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arXiv 2022年

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

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 term 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. © 2022, CC BY-NC-SA.

关键词： Embeddings

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PruneSymNet: A Symbolic Neural network and Pruning Algorithm for Symbolic Regression

arXiv

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arXiv 2024年

作者： Wu, Min Li, Weijun Yu, Lina Li, Wenqiang Liu, Jingyi Li, Yanjie Hao, Meilan AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Microelectronics University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China

Symbolic regression aims to derive interpretable symbolic expressions from data in order to better understand and interpret data. In this study, a symbolic network called PruneSymNet is proposed for symbolic regression. This is a novel neural network whose activation function consists of common elementary functions and operators. The whole network is differentiable and can be trained by gradient descent method. Each subnetwork in the network corresponds to an expression, and our goal is to extract such subnetworks to get the desired symbolic expression. Therefore, a greedy pruning algorithm is proposed to prune the network into a subnetwork while ensuring the accuracy of data fitting. The proposed greedy pruning algorithm preserves the edge with the least loss in each pruning, but greedy algorithm often can not get the optimal solution. In order to alleviate this problem, we combine beam search during pruning to obtain multiple candidate expressions each time, and finally select the expression with the smallest loss as the final result. It was tested on the public data set and compared with the current popular algorithms. The results showed that the proposed algorithm had better accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Gradient methods

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EMO: An Ontology for Supporting the Management of Epidemic

EMO: An Ontology for Supporting the Management of Epidemic

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Applied Physics and computing (ICAPC), International Conference on

作者： Yongchao Gao Qiuyue Wang Mark S. Fox Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences) Jinan China Standard Institute of Emerging Technologies and Innovations Jinan China Urban Data Centre University of Toronto Toronto Canada

In order to improve the coverage of concepts in epidemic management, this paper constructs an epidemic management ontology EMO to help manage and analyse epidemic data. First, based on the ISO/lEC 5087 city data model series of standards and TOVE method, EMO is constructed using Protégé. Secondly, the data types and their relationships required in the epidemic management use cases are defined. Finally, the instance data help to verify the ability of EMO to represent the city epidemic management data. Results show that EMO provides a clear, accurate and unified definition of epidemic management data, improves the efficiency of data sharing and integration, and reduces the difficulty of developing epidemic management tools.

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CubeFlow: Money laundering detection with coupled tensors

arXiv

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arXiv 2021年

作者： Sun, Xiaobing Zhang, Jiabao Zhao, Qiming Liu, Shenghua Chen, Jinglei Zhuang, Ruoyu Shen, Huawei Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Insititute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China China Construction Bank Fintech China

Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities, and always be a fast process involving frequent and chained transactions. How can we detect ML and fraudulent activity in large scale attributed transaction data (i.e. tensors)? Most existing methods detect dense blocks in a graph or a tensor, which do not consider the fact that money are frequently transferred through middle accounts. CubeFlow proposed in this paper is a scalable, flow-based approach to spot fraud from a mass of transactions by modeling them as two coupled tensors and applying a novel multi-attribute metric which can reveal the transfer chains accurately. Extensive experiments show CubeFlow outperforms state-of-the-art baselines in ML behavior detection in both synthetic and real data. Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

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A Novel High Capacity Transmission Frame Structure in Directional-Beam-Based networks with Large Propagation Delays

A Novel High Capacity Transmission Frame Structure in Direct...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Jie Huang Jing Zhang Ming Zhao Jiahao He Sihai Zhang CAS Key Laboratory of Wireless-Optical Communications USTC Hefei China Science Island Branch of University of Science and Technology of China Hefei China Institute of Space Integrated Ground Network Hefei China

ISBN: (纸本)9781665450867

Large propagation delay is a typical feature of wide-range wireless Ad hoc networks. Transmission frame structures in conventional data link protocols designed for such networks mitigate the impact of delays by using techniques such as guard interval, which reduces the effective transmission time of the network. However, large propagation delays can be used to enable more concurrent transmissions. In this paper, we derive the upper bound on the throughput of directional-beam-based networks with large propagation delays. Further, we present a novel transmission frame structure that regards the propagation delay as a buffer to achieve the upper bound of such networks and an algorithm to find an approximate frame length for general delays. Finally, simulation results verify that the proposed frame structure with the frame length obtained by the proposed algorithm achieves the much higher throughput compared with the existing counterparts.

