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"title": "dGG, dRNG, DSC: New Degree-based Shape-based Faithfulness Metrics for Large and Complex Graph Visualization",
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"abstract": "Many aspects of our daily lives now rely on computers, including communications, transportation, government, finance, medicine, and education. However, with increased dependence comes increased vulnerability. Therefore recognizing attacks quickly is critical. In this paper, we introduce a new anomaly detection algorithm based on persistent homology, a tool which computes summary statistics of a manifold. The idea is to represent a cyber network with a dynamic point cloud and compare the statistics over time. The robustness of persistent homology makes for a very strong comparison invariant.",
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"title": "Heat Transfer and Flow in Rotating U-Shaped Cooling Passage with 45° Ribs",
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"abstract": "This paper investigates heat transfer and flow of U-shaped cooling passage with ribs in gas turbine blade by numerical methods. The Reynolds number ranges from 10000 to 40000, and there are three cooling passages with aspect ratio of 0.5, 1, 2, then the study conducts comparative analysis for a variety of programs in case of rotating. The results show that, ribs improve cooling performance of U-shaped passage significantly while obviously increase coolant flow loss. Heat transfer capability of passage wall is proportional to Reynolds number, but overall flow loss remain basically unchanged. Rotation improve first passage heat transfer clearly by Coriolis force while there is little effect on second passage, and it does not affect overall flow loss of U-shaped cooling passage.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper investigates heat transfer and flow of U-shaped cooling passage with ribs in gas turbine blade by numerical methods. The Reynolds number ranges from 10000 to 40000, and there are three cooling passages with aspect ratio of 0.5, 1, 2, then the study conducts comparative analysis for a variety of programs in case of rotating. The results show that, ribs improve cooling performance of U-shaped passage significantly while obviously increase coolant flow loss. Heat transfer capability of passage wall is proportional to Reynolds number, but overall flow loss remain basically unchanged. Rotation improve first passage heat transfer clearly by Coriolis force while there is little effect on second passage, and it does not affect overall flow loss of U-shaped cooling passage.",
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"normalizedAbstract": "This paper investigates heat transfer and flow of U-shaped cooling passage with ribs in gas turbine blade by numerical methods. The Reynolds number ranges from 10000 to 40000, and there are three cooling passages with aspect ratio of 0.5, 1, 2, then the study conducts comparative analysis for a variety of programs in case of rotating. The results show that, ribs improve cooling performance of U-shaped passage significantly while obviously increase coolant flow loss. Heat transfer capability of passage wall is proportional to Reynolds number, but overall flow loss remain basically unchanged. Rotation improve first passage heat transfer clearly by Coriolis force while there is little effect on second passage, and it does not affect overall flow loss of U-shaped cooling passage.",
"fno": "7644a450",
"keywords": [
"Heat Transfer",
"Ribs",
"Coolants",
"Force",
"Turbines",
"Blades",
"Numerical Simulation",
"Gas Turbine",
"Rotation",
"Internal Cooling",
"U Shaped Passage"
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{
"affiliation": null,
"fullName": "Wang Longfei",
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{
"affiliation": null,
"fullName": "Zhou Xun",
"givenName": "Zhou",
"surname": "Xun",
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{
"affiliation": null,
"fullName": "Wang Songtao",
"givenName": "Wang",
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"affiliation": null,
"fullName": "Luo Lei",
"givenName": "Luo",
"surname": "Lei",
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"affiliation": null,
"fullName": "Wen Fengbo",
"givenName": "Wen",
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"proceeding": {
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"title": "2019 IEEE International Conference on Energy Internet (ICEI)",
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"doi": "10.1109/ICEI.2019.00009",
"title": "An Improved Hydraulic Turbine Model and Its Impact on Fast Frequency Response",
"normalizedTitle": "An Improved Hydraulic Turbine Model and Its Impact on Fast Frequency Response",
"abstract": "In view of the need of fast frequency response under high power loss, the common static model cannot depict the dynamic frequency characteristics of hydropower units with external parameters changes accurately. Based on the working nature of the full-parameter model of hydraulic turbines, this paper establishes a dynamic model of the hydraulic turbines that considers external parameters. The model assumes that the stroke of the hydraulic turbines relay changed according to the straight-line law, and the flow coefficient and efficiency of the hydraulic turbines are equal under different working conditions. Case studies shows that the dynamic model of the hydraulic turbine can not only avoid the complicated calculation of parameters of the full-parameter model, but also depict the dynamic characteristics of the fast frequency response of the hydropower units relatively accurately. The model can be applied to related calculations and simulations where the dynamic frequency response capability of the hydropower units can be accurately estimated under different working conditions of the unit, which provides a basis for rational allocation of frequency response resources.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In view of the need of fast frequency response under high power loss, the common static model cannot depict the dynamic frequency characteristics of hydropower units with external parameters changes accurately. Based on the working nature of the full-parameter model of hydraulic turbines, this paper establishes a dynamic model of the hydraulic turbines that considers external parameters. The model assumes that the stroke of the hydraulic turbines relay changed according to the straight-line law, and the flow coefficient and efficiency of the hydraulic turbines are equal under different working conditions. Case studies shows that the dynamic model of the hydraulic turbine can not only avoid the complicated calculation of parameters of the full-parameter model, but also depict the dynamic characteristics of the fast frequency response of the hydropower units relatively accurately. The model can be applied to related calculations and simulations where the dynamic frequency response capability of the hydropower units can be accurately estimated under different working conditions of the unit, which provides a basis for rational allocation of frequency response resources.",
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"normalizedAbstract": "In view of the need of fast frequency response under high power loss, the common static model cannot depict the dynamic frequency characteristics of hydropower units with external parameters changes accurately. Based on the working nature of the full-parameter model of hydraulic turbines, this paper establishes a dynamic model of the hydraulic turbines that considers external parameters. The model assumes that the stroke of the hydraulic turbines relay changed according to the straight-line law, and the flow coefficient and efficiency of the hydraulic turbines are equal under different working conditions. Case studies shows that the dynamic model of the hydraulic turbine can not only avoid the complicated calculation of parameters of the full-parameter model, but also depict the dynamic characteristics of the fast frequency response of the hydropower units relatively accurately. The model can be applied to related calculations and simulations where the dynamic frequency response capability of the hydropower units can be accurately estimated under different working conditions of the unit, which provides a basis for rational allocation of frequency response resources.",
"fno": "149300a013",
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"Fast Frequency Response",
"Common Static Model",
"Dynamic Frequency Characteristics",
"Hydropower Units",
"External Parameters Changes",
