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arXiv 2023年

作者： Liang, Jiaming Monteiro, Renato D.C. Zhang, Honghao Goergen Institute for Data Science Department of Computer Science University of Rochester RochesterNY14620 United States School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332-0205 United States

This paper establishes the iteration-complexity of proximal bundle methods for solving hybrid (i.e., a blend of smooth and nonsmooth) weakly convex composite optimization (HWC-CO) problems. This is done in a unified manner by considering a proximal bundle framework (PBF) based on a generic bundle update framework which includes various well-known bundle update schemes. In contrast to hard-to-check stationary conditions (e.g., the Moreau stationarity) used by other methods for solving HWC-CO, PBF uses a stationarity measure which is easily verifiable. © 2023, CC BY.

关键词： Convex optimization

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Intrusion detection based on phishing detection with machine learning

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Measurement: Sensors 2024年 31卷

作者： Jayaraj, R. Pushpalatha, A. Sangeetha, K. Kamaleshwar, T. Udhaya Shree, S. Damodaran, Deepa Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur TN Chennai India M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology TN Coimbatore India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Dr. Rangarajan Dr.Sagunthala R&D Institute of Science and Technology TN Chennai India Department of Computer Science and Engineering Alpha College of Engineering and Technology Puducherry India VITBS Vellore Institute of Technology Chennai Campus TN India

Machine learning technique which uses artificial neural networks to learn representations. Phishing is a form of fraud in which the attacker tries to learn credential information from the websites. Web phishing is to steal sensitive information such as usernames, passwords and credit card details by way of impersonating a authorized entity. The Hybrid Ensemble Feature Selection is a new feature selection method for machine learning-based phishing detection systems (HEFS). The first step of HEFS involves using a novel Cumulative Distribution Function gradient (CDF-g) algorithm to generate primary feature subsets, which are then fed into a data perturbation ensemble to generate secondary feature subsets. We present the results of our approach and compare them to a few previous studies, with the paper focusing primarily on phishing urls for detecting the unauthorised one by using phishing detection method. © 2023

关键词： Intrusion detection

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Outage-Aware Online Prediction Control for Securing UAV-Aided Communication

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IEEE Transactions on Vehicular Technology 2025年

作者： Sheng, Zhichao Fu, Huabo Huang, Zhiyu Nasir, Ali A. Wu, Qingqing Zeng, Dan Shanghai University Shanghai Institute for Advanced Communication and Data Science Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai200444 China Department of Electrical Engineering and Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Shanghai Jiao Tong University Department of Electronic Engineering Shanghai201210 China

Considering an unmanned aerial vehicle (UAV)-aided multi-user system with multiple mobile eavesdroppers, this paper aims to develop an outage-aware online control strategy to counter the imperfect information of mobile user equipments (UEs) and mobile eavesdroppers. To perform online control effectively, a novel online prediction model is proposed, which models the trajectory of UEs as a stochastic Gauss-Markov mobility model and the trajectory of eavesdroppers as a worst-case model based on the current imperfect location information. The online control problem is formulated as a new multi-stage optimization framework, in which we perform online control by solving per-slot prediction problems and a resource allocation problem for the initial point. In the per-slot prediction problem, we jointly optimize the predictive trajectory and transmit power for the upcoming time slot (TS) by maximizing the minimum achievable secrecy rate (ASR) among all UEs. For resource allocation at the initial TS, we optimize transmit power by maximizing the minimum ASR among all UEs. The per-slot prediction problems and the resources allocation problem are all non-convex problems hence they are intractable in terms of computational complexity. To address them, we propose an efficient inner-approximation methods, by applying successive convex approximation (SCA) methods and S-procedure as well as Bernstein -type inequality. Numerical results demonstrate the superiority of our proposed online control scheme over the conventional benchmark schemes which include existing offline schemes and online scheme without outage based prediction as well as shortest-path navigation scheme. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Hybrid System with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： K. Thinakaran M. Anand T.Rajesh Kumar Saira Banu Atham K.M. Subramanian Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering School of CSE & IS Presidency University Bangaluru Department of Computer Science Engineering Shadan College of Engineering and Technology Hyderabad

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error.

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Emergency Vehicle Detection Using Deep ConvNet2D and Computer Vision

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Elumalaivasan Poongavanam Prathik. A Munirathinam. T Kayalvizhi. V Rajesh. R Mahavishnu. C Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Sona College of Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Shreenivasa Engineering College Dharmapuri India

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and Computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases.

