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Visual analysis of ecological hydrological intelligent process based on the water control of the Internet of Things 5

Visual analysis of ecological hydrological intelligent proce...

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5th International conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Hai, Qing Wang, Changshou Tonglega Li, Na Li, Caiqin Hetao Coll Bayan Nur 015000 Inner Mongolia Peoples R China Hetao Coll Workstn Ecol Governance & Green Dev Bayan Nur 015000 Inner Mongolia Peoples R China

ISBN: (纸本)9781665426428

The WSN system in the ongoing Heihe River Basin Ecology-Hydrological Process Integrated Remote Sensing Observation Joint Experiment has continuously produced a huge amount and a wide variety of automatic observation data. In order to effectively manage and apply these data, an online visualization Web system has been designed and implemented. With the development of the Internet of Things technology, this paper studies the visualization of the ecological hydrological intelligent process of the river basin under the condition of water control, and analyzes the intelligent control of the ecological hydrology of different river basins.

关键词： Intelligent Process visualization Eco-Hydrological visualization Safety Management System Design Visual analysis

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ML-based IoT Communication data analysis Framework for Energy Crisis Management in Commercial Buildings

ML-based IoT Communication Data Analysis Framework for Energ...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International conference on

作者： Ashray Sheth Bansri Choxi Krisha Darji Rajesh Gupta Sudeep Tanwar Jitendra Bhatia Dept. of Elec. and Comm. Engg. Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Sci. and Engg. Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Modern society depends substantially on the consumption of energy, which impacts both environmental sustainability and economic stability. This study analyzes the efficient management of energy use across several zones, with a focus on commercial applications, which account for a considerable amount of worldwide energy consumption. It emphasizes the significance of telecommunications to maintain surveillance on and optimize energy use, utilizing cutting-edge technology like 5G and IoT to send data in real-time and mitigate crises. The proposed system model incorporates multi-sensors to monitor environmental factors such as humidity and temperature, paired with secure data transmission methods to prevent unauthorized access. The findings reveal a significant increase in power consumption from 2018 to 2019, and timely analysis facilitated by telecommunications can substantially assuage the crisis. This research underscores the importance of integrating telecommunication technologies with energy management systems to ensure reliable, efficient, and secure energy usage, thus addressing one of the most pressing challenges in today’s energydependent world.

关键词： Temperature sensors Temperature measurement Energy consumption Crisis management visualization data analysis data visualization Telecommunications data communication Thermal stability

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Spatial-Temporal visualization of Dengue Haemorrhagic Fever Vulnerability in Kediri District, Indonesia, Using K-means Algorithm

Spatial-Temporal Visualization of Dengue Haemorrhagic Fever ...

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International conference on data and Software Engineering (ICoDSE) - data and Software Engineering for Supporting Sustainable Development Goals

作者： Fariza, Arna Mu'arifin Astuti, Defa Widya Politekn Elekt Negeri Surabaya Informat & Comp Eng Dept Surabaya Indonesia

ISBN: (纸本)9781665494533

Dengue Haemorrhagic Fever (DHF) is an infectious disease that remains a major public health concern in Indonesia. Based on data from the Ministry of Health in 2019, the area with the highest number of dengue cases in East Java province is the Kediri district. The spatial and temporal heterogeneity of dengue risk, as well as the factors associated with risk variation, indicate the importance of health issues. The spatial-temporal analysis becomes an important element in preparation for early warning systems for the prevention and controlling dengue fever. This paper provides a new strategy for mapping the level of DHF vulnerability in the villages of Kediri district using the K-means method. Spatial-temporal visualization displays the distribution of DHF susceptibility areas from 2016 to 2019 based on several factors, consisting of the DHF sufferers, patients who died, population, rainfall, and public facilities. The 344 villages are divided into low, medium, and high vulnerability levels using the K-means clustering algorithm. Web-based spatial-temporal visualization is useful to compare the level of DHF vulnerability from year to year. Based on the evaluation of the silhouette coefficient average value of 0.57, it shows a fairly good clustering result.

关键词： Dengue haemorrhagic fever vulnerability spatial-temporal K-means

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Exploring Hybrid Pooling of Pretrained Residual Network for Face Anti-spoofing 23

Exploring Hybrid Pooling of Pretrained Residual Network for ...

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15th International conference on Digital Image Processing, ICDIP 2023

作者： Zhou, Daoxiang Zhang, Yunfei Zhang, Jun College of Data Science Taiyuan University of Technology Taiyuan030024 China

ISBN: (纸本)9798400708237

Face anti-spoofing has become an important part of face recognition system, because it can detect non-living faces in advance, thus the security of the subsequent identification process can be ensured. According to the literature analysis and statistics, the discrimination of handcrafted features is unsatisfying and the high computation burden and overfitting issue of deep learning methods are still unsolved. In this paper, an effective and simple feature extraction method for face anti-spoofing is proposed, which consists of three parts. Firstly, for a given face sample, it is fed to a pretrained residual network, the feature map of a certain layer is extracted. Secondly, first- and second-order pooling are performed simultaneously on the feature map, the goal of which is to learn more discriminative features. Thirdly, the linear support vector machine is exploited to distinguish genuine faces from spoofing ones. The feature distribution visualization and decision scores density show that our learned features possess good intra-class similarity and inter-class diversity. Extensive experiments are conducted on two widely-used spoofing datasets in terms of three standard evaluation metrics: equal error rate, half-total error rate, and area under curve. The comparison results can demonstrate the effectiveness of our proposed method evidently. © 2023 ACM.

