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Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Selamu Shamore Amin Tuni Gure Mohammed Abebe Yimer Department of Computer Science Wachemo University Hossana Ethiopia Department of Information System Arsi University Arsi Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

Speech Recognition focuses on developing advanced technologies that can recognize and translate spoken language into text. Automatic Speech Recognition (ASR) is an Artificial Intelligence (AI) technology that captures human voice from a mi-crophone and analyzes it based on spoken words to comprehend natural language. In Ethiopia, ASR models have been developed for specific local languages such as Amharic, Tigrigna, Wolaytta, and Afaan Oromo, but there has been a lack of research on ASR models for the Hadiyyisa language, which is also spoken in Ethiopia. The research study designed and developed an ASR model for the Hadiyyisa language. The researchers collected data from native speakers of the Hadiyyisa language and used Python programming to design and develop the deep learning model for the Hadiyissa ASR model. Different methods and techniques were used in the study to achieve the research objectives and complete the entire research process. The proposed model utilized deep learning algorithms including RNN, LSTM, BiLSTM, and CNN, along with suitable feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCC). The accuracy of the proposed ASR model was evaluated through experimentation, which demonstrated that the BiLSTM model outperformed the other deep learning models with an overall accuracy of 90.95 % Based on the experimental evidence, the proposed model was found to be better suited for ASR for the Hadiyyisa language.

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Zhejiang Lab Zhejiang-HUST Joint Research Center for Graph Processing China Department of Computer Science University of Warwick United Kingdom

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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A Development of Time-Varying Weight Model Predictive Control for Autonomous Vehicles

A Development of Time-Varying Weight Model Predictive Contro...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Shajari, Arian Najdovski, Zoran Lim, Chee Peng Nahavandi, Saeid Murray State University Murray Department of the Computer Science and Information Systems MurrayKY42071 United States Institute for Intelligent System Research and Innovation Deakin University GeelongVIC43216 Australia Swinburne University of Technology HawthornVIC Australia Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA United States

ISBN: (纸本)9798350337020

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle. © 2023 IEEE.

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Research and Application of Material Data for Digital Manufacturing

Research and Application of Material Data for Digital Manufa...

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Image Processing and computer Applications (ICIPCA), IEEE International Conference on

作者： Ganlong Wang Guoyin Zhang Jichang Wang Jianxun Chen Computer Science and Technology Harbin Engineering University CSIC Information Technology Co. Ltd. Beijing China Computer Science and Technology Harbin Engineering University Harbin China System General Department CSIC Information Technology Co. Ltd. Beijing China

The organization mode of “headquarters and multi-factories” has been widely developed in manufacturing enterprises. Although the multi-production base model meets the needs of rapid business expansion, it puts forward higher requirements for enterprise management. The differences in cost accounting methods, planning methods, and operation modes of the factories under the headquarters lead to different production management organizations, purchasing organizations, and sales organizations. Because the basic materials are managed by the headquarters, each factory can only select the required materials from the headquarters material warehouse. Therefore, there is a phenomenon that the same item corresponds to multiple business views. In this paper, the full attribute management mode of material data is taken as the research object, and a full attribute management solution with material code and factory code as the unique identification is proposed. It provides the theoretical basis and practical guidance for the application and promotion of material data life cycle management in multi-factory mode.

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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"DOMINO - Stop Domestic Violence". An Educational Mobile Application

"DOMINO - Stop Domestic Violence". An Educational Mobile App...

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21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Sakellari, Evanthia Murto, Tiina Häsänen, Marjatta Turunen, Timo Modhwadia, Hitesh Sotto-Mayor De Carvalho Pinto, Maria Luisa Ferreira Dos Santos, Maria Anabela Lagiou, Areti Department of Public and Community Health Laboratory of Hygiene and Epidemiology University of West Attica Athens Greece Faculty of Health and wellbeing Turku University of Applied Sciences Turku Finland Master Degree of Social Science in Business Financial Management Riga Technical University Latvia Master Degree of Engineering in Computer System Riga Technical University Latvia Portugal

