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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Arunoda, D.P.H. Walpola, S.R. Piumira, S.M.I. Athukorala, A.D.M.P. Thilakarathna, Thusithanjana Chandrasiri, Sanjeevi 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 Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students' interactions (attention, emotions) with the system to identify students' ability to use the learning tool, identifying gaps in students' knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students' knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy. © 2023 IEEE.

关键词： Students

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Industrial Automation Information Analogy for Smart Grid Security

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computers, Materials & Continua 2022年第5期71卷 3985-3999页

作者： Muhammad Asif Ishfaq Ali Shahbaz Ahmad Azeem Irshad Akber Abid Gardezi Fawaz Alassery Habib Hamam Muhammad Shafiq Department of Computer Science National Textile UniversityFaisalabad37610PunjabPakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Industrial automation or assembly automation is a strictly monitored environment,in which changes occur at a good *** are many types of entities in the focusing environment,and the data generated by these devices is *** addition,because the robustness is achieved by sensing redundant data,the data becomes *** data generating device,whether it is a sensing device or a physical device,streams the data to a higher-level deception device for calculation,so that it can be driven and configured according to the updated *** the emergence of the Industry 4.0 concept that includes a variety of automation technologies,various data is generated through numerous ***,the data generated for industrial automation requires unique Information Architecture(IA).IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all *** understand its applicability,we used an example smart grid *** smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the *** addition,in a smart grid system,it needs to report changes on either side of the system,i.e.,consumers and suppliers configure and reconfigure the system according to the *** this article,we propose an analogy of a physical phenomenon.A point charge is used as a data generating device,the streamline of electric flux is used as a data flow,and the charge distribution on a closed surface is used as a ***,the intensity changes are used in the physical process,e.g.,the smart *** analogy is explained by metaphors,and the structural mapping framework is used for its theoretical *** proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows,definition changes(deterministic and non-deterministic),events,and i

关键词： Industry 4.0 information architecture industrial automation

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education 5

Enhancing Early Childhood Learning using Image Processing fo...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya 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 Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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AKQ: A Hybrid Quantum-Classical Image Encryption System

AKQ: A Hybrid Quantum-Classical Image Encryption System

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2023 RIVF International Conference on Computing and Communication Technologies, RIVF 2023

作者： Nguyen, Tran Khanh Thi Thanh Truc, Nguyen Hai, Vu Tuan University of Information Technology VNU-HCM Ho Faculty of Computer Science Chi Minh city Viet Nam University of Information Technology VNU-HCM Ho Faculty of Software Engineering Chi Minh city Viet Nam

ISBN: (纸本)9798350315844

Image encryption is an essential field between computer vision and security which aims to protect the image before future attacks. Nowadays, it is threatened by quantum computers, which can break some classical cryptography. The encryption system has been upgraded from quantum key exchange protocol to post-quantum key-establishment mechanisms, but they are not explicitly designed for the image encryption task. In this paper, we propose a hybrid quantum-classical theoretical framework and its instance AKQ system which is more secure and suitable in the post-quantum era. © 2023 IEEE.

关键词： Quantum computers

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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Yijia Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model's accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively. © 2023 IEEE.

关键词： deep learning multimodal pathogenicity protein structure variant

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Air Quality Index Prediction Based on CNN-LSTM-Attention Hybrid Modeling

Air Quality Index Prediction Based on CNN-LSTM-Attention Hyb...

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

作者： Yinyin Li Tao Jiang Hanzhu Gu Weibing Lu Qingguo Wu Yongtao Yu Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

Air Quality Index (AQI) is an important indicator for determining good or bad air quality. The accurate and efficient prediction of AQI plays a positive role in promoting the management of air pollution. However, current algorithms for predicting AQI usually do not comprehensively consider the effects of pollutant factors and meteorological factors on the prediction performance. Therefore, taking pollutant factors and meteorological factors as the basis of the model study, a CNNLSTM-Attention hybrid model is proposed. The CNN-LSTM module is used to obtain the air quality-related features, and the attention mechanism is introduced to weigh and sum the output of the LSTM in order to obtain the final attention-weighted features. The results show that the model has better performance than the single model for the prediction of the air quality index.

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A Semi-Anchoring Guided High-Resolution Capsule Network for Vehicle Logo Recognition

A Semi-Anchoring Guided High-Resolution Capsule Network for ...

