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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Cao, Xi Yin, Lijun Lin, Ying Zhang, Jun La Trobe University Department of Computer Science and Information Technology Melbourne Australia Sun Yat-sen University Department of Psychology Guangzhou China Hanyang University Seoul Korea Republic of Nankai University Tianjin China

ISBN: (纸本)9798350385670

Cognitive diagnostic computerized adaptive testing (CD-CAT) is a cutting-edge testing approach in educational environments. It combines the merits of computerized adaptive testing (CAT) and cognitive diagnosis models (CDMs), allowing for adaptive testing methods to be applied to fine-grained diagnosis. Two crucial factors in CD-CAT item selection are diagnostic accuracy and item bank utilization. Previous research focused either solely on enhancing diagnostic performance or on achieving a balance between bank utilization and diagnostic accuracy, often at the significant expense of the latter. To strike a more effective equilibrium between diagnostic performance and bank utilization, the current study proposes a forwardlooking item selection strategy for CD-CAT, named lookingahead Shannon entropy (laSE). laSE shifts the focus from the immediate next item to a broader perspective, considering sequences of multiple items ahead, thereby offering a longer-term view in item selection. This strategy involves a branch searching scheme to find appropriate multi-item sequences and introduces the laSE metric for evaluating these sequences. The parameter within the laSE method are investigated. Simulation studies based on two item banks are conducted to demonstrate laSE's effectiveness compared to existing methods. The results show that laSE offers a more balanced item selection for CD-CAT in terms of diagnostic accuracy and bank utilization. © 2024 IEEE.

关键词： computer testing

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Requirement engineering for E-Coaching in Higher Education

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engineering（科研） 2023年第5期15卷 275-290页

作者： Essam Almuhsin Ben Soh Alice Li Azmat Ullah Department of Computer Science and Information Technology La Trobe University Melbourne Australia La Trobe Business School La Trobe University Melbourne Australia

This paper presents a requirement engineering for developing an e-coaching environment in the higher education sector. This research demonstrates that IT experts encounter challenges in establishing a system that matches a university’s expectations, as they are usually uncertain about its goals and system requirements. The paper illustrates a business goal-focused requirement induction technique, which encompasses demonstrating the business procedures through Business Process Modelling Notation (BPMN), assessing the university goals via the tree diagram, and drawing out the system requirements from the university objectives through UML state diagrams. A case study of supporting the development of a new IT course is used as a case study and applied using BPMN.

关键词： E-Coaching System Requirements Requirement engineering E-Coaching Process Business Goals

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Unsupervised Representation Learning for 3-D Magnetic Resonance Imaging Superresolution with Degradation Adaptation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4660-4674页

作者： Liu, Jianan Li, Hao Huang, Tao Ahn, Euijoon Han, Kang Razi, Adeel Xiang, Wei Kim, Jinman Feng, David Dagan Vitalent Consulting Gothenburg41760 Sweden University Hospital Heidelberg Department of Neuroradiology Heidelberg69120 Germany James Cook University College of Science and Engineering Cairns4878 Australia La Trobe University School of Computing Engineering and Mathematical Sciences Melbourne3086 Australia Monash University Turner Institute for Brain and Mental Health School of Psychological Sciences Melbourne3800 Australia University of Sydney School of Computer Science SydneyNSW2006 Australia

High-resolution (HR) magnetic resonance imaging (MRI) is essential in aiding doctors in their diagnoses and image-guided treatments. However, acquiring HR images can be time-consuming and costly. Consequently, deep learning-based superresolution reconstruction (SRR) has emerged as a promising solution for generating superresolution (SR) images from low-resolution (LR) images. Unfortunately, training such neural networks requires aligned authentic HR and LR image pairs, which are challenging to obtain due to patient movements during and between image acquisitions. While rigid movements of hard tissues can be corrected with image registration, aligning deformed soft tissues is complex, making it impractical to train neural networks with authentic HR and LR image pairs. Previous studies have focused on SRR using authentic HR images and downsampled synthetic LR images. However, the difference in degradation representations between synthetic and authentic LR images suppresses the quality of SR images reconstructed from authentic LR images. To address this issue, we propose a novel unsupervised degradation adaptation network (UDEAN). Our network consists of a degradation learning network and an SRR network. The degradation learning network downsamples HR images using the degradation representation learned from misaligned or unpaired LR images. The SRR network then learns to map the downsampled HR images to the original ones. Experimental results show that our method outperforms state-of-the-art networks with an improvement of up to 0.051/3.52 dB in structural similarity (SSIM)/peak signal-to-noise ratio (PSNR) on two public datasets, thus is a promising solution to the challenges in clinical settings. © 2020 IEEE.

