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4th International Informatics and Software engineering Conference, IISEC 2023

作者： Idushan, K.A.S. Dilshan, P.G.T. Jayasundera, P.S. Madhushan, K.L.B. Krishara, Jenny Wijendra, Dinuka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350318036

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-To-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users. © 2023 IEEE.

关键词： Learning systems

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A Comparative Study of IDS-Based Deep Learning Models for IoT Network

A Comparative Study of IDS-Based Deep Learning Models for Io...

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2023 International Conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Shaker, Bassam Noori Al-Musawi, Bahaa Qasim Hassan, Mohammed Falih Faculty of Computer Science and Information Technology University of Al-Qadisiyah Iraq Faculty of Engineering University of Kufa Iraq

ISBN: (纸本)9798400708268

The proliferation of connected devices within Internet of Things (IoT) networks has underscored the critical necessity of developing efficient Intrusion Detection Systems (IDS) that can adeptly identify and counteract threats. Deep learning algorithms are better than classical machine learning at automatically learning patterns and representations from raw data, enabling them to detect complex attacks. This research aims to conduct a comparative study of IDSs implemented through three deep learning models: Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN). The study employs three datasets (NF-UNSW-NB15, NF-BoT-IoT, and NF-ToN-IoT) to assess model performance in both binary and multiclass classification scenarios. The findings indicate that for binary traffic classification, DNN outperforms the other two models with an accuracy bout (98%) for all datasets. In the context of multiclass traffic classification, the DNN model surpasses the performance of the other model except for NF-ToN-IoT a CNN model score (69.08%). © 2023 Copyright held by the owner/author(s).

关键词： Internet of things

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Internet of Things(IoT) security: Approaches and existing solutions

Internet of Things(IoT) security: Approaches and existing so...

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2023 TRON Symposium, TRONSHOW 2023

作者： Upadhyay, Vatsala Vaish, Abhishek Kokila, J. Indian Institute of Information Technology Allahabad Dept. of Information Technology Prayagraj211015 India Indian Institute of Information Technology Trichy Dept. of Computer Science and Engineering Tiruchirappalli620012 India

ISBN: (纸本)9784893624093

The world has become dependent on using the Internet of Things(IoT) owing to its unique features - sensing the environment, acting upon the results obtained by sensors, connecting a network of devices for communication, and many more have made human lives simpler. Research done in the area of the Internet of Things(IoT) has revealed that these devices are vulnerable to physical and network attacks that can directly/indirectly result in the malfunctioning in the normal parameters of the devices, as well as cause important data loss or secret information being leaked which can become useful for the hackers to attack the device and tamper with the stored contents in the device. These problems pave the way for designing a robust, security mechanism for the Internet of Things(IoT). This paper shall present an overview of the security aspects of the Internet of Things(IoT) - starting with the attacks that can occur on this network, the survey done in this area, and towards the end, presenting some solutions for making the IoT secure and resilient to vulnerabilities. © 2023 TRON Forum.

关键词： Internet of things

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Accelerating Big Data Application by Eliminating Redundancy on Hadoop Cluster 29

Accelerating Big Data Application by Eliminating Redundancy ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lei, Kelun Du, Shaokang You, Xin Xuan, Zhibo Kong, Haoran Yang, Hailong Shang, Jing Xiao, Zhiwen Wu, Zhihui Luan, Zhongzhi Qian, Depei Beihang University School of Computer Science and Engineering Beijing China China Mobile Information Technology Center China

ISBN: (纸本)9798350330717

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement. © 2023 IEEE.

关键词： Hadoop Performance Optimization Redundant Patterns

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DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

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Journal of Internet technology 2024年第1期25卷 79-86页

作者： Yang, Zhichao Kong, Yan Hsia, Chih-Hsien School of Computer and Software Nanjing University of Information Science and Technology China Department of Computer Science and Information Engineering National Ilan University Taiwan Department of Business Administration Chaoyang University of Technology Taiwan

In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Reinforcement learning

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A comparative study for nuclei segmentation using latest deep learning optimizers 5

A comparative study for nuclei segmentation using latest dee...

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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Eltayab, Engy N Al-Atabany, Walid Salem, Nancy M. Mahmoud, Lamees N. Helwan University Biomedical Engineering Department Egypt Nile University Information Technology and Computer Science School Egypt

ISBN: (纸本)9798350381030

Nuclei segmentation is a critical task in biological image analysis, with numerous applications in cancer diagnosis, grading, staging, and treatment planning. However, this task is challenging, particularly when dealing with low-resolution and low signal-to-noise ratio microscopy images. Segmentation problems arise, such as touching and missing cells, which make the process even more challenging. Deep learning models, including Attention U-Net and TransUNet, have demonstrated exceptional performance in medical image segmentation. Nonetheless, the choice of optimizer can significantly impact the model's performance, especially in nuclei segmentation. In this work, we assessed the efficiency of the latest deep learning optimizers, such as Ranger and GC, on Attention U-Net and TransUNet models and compared their performance with traditional optimizers, i.e., Adam. We conducted experiments on the publicly available Fluo-N2DH-HeLa dataset that was adapted from the cell tracking challenge. The Ranger optimizer, along with the TransUNet model, achieved the highest mean IOU of 95.05% and reasonable recall and precision scores of 94.4% and 96.8%, respectively. The experimental results proved that integrating the TransUNet model with the Ranger optimizer significantly enhanced touching nuclei segmentation performance in fluorescence microscopy images. © 2023 IEEE.

