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arXiv 2024年

作者： Faramarzi, Sajad Zarini, Hosein Javadi, Sepideh Mili, Mohammad Robat Zhang, Rui Karagiannidis, George K. Al-Dhahir, Naofal The School of Electrical Engineering Iran University of Science & Technology Tehran Iran The Dept. of Computer Engineering Sharif University of Technology Tehran Iran Tehran Iran School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece Lebanon The Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

In this paper, we consider the downlink transmission of a multi-antenna base station (BS) supported by an active simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS) to serve single-antenna users via simultaneous wireless information and power transfer (SWIPT). In this context, we formulate an energy efficiency maximisation problem that jointly optimises the gain, element selection and phase shift matrices of the active STAR-RIS, the transmit beamforming of the BS and the power splitting ratio of the users. With respect to the highly coupled and non-convex form of this problem, an alternating optimisation solution approach is proposed, using tools from convex optimisation and reinforcement learning. Specifically, semi-definite relaxation (SDR), difference of concave functions (DC), and fractional programming techniques are employed to transform the non-convex optimisation problem into a convex form for optimising the BS beamforming vector and the power splitting ratio of the SWIPT. Then, by integrating meta-learning with the modified deep deterministic policy gradient (DDPG) and soft actor-critical (SAC) methods, a combinatorial reinforcement learning network is developed to optimise the element selection, gain and phase shift matrices of the active STAR-RIS. Our simulations show the effectiveness of the proposed resource allocation scheme. Furthermore, our proposed active STAR-RIS-based SWIPT system outperforms its passive counterpart by 57% on average. © 2024, CC BY.

关键词： Convex optimization

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LIMIT LAWS FOR GROMOV-WASSERSTEIN ALIGNMENT WITH APPLICATIONS TO TESTING GRAPH ISOMORPHISMS

arXiv

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arXiv 2024年

作者： Rioux, Gabriel Goldfeld, Ziv Kato, Kengo Center for Applied Mathematics Cornell University United States School of Electrical and Computer Engineering Cornell University United States Department of Statistics and Data Science Cornell University United States

The Gromov-Wasserstein (GW) distance enables comparing metric measure spaces based solely on their internal structure, making it invariant to isomorphic transformations. This property is particularly useful for comparing datasets that naturally admit isomorphic representations, such as unlabelled graphs or objects embedded in space. However, apart from the recently derived empirical convergence rates for the quadratic GW problem, a statistical theory for valid estimation and inference remains largely obscure. Pushing the frontier of statistical GW further, this work derives the first limit laws for the empirical GW distance across several settings of interest: (i) discrete, (ii) semi-discrete, and (iii) general distributions under moment constraints under the entropically regularized GW distance. The derivations rely on a novel stability analysis of the GW functional in the marginal distributions. The limit laws then follow by an adaptation of the functional delta method. As asymptotic normality fails to hold in most cases, we establish the consistency of an efficient estimation procedure for the limiting law in the discrete case, bypassing the need for computationally intensive resampling methods. We apply these findings to testing whether collections of unlabelled graphs are generated from distributions that are isomorphic to each other. Copyright © 2024, The Authors. All rights reserved.

关键词： Delta functions

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Cybersecurity Threat Detection using Machine Learning on Network Traffic

Cybersecurity Threat Detection using Machine Learning on Net...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： R. Rithi N. Nithya A. Akila R. Saravanakumar M. Vargheese S. Jeya Lakshmi Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Web phishing is a prevalent online attack in which victims are tricked into revealing their personal information by visiting fraudulent websites. Phishers often use URL shortening tools to disguise malicious links, making it easier to steal sensitive data such as usernames, passwords, and banking details. These phishing websites are designed to mimic legitimate ones, making detection more challenging. As phishing techniques continue to evolve rapidly alongside technological advancements, the need for effective anti-phishing systems has become increasingly crucial. Machine learning has emerged as a powerful tool for recognizing and mitigating phishing scams. This article provides a comprehensive overview of machine learning techniques for phishing detection and their effectiveness in combating online threats.

关键词： Uniform resource locators Accuracy Reviews Phishing Scalability Machine learning Telecommunication traffic Passwords Threat assessment Real-time systems

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Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture

Image-Based Soil Health Analysis Using Deep Learning for Pre...

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International Conference on Communication and Electronics Systems (ICCES)

作者： V. S. Saranya Saikiran Mangali K Srinija Galeiah Medabalimi Meena Devi R Venkata Ramana N Ajanthaa Lakkshmanan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Department of CSBS B V RAJU INSTITUTE OF TECHNOLOGY Medak Telangana Department: Data science MLRIT DUNDIGAL Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Bhimavaram Andhra Pradesh India Department of electrical and electronics engineering KCG college of technology Chennai Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management.