"Full Parameter Model",
"Dynamic Model",
"Dynamic Frequency Response Capability",
"Frequency Response Resources",
"Hydraulic Turbines",
"Frequency Response",
"Hydroelectric Power Generation",
"Employee Welfare",
"Load Modeling",
"Transfer Functions",
"Blades",
"Frequency Response",
"Hydraulic Turbines",
"Water Hammer",
"Dynamic Frequency Response Characteristics"
],
"authors": [
{
"affiliation": "Dalian University of Technology",
"fullName": "Chi Tian",
"givenName": "Chi",
"surname": "Tian",
"__typename": "ArticleAuthorType"
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{
"affiliation": "Dalian University of Technology",
"fullName": "Rao Liu",
"givenName": "Rao",
"surname": "Liu",
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{
"affiliation": "Dalian University of Techonology",
"fullName": "Chenyue Yao",
"givenName": "Chenyue",
"surname": "Yao",
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{
"affiliation": "Dalian University of Techonology",
"fullName": "Quan Lv",
"givenName": "Quan",
"surname": "Lv",
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},
{
"affiliation": "Dalian University of Techonology",
"fullName": "Haixia Wang",
"givenName": "Haixia",
"surname": "Wang",
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"proceeding": {
"id": "1mLM7SzB0oE",
"title": "2020 International Conference on Wireless Communications and Smart Grid (ICWCSG)",
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"article": {
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"doi": "10.1109/ICWCSG50807.2020.00060",
"title": "Study and Application of Multi-valve Micro-Control Technology in Steam Turbine Excitation",
"normalizedTitle": "Study and Application of Multi-valve Micro-Control Technology in Steam Turbine Excitation",
"abstract": "China's power plant steam turbines often encounter steam turbine excitation problems in the use of steam turbines. The linkage micro-control multi-valve regulation technology studied in the paper is to use the turbine operating parameters as a reference. The internal steam excitation problem that occurs in the high-speed operation of the steam turbine, which is caused by the eccentricity of the rotor after the fault analysis; the impeller gap at each stage is unevenly distributed in the circumferential direction when the rotor has a certain eccentricity, which result in uneven distribution of steam entering the flow path in the circumferential direction; thereby it generate the same tangential force as the rotor linear velocity direction and the internal steam flow excitation of the blade. The corresponding parameter range is obtained by the micro-control change of single project parameters, the deviation of the set value and the correction parameters are determined. After the verification and practice of the valve control logic, the multi-valve adjustment linkage based on micro-control is proposed. The micro-control debugging is used to correct the influence of the eccentricity of the turbine bearing blades by optimizing the valve position command and feedback correction. Through testing and verifying the linkage micro-control multi-valve regulation technology, the function and effect of the solution are proved, which guarantees the normal operation of the steam turbine.",
"abstracts": [
{
"abstractType": "Regular",
"content": "China's power plant steam turbines often encounter steam turbine excitation problems in the use of steam turbines. The linkage micro-control multi-valve regulation technology studied in the paper is to use the turbine operating parameters as a reference. The internal steam excitation problem that occurs in the high-speed operation of the steam turbine, which is caused by the eccentricity of the rotor after the fault analysis; the impeller gap at each stage is unevenly distributed in the circumferential direction when the rotor has a certain eccentricity, which result in uneven distribution of steam entering the flow path in the circumferential direction; thereby it generate the same tangential force as the rotor linear velocity direction and the internal steam flow excitation of the blade. The corresponding parameter range is obtained by the micro-control change of single project parameters, the deviation of the set value and the correction parameters are determined. After the verification and practice of the valve control logic, the multi-valve adjustment linkage based on micro-control is proposed. The micro-control debugging is used to correct the influence of the eccentricity of the turbine bearing blades by optimizing the valve position command and feedback correction. Through testing and verifying the linkage micro-control multi-valve regulation technology, the function and effect of the solution are proved, which guarantees the normal operation of the steam turbine.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "China's power plant steam turbines often encounter steam turbine excitation problems in the use of steam turbines. The linkage micro-control multi-valve regulation technology studied in the paper is to use the turbine operating parameters as a reference. The internal steam excitation problem that occurs in the high-speed operation of the steam turbine, which is caused by the eccentricity of the rotor after the fault analysis; the impeller gap at each stage is unevenly distributed in the circumferential direction when the rotor has a certain eccentricity, which result in uneven distribution of steam entering the flow path in the circumferential direction; thereby it generate the same tangential force as the rotor linear velocity direction and the internal steam flow excitation of the blade. The corresponding parameter range is obtained by the micro-control change of single project parameters, the deviation of the set value and the correction parameters are determined. After the verification and practice of the valve control logic, the multi-valve adjustment linkage based on micro-control is proposed. The micro-control debugging is used to correct the influence of the eccentricity of the turbine bearing blades by optimizing the valve position command and feedback correction. Through testing and verifying the linkage micro-control multi-valve regulation technology, the function and effect of the solution are proved, which guarantees the normal operation of the steam turbine.",
"fno": "982000a240",
"keywords": [
"Blades",
"Feedback",
"Machine Bearings",
"Position Control",
"Power Plants",
"Power System Control",
"Rotors",
"Steam Turbines",
"Valves",
"Multivalve Microcontrol Technology",
"China",
"Linkage Microcontrol Multivalve Regulation Technology",
"Turbine Operating Parameters",
"Internal Steam Excitation Problem",
"Eccentricity",
"Rotor",
"Internal Steam Flow Excitation",
"Microcontrol Change",
"Valve Control Logic",
"Multivalve Adjustment Linkage",
"Microcontrol Debugging",
"Turbine Bearing Blades",
"Valve Position Command",
"Feedback Correction",
"Steam Turbine Excitation",
"Power Plant Steam Turbines",
"Turbines",
"Couplings",
"Conferences",
"Wireless Communication",
"Smart Grids",
"Blades",
"Testing",
"Steam Turbine Excitation",
"Blade Eccentricity",
"Multi Valve Micro Control Adjustment"
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"authors": [
{
"affiliation": "Zhonghuan Information College Tianjin University of Technology",
"fullName": "An Zhao",
"givenName": "An",
"surname": "Zhao",
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"abstract": "In the modern era global power demand rises implicitly with exponentially growing power needs. As the global warming becomes a critical factor, emission regulations are set to minimize and eventually seize the traditional power production methods in near future. As a solution, this research aims on presenting a novel methodology for harnessing energy from tidal current streams. In hydro-power sector Cross Flow Turbine (CFT) or “Banki-Michell Turbine” have become a popular choice over the years. But in ocean renewable energy field, limited amount of research has being done to assess the capability of this particular turbine. CFT is a bi-directional turbine, as the turbine runner imparts unidirectional behavior regardless of the flow direction. In this study, tidal passage with cross sectional area of 756.25 m2 having length of 87.5 m consists of equally spaced, 6.1 m diameter four CFT's housed within separate augmentation channels. These specially shaped augmentation channels act as turbines converging and diverging (vice-versa) nozzles passages for the fluid passage. Each turbine runner consists of 18 blades having thin profile. The turbine setup was computer modeled and meshed. The volumetric mesh combines of 28 million, Hexahedral and Tetrahedral mesh elements. Runner blades were extra fined with close mesh elements to capture the boundary layer effect accurately. The quad-turbine setup was simulated with an open sea domain to gain accurate flow field behavior also to eliminate abrupt turbulence behavior pass the tidal passage. Numerical calculations of the turbine setup was carried out using commercial computational fluid dynamics (CFD) code ANSYS CFX. The turbine cluster yields a maximum power output of about 500 kW at optimum tip speed ratio (TSR) of 0.4 for the designed average tidal flow velocity of 2.5 ms-1, with a maximum of about 18% efficiency. As the previous research studies suggests, the efficiency values of tidal current turbines are generally being lower becomes a bearable factor in this study as well. Compared with prevailing tidal turbines designs, the CFT system requires no mechanical or electrical interactions to change the turbine runner blade directions. The simple design of CFT system economically beneficial due to low manufacturing cost and requires considerably less maintenance.",
