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2024 International Conference on computer and Applications, ICCA 2024

作者： Sultana, Rahat Zaman, Muhammad Ivković, Nikola Cengiz, Korhan Rafaqat, Iqra Akhunzada, Adnan The University of Lahore Faculty of Computer Science Sargodha Pakistan Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of Zagreb Faculty of Organization and Informatics Varaždin Croatia Biruni University Department of E&e Engineering Istanbul Turkey University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar

ISBN: (纸本)9798350367560

Knee Osteoporosis (KOP) is a skeletal disease that is caused by low bone mineral density and the degradation of bone tissue. It increases the risk of bone fractures in the knee region and is commonly seen in older people but usually ignored due to its silent nature. Osteoporosis is mostly diagnosed via X-ray images and Dual-Energy X-ray Absorptiometry (DXA) scans that can be difficult to read due to their sheer volume the subtle variations. This study aims to develop a Deep Learning (DL) utilizing Swin Transform model that can accurately recognize osteoporosis in knee X-ray images at an early stage. The dataset used for this research comprises knee X-rays labeled as either 'normal' or 'osteoporotic,' with image preprocessing including resizing to standard dimensions, converting the images into tensors, and normalizing pixel values to improve model performance. The model is trained and validated on arranged dataset, characterized by metrics such as accuracy, precision, recall or F1-score that gives an idea how well our model performs for prediction as acknowledged for its performance in image classification tasks. The results demonstrate that the Swin Transformer model has an overall accuracy of 89.38% on the data set for identifying osteoporosis features on knee X-ray images. This approach demonstrates the potential of the Swin Transformer model to serve as an effective tool for early osteoporosis diagnosis, offering improved accuracy and reliability compared to traditional convolutional neural networks (CNNs). © 2024 IEEE.

关键词： Diseases

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Research on Optimization and Scheduling of MPTCP data Networks in GNSS Network Reference Stations

Research on Optimization and Scheduling of MPTCP Data Networ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hao, Fengqi Wang, Yuxin Kong, Hoiio Ma, Dexin Zhao, Xiyuan Jinan China City University of Macau Faculty of Data Science China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Communication College Qingdao Agricultural University Qingdao266109 China

ISBN: (纸本)9781665410205

The Global Navigation Satellite System Network Reference Stations (GNSS-NRS) are pivotal in modern po-sitioning and navigation applications. However, GNSS-NRS face significant communication challenges, including instability during rapid user movement and inconsistent coverage by different operators, which often results in data transmission interruptions and delays. Moreover, the conventional Multipath Transmission Control Protocol (MPTCP) scheduling strategy does not accommodate the diversity of the data types managed by GNSS-NRS. This default approach treats all data uniformly, even under deteriorating network conditions. To overcome these limitations, this paper introduces a 'Multiple Reception' strategy based on MPTCP that ensures timely and reliable GNSS data transmission. This novel strategy differentiates between observational and control data based on their real-time criticality and applies tailored transmission techniques. Under adverse conditions, it utilizes multiple transmission paths and selectively receives control data, thus prioritizing the transfer of critical information. We conducted simulation experiments using MiniNet to compare the 'Multiple Reception' method against conventional and redundant strategies, demonstrating that it significantly reduces data loss rates in GNSS-NRS. © 2024 IEEE.

关键词： Global positioning system

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Heterogeneous Edge Service Deployment for Cyber Physical Social Intelligence in Internet of Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-13页

作者： Meng, Ke Liu, Zhongjian Xu, Xiaolong Xia, Xiaoyu Tian, Hao Qi, Lianyong Zhou, Xiaokang School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Software Nanjing University of Information Science and Technology Nanjing China School of Computing Technologies RMIT University Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Qingdao China Faculty of Data Science Shiga University Otsu Japan

Cyber Physical Social Intelligence (CPSI) integrates the social intelligence and cyber-physical systems, enabling machines to better interact and respond to human social behaviors. Under CPSI, the Internet of Vehicles (IoV) has given rise to an increasing number of latency-sensitive services. Edge computing, as a distributed computing paradigm, enhances data processing capabilities, reduces data transmission latency, and minimizes bandwidth consumption. However, due to the limited resources of edge servers, striking a balance between service latency and deployment costs remains a highly challenging issue in the process of service deployment. In this paper, we propose a heterogeneous edge service deployment method for CPSI in IoV. Firstly, considering the heterogeneity of IoV services and edge servers, communication model, computational model, and heterogeneous service deployment cost model are constructed. Secondly, to maximize service deployment efficiency and minimize communication latency, a distance and workload-based edge server cluster division method is proposed. Subsequently, heterogeneous service deployment is performed in different clusters based on service category prioritization and minimal deployment quantity prioritization principles. Furthermore, an Analytic hierarchy process-based Heterogeneous edge Service dePloyment algorithm for CPSI in IoV, named AHSP, has been designed to determine optimal service deployment strategies. Finally, extensive numerical experimental results demonstrate the effectiveness of AHSP. IEEE

关键词： Computation theory

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Lin Zou, Qingzhi Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Due to the exceptional performance of Transform-ers in 2D medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%. © 2024 IEEE.

