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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Muhammad Tukur Yehia Boraey Sara Jashari Alberto Jaspe Villanueva Uzair Shah Mahmood Al-Zubaidi Giovanni Pintore Enrico Gobbetti Jens Schneider Marco Agus Noora Fetais College of Science and Engineering HBKU Doha Qatar Department of Computer Science Qatar University Doha Qatar VCCVIS High Perf. Visualization KAUST Jeddah Saudi Arabia Visual and Data-intensive Computing CRS4 Cagliari Italy

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

We discuss virtual staging technologies, focusing on two primary pipelines for creating and exploring immersive indoor environments in the metavers: an AI-based image processing pipeline and a LIDAR-based pipeline. The AI-based image processing pipeline leverages advanced AI algorithms for tasks such as clutter removal, semantic style transfer, and super-resolution, enabling rapid generation of high-quality, photorealistic virtual environments from single panoramic images. The LIDAR-based pipeline captures measurable 3D models of indoor spaces, facilitating immersive editing and collaborative design through real-time interaction with high-fidelity virtual environments. A qualitative comparative analysis of these technologies highlights their strengths and limitations in various applications. The practical implications of these pipelines are discussed, particularly their potential to transform industries such as real estate, furniture retail, interior design, construction, remote collaboration, and immersive training. The paper concludes with suggestions for future research, including conducting user studies, integrating the two pipelines, and optimizing technologies for mobile and edge devices to enhance accessibility and usability.

关键词： Training Solid modeling Three-dimensional displays Image processing Pipelines Superresolution Semantics Collaboration Transforms Usability

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Seeing Your Stories:visualization for Narrative Medicine

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Health data science 2024年第1期4卷 390-405页

作者： Hua Ma Xiaoru Yuan Xu Sun Glyn Lawson Qingfeng Wang Faculty of Science and Engineering University of NottinghamNingbo 315100China Digital Art Department Art&Design Technology InstituteSuzhou 215104China National Key Laboratory of General Artiffcial Intelligence and School of Intelligence Science and Technology Peking UniversityBeijing 100871China Health Data Visualization and Visual Analytics Research Center National Institute of Health Data Science at PKUBeijing 100191China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute University of Nottingham Ningbo ChinaNingbo 315100China Human Factors Research Group Faculty of EngineeringUniversity of NottinghamNottingham NG72RDUK Nottingham University Business School China University of NottinghamNingbo 315100China

Importance:Narrative medicine(NM),in which patient stories play a crucial role in their diagnosis and treatment,can potentially support a more holistic approach to patient care than traditional scientiffc ***,there are some challenges in the implementation of narrative medicine,for example,differences in understanding illnesses between physicians and patients and physicians’increased workloads and overloaded *** paper ffrst presents a review to explore previous visualization research for narrative medicine to bridge the gap between visualization researchers and narrative medicine experts and explore further visualization ***:The review is conducted from 2 perspectives:(a)the contexts and domains in which visualization has been explored for narrative medicine and(b)the forms and solutions applied in these *** applied domains are deffned,including understanding patients from narrative records,medical communication,medical conversation training in education,and psychotherapy and emotional wellness ***:A future work framework illustrates some opportunities for future research,including groups of speciffc directions and future points for the 4 domains and 3 technological exploration opportunities(combination of narrative and medical data visualization,taskaudience-based visual storytelling,and user-centered interactive visualization).Speciffcally,3 directions of future work in medical communication(asynchronous online physician-patient communication,synchronous face-to-face medical conversation,and medical knowledge dissemination)were concluded.

关键词： directions education visualization

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Improving A Bilingual Leaning Material in The Three-Dimensional Space Using Google Translate

Improving A Bilingual Leaning Material in The Three-Dimensio...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： István Károly Boda Erzsébet Tóth László T. Nagy Department of Mathematics and Informatics Debrecen Reformed Theological University Debrecen Hungary Department of Data Science and Visualization University of Debrecen Debrecen Hungary

In our paper we introduce a bilingual language learning material based on the three dimensional virtual library model (3DVLM). This is an inherent part of the virtual library project started in 2013 in the framework of the Cognitive Infocommunications (CogInfoCom) research. The current version of the 3DVLM uses the 3D features and the hypertext-based presentation capabilities of the MaxWhere Seminar System. The latter is especially important because our material is based on web technology and organized so that it is going to be a scale-free network of interconnected nodes. First, we would like to provide a short overview on the virtual library in general, then introduce the organization and structure of the bilingual language learning material we have developed to support English language learning for Hungarian learners at an advanced level. The learning material is based on selected passages from classical literary works, presented both in English and in Hungarian, which we think may improve both English language skills and cultural awareness of language learners through the background knowledge they acquire. In order to check and test the bilingual language learning material we used the Google Translate service which proved to be a very efficient tool for both improving the learning material and deepening the language learning process.

