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"abstract": "We present a robust method for 3D reconstruction of closed surfaces from sparsely sampled parallel contours. A solution to this problem is especially important for medical segmentation, where manual contouring of 2D imaging scans is still extensively used. Our proposed method is based on a morphing process applied to neighboring contours that sweeps out a 3D surface. Our method is guaranteed to produce closed surfaces that exactly pass through the input contours, regardless of the topology of the reconstruction. Our general approach consecutively morphs between sets of input contours using an Eulerian formulation (i.e. fixed grid) augmented with Lagrangian particles (i.e. interface tracking). This is numerically accomplished by propagating the input contours as 2D level sets with carefully constructed continuous speed functions. Specifically this involves particle advection to estimate distances between the contours, monotonicity constrained spline interpolation to compute continuous speed functions without overshooting, and state-of-the-art numerical techniques for solving the level set equations. We demonstrate the robustness of our method on a variety of medical, topographic and synthetic data sets.",
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"abstract": "Multiphase level set model is sensitive to initial contour curve and has huge computation in the process of the multiple objects' segmentation. This paper presents a novel Image segmentation method for multiphase scenario, which initialize the multiphase level set function by coarse image segmentation using fuzzy C-means clustering algorithm and apply graph cut algorithm to acquire multiphase output image. The method effectively reduces the sensitivity of the multiphase level set algorithm to initial contour and is easier to gain the multiphase output image by graph cut algorithm. At the same time, because of using the graph cut algorithm, the multiphase level set function quickly converge to the minimum energy value with small amount of calculation and high computational efficiency. The experiments show that this method has better segmentation effect and higher efficiency of image segmentation.",
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"title": "Multiphase Level Set Model with Local K-means Energy for Histology Image Segmentation",
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"abstract": "In this paper we present a multiphase level set model for histology image segmentation. Global K-means energy is weighted by a Gaussian kernel to cluster image pixels in local neighborhoods. We group these local clusters into different source classes using a multiphase level set model to produce the final segmentation results. Our energy functional is formulated as the integral of local K-means energies across the entire image. Unlike current local region-based active contour methods that update the pixel neighborhood distributions (e.g. local intensity means) in each iteration, we estimate these statistics before contour evolution for more efficient computation. In addition, such pre-derived local intensity distributions enable a model without initial contour selection, i.e., the level set functions can be initialized with a random constant instead of a distance map. In this way our model ameliorates the initialization sensitivity problem of most active contour methods. Experiments on the National Cancer Institute ALTS histology images show the improved performance of our approach over standard multithresholding and K-means clustering, as well as state-of-the-art active contours, mean shift clustering, and Markov random field-based pixel labeling methods.",
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"content": "In this paper we present a multiphase level set model for histology image segmentation. Global K-means energy is weighted by a Gaussian kernel to cluster image pixels in local neighborhoods. We group these local clusters into different source classes using a multiphase level set model to produce the final segmentation results. Our energy functional is formulated as the integral of local K-means energies across the entire image. Unlike current local region-based active contour methods that update the pixel neighborhood distributions (e.g. local intensity means) in each iteration, we estimate these statistics before contour evolution for more efficient computation. In addition, such pre-derived local intensity distributions enable a model without initial contour selection, i.e., the level set functions can be initialized with a random constant instead of a distance map. In this way our model ameliorates the initialization sensitivity problem of most active contour methods. Experiments on the National Cancer Institute ALTS histology images show the improved performance of our approach over standard multithresholding and K-means clustering, as well as state-of-the-art active contours, mean shift clustering, and Markov random field-based pixel labeling methods.",
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"fullName": "Lei He",
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"abstract": "Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to recover the detailed surface geometry, where the representation of a surface needs to be adapted when optimizing different quantities. In this paper, we present a novel multi-view RGB-D based reconstruction method that tackles camera pose, lighting, albedo, and surface normal estimation via the utilization of a gradient signed distance field (gradient-SDF). The proposed method formulates the image rendering process using specific physically-based model(s) and optimizes the surface’s quantities on the actual surface using its volumetric representation, as opposed to other works which estimate surface quantities only near the actual surface. To validate our method, we investigate two physically-based image formation models for natural light and point light source applications. The experimental results on synthetic and real-world datasets demonstrate that the proposed method can recover high-quality geometry of the surface more faithfully than the state-of-the-art and further improves the accuracy of estimated camera poses<sup>1</sup>.",
