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Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

computer Modeling in Engineering & sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam School of computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

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Formal engineering methods for software quality assurance

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Frontiers of computer Science 2012年第1期6卷 1-2页

作者： Shaoying LIU Department of computer Science Faculty of Computer and Information Sciences Hosei University Tokyo 184-8584 Japan

Conventional software engineering on the basis of informal or semi-formal methods is facing tremendous challenges in ensur- ing software quality. Formal methods have attempted to address these challenges by introducing mathematical notation and calculus to support formal specification, refinement, and verification in software development. Their theoretical contributions to the discipline of software engineering are significant. However, in spite of their potential in improving the controllability of software process and reliability, formal methods are generally difficult to apply to large-scale and complex systems in practice because of many constraints （e.g., limited expertise, complexity, changing requirements, and theoretical limitations）. Re- searches on the integration of formal methods into conventional software engineering technologies and processes are likely to create effective ways to transfer formal methods to industry for software productivity and quality. One of the examples is the development of the structured object-oriented formal language （SOFL） and its associated SOFL method for industrial software development.

关键词： Formal engineering methods software quality assurance

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autoC: an efficient translator for model checking deterministic scheduler based OSEK/VDX applications

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Science China(information sciences) 2018年第5期61卷 141-155页

作者： Haitao ZHANG Zhuo CHENG Guoqiang LI Shaoying LIU School of information Science and Engineering Lanzhou University School of information Science Japan Advanced Institute of Science and Technology School of Software Shanghai Jiao Tong University faculty of computer and information sciences Hosei University

The OSEK/VDX automotive OS standard has been widely adopted by many automobile manufacturers, such as BMW and TOYOTA, as the basis for designing and implementing a vehicle-mounted OS. With the increasing functionalities in vehicles, more and more multi-task applications are developed based on the OSEK/VDX OS. Currently, ensuring the reliability of the developed applications is becoming a challenge for developers. As to ensure the reliability of OSEK/VDX applications, model checking as a potential solution has attracted great attention in the automotive industry. However, existing model checkers are often unable to verify a large-scale OSEK/VDX application that consists of many tasks, since the corresponding application model too complex. To make existing model checkers more scalable in verifying large-scale OSEK/VDX applications, we describe a software tool named auto C to tackle this problem by automatically translating a multi-task OSEK/VDX application into an equivalent sequential model. We conducted a series of experiments to evaluate the efficiency of auto C. The experimental results show that auto C is not only capable of efficiently sequentializing OSEK/VDX applications, but also of improving the scalability and efficiency of existing model checkers in verifying large-scale OSEK/VDX applications.

关键词： OSEK/VDX application deterministic scheduler software formal method model checking sequentialization

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Parallelization and sustainability of distributed genetic algorithms on many-core processors

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International Journal of Intelligent Computing and Cybernetics 2014年第1期7卷 2-23页

作者： Yuji Sato Mikiko Sato faculty of computer and information sciences Hosei UniversityTokyoJapan Graduate School of Engineering Tokyo University of Agriculture and TechnologyTokyoJapan

Purpose–The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units(GPUs)and multi-core processors(MCPs).Design/methodology/approach–For distributed genetic algorithm(GA)models,the paper proposes a method where an island’s ID number is added to the header of data transferred by this island for use in fault ***–The paper has shown that the processing time of the proposed idea is practically negligible in applications and also shown that an optimal solution can be obtained even with a single stuck-at fault or a transient fault,and that increasing the number of parallel threads makes the system less susceptible to ***/value–The study described in this paper is a new approach to increase the sustainability of application program using distributed GA on GPUs and MCPs.

