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arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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A Machine Learning Approach for Sentiment Analysis to Nurture Mental Health Amidst COVID-19

A Machine Learning Approach for Sentiment Analysis to Nurtur...

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Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence

作者： Namratha S Khasnis Snigdha Sen Shubhangi S Khasnis Department of Computer Science and Engineering Global Academy of Technology Bangalore Karnataka India Software Engineering Department Sabre India Bangalore Karnataka India

ISBN: (纸本)9781450387637

During the pandemic, when fresh news content is generated every minute about the widespread of the virus, many conversations revolve around the spread and cure of the contagion. At the hands of a commoner who posts news about COVID-19 on social media, the news may manifest itself to accommodate the said person's fear or negative propaganda which can potentially trigger a mass panic outbreak or can disrupt the mental health of a *** research discusses the application of Machine Learning in Sentiment Analysis to classify Tweets about Coronavirus as fear sentiment or panic sentiment. It proposes the idea of a web-based application that caters to filter out the fear-inducing sentiment from a user's daily Twitter feed, thus giving the user accurate and well-spirited *** analysis is performed along with necessary textual data visualization. A substantial accuracy of 91% is achieved in the classification of brief Tweets using the Naïve Bayes method. An accuracy of 74% is achieved using the Logistic Regression classification method for brief tweets. This depicts the advancements in the field of sentimental analysis and sheds light on how it can be employed amidst a challenging situation like the pandemic to preserve mental health.

关键词： COVID-19

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Power Line Aerial Image Restoration under Adverse Weather: Datasets and Baselines

arXiv

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arXiv 2024年

作者： Yang, Sai Hu, Bin Zhou, Bojun Liu, Fan Wu, Xiaoxin Zhang, Xinsong Gu, Juping Zhou, Jun School of Electrical Engineering and Automation Nantong University Nantong226019 China School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China School of Information Science and Technology Nantong University Nantong226019 China College of Computer and Software Engineering Hohai University Nanjing210098 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China School of Information and Communication Technology Griffith University NathanQLD4111 Australia

Power Line Autonomous Inspection (PLAI) plays a crucial role in the construction of smart grids due to its great advantages of low cost, high efficiency, and safe operation. PLAI is completed by accurately detecting the electrical components and defects in the aerial images captured by Unmanned Aerial Vehicles (UAVs). However, the visible quality of aerial images is inevitably degraded by adverse weather like haze, rain, or snow, which are found to drastically decrease the detection accuracy in our research. To circumvent this problem, we propose a new task of Power Line Aerial Image Restoration under Adverse Weather (PLAIR-AW), which aims to recover clean and highquality images from degraded images with bad weather thus improving detection performance for PLAI. In this context, we are the first to release numerous corresponding datasets, namely, HazeCPLID, HazeTTPLA, HazeInsPLAD for power line aerial image dehazing, RainCPLID, RainTTPLA, RainInsPLAD for power line aerial image deraining, SnowCPLID, SnowInsPLAD for power line aerial image desnowing, which are synthesized upon the public power line aerial image datasets of CPLID, TTPLA, InsPLAD following the mathematical models. Meanwhile, we select numerous state-of-the-art methods from image restoration community as the baseline methods for PLAIRAW. At last, we conduct large-scale empirical experiments to evaluate the performance of baseline methods on the proposed datasets. The proposed datasets and trained models are available at https://***/ntuhubin/PLAIR-AW. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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Unsupervised Rail Surface Defect Detection Method

Unsupervised Rail Surface Defect Detection Method

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： ChenYu Zhao Yi Han Ningkang Ouyang YiFan Zhao School of Artificial Intelligence Zhengzhou Software Vocational and Technical College Zhengzhou China Department of Computer Science and Information Anyang Institute of Technology Engineering Anyang China School of Information Engineering North China University of Water Resources and Electric Power Zhengzhou China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Railroad travel has become an essential part of modern life, therefore, rail surface defect detection has become a problem that cannot be ignored. However, most of the currently existing methods often require a large number of defect samples for learning, which are labor-intensive and very difficult to obtain. In order to alleviate the data dependence of the detection model, this paper proposes an unsupervised method. Specifically, we use Transformer’s attention mechanism to construct the encoder-decoder structure and train it only on normal samples with the purpose of reconstruction, so that those regions that cannot be reconstructed will be regarded as defects at the time of detection. To prevent the model from generalizing its generalization ability to those unknown defective samples, i.e., to avoid overgeneralization, we introduce a noise perturbation in the training so that the model focuses on the reconstruction of normal features. Experiments show that the proposed unsupervised approach outperforms a host of supervised models with low data requirements and can effectively address the current challenges of track defect detection.