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"content": "Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to recover the detailed surface geometry, where the representation of a surface needs to be adapted when optimizing different quantities. In this paper, we present a novel multi-view RGB-D based reconstruction method that tackles camera pose, lighting, albedo, and surface normal estimation via the utilization of a gradient signed distance field (gradient-SDF). The proposed method formulates the image rendering process using specific physically-based model(s) and optimizes the surface’s quantities on the actual surface using its volumetric representation, as opposed to other works which estimate surface quantities only near the actual surface. To validate our method, we investigate two physically-based image formation models for natural light and point light source applications. The experimental results on synthetic and real-world datasets demonstrate that the proposed method can recover high-quality geometry of the surface more faithfully than the state-of-the-art and further improves the accuracy of estimated camera poses<sup>1</sup>.",
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"abstract": "In this paper we consider the `Prions' database that describes protein instances stored for human Prion proteins. The Prions database can be viewed as a database of rooted ordered labeled subtrees. Mining frequent substructures from tree databases is an important task and it has gained a considerable amount of interest in areas such as XML mining, bio informatics, Web mining etc. This has given rise to the development of many tree mining algorithms which can aid in structural comparisons, association rule discovery and in general mining of tree structured knowledge representations. Previously we have developed the MB3 tree mining algorithm, which given a minimum support threshold, efficiently discovers all frequent embedded subtrees from a database of rooted ordered labeled subtrees. In this work we apply the algorithm to the Prions database in order to extract the frequently occurring patterns, which in this case are of induced subtree type. Obtaining the set of frequent induced subtrees from the Prions database can potentially reveal some useful knowledge. This aspect will be demonstrated by providing an analysis of the extracted frequent subtrees with respect to discovering interesting protein information. Furthermore, the minimum support threshold can be used as the controlling factor for answering specific queries posed on the Prions dataset. This approach is shown to be a viable technique for mining protein data",
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"content": "In this paper we consider the `Prions' database that describes protein instances stored for human Prion proteins. The Prions database can be viewed as a database of rooted ordered labeled subtrees. Mining frequent substructures from tree databases is an important task and it has gained a considerable amount of interest in areas such as XML mining, bio informatics, Web mining etc. This has given rise to the development of many tree mining algorithms which can aid in structural comparisons, association rule discovery and in general mining of tree structured knowledge representations. Previously we have developed the MB3 tree mining algorithm, which given a minimum support threshold, efficiently discovers all frequent embedded subtrees from a database of rooted ordered labeled subtrees. In this work we apply the algorithm to the Prions database in order to extract the frequently occurring patterns, which in this case are of induced subtree type. Obtaining the set of frequent induced subtrees from the Prions database can potentially reveal some useful knowledge. This aspect will be demonstrated by providing an analysis of the extracted frequent subtrees with respect to discovering interesting protein information. Furthermore, the minimum support threshold can be used as the controlling factor for answering specific queries posed on the Prions dataset. This approach is shown to be a viable technique for mining protein data",
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"title": "Uncertainty Modeling and Analysis, International Symposium on",
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"abstract": "Decision support systems use two basic strategies: the pursuit of a small set of hypotheses and the sequential partitioning of hierarchical hypothesis structures. We present an alternative method based on the maximization of information gain. In each step, we evaluate the difference between the actual and potential future evidence distributions. The data \"promising\" the maximum information gain are then inquired by the system. In simple situations, the new method behaves like traditional strategies but in divergent and inconsistent evidence situations, it avoids the drawbacks induced by the predetermined standard strategies by adapting itself continuously to the actual data configuration. Our method can be extended to layered hierarchical data structures, where its behavior is reminiscent of the cognitive phenomenon of \"restructuring\".",