关键词： Evolutionary computation Genetic algorithms Fault identification Many-core processors Parallelization

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SGNR: A Social Graph Neural Network Based Interactive Recommendation Scheme for E-Commerce

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Tsinghua Science and Technology 2023年第4期28卷 786-798页

作者： Dehua Ma Yufeng Wang Jianhua Ma Qun Jin School of Communications and lnformation Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Department the Faculty of Computer and Information SciencesHosei UniversityTokyo 194-0298Japan Networked information Systems Laboratory Department of Human Informatics and Cognitive SciencesFaculty of Human SciencesWaseda UniversityTokyo 169-8050Japan

Interactive Recommendation(IR)formulates the recommendation as a multi-step decision-making process which can actively utilize the individuals’feedback in multiple steps and optimize the long-term user benefit of *** Reinforcement Learning(DRL)has witnessed great application in IR for ***,user cold-start problem impairs the learning process of the DRL-based recommendation ***,most existing DRL-based recommendations ignore user relationships or only consider the single-hop social relationships,which cannot fully utilize the social *** fact that those schemes can not capture the multiple-hop social relationships among users in IR will result in a sub-optimal *** address the above issues,this paper proposes a Social Graph Neural network-based interactive Recommendation scheme(SGNR),which is a multiple-hop social relationships enhanced DRL *** this framework,the multiple-hop social relationships among users are extracted from the social network via the graph neural network which can sufficiently take advantage of the social network to provide more personalized recommendations and effectively alleviate the user cold-start *** experimental results on two real-world datasets demonstrate that the proposed SGNR outperforms other state-of-the-art DRL-based methods that fail to consider social relationships or only consider single-hop social relationships.

关键词： Interactive Recommendation(IR) Deep Reinforcement Learning(DRL) Graph Neural Network(GNN)

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An Integrated Incentive Framework for Mobile Crowdsourced Sensing

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Tsinghua Science and Technology 2016年第2期21卷 146-156页

作者： Wei Dai Yufeng Wang Qun Jin Jianhua Ma College of Telecommunications and information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China. Department of Human Informatics and Cognitive sciences Waseda UniversitySaitama 359-1192Japan. faculty of computer&information sciences Hosei UniversityTokyo 184-8584Japan

Currently, mobile devices （e.g., smartphones） are equipped with multiple wireless interfaces and rich builtin functional sensors that possess powerful computation and communication capabilities, and enable numerous Mobile Crowdsourced Sensing （MCS） applications. Generally, an MCS system is composed of three components： a publisher of sensing tasks, crowd participants who complete the crowdsourced tasks for some kinds of rewards, and the crowdsourcing platform that facilitates the interaction between publishers and crowd participants. Incentives are a fundamental issue in MCS. This paper proposes an integrated incentive framework for MCS, which appropriately utilizes three widely used incentive methods： reverse auction, gamification, and reputation updating. Firstly, a reverse-auction-based two-round participant selection mechanism is proposed to incentivize crowds to actively participate and provide high-quality sensing data. Secondly, in order to avoid untruthful publisher feedback about sensing-data quality, a gamification-based verification mechanism is designed to evaluate the truthfulness of the publisher＇s feedback. Finally, the platform updates the reputation of both participants and publishers based on their corresponding behaviors. This integrated incentive mechanism can motivate participants to provide high-quality sensed contents, stimulate publishers to give truthful feedback, and make the platform profitable.

关键词： mobile crowdsourced sensing incentive mechanism reverse auction gamification reputation updating

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Applying and Comparison of Chaotic-Based Permutation Algorithms for Audio Encryption

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computers, Materials & Continua 2021年第6期67卷 3161-3176页

作者： Osama M.Abu Zaid Medhat A.Tawfeek Saad Alanazi Department of computer Science Faculty of Computers and InformationKafrelsheikh UniversityEgypt Department of computer Science College of Computer and Information SciencesJouf UniversityKSA Department of computer Science Faculty of Computers and InformationMenoua UniversityEgypt