关键词： Rails Surface reconstruction Perturbation methods Noise Transformers Data models Decoding Image reconstruction Unsupervised learning Defect detection

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks

Identify Initial Nodes for Spreader in A New Diffusion Model...

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International Conference on Web Research (ICWR)

作者： Ehsan Khosravi Fatemeh Yazdanifar Nikoo Salimi Hamid Ahmadi Beni Dept. of Computer Engineering Sharif University of Technology Tehran Iran Dept. of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Dept. of the Electrical Electronics Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Dept. of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. The influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. This problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time.

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Evaluating Ethereum Reentrancy Detection Tools via Mutation Testing

Evaluating Ethereum Reentrancy Detection Tools via Mutation ...

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International Symposium on software Reliability engineering (ISSRE)

作者： Kaitai Zhu Xingya Wang Zhenyu Chen Song Huang Junhua Wu College of Computer and Information Engineering (College of Artificial Intelligence) Nanjing Tech University China State Key Lab for Novel Software Technology Nanjing University China Command & Control Engineering College Army Engineering University of PLA China

Reentrancy vulnerabilities in Ethereum smart contracts have caused huge financial losses in recent years, promoting the development of reentrancy detection tools. Evaluation of reentrancy detection tools has emerged as an essential research focus consequently. However, despite numerous reentrancy detection evaluation approaches, there is no systematically classified set of reentrancy vulnerabilities in evaluation. This oversight may result in a limited understanding of the actual effectiveness of reentrancy detection tools. This paper employs mutation testing to generate classified sets of reentrancy vulnerabilities and evaluate reentrancy detection tools. To determine the types of reentrancy that can be mutated, we use symbolic execution to identify potential reentrancy paths in smart contracts. For each reentrancy type, we design one to three mutation operators. Then, we employ pattern matching to locate statements where mutation operators can be applied, thus generating sets of reentrancy vulnerabilities of different types. We implemented and compared our mutation testing tool with other tools. Results show that we generated at least 29% more classified reentrancy mutants. Furthermore, we evaluated two state-of-the-art reentrancy detection tools based on the classified reentrancy mutants. The findings provide directions for advancing reentrancy detection tools.

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Localization of Image Splicing Under Segment Anything Model With Integrated Compression and Edge Artifacts

Localization of Image Splicing Under Segment Anything Model ...

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IEEE International Conference on Image Processing

作者： Ruhao Zhao Xian Zhong Liang Liao Wenxuan Liu Wenxin Huang Zheng Wang School of Computer Science and Artificial Intelligence Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology School of Electrical and Electronic Engineering Rapid-Rich Object Search Lab Nanyang Technological University College of Computing and Data Science Nanyang Technological University School of Computer Science and Information Engineering Hubei University School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The localization of image splicing involves identifying pixels in an image that have been spliced from other images, necessitating the discernment of splicing features. Despite significant advancements driven by the rise of social media and deep learning, existing methods exhibit limitations, often neglecting the integration of coarse and precise features and lacking the ability to understand objects. This leads to erroneous predictions in identifying spliced regions. This paper proposes Segment Anything Model with Integrated Compression and Edge artifacts (SAM-ICE) for the localization of image splicing, addressing these limitations by fusing forged edge features and compression artifact features. Leveraging SAM’s object understanding ability, our method identifies spliced regions using the fused features as guidance. Specifically, we employ Edge Artifact Extractor (EAE) to extract fine high-frequency edge splicing features and Compression Artifact Extractor (CAE) to extract coarse compression artifact features. By combining these features, our method utilizes coarse-fine features to accurately pinpoint the spliced portions of the image. Experimental results demonstrate the superior accuracy, robustness, and generalizability of our method compared to the state-of-the-arts.

