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An Efficient Video Desnowing and Deraining Method with a Novel Variant dataset 13th

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An Efficient Video Desnowing and Deraining Method with a Nov...

13th International Conference on computer Vision Systems, ICVS 2021

作者： Sadeghzadeh, Arezoo Islam, Md Baharul Zaker, Reza Department of Computer Engineering Bahcesehir University Istanbul Turkey College of Data Science and Engineering American University of Malta Cospicua Malta Department of Electrical Engineering Azarbaijan Shahid Madani University Tabriz Iran

ISBN: (纸本)9783030871550

Video desnowing/deraining plays a vital role in outdoor vision systems, such as autonomous driving and surveillance systems, since the weather conditions significantly degrade their performance. Although numerous approaches have been reported for video snow/rain removal, they are limited to a few videos and did not consider the variations that occurred for the camera and background in real applications. We build a complete snow and rain dataset to overcome this limitation, consisting of 577 videos with synthetic snow and rain, quasi-snow, and real snow and rain. All possible variations of the background and the camera are considered in the dataset. Then, an efficient pixel-wise video desnowing/deraining method is proposed based on the color and temporal information in consecutive video frames. It is highly likely for a single pixel to be a background pixel rather than a snowy pixel at least once in the consecutive frames. Inspiring from this fact along with the color information of the snow pixels, we extract the background pixels from different consecutive frames by searching for the minimum gray-scale intensity. Experimental results demonstrate and validate the proposed method’s robustness to illumination and high-performance static background and camera. © 2021, Springer Nature Switzerland AG.

关键词： Rain

RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

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arXiv 2025年

作者： Shi, Jianrui Zhao, Yong Cui, Zeyang Shen, Xiaoming Zeng, Minhang Liu, Xiaojie Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computing and Data Science The University of Hong Kong Hong Kong Pengcheng Laboratory Shenzhen China

Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural network (DNN)-based detection models, particularly when processing high-resolution video. Conventional strategies, such as input down-sampling and network up-scaling, often compromise detection accuracy for faster performance or lead to higher inference latency. To address these issues, this paper introduces RE-POSE, a Reinforcement Learning (RL)Driven Partitioning and Edge Offloading framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments. Our approach features an RL-Based Dynamic Clustering Algorithm (RL-DCA) that partitions video frames into non-uniform blocks based on object distribution and the computational characteristics of DNNs. Furthermore, a parallel edge offloading scheme is implemented to distribute these blocks across multiple edge servers for concurrent processing. Experimental evaluations show that RE-POSE significantly enhances detection accuracy and reduces inference latency, surpassing existing methods. © 2025, CC BY.

关键词： Computation offloading

Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

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arXiv 2022年

作者： Junayed, Masum Shah Jeny, Afsana Ahsan Islam, Md Baharul Ahmed, Ikhtiar Shahen Shah, A.F.M. Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of Computer Science & Engineering Daffodil International University Dhaka1207 Bangladesh College of Data Science & Engineering American University of Malta Bormla1013 Malta Department of Data Science TU Dortmund University Dortmund44227 Germany Department of Electronics and Communication Engineering Yildiz Technical University Istanbul Turkey

The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model. A comparison study showed that the presented model outperformed pre-trained CNNs and five-fold cross-validation on the same dataset. For ILDs pattern classification, the proposed approach achieved the accuracy scores of 99.09% and the average F score of 97.9% that outperforms three pre-trained CNNs. These outcomes show that the proposed model is relatively state-of-the-art in precision, recall, f score, and accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

An Analysis for Pre-consultations on the Korean Government-Managed Information System Projects 8th

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An Analysis for Pre-consultations on the Korean Government-M...

8th International Conference on Advances in Computing and data sciences, ICACDS 2024

作者： Kim, Myunghee Baek, Nakhoon Department of Digital Platform Government National Information Society Agency Daegu41068 Korea Republic of Department of Information Science Kyungpook National University Daegu41566 Korea Republic of School of Computer Science and Engineering Kyungpook National University Daegu41566 Korea Republic of Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of

ISBN: (纸本)9783031709050

To reduce duplicated information system development projects, Korean government established the pre-consultation body. The pre-consultation body performs preliminary review for various aspects of government-managed information systems. In spite of its good roles and its effectively, the pre-consultation body met over-loaded duties, and now it shows some warning signals mainly due to the increased number of submitted projects. This paper statistically analyzes the results of pre-consultation processes for 1,399 submitted projects in the field of e-Government projects in the year of 2022. Our analysis shows that it is better to exclude some small-size projects from the pre-consultation process. This paper aims to provide the theoretical and statistics-based backgrounds to the claims, and we expect that our suggestions can improve the pre-consultation process of Korean government information systems management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

Investigating the Use of Neural Networks for AI-Based Image Recognition Systems

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Investigating the Use of Neural Networks for AI-Based Image ...