"abstracts": [
{
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"content": "In the modern era global power demand rises implicitly with exponentially growing power needs. As the global warming becomes a critical factor, emission regulations are set to minimize and eventually seize the traditional power production methods in near future. As a solution, this research aims on presenting a novel methodology for harnessing energy from tidal current streams. In hydro-power sector Cross Flow Turbine (CFT) or “Banki-Michell Turbine” have become a popular choice over the years. But in ocean renewable energy field, limited amount of research has being done to assess the capability of this particular turbine. CFT is a bi-directional turbine, as the turbine runner imparts unidirectional behavior regardless of the flow direction. In this study, tidal passage with cross sectional area of 756.25 m2 having length of 87.5 m consists of equally spaced, 6.1 m diameter four CFT's housed within separate augmentation channels. These specially shaped augmentation channels act as turbines converging and diverging (vice-versa) nozzles passages for the fluid passage. Each turbine runner consists of 18 blades having thin profile. The turbine setup was computer modeled and meshed. The volumetric mesh combines of 28 million, Hexahedral and Tetrahedral mesh elements. Runner blades were extra fined with close mesh elements to capture the boundary layer effect accurately. The quad-turbine setup was simulated with an open sea domain to gain accurate flow field behavior also to eliminate abrupt turbulence behavior pass the tidal passage. Numerical calculations of the turbine setup was carried out using commercial computational fluid dynamics (CFD) code ANSYS CFX. The turbine cluster yields a maximum power output of about 500 kW at optimum tip speed ratio (TSR) of 0.4 for the designed average tidal flow velocity of 2.5 ms-1, with a maximum of about 18% efficiency. As the previous research studies suggests, the efficiency values of tidal current turbines are generally being lower becomes a bearable factor in this study as well. Compared with prevailing tidal turbines designs, the CFT system requires no mechanical or electrical interactions to change the turbine runner blade directions. The simple design of CFT system economically beneficial due to low manufacturing cost and requires considerably less maintenance.",
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"affiliation": "Korea Maritime and Ocean University,Interdisciplinary Major of Ocean Renewable Energy Engineering (Gratuagte Resercher),dept. of Mechanical Engineering,Dongsam-dong Youngdo-ku,Busan,Republic of Korea",
"fullName": "A.H Samitha Weerakoon",
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"affiliation": "Korea Maritime and Ocean University,Interdisciplinary Major of Ocean Renewable Energy Engineering (Gratuagte Resercher, Ph.D Student),dept. of Mechanical Engineering,Dongsam-dong Youngdo-ku,Busan,Republic of Korea",
"fullName": "Ho-Seong Young",
"givenName": "Ho-Seong",
"surname": "Young",
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"affiliation": "Devision of Engineering Hanjo Corp, (Resercher),No.20, Namhangseo-ro,Youngdo-gu,Busan,Republic of Korea,49050",
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"abstract": "Recently, learning based video compression methods have attracted increasing attention. However, most learning based video codecs are not adaptive to different video contents. Though adaptation at inference time is a solution to tackle this issue, adapting all the codec’s parameters is computationally expensive and brings heavy bitrate overhead. The recently proposed Lottery Ticket Hypothesis (LTH) states that an over-parameterized neural network contains smaller subnetworks (winning tickets) that can match the performance of the original network. In this paper, we present a novel lottery-ticket adaptation technique on decoder-side multiplicative parameters of a neural network, transferring the concept of winning lottery tickets to video compression tasks. At inference time, the winning multiplicative parameters are overfitted, compressed, and signaled together with encoded frames for decoding. We show that our approach outperforms the Versatile Video Coding (VVC) standard in the Multiscale Structural Similarity (MS-SSIM) at a low bitrate on both the UVG and JVET sequences. To the best of our knowledge, this is the first attempt to apply LTH in the video compression domain. Also, this is the first published end-to-end learned video codec working directly on YUV format, which outperforms VVC on UVG and JVET datasets in MS-SSIM.",
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"content": "Linear cellular automata have many invariant measures in general, but the most natural one is the uniform Bernoulli product measure. There are several studies on their rigidity: The unique invariant measure with a suitable non-degeneracy condition (such as positive entropy or mixing property for the shift map) is the uniform measure. This is related to study of the asymptotic randomization property: Iterates starting from a large class of initial measures converge to the uniform measure (in Cesaro sense). In this paper we consider one-dimensional linear cellular automata with neighborhood of size two, and study limiting distributions starting from a class of shift-invariant probability measures. We characterize when iterates by addition modulo a prime number cellular automata starting from a strong mixing probability measure with full support can converge. This also gives all invariant measures inside the class of those probability measures. In the two-state case, we also obtain a necessary and sufficient condition that a convex combination of strong mixing probability measures is invariant under addition modulo 2 cellular automata. Those results improve previous ones obtained by Marcovici and Miyamoto.",
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"abstract": "This paper describes a study that compared a number of interaction-based measures and their ability to predict cohesion within global software development projects. Messages were collected from three software development projects that involved students from two different countries. The similarities and quantities of such interactions were then analyzed and compared. Results from this analysis show a statistically significant correlation of linguistic characteristics (LSM) and Information Exchange Similarity with Task cohesion, when controlled by culture. In addition, the study found that quantity-based metrics had higher correlations with students' perceptions of their group's cohesiveness than similarity-based measures. More specifically, a word-based measure called Information Exchange Rate had a significant relationship to cohesion. Group rate measures were also tested, but only low significant correlations were found. These results suggest that measures based on quantity of interactions tend to be better predictors of cohesion within distributed learning teams.",
