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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

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. © 2023, CC BY.

关键词： Electric power distribution

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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

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Hybrid Iris Recognition: Leveraging HOG Features and Dense Networks for Enhanced Biometric Accuracy

Hybrid Iris Recognition: Leveraging HOG Features and Dense N...

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Future Telecommunications and Artificial Intelligence (IC-FTAI), International Conference on

作者： Esraa Tarek Shawkat Guirguis Magda Madbouly Information Technology Department Institute of Graduate Studies and Research Alexandria University Alexandria Egypt Dean of the Faculty of Computers and Data Science Alexandria University Alexandria Egypt

ISBN: (数字)9798331534080

ISBN: (纸本)9798331534097

The article describes a new method for iris recognition, based on a hybrid approach combining edge detection, Histogram of Oriented Gradients (HOG) features, and dense neural networks(DNN), enabling improved accuracy and reduced training time. Using Circular Hough Transform (CHT)segmentation and Canny Edge Detection for localization, followed by feature extraction through HOG, the authors train the data using dense networks. The results show a 92.37% accuracy on the CASIA-Iris-Interval dataset and 96.28% on the IITD dataset, outperforming previous techniques. The authors illustrate the proposed method with results from MATLAB simulations on three datasets. The hybrid method allows for a reduction in training time by 10 minutes compared to traditional Convolutional Neural Network (CNN)methods. The effectiveness of the new method is confirmed by accuracy improvements of up to 5 %. These research results supplement existing iris recognition techniques and can be used for real-time biometric applications. The novelty and scientific contribution lie in integrating HOG and dense networks, enhancing both accuracy and computational efficiency in biometric systems.

关键词： Training Histograms Accuracy Image edge detection Neural networks Transforms Feature extraction Real-time systems Telecommunications Iris recognition

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Intrusion Detection System using Modified Arithmetic Optimization Algorithm for Large-scale IoT

Intrusion Detection System using Modified Arithmetic Optimiz...

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Cyber Resilience (ICCR), International Conference on

作者： Sharif Naser Makhadmeh Yousef Sanjalawe Hussam N. Fakhouri Data Science and Artificial Intelligence Department Faculty of Information Technology University of Petra Amman Jordan Department of Cybersecurity American University of Madaba (AUM) Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper introduces an Intrusion Detection System (IDS) designed to enhance security in expansive IoT networks. The proposed IDS leverages one of thee most popular Machine Learning method with a modified Arithmetic Optimization Algorithm (AOA) to identify the optimal feature set and deal with the binary nature of this optimization problem. This set of features is then employed utilizing the K-Nearest Neighbors Algorithm. Our study utilized a dataset, called BotIoT2018. Notably from the results, the proposed IDS using the modified AOA demonstrated a remarkably precise and resilient detection model tailored for IoT networks.

关键词： Machine learning algorithms Intrusion detection Telecommunication traffic Network security Feature extraction Pattern recognition Optimization

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PSO-Based Optimization of Interval Type-2 Fuzzy Logic for Question Difficulty Prediction

PSO-Based Optimization of Interval Type-2 Fuzzy Logic for Qu...

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International Conference on Computer Theory and Applications (ICCTA)

作者： Mai Shaaban Yasser F. Hassan Magda M. Madbouly Information Technology Dept IGSR Alexandria University Alexandria Egypt Faculty of Computers and Data Science Alexandria University Alexandria Egypt

ISBN: (数字)9798350309584

ISBN: (纸本)9798350309591

Educational assessment has a significant role in learning process as a tool of measuring learners' performance and evaluating the knowledge of concepts that achieves learning objectives or outcomes. To design good assessments, it is useful to have a difficulty index for each question. It represents the relative difficulty of a question within a given context or test. Knowing the level of difficulty for each question before running an exam helps to classify questions, identifying the level of challenge, and ensuring a balanced mix of questions. This paper proposes an optimal Interval Type-2 Fuzzy Logic model-based Particle Swarm Optimization (IT2FL-PSO) in order to predict the difficulty of multiple-choice questions (MCQs) with four options and one correct answer format. The results demonstrate that the proposed approach outperforms the baseline approach.

