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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

Contrastive Learning for Robust Android Malware Familial Classification

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arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

The minority matters: a diversity-promoting collaborative metric learning algorithm 22

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The minority matters: a diversity-promoting collaborative me...

Proceedings of the 36th International Conference on Neural Information processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

关键词：

Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims

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arXiv 2021年

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021, CC BY-SA.

关键词： Semantics

FAST Observations of Four Comets to Search for the Molecular Line Emissions between 1.0 and 1.5 GHz Frequencies

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Research in Astronomy and Astrophysics 2024年第10期24卷 130-140页

作者： Long-Fei Chen Chao-Wei Tsai Jian-Yang Li Bin Yang Di Li Yan Duan Chih-Hao Hsia Zhichen Pan Lei Qian Donghui Quan Xue-Jian Jiang Xiaohu Li Ruining Zhao Pei Zuo School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang 550025China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311100China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Key Laboratory of Radio Astronomy and Technology Chinese Academy of SciencesBeijing 100101China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhai 519082China Núcleo de Astronomía Facultad de Ingenieríay CienciasUniversidad Diego PortalesChile Department of Astronomy College of Physics and Electronic EngineeringQilu Normal UniversityJinan 250200China Department of Electronic and Optical Engineering Space Engineering UniversityBeijing 101416China Laboratory for Space Research Faculty of ScienceThe University of Hong KongHong Kong(SAR)China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Astronomy and Space Sciences University of Chinese Academy of SciencesBeijing 100049China

We used the Five-hundred-meter Aperture Spherical radio Telescope(FAST)to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets,C/2020 F3(NEOWISE),C/2020 R4(ATLAS),C/2021 A1(Leonard),and 67P/Churyumov-Gerasimenko during or after their perihelion *** of molecular transition lines fall in this low-frequency range,many attributed to complex organic or prebiotic *** conducted a blind search for the possible molecular lines in this frequency range in those comets and could not identify clear signals of molecular emissions in the *** several molecules have been detected at high frequencies of greater than100 GHz in comets,our results confirm that it is challenging to detect molecular transitions in the L-band frequency *** non-detection of L-band molecular lines in the cometary environment could rule out the possibility of unusually strong lines,which could be caused by the masers or non-LTE *** the line strengths are predicted to be weak,for FAST,using the ultra-wide bandwidth receiver and improving the radio frequency interference environments would enhance the detectability of those molecular transitions at low frequencies in the future.

关键词： astrochemistry ISM:molecules comets:general line:identification

Research on Star/Galaxy Classification Based on Stacking Ensemble Learning

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Chinese Astronomy and Astrophysics 2020年第3期44卷 345-355页

作者： Chao, L.I. Wen-hui, ZHANG Ran, L.I. Jun-yi, W.A.N.G. Ji-ming, L.I.N. College of Information and Communication Engineering Guilin University of Electronic Technology Guilin 541004 Key Laboratory of Education Ministry for Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin 541004 Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin 541004 Guangxi Key Laboratory for Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Satellite Navigation and Position Sensing Guilin 541004

Machine learning has achieved great success in many areas today, but the forecast effect of machine learning often depends on the specific problem. An ensemble learning forecasts results by combining multiple base classifiers. Therefore, its ability to adapt to various scenarios is strong, and the classification accuracy is high. In response to the low classification accuracy of the darkest source magnitude set of stars/galaxies in the Sloan Digital Sky Survey (SDSS), a star/galaxy classification algorithm based on the stacking ensemble learning is proposed in this paper. The complete photometric data set is obtained from the SDSS data Release (DR) 7, and divided into the bright source magnitude set, dark source magnitude set, and darkest source magnitude set according to the stellar magnitude. Firstly, the 10-fold nested cross-validation method is used for the darkest source magnitude set, then the Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms are used to establish the base-classifier model;the Gradient Boosting Decision Tree (GBDT) is used as the meta-classifier model. Finally, based on the classification accuracy of galaxies and other indicators, the classification results are analyzed and compared with the results obtained by the Function Tree (FT), SVM, RF, GBDT, Stacked Denoising Autoencoders (SDAE), Deep Belief Nets (DBN), and Deep Perception Decision Tree (DPDT) models. The experimental results show that the stacking ensemble learning model has improved the classification accuracy of galaxies in the darkest source magnitude set by nearly 10% compared to the function tree algorithm. Compared with other traditional machine learning algorithm, stronger lifting algorithm, and deep learning algorithm, the stacking ensemble learning model also has different degrees of improvement. © 2020

关键词： Galaxies: fundamental parameters methods: data analysis Stars: fundamental parameters techniques: photometric

DITE: Achieving Distributed Intelligent Traffic Engineering With Sparse Network-Wide Guidance

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IEEE Transactions on Network Science and Engineering 2025年

作者： Guo, Yingya Zhou, Weihong Hu, Cheng Lin, Bin Deng, Yuhui Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou 350025 China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350025 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou 350025 China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou 510515 China Jinan University Department of Computer Science Guangzhou 510632 China University of Exeter Department of Computer Science College of Engineering Mathematics and Physical Sciences Exeter EX4 4PY United Kingdom

Distributed Traffic Engineering (TE) adopts decentralized routing and offers natural merits over centralized TE in rapidly responding to dynamic network flows. However, based solely on local network observations, traditional distributed TE methods often lack sufficient routing evidence necessary for generating optimal policies. Furthermore, reinforcement learning, commonly used in distributed TE methods to avoid the need for labeled data, faces challenges in accumulating valuable routing experiences due to its trial-and-error exploration. To handle these challenges, we propose a novel Distributed Intelligent Traffic Engineering (DITE) framework that empowers geographically distributed agents to rapidly and adaptively optimize routing policies for dynamic network flows with low communication overhead. Specifically, to address the limitation of optimizing local routing based solely on partial network observations, we propose enriching the routing evidence for each agent with sparse network-wide information. To promote agents to learn cooperative routing polices efficiently, we explore a new paradigm for training routing agents by seamlessly integrating imitation learning with reinforcement learning. Additionally, to efficiently describe the implicit spatiotemporal features among the routing evidence, we customize a transformer-based agent to utilize the attention mechanism for establishing the relationships between agent states and routing policies. Extensive comparative evaluations on three real network topologies and traffic traces demonstrate the superior performance of DITE in achieving distributed TE. Compared to the existing distributed TE approaches, DITE achieves a notable reduction of 35.95%–42.35% in maximum link utilization and a reduction of 23.62%-68.39% in end-to-end latency. Moreover, the experimental results indicate DITE is robust to network failures and traffic changes. © 2013 IEEE.

关键词： Distributed traffic engineering imitation learning network-wide guidance reinforcement learning transformer

Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

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arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access

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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning