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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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Design and Implementation of Takeaway Ordering Recommendation System Based on Python and Flask 1

引用

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Cao, Junjie Yu, Wendong Wei, Hongwei Yuan, Lin Li, Zhujun Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China Shandong Tianyi Information Technology Co. Ltd. Shandong Jinan250101 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China

ISBN: (数字)9789819619047

ISBN: (纸本)9789819619030

With the rapid development of the economy, people are getting busier and busier, and it is difficult to find time to cook, so ordering takeout or dining at restaurants has become the norm. In the 2020 work report, the government proposed to fully implement the "Internet +" process, which also indicates the arrival of the advantages of the digital economy. Therefore, it has become an inevitable trend to use Internet technology to change our daily life and production methods. Driven by this trend, the catering industry has begun to move towards informatization. In order to make it more convenient for merchants and customers to meet their respective needs and save time and energy, a takeaway ordering recommendation system has emerged. This paper uses a collaborative filtering recommendation algorithm to recommend personalized foods that users may like, so as to provide more convenient services for catering services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bottles

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A Natural Language Processing Approach for Instruction Set Architecture Identification

arXiv

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arXiv 2022年

作者： Sahabandu, Dinuka Mertoguno, Sukarno Poovendran, Radha The Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States The Institute for Information Security & Privacy Georgia Institute of Technology North Avenue AtlantaGA30332 United States

Binary analysis of software is a critical step in cyber forensics applications such as program vulnerability assessment and malware detection. This involves interpreting instructions executed by software and often necessitates converting the software’s binary file data to assembly language. The conversion process requires information about the binary file’s target instruction set architecture (ISA). However, ISA information might not be included in binary files due to compilation errors, partial downloads, or adversarial corruption of file metadata. Machine learning (ML) is a promising methodology that can be used to identify the target ISA using binary data in the object code section of binary files. In this paper we propose a binary code feature extraction model to improve the accuracy and scalability of ML-based ISA identification methods. Our feature extraction model can be used in the absence of domain knowledge about the ISAs. Specifically, we adapt models from natural language processing (NLP) to i) identify successive byte patterns commonly observed in binary codes, ii) estimate the significance of each byte pattern to a binary file, and iii) estimate the relevance of each byte pattern in distinguishing between ISAs. We introduce character-level features of encoded binaries to identify fine-grained bit patterns inherent to each ISA. We use a dataset with binaries from 12 different ISAs to evaluate our approach. Empirical evaluations show that using our byte-level features in ML-based ISA identification results in an 8% higher accuracy than the state-of-the-art features based on byte-histograms and byte pattern signatures. We observe that character-level features allow reducing the size of the feature set by up to 16x while maintaining accuracy above 97%. © 2022, CC BY.

关键词： Extraction

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HSTF-Model: an HTTP-based Trojan Detection Model via the Hierarchical Spatio-Temporal Features of Traffics

arXiv

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arXiv 2023年

作者： Xie, Jiang Li, Shuhao Yun, Xiaochun Zhang, Yongzheng Chang, Peng Institute of Information Engineering Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology University of Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

HTTP-based Trojan is extremely threatening, and it is difficult to be effectively detected because of its concealment and confusion. Previous detection methods usually are with poor generalization ability due to outdated datasets and reliance on manual feature extraction, which makes these methods always perform well under their private dataset, but poorly or even fail to work in real network environment. In this paper, we propose an HTTP-based Trojan detection model via the Hierarchical Spatio-Temporal Features of traffics (HSTF-Model) based on the formalized description of traffic spatio-temporal behavior from both packet level and flow level. In this model, we employ Convolutional Neural network (CNN) to extract spatial information and Long Short-Term Memory (LSTM) to extract temporal information. In addition, we present a dataset consisting of Benign and Trojan HTTP Traffic (BTHT-2018). Experimental results show that our model can guarantee high accuracy (the F1 of 98.62%∼99.81% and the FPR of 0.34%∼0.02% in BTHT-2018). More importantly, our model has a huge advantage over other related methods in generalization ability. HSTF-Model trained with BTHT-2018 can reach the F1 of 93.51% on the public dataset ISCX-2012, which is 20+% better than the best of related machine learning methods. © 2023, CC BY.

