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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Xiang Xu Guanxiao Li Shaozhang Xiao Zheyi Zhang School of Computer and Software Engineering Huaiyin Institute of Technology Huai’ an China

To achieve effective feedback and proficiency tracking of students in contemporary educational settings, we propose a methodology that integrates the RNN-GRU model and dynamic Bayesian networks for predicting students’ engineering competence level based on their online learning behaviors. The approach employed utilizes the RNN-GRU model to analyze students’ progression in algorithm development skills and algorithm implementation ability, resulting in the generation of a historical record of their learning features. These features are then employed to train a dynamic Bayesian network model, facilitating the prediction of students’ engineering competence level. After the amount of data reaches a certain amount, it can compare the previous models such as RNN and LSTM to obtain higher accuracy.

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Deniable authenticated encryption for e-mail applications

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International Journal of computers and Applications 2020年第5期42卷 429-438页

作者： Jin, Chunhua Chen, Guanhua Yu, Changhui Zhao, Jianyang Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

In this paper, we introduce a deniable electronic mail authenticated encryption service. Our design meets the security requirement of the current Pretty Good Privacy and Secure/Multipurpose Internet Mail Extensions to provide message confidentiality and message authentication without the undesired non-repudiation, which can protect the individual privacy of the sender when communication occurs in open channels. Our scheme is based on the bilinear pairings, which is provably secure in the random oracle model under the bilinear Diffie-Hellman and computational Diffie-Hellman assumptions. In addition, our scheme needs a little high communication overhead. However, this extra cost is reasonable for the properties of deniable authentication, confidentiality, and ciphertext anonymity. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electronic mail

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Quantum software Models: Quantum Modules Tomography and Recovery Theorem 35

Quantum Software Models: Quantum Modules Tomography and Reco...

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Exman, Iaakov Zvulunov, Ariel School of Computer Science Faculty of Sciences HIT – Holon Institute of Technology Holon Israel Software Engineering The Jerusalem College of Engineering Azrieli Jerusalem Israel

Quantum Tomography partially measures and then recovers the remaining density matrix quantum state, in order to verify that a certain device – processor or detector – indeed outputs the intended quantum state. However, single matrix element measurements rapidly increase in number with the density matrix dimension and are error-prone. This work proposes a novel quantum software viewpoint on quantum tomography. Instead of individual matrix elements, measurements and density matrix recovery are performed on higher-abstraction modules, i.e. semantically meaningful groups of matrix elements. Quantum Modules Tomography potentially reduce the necessary number of explicit measurements, while still allowing recovery of the whole density matrix. A Recovery Theorem is formulated and proved: density matrix diagonal measurements suffice to recover the whole system density matrix, in terms of modules. The recovery procedure is illustrated by a few case studies. © 2023 Knowledge Systems institute Graduate School. All rights reserved.

关键词： Recovery

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A Cloud-based Smart Wide-area Measurement System for Power Grid Fault Detection 3

A Cloud-based Smart Wide-area Measurement System for Power G...

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2021年第三届先进计算机技术、信息科学与通信国际会议(CTISC2021)

作者： Jingsong Shan Jianyang Zhao Chengfu Sun Chunhua Jin Zhenxiu Zhang Faculty of Computer Engineering Huaiyin Institute of TechnologyHYIT

ISBN: (纸本)9781665418683

Power grid is becoming more and more smart and complex. It is necessary for us to measure, monitor and manage smart devices in power grid. In case of fault, the power grid equipment will suffer serious damage and cause great economic losses. A Cloud-based Wide-area Measurement System approach is proposed for fault detection in this paper. Fault detection using cloud-based WAMS can be divided into two main parts: Edging detection and Cloud detection. In edging devices, we try to find out the suspected local faults in power grid. In Cloud, we try to probe global faults by analysis of global features collected from edges.

关键词： Economics Information science Power measurement Fault detection Image edge detection Power grids Wide area measurements

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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DM-Net for Colorectal Polyp Detection Using Dual Feature and Multi-Layer Aggregation

DM-Net for Colorectal Polyp Detection Using Dual Feature and...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Jingjing Wan Ling Wang Wenjie Zhu Bolun Chen Department of Gastroenterology The Second People's Hospital of Huai'an Huaian China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Early detection of colorectal polyps is crucial for preventing colorectal cancer. Although endoscopy is the current standard diagnostic method, it still faces challenges in terms of accuracy, efficiency, and patient comfort. To address these issues, this paper proposes a colorectal polyp detection model named DM-Net. This model utilizes the Dual Feature Integration Block (DFIB) to integrate channel and spatial features, enhancing feature extraction efficiency. Additionally, the model incorporates the Multi-Layer Path Aggregation Network (MLPAN) to handle the multi-scale variations of polyps. Experimental results demon-strate that DM-Net significantly outperforms existing methods in terms of accuracy and efficiency, offering high clinical applica-bility.

关键词： YOLO Accuracy Endoscopes Colorectal cancer Big Data Feature extraction Real-time systems Standards Faces

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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YOLO-BTM: A Novel Shuttlecock Detection Method for Embedded Badminton Robots

YOLO-BTM: A Novel Shuttlecock Detection Method for Embedded ...

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Automation, Robotics and computer engineering (ICARCE), International Conference on

作者： Yimin Zhang Chuxuan Chen Ronglin Hu Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huai’an China

ISBN: (纸本)9781665475495

Employing robots in badminton training contributes to a more accurate analysis of an athlete's movements and helps avoid injuries. Shuttlecock detection during the flying stage is a critical component of the badminton robot design. However, previous shuttlecock localization methods were unable to detect shuttlecock quickly and accurately in embedded device-based badminton robots, given scale variations, few extractable features, occlusion, and device limitation. In this paper, a deep learning-based shuttlecock localization method is proposed. First, an indoor shuttlecock dataset including 9548 shuttlecock images of various angles and scenes was constructed. Then a shuttlecock detection method YOLO-BTM is proposed, which is based on YOLOv4-Tiny. We proposed a new convolution block to replace the cross-stage partially block in the backbone, to improve the detection speed. To improve the network's ability to detect small objects, the efficient channel attention block is introduced in feature fusion. Finally, a comparative experiment on the accuracy of the method and the detection speed was conducted. The results show that the proposed YOLO-BTM has better performance in detection speed and accuracy compared to the existing state-of-the-art object detection methods on our own shuttlecock dataset. Our method enables real-time, accurate localization of shuttlecock and has the potential to be used in other embedded device based sports robots.

关键词： Location awareness Training Performance evaluation Visualization Object detection Feature extraction Real-time systems

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Lightweight and Roubust Key Agreement for Securing IIoT-Driven Flexible Manufacturing Systems

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IEEE Internet of Things Journal 2025年第11期12卷 17197-17209页

作者： Hammad, Muhammad Badshah, Akhtar Almeer, Mohammed A. Waqas, Muhammad Song, Houbing Chen, Sheng Han, Zhu GIK Institute of Engineering Sciences and Technology Faculty of Mechanical Engineering Topi23640 Pakistan University of Malakand Department of Software Engineering Dir Lower18800 Pakistan University of Bahrain Computer Engineering Department College of Information Technology 32038 Bahrain University of Greenwich Faculty of Engineering and Science Centre for Sustainable Cybersecurity United Kingdom Edith Cowan University School of Engineering PerthWA6027 Australia Department of Information Systems BaltimoreMD United States University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-Or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrate that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages. © 2025 IEEE.

关键词： Flexible manufacturing systems

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