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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Yen, Hew Sok Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Issa, Bayan Yong, Yoke Leng Bashir, Faizah Mohammed Falude, Emmanuel Dodo, Yakubu Aminu Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Petaling Jaya47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria HELP University Persiaran Cakerawala School of Information and Communication Technology Subang Bestari Seksyen Shah AlamU4 40150 Malaysia University of Hail College of Engineering Department of Decoration and Interior Design Engineering Hail55427 Saudi Arabia Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Najran University College of Engineering Architectural Engineering Department Najran66426 Saudi Arabia

ISBN: (纸本)9798350391398

Due to growth in e-commerce, recommender systems have gained importance in providing recommendations to the user to help him or her make a choice. These systems not only assist the users in searching through the extensive product list but also increase businesses' productivity by recommending products. The aim of this project is to suggest a hybrid recommender system which combines the aspects of collaborative and content-based filtering so as to enhance the efficiency of the recommendation system in e-commerce. In addition, it is to evaluate the performance of this model with the individual collaborative and content-based models. The study involved creating the CF and CBF models with the various algorithms like the Matrix Factorization, KNN, SVD, and the TF-IDF vectorization. The best models from CF and CBF were then combined with a Hybrid Filtering (HF) model by using weighted and switching combination to enhance the model's effectiveness. The performance of the CF, CBF and the HF models was compared, in order to check if the proposed hybrid approach is better than the existing models. The results of the experiment show that the hybrid switching model provided better results than the CF and CBF models on their own. The hybrid filtering model had also managed to reduce the RMSE to 1.065 and improved precision to 83.2%. This demonstrates the advantage of the proposed model over the traditional CF and CBF approaches. Our study validates that the hybrid filtering recommender system, especially the switching combination strategy, outperformed the traditional standalone models by integrating the benefits of both collaborative and content-based filtering. This provides a good starting point for future work and there are many directions in which the research can be expanded. © 2024 IEEE.

关键词： Wiener filtering

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YOLO-CCA: A Context-Based Approach for Traffic Sign Detection

arXiv

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arXiv 2024年

作者： Jiang, Linfeng Zhan, Peidong Bai, Ting Yu, Haoyong School of Artificial Intelligence Chongqing University of Technology Chongqing404100 China School of Computing College of Design and Engineering National University of Singapore 119077 Singapore School of Civil and Environmental Engineering Cornell University IthacaNY United States

Traffic sign detection is crucial for improving road safety and advancing autonomous driving technologies. Due to the complexity of driving environments, traffic sign detection frequently encounters a range of challenges, including low resolution, limited feature information, and small object sizes. These challenges significantly hinder the effective extraction of features from traffic signs, resulting in false positives and false negatives in object detection. To address these challenges, it is essential to explore more efficient and accurate approaches for traffic sign detection. This paper proposes a context-based algorithm for traffic sign detection, which utilizes YOLOv7 as the baseline model. Firstly, we propose an adaptive local context feature enhancement (LCFE) module using multi-scale dilation convolution to capture potential relationships between the object and surrounding areas. This module supplements the network with additional local context information. Secondly, we propose a global context feature collection (GCFC) module to extract key location features from the entire image scene as global context information. Finally, we build a Transformer-based context collection augmentation (CCA) module to process the collected local context and global context, which achieves superior multi-level feature fusion results for YOLOv7 without bringing in additional complexity. Extensive experimental studies performed on the Tsinghua-Tencent 100K dataset show that the mAP of our method is 92.1%. Compared with YOLOv7, our approach improves 3.9% in mAP, while the amount of parameters is reduced by 2.7M. On the CCTSDB2021 dataset the mAP is improved by 0.9%. These results show that our approach achieves higher detection accuracy with fewer parameters. The source code is available at https://***/zippiest/yolo-cca. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Community Energy Storage System for Cost Benefits

Community Energy Storage System for Cost Benefits

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IEEE Jordan International Joint Conference on Electrical engineering and Information Technology (JEEIT)

作者： Augustine Ikpehai Olajide Sunkanmi Kelvin Anoh Dept. of Engineering & Mathematics Sheffield Hallam University Sheffield UK School of Engineering Computing & Design University of Chichester Bognor Regis UK

Future low-carbon energy systems will be people-centred. However, optimal utilisation of renewable-based distributed generation in neighbourhood energy market (NEM) remains a limiting factor. This paper investigates a NEM and evaluates the benefits of central energy storage system (ESS) in maximising collective self-consumption and savings, using Sheffield city centre, UK as a case study. The results show that the central ESS has a significant impact on utilization of renewable resources, reduces cost of energy and offers flexibility of the grid through peak reduction. In particular, use of central ESS reduces peak demand by up to 55 % while the community's self-consumption and self-sufficiency increased by 28.8% and 32 % respectively.

