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arXiv 2021年

作者： Wang, Fen Cheung, Gene Li, Taihao Du, Ying Ruan, Yu-Ping Department of Artificial Intelligence Zhejiang Lab Hangzhou China Department of Electrical Engineering & Computer Science York University Canada

Sensor placement for linear inverse problems is the selection of locations to assign sensors so that the entire physical signal can be well recovered from partial observations. In this paper, we propose a fast sampling algorithm to place sensors. Specifically, assuming that the field signal f is represented by a linear model f = Φg, it can be estimated from partial noisy samples via an unbiased least-squares (LS) method, whose expected mean square error (MSE) depends on chosen samples. First, we formulate an approximate MSE problem, and then prove it is equivalent to a problem related to a principle submatrix of ΦΦ indexed by sample set. To solve the formulated problem, we devise a fast greedy algorithm with simple matrix-vector multiplications, leveraging a matrix inverse formula. To further reduce complexity, we reuse results in the previous greedy step for warm start, so that candidates can be evaluated via lightweight vector-vector multiplications. Extensive experiments show that our proposed sensor placement method achieved the lowest sensor sampling time and the best performance compared to state-of-the-art schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Mean square error

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Repetitive nonoverlapping sequential pattern mining

arXiv

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arXiv 2023年

作者： Geng, Meng Wu, Youxi Li, Yan Liu, Jing Fournier-Viger, Philippe Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Shenzhen University Shenzhen China Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Research Center for Knowledge Engineering The Zhejiang Lab Hangzhou311121 China

Sequential pattern mining (SPM) is an important branch of knowledge discovery that aims to mine frequent sub-sequences (patterns) in a sequential database. Various SPM methods have been investigated, and most of them are classical SPM methods, since these methods only consider whether or not a given pattern occurs within a sequence. Classical SPM can only find the common features of sequences, but it ignores the number of occurrences of the pattern in each sequence, i.e., the degree of interest of specific users. To solve this problem, this paper addresses the issue of repetitive nonoverlapping sequential pattern (RNP) mining and proposes the RNP-Miner algorithm. To reduce the number of candidate patterns, RNP-Miner adopts an itemset pattern join strategy. To improve the efficiency of support calculation, RNP-Miner utilizes the candidate support calculation algorithm based on the position dictionary. To validate the performance of RNP-Miner, 10 competitive algorithms and 20 sequence databases were selected. The experimental results verify that RNP-Miner outperforms the other algorithms, and using RNPs can achieve a better clustering performance than raw data and classical frequent patterns. All the algorithms were developed using the PyCharm environment and can be downloaded from https://***/wuc567/Pattern-Mining/tree/master/RNPMiner. Copyright © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Statistical estimation in the spiked tensor model via the quantum approximate optimization algorithm 24

Statistical estimation in the spiked tensor model via the qu...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Leo Zhou Joao Basso Song Mei Walter Burke Institute for Theoretical Physics and the IQIM California Institute of Technology Pasadena CA and Electrical and Computer Engineering Department University of California Los Angeles CA Department of Mathematics University of California Berkeley CA and Quantum Artificial Intelligence Lab (QuAIL) NASA Ames Research Center Moffett Field CA and Research Institute for Advanced Computer Science (RIACS) USRA Mountain View CA Department of Statistics and Department of EECS University of California Berkeley CA

ISBN: (纸本)9798331314385

The quantum approximate optimization algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization that has been a promising avenue for near-term quantum advantage. In this paper, we analyze the performance of the QAOA on the spiked tensor model, a statistical estimation problem that exhibits a large computational-statistical gap classically. We prove that the weak recovery threshold of 1-step QAOA matches that of 1-step tensor power iteration. Additional heuristic calculations suggest that the weak recovery threshold of p-step QAOA matches that of p-step tensor power iteration when p is a fixed constant. This further implies that multi-step QAOA with tensor unfolding could achieve, but not surpass, the asymptotic classical computation threshold Θ(n(q-2)/4) for spiked q-tensors. Meanwhile, we characterize the asymptotic overlap distribution for p-step QAOA, discovering an intriguing sine-Gaussian law verified through simulations. For some p and q, the QAOA has an effective recovery threshold that is a constant factor better than tensor power iteration. Of independent interest, our proof techniques employ the Fourier transform to handle difficult combinatorial sums, a novel approach differing from prior QAOA analyses on spin-glass models without planted structure.