关键词： HTTP

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Detecting Unknown HTTP-based Malicious Communication Behavior via Generated Adversarial Flows and Hierarchical Traffic Features

arXiv

引用

arXiv 2023年

作者： Yun, Xiaochun Xie, Jiang Li, Shuhao Zhang, Yongzheng Sun, Peishuai National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology University of Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Malicious communication behavior is the network communication behavior generated by malware (botnet, spyware, etc.) after victim devices are infected. Experienced adversaries often hide malicious information in HTTP traffic to evade detection. However, related detection methods have inadequate generalization ability because they are usually based on artificial feature engineering and outmoded datasets. In this paper, we propose an HTTP-based Malicious Communication traffic Detection Model (HMCD-Model) based on generated adversarial flows and hierarchical traffic features. HMCD-Model consists of two parts. The first is a generation algorithm based on WGAN-GP to generate HTTP-based malicious communication traffic for data enhancement. The second is a hybrid neural network based on CNN and LSTM to extract hierarchical spatial-temporal features of HTTP-based traffic. In addition, we collect and publish a dataset, HMCT-2020, which consists of large-scale malicious and benign traffic during three years (2018-2020). Taking the data in HMCT-2020(18) as the training set and the data in other datasets as the test set, the experimental results show that the HMCD-Model can effectively detect unknown HTTP-based malicious communication traffic. It can reach F1 ≈ 98.66% in the dataset HMCT-2020(19-20), F1 ≈ 90.69% in the public dataset CIC-IDS-2017 and F1 ≈ 83.66% in the real traffic, which is 20+% higher than other representative methods on average. This validates that HMCD-Model has the ability to discover unknown HTTP-based malicious communication behavior. © 2023, CC0.

关键词： HTTP

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MedRE-DeBGPR: A Novel Model for Medical Relation Extraction Based on DeBERTa, GlobalPointer and RoPE

SSRN

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SSRN 2024年

作者： Deng, Zhiqi Luo, Xudong Gong, Shutao Liu, Jinlu School of Computer Science and Engineering Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China

In medical text analysis, extracting relationships among medical entities is crucial for enhancing clinical decision-making and building comprehensive medical knowledge graphs. This paper proposes a novel model for Relation Extraction (RE) from medical texts, which combines the strengths of DeBERTa (a pre-trained language model known for capturing complex linguistic phenomena) and GPLinker (a framework adept at handling overlapping and nested entities). By integrating these two technologies, our model effectively manages the inherent complexities of medical language, such as domain-specific terminology, complex sentence structures, and nested relationships. The proposed model uses the Fast Gradient Method (FGM) for adversarial training to improve robustness, Efficient GlobalPointer for parameter usage optimisation, and Rotary Position Embedding (RoPE) for better entity recognition. Experimental results on the CMedCausal dataset demonstrate that our approach outperforms existing baseline models and large language models in extracting entity relationships from medical documents. This model has significant implications for developing clinical decision support systems and medical knowledge graphs, and it holds possibility for adaptation across different languages and medical domains. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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PCB Defect Detection Model with Convolutional Modules Instead of Self-Attention Mechanism

PCB Defect Detection Model with Convolutional Modules Instea...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： MingLe Zhou ChangLe Yi HongLin Wan Min Li Gang Li DeLong Han 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 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Physics and Electronic Science Shandong Normal University Jinan China

The detection of defects in printed circuit boards requires high accuracy and realtime performance. Existing industrial detection models generally adopt a pure convolutional structure for ease of deployment. However, the detection accuracy of these models is often insufficient to meet the requirements of the scene. To improve the detection model accuracy and ease of deployment, this paper proposes a convolutional merging Transformer network(CMTRNet). The CMTR-Net model proposes a backbone network (CNN-Former) that uses convolutional modules to replace self-attention, combining the Transformer architecture with a convolutional structure. This approach not only avoids the drawback of self-attention high computation complexity that is detrimental to deployment but also improves the model detection accuracy. Based on CNN-Former, this paper also proposes a feature fusion module that can better fuse the features extracted by CNN-Former. Further-more, based on the CMTRNet model and the characteristics of circuit board defects, this paper proposes a loss function called Melt-IoU, which can make the initial training phase smoother and further improve detection accuracy. Experiments have shown that CMTRNet outperforms existing advanced models on both datasets.

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Joint Secure Beamforming for RIS-Aided Integrated Multibeam-Satellite Terrestrial networks

Joint Secure Beamforming for RIS-Aided Integrated Multibeam-...

