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3rd International Conference on computer science, Engineering and Education Applications, ICCSEEA 2020

作者： Tomashevskiy, Valentyn Pohrebniuk, Iryna Kunanets, Nataliia Pasichnyk, Volodymyr Veretennikova, Nataliia Informatics and Computer Science Department National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kiev Ukraine Economic Management and IT Department Berdyansk University of Management and Business Berdyansk Ukraine Information Systems and Networks Department Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9783030555054

It is proposed technologies for improving digital learning at higher educational institutions, based on the concept of learning considering students’ abilities. Technologies of construction of individual learning scenarios based on a student model and gap maps of user knowledge are analyzed. The student’s model presents the basic parameters of their level of preparation and individual cognitive features. It allows to realize a number of new technologies for forming individual calendar plans of theme repetition, constructing individual trajectories of processes of forgetting the acquired knowledge by a student and developing individual scenarios of adaptive testing. A key feature of the proposed approach is its practical implementation in a real functioning open adaptive learning system, which has been successfully tested and used in several Ukrainian universities. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Design and Development of a Microcontroller Based Solar PV Cooker

Design and Development of a Microcontroller Based Solar PV C...

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Ubiquitous Computing and Intelligent Information systems (ICUIS), International Conference on

作者： Anita Morake Ravi Samikannu L. Akila R. Karpagam R. Rohini Abid Yahya Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Palapye Botswana Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India Department of Electrical and Electronics Engineering Easwari Engineering College Ramapuram India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tiruchengode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This study proposes an innovative approach to improve solar cooking systems by combining microcontroller technology and deep-learning techniques. The Solar-PV Microcontroller-based Cooker concept aims to provide a sustainable and eco-friendly cooking method by harnessing the sun's power. This innovative approach addresses global energy conservation and access to clean cooking options, particularly in areas where conventional methods contribute to deforestation and indoor air pollution. The system includes inputs for cooking duration and temperature monitoring, enhancing solar energy efficiency and user experience. The study investigates how deep learning algorithms can be incorporated into the microcontroller-based solar cooker system to enhance functionality and versatility. Cooking settings can be automatically adjusted to maximize energy consumption and cooking results by analyzing real-time data from sensors and human inputs. The hybrid approach combines the best features of deep learning with microcontroller technology, demonstrating the significance of renewable energy in a cleaner, more efficient future. The Solar-Pv cooker demonstrates the possibilities of fusing culinary technology with computer algorithms, showcasing the potential of renewable energy in a cleaner, more efficient future.

关键词： Temperature sensors Deep learning Temperature measurement Renewable energy sources Microcontrollers Solar energy Ubiquitous computing User experience Sensors Temperature control

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GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation

GLoT: A Novel Gated-Logarithmic Transformer for Efficient Si...

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IEEE Future Networks World Forum (FNWF)

作者： Nada Shahin Leila Ismail Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology UAE University Al-Ain United Arab Emirates Emirates Center for Mobility Research UAE University Al-Ain United Arab Emirates

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community.

关键词： Sign language Adaptation models Translation Accuracy Terminology Neural networks Semantics Logic gates Transformers Testing

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

arXiv

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arXiv 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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Identification of Diabetic Retinopathy through Machine Learning

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Mobile Information systems 2021年 2021卷

作者： Bader Alazzam, Malik Alassery, Fawaz Almulihi, Ahmed Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia

A cross-sectional study of patients with suspected diabetic retinopathy (DR) who had an ophthalmological examination and a retinal scan is the focus of this research. Specialized retinal images were analyzed and classified using OPF and RBM models (restricted Boltzmann machines). Classification of retinographs was based on the presence or absence of disease-related retinopathy (DR). The RBM and OPF models extracted 500 and 1000 characteristics from the images for disease classification after the system training phase for the recognition of retinopathy and normality patterns. There were a total of fifteen different experiment series, each with a repetition rate of 30 cycles. The study included 73 diabetics (a total of 122 eyes), with 50.7% of them being men and 49.3% being women. The population was on the older side, at 59.7 years old on average. The RBM-1000 had the highest overall diagnostic accuracy (89.47) of any of the devices evaluated. The RBM-500 had a better autodetection system for DR signals in fundus images than the competition (100% sensitivity). In terms of specificity, RBM-1000 and OPF-1000 correctly identified all of the images that lacked DR signs. In particular, the RBM model of machine learning automatic disease detection performed well in terms of diagnostic accuracy, sensitivity, and application in diabetic retinopathy screening. © 2021 Malik Bader Alazzam et al.

关键词： Ophthalmology

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Attention-Based Multiscale Localization for Bone Age Assessment

Attention-Based Multiscale Localization for Bone Age Assessm...

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IEEE International Symposium on Biomedical Imaging

作者： Kyunghee Jung Toan Duc Nguyen Duc-Tai Le Junghyun Bum Chang-Hwan Son Hyunseung Choo Department of Superintelligence Engineering Sungkyunkwan University Korea College of Computing and Informatics Sungkyunkwan University Korea Sungkyun AI Research Institute Sungkyunkwan University Korea Department of Software Science & Engineering Kunsan National University Korea Department of Electrical and Computer Engineering Sungkyunkwan University Korea

Bone Age Assessment (BAA) is crucial for the biological maturity evaluation of children. Developing automated techniques of BAA has gained a lot of attention from both academia and medicine. This paper presents a novel deep-learning-based BAA method including refining and multi-scale processing of hand X-ray images. The refining step removes unnecessary background and noises in the images, resulting in a high-quality dataset. Such image refinement is beneficial for Region of Interest (ROI) localization step with self-attention mechanism. The localization model is trained with multiscale hand X-ray images separately to obtain multiple ROIs for each image. Eventually, the multiscale ROIs are used as complementary features of an image in training a regression model for BAA. We evaluate the performance of the proposed method using 2017 RSNA pediatric bone age challenge dataset. The results show mean absolute error of 3.52, which is 24.9% lower than state-of-the-art results.

