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Research Square 2022年

作者： Zhang, Zhenghui Zou, Juan Zheng, Jinhua Yang, Shengxiang Gong, Dunwei Pei, Tingrui Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Information Engineering College of Xiangtan University Hunan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom School of Information and Electronic Engineering China University of Mining and Technology Xuzhou221116 China

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm’s design is to exploit all available information and computationally suggest matches to increase the algorithm’s ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios. © 2022, CC BY.

关键词： Knowledge acquisition

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Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network 6th

Automatic Fault Diagnosis of Smart Water Meter Based on BP N...

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6th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Lin, Jing Mi, Chunqiao School of Computer Science and Engineering Huaihua University HuaihuaHunan418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities HuaihuaHunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province HuaihuaHunan418000 China

ISBN: (纸本)9789811579837

The smart water meter in water supply network can directly affect water production and usage when faults occur. The traditional method of fault detection is inefficient with time lagging, which is not helpful for modernization of water supply system. The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply. In this paper, an automatic fault diagnosis method for the smart device is proposed based on BP neural network. And it was applied on Google Tensorflow platform. Fault symptom vectors were constructed using water meter status data and were used to train the neural network model. In order to improve the learning convergence speed and fault classification effect of the network, a method of weighted symptom was also employed. Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Fault detection

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An Identity-Based Proxy Signature on NTRU Lattice

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Chinese Journal of Electronics 2018年第2期27卷 297-303页

作者： ZHU Hongfei TAN Yu'an YU Xiao XUE Yuan ZHANG Qikun ZHU Liehuang LI Yuanzhang The School of Computer Science and Technology Beijing Institute of Technology Institute of Computer and Communication Engineering Zhengzhou University of Light Industry Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application

Proxy signature plays an important role in distributed systems, mobile agent applications, distributed shared object systems, global distribution networks and mobile communications etc., since it allows the original signer to delegate another proxy signer to sign the message instead of himself. However, the proxy signature schemes constructed on the number theory cannot resist quantum computers attack and depend on the public key infrastructure. To mitigate these problems, the alternative schemes are Identity-based proxy signature constructed on *** this paper, an identity-based proxy signature scheme based on Number theorem research unit(NTRU) lattice is proposed and proven secure in the random oracle. Compared with the other existing identity-based proxy signature schemes, the proposed scheme outperforms them in terms of original signer's signature size and signing key size and proxy signer's signing key size.

关键词： Identity based proxy signature Quantum computers attack NTRU lattice Unforgeability

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SpEx+: A Complete Time Domain Speaker Extraction Network

arXiv

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arXiv 2020年

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker’s reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Extraction

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Adaptive Dynamic Surface Control of Constrained Nonlinear Systems

Adaptive Dynamic Surface Control of Constrained Nonlinear Sy...

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Chinese Control and Decision Conference, CCDC

作者： Ailiang Xing Biao Song Yixin Yin School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Demonstration and Research Department of Network Information System Beijing Institute of Computer Application Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This paper presents an adaptive dynamic surface control for uncertain nonlinear strict feedback systems with constraints on both input and output. Backstepping technology is employed in the controller design. Prescribed performance method with asymmetric barrier Lyapunov function (BLF) is used to deal with the output constrains. The input saturation is solved by using the Nussbaum function. The dynamic surface control (DSC) technique is employed to reduce the complexity of the virtual control. It is illustrated that all closed-loop signals are semi-global uniformly ultimately bounded (SGUUB). Finally, a simulation example proves the proposed results.

关键词： Backstepping Lyapunov methods Adaptive systems Complexity theory Stability analysis Transient analysis

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Mobilecount: An efficient encoder-decoder framework for real-time crowd counting 1

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2nd Chinese Conference on Pattern Recognition and computer Vision, PRCV 2019

作者： Gao, Chenyu Wang, Peng Gao, Ye School of Computer Science Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Beijing China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

In this work, we propose a computation-efficient encoder-decoder architecture, named MobileCount, which is specifically designed for high-accuracy real-time crowd counting on mobile or embedded devices with limited computation resources. For the encoder part, MobileNetV2 is tailored in order to significantly reduce FLOPs at a little cost of performance drop, which has 4 bottleneck blocks preceded by a max pooling layer of stride 2. The design of decoder is motivated by Light-weight RefineNet, which further boosts counting performance with only a 10% increase of FLOPs. In comparison with state-of-the-arts, our proposed network is able to achieve comparable counting performance with 1/10 FLOPs on a number of benchmarks. © Springer Nature Switzerland AG 2019.

关键词： Network coding

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Learning to compare relation: Semantic alignment for few-shot learning

arXiv

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arXiv 2020年

作者： Cao, Congqi Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'An710129 China

Few-shot learning is a fundamental and challenging problem since it requires recognizing novel categories from only a few examples. The objects for recognition have multiple variants and can locate anywhere in images. Directly comparing query images with example images can not handle content misalignment. The representation and metric for comparison are critical but challenging to learn due to the scarcity and wide variation of the samples in few-shot learning. In this paper, we present a novel semantic alignment model to compare relations, which is robust to content misalignment. We propose to add two key ingredients to existing few-shot learning frameworks for better feature and metric learning ability. First, we introduce a semantic alignment loss to align the relation statistics of the features from samples that belong to the same category. And second, local and global mutual information maximization is introduced, allowing for representations that contain locallyconsistent and intra-class shared information across structural locations in an image. Furthermore, we introduce a principled approach to weigh multiple loss functions by considering the homoscedastic uncertainty of each stream. We conduct extensive experiments on several few-shot learning datasets. Experimental results show that the proposed method is capable of comparing relations with semantic alignment strategies, and achieves stateof-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Alignment

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Multi-party Quantum Communication Complexity on Composite Boolean-Valued Function 5th

Multi-party Quantum Communication Complexity on Composite Bo...