关键词： Fuzzy logic Electronic learning Computational modeling Predictive models Indexes Particle swarm optimization Optimization

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Vehicle Check-In data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network 19

Vehicle Check-In Data-driven POI Recommendation Based on Imp...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yuwen Zhang, Jie Dou, Ruihan Zhou, Xiaokang Xu, Xiaolong Wang, Shoujin Qi, Lianyong College of Computer Science and Technology Qingdao China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology China Shanghai Meteorological Information and Technology Support Center China University of Waterloo Faculty of Mathematics Waterloo Canada Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Riken Japan Nanjing University of Information Science and Technology School of Computer and Software China Macquarie University School of Computing Australia Qufu Normal University School of Computer Science China

ISBN: (纸本)9798350346558

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations. © 2022 IEEE.

关键词： Singular value decomposition

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Identifying human values from goal models: An industrial case study

arXiv

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arXiv 2023年

作者： Iqbal, Tahira Taveter, Kuldar Strenze, Tarmo Hussain, Waqar Haggag, Omar Matthews, John Alphonsus Piirisild, Anu Institute of Computer Science University of Tartu Estonia Data 61 CSIRO Australia Faculty of Information Technology Monash University Australia

Human values are principles that guide human actions and behaviour in personal and social life. Ignoring human values during requirements engineering introduces a negative impact on software uptake and continued use. Embedding human values into software is admittedly challenging;however, early elicitation of stakeholder values increases the chances of their inclusion into the developed system. Using Pharaon, a research and innovation project of the European Union’s Horizon 2020 program, as a case study we analysed stakeholder requirements expressed as motivational goal models consisting of functional, quality, and emotional goals in three large-scale trial applications of the project. We were able to elicit 9 of 10 human values according to the theory of human values by Schwartz from the motivational goal models that represent the requirements for the three applications. Our findings highlight the dominant trend of stakeholder values being embedded in emotional goals and show that almost 45% of the identified values belong to the value categories of Security and Self-direction. Our research extends prior work in emotional goal modelling in requirements engineering by linking emotional goals to various stakeholder roles and identifying their values based on the Schwartz theory of human values. © 2023, CC BY.

关键词： Requirements engineering

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Design and Promotion of Cost-Effective IOT-Based Heart Rate Monitoring

Design and Promotion of Cost-Effective IOT-Based Heart Rate ...

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2022 International Conference on Cloud Computing, Internet of Things, and Computer Applications, CICA 2022

作者： Moshayedi, Ata Jahangir Kolahdooz, Amin Roy, Atanu Shuvam Rostami, Seyyed Ali Latifi Xie, Xiaoyun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Faculty of Technology School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou China

ISBN: (纸本)9781510656673

The term telemedicine was first used in the 1920s, although used many years ago and has continued to evolve today. Medical diagnoses usually require visual information, but remote display systems have recently become a special place due to the constant unavailability of the treating physician or the remoteness of medical centers and the constant need of some patients for round-the-clock care. In this article, an Arduino-based heart rate information system is designed and implemented. Due to the reasonable price and easy accessibility of the created system in the fraction, it has many applications. The results of the designed system showed the system's capabilities to track and know the person's heart rate. © 2022 SPIE.

关键词： Cost effectiveness

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Clustering Analysis of Water Usage Structures Based on Knowledge Graph

Clustering Analysis of Water Usage Structures Based on Knowl...

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Computer, Big data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jianzhuo Yan Han Jiao Hang Zhang Yongchuan Yu Jianhui Chen Shiwei Zhou Faculty of Information Technology Beijing University of Technology Beijing China Institute of Big Data Analysis and Applications Beijing Institute of Water Science and Technology Beijing China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

A key issue in the management of water resources in China is the adoption of a water supply total quantity control framework at the regional level based on socioeconomic development. This necessitates more effective management of water resource demand to achieve a balance between supply and demand. This paper aims to conduct an in-depth analysis of the industrial water usage structure, identify the primary influencing factors, and thereby provide insights for the design of demand management policies. Using a typical application scenario of "water extraction and supply" in a subsidiary of a water supply company in Beijing, a water knowledge graph incorporating multiple data sources is constructed. The study proposes a knowledge graph embedding model based on graph convolutional neural networks. Over the 36 months from 2019 to 2021, the water units in the accommodation, office, urban resident, healthcare, retail, education, catering, and residential services industries are clustered, and the water usage is analyzed. The results of this study are expected to provide references for optimizing urban water resource utilization.

关键词： Industries Water Supply and demand Soft sensors Knowledge graphs Water conservation Convolutional neural networks

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