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arXiv 2022年

作者： Li, Hongyu Shen, Shanpu Clerckx, Bruno Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

Reconfigurable intelligent surfaces (RISs) are envisioned as a promising technology for future wireless communications. With various hardware realizations, RISs can work under different modes (reflective/transmissive/hybrid) or have different architectures (single/group/fully-connected). However, most existing research focused on single-connected reflective RISs, mathematically characterized by diagonal phase shift matrices, while there is a lack of a comprehensive study for RISs unifying different modes/architectures. In this paper, we solve this issue by analyzing and proposing a general RIS-aided communication model. Specifically, we establish an RIS model not limited to diagonal phase shift matrices, a novel branch referred to as beyond diagonal RIS (BD-RIS), unifying modes and architectures. With the proposed model, we develop efficient algorithms to jointly design transmit precoder and BD-RIS matrix to maximize the sum-rate for RIS-aided systems. We also provide simulation results to compare the performance of BD-RISs with different modes/architectures. Simulation results show that under the same mode, fully- and group-connected RIS can effectively increase the sum-rate performance compared with single-connected RIS, and that hybrid RIS outperforms reflective/transmissive RIS with the same architecture. Copyright © 2022, The Authors. All rights reserved.

关键词： Reconfigurable architectures

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Cucheb: a GPU implementation of the filtered Lanczos procedure

arXiv

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arXiv 2024年

作者： Aurentz, Jared L. Kalantzis, Vassilis Saad, Yousef Mathematical Institute University of Oxford Andrew Wiles Building Woodstock Road OxfordOX2 6GG United Kingdom Department of Computer Science and Engineering University of Minnesota 4-192 EE/CS Bldg. 200 Union Street S.E. MinneapolisMN55455 United States

This paper describes the software package Cucheb, a GPU implementation of the filtered Lanczos procedure for the solution of large sparse symmetric eigenvalue problems. The filtered Lanczos procedure uses a carefully chosen polynomial spectral transformation to accelerate convergence of the Lanczos method when computing eigenvalues within a desired interval. This method has proven particularly effective for eigenvalue problems that arise in electronic structure calculations and density functional theory. We compare our implementation against an equivalent CPU implementation and show that using the GPU can reduce the computation time by more than a factor of 10. © 2024, CC BY.

关键词： Density functional theory

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Customer Segmentation and Classification Using K-Modes Clustering with Ensemble Learning 2nd

Customer Segmentation and Classification Using K-Modes Clus...

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2nd International Conference on Intelligent Systems and Data science, ISDS 2024

作者： Niloy, Shahriar Rahman Hasan, Toushif Muktashid Apu, Md. Saiduzzaman Hasan, Rakibul Shahin, Kamrul Islam Nguyen, Huu-Hoa Farid, Dewan Md. Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka1212 Bangladesh Software Engineering The Maersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense M5230 Denmark College of Information and Communication Technology Can Tho University 3/2 Street Ninh Kieu District Can Tho City Viet Nam

ISBN: (纸本)9789819796120

In the contemporary landscape of business intelligence and market analysis, customer segmentation serves as a pivotal tool for understanding consumer behavior and preferences. This paper delves into the application of advanced machine learning techniques, specifically K-Modes clustering and ensemble learning with AdaBoost, for the purpose of customer segmentation and classification. The utilization of K-Modes clustering, an extension of the K-Means algorithm tailored for categorical data, facilitates the identification of distinct groups within a heterogeneous customer base. By incorporating categorical variables, K-Modes accommodates the inherent diversity in customer attributes such as demographic information, purchase history, and product preferences. Furthermore, this research integrates ensemble learning techniques, particularly AdaBoost, to enhance the accuracy and robustness of the segmentation process. Through a comprehensive empirical analysis, conducted on a real-world dataset sourced from Kaggle, the proposed methodology demonstrates superior performance compared to traditional clustering approaches. The experimental results showcase the effectiveness of K-Modes clustering combined with AdaBoost ensemble learning in accurately segmenting customers into meaningful groups, thereby enabling businesses to gain deeper insights into consumer behavior and preferences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Driver Status Monitoring System with Feedback from Fatigue Detection and Lane Line Detection

Driver Status Monitoring System with Feedback from Fatigue D...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Kai Yan Chaoyue Zhao Chengkang Shen Peiyan Wang Guoqing Wang Research Centre 2nd Research Institute of CAAC Chengdu China AI Research Centre China Telecom Research Institute (CTRI) Beijing China School of Electronic Science and Engineering Nanjing University Nanjing China Department of Electrical and Computer Engineeering Purdue University West Lafayette USA School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665465007

Automobiles have become an indispensable part of life for both business and pleasure in today's society. Because of the long-term continuous work, fatigue presents a great danger to ride-sharing and truck drivers. Therefore, this paper aims to design a device that provides valuable feedback by evaluating driver status and surroundings. A gradient judgment is made through lane detection and face detection. When a dangerous condition is detected, the driver will be alerted by music and audio announcements with different degrees. The system also has two additional functions. First, a digital record-keeping to assist the professional driver. The other is a security system that if a stranger starts the car, a text message will be sent to the owner's phone. Compared with those in previous works, the proposed system's efficacy and efficiency are validated qualitatively and quantitatively in driver fatigue detection.

