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Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Computers, Materials & Continua 2023年第3期74卷 5417-5430页

作者： Khalid Masood Mahmoud M.Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam Mohammad Hamza Kashif Department of Software Engineering Lahore Garrison UniversityLahore54000Pakistan School of Business Skyline University CollegeSharjahUAE Al-Madinah International University–Faculty of Computer and Information Technology Kuala LumpurMalaysia College of Computer and Information Systems Islamic University of MadinahMadinahKingdom of Saudi Arabia School of IT Skyline University CollegeSharjahUAE Faculty of Engineering Computing and Science(FECS)-Swinburne University of TechnologySarawak Department of General Education Skyline University CollegeUniversity City of SharjahUAE Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan

Generally,conventional methods for anomaly detection rely on clustering,proximity,or *** themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques *** research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured *** node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph *** to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and *** detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of *** Graph-based deep learning networks are designed to predict unknown objects and *** our case,they detect unusual objects in the form of malicious *** edges between nodes represent a relationship of nodes among each *** case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an *** encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible *** show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance *** autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public *** suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniq

关键词： Graph neural network deep learning anomaly detection auto encoders

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An Application of Localized Model Explainability: Identifying Key Disparities in Social Determinants of Health in Food Deserts

An Application of Localized Model Explainability: Identifyin...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Alam, Md Shafiul Ishmakhametov, Namazbai Xie, Ying Chakravarty, Sumit Kennesaw State University Analytics and Data Science Kennesaw United States Kennesaw State University College of Computing and Software Engineering Kennesaw United States Kennesaw State University Department of Electronic Engineering Kennesaw United States

ISBN: (纸本)9781665480451

Food deserts are geographic areas where people have limited access to healthy and affordable food. Millions of people in the US are experiencing adverse effects stemming from living in such areas and it becomes an important challenge for federal and local authorities to improve the quality of life of people living in food deserts. The majority of studies in this field explicitly consider income level when analyzing problems of food deserts, thus shadowing other attributes that might provide deeper insights into the problems of the food desert's population. The current paper assesses the significance of subtler, non-income related characteristics of census tracts by using a new variable attribution technique Appley which approximates Shapley values in linear time. The results show that attributes of census tracts that fall into the set of the so called "social determinants of health"have similar classification power compared to models that explicitly use income variables. The results suggest that each food desert has its own unique set of problems with social determinants of health with varying levels of intensities of each problem and this allows to clearly distinguish not only food deserts from non-food desert areas but also distinguish food deserts from each other. Appley attributes localized importance scores to food deserts' social determinants of health, thus showing which issues are more severe in a given food desert compared to another one. This allows for devising food desert-specific solutions to the health issues of desert residents thus letting authorities improve the quality of life of people in a targeted and budget-efficient manner. © 2022 IEEE.

关键词： Budget control

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Digital Platform to Empower the Self-Employment in Sri Lanka 6

Digital Platform to Empower the Self-Employment in Sri Lanka

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6th International Conference on Information Technology Research, ICITR 2021

作者： Wickramasinghe, H.C.P. Thebuwana, T.D. Wijesinghe, G.K.H.S. Dissanayake, U.N. Kodagoda, Nuwan Suriyawansa, Kushnara Faculty of Computing Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665420006

Unemployment is a huge problem around the world because a lack of job opportunities. People are unable to find the job opportunities according to their preferences and qualifications. As a solution for this, many countries are attempting to empower self-employment. Most of current world problems have been solved using modern technologies. Therefore, the development of self-employment also can be achieved through modern technology. The objective of our proposed platform, HIRELANCER, is empowering self-employment using modern technologies. HIRELANCER is bringing the consumers, service providers, and suppliers into the same platform. HIRELANCER will consist of innovative features that go beyond comparatively to other platforms such as an advanced mechanism to find best suitable service providers/suppliers for the service, handling the virtual front-desk, cost estimation for the services prior to contacting a service provider, and advanced facility to find a suitable career path for the people who are seeking career guidance. This research paper discusses how the innovative features of HIRELANCER will be beneficial for consumers, service providers, and suppliers and ultimately achieve our main objective, which is empowering self-employment in Sri Lanka. © 2021 IEEE.