关键词： Magnetic resonance imaging

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A Novel Approach to Sustainable Energy Trading Optimizing Prosumer Incentives and Carbon Footprint

A Novel Approach to Sustainable Energy Trading Optimizing Pr...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Mayank Arora Gururaj M V Ankush Sharma Naveen Chilamkurti Department of Computer Science and IT La Trobe University Australia Department of Electrical Engineering IIT Kanpur India Department of Electrical Engineering Indian Institute of Technology Kanpur India Department of Computer Science and IT La Trobe University Melbourne VIC Australia

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This paper presents a novel approach to optimizing energy management for prosumers in smart grid environments, balancing financial incentives with carbon emission reduction. We propose a multi-objective optimization model that considers the complex interplay between solar energy generation, battery storage utilization, and energy trading among prosumers. The model is lmplemented using MATlaB, employing a Sequential Quadratic Programming (SQP) algorithm to solve the nonlinear optimization problem. Our approach uniquely integrates both economic and environmental factors by maximizing incentives for solar and battery usage while minimizing carbon emissions associated with energy consumption and storage. The optimization considers various constraints, including energy balance, battery capacity, and solar generation limits. We demonstrate the model's effectiveness using a case study with 10 prosumers, showcasing how the optimal allocation of energy resources can lead to significant reductions in carbon footprint while maximizing financial benefits. This research contributes to the growing field of sustainable energy management in smart grids, offering a practical tool for prosumers to make informed decisions about their energy usage and trading strategies. The results highlight the potential for such optimization models to play a crucial role in promoting more sustainable and economically viable energy systems in the context of increasing decentralization and renewable energy integration.

关键词： Economics Renewable energy sources Energy consumption Optimization models Carbon dioxide Environmental factors Batteries Smart grids Energy management Optimization

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Optimizing Fairness in Peer-to-Peer Energy Systems for Effective Duck Curve Management

Optimizing Fairness in Peer-to-Peer Energy Systems for Effec...

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National Power Systems Conference (NPSC)

作者： Mayank Arora Gururaj M V Ankush Sharma Naveen Chilamkurti Department of Electrical Engineering IIT Kanpur India Department of Computer Science and IT La Trobe University Australia Department of Electrical Engineering Indian Institute of Technology Kanpur India Department of Computer Science and IT La Trobe University Melbourne VIC Australia

ISBN: (数字)9798331519568

ISBN: (纸本)9798331519575

The integration of renewable energy sources, particularly solar, has introduced challenges such as the duck curve phenomenon in energy grids, characterized by midday surplus and evening peaks in demand. This paper proposes an optimization model for peer-to-peer (P2P) energy trading systems aimed at mitigating the duck curve while ensuring fair energy distribution among participants. The model formulates an objective function that minimizes deviations from a target load profile, emphasizing both load flattening and equitable energy sharing. Constraints include energy balance equations, storage limits, and trading bounds, ensuring operational feasibility. MATlaB simulations demonstrate the effectiveness of the proposed approach in achieving significant reductions in peak loads and promoting fairness in energy transactions. By optimizing energy trades based on participant preferences and system-wide objectives, the proposed approach seeks to enhance grid stability and efficiency in decentralized energy markets.

关键词： Renewable energy sources Supply and demand Optimization models Storage management Power system stability Stability analysis Peer-to-peer computing MATlaB Load modeling Energy storage

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Efficient Host Intrusion Detection using Hyperdimensional Computing

Efficient Host Intrusion Detection using Hyperdimensional Co...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Nam, Yujin King, Rachel Burke, Quinn Zhou, Minxuan McDaniel, Patrick Rosing, Tajana Uc San Diego Dept. of Computer Science and Engineering La Jolla United States University of Wisconsin-Madison Dept. of Computer Sciences Madison United States Illinois Institute of Technology Computer Science Department Chicago United States

ISBN: (纸本)9798350362480

Modern host-based intrusion detection systems (HIDS) rely on querying provenance graphs - graph representations of activity history on a system - to detect and respond to security threats present on a system. However, as the complexity and number of applications running on a system increase, the size of provenance graphs also increase, and thus the latency to query them. State-of-the-art designs deliver query latencies that are impractical for modern threat detection. In this paper, we introduce a hyper-dimensional computing (HDC) approach to querying provenance graphs for HIDS. By encoding provenance graphs and attack patterns/signatures into hyper-dimensional vectors, we can implement a query engine using simple vector operations. Our approach is hardware accelerator compatible, providing further speedups under resource-constrained environments. Our evaluation on a real-world dataset shows that our approach achieves > 90% detection accuracy and up to 4, 242× speedups over the state-of-the-art. This shows that HDC-based approaches can effectively deal with scaling issues in modern HIDS. © 2024 IEEE.