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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Explainable Artifacts for Synthetic Western Blot Source Attribution

Explainable Artifacts for Synthetic Western Blot Source Attr...

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IEEE International Workshop on Information Forensics and Security (WIFS)

作者： João P. Cardenuto Sara Mandelli Daniel Moreira Paolo Bestagini Edward Delp Anderson Rocha Artificial Intelligence Lab. Recod.ai Institute of Computing Universidade Estadual de Campinas (UNICAMP) Campinas SP Brazil Department of Computer Science Loyola University Chicago Chicago IL USA Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9798350364422

ISBN: (纸本)9798350364439

Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challenge even for expert scientists habituated to working with such content. When exploited by organizations known as paper mills, which systematically generate fraudulent articles, these technologies can significantly contribute to the spread of misinformation about ungrounded science, potentially undermining trust in scientific research. While previous studies have explored black-box solutions, such as Convolutional Neural Networks, for identifying synthetic content, only some have addressed the challenge of generalizing across different models and providing insight into the artifacts in synthetic images that inform the detection process. This study aims to identify explainable artifacts generated by state-of-the-art generative models (e.g., Generative Adversarial Networks and Diffusion Models) and leverage them for open-set identification and source attribution (i.e., pointing to the model that created the image).

关键词： Forensics Conferences Paper mills Closed box Organizations Generative adversarial networks Diffusion models Security Convolutional neural networks Fake news

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BiLSTM-Autoencoder Architecture for Stance Prediction

BiLSTM-Autoencoder Architecture for Stance Prediction

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2020 International Conference on Data science and engineering, ICDSE 2020

作者： Padnekar, S Meena Kumar, G Santhosh Deepak, P. Cochin University of Science and Technology Artificial Intelligence and Computer Vision Lab Department of Computer Science Kochi India Queen's University School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom

ISBN: (纸本)9781728189192

The recent surge in the abundance of fake news appearing on social media and news websites poses a potential threat to high-quality journalism. Misinformation hurts people, society, science, and democracy. This reason has led many researchers to develop techniques to identify fake news. In this paper, we discuss a stance prediction technique using the Deep Learning approach, which can be used as a factor to determine the authenticity of news articles. The Fake News Stance Prediction is the process of automatically classifying the stance of a news article towards a target into one of the following classes: Agree, Disagree, Discuss, Unrelated. The stance prediction task's input is the news articles containing a pair: a headline as the target and a body as a claim. This paper proposes a deep learning architecture using Bi-directional Long Short Term Memory and Autoencoder for stance prediction. We illustrate, through empirical studies, that the method is reasonably accurate at predicting stance, achieving a classification accuracy as high as 94%. The proposed stance detection method would be useful for assessing the credibility of news articles. © 2020 IEEE.

关键词： Forecasting

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MCLA Task Offloading Framework for 5G-NR-V2X Based Heterogeneous VECNs

TechRxiv

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TechRxiv 2023年

作者： Mirza, Muhammad Ayzed Yu, Junsheng Raza, Salman Ahmed, Manzoor Asif, Muhammad Irshad, Azeem Kumar, Neeraj BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Physics and Electronic Information Anhui Normal University Wuhu241003 China School of Intelligence and Digital Engineering Luoyang Vocational College of Science and Technology Luoyang471000 China Department of Computer Science National Textile University Faisalabad Pakistan School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Faculty of Computer Science Asghar Mall College Govt. of the Punjab Rawalpindi Pakistan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Engineering Graphics Era University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India Department of Electrical and Computer Engineering Lebanese American University Beirut Lebanon Faculty of Computing and IT King Abdulaziz University Jeddah Saudi Arabia

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs. © 2023, CC BY.

