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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Perera, Suresha Varatharaj, A. Perera, Indika Ahangama, Supunmali University of Moratuwa Department of Computer Science and Engineering Katubedda Sri Lanka University of Moratuwa Department of Information Technology Katubedda Sri Lanka

ISBN: (纸本)9798331530983

Hate speech has grown to be a serious problem on social media, encouraging antisocial behaviour and platform abuse due to the ability to disseminate content to millions within seconds, often anonymously. This study contributes to the research community in two ways when addressing this problem. Firstly, a crowdsourcing platform is used to annotate YouTube data to assess the content's severity level, which is used as the training dataset. Secondly, it uses XLM-RoBERTa, a transformer-based multilingual model designed for code-mixed and code-switched text that includes Sinhala, English, and Singlish, to automatically classify the severity level of hate speech. At the initial stage, YouTube text data was initially divided into hate and non-hate categories. Hate texts were then further divided into three severity levels: Bottom, Intermediate, and Top. The XLM-RoBERTa model was used to fine-tune a dataset of 15,000 YouTube titles and comments for multi-class classification. With an F1-score of 0.91 and an accuracy of 92%, the model outperformed deep learning techniques like LSTM and conventional models like Random Forest, Support Vector Machines, and Logistic Regression. This is regarded as the first experiment based on the 'UN Strategy and Plan of Action on hate speech', which offers a unique framework for automatically classifying hate speech according to severity. It gives governments, platform users, and policymakers useful resources to help them make sound decisions. Future researchers can utilize this technique to rank the influence of hate speech on hate users to counteract hate speech propagation at a more severe level. © 2025 IEEE.

关键词： Support vector machines

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Data Recovery and Backup management: A Cloud Computing Impact

Data Recovery and Backup management: A Cloud Computing Impac...

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2023 IEEE International Conference on Emerging Trends in Engineering, sciences and technology, ICES and T 2023

作者： Hasan, Muhammad Zulkifl Sarwar, Nadeem Alam, Intakhab Hussain, Muhammad Zunnurain Siddiqui, Adeel Ahmad Irshad, Asma University of Central Punjab Faculty of Computer Science and Information Technology Lahore Pakistan Bahria University Department of Computer Science Lahore Campus Pakistan University of Sialkot Department of Information Technology Sialkot Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan University of the Punjab School of Biochemistry and Biotechnology Lahore Pakistan

ISBN: (纸本)9781665455602

Cloud computing is the collection of excellent services (storage, databases, etc.) provided to customers and organizations over the internet to fulfill their requirements efficiently. Everything depends on the internet, and you can access your data anywhere. On the other hand, cloud service clients must ensure that the outsourced backup is secure enough to entrust with their data. This research highlighted how the cloud has benefited people by providing unlimited, secure storage without any high cost of investment in hardware. Cloud storage also aids in the security of your data backup, allowing you to access your files and documents even if your hardware is lost or damaged. It is also known as disaster recovery. Google Photos and Google Drive are examples of adequately encrypted backup storage. This paper also discusses the core functionalities of Google photos and Google drive and their advantages in a more comprehensive form. This research's main aim is to provide users with awareness regarding data loss and recovery. Furthermore, threats related to modern technology are also part of this research. In addition, the article also included some graphs and figures to explain the results and facts in detail. © 2023 IEEE.

关键词： Cloud storage

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Learning Semantic Representations and Discriminative Features in Unsupervised Domain Adaptation 11

Learning Semantic Representations and Discriminative Feature...

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11th International Symposium on Electronic Systems Devices and Computing, ESDC 2023

作者： Sidibomma, Rushendra Sanodiya, Rakesh Kumar Indian Institute of Information Technology Department of Computer Science and Engineering Sri City India

ISBN: (纸本)9781665455725

In domain adaptation, the goal is to train a neural network on the source domain and obtain a good accuracy on the target domain. In such a scenario, it is important to transfer the knowledge from the labelled source domain to the unlabelled target domain due to the expensive cost of manual labelling. Following the trail of works in the recent time, feature level alignment seems to be the most promising direction in unsupervised domain adaptation. In most of the recent works using this feature alignment, the semantic information present in the labelled source domain has not been exploited. Among the works that have tried to learn this semantic representations, the discriminative features have not been taken into consideration which results in lower accuracy on target domain. In this paper, we present a novel approach, joint discriminative and semantic transfer network (JDSTN) that not only aligns the semantic representations of source and target domain, but also enhances the discriminative features and thereby improving the accuracy significantly. This is achieved by using pseudo-labels to align the feature centroids of source and target domains while introducing losses that promote the learning of discriminative features. © 2023 IEEE.

