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"doi": "10.1109/ICICIC.2007.341",
"title": "Hybrid Calibration of a Five-axis Robot based on Modeless and Model-based Methods",
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"abstract": "Through Analysis of both model-based robotic calibration and modeless robotic calibration, considering the structure of a five-axis grinding robot, a hybrid robotic calibration method is proposed which combines the advantages of the two common robotic calibration methods. For the translational joint error, a modeless calibration method is adopted, and for the rotational joint error, the hybrid calibration method is adopted according to their respective characteristics. Calibration experiments on a five-axis robot demonstrate its effectivity and feasibility, which can provide certain help for robot calibration of similar structure.",
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"title": "2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)",
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"doi": "10.1109/MASS.2017.56",
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"abstract": "While bringing massive-scale sensing at low cost, mobile participatory sensing is challenged by the low accuracy of the sensors embedded in and/or connected to the smartphones. The mobile measurements that are collected need to be corrected so as to accurately match the phenomena being observed. This paper addresses this challenge by introducing a multi-hop, multiparty calibration method that operates in the background in an automated way. Using our method, sensors that are within a relevant sensing (and communication) range coordinate so that the observations of the participating (previously) calibrated sensors serve calibrating the other participants. As a result, our method is particularly well suited for participatory sensing within crowd meetings, as for instance within public spaces. Our solution leverages multivariate linear regression, together with robust regression so as to discard the measurements that are of too low quality for being meaningful. To the best of our knowledge, we are the first to introduce a multiparty calibration algorithm, while previous work in the area focused on pairwise calibration. The paper further introduces a supporting prototype implemented over Android, and related experiment in the context of noise sensing.We show that the proposed multiparty calibration system enhances the accuracy of the mobile noise sensing application.",
"abstracts": [
{
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"content": "While bringing massive-scale sensing at low cost, mobile participatory sensing is challenged by the low accuracy of the sensors embedded in and/or connected to the smartphones. The mobile measurements that are collected need to be corrected so as to accurately match the phenomena being observed. This paper addresses this challenge by introducing a multi-hop, multiparty calibration method that operates in the background in an automated way. Using our method, sensors that are within a relevant sensing (and communication) range coordinate so that the observations of the participating (previously) calibrated sensors serve calibrating the other participants. As a result, our method is particularly well suited for participatory sensing within crowd meetings, as for instance within public spaces. Our solution leverages multivariate linear regression, together with robust regression so as to discard the measurements that are of too low quality for being meaningful. To the best of our knowledge, we are the first to introduce a multiparty calibration algorithm, while previous work in the area focused on pairwise calibration. The paper further introduces a supporting prototype implemented over Android, and related experiment in the context of noise sensing.We show that the proposed multiparty calibration system enhances the accuracy of the mobile noise sensing application.",
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"proceeding": {
"id": "1Et4qebUc4E",
"title": "2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)",
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"title": "Color Calibration Method for Images Taken by Different Imaging Conditions Which Suppresses False Color",
"normalizedTitle": "Color Calibration Method for Images Taken by Different Imaging Conditions Which Suppresses False Color",
"abstract": "Owing to different imaging conditions, color reproduction of images is usually different; hence, color calibration methods have been developed to unify color reproduction. Iterative distribution transfer (IDT) is one such method that can achieve good color calibration for most pixels of an input image by matching the color distribution of a reference image. This method, however, may lead to unnatural colors, called false colors. In this study, we propose a new color calibration method, which effectively suppresses false colors, whose result is similar to that obtained via the IDT. The input image is projected to specific powers called bases, which are multiplied by the corresponding projection coefficients and added to obtain the projection result. The projection coefficients can be obtained by minimizing the objective function, which is the mean-square error between the projection result and the IDT result. Considering that a false color in the IDT results is expected to be similar to the color of an adjacent pixel, a color-similarity-related item is added to the objective function to address this. Color similarity can be measured by the chromatic aberration between the adjacent pixels of the input image. By ensuring that similar colors in the input image remain similar in the output image, false colors can be suppressed. For the experiment, we use false color index, Kullback-Leibler divergence, and visual evaluation to evaluate the calibration results, which consistently proves that this method offers a better color calibration effect compared with other methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Owing to different imaging conditions, color reproduction of images is usually different; hence, color calibration methods have been developed to unify color reproduction. Iterative distribution transfer (IDT) is one such method that can achieve good color calibration for most pixels of an input image by matching the color distribution of a reference image. This method, however, may lead to unnatural colors, called false colors. In this study, we propose a new color calibration method, which effectively suppresses false colors, whose result is similar to that obtained via the IDT. The input image is projected to specific powers called bases, which are multiplied by the corresponding projection coefficients and added to obtain the projection result. The projection coefficients can be obtained by minimizing the objective function, which is the mean-square error between the projection result and the IDT result. Considering that a false color in the IDT results is expected to be similar to the color of an adjacent pixel, a color-similarity-related item is added to the objective function to address this. Color similarity can be measured by the chromatic aberration between the adjacent pixels of the input image. By ensuring that similar colors in the input image remain similar in the output image, false colors can be suppressed. For the experiment, we use false color index, Kullback-Leibler divergence, and visual evaluation to evaluate the calibration results, which consistently proves that this method offers a better color calibration effect compared with other methods.",
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"normalizedAbstract": "Owing to different imaging conditions, color reproduction of images is usually different; hence, color calibration methods have been developed to unify color reproduction. Iterative distribution transfer (IDT) is one such method that can achieve good color calibration for most pixels of an input image by matching the color distribution of a reference image. This method, however, may lead to unnatural colors, called false colors. In this study, we propose a new color calibration method, which effectively suppresses false colors, whose result is similar to that obtained via the IDT. The input image is projected to specific powers called bases, which are multiplied by the corresponding projection coefficients and added to obtain the projection result. The projection coefficients can be obtained by minimizing the objective function, which is the mean-square error between the projection result and the IDT result. Considering that a false color in the IDT results is expected to be similar to the color of an adjacent pixel, a color-similarity-related item is added to the objective function to address this. Color similarity can be measured by the chromatic aberration between the adjacent pixels of the input image. By ensuring that similar colors in the input image remain similar in the output image, false colors can be suppressed. For the experiment, we use false color index, Kullback-Leibler divergence, and visual evaluation to evaluate the calibration results, which consistently proves that this method offers a better color calibration effect compared with other methods.",
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"affiliation": "School of Information Engineering Inner Mongolia University of Technology,Hohhot,China",
"fullName": "Dongsheng Xu",
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"affiliation": "School of Information Engineering Inner Mongolia University of Technology,Hohhot,China",
"fullName": "Shi Bao",
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"affiliation": "Graduate School of Natural Sciences Nagoya City University,Nagoya-shi,Japan",
"fullName": "Go Tanaka",
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"affiliation": "School of Information Engineering Inner Mongolia University of Technology,Hohhot,China",
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"abstract": "Recognizing the place names within textual labels on historical maps is complicated by many factors, such as curvilinear baselines and dense overlap with other textual or graphical elements. However, maps' alignment with known geography and inter-label typographic style consistencies provide strong cues for resolving uncertainty and reducing text recognition errors. We present a unified probabilistic model to leverage the mutual information between text labels and styles and their geographical locations and categories. This work also introduces likelihood functions to model label placement for polyline and polygon geographical features, such as rivers or provinces. We evaluate the methods on 30 maps from 1866-1927. By interleaving automated map georeferencing with text recognition, we reduce word recognition error by 36% over OCR alone. Incorporating category-style links reduces toponym matching error by 32%.",
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"title": "An Expert System for the Tourism Destinations in IRAQ Based on the Google Maps API",
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"abstract": "The Google Maps API provides many features and solutions for Location Support and Positioning Services on electronic maps, so this paper discusses dealing with tourist destinations by geographically based on Google Maps with a dynamic approach that provides latitude and longitude for each destination on Map based on the information system which provide details of the destination that chosen by the visitor. The system will provide information of the tourist destinations that are available through a special database of the system, in addition to providing brief information and pictures, and review the geographical location of the tourist destination on the map when chosen by the visitor through this system. One of the most important features of working with Google Maps is the ease of dealing with the construction of interactive maps, low cost, no need for tools, and maintenance is not as complex as in other mapping applications, so this system will provide the possibility to create a navigation information system for tourist destinations and provide a great services for the tourists who are exploring their own tourist destinations.",