关键词： Location awareness Image segmentation Image coding Social networking (online) Image edge detection Splicing Feature extraction

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Chatglm-Ppft: Detection Method for Offensive Speech Based on Prompting and Parameter Fine-Tuning

SSRN

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SSRN 2024年

作者： Liu, Jiapeng Fan, Xiaochao Ning, Qian Tuerxun, Palidan Li, Yong Deng, Wenjun Li, Weijie Tang, Jing School of Computer Science and Technology Xinjiang Normal University Urumqi830000 China School of Software Xinjiang University Urumqi830000 China College of Electronics and Information Engineering Sichuan University Sichuan Chengdu610065 China

With the rapid development of social media, the convenience of online communication has led to the frequent occurrence of offensive speech, posing a threat to the online social environment. Ensuring the safety, health, and friendliness of online platforms has made the automatic detection of offensive speech an urgent necessity. Despite the superior performance of large language models in natural language processing tasks due to their extensive parameter sets, their effectiveness in detecting offensive speech is limited by the complexity and diversity of language and the models' tendency to avoid sensitive content. The inherent biases and imbalances in existing datasets further constrain model performance. To address these issues, this paper proposes an offensive speech detection method that combines prompt engineering and parameter fine-tuning (ChatGLM-PPFT), aiming to improve the shortcomings of the large language model ChatGLM in such tasks. By designing strategies such as role-playing, few-shot prompting, and historical response referencing, ChatGLM-PPFT not only mitigates the problem of non-standard responses to offensive content but also enhances detection performance for Chinese offensive speech through lightweight fine-tuning using the LORA method. On the TOXICN and SWSR datasets, ChatGLM-PPFT achieves state-of-the-art detection results and demonstrates significant advantages in fine-grained detection, substantially reducing false positives and false negatives. © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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Online Joint Power Allocation and Task Scheduling for LEO Satellite Networks

Online Joint Power Allocation and Task Scheduling for LEO Sa...

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IEEE Conference on Wireless Communications and Networking

作者： Zheyuan Li Lijun He Juncheng Wang Ziye Jia Yanting Wang Chau Yuen School of Software Northwestern Polytechnical University China School of Information and Control Engineering China University of Mining and Technology China Department of Computer Science Hong Kong Baptist University China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The excessive proliferation of Low Earth Orbit (LEO) satellites inescapably bring the explosive growth of space data in LEO Satellite Networks (LSNs). Meanwhile, the stochastic arrivals of space data together with the time-varying satellite-ground links in LSNs pose significant challenges for offloading a large volume of space data from LSNs to ground stations. To circumvent these challenges, we systematically study the energy-constrained online data offloading problem to jointly optimize power allocation and task scheduling for LSNs. First, we leverage Lyapunov optimization to decouple our formulated long-term stochastic joint optimization problem into a set of per-time-slot subproblems. Then, each subproblem is decoupled into a task scheduling problem and a power allocation problem. Next, we derive the optimal solution to the power allocation problem and propose a multi-armed bandit based quasi-optimal solution to the task scheduling problem. Finally, extensive simulation results show that our proposed algorithm has superior performance over the state-of-the-art solutions.

关键词： Space vehicles Satellites Simulation Low earth orbit satellites Scheduling Explosives Resource management Optimization Space stations

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Federated Learning for Diffusion Models

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Peng, Zihao Wang, Xijun Chen, Shengbo Rao, Hong Shen, Cong The School of Mathematics and Computer Science Nanchang University Nanchang330000 China The School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China The School of Software Nanchang University Nanchang330000 China The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

Diffusion models are powerful generative models that can produce highly realistic samples for various tasks. Typically, these models are constructed using centralized, independently and identically distributed (IID) training data. However, in practical scenarios, data is often distributed across multiple clients and frequently manifests non-IID characteristics. Federated Learning (FL) can leverage this distributed data to train diffusion models, but the performance of existing FL methods is unsatisfactory in non-IID scenarios. To address this, we propose FEDDDPM—Federated Learning with Denoising Diffusion Probabilistic Models, which leverages the data generative capability of diffusion models to facilitate model training. In particular, the server uses well-trained local diffusion models uploaded by each client before FL training to generate auxiliary data that can approximately represent the global data distribution. Following each round of model aggregation, the server further optimizes the global model using the auxiliary dataset to alleviate the impact of heterogeneous data on model performance. We provide a rigorous convergence analysis of FEDDDPM and propose an enhanced algorithm, FEDDDPM+, to reduce training overheads. FEDDDPM+ detects instances of slow model learning and performs a one-shot correction using the auxiliary dataset. Experimental results validate that our proposed algorithms outperform the state-of-the-art FL algorithms on the MNIST, CIFAR10 and CIFAR100 datasets. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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