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8th International Conference on Image, Vision and Computing, ICIVC 2023

作者： Sun, Xiaowen Han, Tongtong Gong, Zheng Huai, Shuaiheng Hu, Qing College of Information Science and Technology Dalian Maritime University Dalian China

ISBN: (纸本)9798350335231

GNSS is the main source of PNT information for maritime ship users. Its availability is related to the safety of ship navigation, and is also an important state that the maritime management department urgently needs to evaluate and monitor. However, the large amount of monitoring data of Satellite navigation brings huge challenges to the analysis and evaluation of its global availability. This paper uses BDS monitoring data as the data source, and first introduces the specific parameters and format characteristics of BDS's broadcast ephemeris data. Then calculate the real-time position of the satellite based on the ephemeris data. Finally, based on grid theory and visualization techniques, divide the grid points, calculate the number of visible satellites and corresponding PDOP values for each grid point, and thus calculate the navigation availability of that grid point. This paper realizes the visualization display of real-time satellite positions, the number of visible satellites at each grid point and the availability data in a global scale on the MATLAB platform. It can also zoom in on local areas and display visually and clearly the availability of BDS navigation services, providing an important reference for maritime management departments to conduct maritime navigation safety assessment and assist decision-making. © 2023 ieee.

关键词： data visualization

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Application of Advanced Systems of User Identification in APCS Control Subsystems 16

Application of Advanced Systems of User Identification in AP...

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16th International Conference Management of large-Scale System Development, MLSD 2023

作者： Meshcheryakov, Roman V. Iskhakov, Andrey Y. Mamchenko, Mark V. V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Laboratory of Cyber-Physical Systems Moscow Russia

ISBN: (纸本)9798350337907

This paper presents research on applying the user identification subsystem for industrial automation systems. Within the framework of this study, we propose software and hardware architecture for the analysis of the effectiveness of identification systems using the browser fingerprints, including a backend server with SQL database and software modules for the analysis and visualization of the data collected. The results of the experiment show that modern frameworks and modules can be used for user identification in industrial automation systems. However, additional verification procedures for user control and management system are required. The maximum average accuracy of user identification based on their integrated browser fingerprint increases with the total number of authorizations (up to 92-93% at the end of the experiment), with almost no new fingerprints generated. © 2023 ieee.

关键词： data visualization

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Zero-Shot Hashing via Asymmetric Ratio Similarity Matrix

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第5期35卷 5426-5437页

作者： Shi, Yang Nie, Xiushan Liu, Xingbo Yang, Lu Yin, Yilong Shandong Univ Sch Software Jinan 250100 Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250100 Peoples R China

Zero-shot hashing targets to learn the hash codes of images in unseen classes based on the limited training data provided by seen classes. In zero-shot hashing, transferring the supervised knowledge, such as attributes and semantic relations, from seen classes to unseen ones is a widely employed method, where the performance is always subject to the ability to capture these supervised knowledge (which is always difficult to obtain). Therefore, in this study, we propose a new methodology for zero-shot hashing via an asymmetric ratio similarity matrix (ASZH), which only needs to calculate the semantic similarity among seen classes for hash learning. Specifically, we use an asymmetric ratio matrix in the similarity calculation to further explore the influence of similarity, where the values of positive weights for similar samples are not equivalent to those of negative ones for dissimilar samples. Additionally, a theoretical analysis regarding the utilization of an asymmetric ratio matrix is provided in this study. The experiments on three large benchmark datasets indicate that the proposed method achieves excellent performance than several state-of-the-art hashing methods.

关键词： Semantics Symmetric matrices Optimization Binary codes Hash functions Hamming distance visualization Hashing zero-shot asymmetric ratio similarity matrix

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A visualization Method for Association analysis and Fusion Based on large-Scale Polymorphic Information 24

A Visualization Method for Association Analysis and Fusion B...