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"content": "Decision support systems use two basic strategies: the pursuit of a small set of hypotheses and the sequential partitioning of hierarchical hypothesis structures. We present an alternative method based on the maximization of information gain. In each step, we evaluate the difference between the actual and potential future evidence distributions. The data \"promising\" the maximum information gain are then inquired by the system. In simple situations, the new method behaves like traditional strategies but in divergent and inconsistent evidence situations, it avoids the drawbacks induced by the predetermined standard strategies by adapting itself continuously to the actual data configuration. Our method can be extended to layered hierarchical data structures, where its behavior is reminiscent of the cognitive phenomenon of \"restructuring\".",
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"affiliation": "Inst. fur Medizinsche Psychol., Ludwig-Maximilians-Univ., Munchen, Germany",
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"abstract": "This paper presents a two-dimensional numerical simulation method for modeling a convective flow structure induced by chemical concentration waves of Belousov-Zhabotinsky (BZ) reaction in a two-dimensional rectangular domain of horizontal space and vertical depth. The method assumes a scenario in which an air-liquid interface of the BZ chemical solution has an elastic property and the Marangoni effect drives the surface motion of the interface. As a result of the surface motion, a convective flow is organized in the bulk of the chemical solution. The bulk flow of the chemical solution is described with the Navier-Stokes equations, and the chemical reaction is described with the Oregonator model. Thus, we couple the three systems of the bulk flow, the chemical reaction and the surface motion described with an elastic equation in the numerical simulation method. Results of several numerical simulations performed with the method show that a single chemical concentration wave propagates with a broad convective flow structure and a chemical concentration wave train propagates with a global flow structure. These flow structures are similar to those observed in real laboratory experiments.",
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"abstract": "The segmentation of Lung lesions is a challenging task because of the complexity of lung lesions surroundings. Lung lesions can be categorized into two types: solid and non-solid. Lots of works have been developed previously to segment one of two types, but only a few are proposed to handle two types at the same time and these methods may be over-segmented or sub-segmented. Therefore, in this study, an effective framework is designed to segment two types of lung lesions in 3-dimension (3D). In the proposed framework, we use a Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method to produce a 3D rough segmentation which is used as the initial contour of Geodesic Active Contour (GAC) method. SBGFRLS method can deal with the non-solid lung lesions very well because it can use the global information to segment inhomogeneous entities. GAC method can accurately locate the edge using the local information. Finally, we reconstruct and visualize the 3D segmentation results of lung lesions using visualization toolkit (VTK). All of our work is based on the Image Segmentation and Registration Toolkit (ITK) platform. We evaluate our method on the lung lesions CT data sets from 300 patients (280 for solid and 20 for non-solid). Experimental results show that our method can achieve better segmentation and more accurate calculation of 3D volume measurement compared to other two methods, especially in the non-solid type lesions.",
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"content": "The segmentation of Lung lesions is a challenging task because of the complexity of lung lesions surroundings. Lung lesions can be categorized into two types: solid and non-solid. Lots of works have been developed previously to segment one of two types, but only a few are proposed to handle two types at the same time and these methods may be over-segmented or sub-segmented. Therefore, in this study, an effective framework is designed to segment two types of lung lesions in 3-dimension (3D). In the proposed framework, we use a Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method to produce a 3D rough segmentation which is used as the initial contour of Geodesic Active Contour (GAC) method. SBGFRLS method can deal with the non-solid lung lesions very well because it can use the global information to segment inhomogeneous entities. GAC method can accurately locate the edge using the local information. Finally, we reconstruct and visualize the 3D segmentation results of lung lesions using visualization toolkit (VTK). All of our work is based on the Image Segmentation and Registration Toolkit (ITK) platform. We evaluate our method on the lung lesions CT data sets from 300 patients (280 for solid and 20 for non-solid). Experimental results show that our method can achieve better segmentation and more accurate calculation of 3D volume measurement compared to other two methods, especially in the non-solid type lesions.",
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"abstract": "In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarse-to-fine framework to effectively and efficiently tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the current state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). On the NIH pancreas segmentation dataset, we outperform the previous best by an average of over 2%, and the worst case is improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.",
