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arXiv 2023年

作者： Di Marco, Niccolò Cinelli, Matteo Alipour, Shayan Quattrociocchi, Walter Department of Mathematics and Computer Science University of Florence Florence Italy Center of Data Science and Complexity for Society Department of Computer Science Sapienza University of Rome Rome Italy

Social media platforms are like giant arenas where users can rely on different content and express their opinions through likes, comments, and shares. However, do users welcome different perspectives or only listen to their preferred narratives? This paper examines how users explore the digital space and allocate their attention among communities on two social networks, Voat and Reddit. By analysing a massive dataset of about 215 million comments posted by about 16 million users on Voat and Reddit in 2019 we find that most users tend to explore new communities at a decreasing rate, meaning they have a limited set of preferred groups they visit regularly. Moreover, we provide evidence that preferred communities of users tend to cover similar topics throughout the year. We also find that communities have a high turnover of users, meaning that users come and go frequently showing a high volatility that strongly departs from a null model simulating users' behaviour. © 2023, CC BY-SA.

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Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation

arXiv

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arXiv 2024年

作者： Vu, Doan Nam Long Igamberdiev, Timour Habernal, Ivan Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security The University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice of granularity at which DP is applied is often neglected. For example, neural machine translation (NMT) typically operates on the sentence-level granularity. From the perspective of DP, this setup assumes that each sentence belongs to a single person and any two sentences in the training dataset are independent. This assumption is however violated in many real-world NMT datasets, e.g., those including dialogues. For proper application of DP we thus must shift from sentences to entire documents. In this paper, we investigate NMT at both the sentence and document levels, analyzing the privacy/utility trade-off for both scenarios, and evaluating the risks of not using the appropriate privacy granularity in terms of leaking personally identifiable information (PII). Our findings indicate that the document-level NMT system is more resistant to membership inference attacks, emphasizing the significance of using the appropriate granularity when working with DP.1 © 2024, CC BY-SA.

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Robust Principal Components by Casewise and Cellwise Weighting

arXiv

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arXiv 2024年

作者： Centofanti, Fabio Hubert, Mia Rousseeuw, Peter J. Department of Industrial Engineering University of Naples Federico II Naples Italy Section of Statistics and Data Science Department of Mathematics KU Leuven Belgium

Principal component analysis (PCA) is a fundamental tool for analyzing multivariate data. Here the focus is on dimension reduction to the principal subspace, characterized by its projection matrix. The classical principal subspace can be strongly affected by the presence of outliers. Traditional robust approaches consider casewise outliers, that is, cases generated by an unspecified outlier distribution that differs from that of the clean cases. But there may also be cellwise outliers, which are suspicious entries that can occur anywhere in the data matrix. Another common issue is that some cells may be missing. This paper proposes a new robust PCA method, called cellPCA, that can simultaneously deal with casewise outliers, cellwise outliers, and missing cells. Its single objective function combines two robust loss functions, that together mitigate the effect of casewise and cellwise outliers. The objective function is minimized by an iteratively reweighted least squares (IRLS) algorithm. Residual cellmaps and enhanced outlier maps are proposed for outlier detection. The casewise and cellwise influence functions of the principal subspace are derived, and its asymptotic distribution is obtained. Extensive simulations and two real data examples illustrate the performance of cellPCA. Copyright © 2024, The Authors. All rights reserved.

关键词： Principal component analysis

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Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video dataset

arXiv

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arXiv 2025年

作者： Panchal, Aneesh Khan, Kainat Katarya, Rahul Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India Big Data Analytics and Web Intelligence Laboratory Department of Computer Science & Engineering Delhi Technological University Delhi110042 India

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remains undiagnosed. In this paper, we worked on a methodology for detection of ASD from 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and using nature-inspired optimization algorithms for feature extraction from dataset. The proposed methodology involve the classification of ASD using supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients for finding the initial leading particle. This selection of particle significantly reduces the computation time and hence, improving the total efficiency and accuracy for the ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combination of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of 100% using random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academia fields, providing a foundation for future research and advancements in ASD diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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Assessment of the Impact of Personality Characteristic on Emotion Resilience 8

Assessment of the Impact of Personality Characteristic on Em...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Garg, Shruti Pandey, Ranjita Rashmi Gara, Ashwani Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi India University of Delhi Department of Statistics Delhi India Griffith University QLD Australia