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"abstract": "Spatial Co-location Pattern (SCP) mining continues to play a critical role in understanding the morphology of urban functional spaces of world cities. It requires a large amount of fine-granular data and computing efficiency to handle the combinatorial explosion of co-location patterns. To this end, this work has two main contributions - i) We showcase a novel approach to perform SCP mining to characterize intra-city scale structure of urban functionality or co-located activity patterns using geosocial Points-of-Interest (POI) vector data. ii) We present a generalized and optimized parallel/distributed SCP mining algorithm implemented on a Hadoop MapReduce system and demonstrate the utility of our approach using the city of Berlin (Germany) as an example. The SCPs tend to vary across Berlin's municipal boroughs and at different spatial scales. Our findings on Berlin's functional structure conform to existing urban geography models. Such a data-driven exploration of massive urban POIs using distributed computing is first of its kind and can help better understand the changing dynamics of urban functionality, as well as physical, and social network structure around the world.",
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"Bars",
"Spatial Databases",
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"Big Data",
"Spatial Data Mining",
"Co Location Pattern",
"Map Reduce",
"Distributed Computing",
"Urban Areas"
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"affiliation": "Penn State University, University Park,GeoVISTA Center,USA",
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"content": "To provide effective services for intelligent transportation systems (ITS), such as optimizing ride services and recommending trips, it is important to predict the distributions of passenger flows from various origins to destinations. However, existing crowd flow prediction models have not sufficiently addressed this problem, and most methods have only focused on in and out flows of individual regions. The main challenges of origin-destination (OD) crowd flow prediction are diverse flow patterns across city networks and data sparsity. To solve these problems, we propose a Multi Attention 3D Residual Network (MAThR) to predict city-wide OD crowd flows. In particular, we develop a multi-component 3D residual structure with a novel global self-attention mechanism to dynamically aggregate the OD spatial-temporal dependencies, by modeling three components: contextual information of the region, and long and short term periodic crowd flows. For each component, we design a tensor criss-cross self-attention block, which can simultaneously discover the global and local correlation of spatial (where), temporal (when) and contextual (which) information between all OD pairs. Evaluation on real-world crowd flow data demonstrates the advantages of our MAThR method on prediction accuracy, compared to other existing state-of-the-art methods.",
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"abstract": "Space-filling visualizations, such as the TreeMap, are well suited for displaying the properties of nodes in hierarchies. To browse the contents of the hierarchy, the primary mode of interaction is by drilling down through many successive layers. In this paper we introduce a distortion algorithm based on fisheye and continuous zooming techniques for browsing data in the TreeMap representation. The motivation behind the distortion approach is for assisting users to rapidly browse information displayed in the TreeMap without opening successive layers of the hierarchy. Two experiments were conducted to evaluate the new approach. In the first experiment (N=20) the distortion approach is compared to the drill down method. Results show that subjects are quicker and more accurate in locating targets of interest using the distortion method. The second experiment (N=12) evaluates the effectiveness of the two approaches in a task requiring context, we define as the context browsing task. The results show that subjects are quicker and more accurate in locating targets with the distortion technique in the context browsing task.",
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"abstract": "Affective computing is a newly trend the main goal is exploring the human emotion things. The human emotion is leaded into a key position of behavior clue, and hence it should be included within the sensible model when an intelligent system aims to simulate or forecast human responses. This research utilizes decision tree one of data mining model to classify the emotion. This research integrates and manipulates the Thayer's emotion mode and color theory into the decision tree model, C4.5 for an innovative emotion detecting system. This paper uses 320 data in four emotion groups to train and build the decision tree for verifying the accuracy in this system. The result reveals that C4.5 decision tree model can be effective classified the emotion by feedback color from human. For the further research, colors will not the only human behavior clues, even more than all the factors from human interaction.",
