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arXiv 2022年

作者： Jia, Xiaojun Zhang, Yong Wu, Baoyuan Ma, Ke Wang, Jue Cao, Xiaochun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyberspace Security University of Chinese Academy of Sciences Beijing China Tencent AI Lab Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Adversarial training (AT) is always formulated as a minimax problem, of which the performance depends on the inner optimization that involves the generation of adversarial examples (AEs). Most previous methods adopt Projected Gradient Decent (PGD) with manually specifying attack parameters for AE generation. A combination of the attack parameters can be referred to as an attack strategy. Several works have revealed that using a fixed attack strategy to generate AEs during the whole training phase limits the model robustness and propose to exploit different attack strategies at different training stages to improve robustness. But those multi-stage hand-crafted attack strategies need much domain expertise, and the robustness improvement is limited. In this paper, we propose a novel framework for adversarial training by introducing the concept of "learnable attack strategy", dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness. Our framework is composed of a target network that uses AEs for training to improve robustness, and a strategy network that produces attack strategies to control the AE generation. Experimental evaluations on three benchmark databases demonstrate the superiority of the proposed method. The code is released at https://***/jiaxiaojunQAQ/LAS-AT. Copyright © 2022, The Authors. All rights reserved.

关键词： Cryptography

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Sakanat: A Competent Mobile Application to Automate Real-Estate

Sakanat: A Competent Mobile Application to Automate Real-Est...

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IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Saeed Saud Sulaiman Al Qassabi N.R Wilfred Blessing Nooritawati Md Tahir College of Computing and Information Sciences University of Technology and Applied Sciences-Muscat Muscat Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Malaysia College of Engineering Universiti Teknologi MARA Malaysia Applied College Princess Nourah Bint Abdul Rahman University (PNU) Riyadh Saudi Arabia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

The traditional methods of searching for flats are often inefficient and lack necessary features, making it difficult for both landlords and tenants to find what they are looking for. The “Sakanat” mobile application is designed to bridge this gap by offering a user-friendly platform for property listing, searching, and management. Landlords can easily upload details, photos, and set rental terms, while tenants can efficiently find suitable flats based on various criteria using advanced search filters. Rent payments can be made directly through the app using a secure payment gateway, and tenants can also access a variety of additional services such as maintenance companies and flat transfer services. Sakanat leverages Flutter for cross-platform app development and Firebase for backend services and data storage. Development will prioritize user-friendliness, performance, and security. By facilitating connections between landlords and tenants, simplifying rent collection, and offering valuable additional services, Sakanat has the potential to revolutionize the flat rental experience.

关键词： Filters Scalability Memory Transforms Logic gates Control systems Mobile applications Maintenance Stakeholders Security

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Research on High Concurrent Access Mechanism Based on Video Platform 2

Research on High Concurrent Access Mechanism Based on Video ...

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2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Gao, Hao Zhou, Xiaolei Zhang, Yonghong Chang, Zhijun Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Big Data Technology and Cognitive Intelligence Laboratory Shandong University Jinan China Documentation and Information Center of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783800757398

In recent years, "online travel" based on surveillance cameras in scenic spots has become a trend, and most of the previous video surveillance platforms did not consider the problem of high concurrency when designing, and large-scale user visits would affect The system server brings great challenges. Therefore, building a video platform based on a high-concurrency technology architecture can make the platform calmly face the access of a large number of users. It is of great significance in maintaining the stability of the platform system and meeting the needs of a large number of users. This article uses MySQL and On top of Redis related technologies, Nginx's built-in weighted round-robin load balancing algorithm is also improved, thereby improving the high concurrency performance of the system. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Security systems

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Multi-dimensional data Visualization Method for Geological Subsidence Deformation Monitoring 2

Multi-dimensional Data Visualization Method for Geological S...

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2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Shao, Yongheng Wang, Hongjun Zhang, Yonghong Liu, Chenxu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China R.C. of Big Data and Artificial Intelligence Technology Shandong University Beijing Zhongke Zhihe Digital Technology Co. Ltd Jinan China

ISBN: (纸本)9783800757398

At present, geological subsidence deformation monitoring is widely used. For the massive multi-dimensional heterogeneous data monitored, how to express and display the data accurately and intuitively, and how to use suitable data visual analysis methods to make relevant monitoring personnel analyze the results of geological subsidence deformation more conveniently and quickly become the main problem. In this research, a visual analysis system combining traditional technology with data visualization technology is designed according to InSAR geological monitoring data of a city in Jiangsu Province, and a visualization model of geological subsidence deformation monitoring based on multi-dimensional data set is proposed. This method has been applied to the evaluation of geological deformation and settlement examples. The appraisal by experts in the field of geology has verified the practicability and effectiveness of the method under the system. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Subsidence

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

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International Conference on data Engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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data interoperability technology based on chain-network convergence

Data interoperability technology based on chain-network conv...

