benchmarks demonstrate that AIDetectVul achieves comparable detection accuracy to the state-of-the-art LineVul model while demonstrating measurable improvements in generalization performance. Compared to LLM-based approaches, our solution maintains significantly lower computational overhead and training costs, making it particularly suitable for industrial applications.

关键词： Training Accuracy Codes Computational modeling Large language models Training data Computer architecture Feature extraction Transformers Vectors

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An Ensemble Learning Approach for Wind Power Forecasting

An Ensemble Learning Approach for Wind Power Forecasting

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Electronic information Engineering and Computer technology (EIECT), International Conference on

作者： Yuesheng Huang Sida Chen Qiling Wu Jiapeng Xiao Yongqi Ren Leijun Wang Rongjun Chen Jiawen Li School of Computer Science Guangdong Polytechnic Normal University Guangzhou China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

With the growing global energy demand and requirement for environmental protection, renewable energy is attracting attention as a vital development direction. Particularly, wind power is rapidly developing as a clean and sustainable form of energy. Nonetheless, wind speed uncertainty and system dynamics make wind power prediction and optimization complex and challenging. Accurate wind power prediction is essential to improve system reliability and performance. In this regard, traditional forecasting methods have limitations in some cases. In this study, based on XGBoost and decision tree with Bagging methods in ensemble learning, wind power data is predicted. The results indicated that better performances than the traditional linear regression and ordinary decision tree models are achieved, revealing the efficiency of the proposed approach.

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Distorted SAR target recognition with virtual SVM and AP-HOG feature 5

Distorted SAR target recognition with virtual SVM and AP-HOG...

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IET International Radar Conference 2020, IET IRC 2020

作者： Yao, Di Liu, Zhenyuan Li, Feng Li, Yang Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839535635

Synthetic aperture radar (SAR) plays an indispensable role in national defence and civil fields due to its unique advantages. Although many methods have been proposed, the recognition to SAR images still faces many difficulties, especially to distorted images. This paper first improves the morphological methods attribute profiles (APs) which is widely employed with optical, and hyperspectral images in particular. We combine the APs method with histogram of oriented gradients (HOG) feature (called AP-HOG) to make it suitable for SAR images. Then, the SVM model is used to identify the labelled samples (i.e., Support Vectors) that are closest to the separating hyperplane with maximum margin. We generate different degrees of distorted images based on support vectors, and use the distorted image and the support vector as a new training set for retraining to improve the recognition effect of the distorted image. Finally, we perform experimental verification based on the MSTAR dataset, which proves the effectiveness of the proposed method. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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Robot Crowd Navigation in Dynamic Environment with Offline Reinforcement Learning

arXiv

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arXiv 2023年

作者： Zhou, Shuai Fu, Hao He, Haodong Liu, Wei School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430081 China Hubei Key Laboratory of Digital Textile Equipment Wuhan430200 China

Robot crowd navigation has been gaining increasing attention and popularity in various practical applications. In existing research, deep reinforcement learning has been applied to robot crowd navigation by training policies in an online mode. However, this inevitably leads to unsafe exploration, and consequently causes low sampling efficiency during pedestrian-robot interaction. To this end, we propose an offline reinforcement learning based robot crowd navigation algorithm by utilizing pre-collected crowd navigation experience. Specifically, this algorithm integrates a spatial-temporal state into implicit Q-Learning to avoid querying out-of-distribution robot actions of the pre-collected experience, while capturing spatial-temporal features from the offline pedestrian-robot interactions. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods by means of qualitative and quantitative analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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SAR target recognition using feature extraction of multi-scale monogenic components 5

SAR target recognition using feature extraction of multi-sca...

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IET International Radar Conference 2020, IET IRC 2020

作者： Li, Feng Yao, Weijun Li, Yang Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839535635

In this paper, we combine the monogenic signal with the histogram of oriented gradient (HOG) feature, and propose a new low-level feature: Monogenic-HOG (MG-HOG);then use the bag of words (BoW) model to construct the middle-level feature. Most of the existed features are directly extracted from grayscale synthetic aperture radar (SAR) images. As an extended representation of analytic signals in high-dimensional space, the monogenic signal has excellent characteristics such as rotation and scale invariance in the potential extraction process, and it is also valuable in the image field. However, in the past related work, it is mostly used directly as the feature representation of the image. After experimental validation on the moving and stationary target acquisition and recognition (MSTAR) data set, the proposed feature extraction method has better performance than a number of existed methods. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Feature extraction

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SAR target recognition combining class information and classification hyperplane 5

SAR target recognition combining class information and class...

引用

IET International Radar Conference 2020, IET IRC 2020

ISBN: (纸本)9781839535635

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role. An important step in SAR target recognition is to transform the raw data to a feature space with the property of within-class compactness and between-class separation. However, due to the problem of target aspect angle sensitivity in SAR images, the difference of some targets belonging to the same class in different poses is greater than the difference of targets belonging to different class in similar poses, which often causes misjudgement. To solve this problem, we first use the twin support vector machine (TWSVM) to obtain the classification hyperplane of each class. Then, based on the obtained hyperplane, the projection matrix is constructed by fusing the prior class information of the target. In this way, the sample can be close to the classification hyperplane of the same class and distant from the classification hyperplane of a different class. This algorithm can improve TWSVM's ability to classify SAR images. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The results verify the effectiveness of the proposed algorithm. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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