关键词： Network intrusion

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3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs 19

3D Scene Graph Prediction on Point Clouds Using Knowledge Gr...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Qiu, Yiding Christensen, Henrik I. University of California San Diego Department of Computer Science and Engineering La Jolla 92122 United States

ISBN: (纸本)9798350320695

3D scene graph prediction is a task that aims to concurrently predict object classes and their relationships within a 3D environment. As these environments are primarily designed by and for humans, incorporating commonsense knowledge regarding objects and their relationships can signifi-cantly constrain and enhance the prediction of the scene graph. In this paper, we investigate the application of commonsense knowledge graphs for 3D scene graph prediction on point clouds of indoor scenes. Through experiments conducted on a real-world indoor dataset, we demonstrate that integrating external commonsense knowledge via the message passing method leads to a 15.0% improvement in scene graph prediction accuracy with external knowledge and 7.96% with internal knowledge when compared to state-of-the-art algorithms. We also tested in the real world with 10 frames per second for scene graph generation to show the usage of the model in a more realistic robotics setting. © 2023 IEEE.

关键词： Message passing

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DPAL-BERT:A Faster and Lighter Question Answering Model

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computer Modeling in engineering & sciences 2024年第10期141卷 771-786页

作者： Lirong Yin Lei Wang Zhuohang Cai Siyu Lu Ruiyang Wang Ahmed AlSanad Salman A.AlQahtani Xiaobing Chen Zhengtong Yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA College of Resources and Environmental Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering ***,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced *** study aims to minimize the inference time of such models while maintaining computational *** also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and *** research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge *** experimental results showed that the distilled models greatly outperform models trained from *** addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the *** demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

关键词： DPAL-BERT question answering systems knowledge distillation model compression BERT Bi-directional long short-term memory(BiLSTM) knowledge information transfer PAL-BERT training efficiency natural language processing

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Measuring Impact of Lean Manufacturing Tools for Continuous Improvement on Economic Sustainability

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Journal of Systems science and Systems engineering 2024年第4期33卷 452-474页

作者： Luis Javier Márquez Figueroa Jorge Luis García-Alcaraz Ahmed I.Osman Alfonso Jesús Gil López Yashar Aryanfar Mika Sillanpää Mamdouh El Haj Assad Department of Electrical Engineering and Computer Science Universidad Autónoma de Ciudad JuárezCiudad JuárezChihuahuaMéxico Department of Industrial Engineering and Manufacturing Universidad Autónoma de Ciudad JuárezCiudad JuárezChihuahuaMéxico School of Chemistry and Chemical Engineering Queens UniversityBelfastNorthern IrelandUK Department of Economic and Managerial Sciences University of La RiojaLa RiojaSpain Department of Chemical Engineering University of JohannesburgJohannesburgSouth Africa Department of Sustainable and Renewable Energy Engineering University of SharjahSharjahUAE

Using Lean Manufacturing(LM)tools in production processes is crucial for companies’economic,environmental,and social sustainability *** study shows a structural equation model(SEM)that shows the relationship between LM Tools like Kaizen(KAI),Gemba(GEM),Value Stream Mapping(VSM)and Key Performance Indicator(KPI)with Economic Sustainability(ECS).Seven hypotheses were evaluated with data from 179 responses to a questionnaire about the Mexican maquiladora industry,showing that these variables are *** a 95%confidence level,the model was evaluated using the partial least squares *** findings indicate that the relationships between KAI and GEM and KAI and VSM have the strongest relationship,followed by VSM and ECS;however,VSM has the strongest effect on *** on these findings,it is recommended that managers adopt a continuous improvement(KAI)approach based on working directly on the shop floor(GEM and VSM)to support their decisions regarding economic growth(ECS).

关键词： Lean manufacturing economic sustainability structural equation model quality improvement

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Evolutionary Algorithm-Based Energy-Aware Path Planning with a Quadrotor for Warehouse Inventory Management

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第4期4卷 829-837页

作者： De Guzman, C.J.P. Chua, A.Y. Chu, T.S. Secco, E.L. Department of Mechanical Engineering De La Salle University 2401 Taft Ave. Malate Manila Philippines Robotics Laboratory School of Mathematics Computer Science & Engineering Liverpool Hope University United Kingdom

Quadrotors have been vital for automating warehouse processes. However, a significant gap in recent studies is that they use a single quadrotor with limited battery life, considering that their objective involves navigation in a large-scale environment such as a warehouse. Using an energy consumption model to enable more efficient navigation can be explored. Conventional data-driven energy models and path planning algorithms are insufficient for describing the various motions that a quadrotor can perform in warehouse operations, such as changes in yaw. This study aims to design a novel exhaustive data-driven energy consumption model and evolutionary algorithm-based path planning algorithm to consider various quadrotor movements involved in warehouse operations. The quadrotor is tasked with performing a set of movements to each be represented as a power equation in terms of their velocity. The obtained equations were subsequently used as the primary optimization objective for the path planning algorithm, which included yaw angle objectives and constraints. A set of experiments was performed with Crazyflie quadrotors to verify the model and the algorithm. The results showcased the accuracy of the energy consumption model, which was kept at a maximum difference of 0.6%. The designed path planning algorithm obtained greater energy efficiency in the generated paths compared to other state-of-the-art evolutionary algorithms with similar objectives and constraints. © Authors retain all.

关键词： Motion planning

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