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"abstract": "A limitation in color constancy research is the inability to establish ground truth colors for evaluating corrected images. Many existing datasets contain images of scenes with a color chart included, however, only the chart's neutral colors (grayscale patches) are used to provide the ground truth for illumination estimation and correction. This is because the corrected neutral colors are known to lie along the achromatic line in the camera's color space (i.e. R=G=B), the correct RGB values of the other color patches are not known. As a result, most methods estimate a 3*3 diagonal matrix that ensures only the neutral colors are correct. In this paper, we describe how to overcome this limitation. Specifically, we show that under certain illuminations, a diagonal 3*3 matrix is capable of correcting not only neutral colors, but all the colors in a scene. This finding allows us to find the ground truth RGB values for the color chart in the camera's color space. We show how to use this information to correct all the images in existing datasets to have correct colors. Working from these new color corrected datasets, we describe how to modify existing color constancy algorithms to perform better image correction.",
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"content": "A limitation in color constancy research is the inability to establish ground truth colors for evaluating corrected images. Many existing datasets contain images of scenes with a color chart included, however, only the chart's neutral colors (grayscale patches) are used to provide the ground truth for illumination estimation and correction. This is because the corrected neutral colors are known to lie along the achromatic line in the camera's color space (i.e. R=G=B), the correct RGB values of the other color patches are not known. As a result, most methods estimate a 3*3 diagonal matrix that ensures only the neutral colors are correct. In this paper, we describe how to overcome this limitation. Specifically, we show that under certain illuminations, a diagonal 3*3 matrix is capable of correcting not only neutral colors, but all the colors in a scene. This finding allows us to find the ground truth RGB values for the color chart in the camera's color space. We show how to use this information to correct all the images in existing datasets to have correct colors. Working from these new color corrected datasets, we describe how to modify existing color constancy algorithms to perform better image correction.",
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"content": "We explore new collective algorithms to optimize MPIBcast, MPIReduce and MPIAllreduce on InfiniBand clusters. Our algorithms are specifically designed for fat-tree networks. We present multi-color k-ary trees with a novel mapping scheme to map the colors to fat-tree network nodes. Our multi-color tree algorithms result in better utilization of network links over traditional algorithms on fat-tree networks. We also present optimizations for clusters of SMP nodes as we explore both hybrid and Multi Leader SMP techniques to achieve the best performance. We show the benefits of our algorithms with performance results from micro-benchmarks on POWER8 and X86 InfiniBand clusters. We also show performance optimizations from our algorithms in the PARATEC and QBOX applications.",
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"abstract": "Given n horizontal segments, each associated with a color from [σ], the Categorical Segment Stabbing problem is to find the distinct K colors stabbed by a vertical line. When the end-points of the segments are distinct and lie in [1, 2n], we present an (2 + ε)n log σ + O(n)-bit index with O(K/ε) query time, where ε ∈ (0, 1].When the end-points are arbitrary real numbers, a standard reduction to the above scenario improves the existing bounds of Janardan and Lopez. We also present results for few other variations: (1) reporting the top-k colors that are stabbed, where each color has a fixed priority. (2) handling these scenarios when the given segments form a tree range.",
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"content": "Given n horizontal segments, each associated with a color from [σ], the Categorical Segment Stabbing problem is to find the distinct K colors stabbed by a vertical line. When the end-points of the segments are distinct and lie in [1, 2n], we present an (2 + ε)n log σ + O(n)-bit index with O(K/ε) query time, where ε ∈ (0, 1].When the end-points are arbitrary real numbers, a standard reduction to the above scenario improves the existing bounds of Janardan and Lopez. We also present results for few other variations: (1) reporting the top-k colors that are stabbed, where each color has a fixed priority. (2) handling these scenarios when the given segments form a tree range.",
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"fullName": "Arnab Ganguly",
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"abstract": "In this papar, we introduce a visualization method for acitivity information sharing system using Self-Organizing Map and the activity information sharing system \"ALKAN2\", which aims at gathering activity information for activity analysis. ALKAN2 gathers users' activity using sensor data and movie data. Moreover, ALKAN2 prepares display environment for users. A user mimics activities of other users, and a user sends the mimicked data to the server. As a result, the mimicked data can be objectively evaluated. In this papar, we explain visualization for sensor data of ALKAN2.",
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"title": "2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops",
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"doi": "10.1109/ICCVW.2009.5457649",
"title": "Acquiring 3D motion trajectories of large numbers of swarming animals",