关键词： Semantics

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Gait Feature Processing for Automated Human Gender Recognition 1st

Gait Feature Processing for Automated Human Gender Recogniti...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Sahu, Saumya Ranjan Bhagat, Surendra Nath Rout, Jyotiranjan Dash, Nilachakra Das, Tapas Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Odisha Balasore India Department of Information Technology Balasore College of Engineering and Technology Odisha Balasore India

ISBN: (纸本)9789811954023

The induction of technologies such as Internet of Everything (IoE) in every aspect of our day-to-day life has made life easier. The increasing use of computational decision-making in problem solving for scientific resolution of research problems is centered on such advanced algorithms that enhance the system performance results. The domain of biometric identification is also growing with great progress in IoT-based research and development. Other than the conventional biometric features, like the iris, fingertips, etc., different studies are done on the features of human movement activity to use it as a potential biometric marker. The proposed work has been done using a standard database collected using multimodal sensors which acquired data from Men as well Female volunteers. The experiments were undertaken in this work to show how feature reduction and methods for analysis of principle components, permit the modeling of a reliable, intelligent authentication system with the help of a conventional machine learning algorithm, for human gender identification, based on gait signature. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Biometrics

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PFed-NS: An Adaptive Personalized Federated Learning Scheme Through Neural Network Segmentation

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IEEE Transactions on computers 2025年第6期74卷 1936-1948页

作者： Liu, Yuchen He, Ligang Zhang, Zhigao Ren, Shenyuan University of Warwick Department of Computer Science CoventryCV4 7AL United Kingdom Wuhan University School of Computer Science Wuhan430072 China China Hubei Luojia Laboratory Wuhan430072 China Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing100044 China

Federated Learning (FL) is typically deployed in a client-server architecture, which makes the Edge-Cloud architecture an ideal backbone for FL. A significant challenge in this setup arises from the diverse data feature distributions across different edge locations (i.e., non-IID data). In response, Personalized Federated Learning (PFL) approaches have been developed. Network segmentation-based PFL is an important approach to achieving PFL, in which the training network is divided into a global segment for server aggregation and a local segment maintained client-side. Existing methods determine the segmentation before the training, and the segmentation remains fixed throughout the PFL training. However, our investigation reveals that model representations vary as PFL progresses and the fixed segmentation may not deliver best performance across various training settings. To address this, we propose PFed-NS, a PFL framework based on adaptive network segmentation. This adaptive segmentation technique is composed of two elements: a mechanism for assessing divergence of clients’ probability density functions constructed from network layers’ outputs, and a model for dynamically establishing divergence thresholds, beyond which server aggregation is deemed detrimental. Further optimization strategies are proposed to reduce the computation and communication costs incurred by divergence modeling. Moreover, we propose a divergence-based BN strategy to optimize BN performance for network segmentation-based PFL. Extensive experiments have been conducted to compare PFed-NS against recent PFL models. The results demonstrate its superiority in enhancing model accuracy and accelerating convergence. © 1968-2012 IEEE.

关键词： Probability density function

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Computation Sourcing in Smart Surveillance Systems for Maximum information Retrieval 9

Computation Sourcing in Smart Surveillance Systems for Maxim...

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9th International Conference on Applied System Innovation, ICASI 2023

作者： Chen, Shuo-Han Guo, Zhen-Yang National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350398380

With the booming of artificial intelligence and the advance of embedded systems, smart surveillance systems can now deploy video analysis techniques, such as human detection and object classification, on surveillance cameras directly. Such an approach decentralizes the computation and allows elastic camera deployment without upgrading the centralized server. Nevertheless, taking human detection as an example, cameras at different locations could have very different numbers of people walking by at different times. This condition could make specific cameras run their detection tasks at the highest frame per second (FPS) but still lose information if FPS is lower than a certain value, while a small group of cameras has almost zero tasks, thus wasting valuable computation power. In addition, as the smart surveillance system grows, cameras deployed at different periods could have different specifications and computation capabilities;therefore, properly utilizing all the computation resources has become essential for smart surveillance systems. The aforementioned observations motivate this study to propose a novel computation souring method by following the concept of crowdsourcing to enable efficient resource sharing within smart surveillance systems and maximize the amount of retrieved information. Promising results have been demonstrated through a series of experiments with different smart cameras of different specifications. © 2023 IEEE.

