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"abstract": "The Voronoi Treemap is a space-filling treemap technique that relaxes the constraints of rectangular nodes. Its organic shapes maintain a one-to-one aspect ratio, are flexible with their placement, allowing stable zooming and dynamic data values. In this paper, we present algorithms for efficient computation and dynamic update of Voronoi Treemaps. Our GPGPU-based technique allows for rapid computation of centroidal Voronoi Diagrams, providing almost two orders of magnitude speedup over previous work. In addition, we present a hierarchical algorithm for stable updates. Finally, we demonstrate the application of Voronoi treemaps to real-world dynamic datasets, including interactive navigation.",
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"doi": "10.1109/DCC.1993.253117",
"title": "Tree compacting transformations",
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"abstract": "This paper concerns one aspect of the construction of compact representations R(T) for trees T which are instances of an abstract tree data type. The ADT supports operations, on certain trees, built around a top-down traversal primitive, and provides the interface between the second and third stages of a general semantic compression system. The mechanisms used ensure that the time taken to perform all such operations using R(T) is linear with respect to performing the operation directly on T. They fall into two categories: invertible transformations on T which produce an equivalent tree with fewer elements (data or child specifications) than the input form; and the exploitation of statistical variations in the occurrence of data elements to reduce the average space required to represent remaining components.<>",
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"title": "1992 Fourth International Conference on Computing and Information",
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"abstract": "An algorithm to generate all maximal independent sets in lexicographic order with polynomial time delay between the output of two successive independent sets was proposed by Johnson and Yannakakis (Inform. Process. Let., vol.27, p.119-23, 1988). This algorithm needs exponential amount of space. Johnson suggested an open problem which is to find such an algorithm with polynomial time-delay and space needed between the output of two successive maximal independent sets. The authors investigate this problem on trees. They first introduce a new problem, the constrained maximal independent set problem, which is NP-complete for general graphs. They show that, for trees, the constrained maximal independent set problem can be solved in theta (n) time, where n is the number of vertices. Based upon this algorithm, they propose another algorithm that generates all maximal independent sets in lexicographic order.<>",
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"content": "An algorithm to generate all maximal independent sets in lexicographic order with polynomial time delay between the output of two successive independent sets was proposed by Johnson and Yannakakis (Inform. Process. Let., vol.27, p.119-23, 1988). This algorithm needs exponential amount of space. Johnson suggested an open problem which is to find such an algorithm with polynomial time-delay and space needed between the output of two successive maximal independent sets. The authors investigate this problem on trees. They first introduce a new problem, the constrained maximal independent set problem, which is NP-complete for general graphs. They show that, for trees, the constrained maximal independent set problem can be solved in theta (n) time, where n is the number of vertices. Based upon this algorithm, they propose another algorithm that generates all maximal independent sets in lexicographic order.<>",
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"affiliation": "Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan",
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"affiliation": "Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan",
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"abstract": "In this paper, we developed a novel method to identify air pollution sources based on remotely sensed aerosol data and Glowworm Swarm Optimization (GSO). In practice, it is usually to identify the air pollution sources to certain industries, such as transportation, power plants, biomass burning, and et.al. To our knowledge, the problem of locating and quantifying the pollution to the specified factories is faced for the first time. In this study, the aerosol retrieved from remotely sensed image and GIS were used to locate and quantify the pollution to each enterprise in the study area based on an improved Glowworm Swarm Optimization and meteorological condition. As a result, the gross and intensity of every enterprise in the study area were achieved. Therefore, the polluting contribution of each factory could be listed, and the most polluting factories could be found. Some experiments were carried out to validate the method, and the Key monitoring factories by local authority was ferreted out accurately.",