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"abstract": "Social behavior of animal group, such as insect swarm, bird flock, fish school, has captivated strong interest of scientists in many fields for years. Acquiring 3D motion trajectory of each individual in a swarm is vital for quantitative study of such behavior, yet this task is challenging due to large numbers of individuals, similar visual feature and frequent occlusions. In this paper, we present a novel approach which provides global optimal results for this task by formulating it as three linear assignment problems (LAP). The first LAP obtains the 2D tracks of particles in video sequences via spatially global assignment; the second one utilizes maximum epipolar co-motion length (MECL) to effectively eliminate matching ambiguities; the last one links the track segments into complete 3D trajectories via spatial-temporal global assignment. The proposed matching cost MECL encodes the global motion information during the whole track and is able to handle the association errors resulting from the first LAP. Our method is computationally efficient and works in near real time on a PC. Experiment results on simulated particle swarms validated the accuracy and efficiency of the proposed method. As real-world case, we successfully acquired 3D trajectories of Drosophila melanogaster (fruit fly) swarm comprising hundreds of individuals, which to our best knowledge is the first such achievement.",
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{
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"content": "Social behavior of animal group, such as insect swarm, bird flock, fish school, has captivated strong interest of scientists in many fields for years. Acquiring 3D motion trajectory of each individual in a swarm is vital for quantitative study of such behavior, yet this task is challenging due to large numbers of individuals, similar visual feature and frequent occlusions. In this paper, we present a novel approach which provides global optimal results for this task by formulating it as three linear assignment problems (LAP). The first LAP obtains the 2D tracks of particles in video sequences via spatially global assignment; the second one utilizes maximum epipolar co-motion length (MECL) to effectively eliminate matching ambiguities; the last one links the track segments into complete 3D trajectories via spatial-temporal global assignment. The proposed matching cost MECL encodes the global motion information during the whole track and is able to handle the association errors resulting from the first LAP. Our method is computationally efficient and works in near real time on a PC. Experiment results on simulated particle swarms validated the accuracy and efficiency of the proposed method. As real-world case, we successfully acquired 3D trajectories of Drosophila melanogaster (fruit fly) swarm comprising hundreds of individuals, which to our best knowledge is the first such achievement.",
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"proceeding": {
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"title": "2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)",
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"article": {
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"title": "Autism Spectrum Disorder diagnosis based on trajectories of eye tracking data",
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"abstract": "The use of Eye Tracking (ET) has been investigated as an auxiliary mechanism to diagnose Autism Spectrum Disorder (ASD). One of the paradigms investigated using ET is Joint Attention (JA), which refers to moments when two individuals are focused on the same object/event so that both are aware that the focus of attention is shared. The computational tools that assist in the diagnosis of ASD have used Image Processing and Machine Learning techniques to process images, videos and ET signals. However, the JA paradigm is still little explored and presents challenges, as it requires analyzing the gaze trajectory and needs innovative approaches. The purpose of this article is to propose a model capable of extracting features from a video used as a stimulus to capture ET signals in order to verify JA and classify individuals as belonging to the ASD or Typical Development (TD) group. The main differential in relation to the approaches in the literature is the definition and implementation of the concept of floating Regions of Interest, which allows monitoring the gaze in relation to an object, considering its semantics, even if the object presents different characteristics throughout the video. A model based on ensembles of Random Forest classifiers was implemented to classify individuals as ASD or TD using the trajectory features extracted from the ET signals. The method reached 0.75 accuracy and 0.82 F1-score, indicating that the proposed approach, based on trajectory and JA, has the potential to be applied to assist in the diagnosis of ASD.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The use of Eye Tracking (ET) has been investigated as an auxiliary mechanism to diagnose Autism Spectrum Disorder (ASD). One of the paradigms investigated using ET is Joint Attention (JA), which refers to moments when two individuals are focused on the same object/event so that both are aware that the focus of attention is shared. The computational tools that assist in the diagnosis of ASD have used Image Processing and Machine Learning techniques to process images, videos and ET signals. However, the JA paradigm is still little explored and presents challenges, as it requires analyzing the gaze trajectory and needs innovative approaches. The purpose of this article is to propose a model capable of extracting features from a video used as a stimulus to capture ET signals in order to verify JA and classify individuals as belonging to the ASD or Typical Development (TD) group. The main differential in relation to the approaches in the literature is the definition and implementation of the concept of floating Regions of Interest, which allows monitoring the gaze in relation to an object, considering its semantics, even if the object presents different characteristics throughout the video. A model based on ensembles of Random Forest classifiers was implemented to classify individuals as ASD or TD using the trajectory features extracted from the ET signals. The method reached 0.75 accuracy and 0.82 F1-score, indicating that the proposed approach, based on trajectory and JA, has the potential to be applied to assist in the diagnosis of ASD.",