关键词： Employment

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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

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Studying and Recommending Information Highlighting in Stack Overflow Answers

arXiv

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arXiv 2024年

作者： Ahmed, Shahla Shaan Wang, Shaowei Tian, Yuan Chen, Tse-Hsun Zhang, Haoxiang Department of Computer Science University of Manitoba Canada School of Computing Queen’s University Canada Department of Computer Science and Software Engineering Concordia University Canada Huawei Canada

Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information. Nonetheless, there have been limited studies on the highlighted information. Objective: We carried out the first large-scale exploratory study on the information highlighted in SO answers in our recent study. To extend our previous study, we develop approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for the Named Entity Recognition task. Method: In this paper, we studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN-based and BERT-based models for each type of formatting (i.e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers. Results: Our models achieve a precision ranging from 0.50 to 0.72 for different formatting types. It is easier to build a model to recommend Code than other types. Models for text formatting types (i.e., Heading, Bold, and Italic) suffer low recall. Our analysis of failure cases indicates that the majority of the failure cases are due to missing identification. One explanation is that the models are easy to learn the frequent highlighted words while struggling to learn less frequent words (i.g., long-tail knowledge). Conclusion: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow. © 2024, CC BY.

关键词： Recommender systems

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Discovering All-chain Set with Direction and Graduality Characteristics over Streaming Time Series 19

Discovering All-chain Set with Direction and Graduality Char...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wang, Shaopeng Feng, Chunkai Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Department of Software Engineering Hohhot China

ISBN: (纸本)9798350346558

Since its introduction over five years ago, time series chain has become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. Recent work has generalized the definition of time series chain, and introduced a novel definition of time series chain with directionality and graduality characteristics (TSC-DG) which can significantly enhance both robustness and usability of the original time series chain. However, previous studies on TSCDG process fixed-length time series. In this work, we focus on the issue of all-chain set with direction and graduality characteristics (all-TSCS-DG) mining over streaming time series for the first time, where all-TSCS-DG is the core of current TSCDG researches. We propose an improved Naive algorithm (IN) to solve this problem. Compared to the Naive, the IN guarantees the same space costs and results firstly, secondly is the IN takes two additional optimal strategies to further improve the time efficiency. The basic ideas of these two strategies are both incremental computing. The first one can make the IN update the IB structure at each time-tick incrementally, where the IB is an important data structure that is used to obtain the all-TSCS-DG. The second one makes the IN obtain mining results at current time-tick based on the ones at the last time-tick incrementally. Extensive experiments on real dataset demonstrate the efficiency and effectiveness of the IN. © 2022 IEEE.

关键词： Time series

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Unmasking Fake News with Emotional Cues: A Comparative Study of Sentiment Reasoning Models

Unmasking Fake News with Emotional Cues: A Comparative Study...

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Advanced Research in computing (ICARC), International Conference on

作者： Vadivel Abishethvarman Kuhaneswaran Banujan B.T.G.S Kumara Nirubikaa Ravikumar Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Australia Department of Software Engineering Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

Discerning truth from deception requires innovative tools in an era besieged by misinformation. Fake news is a form of misleading information delivered as reliable news. It is mainly made up of intentionally manipulating public opinion, decision bias, and exploiting general credulity. Various supervised learning-based fake news detection and classifications only focus on the semantic values due to their subjectivity in the current context. Sentiment analysis is also a common interpretation mechanism of information mining. This research explores the role of sentiment analysis in the context of fake news detection. Sentiment analysis, a facet of natural language processing, delves into the nuanced sentiments within the text, unveiling hidden narratives and differentiating between authentic reporting and carefully crafted deception. The study employs various sentiment reasoning models, including VADER, TextBlob, and Roberta, focusing on comparative analysis. The VADER model, complemented by a Bag of Words approach, emerges as the most effective, outperforming, compared to TextBlob and RoBERTa. Especially BERT sentiment reasoner model underperformed with uneven distribution in this fake news domain. The best VADER sentiments are taken for the transitions of sequential neural network architectures such as Simple Neural Network (SNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) featuring Long Short-Term Memory (LSTM). The LSTM model captures intricate patterns, dependencies, and contextual information. Evaluation metrics, encompassing accuracy, precision, recall, and F1 score, provide a comprehensive understanding of models' performance. The RNN LSTM performed well compared to the other sequential models. It primely emphasised the multifaceted landscape of sentiment analysis, presenting a robust strategy for combating the proliferation of fake news in an information-abundant age. Combining Sentiment Reasoning in VADER and RNN LSTM presents a rob