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24th ieee International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Song, Chao-Nan Cao, Yi Jin, Jin Fu, Qiong-Ying Li, Rong-Sen Wang, Hai-Bo State Key Laboratory of Intelligent Game Beijing China China People's Liberation Army National Defence University Beijing China

ISBN: (纸本)9798350365658

Based on mining the association relationship in the War Game data, this study extracts the key actions to provide support for the auxiliary operation deduction, operation process description and review evaluation. Based on the construction of evaluation system, relationship mining and target value ranking, this paper proposes a technical route to realize the analysis and fusion display method of operational relationship and action impact in war game data. According to the needs of different levels and angles, the distribution display and trend of data situation are presented on the visual interface. It provides theoretical guidance and practical reference for the design of deduction data fusion presentation, which is helpful to reduce the cognitive load of decision-makers, enhance situation awareness and improve decision-making efficiency. © 2024 ieee.

关键词： data fusion

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SoK: A data-driven View on Methods to Detect Reflective Amplification DDoS Attacks Using Honeypots 8

SoK: A Data-driven View on Methods to Detect Reflective Ampl...

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8th ieee European symposium on Security and Privacy (EuroS and P)

作者： Nawrocki, Marcin John, Kristoff Hiesgen, Raphael Kanich, Chris Schmidt, Thomas C. Waehlisch, Matthias Free Univ Berlin Berlin Germany Tech Univ Dresden Dresden Germany Univ Illinois Chicago IL USA NETSCOUT Westford MA USA HAW Hamburg Hamburg Germany

ISBN: (纸本)9781665465120

In this paper, we revisit the use of honeypots for detecting reflective amplification attacks. These measurement tools require careful design of both data collection and data analysis including cautious threshold inference. We survey common amplification honeypot platforms as well as the underlying methods to infer attack detection thresholds and to extract knowledge from the data. By systematically exploring the threshold space, we find most honeypot platforms produce comparable results despite their different configurations. Moreover, by applying data from a large-scale honeypot deployment, network telescopes, and a real-world baseline obtained from a leading DDoS mitigation provider, we question the fundamental assumption of honeypot research that convergence of observations can imply their completeness. Conclusively we derive guidance on precise, reproducible honeypot research, and present open challenges.

关键词： Honeypot DDoS Amplification Attacks Systemization of Knowledge

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Federated analysis of Neuroimaging data Using Coinstac 22

Federated Analysis of Neuroimaging Data Using Coinstac

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22nd ieee International symposium on Biomedical Imaging, ISBI 2025

作者： Basodi, Sunitha Kelly, Ross Romero, Javier Martin, Dylan Panta, Sandeep Plis, Sergey Sarwate, Anand D. Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University TReNDS Center Atlanta United States Rutgers University Department of Electrical & Computer Engineering NJ United States

ISBN: (纸本)9798331520526

Federated analysis can help perform large-scale analyses using neuroimaging datasets across various research groups overcoming the limitations of institutional data-sharing poli-cies, privacy or regulatory concerns as it requires no data sharing. In this work, we showcase our federated platform, COINSTAC, a fully secure, privacy-preserving neuroinfor-matics platform that allows large-scale analysis of federated neuroimaging data (no sharing) and offers several feder-ated algorithms with results on par with centralized analysis (shared/pooled). All federated computations (applications) currently existing in the COINSTAC are trained and tested to ensure that the results of these federated methods perform similarly to their corresponding centralized methods. © 2025 ieee.

关键词： COINSTAC ENIGMA federated freesurfer MRI neuroimaging

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Overview of Machine Learning Methods for Stroke Detection using Weather data 25

Overview of Machine Learning Methods for Stroke Detection us...

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25th International symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Ploscar, Andreea Alina Marc, Anastasia-Daria Aldea, Cristina Caterina Coroiu, Adriana Mihaela Babes Bolyai Univ Dept Comp Sci Cluj Napoca Romania Iuliu Hatieganu Univ Med & Pharm Dept Neurosurg Cluj Napoca Romania

ISBN: (纸本)9798350394139;9798350394122

The study presents a comparison between Machine Learning approaches, such as Decision Trees, XGBoost, Linear Discriminant analysis, and Histogram Boost for detecting days with a higher likelihood of an increased incidence of stroke occurrences in the region of Transylvania, Romania, using meteorological data. The study brings an original contribution to existing work by including air fronts in the analysis and focusing on machine learning methods, being the first one to approach this issue in Romania. Furthermore, it covers a large period of 10 years (2013-2022). The proposed methods include dimensionality reduction and clustering techniques, as the initial dataset contains a large class imbalance, with the positive class representing critical days composing approximate to 1/20 of the entire dataset. Based on the findings outlined, the combination of Kernel PCA, K-means, XGBoost, and Feature Selection emerges as the most effective pipeline, resulting in an overall metric of 74% when evaluated on the test dataset.