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"authors": [
{
"affiliation": "School of Arts, Sciences and Humanities (EACH), University of Sao Paulo (USP),Sao Paulo,SP,Brazil",
"fullName": "Thiago V. Cardoso",
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{
"affiliation": "School of Arts, Sciences and Humanities (EACH), University of Sao Paulo (USP),Sao Paulo,SP,Brazil",
"fullName": "Gabriel C. Michelassi",
"givenName": "Gabriel C.",
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"affiliation": "Institute of Mathematics and Statistics (IME), (USP),Interunit PostGraduate Program on Bioinformatics",
"fullName": "Felipe O. Franco",
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{
"affiliation": "University of Sao Paulo's, School of Medicine (FMUSP),Department of Psychiatry,Sao Paulo,SP,Brazil",
"fullName": "Fernando M. Sumiya",
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"affiliation": "University of Sao Paulo's, School of Medicine (FMUSP),Department of Psychiatry,Sao Paulo,SP,Brazil",
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"abstract": "We present the results of a study comparing five directed-edge representations for use in 2D, screen-based node-link diagrams. The goal of this work is to extend the understanding of tradeoffs and best practices for the representation of edges in directed graphs and to help practitioners choose among different options. Our work applies to graphs in which directed links are depicted using lines connecting the nodes. We tested five different edge representations chosen carefully based on user feedback to thoroughly cover the directed-edge design space. We also investigated how the use of pattern compression affects performance and subjective user preference. The article presents detailed results regarding the significant performance and preference differences between directed-edge representations and provides practical recommendations on their use.",
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"content": "We present the results of a study comparing five directed-edge representations for use in 2D, screen-based node-link diagrams. The goal of this work is to extend the understanding of tradeoffs and best practices for the representation of edges in directed graphs and to help practitioners choose among different options. Our work applies to graphs in which directed links are depicted using lines connecting the nodes. We tested five different edge representations chosen carefully based on user feedback to thoroughly cover the directed-edge design space. We also investigated how the use of pattern compression affects performance and subjective user preference. The article presents detailed results regarding the significant performance and preference differences between directed-edge representations and provides practical recommendations on their use.",
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"abstract": "Augmented Reality is an emerging technology and the applications of technology are still not fully unveiled. This paper explores a new application of augmented reality for a new direction in educational book publishing, which aims to bring interactive learning experience to life. The project takes printed images on book to the next level by applying Augmented Reality technology to provide a unique fascinating experience to its readers on mobile devices. Augmented Reality (AR) technology composing with animation brings new digital entertainment experience to the reader of books. The key feature of this paper uses the technology presents auxiliary information in the field of view of an object printed on book automatically without human intervention. The project uses the technology with iPad mobile device to display 3D models, 3D animations, video splaying, websites and web server connectivity for children education. The results and evaluation of the project shows the interactive 3D animation and self-assessment functions significantly support students to improve their learning experience and performance. The software product of this project, from the business perspective, creates a new business marketing dimension in digital publishing and increases the selling profits in the book publication business.",
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"abstract": "User complaint data are quite valuable because they can reflect deficiencies of companies. Analyzing these short texts can help companies discover what topics users are complaining about. It is critical to locate and respond to these complaints timely so that companies can improve users' satisfaction and loyalty. As the data volume is large, topic model can help discover key complaint topics quickly. The complaint data are in the form of short texts and streams, traditional topic models like LDA and BTM are not suitable in this scenario, for the reason that LDA is designed for long texts and BTM can not handle streams. This paper firstly proposes an improved shorttext topic model called PMITI-BTM to generate topics from user complaint data statically, and then further extends this algorithm into a dynamic one to suit the streaming feature of data. To further analyze the topic evolution, we finally propose a clustering algorithm called TDWAP to acquire the evolution process of these topics in different time slices. For each algorithm, we do several experiments to prove its efficiency. Results show that our methods not only can improve the performance of short texts topic discovery, but also can discover the evolution of topics.",