关键词： Seminars Solid modeling Three-dimensional displays Semantics Learning (artificial intelligence) Organizations Syntactics

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Fusion of DRL and CNN for Effective Face Recognition 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Kumar, Ankit Singh, Sushil Kumar Ravikumar, R.N. Khanna, Ashish Brahma, Biswajit Dr. APJ Abdul Kalam Technical University Lucknow India Department of Computer Engineering Marwadi University Rajkot India Computer Science and Engineering Department Maharaja Agrasen Institute of Technology Delhi India Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Chennai India Senior Data and Visualization Engineer McKesson Corporation USA 32559 Lake Bridgeport St FremontCA94555 United States

ISBN: (数字)9789819765812

ISBN: (纸本)9789819765805

Digital Authentication combined with IOT networks is one of the most innovative practices in innovations. IoT devices such as surveillance cameras have acquired real-time video data in the natural environment. Facial Biometrics for System authentication is considered to be a sophisticated technology used in various devices. The correct processing of digital information in a natural environment is one of the challenging tasks. Noise effect inducts random variations on the subject in the natural environment that may hinder the recognition efficiency of the model. The proposed system recognizes facial biometric features under the natural environment that may contain non-symmetrical randomness of variations. The system's objective is to mitigate the false recognition rates under adverse environmental conditions. It combines digital information with a person’s real-time environment. The detection of the facial region of interest form frames in a real-time dataset has been taken through the Viola-Jones algorithm. Feature extrication using video frames has been developed through Deep Reinforcement Learning (DRL) algorithm, which aims to generate binary trees containing feature vectors. Further, the system uses a convolutional neural network (CNN) model to establish the correlation of feature vectors belonging to the facial identity. The model's objective is to retain high recognition of facial biometric trait feature units under various randomness in a natural environment. The proposed system is also tested under various attacks to test the robustness of the proposed model. The model can secure an average accuracy of 98.85%. Digital forensics, crime investigation, online attendance, etc., are the few applications of Facial data authentication from dynamic input. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep reinforcement learning

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Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems

arXiv

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arXiv 2024年

作者： Lakatos, Róbert Pollner, Péter Hajdu, András Joó, Tamás Data-Driven Health Division of National Laboratory for Health Security Health Services Management Training Centre Semmelweis University Hungary Department of Data Science and Visualization Faculty of Informatics University of Debrecen Hungary Doctoral School of Informatics University of Debrecen Hungary Neumann Technology Platform Neumann Nonprofit Ltd

The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented Generation (RAG) are the techniques that can be used to implement domain adaptation for the development of G-LLM-based knowledge systems. In our study, using ROUGE, BLEU, METEOR scores, and cosine similarity, we compare and examine the performance of RAG and FN for the GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2 language models. Based on measurements shown on different datasets, we demonstrate that RAG-based constructions are more efficient than models produced with FN. We point out that connecting RAG and FN is not trivial, because connecting FN models with RAG can cause a decrease in performance. Furthermore, we outline a simple RAG-based architecture which, on average, outperforms the FN models by 16% in terms of the ROGUE score, 15% in the case of the BLEU score, and 53% based on the cosine similarity. This shows the significant advantage of RAG over FN in terms of hallucination, which is not offset by the fact that the average 8% better METEOR score of FN models indicates greater creativity compared to RAG. © 2024, CC BY.

