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Location Word Embedding: An Approach for Rumor detection Over Social Internet of Things

IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE 2024年第4期10卷 32-39页

作者： Keshta, Ismail Kumar, Akhilesh Kulkarni, Mrunalini Harish Soni, Mukesh Yadav, Kusum Alferaidi, Ali AlMaarefa Univ Coll Appl Sci Dept Comp Sci & Informat Syst Riyadh 13713 Saudi Arabia Gaya Coll Dept Informat Technol Gaya 823001 India Vishwakarma Univ Sch Pharm Pune 411048 India Dr DY Patil Sch Sci & Technol Tathawade 411033 India Univ Hail Coll Comp Sci & Engn Hail 81481 Saudi Arabia

With the emergence of online social networks, the dissemination and acquisition of information have experienced dramatic transformations. While social media makes peoples’ lives easier, it also speeds up the creation and spread of rumors. Therefore, solving the problem of reliably and effectively recognizing words has become a critical need. The global–local attention network (GLAN)-based rumor detection model has been improved to improve its accuracy. Considering the impact of the positioning relationship among words in text on rumor identification, a new relative positional encoding method is utilized to enhance the original model’s local feature extraction module. This method can more precisely extract the semantic and location information of the text in a rumor and aggregate it to provide a better text feature that differentiates rumors from nonrumors. This characteristic is combined with the global element specifying forwarding behavior to increase the effectiveness of word detection. Experimental findings demonstrate that the F 1 value of the suggested method on the Weibo dataset may reach 95%, with a unique detection effect compared to other mainstream detection methods.

关键词： Text processing Fake news Social networking (online) Social Internet of Things Information exchange Data acquisition Electronic messaging Text analysis Accuracy Encoding Text detection detection algorithms Semantics

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Research on Power Information Network Attack detection Method Combining Multiple Kalman Filter algorithms

Research on Power Information Network Attack Detection Metho...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Zheng Hu Yong Tang Xiang Gou Ganzi Power Supply Company State Grid Sichuan Province Electric Power Company Ganzi China Sichuan Shuneng Dianke Energy Technology Co. Ltd. Chengdu China IT Department Sichuan Shuneng Dianke Energy Technology Co. Ltd. Chengdu China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

In order to further improve the attack detection level of power information network, this paper proposes an attack detection method based on improved cubature Kalman filter (ICKF) to detect false data injection attacks. Among them, the cubature Kalman filter algorithm (CKF) is selected as the basic detection algorithm, and it is improved to further improve its detection performance. Simulation results show that compared with other state estimation algorithms, ICKF can estimate the state of power information system more accurately, and its average absolute error is only 2.0124%. In addition, the attack detection method based on ICKF can effectively detect the false data injection attacks. Therefore, the designed detection method based on ICKF can effectively detect false data injection attacks, which further ensures the security of power information, and has certain practical application value.

关键词： Simulation Filtering algorithms Information filters Kalman filters Security State estimation detection algorithms

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Asymptotic Performance Limitations in Cyberattack detection

IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS

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IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS 2023年 4卷 336-346页

作者： Toker, Onur Florida Polytech Univ Elect & Comp Engn Lakeland FL 33805 USA

In this paper, we consider the difficulty of cyberattack detection with $d$ sensors and $n$ observations, and derive performance bounds that are valid independent of the attack detection algorithm used. In other words, regardless of whether it is an artificial intelligence (AI) or sensor fusion based approach or it is derived using a completely new innovative idea, a cyberattack detector using multiple observations does have certain fundamental performance bounds that are independent of the algorithm used. Cyberattacks introduce different forms of anomalies that may be small or large, and given enough measured data, even tiny anomalies will become more noticeable and cyberattack detection problem will be easier provided that a carefully designed attack detection algorithm is used. For example, False Data Injection (FDI) attacks with small injected error may be harder to detect, but such attacks can cause major failures if continued over a long time period. A natural question to ask is to what degree the cyberattack detection problem becomes easier if more and more measurements acquired over a long time period are used for threat assessment, and the risk level reduction achieved for each new observation. For a cyberattack detector, the false alarm rate is the probability of triggering an alarm when there is no cyberattack, and the probability of miss is the probability of not detecting a cyberattack. The risk level of a cyberattack detector is defined as the sum of the probability of false alarm and the probability of miss. By using the notion of Hellinger distance and total variation norm between probability distributions, we derive upper and lower bounds for the minimum possible (achievable) risk level under multiple measurements, and study asymptotic properties of such bounds. These performance bounds are valid regardless of the cyberattack detection algorithm selection;they imply certain fundamental performance limits in cyberattack detection applications with gi