关键词： Image segmentation

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Sentiment Analysis: Indonesia Netflix User's Comment Using Multiple Lexicon-Based Dictionaries 4

Sentiment Analysis: Indonesia Netflix User's Comment Using M...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Mola, Sebastianus Adi Santoso Widiastuti, Tiwuk Roma, Rosa V. K. Isabela Ohe Karnyoto, Andrea Stevens Pardamean, Bens University of Nusa Cendana Faculty of Science and Engineering Kupang Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

A subscription movie streaming service from the United States, Netflix, has been present in Indonesia since 2016 and provides a wide variety of films without showing any single advertisements that can be viewed from anywhere, as long as there is access to the internet. Despite these advantages, many users have complained through the Google Play Store's comments column. Some of the common complaints include frequent buffering and connectivity issues, dissatisfaction with the limited selection of Indonesian movies, lack of subtitles for specific languages, or pricing concerns have also been raised. In this study developed two combined scenario methods using the InSet and SentiStrength-id dictionary to obtain better performance and compare them against independent InSet and SentiStrength-id. This study collected users' comments for the Netflix app in the Google Play Store as a dataset using web scraping techniques through the Google Collaboration tool. The dataset contains 3250 rows spanning the period from January 22, 2024, through June 6, 2024. To ensure proper processing of the text, data cleaning, lowercasing, normalization, tokenization, stemming, and stopwords removal are conducted. The results show that most user opinions are negative. The InSet dictionary has an accuracy of 92%, SentiStrength-id 78%, Combined scenario 1 is 87%, and scenario 2 reaches the highest among others which is 92.52%. © 2024 IEEE.

关键词： Video streaming

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On the interaction phenomena to the nonlinear generalized Hietarinta-type equation

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Journal of Ocean Engineering and science 2024年第1期9卷 89-97页

作者： Usman Younas T.A.Sulaiman Jingli Ren A.Yusuf State Key Laboratory of Power Grid Environmental Protection/Henan Academy of Big Data Zhengzhou UniversityZhengzhou 450001China Faculty of Science Federal University DutseJigawaNigeria Department of Computer Engineering Biruni University IstanbulTurkey

In this paper,we describe the nonlinear behavior of a generalized fourth-order Hietarinta-type equa-tion for dispersive waves in(2+1)*** various wave formations are retrieved by using Hirota’s bilinear method(HBM)and various test function *** Hirota method is a widely used and robust mathematical tool for finding soliton solutions of nonlinear partial differential equa-tions(NLPDEs)in a variety of disciplines like mathematical physics,nonlinear dynamics,oceanography,engineering sciences,and others requires bilinearization of nonlinear *** different wave structures in the forms of new breather,lump-periodic,rogue waves,and two-wave solutions are *** addi-tion,the physical behavior of the acquired solutions is illustrated in three-dimensional,two-dimensional,density,and contour profiles by the assistance of suitable *** on the obtained results,we can assert that the employed methodology is straightforward,dynamic,highly efficient,and will serve as a valuable tool for discussing complex issues in a diversity of domains specifically ocean and coastal *** have also made an important first step in understanding the structure and physical be-havior of complex structures with our findings *** believe this research is timely and relevant to a wide range of engineering *** results obtained are useful for comprehending the fundamental scenarios of nonlinear sciences.

关键词： Hirota’s bilinear method Lump-periodic solutions Breather waves Rouge waves Multi-waves

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Plasticity Loss in Deep Reinforcement Learning: A Survey

arXiv

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arXiv 2024年

作者： Klein, Timo Miklautz, Lukas Sidak, Kevin Plant, Claudia Tschiatschek, Sebastian Faculty of Computer Science UniVie Doctoral School Computer Science University of Vienna Austria Faculty of Computer Science University of Vienna Austria Faculty of Computer Science Research Network Data Science University of Vienna Austria

Akin to neuroplasticity in human brains, the plasticity of deep neural networks enables their quick adaption to new data. This makes plasticity particularly crucial for deep Reinforcement Learning (RL) agents: Once plasticity is lost, an agent’s performance will inevitably plateau because it cannot improve its policy to account for changes in the data distribution, which are a necessary consequence of its learning process. Thus, developing well-performing and sample-efficient agents hinges on their ability to remain plastic during training. Furthermore, the loss of plasticity can be connected to many other issues plaguing deep RL, such as training instabilities, scaling failures, overestimation bias, and insufficient exploration. With this survey, we aim to provide an overview of the emerging research on plasticity loss for academics and practitioners of deep reinforcement learning. First, we propose a unified definition of plasticity loss based on recent works, relate it to definitions from the literature, and discuss metrics for measuring plasticity loss. Then, we categorize and discuss numerous possible causes of plasticity loss before reviewing currently employed mitigation strategies. Our taxonomy is the first systematic overview of the current state of the field. Lastly, we discuss prevalent issues within the literature, such as a necessity for broader evaluation, and provide recommendations for future research, like gaining a better understanding of an agent’s neural activity and behavior. © 2024, CC BY.