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"content": "This paper describes a study that compared a number of interaction-based measures and their ability to predict cohesion within global software development projects. Messages were collected from three software development projects that involved students from two different countries. The similarities and quantities of such interactions were then analyzed and compared. Results from this analysis show a statistically significant correlation of linguistic characteristics (LSM) and Information Exchange Similarity with Task cohesion, when controlled by culture. In addition, the study found that quantity-based metrics had higher correlations with students' perceptions of their group's cohesiveness than similarity-based measures. More specifically, a word-based measure called Information Exchange Rate had a significant relationship to cohesion. Group rate measures were also tested, but only low significant correlations were found. These results suggest that measures based on quantity of interactions tend to be better predictors of cohesion within distributed learning teams.",
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"abstract": "In this study, we experiment the Cubic Spline Interpolation annotation method in order to reduce annotation size for stable or uncertainty cause by the object movement. In the experiment result, we found that Cubic Spline Interpolation method is able to save up to 80 percent of annotation size with 15 frames annotation and below. Moreover, it produces error rate at 31 pixels and averagely less than 4 pixels. Our proposed method to employ dynamic sampling by dynamically change the sampling rate according to object speed shows significant decreases in error rates from 69 percent to 65 percent.",
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"abstract": "Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow 46% less number of samples for accurate signal reconstruction and achieve up to 57% smaller signal reconstruction error under the same noise condition.",
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"content": "Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow 46% less number of samples for accurate signal reconstruction and achieve up to 57% smaller signal reconstruction error under the same noise condition.",
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"abstract": "The first step to deal with the significant issue of air pollution in China and elsewhere in the world is to monitor it. While more physical monitoring stations are built, current coverage is limited to large cities with most other places undermonitored. In this paper we propose a complementary approach to monitor Air Quality Index (AQI): using machine learning models to estimate AQI from social media posts. We propose a series of progressively more sophisticated machine learning models, culminating in a Markov Random Field model that utilizes the text content in social media as well as the spatiotemporal correlation among cities and days. Our extensive experiments on Sina Weibo data from 108 cities during a one-month period demonstrate the accurate AQI prediction performance of our approach.",
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"content": "The first step to deal with the significant issue of air pollution in China and elsewhere in the world is to monitor it. While more physical monitoring stations are built, current coverage is limited to large cities with most other places undermonitored. In this paper we propose a complementary approach to monitor Air Quality Index (AQI): using machine learning models to estimate AQI from social media posts. We propose a series of progressively more sophisticated machine learning models, culminating in a Markov Random Field model that utilizes the text content in social media as well as the spatiotemporal correlation among cities and days. Our extensive experiments on Sina Weibo data from 108 cities during a one-month period demonstrate the accurate AQI prediction performance of our approach.",
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"content": "This paper describes an agent based approach for simulating the control of an air pollution crisis. A Gaussian Plum air pollution dispersion model (GPD) is combined with an Artificial Neural Network (ANN) to predict the concentration levels of three different air pollutants. The two models (GPM and ANN) are integrated with a MAS (multi-agent system). The MAS models pollutant sources controllers and air pollution monitoring agencies as software agents. The population of agents cooperates with each other in order to reduce their emissions and control the air pollution. Leaks or natural sources of pollution are modelled as uncontrolled sources. A cooperation strategy is simulated and its impact on air pollution evolution is assessed and compared. The simulation scenario is built using data about Annaba (a city in North-East Algeria). The simulation helps to compare and assess the efficiency of policies to control air pollution during crises, and takes in to account uncontrolled sources.",
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"fullName": "Julie Dugdale",
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"fullName": "Tarek Khadir",
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"title": "2018 IEEE International Conference on Data Mining Workshops (ICDMW)",
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"abstract": "Air and noise pollution are two major factors that determine the quality of life of the people living in cities. The prime reasons for the rise of air and noise pollution are due to imbalanced urbanization, unregulated increase in traffic and inorganic industrialization. These have resulted in compromising the well-being of the citizens. In this context, the concept of smart cities has been developed. They inherently have the ability to sense and respond to the challenges which characterizes regular cities with the help of embedded intelligence. It has become important to monitor the environmental parameters for policy-making, planning and for making smart cities livable and sustainable. In a bid to make a smart city, in this work, we have studied the spatio-temporal relationship between air and noise pollution in four different locations and have also evaluated the effect of noise in predicting Air Quality (AQ). Data acquisition has been done using customized, self-developed CO<sub>2</sub>; NO<sub>2</sub>; PM2:5, humidity, temperature and intensity of noise. To determine the relationship between air and noise pollution, we have used Pearson correlation. Results show a strong association between the two types of pollution. For predicting the air quality, the impact of noise pollution as a feature has been investigated using three different machine learning models which are Decision Tree, Random Forest and K-Nearest Neighbors. When applicable, the results show that if noise pollution is used as a feature, we get a prediction accuracy of upto 95% which is an improvement of 5% on an average.",
"abstracts": [
{
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"content": "Air and noise pollution are two major factors that determine the quality of life of the people living in cities. The prime reasons for the rise of air and noise pollution are due to imbalanced urbanization, unregulated increase in traffic and inorganic industrialization. These have resulted in compromising the well-being of the citizens. In this context, the concept of smart cities has been developed. They inherently have the ability to sense and respond to the challenges which characterizes regular cities with the help of embedded intelligence. It has become important to monitor the environmental parameters for policy-making, planning and for making smart cities livable and sustainable. In a bid to make a smart city, in this work, we have studied the spatio-temporal relationship between air and noise pollution in four different locations and have also evaluated the effect of noise in predicting Air Quality (AQ). Data acquisition has been done using customized, self-developed CO<sub>2</sub>; NO<sub>2</sub>; PM2:5, humidity, temperature and intensity of noise. To determine the relationship between air and noise pollution, we have used Pearson correlation. Results show a strong association between the two types of pollution. For predicting the air quality, the impact of noise pollution as a feature has been investigated using three different machine learning models which are Decision Tree, Random Forest and K-Nearest Neighbors. When applicable, the results show that if noise pollution is used as a feature, we get a prediction accuracy of upto 95% which is an improvement of 5% on an average.",
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],