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"abstract": "Visualization of urban air pollution requires massive data processing since it should make air pollution maps either in two dimensions or three dimensions and we have to deal with geographical data, that is, GIS data. We used our own cloud computing technology to visualize urban air pollution and found that our approach vastly reduced the processing time of the visualization. In this paper, we present the visualization processing of urban air pollution using our own cloud computing technology, Smart-MapReduce and compare the performance with MPI and MapReduce with Hadoop.",
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"title": "Insulator Pollution Component Identification Method Based on Multi-scale 3D Convolution Network Mode",
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"abstract": "Pollution flashover and discharge caused by pollution deposition on the surface of insulators often occur in the field of power system. In the natural environment, insulators are affected by different weather and pollution sources, and the pollution deposition on the surface dissolved in water damages the insulation performance of insulators, and then leads to power failure of multiple transmission lines when pollution flashover accidents are serious, with serious consequences. Research and investigation have proved that the types of pollution components on the surface of insulators have great differences in their contribution to the pollution flashover phenomenon. In order to carry out regular cleaning of insulators, the identification and detection of pollution components on the surface of insulators is of great significance to predict insulator flashover voltage and prevent pollution flashover accidents. In this paper, based on hyperspectral image technology in the laboratory under ideal conditions to carry out the integrated pollution insulation uncleanness identification analysis experiment, established the insulator samples of hyperspectral image database, set the insulator impurity composition identification model based on deep learning, validate the convolutional neural network model for insulation slices of impurity composition to identify the feasibility and effectiveness.",
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"content": "Pollution flashover and discharge caused by pollution deposition on the surface of insulators often occur in the field of power system. In the natural environment, insulators are affected by different weather and pollution sources, and the pollution deposition on the surface dissolved in water damages the insulation performance of insulators, and then leads to power failure of multiple transmission lines when pollution flashover accidents are serious, with serious consequences. Research and investigation have proved that the types of pollution components on the surface of insulators have great differences in their contribution to the pollution flashover phenomenon. In order to carry out regular cleaning of insulators, the identification and detection of pollution components on the surface of insulators is of great significance to predict insulator flashover voltage and prevent pollution flashover accidents. In this paper, based on hyperspectral image technology in the laboratory under ideal conditions to carry out the integrated pollution insulation uncleanness identification analysis experiment, established the insulator samples of hyperspectral image database, set the insulator impurity composition identification model based on deep learning, validate the convolutional neural network model for insulation slices of impurity composition to identify the feasibility and effectiveness.",
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"affiliation": "Institute of Guangzhou Power Supply Bureau,Electrical Power Test & Research,GuangZhou,China",
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"affiliation": "Institute of Guangzhou Power Supply Bureau,Electrical Power Test & Research,GuangZhou,China",
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"affiliation": "Institute of Guangzhou Power Supply Bureau,Electrical Power Test & Research,GuangZhou,China",
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"affiliation": "Institute of Guangzhou Power Supply Bureau,Electrical Power Test & Research,GuangZhou,China",
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{
"affiliation": "Institute of Guangzhou Power Supply Bureau,Electrical Power Test & Research,GuangZhou,China",
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"title": "Economic loss assessment and Prevention Countermeasures of haze pollution in the process of new urbanization in Shenyang",
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"abstract": "Haze pollution is a major environmental problem in the development of new urbanization in China. Haze is the product of the interaction between human activities and a specific climate. Haze can not only induce cardiovascular, respiratory, and other diseases, seriously damage human health, but also cause extremely low visibility on the road, leading to serious traffic accidents, casualties, and economic losses. This paper analyzes the causes and losses of haze pollution in the process of new urbanization development in Shenyang through big data, and puts forward effective measures to solve the problem of economic losses in haze control in Shenyang, and speed up the process of new urbanization development in Shenyang.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Haze pollution is a major environmental problem in the development of new urbanization in China. Haze is the product of the interaction between human activities and a specific climate. Haze can not only induce cardiovascular, respiratory, and other diseases, seriously damage human health, but also cause extremely low visibility on the road, leading to serious traffic accidents, casualties, and economic losses. This paper analyzes the causes and losses of haze pollution in the process of new urbanization development in Shenyang through big data, and puts forward effective measures to solve the problem of economic losses in haze control in Shenyang, and speed up the process of new urbanization development in Shenyang.",
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"normalizedAbstract": "Haze pollution is a major environmental problem in the development of new urbanization in China. Haze is the product of the interaction between human activities and a specific climate. Haze can not only induce cardiovascular, respiratory, and other diseases, seriously damage human health, but also cause extremely low visibility on the road, leading to serious traffic accidents, casualties, and economic losses. This paper analyzes the causes and losses of haze pollution in the process of new urbanization development in Shenyang through big data, and puts forward effective measures to solve the problem of economic losses in haze control in Shenyang, and speed up the process of new urbanization development in Shenyang.",
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"authors": [
{
"affiliation": "Shenyang Jianzhu University,School of management,Shenyang,China",
"fullName": "Sun Yanli",
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{
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"fullName": "Liu Shanglai",
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"title": "AiR: An Augmented Reality Application for Visualizing Air Pollution",
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"content": "In order to effectively combat Air Pollution, it is necessary for the government and the community to work together. Easily comprehensible visualizations can play a major role in drawing public attention and spreading awareness about seemingly intangible air pollution. Considering the widespread usage of Android-based devices, in this paper, we propose an Augmented Reality based application called AiR, to help users to visualize pollutants in the air and to create an immersive user experience. It aims to interactively engage a wide variety of users and create awareness without overwhelming them with data. AiR visualizes 12 pollutants $[PM _{10}$, $PM _{2.5}$, NO, NO<inf>2</inf>, $NO _{x}$, CO, SO<inf>2</inf>, O<inf>3</inf>, NH<inf>3</inf>, C<inf>6</inf> H<inf>6</inf>, (CH<inf>3</inf>)C<inf>6</inf> H<inf>5</inf> and (CH<inf>3</inf>)<inf>2</inf> C<inf>6</inf> H<inf>5</inf>] through unique models. We demonstrate our application on pollution data by CPCB from various weather stations across India collected over the initial lockdown period due to COVID-19 in India.",
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"abstract": "Air pollution becomes a severe obstacle which restricts the development of urbanization in developing countries. In order to predict the concentration of PM2.5 accurately, in this research, a PM2.5 prediction model, named Air-S2S-LSTM, is constructed based on Seq2seq. LSTM, that is long short-term memory neural network, is imported into this model to study the historical meteorological data. Experiments show that the model performs better accuracy in different time span prediction task. The RMSE and MAE of s2s-LSTM model are reduced by 57.49%, 53.35%, 32.835% and 60.52%, 50.95%, 58.16% while comparing with ARIMA, Holt-Winters, and SVR respectively. What’s more, results of Pearson correlation coefficient and principal component analysis indicated that dew point, wind speed, air temperature, air pressure, rainfall in all characteristics have a predominant influence upon the concentration of PM2.5.",
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"content": "Air pollution becomes a severe obstacle which restricts the development of urbanization in developing countries. In order to predict the concentration of PM2.5 accurately, in this research, a PM2.5 prediction model, named Air-S2S-LSTM, is constructed based on Seq2seq. LSTM, that is long short-term memory neural network, is imported into this model to study the historical meteorological data. Experiments show that the model performs better accuracy in different time span prediction task. The RMSE and MAE of s2s-LSTM model are reduced by 57.49%, 53.35%, 32.835% and 60.52%, 50.95%, 58.16% while comparing with ARIMA, Holt-Winters, and SVR respectively. What’s more, results of Pearson correlation coefficient and principal component analysis indicated that dew point, wind speed, air temperature, air pressure, rainfall in all characteristics have a predominant influence upon the concentration of PM2.5.",