关键词： Face recognition

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Assessing the Importance of Video visualization Timings in a Flipped Classroom Context 53

Assessing the Importance of Video Visualization Timings in a...

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53rd IEEE ASEE Frontiers in Education International conference, FIE 2023

作者： Liz-Domínguez, Martín Caeiro-Rodríguez, Manuel Mikic-Fonte, Fernando Llamas-Nistal, Martín AtlanTTic Research Center University of Vigo Vigo36310 Spain

ISBN: (纸本)9798350336429

This Research Work-in-Progress focuses on how students make use of supporting videos provided in a flipped classroom context. More specifically, this study analyzes the academic implications of the timing of video accesses: late (during the few days preceding an exam) or early. The experiment was performed in a first-year engineering course. The course is divided into 6 splits, ranging from 2 to 4 weeks each. Every split introduces new videos and ends with an exam. data from 90 first-year students were collected. Two main analysis tasks were undertaken: assessing the correlation between the number of videos accessed early or late and exam scores, and student clustering depending on the general timing of video accesses. Significant correlation between early accesses and exam scores was only observed on the first split. Also in this first split, 3 student groups were identified via clustering: low activity, mostly late and mostly early. Interestingly, there were significant differences in long-term performance between these groups. This is a valuable result towards the development of an early warning system, as it helps identify students at risk at a early stage. © 2023 IEEE.

关键词： continuous assessment data correlation flipped classroom higher education learning analytics

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Interactive Visual analysis of COVID-19

Interactive Visual Analysis of COVID-19

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International conference on Information Visualisation (IV)

作者： Margaret Varga Adelica Ndoni Susan Traeber-Burdin April Rose Panganiban Valérie Lavigne Department of Biology University of Oxford Oxford United Kingdom NATO Communication and Information Agency (NCIA) The Hague Netherlands Human Machine Systems Fraunhofer FKIE Bonn Germany Air Force Reserch Laboratory Dayton USA Defence Research and Development Canada Valcartierr Canada

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. It was first detected in China in December 2019. It spreads between people in close contact. On the 11 th March 2020 the WHO declared the outbreak of the virus as a pandemic, which signaled a significant acceleration in the global response to the COVID-19 outbreak, and recognized the widespread transmission of the virus across multiple countries and continents. data regarding COVID-19 was gathered and made available for open access. These data sources offer invaluable information for tracking, raising awareness and understanding of COVID-19, recognizing its impact, as well as informing the general public, health authorities, policy makers, situation managers and decision makers. However, COVID-19 data in its raw form is complex and difficult to understand and analyze. The application of visualization together with human factor design principles in a complex systems framework provides an effective means for exploiting these big and complex datasets. These techniques can transform such inherently non-visual data into intuitive visual forms that enable users to gain insight into, and understanding of, information contained within the data. This paper discusses the application of visualization and development of interactive dashboards, set in a complex systems framework, to provide an effective means for the users to explore, analyze and gain awareness of the situation, thus enabling informed decision making.

关键词： COVID-19 visualization Analytical models Pandemics Decision making User centered design data visualization Transforms Complexity theory Complex systems

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Visualizing COVID-19 Vaccination Rate and Vaccination Centre in Malaysia using DBSCAN Clustering model 3

Visualizing COVID-19 Vaccination Rate and Vaccination Centre...

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3rd IEEE International Power and Renewable Energy conference (IPRECON)

作者： Said, Sofiyah Insyirah Mohd Aminuddin, Raihah Abidin, Nor Afirdaus Zainal Nasir, Siti Diana Nabilah Mohd Ibrahim, Asma Zubaida Mohamed Univ Teknol MARA Malacca Branch Fac Comp & Math Sci Jasin Campus Malacca Malaysia Univ Teknol MARA Malacca Branch Coll Comp Informat & Media Jasin Campus Malacca Malaysia Kingston Univ London England City Univ Petaling Jaya Selangor Malaysia

ISBN: (纸本)9781665491754

In February 2021, the Malaysian government launched a vaccination campaign against coronavirus disease 2019 (COVID-19). However, there is a problem in identifying suitable location for vaccination centre should be allocated. At the same time, there are population that living in the rural area and has difficulty to travel to the nearest vaccination centres. Therefore, based on the data of vaccination rate collected by Ministry of Health, the proposed project aims to classify and visualise the data based on number of COVID-19 vaccination rate and centre in Malaysia for the adult and adolescent populations. This project uses machine learning technique called Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The system is developed in Python language platform for back-end development, and PyCharm is utilised for front-end development in web-based platform. This project follows four phases in Waterfall model: requirement analysis, design, implementation, and testing. The system is evaluated for functionality and usability based on user satisfaction and the accuracy of the model. The results of the testing shows that all the functionality of the system have been implemented successfully in the system. The system also rated good according to SUS Questionnaire in usability testing with score of 88.5%. The model of machine learning also achieved a good accuracy score which is greater than 0.3. In conclusion, the data visualization web-based application helps the Malaysian government to identify location for additional vaccination centres in strategic locations and it helps Malaysian people to locate nearby vaccination centres in their area.