ISBN: (纸本)9781643684000

Domestic violence affects people of all socioeconomic backgrounds and education levels and can happen to anyone. It is a public health issue that needs to be addressed with health and social care professionals playing an essential role in prevention and early intervention. These professionals need to be prepared through proper education. A European funded project developed 'DOMINO - Stop domestic violence' educational mobile application which was piloted among 99 social and/or health care students and professionals. Most of the participants (n= 59, 59.6%) indicated that the DOMINO mobile application was easy to install and over half of them (n=61, 61.6%) would recommend the app. They found it easy to use, and quick access to useful materials and tools. Participants found case studies and the checklist good and useful tools for them. The DOMINO educational mobile application is available open access, in English, Finnish, Greek, Latvian, Portuguese and Swedish, for any stakeholder worldwide who is interested to learn more about domestic violence prevention and intervention. © 2023 The authors and IOS Press.

关键词： Mobile computing

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Empowering Deaf Children with Sinhala Sign Language, Emotion Detection, and Sound Recognition

Empowering Deaf Children with Sinhala Sign Language, Emotion...

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Advancements in Computing (ICAC), International Conference on

作者： W.H.K Fernando P.L.D.N Perera M.I.N Fernando P.T.E.W Gunasekara Harinda Fernando Rivoni De Zoysa Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This study describes a broad endeavor to use cutting-edge technologies to empower deaf primary school students in Sri Lanka. Three key elements make up the study: a sound recognition and classification system, an Android software for translating gestures in Sinhala Sign Language (SSL), and a mobile app for emotion recognition and text-tospeech in Sinhala. By promptly alerting caregivers to potential dangers, the sound recognition technology secures the security of deaf children at home, providing an invaluable layer of security. The Android software bridges the communication gap and improves family interactions by translating SSL gestures captured by the device's camera into textual representations. This promotes successful communication between deaf children and their parents. Convolutional Neural Networks (CNNs) are used in the mobile application to detect and understand emotions based on facial expressions. This enables non-verbal youngsters to communicate successfully through Sinhala textto-speech output with adaptive tone modulation. This research aims to build a caring and inclusive atmosphere, enabling increased independence and enhanced communication skills in deaf children by combining these advances. The suggested solutions could significantly improve the lives of deaf kids, providing them with opportunities for better learning, social interaction, and emotional health.

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Towards an Intelligent Industrial Environment: Blockchain-Empowering Human-Centered Artificial Intelligence for Risk Assessment and Strategic Evaluation

SSRN

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SSRN 2023年

作者： Khan, Abdullah Ayub Chen, Yen-Lin Hajjej, Fahima Shaikh, Aftab Ahmed Yang, Jing Ku, Chin Soon Por, Lip Yee Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

With the recent introduction of high-level human control and high-level IoT control in intelligent transportation systems, their association with blockchain increases the performance of the Internet of Vehicles (IoVs) and E-Healthcare, which leads to wider adaptation in the future as well. In this paper, the contributions of this systematic article are divided into three different folds: first, this paper evaluates more than a hundred previously published articles and highlights the gaps in futuristic concerns. Second, the paper presents a novel and secure human-centered AI-enabled risk assessment framework for the IoVs with blockchain distributed ledger technology (DLT). Third, a distributed public permissionless network with two operational channels of inter-communication for vehicle-to-vehicle (V2V) is proposed. It is considered an extension in the Vehicle-to-Everything (V2X) environment. Along with that, the pseudo-development of chaincodes to automate human-centered IoV node transactions. In addition, a Practical Byzantine Fault Tolerance (PBFT) consensus protocol and related controlled executions are programmed for fast scheduling, offloading, managing, and optimizing with reliability. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording

arXiv

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arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

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Two-Layer SVM, Towards Deep Statistical Learning 1

Two-Layer SVM, Towards Deep Statistical Learning

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1st International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Kazemi, Alireza Boostani, Reza Odeh, Mahmoud Al-Mousa, Mohammad Rasmi Shiraz University Shiraz Faculty of Science Mathematics Department Fars Iran Electrical and Computer Engineering School Shiraz University Shiraz CSEandIT Dept. Fars Iran Zarqa University Department of Management Information System Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350332742

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs). © 2022 IEEE.

关键词： Support vector machines

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