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"content": "In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarse-to-fine framework to effectively and efficiently tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the current state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). On the NIH pancreas segmentation dataset, we outperform the previous best by an average of over 2%, and the worst case is improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.",
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"doi": "10.1109/CBMS55023.2022.00069",
"title": "Integrating with Segmentation by Using Multi-Task Learning Improves Classification Performance in Medical Image Analysis",
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"abstract": "Diagnosis of tumors is an important direction of computer-aided diagnosis (CAD). The shape, size, and boundary of the tumor are widely-used diagnostic evidence, and the corresponding segmentation annotated by the radiologists is a vital expert knowledge, which can be used as supervision to guide feature extraction. Therefore, this study firstly introduces a multi-task learning (MTL) network integrating segmentation task for predicting grading of pancreatic neuroendocrine neoplasms (pNENs) and the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The proposed network combines a powerful split-attention-based encoder and a U-net decoder, and achieves the best performance in comparisons of other popular networks and previous studies. In addition, feature map visualization suggests that the reason for the improved classification performance may be that MTL makes the encoder pay more attention to lesions and extract more semantic information.",
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"content": "Diagnosis of tumors is an important direction of computer-aided diagnosis (CAD). The shape, size, and boundary of the tumor are widely-used diagnostic evidence, and the corresponding segmentation annotated by the radiologists is a vital expert knowledge, which can be used as supervision to guide feature extraction. Therefore, this study firstly introduces a multi-task learning (MTL) network integrating segmentation task for predicting grading of pancreatic neuroendocrine neoplasms (pNENs) and the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The proposed network combines a powerful split-attention-based encoder and a U-net decoder, and achieves the best performance in comparisons of other popular networks and previous studies. In addition, feature map visualization suggests that the reason for the improved classification performance may be that MTL makes the encoder pay more attention to lesions and extract more semantic information.",
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"normalizedAbstract": "Diagnosis of tumors is an important direction of computer-aided diagnosis (CAD). The shape, size, and boundary of the tumor are widely-used diagnostic evidence, and the corresponding segmentation annotated by the radiologists is a vital expert knowledge, which can be used as supervision to guide feature extraction. Therefore, this study firstly introduces a multi-task learning (MTL) network integrating segmentation task for predicting grading of pancreatic neuroendocrine neoplasms (pNENs) and the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The proposed network combines a powerful split-attention-based encoder and a U-net decoder, and achieves the best performance in comparisons of other popular networks and previous studies. In addition, feature map visualization suggests that the reason for the improved classification performance may be that MTL makes the encoder pay more attention to lesions and extract more semantic information.",
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"affiliation": "School of Biomedical Engineering Shenzhen University,Shenzhen,China",
"fullName": "Yi Li",
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"affiliation": "School of Biomedical Engineering Shenzhen University,Shenzhen,China",
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"affiliation": "School of Biomedical Engineering Shenzhen University,Shenzhen,China",
"fullName": "Mingyu Wang",
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"affiliation": "School of Biomedical Engineering Shenzhen University,Shenzhen,China",
"fullName": "Fei Li",
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"surname": "Li",
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{
"affiliation": "Shenzhen Institute for Drug Control,Shenzhen,China",
"fullName": "Jia Chen",
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{
"affiliation": "The First Affiliated Hospital of Sun Yat-sen University,Department of Radiology,Guangzhou,China",
"fullName": "Yanji Luo",
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{
"affiliation": "School of Biomedical Engineering, The First Affiliated Hospital of Sun Yat-sen,Guangzhou,China",
"fullName": "Shi-Ting Feng",
"givenName": "Shi-Ting",
"surname": "Feng",
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{
"affiliation": "University Shenzhen Second People's Hospital,Department of Radiology,Shenzhen,China",
"fullName": "Xiaoyi Lin",
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"surname": "Lin",
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{
"affiliation": "School of Biomedical Engineering Shenzhen University,Shenzhen,China",
"fullName": "Bingsheng Huang",
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"surname": "Huang",
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"title": "2019 International Conference on 3D Vision (3DV)",
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"doi": "10.1109/3DV.2019.00035",