关键词： Wireless communication Schedules Upper bound Simulation Signal processing algorithms Signal processing Throughput

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A neural-guided dynamic symbolic network for exploring mathematical expressions from data 24

A neural-guided dynamic symbolic network for exploring mathe...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Wenqiang Li Weijun Li Lina Yu Min Wu Linjun Sun Jingyi Liu Yanjie Li Shu Wei Yusong Deng Meilan Hao AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China and School of Electronic Electrical and Communication Engineering & School of Integrated Circuits University of Chinese Academy of Sciences Beijing China and Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China and School of Electronic Electrical and Communication Engineering & School of Integrated Circuits and Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China and Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China

Symbolic regression (SR) is a powerful technique for discovering the underlying mathematical expressions from observed data. Inspired by the success of deep learning, recent deep generative SR methods have shown promising results. However, these methods face difficulties in processing high-dimensional problems and learning constants due to the large search space, and they don't scale well to unseen problems. In this work, we propose DYSYMNET, a novel neural-guided Dynamic Symbolic network for SR. Instead of searching for expressions within a large search space, we explore symbolic networks with various structures, guided by reinforcement learning, and optimize them to identify expressions that better-fitting the data. Based on extensive numerical experiments on low-dimensional public standard benchmarks and the well-known SRBench with more variables, DYSYMNET shows clear superiority over several representative baseline models. Open source code is available at https://***/AILWQ/DySymNet.

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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AsyncSC: An Asynchronous Sidechain for Multi-Domain data Exchange in Internet of Things

arXiv

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-Blockchain as a Service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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Conditional GANs with Auxiliary Discriminative Classifier

arXiv

引用

arXiv 2021年

作者： Hou, Liang Cao, Qi Shen, Huawei Pan, Siyuan Li, Xiaoshuang Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Shanghai Jiao Tong University Shanghai China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generative adversarial network (AC-GAN) has been widely used, but suffers from the problem of low intra-class diversity of the generated samples. The fundamental reason pointed out in this paper is that the classifier of AC-GAN is generator-agnostic, which therefore cannot provide informative guidance for the generator to approach the joint distribution, resulting in a minimization of the conditional entropy that decreases the intra-class diversity. Motivated by this understanding, we propose a novel conditional GAN with an auxiliary discriminative classifier (ADC-GAN) to resolve the above problem. Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively. Our theoretical analysis reveals that the generator can faithfully learn the joint distribution even without the original discriminator, making the proposed ADC-GAN robust to the value of the coefficient hyperparameter and the selection of the GAN loss, and stable during training. Extensive experimental results on synthetic and real-world datasets demonstrate the superiority of ADC-GAN in conditional generative modeling compared to state-of-the-art classifier-based and projection-based conditional GANs. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Ε-weakened Robustness of deep neural networks

arXiv

引用

arXiv 2021年

作者： Huang, Pei Jia, Fuqi Yang, Yuting Ma, Feifei Liu, Minghao Zhang, Jian State Key Laboratory of Computer Science Institution of Software CAS Beijing China Key Laboratory of Intelligent Information Institute of Computing Technology CAS Beijing China

This paper introduces a notation of Ε-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs). Unlike the conventional robustness, which focuses on the "perfect" safe region in the absence of adversarial examples, Ε-weakened robustness focuses on the region where the proportion of adversarial examples is bounded by user-specified Ε. Smaller Ε means a smaller chance of failure. Under such robustness definition, we can give conclusive results for the regions where conventional robustness ignores. We prove that the Ε-weakened robustness decision problem is PP-complete and give a statistical decision algorithm with user-controllable error bound. Furthermore, we derive an algorithm to find the maximum Ε-weakened robustness radius. The time complexity of our algorithms is polynomial in the dimension and size of the network. So, they are scalable to large real-world networks. Besides, We also show its potential application in analyzing quality issues. © 2021, CC BY.

关键词： Deep neural networks

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