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"abstract": "Scenarios are a well-explored technique for working with and understanding a system's requirements. However, comprehending a large group of scenarios for a system can be difficult, especially for non-experts. Our previous work proposed that visualizing scenarios using social animated characters could assist this process. However, assessing the efficacy of visualization techniques can be challenging. This paper proposes that a mixed-method study combining qualitative and quantitative analysis can be effective for evaluating a social visualization of a group of scenarios. Specifically, we found that the quantitative data addressed focused hypotheses, while the qualitative data gave us insight into the nature of scenarios in requirements, the goals of scenario visualization, and how the technology can support these goals more effectively. Both forms of analysis can be valuable and mutually reinforcing in developing and evaluating effective social visualizations of scenarios, and by extension for other work in RE as well.",
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"abstract": "Stream Processing has become the de facto standard way of supporting real-time data analytics. Stream Processing applications are typically shaped as pipelines of operators, each record of the stream traversing all the operators of the graph. The placement of these operators on nodes of the platform can evolve through time according to different parameters such as the velocity of the input stream and the capacity of nodes. Such an adaptation calls for mechanisms such as dynamic operator scaling and migration. With the advent of Fog Computing, gathering multiple computationally-limited geographically-distributed resources, these mechanisms need to be decentralized, as a central coordinator orchestrating these actions is not a scalable solution any more. In a fully decentralized vision, each node hosts part of the pipeline. Each node is responsible for the scaling of the operators it runs. More precisely speaking, nodes trigger new instances of the operators they runs or shut some of them down. The number of replicas of each operator evolving independently, there is a need to maintain the connections between nodes hosting neighbouring operators in the pipeline. One issue is that, if all these operators can scale in or out dynamically, maintaining a consistent view of their neighbours becomes difficult, calling for synchronization mechanisms to ensure it, to avoid routing inconsistencies and data loss. In this paper, we show that this synchronization problem translate into a particular Group Mutual Exclusion (GME) problem where a group comprises all instances of a given operator of the pipeline and where conflicting groups are those hosting neighbouring operators in the pipeline. The specificity of our problem is that groups are fixed and that each group is in conflict with only one other groups at a time. Based on these constraints, we formulate a new GME algorithm whose message complexity is reduced when compared to algorithms of the literature, while being able to ensure a high level of concurrent occupancy (the number of processes of the same group in the critical section (the scaling mechanism) at the same time.",
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"abstract": "Diffusion tensor magnetic resonance imaging (DTI) is the main non-invasive utility to reveal the information of local diffusivity of white matter and other fibrous human tissues. Because the diffusion weighted image (DWI) which is used to acquire DTI is sensitive to noise, effective noise removal is required to improve the accuracy of the DTI data and its subsequent applications. Instead of denoise the DWI in different direction separately, in this paper an anisotropic filtering method which synthetically considering the structure information and characteristic of the DTI is proposed. The eigenvalue and eigenvector of the smoothing structure tensor is reconstructed to denoise and keep the structure characteristic simultaneously. By the method, denoising simulations using a synthetic DTI dataset and experiments using an in vivo brain DTI dataset have been done. The results of the simulations and experiments demonstrate that using the method proposed the noise can be removed significantly and is more effective compared with the common methods.",
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"content": "Diffusion tensor magnetic resonance imaging (DTI) is the main non-invasive utility to reveal the information of local diffusivity of white matter and other fibrous human tissues. Because the diffusion weighted image (DWI) which is used to acquire DTI is sensitive to noise, effective noise removal is required to improve the accuracy of the DTI data and its subsequent applications. Instead of denoise the DWI in different direction separately, in this paper an anisotropic filtering method which synthetically considering the structure information and characteristic of the DTI is proposed. The eigenvalue and eigenvector of the smoothing structure tensor is reconstructed to denoise and keep the structure characteristic simultaneously. By the method, denoising simulations using a synthetic DTI dataset and experiments using an in vivo brain DTI dataset have been done. The results of the simulations and experiments demonstrate that using the method proposed the noise can be removed significantly and is more effective compared with the common methods.",
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"abstractUrl": "/proceedings-article/isspit/2018/08642639/17QjJc0dz6L",