关键词： Cameras

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Assessing Risk Factors with Generative Adversarial Networks for Cardiac Arrest Detection

Assessing Risk Factors with Generative Adversarial Networks ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Gaur, Sunil Kumar Preethi, D. Abrol, Monika Teetthanker Mahaveer University College of Engineering Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

This paper seeks to evaluate the performance of generative adverse networks (GANs) in opposition to conventional strategies for predicting cardiac arrests. Via the usage of GANs, the paper examines the capability to assess hazard factor accuracy and generate new synthetic facts regarding the threat of cardiac arrest. The paper explores methods that GANs can be applied to generate new representations of respective cardiac arrest danger factors. Moreover, it evaluates the superiority of the GANs-based model in evaluation to traditional gadget learning techniques constructed on existing data. ultimately, the look tries to assess the accuracy of GANs in cardiac arrest prediction and its capability to assess hazard elements. This paper investigates the capability of using Generative antagonistic Networks (GANs) to assess chance factors for the early detection of cardiac arrest. First, a deep generative community consisting of two convolutional vehicle Encoder (CAE) sub-networks is employed to examine discriminative representations from clinical databases. Then, a supervised discriminative network is used to analyze the encodings and classify hazard factors that hint at the opportunity of cardiac arrest. The paper also demonstrates strategies for optimizing the GAN's training technique to further improve the device's accuracy. Subsequently, experimental consequences at the MIMIC scientific database display the effectiveness of the proposed GAN architecture in ascertaining cardiac arrest hazard elements.. © 2024 IEEE.

关键词： antagonistic capability hazard prediction strategies

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Robust Auto-Scaling with Probabilistic Workload Forecasting for Cloud Databases 40

Robust Auto-Scaling with Probabilistic Workload Forecasting ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Hang, Haitian Tang, Xiu Sun, Jianling Bao, Lingfeng Lo, David Wang, Haoye College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore School of Computer and Computing Science Hangzhou City University China

ISBN: (纸本)9798350317152

Auto-scaling is crucial for achieving elasticity in cloud databases as well as other cloud systems. Predictive auto-scaling, which leverages forecasting techniques to adjust resources based on predicted workload, has been widely adopted. However, the inherent inaccuracy of forecasting presents a significant challenge, potentially causing resource under-provisioning. To address this challenge, we propose robust predictive auto-scaling that considers the uncertainty in forecasts. Unlike previous predictive approaches that rely on single-valued forecasts, we leverage probabilistic forecasting techniques to generate quan-tile forecasts, providing a more comprehensive understanding of the potential future workloads. By formulating the auto-scaling problem as a robust optimization problem, we enable the implementation of auto-scaling strategies with customizable levels of robustness, which can be determined by considering various quantile levels of forecasts. Moreover, we enhance the adaptability of our strategy by incorporating different quantile levels through-out the entire decision horizon, allowing for dynamic adjustments in the conservatism of our auto-scaling decisions. This enables us to strike a balance between resource efficiency and system robustness. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving robust auto-scaling in cloud databases, while maintaining reasonable resource efficiency. © 2024 IEEE.

关键词： Forecasting

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A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

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computers, Materials & Continua 2022年第9期72卷 5395-5413页

作者： Huanrong Tang Changlin Hu Tianming Liu Jianquan Ouyang Key Laboratory of Intelligent Computing and Information Processing Ministry of EducationComputer Science College of Xiangtan UniversityXiangtanHunan ProvinceChina Department of Computer Science The University of GeorgiaAthensGeorgiaUSA

In response to problems in the centralized storage of personal resumes on third-party recruitment platforms,such as inadequate privacy protection,inability of individuals to accurately authorize downloads,and inability to determine who downloaded the resume and when,this study proposes a blockchain-based framework for secure storage and sharing of *** can employ an authorized access mechanism to protect their privacy *** proposed framework uses smart contracts,interplanetary file system,symmetric encryption,and digital signatures to protect,verify,and share *** keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these *** key escrow incentives are implemented using smart contracts to automatically verify the correctness of keys and encourage the active participation of honest key escrow *** framework combines blockchain and searchable symmetric encryption technology to realize multikeyword search using inverted indexing and Bloom filters and verify search results on the *** search service fees are charged through *** after the search results are verified can the search service provider obtain the search fee,thus ensuring fair and efficient search for encrypted *** framework is decentralized,secure,and tamper-evident,and achieves controlled sharing while protecting personal privacy and information security.

关键词： Resume blockchain interplanetary file system secret sharing searchable encryption

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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