"normalizedAbstract": "Air and noise pollution are two major factors that determine the quality of life of the people living in cities. The prime reasons for the rise of air and noise pollution are due to imbalanced urbanization, unregulated increase in traffic and inorganic industrialization. These have resulted in compromising the well-being of the citizens. In this context, the concept of smart cities has been developed. They inherently have the ability to sense and respond to the challenges which characterizes regular cities with the help of embedded intelligence. It has become important to monitor the environmental parameters for policy-making, planning and for making smart cities livable and sustainable. In a bid to make a smart city, in this work, we have studied the spatio-temporal relationship between air and noise pollution in four different locations and have also evaluated the effect of noise in predicting Air Quality (AQ). Data acquisition has been done using customized, self-developed CO2; NO2; PM2:5, humidity, temperature and intensity of noise. To determine the relationship between air and noise pollution, we have used Pearson correlation. Results show a strong association between the two types of pollution. For predicting the air quality, the impact of noise pollution as a feature has been investigated using three different machine learning models which are Decision Tree, Random Forest and K-Nearest Neighbors. When applicable, the results show that if noise pollution is used as a feature, we get a prediction accuracy of upto 95% which is an improvement of 5% on an average.",
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"Air Quality Prediction",
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"Air Quality",
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"fullName": "Arindam Ghosh",
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"fullName": "Prithviraj Pramanik",
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"fullName": "Kartick Das Banerjee",
"givenName": "Kartick Das",
"surname": "Banerjee",
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{
"affiliation": null,
"fullName": "Ashutosh Roy",
"givenName": "Ashutosh",
"surname": "Roy",
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"fullName": "Subrata Nandi",
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"proceeding": {
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"title": "2022 IEEE International Conference on Big Data (Big Data)",
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"article": {
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"doi": "10.1109/BigData55660.2022.10020701",
"title": "A Data Integration Approach to Estimating Personal Exposures to Air Pollution",
"normalizedTitle": "A Data Integration Approach to Estimating Personal Exposures to Air Pollution",
"abstract": "Globally, air pollution is the largest environmental risk to public health. In order to inform policy and target mitigation strategies there is a need to increase our understanding of the (personal) exposures experienced by different population groups. The Data Integration Model for Exposures (DIMEX) integrates data on daily travel patterns and activities with measurements and models of air pollution using agent-based modelling to simulate the daily exposures of different population groups. Here we present the results of a case study using DIMEX to model personal exposures to PM2.5 in Greater Manchester, UK, and demonstrate its ability to explore differences in time activities and exposures for different population groups. DIMEX can also be used to assess the effects of reductions in ambient air pollution and when run with concentrations reduced to 5 µg/m<sup>3</sup> (new WHO guidelines) lead to an estimated (mean) reduction in personal exposures between 2.7 and 3.1 µg/m<sup>3</sup> across population (gender-age) groups.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Globally, air pollution is the largest environmental risk to public health. In order to inform policy and target mitigation strategies there is a need to increase our understanding of the (personal) exposures experienced by different population groups. The Data Integration Model for Exposures (DIMEX) integrates data on daily travel patterns and activities with measurements and models of air pollution using agent-based modelling to simulate the daily exposures of different population groups. Here we present the results of a case study using DIMEX to model personal exposures to PM2.5 in Greater Manchester, UK, and demonstrate its ability to explore differences in time activities and exposures for different population groups. DIMEX can also be used to assess the effects of reductions in ambient air pollution and when run with concentrations reduced to 5 µg/m<sup>3</sup> (new WHO guidelines) lead to an estimated (mean) reduction in personal exposures between 2.7 and 3.1 µg/m<sup>3</sup> across population (gender-age) groups.",
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],
"normalizedAbstract": "Globally, air pollution is the largest environmental risk to public health. In order to inform policy and target mitigation strategies there is a need to increase our understanding of the (personal) exposures experienced by different population groups. The Data Integration Model for Exposures (DIMEX) integrates data on daily travel patterns and activities with measurements and models of air pollution using agent-based modelling to simulate the daily exposures of different population groups. Here we present the results of a case study using DIMEX to model personal exposures to PM2.5 in Greater Manchester, UK, and demonstrate its ability to explore differences in time activities and exposures for different population groups. DIMEX can also be used to assess the effects of reductions in ambient air pollution and when run with concentrations reduced to 5 µg/m3 (new WHO guidelines) lead to an estimated (mean) reduction in personal exposures between 2.7 and 3.1 µg/m3 across population (gender-age) groups.",
"fno": "10020701",
"keywords": [
"Air Pollution Control",
"Data Integration",
"Environmental Factors",
"Government Policies",
"Health And Safety",
"Risk Analysis",
"Air Pollution",
"Daily Travel Patterns",
"Data Integration Model",
"DIMEX",
"Environmental Risk",
"Greater Manchester",
"Mitigation Strategies",
"Public Health",
"UK",
"WHO",
"Atmospheric Modeling",
"Sociology",
"Data Integration",
"Big Data",
"Air Pollution",
"Data Models",
"Pollution Measurement",
"Air Pollution",
"Data Integration",
"Micro Simulation",
"Health Effects"
],
"authors": [
{
"affiliation": "University of Exeter,Joint Centre for Excellence in Environmental Intelligence,Exeter,UK",
"fullName": "Matthew L Thomas",
"givenName": "Matthew L",
"surname": "Thomas",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of London,School of Engineering, Physical and Mathematical Sciences Royal Holloway,Egham,UK",
"fullName": "Gavin Shaddick",
"givenName": "Gavin",
"surname": "Shaddick",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Manchester,Department of Earth and Environmental Sciences,Manchester,UK",
"fullName": "David Topping",
"givenName": "David",
"surname": "Topping",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Denmark,Management and Economics Technical,Department of Technology,Lyngby,Denmark",
"fullName": "Karyn Morrissey",
"givenName": "Karyn",
"surname": "Morrissey",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Manchester,Department of Earth and Environmental Sciences,Manchester,UK",
"fullName": "Thomas J. Brannan",
"givenName": "Thomas J.",
"surname": "Brannan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Newcastle University,School of Computing,Newcastle,United Kingdom",
"fullName": "Mike Diessner",
"givenName": "Mike",
"surname": "Diessner",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Alan Turing Institute,AI for Science and Government,London,UK",
"fullName": "Ruth C. E. Bowyer",
"givenName": "Ruth C. E.",
"surname": "Bowyer",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Exeter,Department of Mathematics and Statistics,Exeter,UK",
"fullName": "Stefan Siegert",
"givenName": "Stefan",
"surname": "Siegert",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Manchester,Department of Earth and Environmental Sciences,Manchester,UK",
"fullName": "Hugh Coe",
"givenName": "Hugh",
"surname": "Coe",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Manchester,Department of Geography,Manchester,UK",
"fullName": "James Evans",
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"title": "2017 International Conference on Computer Network, Electronic and Automation (ICCNEA)",
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"title": "Evaluation on Traffic Guidance Plan During Construction Period Based on Vissim Simulation",