关键词： Deep reinforcement learning

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Editorial Deep Learning-Empowered Big data Analytics in Biomedical Applications and Digital Healthcare

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年第4期21卷 516-520页

作者： Zhou, Xiaokang Leung, Carson K. Wang, Kevin I-Kai Fortino, Giancarlo Shiga University Faculty of Data Science Shiga Hikone522-0069 Japan University of Manitoba Department of Computer Science WinnipegR3T 2N2 Canada University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand University of Calabria DIMES Department Rende CS87036 Italy

Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of artificial intelligence (AI) and Internets of Things (IoT) technologies, deep learning (DL) for big data analytics - including affective learning, reinforcement learning, and transfer learning - are widely applied to sense, learn, and interact with human health. Examples of biomedical applications include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, DL and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, DL, and big data analytic technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and DL technologies. Hence, exploring the possibility of DL and big data analytics in the fields of biomedical applications and digital healthcare is in high demand. © 2004-2012 IEEE.

关键词： Big data

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The role of data governance in a high-level approach of data migration to open data

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Business Information Review 2024年第3期41卷 94-103页

作者： Yulfitri, Alivia Sensuse, Dana Indra Sumirat Hidayat, Deden Suryono, Ryan Randy Kautsarina Purwaningsih, Erisva Hakiki Prabuwono, Anton Satria Faculty of Computer Science Universitas Indonesia Depok Indonesia Faculty of Computer Science Universitas Esa Unggul West Jakarta Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Central Jakarta and Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Information and Communications Technology Human Resources Development Agency Ministry of Communication and Information Technology of the Republic of Indonesia Jakarta Central Jakarta Indonesia Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia

data quality issues are still a challenge in data migration. Existing methods mainly focus on technical issues. However, the problem of data quality is not only a technical problem, but the main one is the non-technical aspect. This paper aims to identify the role of data governance in the data migration process. The qualitative research method uses two primary references, namely the high-level data migration approach from Kazimir et al. and the DAMA-DMBOK framework. Both are highly compatible with data migration processes focusing on data quality and referencing governance roles. The case study was conducted in a central government working on data migration to the new One data application. The results show that eight data governance roles are involved in 20 migration processes (65%). Non-technical activities related to data quality involve more than half of the data migration process. This paper proposes the role of data governance for non-technical aspects of the migration process. This paper presents a case study of the high-level data migration process and how data governance comes into play. This research can help the government and practitioners overcome problems that often occur in data migration, especially related to data quality. © The Author(s) 2024.

关键词： DAMA-DMBOK framework data governance data migration Indonesia’s one data Kazimir et al.’s approach open data

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A Comprehensive Exploration of Fine-Tuning WavLM for Enhancing Speech Emotion Recognition 6

A Comprehensive Exploration of Fine-Tuning WavLM for Enhanci...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Ali, Fadel Arymurthy, Aniati Murni Prasojo, Radityo Eko Universitas Indonesia Faculty of Computer Science Depok Indonesia Pitik Digital Indonesia Department of AI & Data South Tangerang Indonesia

ISBN: (纸本)9798350358346

Speech Emotion Recognition (SER) is a pivotal area in Human-computer Interaction (HCI) with numerous applications. Traditional SER models rely on supervised learning but face challenges due to limited labeled data and the subjective nature of emotions. Self-supervised learning (SSL) offers an alternative by leveraging unlabeled audio data. WavLM, a large-scale SSL audio model, has shown promise in various speech processing tasks. This paper investigates the fine-Tuning of WavLM for SER, analyzing the impact of different segment of layers on performance. We conducted experiments on the IEMOCAP and RAVDESS datasets, comparing various fine-Tuned WavLM models with Wav2Vec 2.0 and HuBERT as SSL baselines. Results reveal that WavLM outperforms SSL base-lines, with all-layer fine-Tuned WavLM achieving state-of-The-Art results. Interestingly, fine-Tuning top layers significantly enhances performance, suggesting their role in encoding paralinguistic information. However, fine-Tuning all layers remains superior. These findings shed light on optimizing SSL audio models for SER and highlight WavLM's potential in emotion recognition. © 2023 IEEE.

关键词： Supervised learning

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