引用

Future Communications and networks (FCN), International Conference on

作者： Yu Zhang Yang Yang Shuangrui Zhao Teng Li Yulong Shen Xiaohong Jiang Jianfeng Ma School of Computer Science and Technology Xidian University Xi’an China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China School of Cyber Engineering Xidian University Xi’an China School of Systems Information Science Future University Hakodate Japan

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

This paper focuses on a reconfigurable intelligent surface (RIS) enabled integrated multibeam-satellite terrestrial network, which consists of a satellite, a base station (BS), an RIS, multiple earth stations (ESs), an eavesdropper (Eve) and a terrestrial user (TU). In such a network, the satellite provides broadband services to multiple ESs, while the BS transmits friendly interference to the Eve for crippling confidential information interception with the help of the RIS. To enhance the security performance, we provide a theoretical modelling secrecy rate (SR) and formulate the SR maximization problem subject to the signal-to-interference-plus-noise ratio (SINR) requirements, the total transmit power limit and the RIS unitmodulus constraints. By combining approaches like alternative optimization (AO), bisection search, semi-definite programming (SDP), eigenvalue decomposition, and Gaussian randomization, we then propose a joint secure beamforming scheme to solve the non-convex optimization problem and thus identify the optimal beamforming designs for the satellite, the BS, and the RIS. Simulation results indicate the effectiveness of security enhancement via the proposed joint secure beamforming scheme.

关键词： Satellites Array signal processing Simulation Interference Reconfigurable intelligent surfaces Space-air-ground integrated networks Search problems

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Mining unseen classes via regional objectness: a simple baseline for incremental segmentation 22

Mining unseen classes via regional objectness: a simple base...

引用

Proceedings of the 36th International Conference on Neural information Processing Systems

作者： Zekang Zhang Guangyu Gao Zhiyuan Fang Jianbo Jiao Yunchao Wei School of Computer Science Beijing Institute of Technology School of Computer Science University of Birmingham WEI Lab Institute of Information Science Beijing Jiaotong University and Beijing Key Laboratory of Advanced Information Science and Network

ISBN: (纸本)9781713871088

Incremental or continual learning has been extensively studied for image classification tasks to alleviate catastrophic forgetting, a phenomenon that earlier learned knowledge is forgotten when learning new concepts. For class incremental semantic segmentation, such a phenomenon often becomes much worse due to the background shift, i.e., some concepts learned at previous stages are assigned to the background class at the current training stage, therefore, significantly reducing the performance of these old concepts. To address this issue, we propose a simple yet effective method in this paper, named Mining unseen Classes via Regional Objectness for Segmentation (MicroSeg). Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages. Therefore, to avoid forgetting old knowledge at the current training stage, our MicroSeg first splits the given image into hundreds of segment proposals with a proposal generator. Those segment proposals with strong objectness from the background are then clustered and assigned newly-defined labels during the optimization. In this way, the distribution characterizes of old concepts in the feature space could be better perceived, relieving the catastrophic forgetting caused by the background shift accordingly. Extensive experiments on Pascal VOC and ADE20K datasets show competitive results with state-of-the-art, well validating the effectiveness of the proposed MicroSeg.

关键词：

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Civil Engineering Design in IoT: Leveraging Improved Swarm Intelligence Optimization

Civil Engineering Design in IoT: Leveraging Improved Swarm I...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Weiheng Fu Rongzhong Chen Kaiwen Chen Xiaojun Xie College of Computer and Data Science Fuzhou University Fuzhou China Xiamen Key Laboratory of Straits Meteorology Xiamen Meteorological Bureau Xiamen China Roadway Design Department Florida Transportarion Engineeeing INC Punta Gorda United States Office of Network Security and Information Fuzhou University Zhicheng College Fuzhou China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Nowadays, swarm intelligence algorithms are used to solve various problem in IoT environments because of their excellent performance, and the particle swarm algorithm(PSO) is a superior algorithm in swarm intelligence algorithms. The original PSO, however, exhibits certain limitations. For instance, the utilization of random initialization results in a less diverse population distribution for PSO. During the algorithm iteration process, excessively large or small weight factors can also detrimentally impact the performance of the algorithm. Moreover, when dealing with multi-modal problems, the PSO algorithm can quickly converge to local optimal because of its "precocious". In this paper, a Double-Population Particle Swarm Optimization(DP-PSO) algorithm is proposed. Firstly, the chaotic mechanism is used to initialize the population to make its distribution more uniform and obtain better convergence results. Secondly, the oscillation characteristic of the sine function is used to improve the updating method of weight by adding the sine factor, and the search area of the particle is expanded. Finally, the double-population variation mechanism is introduced to enhance the variation ability and improve the solving accuracy in the later iteration. The experimental results show that the DP-PSO is superior to other algorithms in performance and has obvious improvement in the ability to expand the search space of the population and jump out of the local optimal solution. When applied to cantilever beam design, DP-PSO also has good competitiveness in terms of stability, robustness, and scalability.

关键词： Social computing Scalability Optimization methods Robustness Structural beams Particle swarm optimization Thermal stability Oscillators Civil engineering Convergence

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