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Boosting speech recognition performance: a robust and accurate ensemble method based on HMMs

International Journal of Intelligent systems Technologies and Applications 2024年第1期22卷 41-76页

作者： Hazmoune, Samira Bougamouza, Fateh Mazouzi, Smaine Benmohammed, Mohamed Department of Computer Science Faculty of Sciences University of 20 Août 1955-Skikda Skikda Algeria Department of Software Technologies and Information Systems Faculty of New Technologies of Information and Communication University Constantine 2 Constantine Algeria

In this paper, we propose an ensemble method based on hidden Markov models (HMMs) for speech recognition. Our objective is to reduce the impact of the initial setting of training parameters on the final model while improving accuracy and robustness, particularly in speaker independent systems. The main idea is to exploit the sensitivity of HMMs to the initial setting of training parameters, thus creating diversity among the ensemble members. Additionally, we perform an experimental study to investigate the potential relationship between initial training parameters and ten diversity measures from literature. The proposed method is assessed on a standard dataset from the UCI machine-learning repository. Results demonstrate its effectiveness in terms of accuracy and robustness to intra-class variability, surpassing basic classifiers (HMM, KNN, NN, SVM) and some previous works in the literature including those using deep learning algorithms such as convolutional neural networks (CNNs) and long short-term memory (LSTM). Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Hidden Markov models

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A Proposal of the Gamification of Programming Learning Assistant System

A Proposal of the Gamification of Programming Learning Assis...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Kyaw, Htoo Htoo Sandi Kaneko, Keiichi Fuanbiki, Nobuo Tokyo University of Agriculture and Technology Department of Computer and Information Science Tokyo Japan Okayama University Department of Information and Communication Systems Okayama Japan

ISBN: (纸本)9798331530839

Despite the rapid expansion of the ICT sector, there's a disconnect with STEM subjects, leading to high dropout rates in computer science and engineering programs. This contributes to many unfilled positions in the ICT job market. One key issue is that traditional teaching methods struggle to engage students in challenging and abstract STEM subjects like programming. To address this, innovative technology-enhanced learning solutions are needed to be developed. In the past, we have developed a Programming Learning Assistant System (PLAS) to support programming learning for novice students. PLAS offers several types of exercise problems to foster code reading and writing skills at different levels. In this paper, we propose the initial prototype of the gamification of PLAS to enhance students' comprehension of programming concepts, improve their confidence, cultivate their interest in STEM fields, and increase course engagement through dynamic and engaing scenarios. © 2024 IEEE.

关键词： Students

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Analysis of Code Writing Problem Solution Results in C Programming Course

Analysis of Code Writing Problem Solution Results in C Progr...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Kyaw, Htoo Htoo Sandi Kaneko, Keiichi Funabiki, Nobuo Tokyo University of Agriculture and Technology Department of Computer and Information Science Tokyo Japan Okayama University Department of Information and Communication Systems Okayama Japan

ISBN: (纸本)9798350324136

To advance the educational quality of C programming, we have proposed and implemented the C Programming Learning Assistant System (CPLAS) as a Web application system. CPLAS offers a Code Writing Problem (CWP) that requests students to write the source code from scratch to improve their code-writing skills. In past years, we have utilized code writing problems in our C programming courses. In this paper, we analyze the solution results of students taking the course in 2022. The results confirmed the effectiveness of the code validation function for the automatic marking and the coding rule checking function to improve readable code writing skills. © 2024 IEEE.

关键词： C Programming Learning Assistant System code validation function Code Writing Problem coding rule checking function

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Distributional learning in multi-objective optimization of recommender systems

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10849-10865页

作者： Candelieri, Antonio Ponti, Andrea Giordani, Ilaria Bosio, Anna Archetti, Francesco Department of Economics Management and Statistics University of Milano-Bicocca Milan Italy Department of Computer Science Systems and Communication University of Milano-Bicocca Milan Italy OAKS Srl Milan Italy

Metrics such as diversity and novelty have become important, beside accuracy, in the design of Recommender systems (RSs), in response the increasing users' heterogeneity. Therefore, the design of RSs is now increasingly modelled as a multi-objective optimization problem (MOP) for whose solution Multi-objective evolutionary algorithms (MOEAs) have been increasingly considered. In this paper we focus on the k-top recommendation problem in which a solution is encoded as a matrix whose rows correspond to customers and column to items. The value of accuracy, novelty, and coverage for each candidate list, is evaluated as a sample and can be represented as a 3-d histogram which encodes the knowledge obtained from function evaluations. This enables to map the solution space into a space, whose elements are histograms, structured by the Wasserstein (WST) distance between histograms. The similarity between 2 users in this probabilistic space is given by the Wasserstein distance between their histograms. This enables the construction of the WST graph whose nodes are the users and the weights of the edges are the WST distance between users. The clustering of users takes then place in the WST-graph. In the optimization phase the difference between two top-k lists can be encoded as the WST distance between their 3-dimensional histograms. This enables to derive new selection operators which provide a better diversification (exploration). The new algorithm Multi-objective evolutionary optimization/Wasserstein (MOEA/WST), compared with the benchmark NSGA-II, yields better hypervolume and coverage, in particular at low generation counts. © 2022, The Author(s).