"title": "V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation",
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"abstract": "Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation. However, 2D convolutions cannot fully leverage the rich spatial information along the third axis, while 3D convolutions suffer from the demanding computation and high GPU memory consumption. In this paper, we propose to automatically search the network architecture tailoring to volumetric medical image segmentation problem. Concretely, we formulate the structure learning as differentiable neural architecture search, and let the network itself choose between 2D, 3D or Pseudo-3D (P3D) convolutions at each layer. We evaluate our method on 3 public datasets, i.e., the NIH Pancreas dataset, the Lung and Pancreas dataset from the Medical Segmentation Decathlon (MSD) Challenge. Our method, named V-NAS, consistently outperforms other state-of-the-arts on the segmentation tasks of both normal organ (NIH Pancreas) and abnormal organs (MSD Lung tumors and MSD Pancreas tumors), which shows the power of chosen architecture. Moreover, the searched architecture on one dataset can be well generalized to other datasets, which demonstrates the robustness and practical use of our proposed method.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation. However, 2D convolutions cannot fully leverage the rich spatial information along the third axis, while 3D convolutions suffer from the demanding computation and high GPU memory consumption. In this paper, we propose to automatically search the network architecture tailoring to volumetric medical image segmentation problem. Concretely, we formulate the structure learning as differentiable neural architecture search, and let the network itself choose between 2D, 3D or Pseudo-3D (P3D) convolutions at each layer. We evaluate our method on 3 public datasets, i.e., the NIH Pancreas dataset, the Lung and Pancreas dataset from the Medical Segmentation Decathlon (MSD) Challenge. Our method, named V-NAS, consistently outperforms other state-of-the-arts on the segmentation tasks of both normal organ (NIH Pancreas) and abnormal organs (MSD Lung tumors and MSD Pancreas tumors), which shows the power of chosen architecture. Moreover, the searched architecture on one dataset can be well generalized to other datasets, which demonstrates the robustness and practical use of our proposed method.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation. However, 2D convolutions cannot fully leverage the rich spatial information along the third axis, while 3D convolutions suffer from the demanding computation and high GPU memory consumption. In this paper, we propose to automatically search the network architecture tailoring to volumetric medical image segmentation problem. Concretely, we formulate the structure learning as differentiable neural architecture search, and let the network itself choose between 2D, 3D or Pseudo-3D (P3D) convolutions at each layer. We evaluate our method on 3 public datasets, i.e., the NIH Pancreas dataset, the Lung and Pancreas dataset from the Medical Segmentation Decathlon (MSD) Challenge. Our method, named V-NAS, consistently outperforms other state-of-the-arts on the segmentation tasks of both normal organ (NIH Pancreas) and abnormal organs (MSD Lung tumors and MSD Pancreas tumors), which shows the power of chosen architecture. Moreover, the searched architecture on one dataset can be well generalized to other datasets, which demonstrates the robustness and practical use of our proposed method.",
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"affiliation": "Johns Hopkins University",
"fullName": "Zhuotun Zhu",
"givenName": "Zhuotun",
"surname": "Zhu",
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{
"affiliation": "Johns Hopkins University",
"fullName": "Chenxi Liu",
"givenName": "Chenxi",
"surname": "Liu",
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{
"affiliation": "NVIDIA Corporation",
"fullName": "Dong Yang",
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{
"affiliation": "Johns Hopkins University",
"fullName": "Alan Yuille",
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{
"affiliation": "NVIDIA Corporation",
"fullName": "Daguang Xu",
"givenName": "Daguang",
"surname": "Xu",
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"title": "Deep Distance Transform for Tubular Structure Segmentation in CT Scans",
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"abstract": "Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases. But automatic tubular structure segmentation in CT scans is a challenging problem, due to issues such as poor contrast, noise and complicated background. A tubular structure usually has a cylinder-like shape which can be well represented by its skeleton and cross-sectional radii (scales). Inspired by this, we propose a geometry-aware tubular structure segmentation method, Deep Distance Transform (DDT), which combines intuitions from the classical distance transform for skeletonization and modern deep segmentation networks. DDT first learns a multi-task network to predict a segmentation mask for a tubular structure and a distance map. Each value in the map represents the distance from each tubular structure voxel to the tubular structure surface. Then the segmentation mask is refined by leveraging the shape prior reconstructed from the distance map. We apply our DDT on six medical image datasets. Results show that (1) DDT can boost tubular structure segmentation performance significantly (e.g., over 13% DSC improvement for pancreatic duct segmentation), and (2) DDT additionally provides a geometrical measurement for a tubular structure, which is important for clinical diagnosis (e.g., the cross-sectional scale of a pancreatic duct can be an indicator for pancreatic cancer).",
