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

作者： Xiu Su Shan You Hongyan Xu Xiuxing Li Jun Long Yi Chen Chang Xu Big Data Institute Central South University School of Computer Science Faculty of Engineering The University of Sydney SenseTime Research School of Computer Science and Engineering University of New South Wales Institute of Computing Technology Chinese Academy of Sciences School of Computer Science Hong Kong University of Science and Technology

Large language models (LLMs) offer impressive performance across diverse fields, but their increasing complexity raises both design costs and the need for specialized expertise. These challenges are intensified for Neural Architecture Search (NAS) methods reliant on weight-sharing techniques. This paper introduces GNAS, a new NAS method that boosts the search process with the aid of LLMs for efficient model discovery. With insights from existing architectures, GNAS swiftly identifies superior models that can adapt to changing resource constraints. We provide a mathematical framework to facilitate the transfer of knowledge across different model sizes, thereby improving search efficiency. Our experiments conducted on ImageNet, NAS-Bench-Macro, and ChannelBench-Macro confirm the effectiveness of GNAS across both CNN and Transformer architectures.

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Lane Detection Using Deep Learning for Rainy Conditions

Lane Detection Using Deep Learning for Rainy Conditions

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International Conference on Computer and Communication Engineering, ICCCE

作者： Hadhrami Ab Ghani Atiqullah Mohamed Daud Rosli Besar Zamani Md Sani Mohd Nazeri Kamaruddin Syabeela Syahali Faculty of Data Science and Computing Universiti Malaysia Kelantan Kelantan Malaysia Faculty of Engineering and Technology Multimedia University Melaka Malaysia Faculty of Mechatronic Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

Prior research has shown that various road marker classification mechanisms in clear or dry weather conditions have high accuracy performance. However, the performance tends to be lower under rainy driving conditions due to the reduced quality of the road image when detecting the five classes of road markers which are Single, Single-Single, Dashed, Solid-Dashed, and Dashed-Solid. To address this challenging condition, lane marker detection based on deep learning approach is proposed in this paper. The target weather condition is rainy, which is very challenging as it causes the surface of the roads, especially the area which includes the lane marker to become blurry and unclear due to the rainwater. In order to carefully select the right features of the road such that the lane marker can be classified and detected successfully. The lane marker object is captured from the frames of the video clips taken from established published video datasets. With this fast and better lane marker detection, the achievable classification precision is satisfactory although the weather is rainy.

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Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-Based Approaches to Offline and Online Detection

Non-Intrusive Load Monitoring for Feeder-Level EV Charging D...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Cameron Martin Fucai Ke Hao Wang Department of Data Science and AI Faculty of Information Technology Monash University Australia Department of Human Centred Computing Faculty of Information Technology Monash University Australia Monash Energy Institute Monash University Australia

Understanding electric vehicle (EV) charging on the distribution network is key to effective EV charging management and aiding decarbonization across the energy and transport sectors. Advanced metering infrastructure has allowed distribution system operators and utility companies to collect high-resolution load data from their networks. These advancements enable the non-intrusive load monitoring (NILM) technique to detect EV charging using load measurement data. While existing studies primarily focused on NILM for EV charging detection in individual households, there is a research gap on EV charging detection at the feeder level, presenting unique challenges due to the combined load measurement from multiple households. In this paper, we develop a novel and effective approach for EV detection at the feeder level, involving sliding-window feature extraction and classical machine learning techniques, specifically models like XGBoost and Random Forest. Our developed method offers a lightweight and efficient solution, capable of quick training. Moreover, our developed method is versatile, supporting both offline and online EV charging detection. Our experimental results demonstrate high-accuracy EV charging detection at the feeder level, achieving an F-Score of 98.88% in offline detection and 93.01% in online detection.

