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19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2023

作者： Zhang, Qiao School of Interactive Computing College of Computing Georgia Institute of Technology Technology Square Research Building 85 5th St NW AtlantaGA30332 United States

ISBN: (纸本)157735883X

With the goal of better understanding Human-machine Teaming (HMT) dynamics and how team competencies that are transportable across contexts can lead to different teaming behaviors and team performances, I propose a series of three studies to explore communication, coordination and adaptation in HMT paradigms. I implement and integrate multiple AI agents and use collaborative games as testing environments to evaluate teaming effects. My work can provide findings to two higher level research questions that are widely studied in HMT: 1) the bidirectional behaviors that human and AI agents may develop when working as a team and, 2) how different types of AI agents can impact the teaming efficiency in human-AI teaming. Besides, my work can also contribute to Human-Computer Interaction and Game AI scholarship with insights into teaming dynamics in Human-AI teaming. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Computer games

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Evaluation of Virtual Reality for Early-Stage Structure and Production Planning for Industrial buildings

Evaluation of Virtual Reality for Early-Stage Structure and ...

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2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Podkosova, Iana Reisinger, Julia Zahlbruckner, Maria Antonia Kovacic, Iva Kaufmann, Hannes Tu Wien Institute of Visual Computing and Human-Centered Technology Austria Tu Wien Institute of Building and Industrial Construction Austria

ISBN: (纸本)9798350328912

Efficient communication between stakeholders is key to successful project planning in the AEC industry. The workflow proposed in the research project BIMFlexi enables interdisciplinary exchange already at the early stages of industrial building planning by using building Information Modeling for integral design of the future production and the building structure. Communication in BIMFlexi is supported by a Virtual Reality platform, BIMFlexi-VR, that provides a multi-user virtual environment in which architects, structural engineers, production planners and production owners can collaboratively change parameters of the planned building and see the resulting changes in the building geometry and various fitness metrics in real-time. This paper discusses the results of the evaluation of BIMFlexi-VR by interdisciplinary teams of professionals from the AEC industry, which identified a strong potential of our platform for interdisciplinary presentation and communication. © 2023 IEEE.

关键词： Virtual reality

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AI-Driven Risk Assessment in Food Safety Using EU RASFF Database

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Food and Bioprocess technology 2025年 1-22页

作者： Sari, Omer Faruk Amer, Eslam Bader-El-Den, Mohamed Ince, Volkan Leadley, Craig School of Computing University of Portsmouth Buckingham Building Lion Terrace England PortsmouthPO1 3HE United Kingdom Institute of Food Science & Technology 5 Cambridge Court 210 Shepherds Bush Road England LondonW6 7NJ United Kingdom

Abstract: Food safety remains a critical public health conceated food causing significant morbidity and mortality worldwide. Accurate and transparent risk assessment is essential to safeguard consumer health and support evidence-based regulatory decisions. This study introduces a comprehensive and novel AI framework that leverages machine learning, deep learning, and transformer-based models to classify food safety risks using the Rapid Alert System for Food and Feed (RASFF) dataset, enhanced through advanced data enrichment techniques. The enriched dataset addressed challenges such as short explanations and class imbalances, resulting in significant performance improvements across all model categories. Transformer-based models, including BERT with an accuracy of 0.978 and RoBERTa with an accuracy of 0.979, outperformed traditional machine learning methods such as logistic regression, which achieved an accuracy of 0.954, and SVM, which reached 0.959, as well as deep learning models like LSTM, which obtained 0.971, and BiLSTM, which achieved 0.97 3. Explainable Artificial Intelligence (XAI) techniques were applied to uncover critical insights into the models’ decision-making processes. Influential features such as salmonella, aflatoxins, and listeria were identified, enhancing model transparency and interpretability. The framework’s combination of data augmentation and XAI improves predictive accuracy while ensuring interpretability, making it suitable for real-time risk prioritization in regulatory surveillance. This study demonstrates the potential of AI-powered approaches for food safety risk assessment, bridging predictive accuracy with transparency to support more reliable and actionable food safety management systems. Graphic abstract: (Figure presented.) © The Author(s) 2025.