"abstracts": [
{
"abstractType": "Regular",
"content": "Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases. But automatic tubular structure segmentation in CT scans is a challenging problem, due to issues such as poor contrast, noise and complicated background. A tubular structure usually has a cylinder-like shape which can be well represented by its skeleton and cross-sectional radii (scales). Inspired by this, we propose a geometry-aware tubular structure segmentation method, Deep Distance Transform (DDT), which combines intuitions from the classical distance transform for skeletonization and modern deep segmentation networks. DDT first learns a multi-task network to predict a segmentation mask for a tubular structure and a distance map. Each value in the map represents the distance from each tubular structure voxel to the tubular structure surface. Then the segmentation mask is refined by leveraging the shape prior reconstructed from the distance map. We apply our DDT on six medical image datasets. Results show that (1) DDT can boost tubular structure segmentation performance significantly (e.g., over 13% DSC improvement for pancreatic duct segmentation), and (2) DDT additionally provides a geometrical measurement for a tubular structure, which is important for clinical diagnosis (e.g., the cross-sectional scale of a pancreatic duct can be an indicator for pancreatic cancer).",
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"normalizedAbstract": "Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases. But automatic tubular structure segmentation in CT scans is a challenging problem, due to issues such as poor contrast, noise and complicated background. A tubular structure usually has a cylinder-like shape which can be well represented by its skeleton and cross-sectional radii (scales). Inspired by this, we propose a geometry-aware tubular structure segmentation method, Deep Distance Transform (DDT), which combines intuitions from the classical distance transform for skeletonization and modern deep segmentation networks. DDT first learns a multi-task network to predict a segmentation mask for a tubular structure and a distance map. Each value in the map represents the distance from each tubular structure voxel to the tubular structure surface. Then the segmentation mask is refined by leveraging the shape prior reconstructed from the distance map. We apply our DDT on six medical image datasets. Results show that (1) DDT can boost tubular structure segmentation performance significantly (e.g., over 13% DSC improvement for pancreatic duct segmentation), and (2) DDT additionally provides a geometrical measurement for a tubular structure, which is important for clinical diagnosis (e.g., the cross-sectional scale of a pancreatic duct can be an indicator for pancreatic cancer).",
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"affiliation": "Johns Hopkins University",
"fullName": "Fengze Liu",
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{
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"abstract": "This work presents an intuitive method to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. To provide radiologists with tumor locations, we adopt a segmentation framework to classify CT volumes by checking if at least a sufficient number of voxels is segmented as tumors. To quantitatively analyze our method, we collect and voxelwisely label a new abdominal CT dataset containing 376 cases with both arterial and venous phases available for each case, in which 228 cases were diagnosed with PNETs while the remaining 148 cases are normal, which is currently the largest dataset for PNETs to the best of our knowledge. In order to incorporate rich knowledge from radiologists to our framework, we annotate dilated pancreatic duct as well, which is regarded as an abnormality indicator. Quantitatively, our approach outperforms state-of-the-art segmentation networks and achieves a sensitivity of 89.47% at a specificity of 81.08%, which indicates a potential direction to achieve a clinical impact related to cancer diagnosis by earlier tumor detection.",
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"abstract": "The pancreatic disease taxonomy includes ten types of masses (tumors or cysts) [20], [8]. Previous work focuses on developing segmentation or classification methods only for certain mass types. Differential diagnosis of all mass types is clinically highly desirable [20] but has not been investigated using an automated image understanding approach.We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging. Both image appearance and the 3D organ-mass geometry relationship are critical. We propose a holistic segmentation-mesh-classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information, which is accomplished by combining the anatomical structure and the semantic detection-by-segmentation network. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask (i.e., mask-to-mesh). A new graph-based residual convolutional network (Graph-ResNet), whose nodes fuse the information of the mesh model and feature vectors extracted from the segmentation network, is developed to produce the patient-level differential classification results. Extensive experiments on 661 patients’ CT scans (five phases per patient) show that SMCN can improve the mass segmentation and detection accuracy compared to the strong baseline method nnUNet (e.g., for nonPDAC, Dice: 0.611 vs. 0.478; detection rate: 89% vs. 70%), achieve similar sensitivity and specificity in differentiating PDAC and nonPDAC as expert radiologists (i.e., 94% and 90%), and obtain results comparable to a multimodality test [20] that combines clinical, imaging, and molecular testing for clinical management of patients.",