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"abstract": "With the urbanization process speeding up, the construction of City Road increased rapidly. So limited road resource is often occupied during the road construction, which makes road capacity of construction affected area greatly decrease. Then the contradiction of city original transport supply becomes more prominent. Therefore, it is very important to make a reasonable traffic organization plan. At first According to the present traffic investigation and construction site management program, combining with the OD (Origin - Destination) distribution principle, the traffic impact of construction section is analyzed, the scope of influence is determined. The Taihangshan Road of Qingdao No.1 subway line is taken as an example, three evaluation indicators, such as: average delays, queue length, number of vehicles between OD points are selected combined with current traffic flow. The available traffic guiding schemes include temporary widening of roads, traffic management measures and construction safety guarantee measures, and so on. Then VISSIM is used for the guidance measures simulation. Finally, According to the simulation results, quantitative guidance measures are evaluated. The choice of reasonable conduct measures is based on relevant data.",
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"content": "With the urbanization process speeding up, the construction of City Road increased rapidly. So limited road resource is often occupied during the road construction, which makes road capacity of construction affected area greatly decrease. Then the contradiction of city original transport supply becomes more prominent. Therefore, it is very important to make a reasonable traffic organization plan. At first According to the present traffic investigation and construction site management program, combining with the OD (Origin - Destination) distribution principle, the traffic impact of construction section is analyzed, the scope of influence is determined. The Taihangshan Road of Qingdao No.1 subway line is taken as an example, three evaluation indicators, such as: average delays, queue length, number of vehicles between OD points are selected combined with current traffic flow. The available traffic guiding schemes include temporary widening of roads, traffic management measures and construction safety guarantee measures, and so on. Then VISSIM is used for the guidance measures simulation. Finally, According to the simulation results, quantitative guidance measures are evaluated. The choice of reasonable conduct measures is based on relevant data.",
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"abstract": "Understanding the Origin-Destination (OD) patterns between different regions of a city is important in urban planning. In this work, based on taxi GPS data, we propose OD-Wheel, a novel visual design and associated analysis tool, to explore OD patterns. Once users define a region, all taxi trips starting from or ending to that region are selected and grouped into OD clusters. With a hybrid circular-linear visual design, OD-Wheel allows users to explore the dynamic patterns of each OD cluster, including the variation of traffic flow volume and traveling time. The proposed tool supports convenient interactions and allows users to compare and correlate the patterns between different OD clusters. A use study with real data sets demonstrates the effectiveness of the proposed OD-Wheel.",
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"content": "Understanding the Origin-Destination (OD) patterns between different regions of a city is important in urban planning. In this work, based on taxi GPS data, we propose OD-Wheel, a novel visual design and associated analysis tool, to explore OD patterns. Once users define a region, all taxi trips starting from or ending to that region are selected and grouped into OD clusters. With a hybrid circular-linear visual design, OD-Wheel allows users to explore the dynamic patterns of each OD cluster, including the variation of traffic flow volume and traveling time. The proposed tool supports convenient interactions and allows users to compare and correlate the patterns between different OD clusters. A use study with real data sets demonstrates the effectiveness of the proposed OD-Wheel.",
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"normalizedAbstract": "Understanding the Origin-Destination (OD) patterns between different regions of a city is important in urban planning. In this work, based on taxi GPS data, we propose OD-Wheel, a novel visual design and associated analysis tool, to explore OD patterns. Once users define a region, all taxi trips starting from or ending to that region are selected and grouped into OD clusters. With a hybrid circular-linear visual design, OD-Wheel allows users to explore the dynamic patterns of each OD cluster, including the variation of traffic flow volume and traveling time. The proposed tool supports convenient interactions and allows users to compare and correlate the patterns between different OD clusters. A use study with real data sets demonstrates the effectiveness of the proposed OD-Wheel.",
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"affiliation": "Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University, China",
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"proceeding": {
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"title": "2014 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)",
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"title": "Event Based Robot Prognostics Using Principal Component Analysis",
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"abstract": "As industrial systems are getting complicated, challenges in coming up with efficient maintenance strategies which include predicting failures in the system become important industry specific research topic. Traditionally, research focuses on developing failure prediction models based on physical understanding of the system. But, development of such models are often time consuming and labour intensive for complex systems. Inrecent past, due to advent of cheaper data collection mechanisms and efficient algorithms, data driven approaches for predicting failures are gaining significant interest in industrial research community. In this paper, we provide a Principal component Analysis (PCA) based approach of failure prediction in industrial robots using event log information. The event logs are collected through remote service set-up from a robot controller. The proposed method will reduce the dimensionality of the original data which consist of interrelated events while retaining the variation present in the data. Using PCA and multivariate statistics such as Hotelling T2, Q Residuals and Q contributions charts, we are able to detect abnormal behavior of event pattern within 30 days before failure.",
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"content": "As industrial systems are getting complicated, challenges in coming up with efficient maintenance strategies which include predicting failures in the system become important industry specific research topic. Traditionally, research focuses on developing failure prediction models based on physical understanding of the system. But, development of such models are often time consuming and labour intensive for complex systems. Inrecent past, due to advent of cheaper data collection mechanisms and efficient algorithms, data driven approaches for predicting failures are gaining significant interest in industrial research community. In this paper, we provide a Principal component Analysis (PCA) based approach of failure prediction in industrial robots using event log information. The event logs are collected through remote service set-up from a robot controller. The proposed method will reduce the dimensionality of the original data which consist of interrelated events while retaining the variation present in the data. Using PCA and multivariate statistics such as Hotelling T2, Q Residuals and Q contributions charts, we are able to detect abnormal behavior of event pattern within 30 days before failure.",