关键词： Cyberattack Detectors Time measurement Probability distribution Artificial intelligence Sensor fusion detection algorithms Cyberattack detection with multiple observations performance bounds Hellinger distance

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Developer Cooperation Relationship and Attribute Similarity Based Community detection in Software Ecosystem

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Chinese Journal of Electronics 2023年第1期32卷 39-50页

作者： SHEN Xin DU Junwei GONG Dunwei YAO Xiangjuan School of Information and Control Engineering China University of Mining and Technology School of Information Science and Technology Qingdao University of Science and Technology School of Mathematics China University of Mining and Technology

A software ecosystem(SECO) can be described as a special complex network. Previous complex networks in an SECO have limitations in accurately reflecting the similarity between each pair of nodes. The community structure is critical towards understanding the network topology and function. Many scholars tend to adopt evolutionary optimization methods for community detection. The information adopted in previous optimization models for community detection is incomprehensive and cannot be directly applied to the problem of community detection in an SECO. Based on this, a complex network in SECOs is first built. In the network, the cooperation intensity between developers is accurately calculated, and the attribute contained by each developer is considered. A multi-objective optimization model is formulated. A community detection algorithm based on NSGA-II is employed to solve the above model. Experimental results demonstrate that the proposed method of calculating the developer cooperation intensity and our model are advantageous.

关键词： Biological system modeling Ecosystems Optimization methods Complex networks Software detection algorithms

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Contrastive Self-Supervised Learning for Globally Distributed Landslide detection

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IEEE ACCESS 2024年 12卷 118453-118466页

作者： Ghorbanzadeh, Omid Shahabi, Hejar Piralilou, Sepideh Tavakkoli Crivellari, Alessandro La Rosa, Laura Elena Cue Atzberger, Clement Li, Jonathan Ghamisi, Pedram Univ Nat Resources & Life Sci BOKU Inst Geomat A-1190 Vienna Austria INRS Ctr Eau Terre Environm Quebec City PQ G1K 9A9 Canada IARAI A-1030 Vienna Austria Natl Taiwan Univ Dept Geog Taipei 106319 Taiwan Wageningen Univ & Res Lab Geoinformat Sci & Remote Sensing NL-6708 PB Wageningen Netherlands Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Helmholtz Inst Freiberg Resource Technol Helmholtz Zent Dresden Rossendorf Freiberg Germany

The Remote Sensing (RS) field continuously grapples with the challenge of transforming satellite data into actionable information. This ongoing issue results in an ever-growing accumulation of unlabeled data, complicating interpretation efforts. The situation becomes even more challenging when satellite data must be used immediately to identify the effects of a natural hazard. Self-supervised learning (SSL) offers a promising approach for learning image representations without labeled data. Once trained, an SSL model can address various tasks with significantly reduced requirements for labeled data. Despite advancements in SSL models, particularly those using contrastive learning methods like MoCo, SimCLR, and SwAV, their potential remains largely unexplored in the context of instance segmentation and semantic segmentation of satellite imagery. This study integrates SwAV within an auto-encoder framework to detect landslides using deca-metric resolution multi-spectral images from the globally-distributed large-scale landslide4sense (L4S) 2022 benchmark dataset, employing only 1% and 10% of the labeled data. Our proposed SSL auto-encoder model features two modules: SwAV, which assigns features to prototype vectors to generate encoder codes, and ResNets, serving as the decoder for the downstream task. With just 1% of labeled data, our SSL model performs comparably to ten state-of-the-art deep learning segmentation models that utilize 100% of the labeled data in a fully supervised manner. With 10% of labeled data, our SSL model outperforms all ten fully supervised counterparts trained with 100% of the labeled data.

关键词： Terrain factors Feature extraction Data models Codes Decoding Benchmark testing Deep learning Landslides detection algorithms Remote sensing Hazardous areas landslide detection multispectral imagery natural hazard remote sensing

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Box Decoding: A Low-Complexity Algorithm for MIMO detection 37

Box Decoding: A Low-Complexity Algorithm for MIMO Detection

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2024 International Workshop on Signal Processing Systems

作者： Qureshi, Sheikh Faizan Damjancevic, Stefan A. Matus, Emil Utyansky, Dmitry van der Wolf, Pieter Fettweis, Gerhard P. Tech Univ Dresden Vodafone Chair Dresden Germany Synopsys Inc Sunnyvale CA USA Synopsys Inc Eindhoven Netherlands Barkhausen Inst Dresden Germany Tech Univ Dresden CeTI Dresden Germany

