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1st Renewable Energy Technologies and Modern Communications Systems: Future and Challenges Conference, RETMCS 2024

作者： AL-Shehri, Bushra A. Sabbeh, Sahar F. Dessouky, Mohamed M. Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Faculty of Computers and Artificial Intelligence Information Systems Department Benha University Benha13518 Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9798331519445

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography dataset (Kaggle), the SARS-CoV-2 CT-Scan dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography dataset and 99% accuracy on the SARS-CoV-2 CT-Scan dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions. ©2024 IEEE.

关键词： COVID-19

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MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles 26

MapsTP: HD Map Images Based Multimodal Trajectory Prediction...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Sharma, Sushil Das, Arindam Sistu, Ganesh Halton, Mark Eising, Ciarán Department of Electronic & Computer Engineering University of Limerick Ireland SFI CRT Foundations in Data Science University of Limerick Ireland

ISBN: (纸本)9781837242672

Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering multiple possible future trajectories based on diverse sources of environmental data. In this approach, we leverage ResNet-50 to extract image features from high-definition map data and use IMU sensor data to calculate speed, acceleration, and yaw rate. A temporal probabilistic network is employed to compute potential trajectories, selecting the most accurate and highly probable trajectory paths. This method integrates HD map data to improve the robustness and reliability of trajectory predictions for autonomous vehicles. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Autonomous Vehicles HDMap Images MultiModel Trajectory Prediction Probabilistic Network

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Towards Log-based Execution Status Estimation Using Graph Neural Networks 31

Towards Log-based Execution Status Estimation Using Graph Ne...

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31st Asia-Pacific Software Engineering Conference, APSEC 2024

作者： Sumita, Shimon Nakagawa, Hiroyuki Saito, Shinobu Tsuchiya, Tatsuhiro Osaka University Osaka Japan Okayama University Okayama Japan NTT Computer and Data Science Laboratories Tokyo Japan

ISBN: (纸本)9798331534011

This study addresses software bloat, a prevalent issue in modern software development, causing excessive size and complexity due to feature additions and unnecessary functions. Such bloat leads to decreased efficiency, performance degradation, and increased vulnerability. To combat this issue, the concept of software 3R (reduce, reuse, recycle) is proposed;however, accurately reproducing the internal state of black-box software for 3R requires both source code and execution log data, posing practical challenges. In this paper, we conduct a software execution status estimation using limited execution log. Graph Neural Networks (GNNs) are employed for analysis, offering effective processing of graph data. The task is framed as link prediction and node classification, comparing traditional deep learning methods with GNNs using Apache OFBiz ERP software logs. Preliminary results validate GNN applicability. © 2024 IEEE.

关键词： Software design

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Expressive Access Control Encryption With Bilateral Policy Privacy for Consumer Electronics in the Metaverse

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 90-101页

作者： Peng, Zongfeng Peng, Changgen Xu, Dequan Ding, Hongfa Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University of Finance and Economics College of Information Guiyang550025 China

The metaverse offers an unprecedented immersive experience and a fresh connection paradigm for consumers, which brings security threats related to unauthorized data access, potential misuse, and increasing attacks in the bilateral information flow. Therefore, it is necessary to provide an efficient and secure access control scheme. However, current efforts struggle to address these more complicated security issues. To do this, in this paper, we present an expressive access control encryption (EACE) scheme for bilateral control of information flow in metaverse-enabled consumer electronics. Our EACE supports fine-grained and bilateral control of senders and receivers by carefully taking advantage of access control encryption, dual-policy attribute-based encryption, and matchmaking encryption. Furthermore, we propose a novel sanitization algorithm with sender anonymity and bilateral policy privacy against the malicious sanitizer. We formalize the corresponding security definitions and give rigorous proofs. Performance analysis demonstrates that our EACE can simultaneously achieve bilateral control with policy privacy, sender anonymity against sanitizer, and lightweight decryption. The experimental results show that our proposal costs less computational and storage overhead in the symmetric bilinear group. The impressive performance indicates that our EACE is an excellent choice to balance security and efficiency when consumer electronics meet the metaverse. © 1975-2011 IEEE.

关键词： Differential privacy

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Efficient and Reliable Federated Recommendation System in Temporal Scenarios 18th

Efficient and Reliable Federated Recommendation System in ...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Ye, Jingzhou Lin, Hui Wang, Xiaoding Dong, Chen Liu, Jianmin The College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China College of Computer and Data Science Fuzhou University Fuzhou350116 China

ISBN: (纸本)9789819998951

Addressing privacy concerns and the evolving nature of user preferences, it is crucial to explore collaborative training methods for federated recommendation models that match the performance of centralized models while preserving user privacy. Existing federated recommendation models primarily rely on static relational data, overlooking the temporal patterns that dynamically evolve over time. In domains like travel recommendations, factors such as the availability of attractions, introduction of new activities, and media coverage constantly change, influencing user preferences. To tackle these challenges, we propose a novel approach called FedNTF. It leverages an LSTM encoder to capture multidimensional temporal interactions within relational data. By incorporating tensor factorization and multilayer perceptrons, we project users and items into a latent space with time encoding, enabling the learning of nonlinear relationships among diverse latent factors. This approach not only addresses the privacy concerns by preserving the confidentiality of user data but also enables the modeling of temporal dynamics to enhance the accuracy and relevance of recommendations over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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Research on Traceable Handwritten Signature Anti-Counterfeiting and Verification System 6

Research on Traceable Handwritten Signature Anti-Counterfeit...