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"normalizedAbstract": "As industrial systems are getting complicated, challenges in coming up with efficient maintenance strategies which include predicting failures in the system become important industry specific research topic. Traditionally, research focuses on developing failure prediction models based on physical understanding of the system. But, development of such models are often time consuming and labour intensive for complex systems. Inrecent past, due to advent of cheaper data collection mechanisms and efficient algorithms, data driven approaches for predicting failures are gaining significant interest in industrial research community. In this paper, we provide a Principal component Analysis (PCA) based approach of failure prediction in industrial robots using event log information. The event logs are collected through remote service set-up from a robot controller. The proposed method will reduce the dimensionality of the original data which consist of interrelated events while retaining the variation present in the data. Using PCA and multivariate statistics such as Hotelling T2, Q Residuals and Q contributions charts, we are able to detect abnormal behavior of event pattern within 30 days before failure.",
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"fullName": "V. Sathish",
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"content": "Due to the high complexity and dimensionality of stock price data, high volatility and uncertainty cause an over-fitting problem in predicting stock price. In this paper, we compare different combinations of variant Principal Component Analysis (PCA) methods and Recurrent Neural Network (RNN) based frameworks that can be applied to extract high-level features from a rich set of initial variables for producing 2-year ahead forecasts of the daily stock price of IBM and Wells Fargo. The objective of our comparative study is to find which combination performs best in forecasting different stock price data. The experimental results show that the best combination for IBM is 2-Directional 2-Dimensional PCA (2d2d-PCA) with Long Short-Term Memory (LSTM), which achieves accuracy of 91.77% based on R-Square (R<sup>2</sup>) and for Wells Fargo is 2d2d-PCA with Gated Recurrent Unit (GRU), which achieves accuracy of 92.47%. Our comparative study indicates that the best combination of dimension reduction method and deep learning model is unfixed for predicting different stock price data. In addition, it is confirmed that GRU is an efficient model in predicting stock price; and 2d2d-PCA performs as well as PCA with respect to other dimension reduction methods with less error.",
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"abstract": "Anonymization methods based on principal component analysis (PCA), one of dimensionality reduction methods, have been well studied. These combine PCA with noise addition. Some of these methods theoretically guarantee privacy but decrease utility because of noise addition. Chen et al. proposed a PCA-based privacy-enhancing method adding noise to com-pressed data and assumed an attack model for privacy evaluation where an attacker can obtain the transformation matrix used to transform the original data matrix into the compressed data matrix. However, it is not always possible for an attacker to obtain the transformation matrix in practice. To the best of our knowledge, there is no study about privacy of the regular PCA. In this study, we propose an attack model for PCA that restricts the information that the attacker can obtain, and under this model, we perform an attack on plain PCA, which doesn't add noise. We conducted experiments to see how our model affects privacy. We evaluated privacy using Re-identification Attack, in which an attacker tries to link records in compressed data to corresponding records in original data. We found that the accuracy of the attack was significantly lower than when using the usual model while keeping the utility high. These results suggest that PCA can sufficiently protect privacy when a realistic attacker is assumed in the proposed model.",
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"content": "Anonymization methods based on principal component analysis (PCA), one of dimensionality reduction methods, have been well studied. These combine PCA with noise addition. Some of these methods theoretically guarantee privacy but decrease utility because of noise addition. Chen et al. proposed a PCA-based privacy-enhancing method adding noise to com-pressed data and assumed an attack model for privacy evaluation where an attacker can obtain the transformation matrix used to transform the original data matrix into the compressed data matrix. However, it is not always possible for an attacker to obtain the transformation matrix in practice. To the best of our knowledge, there is no study about privacy of the regular PCA. In this study, we propose an attack model for PCA that restricts the information that the attacker can obtain, and under this model, we perform an attack on plain PCA, which doesn't add noise. We conducted experiments to see how our model affects privacy. We evaluated privacy using Re-identification Attack, in which an attacker tries to link records in compressed data to corresponding records in original data. We found that the accuracy of the attack was significantly lower than when using the usual model while keeping the utility high. These results suggest that PCA can sufficiently protect privacy when a realistic attacker is assumed in the proposed model.",
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"normalizedAbstract": "Anonymization methods based on principal component analysis (PCA), one of dimensionality reduction methods, have been well studied. These combine PCA with noise addition. Some of these methods theoretically guarantee privacy but decrease utility because of noise addition. Chen et al. proposed a PCA-based privacy-enhancing method adding noise to com-pressed data and assumed an attack model for privacy evaluation where an attacker can obtain the transformation matrix used to transform the original data matrix into the compressed data matrix. However, it is not always possible for an attacker to obtain the transformation matrix in practice. To the best of our knowledge, there is no study about privacy of the regular PCA. In this study, we propose an attack model for PCA that restricts the information that the attacker can obtain, and under this model, we perform an attack on plain PCA, which doesn't add noise. We conducted experiments to see how our model affects privacy. We evaluated privacy using Re-identification Attack, in which an attacker tries to link records in compressed data to corresponding records in original data. We found that the accuracy of the attack was significantly lower than when using the usual model while keeping the utility high. These results suggest that PCA can sufficiently protect privacy when a realistic attacker is assumed in the proposed model.",
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"affiliation": "Bangladesh University of Engineering and Technology (BUET)",
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"abstract": "Protein structure prediction is a key topic in computational structural proteomics. The hypothesis that protein biological functions are implied by their three-dimensional structure makes the protein tertiary structure prediction a relevant problem to be solved. Predicting the tertiary structure of a protein by using its residue sequence is called the protein folding problem. Recently, novel approaches to the solution of this problem have been found and many of them use contact maps as a guide during the prediction process. Contact map structures are bidimensional objects which represent some of the structural information of a protein. Many approaches and bioinformatics tools for contact map prediction have been presented during the past years, having different performances for different protein families. In this work we present a novel approach based on the integration of contact map predictions in order to improve the quality of the predicted contact map with a consensus-based algorithm.",
"abstracts": [
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"content": "Protein structure prediction is a key topic in computational structural proteomics. The hypothesis that protein biological functions are implied by their three-dimensional structure makes the protein tertiary structure prediction a relevant problem to be solved. Predicting the tertiary structure of a protein by using its residue sequence is called the protein folding problem. Recently, novel approaches to the solution of this problem have been found and many of them use contact maps as a guide during the prediction process. Contact map structures are bidimensional objects which represent some of the structural information of a protein. Many approaches and bioinformatics tools for contact map prediction have been presented during the past years, having different performances for different protein families. In this work we present a novel approach based on the integration of contact map predictions in order to improve the quality of the predicted contact map with a consensus-based algorithm.",