关键词： Grading

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BenCo: First Step Towards Coreference Resolution in Bengali

BenCo: First Step Towards Coreference Resolution in Bengali

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Hossain, Mojammel Islam, Md Samiul Uddin Ahmed, Jashim Kader Khan, Md Rahat University of Padova Information Engineering Padova Italy University of Alberta Computing Science AB Canada Ahsanullah University of Science & Technology Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350322972

Coreference resolution is well-studied in NLP;however, Bengali coreference resolution research has not been as well investigated as it has been for English and other rich languages. Bengali has a richer morphology than English despite having fewer resources. This research introduce a tiny new dataset of coreference annotations over Bengali texts from four domains in this article called BenCo. In this dataset, there are 48,610 tokens and 5488 mention annotations structured into 470 mention clusters. The article outline the procedure used to generate this dataset and use it to build an end-to-end neural network-based system. This study will help to clarify how Bengali coreference phenomena differ depending on the domain and inspires others to produce more materials in the language. Also, insufficient information transfer in the zero-shot multilingual situations, which may indicate the need for language-specific resources for this job. © 2023 IEEE.

关键词： Zero-shot learning

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Optimal Task Allocation for Battery-Assisted and Price-Aware Mobile Edge Computing

IEEE Networking Letters

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IEEE Networking Letters 2023年第4期5卷 199-203页

作者： Deng, Tao You, Lei Yu, Zhanwei Yuan, Di Soochow University School of Computer Science and Technology Suzhou215006 China Technical University of Denmark Department of Engineering Technology Ballerup2750 Denmark Uppsala University Department of Information Technology Uppsala751 05 Sweden

In this letter, we propose a battery-assisted approach to improve energy efficiency for mobile edge computing (MEC) networks by utilizing the space-time-varying characteristics of electricity price. We formulate a price-aware task allocation problem (PATA) that jointly considers the cost for task computation, the cost of task offloading, and the cost of battery degradation. PATA is seemingly a mixed integer non-linear programming problem. By a graph-based reformulation, solving PATA is mapped to finding minimum cost flows or convex cost flows in the graph. This discovery reveals that the global optimum of PATA is obtained in polynomial time. Performance evaluation manifests that the proposed approach significantly outperforms other approaches. © 2019 IEEE.

关键词： Integer programming

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Modelling High-Order Social Relations for Item Recommendation (Extended Abstract) 39

Modelling High-Order Social Relations for Item Recommendatio...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Liu, Yang Chen, Liang He, Xiangnan Peng, Jiaying Zheng, Zibin Tang, Jie Sun Yat-sen University School of Computer Science and Engineering Guangzhou China University of Science and Technology of China School of Data Science School of Information Science and Technology Hefei China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350322279

Personalized recommendation is becoming increasingly important in online information systems in the current era of information explosion. In real-world scenarios, when a user considers which items to consume, the decision choice may be affected by her friends. For example, she may ask her friends for suggestions or be attracted by products purchased by one friend. As such, to provide satisfactory recommendation service, it is important to account for the evidence in social relations when they are available to use. Several prior efforts have been made to leverage social relations to build the recommender system and verified their utility. However, most existing methods, such as the well-known TrustSVD, leverage only first-order social relations, i.e., the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which are very informative to reveal user preference, have been largely ignored. © 2023 IEEE.

关键词： Online systems

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DB Workload Management Through Characterization and Idleness Detection 26

DB Workload Management Through Characterization and Idleness...

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Mateen, Abdul Mahmood, Khawaja Tahir Nam, Seung Yeob Federal Urdu University of Arts Science and Technology Department of Computer Science Islamabad45570 Pakistan Yeungnam University Department of Information and Communication Engineering Gyeongsan Korea Republic of

ISBN: (纸本)9791188428120

It is difficult to handle the database (DB) workload due to the huge increase in data., the functionality demand from the user., and the rapid changes in data. It is not easy to manage the DB workload., which therefore leads to malnourishment. To get efficient results., there must be complete knowledge about the type and changes in workload. The versatility and complexity of DBMSs led the DB researchers towards new philosophy and thoughts. A novel approach is introduced for DB workload management through characterization., scheduling., and database idleness detection. In workload characterization., workload is observed., and effective workload characterization parameters are selected. After that., scheduling is performed in order to arrange the DB workload to reduce the waiting time for each workload. Lastly., database idleness is identified at run-time and exploited for system as well as user-initiated workloads to improve efficiency. The proposed approach for workload management is validated through experiments using benchmark workloads. © 2024 Global IT Research Institute - GIRI.

关键词： Database systems

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