关键词： Precision agriculture Deep learning Moisture Soil Thermal sensors Nutrients Feature extraction Real-time systems Convolutional neural networks Monitoring

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Adaptive finite-time backstepping control tracker for quadrotor UAV with model uncertainty and external disturbance

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Aerospace science and Technology 2023年第1期133卷

作者： Wang, Jianghua Alattas, Khalid A. Bouteraa, Yassine Mofid, Omid Mobayen, Saleh Institute of Plasma Physics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia University of Sfax Sfax3038 Tunisia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou640301 Taiwan

In this study, for the purpose of finite-time tracking control of quad-rotor Unmanned Aerial Vehicle (UAV) system under modeling uncertainties and external disturbances, an adaptive command-filtered backstepping sliding mode control scheme is presented. At first, a state-space model of quad-rotor's system is considered in the existence of bounded model uncertainties and exterior perturbations. Then, a command-filter according to the virtual control input is planned to reduce the computing complications. Subsequently, the output of command filter is considered instead of high-order derivative of virtual input. Besides, in order to remove the differential signals which are obtained during design of command-filter, the compensation error is used. Afterward, the sliding surfaces are designed according to tracking errors which are defined between actual states of the quad-rotor, desired values and compensation error system. Furthermore, backstepping control procedure combined with Lyapunov stability theory is used for finite-time convergence of switching surface. Besides, the adaptive control strategy is applied to estimate the upper bound of uncertainty and disturbance which is unknown in advance. At last, in order to validate the high efficacy of the designed control scheme, simulation results based on the actual parameters of quadrotor are provided. In addition, the comparison outcomes are provided to show proficiency and validity of the recommended technique compared with the existing method. © 2022 Elsevier Masson SAS

关键词： Sliding mode control

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User preference Based Personalized Collaborative Filtering for Large Scale Recommender Systems

User preference Based Personalized Collaborative Filtering f...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Priya V Keerthika J L.R Sujithra Department of Artificial Intelligence and Data Science Dr NGP Institute of Technology Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore India

The Indian market for internet shopping has already been penetrated by E-Commerce. People are increasingly attracted to shop online, which has altered the market landscape. Amazon, Snap deal, Flipkart, and more companies offer online shopping sites that are gaining traction. As the number of online buyers and dealers grows, smart business approaches are required to deal with the massive amounts of data generated every day. Recommendation systems play a crucial role in sifting this data and giving consumers with relevant information. Various strategies for recommender systems have been used, such as Collaborative Filtering, Content-based, and Demographic, but these techniques have a number of flaws that make them ineffective. Among these collaborative filtering approach is widely popular because of its simplicity and accuracy. But still these systems can be improved in their accuracy when used for multiple domains. As a result, more distinguishing traits must be identified in order to optimize these procedures. This can be accomplished by combining the benefits of multiple strategies in a hybrid approach. This paper describes a hybrid approach that identifies the users with shared preferences (semantic users) and their dynamic interests with parallel training to improve recommendation efficiency. Results were compared with other state of art techniques and the accuracy of our system is found to be improved by 2% and seems to give promising results for multiple domains.

关键词： Training Filtering Collaborative filtering Semantics Collaboration Companies Filtering algorithms

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Efficient Video Summarization with Hydra Attentive Vision Transformer

Efficient Video Summarization with Hydra Attentive Vision Tr...

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Frontiers of Information Technology (FIT)

作者： Muhammad Shafqat Ali Muhammad Azhar Saba Masood Bumshik Lee Tanzeela Iqbal Adeen Amjad Department of Computer Science Comsats University Islamabad Sahiwal Pakistan Department of Applied Data Science Hong Kong Shue Yan University Hong Kong Department of Information and Communication Engineering Chosun University Gwangju Republic of Korea

A video summary’s objective is to produce succinct and condensed synopses that accurately depict the content of the original video while sacrificing none of its vital features. Effective deep summarization models have arisen in the field of video summarizing, made feasible by the advancement of gated recursive unit (GRU) and long and short-term memory (LSTM) technologies. However, if the video is very long, GRU and LSTM models are unlikely to capture long-term dependencies as well as they could otherwise. In recent years, significant progress in the field of supervised video summarization has been accomplished through the use of techniques involving sequence-to-sequence learning. However, it is important to remember that traditional recurrent neural networks (RNNs) have some limitations when it comes to modeling long sequences and that using transformers to represent sequences necessitates a large number of input parameters, resulting in increased computational complexity. We present a new video summarizing methodology that addresses the aforementioned issues by utilizing a Hydra Attention-based Vision Transformer framework. The suggested method captures long-term dependencies and extracts significant characteristics from video sequences well. The proposed architecture improves the accuracy and reduces the processing time of video summaries by using the capabilities of Hydra Attention and transformer-based sequence modeling. Our solution outperforms state-of-the-art approaches in terms of performance and computing economy in empirical evaluations of the SumMe and TVSum datasets.