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Inductive Graph-based Knowledge Tracing

Inductive Graph-based Knowledge Tracing

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Donghee Han Daehee Kim Keejun Han Mun Yong Yi Graduate School of Data Science Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Division of Computer Engineering Hansung University Seoul Republic of Korea Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

The rise of virtual education and increase in distance, partly owing to the spread of COVID-19 pandemic, has made it more difficult for teachers to determine each student’s learning status. In this situation, knowledge tracing (KT), which tracks a student’s mastery of specific knowledge concepts, is receiving increasing attention. KT utilizes a sequence of studentexercise interactive activities to predict the mastery of concepts corresponding to a target problem, recommending appropriate learning resources to students and optimizing learning sequences for adaptive learning. With the development of deep learning, various studies have been proposed, such as sequential models using recurrent neural networks, attention models influenced by transformers, and graph-based models that depict the relationships between knowledge concepts. However, they all have common limitations in that they cannot utilize the learning activities of students other than the target student and can only use a limited form of exercise information. In this study, we have applied the concept of rating prediction to the studentexercise knowledge tracing problem and solved the limitations of the existing models. Our proposed Inductive Graph-based Knowledge Tracing (IGKT) designed to integrate structural information and various unrestricted types of additional information into the model through subgraph sampling, has been found superior over the existing models across two different datasets in predicting student performances.

关键词： Knowledge engineering Deep learning Adaptation models Target tracking Recurrent neural networks Pandemics Predictive models

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RIS-Assisted Passive Localization (RAPL): An Efficient Zero-Overhead Framework Using Conditional Sample Mean

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe Hu, Ye School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States

Reconfigurable Intelligent Surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. To address these challenges, in this work, we move beyond conventional methods and introduce a novel data-driven, multiple RISs-assisted passive localization approach (RAPL). The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user’s position is determined based on the estimated multiple AoD pairs in the second stage. This approach only utilizes the existing communication signals between the user and the BS, relying solely on the measurement of received signal power at each BS antenna for a set of randomly generated phase shifts across all RISs. Moreover, by obviating the need for real-time RIS phase shift optimization or user-to-BS pilot transmissions, the method introduces no additional communication overhead, making it highly suitable for deployment in real-world networks. The proposed scheme is then extended to multi-RIS scenarios considering both parallel and cascaded RIS topologies. Numerical results show that the proposed RAPL improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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

作者： Marvin Borsdorf Zexu Pan Pascal Himmelmann Haizhou Li Tanja Schultz Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time.

关键词： Training Measurement databases Signal processing data models Blind source separation Robustness Acoustics Speech processing

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Context-aware Inductive Graph Matrix Completion with Sentence BERT

Context-aware Inductive Graph Matrix Completion with Sentenc...

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IEEE International Conference on Web Services (ICWS)

作者： Donghee Han Daehee Kim Keejun Han Mun Yong Yi Graduate School of Data Science Korea Advanced Institute of Science and Technology Daejeon Republic of Korea School of Computer Engineering Hansung University Seoul Republic of Korea Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

Existing graph neural network (GNN) based recommendation models depend highly on initial node features and graph structures. Most prior studies do not support the use of additional information on user-item interactions and adopt a transductive method, which has limitations for actual web service. To overcome these problems, we propose Context-aware Inductive Graph Matrix Completion (CGMC), which utilizes context vectors that represent user-item interactions and user-item bipartite graph structures in an inductive manner. Our method constructs context vectors from the review, timestamp, and rating and uses them as edge features of the user-item bipartite graph. Relations between homogeneous nodes are also constructed based on common neighbors. Our model combines context vectors and relational information using Context-aware Graph Attention Networks and Edge Fusion Graph Convolutional Networks. We conducted extensive experiments using six real-world datasets, and the results show that the proposed model achieves superior performances over other competing models. Furthermore, we analyzed the inductive characteristics of CGMC through the cross-domain transferability. The source code is available in the repository at [https://***/venzino-han/CGMC_SBERT].

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Label-Free Biomolecule Detection with InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Elshafie, Hashim Alqahtani, Abdulrahman Saad Mubarakali, Azath Adarsh Rag, S. Venkatesh, M. Parthasarathy, P. King Khalid University College of Computer Science Department of Computer Engineering Abha61421 Saudi Arabia University of Bisha College of Computing and Information Technology Bisha67714 Saudi Arabia King Khalid University College of Computer Science Department of Informatics and Computer Systems Abha61421 Saudi Arabia Manipal Institute of Technology Department of Data Science and Computer Applications Manipal Academy of Higher Education Manipal576104 India CMR Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru560037 India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of 2.5 × 10 cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of 3.5 × 103, outperforming configurations without overlap (2 × 103), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection. © 2013 IEEE.

关键词： Junction gate field effect transistors

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