关键词： Delay-sensitive applications

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The Age of Synthetic Realities: Challenges and Opportunities

arXiv

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arXiv 2023年

作者： Cardenuto, João Phillipe Yang, Jing Padilha, Rafael Wan, Renjie Moreira, Daniel Li, Haoliang Wang, Shiqi Andaló, Fernanda Marcel, Sébastien Rocha, Anderson Artificial Intelligence Lab. Recod.ai Institute of Computing University of Campinas SP Campinas Brazil Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computer Science Loyola University Chicago ChicagoIL United States Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Computer Science City University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland University of Lausanne Lausanne Switzerland

Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive. In this paper, we delve into the concept of synthetic realities and their implications for Digital Forensics and society at large within the rapidly advancing field of AI. We highlight the crucial need for the development of forensic techniques capable of identifying harmful synthetic creations and distinguishing them from reality. This is especially important in scenarios involving the creation and dissemination of fake news, disinformation, and misinformation. Our focus extends to various forms of media, such as images, videos, audio, and text, as we examine how synthetic realities are crafted and explore approaches to detecting these malicious creations. Additionally, we shed light on the key research challenges that lie ahead in this area. This study is of paramount importance due to the rapid progress of AI generative techniques and their impact on the fundamental principles of Forensic science. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital forensics

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Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for distributed Traffic Data Recovery

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IEEE Transactions on Network science and engineering 2025年

作者： Deng, Lei Liu, Xiao-Yang Zheng, Haifeng Feng, Xinxin Zhu, Ming Tsang, Danny H. K. Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China Internet of Things Thrust Guangdong Guangzhou511400 China Columbia University Department of Electrical Engineering NY United States Chinese Academy of Sciences Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Hong Kong

In intelligent transportation systems, deep learning is a widely adopted technique for traffic data recovery. In city-wide traffic data recovery tasks, traditional centralized deep-learning-model training strategies become inapplicable because of the expensive storage costs for large-scale traffic datasets. In this scenario, edge computing emerges as a natural choice, allowing decentralized data storage and distributed training on edge nodes. However, there is still a challenge: distributed training on edge nodes suffers from high communication costs for parameter transmission. In this paper, we propose a communication-efficient Graph-Tensor Fast Iterative Shrinkage-Thresholding Algorithm-based neural Network (GT-FISTA-Net) for distributed traffic data recovery. Firstly, we model the recovery task as a graph-tensor completion problem to better capture the low-rankness of traffic data. A recovery guarantee is also provided to characterize the performance bounds of the proposed scheme in terms of recovery error. Secondly, we propose a distributed graph-tensor completion algorithm and unfold it into a deep neural network called GT-FISTA-Net. GT-FISTA-Net requires small communication costs for distributed model training on edge nodes and thus it is applicable for city-wide traffic data recovery. Extensive experiments on real-world datasets show that the proposed GT-FISTA-Net can also provide excellent recovery accuracy compared with state-of-the-art distributed recovery methods. © 2013 IEEE.

关键词： distributed learning Edge computing Graph-tensor tensor completion traffic data recovery

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Towards more effective and economic sparsely-activated model

arXiv

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arXiv 2021年

作者： Jiang, Hao Zhan, Ke Qu, Jianwei Wu, Yongkang Fei, Zhaoye Zhang, Xinyu Chen, Lei Dou, Zhicheng Qiu, Xipeng Guo, Zikai Lai, Ruofei Wu, Jiawen Hu, Enrui Zhang, Yinxia Jia, Yantao Yu, Fan Cao, Zhao Distributed and Parallel Software Lab Huawei China School of Computer Science Fudan University Department of Computer Science and Engineering Hong Kong University of Science and Technology Gaoling School of Artificial Intelligence Renmin University of China

The sparsely-activated models have achieved great success in natural language processing through large-scale parameters and relatively low computational cost, and gradually become a feasible technique for training and implementing extremely large models. Due to the limit of communication cost, activating multiple experts is hardly affordable during training and inference. Therefore, previous work usually activate just one expert at a time to alleviate additional communication cost. Such routing mechanism limits the upper bound of model performance. In this paper, we first investigate a phenomenon that increasing the number of activated experts can boost the model performance with higher sparse ratio. To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU). Our methods shed light on the training of extremely large sparse models and experiments prove that our models can achieve significant performance gain with great efficiency improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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