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ImagineNet: Target Speaker Extraction with Intermittent Visual Cue Through Embedding Inpainting

ImagineNet: Target Speaker Extraction with Intermittent Visu...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zexu Pan Wupeng Wang Marvin Borsdorf Haizhou Li Institute of Data Science National University of Singapore (NUS) Singapore Department of Electrical and Computer Engineering NUS Singapore Machine Listening Lab University of Bremen Germany Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the auxiliary reference. The use of visual cue is not only feasible, but also effective due to its noise robustness, and becoming popular. However, it is difficult to guarantee that such parallel visual cue is always available in real-world applications where visual occlusion or intermittent communication can occur. In this paper, we study the audio-visual speaker extraction algorithms with intermittent visual cue. We propose a joint speaker extraction and visual embedding inpainting framework to explore the mutual benefits. To encourage the interaction between the two tasks, they are performed alternately with an interlacing structure and optimized jointly. We also propose two types of visual inpainting losses and study our proposed method with two types of popularly used visual embeddings. The experimental results show that we outperform the baseline in terms of signal quality, perceptual quality, and intelligibility.

关键词： Visualization Lips Design methodology Signal processing algorithms Signal processing Noise robustness Synchronization

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Transition between distribution patterns in human dynamics with high activity

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Physical Review Research 2023年第3期5卷 033179-033179页

作者： Ming Zhao Jiani Chen Jiasheng Lao Yanqing Hu Jiarong Xie College of Physical Science and Technology Guangxi Normal University Guilin 541004 People's Republic of China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen 518055 People's Republic of China School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 People's Republic of China

Complex interactions among a large number of individuals lead to multiple patterns of collective human behavior. However, the theoretical prediction of pattern transitions has not been empirically confirmed. This is because in previous empirical studies, different patterns were observed in different systems, and the coexistence of multiple patterns in the same system was rarely found. By investigating nearly 10 million messages in 252 QQ groups, we find rich distribution patterns of interevent time for human collective behavior, including the transitions between bimodal distribution, double-power-law distribution, and single-power-law distribution. The model developed in this paper suggests that human physiological rhythms and the high collective activity play key roles in the presentation of the single-power-law distribution. These results enhance the empirical research in the field of human dynamics, and are helpful for understanding many complex socioeconomic phenomena.

关键词： Collective behavior in networks Stochastic processes Social dynamics Time series analysis

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A Tiered Quadruplet Network with Patient-Specific Mining and Dynamic Margin for Improved ugly Duckling Lesion Classification

A Tiered Quadruplet Network with Patient-Specific Mining and...

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IEEE International Symposium on Biomedical Imaging

作者： Nathasha Naranpanawa H. Peter Soyer Adam Mothershaw Gayan K. Kulatilleke Zongyuan Ge Brigid Betz-Stablein Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Australia Dermatology Research Centre Frazer Institute The University of Queensland Australia Monash Medical AI Monash University Australia Department of Data Science and AI Faculty of IT Monash University Australia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The concept of the ‘ugly duckling sign’ is a criterion used in diagnosing cutaneous melanoma by identifying visually distinct skin lesions. However, variations in pigmented lesions among patients make it challenging to visually distinguish ugly ducklings. Hence, we propose DMT-Quadruplet -a deep metric learning network to learn lesion features at two tiers - patient-level and lesion-level. We introduce a patient-specific quadruplet mining approach within a tiered quadruplet network, to capture contextual information both globally and locally. We further incorporate a dynamic margin within the patient-specific mining to allow more useful quadruplets to be mined within individuals. Comprehensive experiments show that our proposed method outperforms traditional classifiers, achieving significantly higher sensitivity than baseline models, successfully classifying ugly duckling lesions both in patient-specific and patient-agnostic contexts.

关键词： Manifolds Learning systems Sensitivity Pipelines Melanoma Feature extraction Extraterrestrial measurements

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