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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Zhang, Yiqian Wang, Yue Department of Computer Science Jinan Vocational College Jinan250103 China Division of Public Teaching Jinan Vocational College Jinan250103 China

ISBN: (纸本)9783031211638

The traditional online education system data query method has the problem of unclear track data type, which leads to a long query time. To solve this problem, this study designs a data optimization query method of online education system based on decision tree. According to the teaching objectives of teachers and the characteristics of courses, extract the course characteristics of online education system, and adjust the data segmentation process, so as to extract the grid memory query index structure. Then select a field as the segmentation basis and mark, and use the decision tree to identify the trajectory data type. Finally, set the data optimization query mode by continuously predefined query interval. The experimental results show that compared with the other two query methods, the query time of this method is less, which shows that the application performance of the data optimization query method of online education system integrated with decision tree is better. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Decision trees

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Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

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computers, Materials & Continua 2023年第5期75卷 2575-2591页

作者： Badriyya BAl-onazi Abdulkhaleq Q.A.Hassan Mohamed K.Nour Mesfer Al Duhayyim Abdullah Mohamed Amgad Atta Abdelmageed Ishfaq Yaseen Gouse Pasha Mohammed Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24231Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the *** SA technique is specifically applied to the data collected from social media *** research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction *** this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in ***,the data pre-processing is performed to convert the raw tweets into a useful ***,the word2vec model is applied to generate the feature *** Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the ***,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU *** proposed QPSODL-SAAT model was experimentally validated using the standard *** extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

关键词： Sentiment analysis Arabic tweets quantum particle swarm optimization deep learning word embedding

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Visualising Bibliographic Metadata Using CAQDAS in the Research on the Gender Gap in STEM Studies in Higher Education 7th

Visualising Bibliographic Metadata Using CAQDAS in the Resea...

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7th World Conference on Qualitative Research, WCQR 2023

作者： Verdugo-Castro, Sonia Sánchez-Gómez, Mª Cruz García-Holgado, Alicia García-Peñalvo, Francisco José Costa, António Pedro GRIAL Research Group Department of Didactics Organization and Research Methods Universidad de Salamanca Salamanca Spain GRIAL Research Group Computer Science Department Universidad de Salamanca Salamanca Spain University of Aveiro Aveiro Portugal

ISBN: (纸本)9783031313455

Besides providing the consulted publication’s findings, the literature review can offer information through metadata. computer-Assisted Qualitative Data Analysis Software (CAQDAS) can support visualising bibliometric metadata through RIS files. The usefulness is to provide an interactive image of the reality and actuality of scientific production. This article presents a case study to exemplify how metadata can be analysed and visualised using CAQDAS. The topic for the case study is the gender gap in STEM studies in higher education. The study aims to identify the value and usefulness of data visualisation in representing bibliometric data to support literature review processes. The phenomenon of the gender gap in the STEM education sector is used as a case study. The research questions addressed by the study are: (1) What does CAQDAS contribute to the results obtained?;(2) What are the possible causes of the gender gap? The analysis concludes that the cultural, social, educational, family, and peer group environment generates positive and negative force fields when deciding which studies to pursue;some people follow the patterns expected of them according to their gender. Finally, data visualisation helps understand the scientific evolution of a phenomenon and supports the research on a particular topic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Metadata

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Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering

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Tsinghua science and Technology 2022年第5期27卷 855-868页

作者： Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province the College of Mathematics and Information ScienceHebei UniversityBaoding 071002China School of Applied Mathematics Beijing Normal University ZhuhaiZhuhai 519087China Department of Computer Teaching Hebei UniversityBaoding 071002China

The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation *** a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among *** these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering ***,the diversity and amount of input information are enhanced to generate highly useful representations for downstream ***,the topological graph is constructed to express the spatial relationship of *** a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively *** external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering *** results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service *** GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech ***,DBI indicates the dispersion of cluster distribution and compactness within the cluster.

关键词： self-supervised clustering graph convolutional network feature correspondence semantic feature guidance confusion matrix evaluation indicator

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Research on Generating Xi'an Drum Music Based on Generative Adversarial Network

Research on Generating Xi'an Drum Music Based on Generative ...

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2023 IEEE International Conference on Sensors, Electronics and computer Engineering, ICSECE 2023

作者： Liang, Tianmian Li, Peng Cao, Yumei Wu, Xiaojun Zhao, Huifang Qi, Chenye School of Computer Science Shaanxi Normal University Xi'an710119 China Shaanxi Normal University School of Computer Science Xi'an710119 China Ministry of Education Key Laboratory of Modern Teaching Technology Xi'an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi'an710119 China Xi'An Key Laboratory of Cultural Tourism Resources Development and Utilization Xi'an710062 China Shaanxi Normal University School of Music Xi'an710119 China

ISBN: (纸本)9798350313734

Currently most AI music generator platforms mainly focus on creating modern music, without focusing on the creation of folk music. And most music generation neural network models use recursive neural networks and their variants, resulting in excessively repetitive music information, greatly reducing the fun of music. When using only generative adversarial networks (GANs) to generate music, it is easy to experience unstable training, vanishing gradients, and mode collapse. Failure to consider time dependence can reduce the authenticity of generated music. In order to solve the problem that most of the conventional deep learning GAN music generation network models do not consider music theory rules in music generation and Xi'an drum music faces the scarcity of tracks and difficulties in composition, we proposed a GAN network music generative model based on music theory rules and characteristics of Chinese Folk Songs. When generating music, the generator CNN and the regulator CNN train together, and generate music melodies under the constraints of the voice column CNN module. The generated results are sent to the discriminator CNN, which submits feedback to the generator CNN for competition, to compensate for the shortcomings of previous technology and ultimately generate better melodies, thereby improving the authenticity, sweetness, and rhythm of the generated music. © 2023 IEEE.