关键词： Knowledge based systems

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Enhancing medical vision-language contrastive learning via inter-matching relation modelling

arXiv

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arXiv 2024年

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman The Biomedical data analysis and visualization lab School of Computer Science The University of Sydney NSW Australia The Department of Molecular Imaging Royal Prince Alfred Hospital NSW Australia The Institute of Translational Medicine Shanghai Jiao Tong University Shanghai China

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Kbpt: Knowledge-Based Prompt Tuning for Zero-Shot Relation Triplet Extraction

SSRN

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SSRN 2023年

作者： Guo, Qian Guo, Yi Zhao, Jin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200436 China Shanghai Engineering Research Center of Big Data & Internet Audience Shanghai200072 China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai200433 China

Knowledge representation is becoming an effective way of information extraction. However, considerable studies ignored its application scenarios in the zero-shot setting. In this paper, we propose a novel framework for prompt language models based on external ontology knowledge called Knowledge-Based Prompt Tuning for the Zero-shot Relation Triplet Extraction (KBPT), which encourages further investigation in low-resource regimes to address the data scarcity problem in Relation Triplet Extraction (RTE). The major task of relation triplet extraction in zero-shot learning is to extract multiple triplets that are consisted of head entities, tail entities, and relation labels from an input sentence where the extracted relation labels do not exist in the training set. The fundamental idea of prompt tuning is to construct a prompt template, then append it behind the input text as the pre-trained language models (PLMs) input, thus, transforming the classification task into a masked language model prediction. However, our proposed model does not involve the mask language model prediction but a well-designed prompt template in a structured text format to generate synthetic training data containing the unseen relation category. Concretely, we utilize the relation labels and incorporate virtual tokens sources from the relation semantics to construct a structured prompt template for generating synthetic training instances. Moreover, to further enrich and supplement prior knowledge, we draw on ontology schema based on external knowledge bases to enhance the capability of semantic representation in the prompt template. To address the problem of knowledge heterogeneity, we synergistically optimize these embedding representations by way of collective training. In addition, we carefully design a Multiple Triplets Decoding (MTD) algorithm to break through the limitation of extracting multiple relation triplets in a sentence, and our proposed model is model-agnostic and can be orthogon

关键词： Ontology

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An updated classification of cyanobacterial orders and families based on phylogenomic and polyphasic analysis (vol 59, pg 12, 2023)

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JOURNAL OF PHYCOLOGY 2023年第3期59卷 635-635页

作者： Strunecky, O. Ivanova, A. P. Mares, J. Faculty of Fisheries and Protection of Waters CENAKVA Institute of Aquaculture and Protection of Waters University of South Bohemia in České Budějovice Na Sádkách 1780 370 05 České Budějovice Czech Republic Contribution: Conceptualization (equal) Formal analysis (lead) Funding acquisition (supporting) Methodology (lead) Visualization (equal) Writing - original draft (equal) Contribution: Data curation (supporting) Formal analysis (supporting) Visualization (supporting) Biology Centre of the CAS Institute of Hydrobiology Na Sádkách 702/7 370 05 České Budějovice Czech Republic Faculty of Science Department of Botany University of South Bohemia Branišovská 1760 370 05 České Budějovice Czech Republic Contribution: Conceptualization (equal) Formal analysis (supporting) Funding acquisition (lead) Methodology (supporting) Writing - original draft (equal)

Cyanobacterial taxonomy is facing a period of rapid changes thanks to the ease of 16S rRNA gene sequencing and established workflows for description of new taxa. Since the last comprehensive review of the cyanobacterial system in 2014 until 2021, at least 273 species in 140 genera were newly described. These taxa were mainly placed into previously defined orders and families although several new families were proposed. However, the classification of most taxa still relied on hierarchical relationships inherited from the classical morphological taxonomy. Similarly, the obviously polyphyletic orders such as Synechococcales and Oscillatoriales were left unchanged. In this study, the rising number of genomic sequences of cyanobacteria and well-described reference strains allowed us to reconstruct a robust phylogenomic tree for taxonomic purposes. A less robust but better sampled 16S rRNA gene phylogeny was mapped to the phylogenomic backbone. Based on both these phylogenies, a polyphasic classification throughout the whole phylum of Cyanobacteria was created, with ten new orders and fifteen new families. The proposed system of cyanobacterial orders and families relied on a phylogenomic tree but still employed phenotypic apomorphies where possible to make it useful for professionals in the field. It was, however, confirmed that morphological convergence of phylogenetically distant taxa was a frequent phenomenon in cyanobacteria. Moreover, the limited phylogenetic informativeness of the 16S rRNA gene, resulting in ambiguous phylogenies above the genus level, emphasized the integration of genomic data as a prerequisite for the conclusive taxonomic placement of a vast number of cyanobacterial genera in the future.