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DMSDRec: Dynamic Structure-aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation

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IEEE Transactions on Services computing 2025年

作者： Li, Yue Zeng, Jun Tang, Haoran Wen, Junhao Gao, Min Zhou, Wei Chongqing University School of big data and software engineering Chongqing40044 China Hong Kong Polytechnic University Department of Computing Hung Hom Kowloon Hong Kong

The rise of smart devices has accelerated location-based services, creating vast trajectory data for Point-of-Interest (POI) recommendations. However, user omissions or privacy concerns often result in incomplete trajectory data, compromising sequential patterns and spatiotemporal relationships. Existing solutions using graph structures, self-supervised learning (SSL), or spatiotemporal contexts still face two limitations: (1) graph-based and SSL methods produce suboptimal trajectory representations due to respective inherent constraints;(2) noise interference persists when modeling distorted spatiotemporal signals. To mitigate these issues, we propose a Dynamic Structure-aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation (DMSDRec). Specifically, we introduce a dynamic structure-aware improved graph masked autoencoder that adaptively and dynamically distills global transitional information for self-supervised augmentation. It naturally avoids the noise introduced by existing SSL methods' dependency on manual views augmentation. Meanwhile, the masked reconstruction task synergistically enhances trajectory representations by capturing deeper cross-sequence dependencies. Additionally, we propose an effective latent-space spatiotemporal diffusion denoising method. First, we employ graph structures to model spatiotemporal relationships, utilizing higher-order structural information to alleviate the linear spatiotemporal relationship deviations caused by incomplete trajectories. Building on this, we implement diffusion models in the latent space to systematically identify and remove noise from spatiotemporal representations. Through experimental results on two real-world datasets, we demonstrate the superiority of our proposed DMSDRec in terms of recommendation accuracy and robustness. © 2008-2012 IEEE.

关键词： Dynamic Structure-awareness Graph Masked Autoencoder Next POI Recommendation Spatiotemporal Diffusion

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Key Features and Formation of Transactions in the Case of Using UTxO, EUTxO and Account Based Data Storage Models 4

Key Features and Formation of Transactions in the Case of Us...

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4th International Science and Technology Conference "Modern Network Technologies", MoNeTec 2022

作者： Pomogalova, Albina V. Martyniuk, Anastasia A. Yesalov, Kirill E. Bonch-Bruevich Saint-Petersburg State University of Telecommunications Infocommunication Systems Department Saint Petersburg Russia Bonch-Bruevich Saint-Petersburg State University of Telecommunications Software Engineering and Computing Department Saint Petersburg Russia

ISBN: (数字)9781665472463

ISBN: (纸本)9781665472463

At the moment there are various logical concepts of constructing data storage and making transactions in blockchain networks. These concepts can be divided into account-based and (Extended) Unspent Transaction Output (E)UTxO. Account-based is a system with multiple accounts that can interact with each other through the creation and signing the transaction by a private key. While (E)UTxO is a model in which the values at the transaction outputs determine the current state of both a single smart contract and the entire blockchain network. To move from one state to another, transactions use the unspent outputs of previous transactions. The unspent outputs then become spent outputs: inputs for the new transaction also forming its unspent transaction outputs. These outputs can store both ADA and native tokens assigned to the public key. They can also store other information depending on the implementation of the script. The paper investigates the architectural features and differences of systems with two concepts of data storage construction, application features, as well as transaction construction models and the impact of models on network performance considered on the example of a practical case - development of an authorization module in the system. © 2022 IEEE.

关键词： Blockchain

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Bias Analysis of AI Models for Undergraduate Student Admissions

arXiv

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arXiv 2024年

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Indiana University Luddy School of Informatics Computing and Engineering Indiana University Indianapolis Indianapolis IN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors [1] to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © 2024, CC BY.

关键词： Contrastive Learning

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