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Review and concepts on how construction and facilities management can utilize barcode and QR code technology to access documents onsite

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Procedia Computer Science 2025年 253卷 1313-1322页

作者： Robert Lopez Alexander Rose Lecturer Construction Management Discipline School of Design and the Built Environment (DBE) Curtin University GPO Box U1987 Perth 6845 Australia Innovation Central Perth (ICP) Intern Computing Discipline School of Electrical Engineering Computing and Mathematical Sciences (EECMS) Curtin University GPO Box U1987 Perth 6845 Australia

This research aimed to determine the applicability of utilizing barcode and QR code technology to improve construction and facilities management efficiency. The significance is that this technology can automate onsite access of construction practitioners and facilities management to documents. An investigation of the technological maturity to achieve this is presented, focusing on what their technology companies are capable of providing. Comparative analyses of two relevant technology provider companies and the four innovative concepts they proposed were performed. Their business models, functions, capabilities and devices were compared. Each technology provider company is capable of offering a greater diversity of products and services than their involvement in proposing these concepts. This research is timely due to limited previous studies on the utilization of this technology to automate construction and facilities management. Fundamental to their implementation feasibility are issues of company policies, quality, cost, customer service, terms and conditions, capabilities, use, storage, security, building information modeling, other devices or operating systems.

关键词： Barcode QR Code Documentation Construction Management Facilities Management

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CE-FFT: Communication-Efficient Federated Fine-Tuning for Large Language Models via Quantization and In-Context Learning

CE-FFT: Communication-Efficient Federated Fine-Tuning for La...

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

作者： Pengyu Zhang Yingjie Liu Yingbo Zhou Ming Hu Xian Wei Mingsong Chen MoE Engineering Research Center of SW/HW Co-Design Technology and Application East China Normal University School of Computing and Information Systems Singapore Management University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Although Federated Fine-Tuning (FFT) facilitates the fine-tuning of Large Language Models (LLMs) across data owners without compromising their privacy, it suffers from severe communication overheads caused by numerous parameters of LLMs even with Parameter-Efficient Fine-Tuning (PEFT) methods. To address this, we propose a novel communication-efficient FFT framework by reducing per-round transmission costs and simultaneously diminishing the required convergence rounds. Specifically, we freeze the parameter-intensive backbone of LLMs, activate low-rank adapters for fine-tuning, and design a parameter quantization mechanism to reduce per-round communication costs. Additionally, inspired by in-context learning, we align the distributions of local data by identifying and rewriting unimportant samples using the global model, retaining important local information, and guiding local fine-tuning towards a uniform trajectory, thereby accelerating convergence and reducing transmission burdens. Experiments on two instruction datasets show that our method can reduce communication costs by up to 7.76× without compromising performance.

关键词： Training Data privacy Costs Quantization (signal) Large language models LoRa Data models Trajectory Speech processing Convergence

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Teacher-Led Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts 15

Teacher-Led Debriefing in Computer-Supported Collaborative L...

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15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022

作者： Amarasinghe, Ishari Hernández-Leo, Davinia Manathunga, Kalpani Pérez, Jonathan Chacón Dimitriadis, Yannis Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka ELISAVA Barcelona School of Design and Engineering Barcelona Spain School of Telecommunication Engineering Universidad de Valladolid Valladolid Spain

ISBN: (纸本)9781737330646

Debriefing is an integral part of orchestration and provides a space for teachers to review the learning experience. Although this concept is not new, little is known about how debriefing is conducted in scripted computer-supported collaborative learning situations, and its effects on students' learning gains. Moreover, there is a lack of studies providing evidence of how learning analytics can be effectively utilised to support teacher-led debriefing. The objective of this study is twofold: Firstly, it studies how debriefing impacts students' learning gains in Pyramid pattern-based learning situations. Secondly, it explores the types of learning analytics indicators that can support debriefing. Results indicated that debriefing can contribute to improve students' learning gains, however, it does not always lead to the optimal outcomes and the type of task can have a major influence. Mechanisms such as semantic similarity score, knowledge graph visualisations and flag features are scrutinized as options to support debriefing. © ISLS.