ISBN: (纸本)9781665408165

This study assesses emotional resilience (ER) among people residing in the eastern region of India based on six demographic factors age, gender, education, marital status, working hours and financial status. MATERIAL/METHODS: Researchers conducted a survey through questionnaire among people aged 25 and 75 (mean age =50, mathrm{N}=508) from 1 September 2021 to 20 October 2021 after two COVID-19 outbreaks and analysed the data using the chi-square test, Cronbach alpha, multiple regression (MR) and logistic regression (LR). RESULTS: Results of present research show that ER negatively correlates with age (coeff. =14.315, mathrm{p}=5.298mathrm{E}-11) and positively with financial status (mathrm{t}=8.924, mathrm{p}=1.876mathrm{E}-09). The three personality characteristics impacting ER most were: 1. I am playful. I find humour in rough situations and can laugh at myself. 2. I am curious. I ask questions. I want to know how things work. I like to try new ways of doing things. 3. I learn valuable lessons from my experiences and the experiences of others. CONCLUSION: Of six demographic variables, only two (age and financial status) correlated with ER. © 2022 IEEE.

关键词： Surveys

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A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity Threat Detection and Classification

A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity T...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Akilandeswari, S. Soumya, T.R. Sumathi, S. Sushith, Mishmala Brinda, V. Rajan, S. E.G.S. Pillay Engineering College Department of Artificial Intelligence & Data Science Nagapattinam611002 India Prathyusha Engineering College Department of Computer Science Engineering Tiruvallur602025 India Velammal Engineering College Department of Electronics and Communication Engineering Chennai600066 India Adithya institute of Technology Department of Information Technology Kurumbapalayam India K.Ramakrishnan College Of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode638012 India

ISBN: (纸本)9798331509828

With the advancement of digital era comes an increased value for cybersecurity, and these need to be dealt with using advanced techniques. Deep learning - metaheuristic optimization hybrid model for advanced threat detection and mitigation is presented in this paper. With a Convolutional Neural Network (CNN) for feature extraction and Long Short-Term Memory (LSTM) for sequence analysis, the proposed framework integrates all the modules in order to provide robust cyber-attack detection. In order to optimize the parameters of the model, a Particle Swarm Optimization (PSO) algorithm is used in order to achieve better classification accuracy and faster computation. Benchmark datasets are used for extensive experiments, where existing methods outperformed in terms of precision, recall, and Fl-score. The results demonstrate that with detected flagged FPs and the hybrid model, the increased detection rates are achieved without the reduction in true negatives, rendering it a practical solution for real world applications. This research adds to the field by providing a solution to cybersecurity problems that is scalable and adaptable to the present and beyond. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing Autofocus Performance through Predictive Motion-Targeting and Self-Attention in a Deep Reinforcement Learning Framework

Enhancing Autofocus Performance through Predictive Motion-Ta...

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

作者： Wei, Xiaolin Yang, Ruilong Wu, Xing Wang, Chengliang Wang, Haidong Wang, Hongqian Tang, Tao College of Computer Science Chongqing University Chongqing China Southwest Hospital of Army Medical University Department of Thoracic Surgery Chongqing China Southwest Hospital of Army Medical University Medical Big Data Center Chongqing China

ISBN: (纸本)9781665410205

In focusing tasks on moving targets, traditional methods that rely on maximizing contrast struggle to capture moving objects due to insufficient focusing speed. Deep learning-based methods have attempted to directly predict the optimal focal length for the target;however, due to low prediction accuracy, they often lead to out-of-focus situations when capturing moving objects. In recent years, some approaches have utilized reinforcement learning to automatically explore focal length adjustment patterns, thus achieving better results than traditional methods. However, these approaches have not considered the motion characteristics of the targets, leading to a need for further improvement in focusing performance. To overcome these limitations, we introduce a motion-based feature and deep reinforcement learning-driven autofocus algorithm named MF-DRLAF (Motion Features based Deep Reinforcement Learning Autofocus Model) for moving targets. This novel method tracks the object, predicts its motion state through feature extraction, and uses deep reinforcement learning to dynamically adjust the focus. We utilize a self-attention mechanism to adaptively learn various motion patterns and employ a feature pool structure to enhance processing efficiency. Experiments and real-world testing on a Google Pixel3 demonstrate that our approach significantly enhances autofocus performance on moving objects, highlighting its potential for broader imaging applications. This approach offers a promising direction for future development in autofocus technology. © 2024 IEEE.