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6th International Conference on Machine Learning and Intelligent Systems, MLIS 2024

作者： Ye, Jiahong Ye, Feiyue School of Data Science The Chinese University of HongKong Shenzhen China School of Computer Engineering Jiangsu University of technology China

ISBN: (纸本)9781643685533

Addressing the anti-counterfeiting and verification issues of handwritten signatures, a traceable offline handwritten signature anti-counterfeiting and verification system is proposed. The system consists of four parts: a front-end mobile terminal application system, a database storage system, a backend management system, and a portable dynamic encoding stamp device. The system can achieve traceability of signatures, thereby more effectively preventing counterfeit signatures. The front-end mobile terminal application system can generate a unique two-dimensional code based on the relevant information of the signature file and send it to the database system for storage, while also sending it to the portable dynamic encoding stamp device. The portable dynamic encoding stamp device can receive the dynamic two-dimensional code from the front-end mobile terminal application system and produce a two-dimensional code imprint. The database system can store information such as signature file details, corresponding QR codes, and signature photos, which can be used for traceability queries and verification. The proposed system utilizes the SHA algorithm to generate unique codes and can generate corresponding QR code prints on a portable dynamic encoding device. When signing, a QR code seal is affixed, and during signature verification, the QR code on the signature file can be compared with the information in the database to verify the authenticity of the signature. When needed, signature photos stored in the database can also be used for further verification through machine learning. Compared with traditional signature verification methods, this method has better effectiveness. © 2024 The Authors.

关键词： database systems

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Griffin: Fast Transactional database Index with Hash and B+-Tree 20

Griffin: Fast Transactional Database Index with Hash and B+-...

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20th IEEE International Conference on e-science, e-science 2024

作者： Nakazono, Sho Bessho, Yutaro Kawashima, Hideyuki Nakamori, Tatsuhiro NTT Computer and Data Science Laboratories Tokyo Japan Keio University Faculty of Environment and Information Studies Kanagawa Japan

ISBN: (纸本)9798350365610

Index access is one of the dominant performance factors in transactional database systems. Many systems use a B+tree or one of its variants to handle point and range operations. This access pattern has room for performance improvement. Firstly, point operations can potentially be processed in O(1) with a hash table. Secondly, to ensure serializability of transactions, range operations incur overhead from phantom avoidance techniques that involve additional processing or synchronization, such as an extra traversal of the B+-tree. To address these issues, we propose a hybrid index architecture, Griffin. For point operations, Griffin has a hash table that provides access paths in O(1) time, along with a B+-tree. For phantom avoidance, Griffin employs a precision locking method, which does not involve additional traversal of the B+-tree. Despite its hybrid architecture, Griffin transparently provides linearizable operations and an interface of a single database index. We built a Griffin index combining a hash table and BwTree. Compared to a baseline index that is composed of a BwTree only, it achieves up to 3.1x higher throughput in a point operation dominant workload, and up to 5.4x higher throughput in a range operation dominant workload. © 2024 IEEE.

关键词： database systems

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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Hierarchical vectorization for facial images

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Computational Visual Media 2024年第1期10卷 97-118页

作者： Qian Fu Linlin Liu Fei Hou Ying He School of Computer Science and Engineering Nanyang Technological University639798SingaporeSingapore Data61 Commonwealth Scientific and Industrial Research OrganisationSydney2015Australia Interdisciplinary Graduate School Nanyang Technological University and Alibaba Group639798SingaporeSingapore State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China

The explosive growth of social media means portrait editing and retouching are in high *** portraits are commonly captured and stored as raster images,editing raster images is non-trivial and requires the user to be highly *** at developing intuitive and easy-to-use portrait editing tools,we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical *** base layer consists of a set of sparse diffusion curves(DCs)which characterize salient geometric features and low-frequency colors,providing a means for semantic color transfer and facial expression *** middle level encodes specular highlights and shadows as large,editable Poisson regions(PRs)and allows the user to directly adjust illumination by tuning the strength and changing the shapes of *** top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and *** train a deep generative model that can produce high-frequency residuals *** to the inherent meaning in vector primitives,editing portraits becomes easy and *** particular,our method supports color transfer,facial expression editing,highlight and shadow editing,and automatic *** quantitatively evaluate the results,we extend the commonly used FLIP metric(which measures color and feature differences between two images)to consider *** new metric,illumination-sensitive FLIP,can effectively capture salient changes in color transfer results,and is more consistent with human perception than FLIP and other quality measures for portrait *** evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks,such as retouching,light editing,color transfer,and expression editing.

关键词： face editing vectorization Poisson editing color transfer illumination editing expression editing

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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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