关键词： E-learning

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TherAIssist: Assisting Art Therapy Homework and Client-Practitioner Collaboration through Human-AI Interaction

arXiv

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arXiv 2025年

作者： Liu, Di Bai, Jingwen Zhang, Zhuoyi Zhang, Yilin Zhang, Zhenhao Zhao, Jian An, Pengcheng School of Design SUSTech Shenzhen China School of Computing National University of Singapore Singapore Singapore Department of Human Centered Design & Engineering University of Washington Seattle United States Carnegie Mellon University Pittsburgh United States University of Waterloo Waterloo Canada

Art therapy homework is essential for fostering clients’ reflection on daily experiences between sessions. However, current practices present challenges: clients often lack guidance for completing tasks that combine art-making and verbal expression, while therapists find it difficult to track and tailor homework. How HCI systems might support art therapy homework remains underexplored. To address this, we present TherAIssist, comprising a client-facing application leveraging human-AI co-creative art-making and conversational agents to facilitate homework, and a therapist-facing application enabling customization of homework agents and AI-compiled homework history. A 30-day field study with 24 clients and 5 therapists showed how TherAIssist supported clients’ homework and reflection in their everyday settings. Results also revealed how therapists infused their practice principles and personal touch into the agents to offer tailored homework, and how AI-compiled homework history became a meaningful resource for in-session interactions. Implications for designing human-AI systems to facilitate asynchronous client-practitioner collaboration are discussed. © 2025, CC BY.

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Statistical Analysis of Digital Financial Technology Adoption Research

Statistical Analysis of Digital Financial Technology Adoptio...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Temiloluwa Iyanuoluwa Ayodele, Olamide Emmanuel Ajibade, Samuel-Soma M. Goles, Nanet A. Dayupay, Johnry P. Adediran, Anthonia Oluwatosin Scholar School System Department of Business Management Birmingham United Kingdom Ekiti State University Department of Banking and Finance Ekiti State Ado Ekiti Nigeria Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Malaysia Cebu Technological University Moalboal Cebu Philippines Federal University of Uberlandia Faculty of Architecture and Urban Design Uberlandia Brazil

ISBN: (纸本)9798350358155

Digital financial technology adoption (DFTA) studies published in Scopus between 2019 and 2022 were examined in this study. After that, we assessed relevant literature, did bibliometric studies, and examined publication and citation patterns. The statistics showed 2643 citations and 176 publications. DFTA research has gained popularity when time-varying TP and TC values increased gradually. This topic's most popular sources are "articles"and "Sustainability (Switzerland)". A search of DFTA research stakeholders revealed several authors, organizations, and nations. Most famously associated with the University of Johannesburg in South Africa, David Mhlanga has produced more publications than anyone else. The National Natural Science Foundation and other Chinese donors who dominate DFTA research funding contributed to researchers' and affiliations' output. Cluster and hotspot analysis revealed two themes, "Digital financial technology adoption"and "Sustainable Banking Inclusion,"and three keywords, Digital financial technology adoption, monetary inclusion, and finance. According to the examined literature, DFTA's effects on international financial stability and GDP growth are understudied. Future research should examine DFTA's environmental impact, long-term profitability, and commercial strategies for low-income and rural communities. © 2024 IEEE.

关键词： Decentralized finance

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Semantic Analysis Using Deep Learning for Predicting Stock Trends

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Procedia Computer Science 2024年 235卷 820-829页

作者： Manisha Galphade V.B. Nikam Dhanalekshmi Yedurkar Prabhishek Singh Thompson Stephan Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set.

关键词： Deep learning emotion mining financial analysts investors sentiment analysis

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Bridging the Modality Gap: Generative Adversarial Networks for T1-T2 MRI Image Translation

Bridging the Modality Gap: Generative Adversarial Networks f...

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International Conference on computing and Networking Technology (ICCNT)

作者： Mayank Pandey Naga Sai Shreya Kunda Pranave Kc Tripty Singh Rekha R Nair Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Dept. of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

CycleGAN, a significant deep learning model, has transformed medical imaging by providing a remedy for translating MRI images between different modalities. Its ability to transform T1-weighted to T2-weighted images is crucial in healthcare environments where obtaining multiple scans can be time-consuming and burdensome for patients. This can be achieved by utilizing CycleGAN, which generates images that contains vital information that will help the healthcare professionals to diagnose the patient without exposing them to further radiations. This unleashes an effective and safer way of diagnosing patients. The adaptability of CycleGAN also allows scalability and the ability to customize it for different types of medical images, where scanning can be achieved with less exposure to radiation, without loosing or compromising any important information.

关键词： Deep learning Magnetic resonance imaging Scalability Magnetic resonance Medical services Transforms Generative adversarial networks Medical diagnostic imaging

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