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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Ajay S Ram Arya Thalakkat Devika Udayan Aiby A Khader Hari K Vipin George Dept. of Computer Science and Engg. Government Engineering College Thrissur India Dept. of Cyber Forensics School of Technology and Applied Science Kottayam India Dept. of Computer Application Kerala Police Academy Thrissur India Police Computer Lab Kerala Police Academy Thrissur India Faculty Kerala Police Academy Thrissur India

The Internet's growth has connected users globally, forming a vast network of data. As new social media platforms with diverse features emerge, individuals are drawn to sharing their personal data online. This inclination stems from a desire to display social status. However, this openness poses risks, as personal data uploaded to the internet can easily unveil one's online identity. Extracting such data is a straightforward process, as a simple correlation of evidence with an online profile can summarize a user's identity. By combining various sources of information linked to a specific individual, a comprehensive profile can be created. As the digital landscape expands, Open Source Intelligence (OSINT) has become an invaluable tool for gathering information. OSINT involves searching and accessing publicly available data to extract relevant information, analyze patterns, and draw conclusions. It encompasses activities like web scraping, data mining, social media monitoring, and online research. OSINT is crucial for understanding public sentiment, identifying potential threats, conducting background checks, investigating crimes, and performing competitive analysis. This paper investigates the impact of OSINT as a modern day reconnaissance tool. Through case studies and real-world examples, the research demonstrates the effectiveness of OSINT in enhancing situational awareness, supporting decision-making for law enforcement agencies, and using OSINT as a modern-day reconnaissance tool.

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Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

arXiv

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arXiv 2023年

作者： Wu, Peng Liu, Jing He, Xiangteng Peng, Yuxin Wang, Peng Zhang, Yanning The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Poly-technical University China The Guangzhou Institute of Technology Xidian University China The Wangxuan Institute of Computer Technology Peking University China

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., "vandalism", is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks and design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method. Captions are publicly released at https://***/Roc-Ng/VAR. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Stereo Image Dense Matching Based on SGM Constrained by Feature Matching

Stereo Image Dense Matching Based on SGM Constrained by Feat...

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International Conference on computer and Communications (ICCC)

作者： Tao Ma Hangbiao Zhu Weijian Huang Pei An Chun Wang Kun Yu Institute of Computer Application China Academy of Engineering Physics Mianyang China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China National Key Laboratory of Science and Technology on Electromagnetic Energy Naval University of Engineering Wuhan China

Dense image matching plays a important role in stereo vision and remote sensing. Semi-global matching (SGM) is a widely used dense image matching framwork, which uses dynamic programming calculation strategy to optimize the global energy function. SGM has the problem of wrong disparity accumulation along the path. To solve the problem, this paper proposes a new dense image matching method based on feature matching and SGM. First, the feature matching is used to extract the reliable reference points. And then, reference points are used as additional constraints in matching cost aggregation to cut off the wrong disparity delivering along the path. In order to evaluate our method, qualitative and quantitative experiments are carried out on three stereo image pairs. The experimental results show that our method can achieve high matching accuracy by effectively reducing the error propagation.

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RIS-Assisted Indoor Visible Light Positioning Based on Sparse Bayesian Learning

RIS-Assisted Indoor Visible Light Positioning Based on Spars...

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Intelligent Communications and Computing (ICC), International Conference on

作者： Yuhao Wang Sifan Wu Lisu Yu Chuan Xu Zhenghai Wang Xiangxiang Cai School of Information Engineering Nanchang University Nanchang Jiangxi China Digital Technology Application Industry College Shangrao Normal University Shangrao Jiangxi China School of Mathematics and Computer Science Nanchang University Nanchang Jiangxi China

Visible light communication (VLC) relies heavily on the line of sight (LoS). Once the LoS link is blocked, the communication efficiency will be greatly reduced. The indoor environment is complex, and it is inevitable that the LoS link will be blocked. In order to solve such problem, we have integrated the reconfigurable intelligence surface (RIS) into the indoor visible light positioning (VLP) system to enhance the positioning accuracy when the LoS link is blocked. We have proposed a sparse Bayesian learning (SBL)-Based algorithm to solve the formulated joint-optimization problem of estimated position, noise variance, and sparsity. This algorithm uses Bayesian reconstruction theory and conditional probability expressions to calculate the mean and covariance matrix of the formula, and further obtain the updated noise variance value and sparsity value, thereby finally attain the accurate position. From the simulation results, the positioning accuracy can reach 0.41m when the cascade channel length is 10m.

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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DRL-Based Pricing-Driven for Task Offloading and Dynamic Resource in Vehicle Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Sijun Yang, Liang Li, Junjie Guo, Hongzhi Ahmad, Ishtiaq Da Costa, Daniel Benevides Jiang, Hongbo Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China Czech Technical University in Prague Department of Telecommunication Engineering Faculty of Electrical Engineering Prague166 27 Czech Republic Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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A Fast Matrix Completion Method Based on Matrix Bifactorization and QR Decomposition

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Liu, Qing Peng, Chen Yang, Pei Zhou, Xiancun Liu, Zhengyu School of Electronic and Information Engineering West Anhui University Anhui Lu'an237012 China School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan232001 China Department of Computer Technology and Application Qinghai University Qinghai Xining810016 China

The problem of recovering the missing values in an incomplete matrix, i.e., matrix completion, has attracted a great deal of interests in the fields of machine learning and signal processing. A matrix bifactorization method, which is abbreviated as MBF, is a fast method of matrix completion that has a better speed than the traditional nuclear norm minimization methods. However, it may become inaccurate and slow when solving matrices of not low rank. In this paper, an improved fast and accurate MBF method based on Qatar Riyal (QR) decomposition is proposed, which can be called FMBF-QR. On one side, the optimization problem of MBF is improved to be an iteratively reweighted L2,1 norm minimization problem to enhance the accuracy of MBF. On the other side, the minimization problem of FMBF-QR is optimized very efficiently by using QR decomposition for improving the speed of MBF. Sufficient experimental results verify that FMBF-QR can converge with a higher accuracy and a faster speed than the traditional matrix completion methods. © 2023 Qing Liu et al.