关键词： Deep learning Food safety Risk assessment Transformers models XAI

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Consequential effects of leading technology-driven offensive strategy in a universal bank

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International Journal of Information technology and Management 2024年第1期23卷 13-32页

作者： Obeng, Asare Yaw Coleman, Alfred Computer Science Department Kumasi Technical University P.O. Box 854 Kumasi Ghana School of Computing College of Science Engineering and Technology University of South Africa GJ Gerwel Building C4-61 Private Bag X6 Florida1710 South Africa

Globally, banking industry is saddled with intense competition. Management of banking firms are required to formulate effective strategies to drive their capabilities to compete, discover and defend their positions in a competitive industry. İn Ghana, universal banks are leveraging the capabilities of information technology to devise technology-driven offensive strategies. The core of these strategies is embarking on innovative activities to enhance performance and respond to business challenges. A qualitative analysis was performed using primary data from 17 participants. Using NVivo, consequential effects of IS-technological innovation (ISTI) on business challenges, innovation performance, operational performance;and specific moderating factors of ISTI were assessed. Strategic-IS Project impacts strong (r = 0.634601) and positive on ISTI than the other moderators. ISTI impacts strongly (r = 0.644951) and positively on operational performance. With r = 0.7422, innovation performance positively and strongly influences operational performance. ISTI impacts positively on business challenges. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Digital storage

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Implementation of YOLOv5 and Vision OCR Hybrid Model for GD&T Recognition 6

Implementation of YOLOv5 and Vision OCR Hybrid Model for GD&...

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6th IEEE Symposium on Computers and Informatics, ISCI 2024

作者： Yazed, Muhammad Syukri Mohd Shaubari, Ezak Fadzrin Ahmad Yap, Moi Hoon Faculty of Computer Science and Information Technology Department of Multimedia Johor Batu Pahat Malaysia Manchester Metropolitan University Department of Image Vision and Computing E116b John Dalton Building United Kingdom

ISBN: (纸本)9798350353853

The interpretation of Geometric Dimensioning and Tolerancing (GD&T) in engineering drawings is a critical aspect of design and manufacturing processes. However, traditional methods of manual annotation are time-consuming and often lead to variability in understanding, which can impact product functionality and inspection outcomes. To address these challenges, this paper proposes an automated approach to recognize GD&T in engineering drawings by combining deep learning techniques. The primary objective of this paper is to develop a hybrid model that integrates the YOLOv5 object detection model and Vision OCR for symbol, text, and character extraction. The purpose is to streamline the interpretation process and improve the accuracy of GD&T recognition in engineering drawings. By training the YOLOv5 model on a diverse dataset and employing Vision OCR for text retrieval, the model aims to detect objects and extract relevant text efficiently. Performance evaluation metrics, including precision, recall, and mean Average Precision (mAP), are used to assess the effectiveness of the proposed hybrid model. Experimental results demonstrate promising outcomes, with the model achieving high precision and recall rates, as well as a strong mAP score. These results indicate that the hybrid model can accurately recognize objects and text within engineering drawings up to 80%, thereby addressing the problem of inefficiency and variability associated with manual GD&T interpretation. This paper offers a novel solution to automate GD&T recognition in engineering drawings, contributing to enhanced efficiency and accuracy in design interpretation. The proposed model has significant implications for engineering graphics and design practices, as it facilitates better communication and collaboration among engineers, designers, and manufacturers. By streamlining design documentation processes, the hybrid model can be integrated into manufacturing workflows to improve productivity and quality

关键词： Precision engineering

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Modelling Student Performance in a Structural Steel Graduate-based Module: A Comparative Analysis Between K-Nearest Neighbor and Dummy Classifiers 16

Modelling Student Performance in a Structural Steel Graduate...

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16th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2025

作者： Ilunga, Masengo Zimbili, Omphemetse Mampilo, Phahlani Abhishek, Agarwal Department of Civil and Environmental Engineering and Building Science University of South Africa Pretoria South Africa School of Computing University of South Africa Pretoria South Africa Department of Mechanical Engineering College of Science and Technology Royal University of Bhutan Phuentsholing Bhutan