关键词： 16S rRNA Cyanophyceae multilocus phylogenomic polyphasic approach taxonomy Vampirovibriophyceae

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Detecting Oil Palm Plantations and Mangroves in Se Asia Among Igbp Land Cover Units Mapped by the Modis

SSRN

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SSRN 2025年

作者： Ye Myint, Yadanar Percival, Joseph Emile Honour Kitajima, Kaoru Division of Forest and Biomaterials Sciences Graduate School of Agriculture Kyoto University Kyoto Japan Spatial Data Analysis and Visualization Lab University of Hawaii at Hilo Hilo WI96720-4091 United States Department of Geography & Environmental Science University of Hawai‘i at Hilo HiloHI96720 United States

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover type product (MODIS-LC) is widely used for global land cover classification to follow the International Geosphere-Biosphere Programme (IGBP) scheme. The geographical dimension of 1200 km x 1200 km is appropriate for regional and national level tracking of deforestation in SE Asia (4.5 million ha), where oil palm plantations are a key driver of forest loss in SE Asia. Here, we examined forest conversion to other land cover types in eight SE Asian countries during two periods (2001-2010 and 2010-2018) using the MODIS MCD12Q1 data in ArcMap 10.8.2. The results showed that 12% and 9% of forested areas were converted to woody savanna and savanna in 2001-2010 and 2010-2018, respectively. Then, we selected 260 random subsamples (500 m polygons) from each of the common land cover types in SE Asia and verified or reclassified their vegetation with visual examination of high-resolution Google Earth imagery. However, verification with high-resolution images reduced these estimates to 5.1 % and 4.1 %, because 44% and 46% of areas initially classified as woody savannas and savannas were actually oil palms. Using calibration factor for each vegetation type, 47.2 % and 47.9 % of forest losses were estimated to be attributable to conversion to oil palm plantations, whereas the remaining forest loss was attributed to degradation to savannas and grassland. In addition, the IGBP land cover type of permanent wetland was found to consist of mangroves (63%), aqua farms (20%), and oil palms (8%) rather than permanent wetland per se. While MODIS-LC is a powerful dataset for global analyses, regional calibration with high-resolution imagery, as demonstrated in this study, provides insight that can better inform policy makers. © 2025, The Authors. All rights reserved.

关键词： Deforestation

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Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study

arXiv

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arXiv 2024年

作者： Dhar, Mrinal Kanti Wang, Chuanbo Patel, Yash Zhang, Taiyu Niezgoda, Jeffrey Gopalakrishnan, Sandeep Chen, Keke Yu, Zeyun Big Data Analytics and Visualization Lab Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI United States Auxillium Health MilwaukeeWI United States Wound Healing and Tissue Repair Analytics Laboratory School of Biomedical Sciences & Health Care Administration University of Wisconsin Milwaukee MilwaukeeWI United States Department of Computer Science Marquette University MilwaukeeWI United States

Identifying individual tissues, so-called tissue segmentation, in diabetic foot ulcer (DFU) images is a challenging task and little work has been published, largely due to the limited availability of a clinical image dataset. To address this gap, we have created a DFUTissue dataset for the research community to evaluate wound tissue segmentation algorithms. The dataset contains 110 images with tissues labeled by wound experts and 600 unlabeled images. Additionally, we conducted a pilot study on segmenting wound characteristics including fibrin, granulation, and callus using deep learning. Due to the limited amount of annotated data, our framework consists of both supervised learning (SL) and semi-supervised learning (SSL) phases. In the SL phase, we propose a hybrid model featuring a Mix Transformer (MiT-b3) in the encoder and a CNN in the decoder, enhanced by the integration of a parallel spatial and channel squeeze- and-excitation (P-scSE) module known for its efficacy in improving boundary accuracy. The SSL phase employs a pseudo-labeling-based approach, iteratively identifying and incorporating valuable unlabeled images to enhance overall segmentation performance. Comparative evaluations with state-of-the-art methods are conducted for both SL and SSL phases. The SL achieves a Dice Similarity Coefficient (DSC) of 84.89%, which has been improved to 87.64% in the SSL phase. Furthermore, the results are benchmarked against two widely used SSL approaches—Generative Adversarial Networks and Cross-Consistency Training. Additionally, our hybrid model outperforms the state-of-the-art methods with a 92.99% DSC in performing binary segmentation of DFU wound areas when tested on the Chronic Wound dataset. Codes and data are available at https://***/uwm-bigdata/DFUTissueSegNet. © 2024, CC BY-NC-SA.

关键词： Image segmentation

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