This research presents,and claries the application of two permutation algorithms,based on chaotic map systems,and applied to a le of speech *** are the Arnold cat map-based permutation algorithm,and the Baker’s chaotic map-based permutation *** algorithms are implemented on the same speech signal ***,both the premier and the encrypted le histograms are documented and *** speech signal amplitude values with time signals of the original le are recorded and plotted against the encrypted and decrypted ***,the original le is plotted against the encrypted le,using the spectrogram frequencies of speech signals with the signal *** permutation algorithms are used to shufe the positions of the speech les signals’values without any changes,to produce an encrypted speech le.A comparative analysis is introduced by using some of sundry statistical and experimental analyses for the procedures of encryption and decryption,e.g.,the time of both procedures,the encrypted audio signals histogram,the correlation coefcient between specimens in the premier and encrypted signals,a test of the Spectral Distortion(SD),and the Log-Likelihood Ratio(LLR)*** outcomes of the different experimental and comparative studies demonstrate that the two permutation algorithms(Baker and Arnold)are sufcient for providing an efcient and reliable voice signal encryption ***,the Arnold’s algorithm gives better results in most cases as compared to the results of Baker’s algorithm.

关键词： Arnold’s cat map chaotic maps permutation algorithms speech encryption Baker’s chaotic map

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CAN:Effective Cross Features by Global Attention Mechanism and Neural Network for Ad Click Prediction

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Tsinghua Science and Technology 2022年第1期27卷 186-195页

作者： Wenjie Cai Yufeng Wang Jianhua Ma Qun Jin Nanjing university of Posts and Telecommunications(NJUPT) Nanjing210003China Digital Media Department in the faculty of computer and information sciences Hosei UniversityTokyo 163-8001Japan Networked information Systems Laboratory Department of Human Informatics and Cognitive SciencesWaseda UniversityTokyo 163-8001Japan

Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent *** of the hot topics in this area is the construction of feature interactions to facilitate accurate *** machine provides second-order feature interactions by linearly multiplying hidden feature ***,real-world data present a complex and nonlinear ***,second-order feature interactions are unable to represent cross information *** drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature ***,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original *** this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN *** attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature *** experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.

关键词： click-through rate prediction global attention mechanism feature interaction neural network

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Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

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computers, Materials & Continua 2023年第7期76卷 367-382页

作者： Mona Jamjoom Ahmed Elhadad Hussein Abulkasim Safia Abbas Department of computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of computer Science Faculty of Computers and InformationSouth Valley UniversityQena83523Egypt faculty of Science New Valley UniversityEl-Kharga72511Egypt Department of computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt

Several pests feed on leaves,stems,bases,and the entire plant,causing plant *** a result,it is vital to identify and eliminate the disease before causing any damage to *** detecting plant disease and treating it is pretty challenging in this *** processing is employed to detect plant disease since it requires much effort and an extended processing *** main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf ***,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and *** gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant *** to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.

关键词： SVM machine learning GLCM algorithm K-means clustering LBP

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An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

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computers, Materials & Continua 2023年第2期74卷 3471-3487页

作者： Mohammed Hadwan Hamzah A.Alsayadi Salah AL-Hagree Department of information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen computer Science Department Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt computer Science Department Faculty of SciencesIbb UniversityYemen Department of computer sciences&information Ibb UniversityYemen

The attention-based encoder-decoder technique,known as the trans-former,is used to enhance the performance of end-to-end automatic speech recognition(ASR).This research focuses on applying ASR end-toend transformer-based models for the Arabic language,as the researchers’community pays little attention to *** Muslims Holy Qur’an book is written using Arabic diacritized *** this paper,an end-to-end transformer model to building a robust Qur’an *** is *** acoustic model was built using the transformer-based model as deep learning by the PyTorch framework.A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic *** filter bank is used for feature *** build a language model(LM),the Recurrent Neural Network(RNN)and Long short-term memory(LSTM)were used to train an n-gram word-based *** a part of this research,a new dataset of Qur’an verses and their associated transcripts were collected and processed for training and evaluating the proposed model,consisting of 10 h *** recitations performed by 60 *** experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate(CER)of 1.98%and a word error rate(WER)of 6.16%.We have achieved state-of-the-art end-to-end transformer-based recognition for Qur’an reciters.

关键词： Attention-based encoder-decoder recurrent neural network long short-term memory qur’an reciters recognition diacritized arabic text

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