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Capturing the Complexity of Human Strategic Decision-Making with Machine Learning

SSRN

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SSRN 2024年

作者： Zhu, Jian-Qiao Peterson, Joshua C. Enke, Benjamin Griffiths, Thomas L. Department of Computer Science Princeton University NJ United States Faculty of Computing and Data Science Boston University MA United States Department of Economics Harvard University CambridgeMA United States Department of Computer Science Department of Psychology Princeton University NJ United States

Understanding how people behave in strategic settings–where they make decisions based on their expectations about the behavior of others–is a longstanding problem in the behavioral sciences. We conduct the largest study to date of strategic decision-making in the context of initial play in two-player matrix games, analyzing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on these data predicts people’s choices better than leading theories of strategic behavior, indicating that there is systematic variation that is not explained by those theories. We then modify the network to produce a new, interpretable behavioural model, revealing what the original network learned about people: their ability to optimally respond and their capacity to reason about others are dependent on the complexity of individual games. This context-dependence is critical in explaining deviations from the rational Nash equilibrium, response times, and uncertainty in strategic decisions. More broadly, our results demonstrate how machine learning can be applied beyond prediction to further help generate novel explanations of complex human behavior. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection

arXiv

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arXiv 2023年

作者： Martin, Cameron Ke, Fucai Wang, Hao Department of Data Science and AI Faculty of Information Technology Monash University Australia Department of Human Centred Computing Faculty of Information Technology Monash University Australia Monash Energy Institute Monash University Australia

关键词： Electric power distribution

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A Survey on Fairness-aware Recommender Systems

arXiv

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arXiv 2023年

作者： Jin, Di Wang, Luzhi Zhang, He Zheng, Yizhen Ding, Weiping Xia, Feng Pan, Shirui College of Intelligence and Computing Tianjin University China Department of Data Science and AI Faculty of IT Monash University Australia School of Information Science and Technology Nantong University China School of Computing Technologies RMIT University Australia School of Information and Communication Technology Griffith University Australia

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems. © 2023, CC BY.

关键词： Recommender systems

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Remote Sensing data-Based Satellite Image Analysis in Water Quality Detection for Public Health data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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A Survey on Fairness-Aware Recommender Systems

SSRN

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SSRN 2023年

关键词： Recommender systems

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Quantifying the Benefits of Implementing Big data Analytics on Consumers’ Online Purchase Intentions in e-Commerce

Quantifying the Benefits of Implementing Big Data Analytics ...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Wei, Justin Ang Kang Mansor, Mahayaudin M. Derasit, Zuraidah Bakar, Mohd Aftar Abu Ul-Saufie, Ahmad Zia Hamzah, Firdaus Mohamad Faculty of Business Economics and Accounting HELP University Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Malaysia Centre for Defence Foundation Studies Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798331530303

The digital revolution has reshaped the global economy, especially e-commerce. Innovative technologies such as big data analytics, artificial intelligence, and cloud computing drive this transformation, enabling companies to make informed business decisions and influence consumers’ online purchasing behaviour. This study explores empirical evidence for the benefits of using big data analytics (BDA) to influence consumers’ online purchasing decisions in e-commerce. Primary data was collected via an online survey targeting 300 customers with online shopping experience on platforms that have adopted the digital technologies of BDA. Statistical methods used in this study included questionnaire design, reliability test, descriptive analysis, and multiple regression modelling and diagnostics. The findings indicate that the benefits of implementing BDA in e-commerce for information search, recommendation systems, consumer services, dynamic pricing, security and fraud detection are strongly associated with consumers’ online purchasing intentions. The study underscores the importance of businesses leveraging BDA, as the benefits significantly and positively impact consumers’ purchasing intentions in e-commerce while addressing security and fraud detection concerns that undermine its advantage. Nevertheless, we encourage all companies to leverage BDA implementation in their digital marketplace, which ultimately contributes to achieving e-commerce goals, such as increased market reach, cost reduction, and revenue growth. ©2024 IEEE.

关键词： Marketplaces

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain Technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important Computer science, Engineering, and Decision sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar university (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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