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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Rajhi, Mohammed Pissinou, Niki Florida International University School of Computing and Information Sciences MiamiFL United States Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia

ISBN: (纸本)9798350329223

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. This study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. This adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats. © 2023 IEEE.

关键词： Computational efficiency

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Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue

Sequential Topic Selection Model with Latent Variable for To...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Wen, Xiaofei Wei, Wei Mao, Xian-Ling Cognitive Computing and Intelligent Information Processing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science and Technology Beijing Institute of Technology China

Recently, topic-grounded dialogue system has attracted significant attention due to its effectiveness in predicting the next topic to yield better responses via the historical context and given topic sequence. However, almost all existing topic prediction solutions focus on only the current conversation and corresponding topic sequence to predict the next conversation topic, without exploiting other topic-guided conversations which may contain relevant topic-transitions to current conversation. To address the problem, in this paper we propose a novel approach, named Sequential Global Topic Attention (SGTA) to exploit topic transition over all conversations in a subtle way for better modeling post-to-response topic-transition and guiding the response generation to the current conversation. Specifically, we introduce a latent space modeled as a Multivariate Skew-Normal distribution with hybrid kernel functions to flexibly integrate the global-level information with sequence-level information, and predict the topic based on the distribution sampling results. We also leverage a topic-aware prior-posterior approach for secondary selection of predicted topics, which is utilized to optimize the response generation task. Extensive experiments demonstrate that our model outperforms competitive baselines on prediction and generation tasks. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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UMixer: A Novel U-shaped Convolutional Mixer for Multi-scale Feature Fusion in Medical Image Segmentation 16th

UMixer: A Novel U-shaped Convolutional Mixer for Multi-scal...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Su, Yongxin Huang, Hongbo Song, Zun Lin, Lei Liu, Jinhan Mechanical Electrical Engineering School Beijing Information Science and Technology University Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Economics and Management Beijing Information Science and Technology University Beijing China Institute of Computing Intelligence Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9783031202322

Medical image segmentation plays a critical role in assisting diagnosis and prognosis. Since the transformer was first introduced to the field, the neural network structure has experienced a transition from ConvNet into Transformer. However, some redesigned ConvNets in recent works show astonishing effects, which outperforms classic elaborate ConvNets, and even complicated transformers. Inspired by these works, we introduced large-kernel convolutions to improve the ConvNets in capturing the long-range dependency. Cooperated with a novel multi-scale feature fusion method, we proposed a U-shaped convolutional structure, dubbed UMixer, which effectively integrates shallow spatial information with deep semantic information and high-resolution detailed information with low-resolution global information. Without any attention mechanism and pre-training on large datasets, UMixer achieves more accurate segmentation results than traditional ConvNets and Transformers on the Synapse dataset. Experiments demonstrate the effectiveness of this multi-scale feature fusion structure and its capability in modeling long-range dependency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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An identity-based message authentication scheme for Internet of vehicles 8

An identity-based message authentication scheme for Internet...

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8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Yan, Chenyang Wang, Hongshuo Yan, Songsong QILU Institute of Technology School of Computing Science and Information Engineering Jinan China The University of Nottingham Nottingham United Kingdom School of Computing Science and Information Engineering Jinan China

ISBN: (纸本)9781665480697

The Internet of Vehicles realizes information sharing through the interconnection between entities such as vehicles and vehicles, vehicles and roads. As the computing tasks of vehicles become more and more complex, the data load of traditional cloud computing centers also increases. If a large amount of data generated by the vehicle is all uploaded to the cloud server, and the data is processed by the cloud computing center, it will increase the burden on the cloud server. In order to reduce the request time in the vehicle and reduce the data transmission delay, edge computing is introduced, and an identity-based vehicle networking message authentication scheme in the edge computing scenario is designed to realize the rational use of computing and storage resources. The scheme satisfies unlinkability, unforgeability, identity privacy protection, and anti-replay attack. © 2022 IEEE.

关键词： Edge computing

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Diabetes Prediction using Enhanced and Optimized DRL-CNN Approaches 21

Diabetes Prediction using Enhanced and Optimized DRL-CNN App...

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21st International Learning and technology Conference, L and T 2024

作者： Usha Nandhini, A. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Ben Dhaou, Imed Vistas School of Computing Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances with the WB-SMOTE approach, and introduces two novel classification methods, DRL-CNN and DRL-OCNN. Reliable datasets from sources like The Data World repository, PIMA Indian Diabetes (PIDD) dataset, and Real Time (RT) Diabetes Dataset are used, with an emphasis on data consistency and feature relevance. If we examine these datasets, it contains general attributes and no internal specific attributes related to diabetes is used. So, prediction results will be what type of diabetes we won't get any specific characteristics and risk factors. Evaluation is based on Accuracy, Precision, and Recall criteria. The DRL-OCNN model, integrating SGD optimization and Delta Rule Learning, outperforms other ML models with a accuracy of 96%, precision of 0.93 and recall of 0.91, demonstrating its potential for accurate early diabetes identification and classification. © 2024 IEEE.

关键词： Machine learning

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Exploring the Role of Generative AI in Medical Microbiology Education: Enhancing Bacterial Identification Skills in Laboratory Students 1st

Exploring the Role of Generative AI in Medical Microbiology ...