ISBN: (纸本)9798350373769;9798350373752

The selection of an algorithm for MIMO detection often involves a trade-off between detection performance and the efficiency of the implementation. Tree-based algorithms, such as Sphere Decoding (SD), offer superior performance compared to linear schemes like MMSE and ZF and non-linear schemes like successive interference cancellation (SIC). In the SD approach, the algorithm must select the most suitable branch at each tree node. Due to this conditional behavior, the number of computational steps is data-dependent and is only determined at run-time. Moreover, due to the sequential nature of the tree traversal, it is not well suited for parallel implementation on a modern vector DSP. Approaches like K-Best exist to address this challenge, but they remain computationally intense, especially for higher-order QAM modulations. We study the use case of the 5G-based IoT standard NR-Redcap, which requires a low-complexity baseband kernel implementation for a cost-effective modem design. We propose a novel MIMO detection scheme called Box Decoding, whose computational complexity does not depend on the QAM modulation order and offers a significant reduction in complexity compared to K-Best for our chosen use case. Furthermore, the algorithm doesn't necessitate the expensive sorting of candidate symbols, making it particularly appealing for implementation on contemporary vector Digital Signal Processors (DSPs). Testing on a 64QAM modulation system for Redcap indicates negligible MIMO detection performance degradation compared to K-Best, with significantly lower complexity. For our use case, the Box Decoding algorithm proves to be a cost-effective implementation scheme for MIMO detection in 5G IoT modems, offering a 2dB SNR gain over MMSE-based SIC for an extra 23% of clock cycles consumed for this task on a 512-bit vector DSP.

关键词： Multi-antenna systems 5G new radio (NR) Redcap detection algorithms Vector DSP mobile communications.

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RTM-UAVDet: A Real-Time Multimodal UAV Detector

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第1期61卷 473-489页

作者： Wang, Guorui Jiang, Qian Jin, Xin Wozniak, Michal Wang, Puming Yao, Shaowen Yao Yunnan Univ Kunming 650031 Peoples R China Wroclaw Univ Sci & Technol PL-50370 Wroclaw Poland

As unmanned aerial vehicles (UAVs) become more prevalent, the need for accurate and reliable detection algorithms becomes increasingly important. UAV detection algorithms are essential for maintaining security, managing airspace, safeguarding privacy, responding to emergencies, protecting critical infrastructure, ensuring regulatory compliance, and enabling responsible UAV usage across various domains. The multiscene images and videos based on visible light (RGB) and thermal infrared (TIR) remote sensing of UAVs are considered crucial data sources for public safety. However, as imaging techniques move from single to multimodality, using object detection algorithms to detect RGB and TIR modalities simultaneously in real -time is a significant challenge. This study proposes a multimodal UAV object detection framework for static images. Based on convolutional neural network architecture, the multimodal dynamic convolution model extracts features from ground TIR images and videos of forward-looking infrared and visible light cameras. Our approach circumvents the constraints of conventional multimodal fusion methods by effectively detecting UAVs within single-frame, multimodal static images without requiring image alignment. The results show that the average precision 0.5:0.95 (AP(0.5:0.95)) of the UAV instance in the validation task is 66.1%. Furthermore, RTM-UAVDet operates at a speed of 72.4 frames per second, fully satisfying the requirements for real-time processing and effectively recognizing UAVs larger than 48 (6 x 8) pixels.

关键词： Autonomous aerial vehicles Feature extraction Accuracy detection algorithms Real-time systems Radar detection Aerospace and electronic systems Multimodal object detection multimodal image processing real-time image processing unmanned aerial vehicle (UAV) detection

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Design of Non-Coherent RIS-Empowered DCSK With Two-Level Nested Index Modulation