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"title": "Complexity and Data Mining, International Workshop on",
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"doi": "10.1109/IWCDM.2011.24",
"title": "Discriminative Analysis of Cerebral Motor Function Tissue for Left and Right Handedness Based on DTI",
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"abstract": "Objective: To investigate the relationship between handedness and motorial brain tissue (corticospinal fasciculus and corpus striatum) with diffusion tensor imaging (DTI). Methods: In this study, thirty-five healthy subjects (24 right-handers and 11 left-handers) were enrolled from the International Consortium of Brain Mapping (ICBM) database, and the DTI data were obtained on Siemens Sonata 1.5T MR units. The parameters including fractional anisotropy (FA), mean diffusivity (MD), longitudianl diffusivity (¦ËL) and transverse diffusivity (¦ËT) were acquired from the corticospinal fasciculus and corpus striatum in both hemispheres of brain. Discriminative analysis for left and right handed nesses was employed. Results: Handedness relates to motorial brain tissue, and the asymmetry of the motorial brain tissue. Furthermore, the major differences between left-and right-handedness in DTI index parameters were ¦ËL and ¦ËT in the caudate nucleus. Conclusion: The major differences between left- and right-handedness in microcosmic tissue were axon and myelin organization in the caudate nucleus. This finding is discovered by analyzing the DTI microcosmic information using multi-patterns.",
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"content": "Objective: To investigate the relationship between handedness and motorial brain tissue (corticospinal fasciculus and corpus striatum) with diffusion tensor imaging (DTI). Methods: In this study, thirty-five healthy subjects (24 right-handers and 11 left-handers) were enrolled from the International Consortium of Brain Mapping (ICBM) database, and the DTI data were obtained on Siemens Sonata 1.5T MR units. The parameters including fractional anisotropy (FA), mean diffusivity (MD), longitudianl diffusivity (¦ËL) and transverse diffusivity (¦ËT) were acquired from the corticospinal fasciculus and corpus striatum in both hemispheres of brain. Discriminative analysis for left and right handed nesses was employed. Results: Handedness relates to motorial brain tissue, and the asymmetry of the motorial brain tissue. Furthermore, the major differences between left-and right-handedness in DTI index parameters were ¦ËL and ¦ËT in the caudate nucleus. Conclusion: The major differences between left- and right-handedness in microcosmic tissue were axon and myelin organization in the caudate nucleus. This finding is discovered by analyzing the DTI microcosmic information using multi-patterns.",
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"fullName": "Jiaolong Qin",
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"fullName": "Wei Wang",
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"abstract": "Diffusion Tensor Imaging (DTI) is an MRI-based technique for quantifying water diffusion in living tissue. In the white matter of the brain, water diffuses more rapidly along the neuronal axons than in the perpendicular direction. By exploiting this phenomenon, DTI can be used to determine trajectories of fiber bundles, or neuronal connections between regions, in the brain. The resulting bundles can be visualized. However, the resulting visualizations can be complex and diffi- cult to interpret. An effective approach is to pre-determine trajectories from a large number of positions throughout the white matter (full brain fiber tracking) and to offer facilities to aid the user in selecting fiber bundles of interest. Two factors are crucial for the use and acceptance of this technique in clinical studies: Firstly, the selection of the bundles by brain experts should be interactive, supported by real-time visualization of the trajectories registered with anatomical MRI scans. Secondly, the fiber selections should be reproducible, so that different experts will achieve the same results. In this paper we present a practical technique for the interactive selection of fiber-bundles using multiple convex objects that is an order of magnitude faster than similar techniques published earlier. We also present the results of a clinical study with ten subjects that show that our selection approach is highly reproducible for fractional anisotropy (FA) calculated over the selected fiber bundles.",
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"abstract": "In this paper, we present a novel set of features which mainly comprises Fourier descriptors to facilitate the classification of white matter (WM) fiber tracts reconstructed from diffusion tensor imaging (DTI) tractography. Particularly, Fourier descriptors composing the feature vectors are different frequency terms of the discrete Fourier transform of the Euclidean coordinates of the step points of WM fiber tracts. This approach can effectively reduce the computing cost in WM fiber tract classification due to the decreased feature vector dimensionality. As integrals, Fourier descriptor features make the algorithm less sensitive to DTI noise. The fact that Fourier descriptors achieve spatial independent representation and normalization of WM fiber tracts makes it useful for group studies of WM fiber tracts. Specially, a new strategy for using Fourier descriptors is proposed. Experimental results with real datasets show that this technique is feasible and may be an alternative to existing approaches.",