关键词： Lane detection Multicore processing Face recognition Roads Fatigue Safety Automobiles

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Metal-insulator transition switching in VOx−VSe2 heterojunctions

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Physical Review Materials 2022年第1期6卷 014003-014003页

作者： Muhammad Fauzi Sahdan Arramel Sharon Lim Xiaodai Hong Wang Muhammad Danang Birowosuto Sow Chorng Haur Kah-Wee Ang Andrew Thye Shen Wee Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117551 Singapore CINTRA UMI CNRS/NTU/THALES Singapore 637553 Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Centre for Advanced 2D Materials National University of Singapore Block S14 6 Science Drive 2 Singapore 117546 Singapore

First-order metal-insulator transition (MIT) observed in strongly correlated systems such as vanadium dioxide (VO2) holds potential in electronics, energy, to optical applications. Starting from a vanadium diselenide (VSe2) bulk crystal, we demonstrated a direct surface conversion from VSe2 to VO2 via laser exposure in ambient condition. The process generates defects, and the heat from the laser promotes oxidation forming VOx. Raman spectra at room temperature suggest the resulting oxide formed is monoclinic (M1) VO2. Above the transition temperature (TC), all the phonon modes are damped indicating formation of the rutile phase (metallic). Photoluminescence (PL) intensity enhancement and peak shifts observed at TC suggest correlation to the band structure transformation. In addition, we observed electrically induced MIT in our lateral VSe2−VOx heterojunction device.

关键词： Metal-insulator transition Raman lasers Transition metal dichalcogenides Transition metal oxides Photoluminescence Time-resolved photoluminescence

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Identification of Critical Links in Transportation Networks: An Integrated Neural Networks Approach that Incorporates Network Topology

SSRN

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SSRN 2024年

作者： Barati, Hojjat Yazici, Anil Bargahi, Mahsa Johnson Mirmiran & Thompson 121 Continental Dr. NewarkDE19713 United States Department of Civil Engineering Stony Brook University 2425 Computer Science Stony BrookNY11794 United States MTA New York City Transit MTA 2 Broadway New YorkNY10004 United States

This paper introduces a novel integrated Multi-layer Perceptron Neural Network (MLP) and Convolutional Neural Network (CNN) approach to predict the criticality ranking of links in transportation networks. Traditional methods for critical link identification often overlook the complex interdependencies of network flows or are hindered by computational inefficiencies and extensive data requirements due to running multiple User Equilibrium (UE) traffic assignments for all individual networks at hand. The proposed methodology overcomes these challenges by leveraging the computational efficiency of NNs and capability to process network topology as an input feature to identify the critical links. For this purpose, real-life U.S. County road networks with varying topological characteristics are extracted through the OSMnx library. For all networks, User Equilibrium (UE) traffic assignments is run under various disruption scenarios to generate necessary training data that include 1) network topology, 2) link flows and 3) the statistical moments (mean, variance, skewness) of the criticality score distribution for each network link under various disruption scenarios. Accordingly, the trained MLP-CNN model provides the critical links in a network with the sole input of network graph and the link volumes. The model’s effectiveness and capability to accurately forecast the criticality scores were illustrated through tests across diverse and complex sample networks. The model’s practical applicability was also illustrated within Long Island (NY) network. Overall, the developed model takes both the topological and operational characteristics of transportation networks into account while identifying the critical links in transportation networks, addressing a gap in the existing literature. © 2024, The Authors. All rights reserved.

关键词： Network topology

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Implementation of a Scalable Platform for Real-Time Monitoring of Machine Tools

SSRN

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SSRN 2024年

作者： Tapia, Endika Lopez-Novoa, Unai Sastoque-Pinilla, Leonardo López-De-Lacalle, Luis Norberto Aeronautics Advanced Manufacturing Center University of the Basque Country Bizkaia Science and Technology Park 202 Zamudio48170 Spain Department of Computer Languages and Systems University of the Basque Country Rafael Moreno pasealekua 2-3 Bilbao48013 Spain Department of Mechanical Engineering University of the Basque Country Torres Quevedo enparantza s/n Bilbao48013 Spain

In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an industrial manufacturing process using scalable software tools. The platform collects data from a machine, processes it, and displays visualizations in a dashboard along with the results. A statistical method is used to detect outliers in the manufacturing process. The performance of the platform is assessed in two ways: firstly by monitoring a five-axis milling machine and secondly, using simulated tests. Former tests prove the suitability of the platform and reveal the issues that arise in a real environment, and latter tests prove the scalability of the platform with higher data processing needs than the previous ones. © 2024, The Authors. All rights reserved.