关键词： Signal processing

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A self-supervised CNN for image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Jiao, Tiancai Zuo, Wangmeng Zhang, Yanning Lin, Chia-Wen the School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China the Peng Cheng Laboratory Laboratory Guangdong Shenzhen518055 China the School of Computer Science Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu300 Taiwan

Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real world, which results in poor robustness of image watermark removal techniques. In this paper, we propose a self-supervised convolutional neural network (CNN) in image watermark removal (SWCNN). SWCNN uses a self-supervised way to construct reference watermarked images rather than given paired training samples, according to watermark distribution. A heterogeneous U-Net architecture is used to extract more complementary structural information via simple components for image watermark removal. Taking into account texture information, a mixed loss is exploited to improve visual effects of image watermark removal. Besides, a watermark dataset is conducted. Experimental results show that the proposed SWCNN is superior to popular CNNs in image watermark removal. Codes can be obtained at https://***/hellloxiaotian/SWCNN. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Role and application of Ribs in Solar Air Heater

Role and Application of Ribs in Solar Air Heater

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Amandeep Singh Sanjeev Kumar Joshi Dharmendra Kumar Rajiv Kumar Shiv Dayal Pandey Madhur Thapliyal Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun Uttarakhand India Computer Science and Engineering Department Shivalik College of Engineering Dehradun Uttarakhand India Department of Computer Application Graphic Era Hill University Dehradun Uttarakhand India

Maintaining the clean environment and reducing the usage of fossil fuels requires switching to renewable sources of energy, because of limited resources of fossil fuels and having insufficient fossil fuel storage. The world is facing from shortage of fossil fuel and its cost is going too high and its smoke is very harmful to the environment. In this crisis, the world is starting the use of renewable energy. Renewable energies are easily available, and it reduces air pollution and fuel cost. non-conventional energy sources, like solar energy, atomic energy, hydroelectric power, wind energy etc. are a substitute for conventional energy. Solar energy is the best one, is used in various utilization, like solar air heaters, solar water heaters, solar power generation, solar pumps, solar cooking etc. Solar air heater that absorbs heat from the sun's radiations to heat air and it is collected by the collector & converted into kinetic energy and also restored in the form of potential energy. In this review, we will discuss the role of ribs in solar air heating systems to enhance the heat transfer rate, types of rib, and the purpose of ribs arrangement in solar air heating systems. Ribs are used to transfer maximum heat and increase the pressure inside of the cabin and it located on the absorber plate surface and it help to increase the solar air heaters performance. This study emphasizes exploring the effects of ribs and the behaviour of fluid after impacting on the ribs. For drying crops, heating buildings, and other uses, solar air heaters are extremely important in the agriculture sector, small-scale industries, in urban and villages for heating and drying purposes, etc. The use of ribs can enhance the effectiveness of a solar air heating system and this technology can be used for heat transmission enhancement, power generation & can fulfil the world's energy requirements. The primary objectives of this review article are to assist and summaries the research work carried

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Multi-Domains Personalized Local Differential Privacy Frequency Estimation Mechanism for Utility Optimization

SSRN

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SSRN 2024年

作者： Li, Yunfei Fu, Xiaodong Liu, Li Ding, Jiaman Peng, Wei Jia, Lianyin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Graduate School Yunnan University of Finance and Economics Kunming650221 China Yunnan Provincial Key Laboratory of Computer Technology Application Kunming University of Science and Technology Kunming650500 China

Local Differential Privacy (LDP) has garnered considerable attention in recent years because it does not rely on trusted third parties and has low interactivity and high operational efficiency. However, current LDP frequency estimation mechanisms aggregate data using different privacy budgets within the same data domain, overlooking the aggregation requirements across different data domains. This limits the potential for enhancing the data utility under fixed privacy budgets and meeting user preferences in multiple data domains and privacy budgets. To address this issue, we define a Multi-Domains Personalized Local Differential Privacy (MDPLDP) model that allows users to freely choose data domains and privacy budgets according to their privacy preferences. Furthermore, based on the MDPLDP model, two new frequency estimation mechanisms are proposed: MDPLDP-Generalized Randomized Response and MDPLDP-basic Randomized Aggregatable Privacy-Preserving Ordinal Response. These mechanisms support cross-domains data aggregation and optimize data utility by adjusting the data domains and increasing privacy budgets. Theoretical analysis reveals that these new mechanisms have lower estimation errors than the traditional LDP mechanisms. Experiments on real and synthetic datasets demonstrate that the proposed mechanisms effectively reduce estimation errors and enhance the utility of data-frequency estimation. © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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