关键词： stroke machine learning classification subarachnoid hemorrhage weather air front

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PhenoAlign: A Hybrid data-Knowledge-Driven Approach for Precisely Aligning Phenotype Information in Medical Texts

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ieee Journal of Biomedical and Health Informatics 2025年第6期PP卷 PP页

作者： Yang, Tao Chen, Luming Deng, Lizong Jiang, Taijiao Guangzhou Medical University Guangzhou510182 China Guangzhou National Laboratory Guangzhou510005 China Chinese Academy of Medical Sciences Peking Union Medical College Institute of Systems Medicine Beijing100005 China Suzhou Institute of Systems Medicine Suzhou215123 China The First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease The Key Laboratory of Advanced Interdisciplinary Studies Center Guangzhou510120 China

Precisely aligning phenotypic information within medical texts is paramount in advancing intelligent medical applications, such as similar patient case retrieval. However, despite its criticality, an algorithm specifically designed for this task is lacking. We previously introduced a fine-grained semantic information model, the semantic structured unit of phenotypes (PhenoSSU), and an automatic extraction algorithm. This model accurately characterizes and extracts phenotypic information from medical texts. In this study, we explore different PhenoSSU alignment strategies. The results show that the data-knowledge-driven approach best aligns PhenoSSUs. Specifically, employing a BERT-based pre-trained language model (PLMs) to align phrase-type PhenoSSUs and a knowledge-based method for logic-type PhenoSSUs demonstrates efficacy. Moreover, by successfully integrating the PhenoSSU alignment and extraction algorithms, we have developed PhenoAlign, a novel medical text phenotype alignment tool. This tool facilitates precisely aligning phenotypic information by processing two medical texts, generating accurate alignment outcomes. PhenoAlign exhibited satisfactory medical text phenotypic alignment using the expert annotated gold standard test set, with an end-to-end F1 score of 0.820. The F1 scores for phenoSSU extraction and alignment were 0.885 and 0.927, respectively. Our analysis extends to the potential application of ChatGPT in phenotype alignment tasks, and find significant challenges encountered by large language models in this domain. We developed a simple, effective tool for medical text phenotype information alignment. This tool will be valuable to intelligent medical applications, facilitating patient care and medical research advancements. © 2013 ieee.

关键词： Semantics

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Semi-Supervised Root-Cause analysis with Co-Training for Integrated Systems 40

Semi-Supervised Root-Cause Analysis with Co-Training for Int...

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40th ieee VLSI Test symposium (VTS)

作者： Pan, Renjian Li, Xin Chakrabarty, Krishnendu Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781665410601

The increasing complexity of integrated systems has exacerbated the challenges associated with system diagnosis. To tackle these challenges, intelligent root-cause-analysis facilitated by machine learning has been proposed in recent years. However, most of these methods rely on a large amount of data with root-cause labels, which are often either not available or difficult to obtain. In this paper, we propose a semi-supervised root-cause-analysis method with co-training, where only a small set of labeled data is required. Using random forest as the learning kernel, a co-training technique is proposed to leverage the unlabeled data by automatically pre-labeling a subset of them and retraining each decision tree. In addition, several novel techniques are proposed to avoid over-fitting and determine hyper-parameters. Two case studies based on industrial designs demonstrate that the proposed approach significantly outperforms state-of-the-art methods by saving up to 43% of labeling efforts by human experts.

关键词： Very large scale integration Complexity theory Labeling Decision trees Kernel Random forests

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Neural Flow Map Reconstruction

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COMPUTER GRAPHICS FORUM 2022年第3期41卷 391-402页

作者： Sahoo, S. Lu, Y. Berger, M. Vanderbilt Univ Nashville TN 37235 USA

In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time-varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generate in situ that are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time-varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem - learning a function-space neural network to reproduce flow map samples under a fixed integration scheme - leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.

关键词： CCS Concepts • Computing methodologies → Neural networks Reconstruction • Human-centered computing → Scientific visualization

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