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1st International Workshop on Breaking Barriers with Generative Intelligence, BBGI 2024

作者： Al-Barazie, Ray Mohamed, Azza Lin, Fuhua Faculty of Medical and Health Sciences Liwa College Al-Ain United Arab Emirates Faculty of Information Technology Liwa College Al-Ain United Arab Emirates School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaAB Canada

ISBN: (纸本)9783031659959

The precise identification of pathogens in biological material is critical for appropriate medical diagnosis and therapy. Medical laboratory students must be proficient in laboratory skills since they play a critical part in the diagnostic process. Using appropriate microscope techniques, one must be able to identify a wide range of pathogens, including bacteria, viruses, parasites, and fungi. Traditional methods of skill development include on-campus practical lessons and field training in hospital microbiology departments. However, the emergence of generative artificial intelligence (AI) opens new avenues for educational enhancement. This study investigates the feasibility of using generative AI, specifically Gemini, to train medical laboratory students in bacterial identification using morphological traits seen in micrographs. The study assessed student learning results using Gemini-generated case studies and quizzes. The results showed that Gemini-generated quizzes helped pupils identify different bacteria based on the micrographs supplied. However, limitations were identified, such as the requirement for teachers to manually add photographs. Overall, the study highlights the potential of generative AI tools in educational contexts, arguing that they could supplement traditional teaching techniques and improve the learning experience for medical laboratory students. More research into generative AI's educational applications is needed to fully realize its potential in medical education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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Dense Visible Watermark Removal with Progressive Feature Propagation 12th

Dense Visible Watermark Removal with Progressive Feature Pr...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Ren, Chunchi Zhao, Jiangfeng He, Hongjie Chen, Fan School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9783031463167

Visible watermark removal is an important task to ensure the robustness of visible watermarks. However, existing techniques are unable to efficiently remove dense visible watermarks in stock content market images. To address this issue, we propose a novel solution called the Progressive Visible Watermark Removal Network (PVWRNet) that achieves efficient and effective dense visible watermark removal. Our approach utilizes a cross-combination structure in the backbone module to balance dense visible watermark spatial and contextual features. Furthermore, progressive modules are incorporated to multi-fuse shallow features of the backbone module, leading to significant improvement in image restoration metric scores and visual quality. To enhance local features of the backbone module, Reinforced Dual Attention Modules (RDAM) are introduced, which reweight the local features. Experimental results demonstrate that our proposed PVWRNet outperforms state-of-the-art approaches in the dense visible watermark dataset, especially in high definition images. In conclusion, the proposed method provides a potent and efficient solution for removing dense visible watermarks in stock content market images, and the use of cross-combination structure, progressive modules, and reinforced dual attention modules enables PVWRNet to outperform existing techniques in solving the challenging dense visible watermark removal problem. The code is available at: https://***/HyuuHyuuNeko/PVWRNet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image reconstruction

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User Centric Block-Level Attribute Based Encryption in Cloud Using Blockchains

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Computer Systems science & Engineering 2022年第8期42卷 605-618页

作者： S.Godfrey Winster A.Siva Kumar R.Ramesh Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Sri Sai Ram Engineering CollegeChennai600044India Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India

Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient *** advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation(RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any *** to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient *** improve the performance in data security,the block chains are used for securing the data in the cloud ***,the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the *** this paper,an efficient user centric block level Attribute Based Encryption(UCBL-ABE)scheme is presented to provide the efficient security of cloud data in cloud *** proposed approach performs data transaction by employing the block *** proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust(DLAT).Based on DLAT,the user access has been restricted in the cloud *** proposed protocol is implemented in real time using Java programming language and uses IBM *** implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.

关键词： Cloud blockchain data security ABE block-level

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The Role of Blockchain Integration in Edge computing for 6G IoT: Challenges and Applications 7

The Role of Blockchain Integration in Edge Computing for 6G ...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Alawadhi, Abdulwadood Almogahed, Abdullah Shtayt, Bushra Abdullah Barraood, Samera Obaid Adawy, Mohammad Ibrahim Al-Jamili, Omar InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Parit Raja Malaysia Information Technologies Management Management Technical College of Basra Southern Technical University Basra Iraq Department of Computer Science College of Computers and Information Technology Hadhramout University Hadhramout Yemen Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia

ISBN: (纸本)9798331519513

Blockchain is the technology on which cryptocurrencies operate within the 6G Internet of Things. Due to its flexibility, it has received significant adoption in numerous applications as a possible distributed data management solution. Nonetheless, a significant obstacle that impedes blockchain's potential is its constrained scalability, which limits its capacity to facilitate applications requiring frequent transactions. Conversely, edge computing, an innovative technology, addresses the limitations of conventional cloud-based systems by decentralizing cloud functionalities and alleviating problems such as elevated latency and insufficient security. Nonetheless, edge computing presently encounters challenges related to decentralized governance and security. As a result, the integration of blockchain and edge computing within a cohesive framework offers substantial benefits, such as dependable network access and governance, decentralized storage, and computing at the peripheries. This integration effectively mitigates many security problems and guarantees data integrity in edge computing contexts. This article offers a comprehensive analysis of blockchain technology, its attributes, and its relevance in the context of 6G. Additionally, it investigates the amalgamation of blockchain and edge computing, analyzes diverse application cases, and addresses the related issues. Finally, the report delineates contemporary research trajectories and draws conclusions based on the findings. © 2024 IEEE.

关键词： Mobile edge computing

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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