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Arbitrary Motion Style Transfer with Multi-Condition Motion Latent Diffusion Model

Arbitrary Motion Style Transfer with Multi-Condition Motion ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wenfeng Song Xingliang Jin Shuai Li Chenglizhao Chen Aimin Hao Xia Hou Ning Li Hong Qin Beijing Information Science and Technology University China Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University College of Computer Science and Technology China University of Petroleum (East China) Research Unit of Virtual Human and Virtual Surgery (2019RU004) Chinese Academy of Medical Sciences Department of Computer Science Stony Brook University (SUNY at Stony Brook) Stony Brook New York USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

computer animation's quest to bridge content and style has historically been a challenging venture, with previous efforts often leaning toward one at the expense of the other. This paper tackles the inherent challenge of content-style duality, ensuring a harmonious fusion where the core narrative of the content is both preserved and elevated through stylistic enhancements. We propose a novel Multi-condition Motion Latent Diffusion Model (MCM-LDM) for Arbitrary Motion Style Transfer (AMST). Our MCM-LDM significantly emphasizes preserving trajectories, recognizing their fundamental role in defining the essence and fluidity of motion content. Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory, motion content, and motion style. The critical insight of MCM-LDM is to embed multiple conditions with distinct priorities. The content channel serves as the primary flow, guiding the overall structure and movement, while the trajectory and style channels act as auxiliary components and synchronize with the primary one dynamically. This mechanism ensures that multi-conditions can seamlessly integrate into the main flow, enhancing the overall animation without overshadowing the core content. Empirical evaluations underscore the model's proficiency in achieving fluid and authentic motion style transfers, setting a new benchmark in the realm of computer animation. The source code and model are available at https://***/XingliangJin/***.

关键词： Bridges computer vision Fluids Computational modeling Source coding Animation Diffusion models

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Cybernated Watermarking Approach on Image with Cryptographic Encryption

Cybernated Watermarking Approach on Image with Cryptographic...

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computer, Communication and Control (IC4), International Conference on

作者： R. Sivasankari T Veeramakali S M Mustafa Nawaz P. Prakash P Umadevi A Alvin Ancy Dept. of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Sri Sai Ram Engineering College Department of S & H KPR Institute of Engineering and Technology Coimbatore Dept of Electronics and Communication Engineering Anna University Chennai

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types; a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text.

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Robust Recovery of Low Rank Matrix by Nonconvex Rank Regularization 11th

Robust Recovery of Low Rank Matrix by Nonconvex Rank Regular...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Zhang, Hengmin Luo, Wei Du, Wenli Qian, Jianjun Yang, Jian Zhang, Bob Department of Computer and Information Science University of Macau 999078 China School of Information Science and Engineering Key Laboratory of Advanced Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783030873578

As we know, nuclear norm based regularization methods have the real-world applications in pattern recognition and computer vision. However, there exists a biased estimator when nuclear norm relaxes the rank function. To solve this issue, we focus on studying nonconvex rank regularization problems for both robust matrix completion (RMC) and low rank representation (LRR), respectively. By extending both to a general low rank matrix minimization problem, we develop a nonconvex alternating direction method of multipliers (ADMM). Moreover, the convergence results, i.e., the variable sequence generated by the nonconvex ADMM is bounded and its subsequence converges to a stationary point. Meanwhile, its limiting point satisfies the Karush-Kuhn-Tucher (KKT) conditions provided under some milder assumptions. Numerical experiments can verify the convergence properties of the theoretical results and the performance shows its superiority on both image inpainting and subspace clustering. © 2021, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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A Two-Stage Packetized Energy Trading and Management Framework for Virtual Power Plants

A Two-Stage Packetized Energy Trading and Management Framewo...

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IEEE General Meeting Power& Energy Society

作者： Yuanliang Li Luyang Hou Jun Yan Yuhong Liu Mohsen Ghafouri Peng Zhang Concordia Institute for Information Systems Engineering Concordia University Montréal QC Canada School of Computer Science Beijing University of Posts and Telecommunications Beijing China Department of Computer Engineering Santa Clara University Santa Clara CA USA College of Information Engineering Shenzhen University Shenzhen China

Packetized energy management (PEM) enables virtual power plants (VPPs) to manage a population of controllable and distributed loads and generators in a request-reply manner by encapsulating the energy into fixed-duration/fixed-power energy packets. To participate in and benefit from the energy market in a transactive energy system, the VPP with PEM technique should customize its trading strategy by considering the feature of PEM. With this goal, this paper develops a dedicated two-stage packetized energy trading and management (PETM) framework for VPPs facilitated by using a co-simulation platform for a transactive energy system. Particularly, VPP bids for the energy quantity as a participant of the market with an objective of maximizing its designed utility in the first stage; and then in the second stage, the VPP launches PEM to manage a bunch of controllable devices of its customers by tracking a balancing signal produced in the first stage. We validate the proposed PETM framework using the developed co-simulation platform (PEMT-CoSim), and the results demonstrate the feasibility of the proposed framework.

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