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

作者： Lanfang Wang Zhenyang Yu Yongtao Yu Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

As a unique identity reflecting the manufacturer of the vehicle, the vehicle logo information plays an important role in many transportation-related applications. However, due to the challenges of size variations, shape and form diversities, deformations, occlusions, and complex scenarios, it is still not an easy task to realize highly accurate vehicle logo recognition from images. This paper proposes a novel semi-anchoring guided high-resolution capsule network (SAGHR-CapsNet) for vehicle logo recognition. First, constructed with a multibranch high-resolution capsule network architecture functioned with repeated multiresolution feature fusion for feature extraction, the SAGHR-CapsNet can extract semantically strong and spatially accurate feature representations at each scale. Second, designed with a capsule-based efficient self-attention mechanism for feature semantic promotion, the SAGHRCapsNet functions excellently to attend to channel-wise informative features and target-oriented spatial features. Finally, adopted with a semi-anchoring guided strategy for vehicle logo recognition, the SAGHR-CapsNet performs promisingly to simultaneously improve the processing efficiency and guarantee the recognition accuracy. Intensive quantitative evaluations and comparative analyses on two large-scale data sets demonstrated the applicability and superiority of the SAGHR-CapsNet in vehicle logo recognition tasks.

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A Ternary-attention Capsule Feature Pyramid Network for Building Footprint Extraction from Remote Sensing Imagery

A Ternary-attention Capsule Feature Pyramid Network for Buil...

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

作者： Haiyan Zhang Yazhou Li Meifeng Wang Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

The up-to-date and accurate building footprint database plays a significant role in a large variety of applications. Recently, remote sensing images have provided an important data source for building footprint extraction tasks. However, due to topology variations, color diversities, and complicated rooftop and environmental scenarios, it is still a challenging task to realize fully automated and highly accurate extraction of building footprints from remote sensing images. In this paper, we propose a novel ternary-attention capsule feature pyramid network (TA-CapsFPN), which is formulated with a capsule feature pyramid network architecture and integrated with context-augmentation and feature attention modules, aiming at improving the building footprint extraction accuracy by combining the superior properties of capsule representations and the powerful capability of attention mechanisms. Quantitative evaluations and comparative analyses show that the TA-CapsFPN provides a promising and competitive performance in processing buildings of varying conditions.

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A Bearing Fault Diagnosis Method Based on Improved Deep Residual Contraction Network

A Bearing Fault Diagnosis Method Based on Improved Deep Resi...

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

作者： Shuxin Guo Xiaobing Chen Chuangchuang Zhang Rongrong Wang Heng Lu Chengfa Lu Faculty of computer and software engineering Huaiyin insititute of technology Huai’an China

In this paper, a method for bearing fault diagnosis based on an improved deep residual contraction network is proposed. The method utilizes the residual contraction module in the deep residual contraction network, which is improved in combination with the Inception network, in order to enhance the diagnostic accuracy and efficiency of bearing faults. The method divides the bearing fault diagnosis problem into several sub-problems and designs the corresponding residual contraction module and Inception network structure for each sub-problem. Through experimental validation using an actual bearing fault dataset, the results demonstrate that the method achieves high accuracy and stability in bearing fault diagnosis, providing a new idea and method for research in the field of bearing fault diagnosis.

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A Travelling Recommendation System based on Knowledge Graph 3

A Travelling Recommendation System based on Knowledge Graph

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3rd International Academic Exchange Conference on Science and Technology Innovation, IAECST 2021

作者： Dai, G.R. Xu, J.Y. Zhou, H. Zhou, L. Cao, Z.H. Zhu, M.D. Huaiyin Institute of Technology Faculty of Computer and Software Engineering China

ISBN: (纸本)9781665402675

In recent years, the improvement of people's live standard lead to an increasing demand for travelling, but the information on scenic spots on the Internet is ponderous and the accuracy of scenic spot recommendations is not high. This study aims to solve these problems to realize a more accurate scenic spot recommendation for self-drivings and provide a better visualization of recommendation results. A recommendation model based on the Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (MKR) algorithm is designed in this study. Then, based on this model, an intelligent knowledge graph based recommendation system for scenic spots is constructed. Rich unstructured text data of scenic spots in major scenic spot websites are crawled using Selenium and are stored and managed by the neo4j graph database. Through experimental simulation, the recommendation accuracy can reach over 84%. Compared to the Propagating User Preferences on the Knowledge Graph for Recommender Systems (RippleNet), the accuracy rate is enhanced by 6.2%. © 2021 IEEE.

关键词： Knowledge graph

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