关键词： Music

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Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

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computers, Materials & Continua 2023年第8期76卷 1537-1554页

作者： Badriyya BAl-onazi Najm Alotaibi Jaber SAlzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Mahmoud Othman Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or *** the number of labels is limited to one,the task becomes single-label text *** Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical *** recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so *** paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model *** presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic *** achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible ***,the ELM model is utilized for the representation and recognition of the Arabic *** last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier *** experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.

关键词： Arabic corpus dragonfly algorithm machine learning text mining extreme learning machine

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ROT: Retention and Operation Limitation Using TEE

ROT: Retention and Operation Limitation Using TEE

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Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Sumit Kumar Paul D.A. Knox Electrical and Computer Engineering University Of Ottawa Ottawa Canada School of Engineering Design and Teaching Innovation (and School of Electrical Engineering and Computer Science) University Of Ottawa Ottawa Canada

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

Service users seek guarantees that their private data shared with service providers is handled securely and used only for intended purposes. Additionally, they may want assurances that their data will not be shared with other parties and deleted after a time. However, they have no control over the remote environments where the service provider stores and processes their data. While related privacy legislation exists, service providers do not always comply with such policies. Therefore, copies of private data can be retained and used later for malicious purposes or shared unethically. To solve these problems, a framework is proposed leveraging the trusted execution environment, which is protected against rollback and other attacks without involving additional parties, trust assumptions, or resources. Some freely available blockchain byproducts are used in a simple yet novel way to achieve that. Performance metrics of the framework's implementation show its suitability for dealing with real-life scenarios.

关键词： Measurement Privacy Data privacy Costs Process control Legislation Blockchains

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Automatic Traffic Control of Autonomous Vessels in Coastal Waters

Automatic Traffic Control of Autonomous Vessels in Coastal W...

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2023 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2023

作者： Sedova, Nelly A. Sedov, Viktor A. Bazhenov, Ruslan I. Alutina, Elena F. Gorbunova, Tatiana N. Ledovskikh, Irina A. Maritime State University named after G.I. Nevelskoy Automatic and Information Systems Vladivostok Russia Maritime State University named after G.I. Nevelskoy Electrician Theoretical Bases Vladivostok Russia Sholom-Aleichem Priamursky State University Information Systems and Mathematics Birobidzhan Russia Blagoveschensk State Pedagogical University Computer Science and Methods of Teaching Computer Science Blagoveschensk Russia Moscow State University of Civil Engineering Applied Mathematics Moscow Russia Mathematics and Information Technology Pacific National University Khabarovsk Russia

ISBN: (纸本)9798350340532

The paper examines an approach to arrange marine autonomous surface ships (both with and without crew) traffic surveillance illustrated with the Amur Bay and the Golden Horn Bay water area of the city of Vladivostok. The approach involves real-time application of a computer alliance based on General Packet Radio Service (GPRS) and Global Positioning System (GPS) satellite navigation technologies. The research presents the diagram of the system to observe autonomous surface ships located in the coastal area, as well as the assessment algorithm of the current navigation. Machine learning methods applied for analyzing the obtained information contribute to the fast and quite accurate review of the surface navigation, as well as hazard evaluation of vehicles caught in the area of heading in a sea autonomous surface vessel assessment. © 2023 IEEE.

关键词： Global positioning system

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Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

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Intelligent Automation & Soft Computing 2023年第6期36卷 2529-2544页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hassan Alshamrani Mesfer Al Duhayyim Heba Mohsen Amgad Atta Abdelmageed Gouse Pasha Mohammed Abu Sarwar Zamani Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Teachers Training Arabic Linguistics InstituteKing Saud UniversityP.O.BOX 145111RiyadhZIP 4545Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male *** had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for *** of the research works are done mainly for English and least amount of effort for non-English *** study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for ***,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)*** presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter *** attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word ***,the MSDAE model is exploited for the identification of gender into two classes namely male and *** the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our *** performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct *** comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

关键词： Arabic twitter gender identification bat algorithm hybrid deep learning social media arabic corpus

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Privacy-Aware Examination Results Ranking for the Balance Between Teachers and Mothers

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Tsinghua science and Technology 2022年第3期27卷 581-588页

作者： Qunying Yuan Dongxing Wang Yuanyuan Zhao Yong Sang Fan Wang Yuwen Liu Ying Miao Department of College English Teaching Qufu Normal UniversityRizhao 276826China Department of Physical Education Qufu Normal UniversityRizhao 276826China Department of Basketball Rizhao Sports SchoolRizhao 276800China School of Computer Science Qufu Normal UniversityRizhao 276826China

As the main parent and guardian, mothers are often concerned with the study performance of their *** specifically, most mothers are eager to know the concrete examination scores of their children. However,with the continuous progress of modern education systems, most schools or teachers have now been forbidden to release sensitive student examination scores to the public due to privacy concerns, which has made it infeasible for mothers to know the real study level or examination performance of their children. Therefore, a conflict has come to exist between teachers and mothers, which harms the general growing up of students in their study. In view of this challenge, we propose a Privacy-aware Examination Results Ranking(PERR) method to attempt at balancing teachers’ privacy disclosure concerns and the mothers’ concerns over their children’s examination *** drawing on a relevant case study, we prove the effectiveness of the proposed PERR method in evaluating and ranking students according to their examination scores while at the same time securing sensitive student information.

关键词： user privacy protection evaluation and ranking perfeormance balance

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