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"abstract": "For diagnosis and risk assessment in patients with stable ischemic heart disease, myocardial perfusion scintigraphy is one of the most common cardiological examinations performed today. There are however many motivations for why an artificial intelligence algorithm would provide useful input to this task. For example to reduce the subjectiveness and save time for the nuclear medicine physicians working with this time consuming task. In this work we have developed a deep learning algorithm for multi-label classification based on a convolutional neural network to estimate the probability of obstructive coronary artery disease in the left anterior artery, left circumflex artery and right coronary artery. The prediction is based on data from myocardial perfusion scintigraphy studies conducted in a dedicated Cadmium-Zinc-Telluride cardio camera (D-SPECT Spectrum Dynamics). Data from 588 patients was available, with stress images in both upright and supine position, as well as a number of auxiliary parameters such as angina symptoms and age. The data was used to train and evaluate the algorithm using 5-fold cross-validation. We achieve state-of-the-art results for this task with an area under the receiver operating characteristics curve of 0.89 as average on per-vessel level and 0.95 on per-patient level.",
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"title": "2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)",
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"abstract": "Breast cancer is one of the most important cancers in women around the world. The number of new cases of breast cancer in Taiwan has already broken the 10,000 mark each year to cause more than 2,000 unfortunate deaths. Many academic studies point out that the external environmental factor of breast cancer may be attributed to air pollution. Although there were relevant past studies on the external environment factor of breast cancer in Taiwan, but most statistical analyses had done little interactive visualization differences between air pollution indexes of counties/cities and the general trend status of breast cancer.in this study, a cross-domain integration was conducted based on air pollution and the density of cars/motorcycles in counties/cities to present it in a data visualization manner. The aim is to explore the correlation between the exposure to external environmental factor and breast cancer incidence rate in Taiwan. This involved integrating the open government data and establishing an entity-relationship model (ERD) using Power BI to visualize it, and exploring the correlation between the exposure to external environmental factor and breast cancer incidence rate in Taiwan. Supplemented by data visualization, data drilling was done on the impact of air pollution indexes in counties/cities to breast cancer, thereby providing a more effective platform for relevant institutions and the public to perform more beneficial breast cancer prevention advocacy, raising public awareness on breast cancer, and lowering the harmfulness of external environmental factor on breast cancer in the future.",
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"content": "Breast cancer is one of the most important cancers in women around the world. The number of new cases of breast cancer in Taiwan has already broken the 10,000 mark each year to cause more than 2,000 unfortunate deaths. Many academic studies point out that the external environmental factor of breast cancer may be attributed to air pollution. Although there were relevant past studies on the external environment factor of breast cancer in Taiwan, but most statistical analyses had done little interactive visualization differences between air pollution indexes of counties/cities and the general trend status of breast cancer.in this study, a cross-domain integration was conducted based on air pollution and the density of cars/motorcycles in counties/cities to present it in a data visualization manner. The aim is to explore the correlation between the exposure to external environmental factor and breast cancer incidence rate in Taiwan. This involved integrating the open government data and establishing an entity-relationship model (ERD) using Power BI to visualize it, and exploring the correlation between the exposure to external environmental factor and breast cancer incidence rate in Taiwan. Supplemented by data visualization, data drilling was done on the impact of air pollution indexes in counties/cities to breast cancer, thereby providing a more effective platform for relevant institutions and the public to perform more beneficial breast cancer prevention advocacy, raising public awareness on breast cancer, and lowering the harmfulness of external environmental factor on breast cancer in the future.",
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"affiliation": "National Taipei University of Nursing and Health Sciences,Department of Information Management,Taipei,Taiwan",
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