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Nitin N. Sakhare Dipannita Mondal M. Vigenesh Geetha. B Dharmesh Dhabliya Ashok Kumar Purohit Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Commerce Mangalayatan University Aligarh India

This paper examines the usage of synthetic intelligence (AI) to broaden photo recognition structures through neural networks. Strategies along with supervised gaining knowledge, unsupervised gaining knowledge, deep studying, and convolutional neural networks are investigated, as are their benefits and barriers. The paper appears at many demanding situations involved in neural networks for photo recognition, including the need for categorized information, the magnificence imbalance trouble, and the scalability issue. It additionally describes several methods to improve accuracy and velocity, combining different architectures and photograph pre-processing techniques. Eventually, the paper discusses the ability packages and future research directions of AI-based total image popularity structures.

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How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?

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arXiv 2024年

作者： Liu, Tianchi Zhang, Lin Das, Rohan Kumar Ma, Yi Tao, Ruijie Li, Haizhou Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore National Institute of Informatics Tokyo Japan Fortemedia Singapore Singapore School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Partially manipulating a sentence can greatly change its meaning. Recent work shows that countermeasures (CMs) trained on partially spoofed audio can effectively detect such spoofing. However, the current understanding of the decision-making process of CMs is limited. We utilize Grad-CAM and introduce a quantitative analysis metric to interpret CMs’ decisions. We find that CMs prioritize the artifacts of transition regions created when concatenating bona fide and spoofed audio. This focus differs from that of CMs trained on fully spoofed audio, which concentrate on the pattern differences between bona fide and spoofed parts. Our further investigation explains the varying nature of CMs’ focus while making correct or incorrect predictions. These insights provide a basis for the design of CM models and the creation of datasets. Moreover, this work lays a foundation of interpretability in the field of partial spoofed audio detection that has not been well explored previously. © 2024, CC BY-NC-ND.

关键词： Decision making

Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges

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arXiv 2024年

作者： Cao, Yuchen Dai, Jianglai Wang, Zhongyan Zhang, Yeyubei Shen, Xiaorui Liu, Yunchong Tian, Yexin Khoury College of Computer Science Northeastern University United States Department of EECS University of California Berkeley United States Center for Data Science New York University United States School of Engineering and Applied Science University of Pennsylvania United States Georgia Institute of Technology College of Computing United States

The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through user-generated content. This systematic review examines machine learning (ML) models for detecting mental illness, with a particular focus on depression, using social media data. It highlights biases and methodological challenges encountered throughout the ML lifecycle. A search of PubMed, IEEE Xplore, and Google Scholar identified 47 relevant studies published after 2010. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was utilized to assess methodological quality and risk of bias. The review reveals significant biases affecting model reliability and generalizability. A predominant reliance on Twitter (63.8%) and English-language content (over 90%) limits diversity, with most studies focused on users from the United States and Europe. Non-probability sampling methods (approximately 80%) limit representativeness. Only 23% of studies explicitly addressed linguistic nuances like negations, crucial for accurate sentiment analysis. Inconsistent hyperparameter tuning was observed, with only 27.7% properly tuning models. About 17% did not adequately partition data into training, validation, and test sets, risking overfitting. While 74.5% used appropriate evaluation metrics for imbalanced data, others relied on accuracy without addressing class imbalance, potentially skewing results. Reporting transparency varied, often lacking critical methodological details. These findings highlight the need to diversify data sources, standardize preprocessing protocols, ensure consistent model development practices, address class imbalance, and enhance reporting transparency. By overcoming these challenges, future research can develop more robust and generalizable ML models for depression detection on social media, contributing to improved mental health outcomes globally. © 2024, CC BY.

关键词： Adversarial machine learning