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"affiliation": "Department of Experimental and Clinical Medicine, Magna Græcia, University of Catanzaro, Viale Europa, 88100 Germaneto, Catanzaro, Italy",
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"abstract": "Deriving the exact casual model that governs the relations between variables in a multidimensional dataset is difficult in practice. It is because causal inference algorithms by themselves typically cannot encode an adequate amount of domain knowledge to break all ties. Visual analytic approaches are considered a feasible alternative to fully automated methods. However, their application in real-world scenarios can be tedious. This paper focuses on these practical aspects of visual causality analysis. The most imperative of these aspects is posed by Simpson' Paradox. It implies the existence of multiple causal models differing in both structure and parameter depending on how the data is subdivided. We propose a comprehensive interface that engages human experts in identifying these subdivisions and allowing them to establish the corresponding causal models via a rich set of interactive facilities. Other features of our interface include: (1) a new causal network visualization that emphasizes the flow of causal dependencies, (2) a model scoring mechanism with visual hints for interactive model refinement, and (3) flexible approaches for handling heterogeneous data. Various real-world data examples are given.",
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"abstract": "While training a machine learning model, data scientists often need to determine some hyperparameters to set up the model. The values of hyperparameters configure the structure and other characteristics of the model and can significantly influence the training result. However, given the complexity of the model algorithms and the training processes, identifying a sweet spot in the hyperparameter space for a specific problem can be challenging. This paper characterizes user requirements for hyperparameter tuning and proposes a prototype system to provide model-agnostic support. We conducted interviews with data science practitioners in industry to collect user requirements and identify opportunities for leveraging interactive visual support. We present HyperTuner, a prototype system that supports hyperparameter search and analysis via interactive visual analytics. The design treats models as black boxes with the hyperparameters and data as inputs, and the predictions and performance metrics as outputs. We discuss our preliminary evaluation results, where the data science practitioners deem HyperTuner as useful and desired to help gain insights into the influence of hyperparameters on model performance and convergence. The design also triggered additional requirements such as involving more advanced support for automated tuning and debugging.",
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"abstract": "This paper presents a framework which enables a user to more easily make corrections to adversarial texts. While attack algorithms have been demonstrated to automatically build adversaries, changes made by the algorithms can often have poor semantics or syntax. Our framework is designed to facilitate human intervention by aiding users in making corrections. The framework extends existing attack algorithms to work within an evolutionary attack process paired with a visual analytics loop. Using an interactive dashboard a user is able to review the generation process in real time and receive suggestions from the system for edits to be made. The adversaries can be used to both diagnose robustness issues within a single classifier or to compare various classifier options. With the weaknesses identified, the framework can also be used as a first step in mitigating adversarial threats. The framework can be used as part of further research into defense methods in which the adversarial examples are used to evaluate new countermeasures. We demonstrate the framework with a word swapping attack for the task of sentiment classification.",
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"abstract": "In order to effectively detect faults and maintain heavy machines, a standard practice in several organizations is to conduct regular manual inspections. The procedure for conducting such inspections requires marking of the damaged components on a standardized inspection sheet which is then camera scanned. These sheets are marked for different faults in corresponding machine zones using hand-drawn arrows and text. As a result, the reading environment is highly unstructured and requires a domain expert while extracting the manually marked information. In this paper, we propose a novel pipeline to build an information extraction system for such machine inspection sheets, utilizing state-of-the-art deep learning and computer vision techniques. The pipeline proceeds in the following stages: (1) localization of different zones of the machine, arrows and text using a combination of template matching, deep learning and connected components, and (2) mapping the machine zone to the corresponding arrow head and the text segment to the arrow tail, followed by pairing them to get the correct damage code for each zone. Experiments were performed on a dataset collected from an anonymous real world manufacturing unit. Results demonstrate the efficacy of the proposed approach and we also report the accuracy for each step in the pipeline.",
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"content": "In order to effectively detect faults and maintain heavy machines, a standard practice in several organizations is to conduct regular manual inspections. The procedure for conducting such inspections requires marking of the damaged components on a standardized inspection sheet which is then camera scanned. These sheets are marked for different faults in corresponding machine zones using hand-drawn arrows and text. As a result, the reading environment is highly unstructured and requires a domain expert while extracting the manually marked information. In this paper, we propose a novel pipeline to build an information extraction system for such machine inspection sheets, utilizing state-of-the-art deep learning and computer vision techniques. The pipeline proceeds in the following stages: (1) localization of different zones of the machine, arrows and text using a combination of template matching, deep learning and connected components, and (2) mapping the machine zone to the corresponding arrow head and the text segment to the arrow tail, followed by pairing them to get the correct damage code for each zone. Experiments were performed on a dataset collected from an anonymous real world manufacturing unit. Results demonstrate the efficacy of the proposed approach and we also report the accuracy for each step in the pipeline.",
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"abstract": "The nutritional fact label (NFL) is the medium used to communicate nutritional information that help people having a balance-dietary, while there were evidences claimed that current format of NFL had an interface issues. Thus, the purpose of this study was to evaluate usability of nutrition fact label concerning people's health literacy. This study conducted a quantitative usability testing together with an in-depth interview in order to understand how people accessed the nutrition information in current design. Forty participants with 10 people that are high health literacy and 30 people that are low health literacy participated in the usability testing. The individual differences of health literacy were found and the major interface issues were illustrated and discussed.",
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"abstract": "Effective and efficient retargeting techniques may enrich users' browsing experiences in mobile devices. Existing mesh-based retargeting solutions put less efforts in making well-tuned meshes. In this paper, we propose a novel adaptive grid based optimization method to retarget an image. First, we present an entropy based measure to guide the grid construction. Then we employ the quadtree structure to adjust the grid granularity adaptively. Furthermore, to reduce the inappropriate deformation from inconsistent importance assignment, we build a global optimization model to alleviate serious shape deformation in retargeting. Comparison experiments show our method's superiority over the state-of-the-art approaches.",
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