ISBN: (纸本)9781950492848

The predictive strength of the K-Nearest Neighbor (K-NN) and Dummy machine learning classification algorithms is investigated for students’ final score. The dependent variable (label) is defined by a binary class, while the different assessments define the independent variables (features). The latter are the module student assessment marks, and the former covers students’ final score. The two algorithms have been applied to the Structural Analysis, which is an engineering technology module taught at the University of South Africa. Competency level or graduate attribute characterises such a module. The results showed that the accuracy values of K-Nearest Neighbor (K-NN) and Dummy algorithms were 0.95 and 0.79 respectively. However, the values of recall, precision, f1-score, support, kappa coefficient and Matthews correlation coefficient, showed that the Dummy model predicted very poorly the "fail" instances, as opposed to the "pass" instances. Thus, the K-NN classifier outperformed the Dummy classifier. The two algorithms could be simultaneously recommended as guiding tools for academics in predicting students’ final score (as fail or pass). However, K-NN is the only algorithm that could be used for both fail and pass. © 2025 Proceedings IMCIC - International Multi-Conference on Complexity, Informatics and Cybernetics. All rights reserved.

关键词： Nearest neighbor search

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Research on Time-series Visualization Methods for Scientific and Technical Consulting Graph Data

Research on Time-series Visualization Methods for Scientific...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： Hu, Xinyang Wang, Lei Wei, Qiushi Shang, Jin Haihong, E. Beijing University of Posts and Telecommunications School of Computing Beijing China China Mobile Information Technology Co. Ltd Beijing China Jinze Building No.2 Guangningbo Street Xicheng District Beijing China

ISBN: (纸本)9798350301113

The existing open-source knowledge visualization components do not support knowledge graph temporal visualization exploration capabilities and cannot meet the demand of analyzing the dynamic evolutionary changes of industries in science and technology consulting scenarios. In this paper, to address the above problems, we propose a knowledge graph-based temporal visualization component construction and optimization method to build a graph visualization component that supports temporal dynamic exploration based on existing open-source knowledge visualization components to address the demand for industry insight analysis in science and technology consulting graph analysis services. At the same time, a data processing and graph rendering optimization strategy is proposed to improve the overall task execution efficiency in response to the problems of low rendering efficiency and high computational resource occupation rate in the time-series visualization exploration scenario. © 2023 IEEE.

关键词： Time series

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Joint Multiple Efficient Neighbors and Graph Learning for Multi-view Clustering 1

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10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023

作者： Sadjadi, Fatemeh Torra, Vicenç Department of Applied Mathematics Faculty of Sciences and Modern Technologies Graduate University of Advanced Technology Kerman117-76315 Iran Department of Computing Science MIT Building Umeå University Västerbotten Umeå901 87 Sweden

ISBN: (数字)9783031467813

ISBN: (纸本)9783031467806

Graph-based multi-view clustering is a popular method for identifying informative graphs for e.g. computer vision applications. Nevertheless, optimizing sparsity and connectivity simultaneously is challenging. Multi-view clustering (MC) integrates complementary information from different views. However, most existing methods introduce noise or ignore relevant data structures. This paper introduces a Joint multiple efficient neighbors and Graph (JMEG) learning method for MC. Our approach includes a post-processing technique to optimize sparsity and connectivity by means of identifying neighbors efficiently. JMEG also uses partition space and consensus graph learning to uncover data structures efficiently. Experiments show that JMEG outperforms state-of-the-art methods with negligible additional computation cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Data structures

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Rendering Optimization of Massive Map Annotation Point Data Based on Multi Strategy Fusion 12

Rendering Optimization of Massive Map Annotation Point Data ...

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12th International Conference of Information and Communication technology, ICTech 2023

作者： Wei, Zhijie Zhou, Xiaolei Xing, Wentai Bao, Changlin Zhou, Cheng Cao, Ruipu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Qinghai Academy of Building Materials Science Xining China Suzhou Jiaoke Group Co. Ltd Nanjing China

ISBN: (纸本)9798350332919

Aiming at the problems of poor visualization effect and system lag delay caused by the rendering of massive map label points, an optimization strategy combining visual optimization technology and point aggregation algorithm is proposed, and it is officially put into use in a city and town pavement collapse monitoring visualization platform. Compared with the traditional annotation point optimization scheme, firstly, the annotation point classification processing according to the specific business scenario, asynchronous traversal of data combined with documentFragment and other optimization technologies, combined with the K-D tree-based Grid-Distance-based Clustering, by comparing the performance report detected by Google LightHouse, the performance indicators in the page display are improved by about 26%. At the same time, in terms of optimization effect, the number of callout points rendered on the page is controlled, so that the number of reflow redraws caused by page rendering is further reduced. © 2023 IEEE.

关键词： Visualization

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