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第4期24卷 3044-3058页

作者： Cai, Xiangming Huang, Chongwen Chen, Pingping Basar, Ertugrul Yuen, Chau Fuzhou Univ Sch Phys & Informat Engn Fuzhou 350108 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Multimodal Commun Networks & Hangzhou 310027 Peoples R China Koc Univ Dept Elect & Elect Engn Commun Res & Innovat Lab CoreLab TR-34450 Istanbul Turkiye Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Non-coherent chaotic communication has gained increasing attention as it provides an efficient solution for reliable communications without requiring channel state information. In this paper, we propose a non-coherent reconfigurable intelligent surface (RIS)-empowered differential chaos shift keying scheme with two-level nested index modulation (RIS-DCSK-TLNIM). In the proposed RIS-DCSK-TLNIM scheme, the reference index and information index are nested to form two-level nested index modulation. This design enhances both the spectral efficiency and bit error rate (BER) performance of RIS-DCSK-TLNIM, albeit at the expense of slightly increased complexity. Furthermore, we propose a joint detection algorithm to recover the information bits transmitted via the reference index, information index, and two distinct-mode signals. We then extend RIS-DCSK-TLNIM into an enhanced system to achieve higher spectral efficiency. Subsequently, we analyze the BER performance, spectral efficiency, and system complexity of RIS-DCSK-TLNIM, and compare these metrics with those of benchmark systems. Comparison results demonstrate that the proposed RIS-DCSK-TLNIM, when configured with a small number of time slots, can achieve more than twice the spectral efficiency and at least a 6 dB gain in BER performance compared to benchmark systems.

关键词： Indexes Modulation Chaotic communication Wireless communication Bit error rate Symbols Receiving antennas Electronic mail detection algorithms Complexity theory Non-coherent chaotic communication differential chaos shift keying (DCSK) reconfigurable intelligent surface (RIS) two-level nested index modulation (TLNIM)

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A Lightweight Self Attention Based Multi-Task Deep Learning Model for Industrial Solar Panel and Environmental Monitoring

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1502-1513页

作者： Gangopadhyay, Tuhinangshu Meena, Tanushree Pal, Debojyoti Roy, Sudipta Jio Inst Artificial Intelligence & Data Sci Navi Mumbai 410206 India

Environmental monitoring has become a serious topic of discussion and is gaining mass attention. The reason is the severe consequences of environmental depletion, which has led to circumstances like climate change, rise in floods and droughts, changed rainfall patterns, etc. So, various measures are being taken to protect the environment, like shifting to renewable and pollution-free energy alternatives, like solar energy, and handling the after-effects of disasters, like flood management and oil spill accident management. However, their identification still remains a huge challenge, which is laborious and extensive. Thus, this work proposed a lightweight and efficient segmentation model, SA U-Net++, for the automatic identification of solar panels and their associated defects, flood affected-areas and oil spill accident regions. The model's novel blend of level-wise self-attention modules is embedded with the revised bridge connections and the dropouts. It has helped in better efficient global context understanding and feature extraction from the inputs, besides maintaining the integrity of the training process and avoiding some major learning and run-time issues, like overfitting and memory exhaustion. Our detailed experiments demonstrate that the proposed model outperforms state-of-the-art models. The results confirm its high generalizability, cost-effectiveness, and robustness.

关键词： Environmental monitoring Solar panels Solar energy Renewable energy sources Pollution measurement Climate change detection algorithms Weather forecasting Modeling Floods Energy management Disaster management Sustainable development Flood segmentation oil spill segmentation self-attention semantic segmentation solar panel segmentation and defect identification

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Algorithm for Mining Maximal Balanced Bicliques Using Formal Concept Analysis

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IEEE ACCESS 2025年 13卷 35113-35123页

作者： Sadriddinov, Ilkhomjon Peng, Sony Siet, Sophort Kim, Dae-Young Park, Doo-Soon Yi, Gangman Soonchunhyang Univ Dept Software Convergence Asan 31538 South Korea Soonchunhyang Univ Dept Comp Sci Engn Asan 31538 South Korea Dongguk Univ Dept AI Software Convergence Seoul 04260 South Korea

One of the most fundamental models for cohesive subgraph mining in network analysis is that which involves the use of cliques. In bipartite graph analysis, the detection of maximal balanced bicliques (MBB) is an important problem with numerous applications, including VLSI design, protein interactions, and social networks. However, MBB detection is difficult, complex, and time-consuming. In the current paper, to address these disadvantages, we propose a new algorithm for detecting MBB using formal concept analysis (FCA) on bipartite graphs. We applied an algorithm to compute formal concepts from the formal context, which is an alternative way of representing a bipartite graph. We proved that the MBB problem is equivalent to the semi-equiconcept enumeration problem in the formal context. Therefore, the semi-equiconcept mining algorithm was applied to the MBB enumeration problem. However, since the existing FCA algorithm cannot be directly applied to mine all MBBs, the FCA algorithm was modified in its entirety. Thorough asymptotic analysis was performed on the proposed algorithm. Experiments were also conducted on various real-world bipartite graphs to which the proposed algorithm was applied, and our results were found to be significantly better than those obtained by the preexisting algorithm.

关键词： Bipartite graph Formal concept analysis Lattices Web pages Time complexity Social networking (online) detection algorithms Data mining formal concept analysis maximal balanced biclique

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