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Temporal Dynamics and Anomaly Detection in Transactional Networks

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Temporal Dynamics and Anomaly Detection in Transactional Net...

IEEE/ACM International Symposium on Big data Computing (BDC)

作者： Ethan Wei Tommy Dang Anson L. Clark Scholar 2024 Texas Tech University Lubbock United States of America Interactive Data Visualization Lab Texas Tech University Lubbock United States of America

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

In this paper, we use data visualization and visual analytics to tackle the IEEE VAST Challenge 2024 Mini Challenge 3. We use TimeArcs, measures of centrality, and multiple anomaly detection algorithms to visualize the evolution of corporate structure and identify suspicious behavior in the Oceanus Fishing Market. We then create an anomaly detection algorithm combining 3 measures of centrality and isolation forest and local outlier factor algorithms to analyze transactional networks over time and rank nodes by an anomaly score. The algorithm is tested on the VAST Challenge data as well as the 1999 DARPA Intrusion Detection dataset. The algorithm succeeds in ranking many nodes labeled as suspicious during the VAST Challenge with high anomaly scores. It achieves a peak 97.31 percent accuracy on the DARPA dataset. This demonstrates the power of representing different types of data as transactional networks and shows the potential of this algorithm to be used as a versatile tool for other datasets.

关键词： Accuracy Visual analytics Heuristic algorithms data visualization Sea measurements Intrusion detection Forestry Big data Time measurement Anomaly detection

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

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman University of Sydney Biomedical data analysis and visualization lab School of Computer Science NSW Australia Royal Prince Alfred Hospital Department of Molecular Imaging NSW Australia Shanghai Jiao Tong University Institute of Translational Medicine 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. © 2025 IEEE.

关键词： Contrastive Learning

VizLinter: A linter and fixer framework for data visualization

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arXiv 2021年

作者： Chen, Qing Sun, Fuling Xu, Xinyue Chen, Zui Wang, Jiazhe Cao, Nan Intelligent Big Data Visualization Lab Tongji University

— Despite the rising popularity of automated visualization tools, existing systems tend to provide direct results which do not always fit the input data or meet visualization requirements. Therefore, additional specification adjustments are still required in real-world use cases. However, manual adjustments are difficult since most users do not necessarily possess adequate skills or visualization knowledge. Even experienced users might create imperfect visualizations that involve chart construction errors. We present a framework, VizLinter, to help users detect flaws and rectify already-built but defective visualizations. The framework consists of two components, (1) a visualization linter, which applies well-recognized principles to inspect the legitimacy of rendered visualizations, and (2) a visualization fixer, which automatically corrects the detected violations according to the linter. We implement the framework into an online editor prototype based on Vega-Lite specifications. To further evaluate the system, we conduct an in-lab user study. The results prove its effectiveness and efficiency in identifying and fixing errors for data visualizations. © 2021, CC BY.

关键词： visualization

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

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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

关键词： Contrastive Learning

ChartKG: A Knowledge-Graph-Based Representation for Chart Images

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arXiv 2024年

作者： Zhou, Zhiguang Wang, Haoxuan Zhao, Zhengqing Zheng, Fengling Wang, Yongheng Chen, Wei Wang, Yong School of Media and Design Hangzhou Dianzi University CO Hangzhou310018 China School of Media and Design Hangzhou Dianzi University China Big Data Visualization and Human Computer Collaborative Intelligent Laboratory Hangzhou Dianzi University China Research Center of Big Data Intelligence Zhejiang Lab Hangzhou China State Key Lab of CAD&CG Zhejiang University China College of Computing and Data Science Nanyang Technological University Singapore

Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified ***, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG. © 2024, CC BY.

关键词： Knowledge graph

OpenKG Chain:A Blockchain Infrastructure for Open Knowledge Graphs

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data Intelligence 2021年第2期3卷 205-227页

作者： Huajun Chen Ning Hu Guilin Qi Haofen Wang Zhen Bi Jie Li Fan Yang College of Computer Science&AZFT Knowledge Engine Lab Zhejiang UniversityHangzhou 310058China Ontology Foundation Venture Drive#11-31Vision ExchangeSingapore 608526Singapore School of Computer Science and Engineering Southeast UniversityNanjing 211189China Intelligent Big Data Visualization Lab Tongji UniversityShanghai 200092China

The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between *** publishing knowledge bases as open data on the Web has gained significant *** China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge *** existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge *** article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust *** have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple *** have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,*** the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity *** present,all operations on the data sets and tool sets at ***,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted *** this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.

关键词： Open knowledge graph Blockchain Decentralized distributed network Decentralized knowledge graph

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

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SSRN

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

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

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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

visualization of data from HACC Simulations by Paraview

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Visualization of Data from HACC Simulations by Paraview

2019 IEEE Scientific visualization Conference, SciVis 2019

作者： Nguyen, Bao D. Nguyen, Ngan V.T. Pham, Vung Dang, Tommy Interactive Data Visualization Lab Texas Tech University United States

ISBN: (纸本)9781728135120

In this project, we visualize and analyze data of Hybrid Accelerated Cosmology Code (HACC) cosmology simulations. We investigate in point rendering as well as Smoothed-particle hydrodynamics (SPH) interpolation and apply them to visualize the data in a 3D environment. Paraview was used for the rendering task. Some parameters of points in the software were adjusted to find well-qualified visualizations. These approaches provide a clear vision to the impact of Active Galactic Nuclei (AGN) on surrounding matter, and thus, we can gain more information for understanding the nature of AGNs and the universe. © 2019 IEEE.

关键词： data visualization