关键词： Machine tools

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Bangla Social Media Cyberbullying Detection Using Deep Learning 1st

Bangla Social Media Cyberbullying Detection Using Deep Learn...

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1st International Conference on Intelligent Systems and Data science, ISDS 2023

作者： Rodela, Anika Tasnim Nguyen, Huu-Hoa Farid, Dewan Md. Huda, Mohammad Nurul Department of Information and Communication Technology Bangladesh University of Professionals Mirpur Cantonment Dhaka Bangladesh College of Information and Communication Technology Can Tho University 3/2 Street Ninh Kieu District Can Tho City Viet Nam Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka1212 Bangladesh

ISBN: (纸本)9789819976485

The growth of social media over the past decade has been nothing short of phenomenal. An exponential increase has been seen on platforms like Facebook, Twitter, Instagram, LinkedIn, and YouTube, in their user base, accumulating billions of active users worldwide. Technology advancements, the ubiquitous use of smartphones, and the innate human desire for connection don’t always contribute constructively;they might additionally end up in spreading violence in the form of bullying of others which is known as cyberbullying. As a result of cyberbullying, the victims will frequently experience anxiety, depression, and other mental diseases which can even result in suicide. Therefore the extensive need of detecting and controlling cyberbullying motivated us to automate this process. In this paper, we have introduced an approach to detect cyberbullying from social media data by using deep learning models. We have used Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network in the proposed hybrid approach along with word embedding technique called fastText. We achieved an accuracy of 91.63% with the proposed model by using a publicly available dataset containing 16,073 samples which outperformed all the state of the art models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation

arXiv

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arXiv 2024年

作者： Chen, Xuexin Cai, Ruichu Huang, Zhengting Zhu, Yuxuan Horwood, Julien Hao, Zhifeng Li, Zijian Hernández-Lobato, José Miguel School of Computer Science Guangdong University of Technology Guangzhou China Guangzhou China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Shantou University Shantou515063 China Mohamed bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests. Despite their utility, FAMs struggle to distinguish the contributions of different features, when their prediction changes are similar after perturbation. To enhance FAMs' discriminative power, we introduce Feature Attribution with Necessity and Sufficiency (FANS), which find a neighborhood of the input such that perturbing samples within this neighborhood have a high Probability of being Necessity and Sufficiency (PNS) cause for the change in predictions, and use this PNS as the importance of the feature. Specifically, FANS compute this PNS via a heuristic strategy for estimating the neighborhood and a perturbation test involving two stages (factual and interventional) for counterfactual reasoning. To generate counterfactual samples, we use a resampling-based approach on the observed samples to approximate the required conditional distribution. We demonstrate that FANS outperforms existing attribution methods on six benchmarks. Please refer to the source code via https://***/DMIRLAB-Group/FANS. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Joint Communication and Computation Framework for Goal-Oriented Semantic Communication with Distortion Rate Resilience

arXiv

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arXiv 2023年

作者： Nguyen, Minh-Duong Do, Quang-Vinh Yang, Zhaohui Hwang, Won-Joo Pham, Quoc-Viet The Department of Information Convergence Engineering Pusan National University Busan46241 Korea Republic of The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang University Hangzhou310027 China The School of Computer Science and Statistics Trinity College Dublin The University of Dublin Dublin2 D02 PN40 Ireland

Recent research efforts on semantic communication have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems. However, these approaches introduce a paradox: the accuracy of artificial intelligence (AI) tasks should naturally emerge through training rather than being dictated by network constraints. Acknowledging this dilemma, this work introduces an innovative approach that leverages the rate-distortion theory to analyze distortions induced by communication and semantic compression, thereby analyzing the learning process. Specifically, we examine the distribution shift between the original data and the distorted data, thus assessing its impact on the AI model’s performance. Founding upon this analysis, we can preemptively estimate the empirical accuracy of AI tasks, making the goal-oriented semantic communication problem feasible. To achieve this objective, we present the theoretical foundation of our approach, accompanied by simulations and experiments that demonstrate its effectiveness. The experimental results indicate that our proposed method enables accurate AI task performance while adhering to network constraints, establishing it as a valuable contribution to the field of signal processing. Furthermore, this work advances research in goal-oriented semantic communication and highlights the significance of data-driven approaches in optimizing the performance of intelligent *** Codes 68T05 Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent systems

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