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Yu, Wenbin Dong, Zangqiang Liu, Wenjie Qu, Zhiguo Xu, Xiaolong Liu, Alex Xiangyang Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu Province210044 China The Department of Computer Science and Application Zhengzhou Institute of Aeronautical Industry Management ZhengzhouHenan Province450015 China Department of Computer Science and Engineering Michigan State University East LansingMI48824-1226 United States

ISBN: (纸本)9783030242671

The performance of communication complexity depends on the selected computation model. Even on the specific model the quantum communication complexity is not always better than the classical one. This paper investigates the quantum communication complexity based on a multi-party computation model of the composite Boolean-valued function. On this model we design a quantum distributed algorithm to obtain the upper bound of quantum communication complexity. The result shows that the performance gap between quantum and classical communication complexity depends on the infinity order of function domain’s square root and users’ number. In the best situation the performance of the quantum communication complexity wins the quadratic level of advantage than the classical one. And sometimes the classical way is more efficient than the quantum one. © 2019, Springer Nature Switzerland AG.

关键词： Quantum communication

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Kernel contextual fuzzy rule model based on conditional input space partitioning driven by data reconstruction in autoencoder and randomization-based neural networks

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Knowledge-Based Systems 2025年 320卷

作者： Zhang, Congcong Oh, Sung-Kwun Fu, Zunwei Pedrycz, Witold School of Mathematics and Statistics Linyi University Shandong Linyi276005 China Shandong Data Open Innovative Application Laboratory Linyi276005 China School of Automation and Electrical Engineering Linyi University Shandong Linyi276005 China School of Electrical & Electronic Engineering The University of Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Department of Electronic Engineering Seokyeong University Seoul 02713 Korea Republic of Department of Computer Science The University of Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonAB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw 00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkey

Fuzzy Rule-based Models (FRMs) have attracted significant interest in the field of machine learning owing to their modular architecture, robust design methodologies, and sound interpretability. This study introduces a novel Kernel Contextual Fuzzy Rule Model (KCFRM) designed to cope with regression tasks. A Kernel Contextual Fuzzy Clustering (KCFC) algorithm is proposed for conditional partitioning of the input space. Specifically, we incorporate the Mercer kernel into context fuzzy clustering to enhance the abilities of the model to distinguish, extract, and amplify useful features via nonlinear mapping, thereby generating more suitable information granules. Additionally, we employ an autoencoder's "encoding-decoding" mechanism to extract differences between data patterns and subsequently transform these into KCFC contexts via a conversion function, therefore leading to the creation of high-quality fuzzy sets. In the conclusion portion of fuzzy rules, conventional numerical or linear functions struggle to adequately describe the complex behavior present in local fuzzy regions. To mitigate this, we incorporate a Randomization-based Neural Network (RANN), capable of providing superior approximation capabilities and substantial computational efficiency. RANN overcomes traditional method constraints in representing complex behaviors within the fuzzy region. The inclusion of RANN results in more accurate and efficient conclusions for fuzzy rules. The uniqueness of this study lies in its holistic approach to designing fuzzy models with KCFC and RANN, improving the expressiveness of the rules and enhancing the generalization of the models. KCFRM's performance is assessed using various publicly available machine learning datasets, with experimental results underscoring its effectiveness and performance enhancements. © 2025

关键词： Fuzzy rules

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A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm

A Novel Strategy for Prediction of Cellular Cholesterol Sign...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Rudra Kalyan Nayak Ramamani Tripathy V. Saravanan Siva Shankar S. Priti Das Dinesh Kumar Anguraj Faculty of Computer Science & Engineering Koneru Lakshmaiah Education Foundation (Deemed to be University) Green Fields Vaddeswaram Andhra Pradesh India Faculty of Master in Computer Application United School of Business Management Bhubaneswar Odisha India Faculty of Computer Science & Engineering KG Reddy College of Engineering and Technology Hyderabad Telengana India SCB Medical College and Hospital Cuttack Odisha India

ISBN: (数字)9781728148892

ISBN: (纸本)9781728148908

In present era, both soft computing and artificial intelligence techniques have been implemented as problem solver in bioinformatics, as conventional methods are not sufficient for handling huge amount of data. The principal focus of research in all pharmaceutical industries is on human proteins like G protein-coupled receptors (GPCRs), ATP-binding cassette (ABC) transporter etc. Membrane protein plays a significant role for every human being and all drug targets have been considered using this protein. In this manuscript, our focal point of research is on membrane protein that is GPCR family and cholesterol target process on the transmembrane region. Among all living organisms, GPCR acts as the largest superfamily and it includes numerous classes. From the last decades to till date GPCR sequence prediction and classification have been a challenging factor for all biomedicine scientists. GPCR family is also known as 7-pass transmembrane protein receptors. Every time membrane cholesterol has targets for the binding sites of membrane protein in both N terminus and C terminus of the cell membrane. For this purpose, a computational Rough-Fuzzy C-Means (FCM) based approach is implemented for prediction and this method helps to find out valid amino acid sequence which has biological relevance for clinical drug discovery.

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