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"content": "User complaint data are quite valuable because they can reflect deficiencies of companies. Analyzing these short texts can help companies discover what topics users are complaining about. It is critical to locate and respond to these complaints timely so that companies can improve users' satisfaction and loyalty. As the data volume is large, topic model can help discover key complaint topics quickly. The complaint data are in the form of short texts and streams, traditional topic models like LDA and BTM are not suitable in this scenario, for the reason that LDA is designed for long texts and BTM can not handle streams. This paper firstly proposes an improved shorttext topic model called PMITI-BTM to generate topics from user complaint data statically, and then further extends this algorithm into a dynamic one to suit the streaming feature of data. To further analyze the topic evolution, we finally propose a clustering algorithm called TDWAP to acquire the evolution process of these topics in different time slices. For each algorithm, we do several experiments to prove its efficiency. Results show that our methods not only can improve the performance of short texts topic discovery, but also can discover the evolution of topics.",
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"abstract": "One feasible way to handle the difficulties in the Chinese short-text classification is doing feature extension for its content. And a key technique of it is the construction of feature extension mode library whose quality would directly influence the effect of feature extension. This paper proposes a method of constructing a high-quality feature extension mode library. In our method, firstly, we obtained the feature extension modes from the training set, and then introduced three measures, i.e., confidence, category homoplasy and relevancy strength of the obtained feature extension mode, to improve their quality. Finally, we used the high-quality feature extension modes to construct a high-quality feature extension mode library. Experimental results show that (1) Introducing the three measures can improve the quality of feature extension mode in some degree respectively; (2)A high-quality feature extension mode library is helpful to improving the effect of Chinese short-text classification.",
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"abstract": "Mobile IPv6 (MIPv6) is a protocol to support mobility for IPv6. Fast Handovers for MIPv6 (FMIPv6) have been studied, since in MIPv6 handover latency is long, and all packets are lost during handover. FMIPv6 provides seamless handover by minimizing handover latency, and prevents packet loss through buffering and tunneling. FMIPv6 uses anticipation based on layer 2 trigger information, and consists of two modes such as predictive mode and reactive mode. Several works have been done to evaluate the performance of FMIPv6 in different network environments. However, the previous works did not consider probability of predictive mode failure that classifies the two modes. Also, in the most previous works the two modes of FMIPv6 are evaluated separately. In this paper, FMIPv6 combining the two modes is analyzed through probability of predictive mode failure, since FMIPv6 is just a protocol. We investigate probability of predictive mode failure to find important elements in network environment. Then, FMIPv6 is analyzed by these elements for performance evaluation with respect to various metrics like signaling cost, delivery cost, and buffering cost. Numerical results show a trade-off between performance and system parameters. Then, we shows methods to optimize FMIPv6 using probability of predictive mode failure and layer 2 trigger time.",
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"content": "Mobile IPv6 (MIPv6) is a protocol to support mobility for IPv6. Fast Handovers for MIPv6 (FMIPv6) have been studied, since in MIPv6 handover latency is long, and all packets are lost during handover. FMIPv6 provides seamless handover by minimizing handover latency, and prevents packet loss through buffering and tunneling. FMIPv6 uses anticipation based on layer 2 trigger information, and consists of two modes such as predictive mode and reactive mode. Several works have been done to evaluate the performance of FMIPv6 in different network environments. However, the previous works did not consider probability of predictive mode failure that classifies the two modes. Also, in the most previous works the two modes of FMIPv6 are evaluated separately. In this paper, FMIPv6 combining the two modes is analyzed through probability of predictive mode failure, since FMIPv6 is just a protocol. We investigate probability of predictive mode failure to find important elements in network environment. Then, FMIPv6 is analyzed by these elements for performance evaluation with respect to various metrics like signaling cost, delivery cost, and buffering cost. Numerical results show a trade-off between performance and system parameters. Then, we shows methods to optimize FMIPv6 using probability of predictive mode failure and layer 2 trigger time.",
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"abstract": "Most network traffic analysis applications are designed to discover malicious activity by only relying on high-level flowbased message properties. However, to detect security breaches that are specifically designed to target one network (e.g., Advanced Persistent Threats), deep packet inspection and anomaly detection are indispensible. In this paper, we focus on how we can support experts in discovering whether anomalies at message level imply a security risk at network level. In SNAPS (Semantic Network traffic Analysis through Projection and Selection), we provide a bottom-up pixel-oriented approach for network traffic analysis where the expert starts with low-level anomalies and iteratively gains insight in higher level events through the creation of multiple selections of interest in parallel. The tight integration between visualization and machine learning enables the expert to iteratively refine anomaly scores, making the approach suitable for both post-traffic analysis and online monitoring tasks. To illustrate the effectiveness of this approach, we present example explorations on two real-world data sets for the detection and understanding of potential Advanced Persistent Threats in progress. ",
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