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IEEE International Conference on Blockchain (Blockchain)

作者： Haoyu Wang Jiping Xu Xin Zhang Cheng Chi Zihang Yin Yanjie Shao Beijing Technology and Business University Beijing School of Computing and Artificial Intelligence Beijing China Key Laboratory of Industrial Internet and Big Data Chinese National Light Industry Beijing China China Academy of information and communication Institute Of Industrial Internet And Internet Of Things Beijing China Institute of Software Chinese Academy of Sciences Beijing China

With the rapid development of industrial Internet services, the era of the Internet of everything has arrived, which puts forward higher requirements for the network security of the industrial Internet. In order to guarantee the safe and trustworthy flow of industrial Internet data, this paper proposes a chain network connector architecture that integrates blockchain technology and industrial Internet technology, which utilizes the characteristics of blockchain traceability, tampering, collective maintenance, and openness and transparency to guarantee that the identification data in the industrial Internet is more secure and trustworthy. In addition, the architecture adopts quantum identity authentication technology in the identity authentication part to reduce the problem of illegal user access due to identity forgery. Meanwhile, for the problem of low efficiency of large-capacity and high-concurrency data processing generated by actual enterprises in the production process, this paper proposes to use analytical hierarchy process and Hungarian algorithm to co-optimize the chain network connector in order to make the resource allocation more reasonable and the transmission efficiency higher. The experimental results show that different algorithms are used for task allocation in different scenarios to maximize the data transmission efficiency, and also greatly improve the security of industrial Internet identification data.

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A prediction-correction based iterative convolution-thresholding method for topology optimization of heat transfer problems

arXiv

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arXiv 2024年

作者： Chen, Huangxin Dong, Piaopiao Wang, Dong Wang, Xiao-Ping School of Mathematical Sciences Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University Fujian361005 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China

In this paper, we propose an iterative convolution-thresholding method (ICTM) based on prediction-correction for solving the topology optimization problem in steady-state heat transfer equations. The problem is formulated as a constrained minimization problem of the complementary energy, incorporating a perimeter/surface-area regularization term, while satisfying a steady-state heat transfer equation. The decision variables of the optimization problem represent the domains of different materials and are represented by indicator functions. The perimeter/surface-area term of the domain is approximated using Gaussian kernel convolution with indicator functions. In each iteration, the indicator function is updated using a prediction-correction approach. The prediction step is based on the variation of the objective functional by imposing the constraints, while the correction step ensures the monotonically decreasing behavior of the objective functional. Numerical results demonstrate the efficiency and robustness of our proposed method, particularly when compared to classical approaches based on the ICTM. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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A Prediction-Correction Based Iterative Convolution-Thresholding Method for Topology Optimization of Heat Transfer Problems

SSRN

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SSRN 2024年

关键词： Convolution

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation

arXiv

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arXiv 2024年

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Addressing the traveling salesperson problem with frequency fitness assignment and hybrid algorithms

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Soft computing 2024年第17-18期28卷 9495-9508页

作者： Liang, Tianyu Wu, Zhize Lässig, Jörg Berg, Daan van den Thomson, Sarah L. Weise, Thomas Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Jinxiu Dadao 99 Anhui Hefei230601 China Fakultät Elektrotechnik und Informatik Hochschule Zittau/Görlitz Brückenstraße 1 Saxony Görlitz02826 Germany Abteilung Kognitive Energiesysteme Fraunhofer IOSB-AST Wilhelmsplatz 11 Saxony Görlitz02826 Germany Department of Computer Science Vrije Universiteit Amsterdam De Boelelaan 1111 Amsterdam1081 HV Netherlands School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Jiulong Road 111 Anhui Hefei230601 China

The traveling salesperson problem (TSP) is one of the most iconic hard optimization tasks. With frequency fitness assignment (FFA), a new approach to optimization has recently been proposed: instead of directing the search towards better solutions, the optimization process prefers those with rarely encountered objective values. FFA can be plugged into existing algorithms and can improve their performance on some problems that are hard for them. However, problems like the TSP, where the number of possible objective values (here: tour lengths) is high, cause the performance of FFA-based algorithms to deteriorate. We choose 56 symmetric instances from TSPLib as a testbed for our experiments. We plug FFA both into the (1+1) EA and simulated annealing (SA) and obtain the FEA and FSA, respectively. We find that the resulting FEA substantially outperforms the (1+1) EA and that FSA can reach the global optima more often than SA within our computational budget. We propose two ways to hybridize FFA and traditional optimization in a round-robin scheme where objective function evaluations are alternatingly assigned to both components. The FFA-based parts of the algorithms sometimes send a solution over to the traditional-optimization-based ones. This can lead to further performance improvements. In particular, the hybrids combining simulated annealing with the FEA solve most problems and find close-to-optimal solutions on other instances. Even for several instances with many possible different objective values, which are therefore not suited to FFA, hybrids combining FFA and traditional objective-guided optimization algorithms can offer substantial performance improvements. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Simulated annealing

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