关键词： Recommender systems

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Student academic success prediction in multimedia-supported virtual learning system using ensemble learning approach

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Multimedia Tools and Applications 2024年第40期83卷 87553-87578页

作者： Saidani, Oumaima Umer, Muhammad Alshardan, Amal Alturki, Nazik Nappi, Michele Ashraf, Imran Department of Information Systems College of Computer and Information Sciences and Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano84084 Italy Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Multimedia systems and metaverse has been gaining increasing interest for education in virtual environments. With wide adoption of these technologies, the data is expected to grow exponentially. The increasing rise of educational data indicates that standard processing methods may be limited. As a result, constructing data mining-based academic performance assessment methods is becoming increasingly important. Students’ academic performance prediction can raise an alert for students at risk of failure and can help academia plan accordingly. This research suggests utilizing features derived from a convolutional neural network (CNN) in conjunction with machine learning models to enhance the overall performance of the model. By doing so, the necessity for manual feature extraction is eliminated, leading to superior outcomes compared to using machine learning and deep learning models separately. At the outset, nine machine learning models are employed to assess both the original features and the convoluted features. The top two individual models are later used to build an ensemble model using soft voting criteria. The ensemble model is built using random forest (RF) and support vector machine (SVM) for predicting student academic performance. The performance of the proposed approach is compared to existing models for validation indicating the superior performance of the former. With an accuracy of 98.99%, as well as precision, recall, and F1 scores of 99%, the proposed method achieved outstanding results for predicting student academic performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

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computers, Materials & Continua 2023年第1期74卷 881-895页

作者： Nitin Sharma Mohd Anul Haq Pawan Kumar Dahiya B.R.Marwah Reema Lalit Nitin Mittal Ismail Keshta Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Electronics&Communication Engineering DCRUST Murthal131039India Department of Transportation Engineering IITKanpur208016Uttar PradeshIndia Department of Computer Science Applications PIETSmalkha132102India Skill Faculty of Science and Technology Shri Vishwakarma Skill UniversityPalwal121102India Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile *** is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight *** the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency *** Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic *** study is based on a comprehensive and detailed literature evaluation in the field of *** major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car *** research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for *** properties of characters are employed to recognise the Indian licence plate in this *** licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were *** comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

关键词： Intelligent transportation system automatic license plate recognition system neural network random forest convolutional neural network support vector machine

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Training Large Models on Heterogeneous and Geo-distributed Resource with Constricted Networks

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Big Data Mining and Analytics 2025年第4期8卷 966-980页

作者： Zong, Zan Guo, Minkun Zhai, Mingshu Tang, Yinan Li, Jianjiang Zhai, Jidong Tsinghua University Department of Computer Science and Technology Beijing100084 China IEIT SYSTEMS Co. Ltd. Jinan250014 China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing100083 China

As the computational demands driven by large model technologies continue to grow rapidly, leveraging GPU hardware to expedite parallel training processes has emerged as a commonly-used strategy. When computational resources within a single cluster are insufficient for large-model training, the hybrid utilization of heterogeneous acceleration hardware has emerged as a promising technical solution. The utilization of heterogeneous acceleration hardware and scheduling of diverse cloud resources have become a focal point of considerable interest. However, these computing resources are often geographically distributed. Due to the lack of awareness of heterogeneous devices and network topologies, existing parallel training frameworks struggle to leverage mixed GPU resources across constrained networks effectively. To boost the computing capability of the connected heterogeneous clusters, we propose HGTrainer, an optimizer designed to plan heterogeneous parallel strategies across distributed clusters for large model training. HGTrainer can adaptively saturate heterogeneous clusters because of the expanded tunable parallelism space for heterogeneous accelerators, with the awareness of relatively lower inter-cluster bandwidth. To achieve this goal, we formulate the model partitioning problem among heterogeneous hardware and introduce a hierarchical searching algorithm to solve the optimization problem. Besides, a mixed-precision pipeline method is used to reduce the cost of inter-cluster communications. We evaluate HGTrainer on heterogeneous connected clusters with popular large language models. The experimental result shows that HGTrainer effectively improves 1.49× training throughput on average for the mixed heterogeneous cluster compared with the state-of-the-art Metis. © 2018 Tsinghua University Press.

关键词： Cluster computing

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain systems

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computer Modeling in Engineering & sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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Database Deadlock Diagnosis for Large-Scale ORM-Based Web Applications 39

Database Deadlock Diagnosis for Large-Scale ORM-Based Web Ap...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Dong, Zhiyuan Wang, Zhaoguo Yi, Chuanwei Xu, Xian Zhang, Jinyuan Li, Jinyang Chen, Haibo Shanghai Jiao Tong University Institute of Parallel and Distributed Systems China New York University Department of Computer Science United States

ISBN: (纸本)9798350322279

Today, most database-backed web applications depend on the database to handle deadlocks. At runtime, the database monitors the progress of transaction execution to detect deadlocks and abort affected transactions. However, this common detect-and-recover strategy is costly to performance as aborted transactions waste CPU *** avoid deadlock-induced performance degradation, developers aim to reorganize the application code to remove deadlocks. Unfortunately, doing so is difficult for web applications. Not only do their implementations include hundreds of thousands of LoCs, but they also use third-party object-relational mapping (ORM) frameworks which hide database access details. Consequently, it is hard for developers to accurately diagnose *** propose WeSEER, a deadlock diagnosis tool for web applications. To overcome the opacity of ORMs, WeSEER performs concolic execution on unit tests to extract a web application's transactions as a sequence of template statements with symbolic inputs as well as path conditions that enable the sequence. WeSEER then analyzes the extracted transactions based on fine-grained lock modeling to identify potential deadlocks and report the code locations that cause them. We implement WeSEER for Java-based (OpenJDK) web applications, and use it to analyze two popular open-source e-commerce applications, Broadleaf and Shopizer. WeSEER has successfully identified 18 potential deadlocks in Broadleaf and Shopizer. Eliminating these identified deadlocks can result in up to 39.5× and 4.5× throughput improvement for Broadleaf and Shopizer, respectively. © 2023 IEEE.

关键词： Database systems

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