关键词： Deep reinforcement learning

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Auto Patterns Recognition for Chinese Open Information Extraction

SSRN

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SSRN 2023年

作者： Hua, Jialin Luo, Liangqing Ping, Weiying Tao, Chunhai Liao, Yan Lu, Xuewen Key Laboratory of Data Science in Finance and Economics School of Statistics and Data Science Jiangxi University of Finance and Economics Nanchang China Department of Mathematics and Statistics University of Calgary Calgary Canada

Open information extraction (OIE) aim to extract surface relations and their corresponding arguments from natural language text, irrespective of domain. In this paper, we present an innovative OIE model, APRCOIE, tailored for Chinese text. Diverging from previous models, our model generates extraction patterns in an autonomous way. The model defines a new form of pattern for Chinese OIE and proposes an automated pattern generation methodology. In that way, the model can handle a wide array of complex and diverse Chinese grammatical phenomena. To efficiently conduct extraction procedure, we design a preliminary filter based on tensor computing. To train the model, we manually annotated a large-scale Chinese OIE dataset. In the comparative evaluation, we demonstrate APRCOIE outperforms state-of-the-art Chinese OIE models and significantly expand the boundaries of achievable OIE performance. The code of APRCOIE and the annotated dataset are released in GitHub (https://***/jialin666/APRCOIE_v1) © 2023, The Authors. All rights reserved.

关键词： Tensors

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Contrastive Learning based Item Representation with Asymmetric Augmentation for Sequential Recommendation 23

Contrastive Learning based Item Representation with Asymmetr...

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2nd International Conference on Algorithms, data Mining, and Information Technology, ADMIT 2023

作者： Zeng, Kexiang Shi, Xiaoyu Xie, Hong Shang, Mingsheng Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Big Data and IntelligentComputing Chongqing Institute of Green and Intelligent Technology Chongqing China

ISBN: (纸本)9798400707629

Contrastive learning has been widely applied in sequential recommendation to improve the recommendation performance. Existing contrastive learning methods focus on adjusting the views number of positive and negative samples to enhance the item representation via data-level augmentation(e.g., MMInfoRec). However, they generally ignore the sampled items still follow a long-tail distribution, i.e., a few popular items have a high-frequency occurrence in contrastive learning, while the majority of unpopular items have a low-frequency in contrastive learning. As a result, this imbalance in sample extraction leads to insufficient contrastive learning between popular and unpopular items, resulting in sub-optimal item representation. In this paper, we propose CA4Rec, a Contrastive learning based item representation with Asymmetric augmentation for Sequential Recommendation, that adopts an asymmetric augmented multi-instance contrastive learning strategy to enhance the item representation. Specifically, we first use the popularity-aware method to divide the entire item set into popular and unpopular items, then we generate k augmented views for popular items, and k + n augmented views for unpopular items. Finally, we use our proposed asymmetric multi-instance noise contrastive estimation (AMINCE) to perform contrastive learning calculation. Make sure there is sufficient contrast between popular and unpopular items to mitigate the issue. we implement the improvements on the state-of-the-art model and conduct extensive experiments on three benchmark datasets. The experimental results demonstrate that CA4Rec outperforms the state-of-the-art baselines. © 2023 Owner/Author.

关键词： Learning systems

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AARIN: Affordable, accurate, reliable and innovative mechanism to protect a medical cyber-physical system using blockchain technology

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International Journal of Intelligent Networks 2021年 2卷 175-183页

作者： Tyagi, Amit Kumar Aswathy, S.U. Aghila, G. Sreenath, N. Centre for Advanced Data Science Vellore Institute of Technology Tamilnadu Chennai600127 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai600127 India Department of Computer Science and Engineering Jyothi Engineering College Kerala Thrissur679531 India Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India Department of Computer Science and Engineering Pondicherry Engineering College Pudcuherry India

The enhancement (in the past decade) in electronics devices/technology, a rise in most of the accidents concerning security and surveillance intruding the private lives of the users question the existing systems being used to combat this challenge wherein, the third-parties gather and handle large amounts of individual details. As part of the constant evolution of the cyber physical system architecture, one of the goals of our system is to reduce the latency time for enrollment of new information. Generally, the efficiency and benefit of a Cyber Physical Systems (CPS) depends heavily on interconnection of individual devices or nodes. Exchange of data and information relevant to an overall task or functionality is the key to many applications such as smart grids, smart cities, and many others. Trustworthiness of data is needed to make such systems (especially in MCPS systems) successful. To be able to fulfill policies to guarantee the safety of all entities within a Medical Cyber Physical Systems (MCPS) and to provide security measures to enforce these cryptographic solutions have to be embedded. This paper describes a decentralized e-healthcare application framework for personal data management that ensures that users own and access their data. This work proposes a novel mechanism to secure Medical Cyber Physical Systems (MCPS), i.e., as an automated access-control manager (including building trust in a third party). This work also integrates some features in security building blocks in ultra-small devices to provide essential properties to secure embedded systems. © 2021 The Author(s)

关键词： Cryptography

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