关键词： COVID-19 vaccination centre clustering DBSCAN data visualization web-based

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A Visual data Science Solution for visualization and Visual Analytics of Big Sequential data 25

A Visual Data Science Solution for Visualization and Visual ...

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25th International conference Information Visualisation (IV) - AI and Visual Analytics and data Science

作者： Leung, Carson K. Wen, Yan Zhao, Chenru Zheng, Hao Jiang, Fan Cuzzocrea, Alfredo Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Northern British Columbia UNBC Dept Comp Sci Prince George BC Canada Univ Calabria iDEA Lab Arcavacata Di Rende Italy

ISBN: (纸本)9781665438278

In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the initiates of open data also led to the willingness of many government, researchers, and organizations to share their data and make them publicly accessible. An example of open big data is healthcare, disease and epidemiological data such as privacy-preserving statistics on patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Analyzing these open big data can be for social good. For instance, analyzing and mining the disease statistics helps people to get a better understanding of the disease, which may inspire them to take part in preventing, detecting, controlling and combating the disease. As "a picture is worth a thousand words", having the pictorial representation further enhances people's understanding of the data and the corresponding results for the analysis and mining. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. We illustrate the ideas through the visualization and visual analytics of sequences of real-life COVID-19 epidemiological data. Our solution enables people to visualize COVID-19 epidemiological data and their temporal trends. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. Evaluation of these real-life sequential COVID-19 epidemiological data demonstrates the effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data.

关键词： information visualization big data sequences data science visual data science data mining data analytics visual analytics COVID-19

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8th Workshop on Topological Methods in data analysis and visualization, TopoInVis 2019

8th Workshop on Topological Methods in Data Analysis and Vis...

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8th Workshop on Topological Methods in data analysis and visualization, TopoInVis 2019

ISBN: (纸本)9783030834999

The proceedings contain 18 papers. The special focus in this conference is on Topological Methods in data analysis and visualization. The topics include: Topological Feature Search in Time-Varying Multifield data;tensor Fields for data Extraction from Chart Images: Bar Charts and Scatter Plots;a Fast Approximate Skeleton with Guarantees for Any Cloud of Points in a Euclidean Space;topologically Robust B-spline Reconstruction of Fibers from 3D Images;introduction to Vector Field Topology;an Overview of the Topology ToolKit;implementing Persistence-Based Clustering of Point Clouds in the Topology ToolKit;Report of the TopoInVis TTK Hackathon: Experiences, Lessons Learned, and Perspectives;mergemaps: Treemaps for Scientific data;notes on Percolation analysis of Sampled Scalar Fields;distributed Task-Parallel Topology-Controlled Volume Rendering;topology-Based Feature Design and Tracking for Multi-center Cyclones;using Contour Trees in the analysis and visualization of Radio Astronomy data Cubes;objective Finite-Time Flow Topology from Flowmap Expansion and Contraction;coreline Criteria for Inertial Particle Motion;implicit visualization of 2D Vector Field Topology for Periodic Orbit Detection.

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Unsteady Flow Field Prediction: Understanding the Dynamics in an Intuitive Physics Way 26

Unsteady Flow Field Prediction: Understanding the Dynamics i...

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26th International conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Qu, Xiyao Liu, Zijing An, Wei Lyu, Hongqiang Liu, Xuejun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Key Lab Aerodynam Noise Control Mianyang Sichuan Peoples R China State Key Lab Aerodynam Mianyang Sichuan Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

The prediction of unsteady flow field is a challenge in the area of fluid dynamics due to the spatiotemporally dynamic behavior. Given high-dimensional spatial information of the unsteady flow field for a number of previous time steps, the subsequent multi-step or even multi-cycle information is to be obtained. Inspired by human intuitive physics, we investigate a purely visual approach for unsteady flow field prediction directly from images which are obtained by mapping physical quantities to the color space, and propose a novel stochastic latent variable model based on deep neural networks. Our model is evaluated on two numerical simulation datasets of different complexities in terms of conditions and scopes of the flow field. The results show that trained with two-cycle data, over ten-cycle future flow fields are accurately predicted by our model on the low-complexity dataset. For the high-complexity one, our model also gives accurate multi-cycle predictions after trained with data of more cycles.

关键词： Deep learning visualization Fluid dynamics Time series analysis Stochastic processes Predictive models data models

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