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"content": "The pancreatic disease taxonomy includes ten types of masses (tumors or cysts) [20], [8]. Previous work focuses on developing segmentation or classification methods only for certain mass types. Differential diagnosis of all mass types is clinically highly desirable [20] but has not been investigated using an automated image understanding approach.We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging. Both image appearance and the 3D organ-mass geometry relationship are critical. We propose a holistic segmentation-mesh-classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information, which is accomplished by combining the anatomical structure and the semantic detection-by-segmentation network. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask (i.e., mask-to-mesh). A new graph-based residual convolutional network (Graph-ResNet), whose nodes fuse the information of the mesh model and feature vectors extracted from the segmentation network, is developed to produce the patient-level differential classification results. Extensive experiments on 661 patients’ CT scans (five phases per patient) show that SMCN can improve the mass segmentation and detection accuracy compared to the strong baseline method nnUNet (e.g., for nonPDAC, Dice: 0.611 vs. 0.478; detection rate: 89% vs. 70%), achieve similar sensitivity and specificity in differentiating PDAC and nonPDAC as expert radiologists (i.e., 94% and 90%), and obtain results comparable to a multimodality test [20] that combines clinical, imaging, and molecular testing for clinical management of patients.",
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"title": "PunyVis: A Visual Analytics Approach for Identifying Homograph Phishing Attacks",
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"abstract": "Attackers seeking to deceive web users into visiting malicious websites can exploit limitations of the tools intended to help browsers translate domain names containing non-ASCII characters, or internationalized domain names (IDNs). These attacks, called homograph phishing, involve registering Unicode domain names that are visually similar to legitimate ones but direct users to distinct servers. Tools exist to identify when domains use non-ASCII characters, which get translated by the Punycode protocol to work with the Domain Name System (DNS); however, these tools cannot automatically distinguish between benign use cases and ones with malicious intent, leading to high rates of false-positive alerts and increasing the workload of analysts looking for evidence of homograph phishing.To address this problem, we present PunyVis, a visual analytics system for exploring and identifying potential homograph attacks on large network datasets. By targeting instances of Punycode that use easily-confusable ASCII characters to spoof popular websites, PunyVis quickly condenses large datasets into a small number of potentially malicious records. Using the interactive tool, analysts can evaluate potential phishing instances and view supporting information from multiple data sources, as well as gain insight about overall risk and threat regarding homograph attacks. We demonstrate how PunyVis supports analysts in a case study with domain experts, and identified divergent analysis strategies and the need for interactions that support how analysts begin exploration and pivot around hypotheses. Finally, we discuss design implications and opportunities for cyber visual analytics.",
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"title": "Nonlinear Simulation of Thermo-viscous Fingering in Miscible Displacements in Porous Media",
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"abstract": "The nonlinear simulation of interfacial instabilities in miscible displacements in porous media often requires sophisticated numerical algorithms as well as very fine spatial and temporal resolutions. In this study, Hartley transform based Pseudo-spectral method is used to simulate time evolution of thermo-viscous fingers in rectilinear geometry. The problem is formulated using continuity equation, Darcy?s law, and volume-averaged forms of convection-diffusion equation for mass and energy balance. The numerical code is validated against established results for isothermal displacements. The effects of exponential dependence of viscosity on concentration and temperature, Lewis number, and porosity on the stability of the thermo-viscous flow are examined. It has been generally observed that at practical values of porosity and Lewis number, the thermal front always lags behind the fluid front and the instability is dominated by the viscosity variation due to concentration.",
"abstracts": [
{
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"content": "The nonlinear simulation of interfacial instabilities in miscible displacements in porous media often requires sophisticated numerical algorithms as well as very fine spatial and temporal resolutions. In this study, Hartley transform based Pseudo-spectral method is used to simulate time evolution of thermo-viscous fingers in rectilinear geometry. The problem is formulated using continuity equation, Darcy?s law, and volume-averaged forms of convection-diffusion equation for mass and energy balance. The numerical code is validated against established results for isothermal displacements. The effects of exponential dependence of viscosity on concentration and temperature, Lewis number, and porosity on the stability of the thermo-viscous flow are examined. It has been generally observed that at practical values of porosity and Lewis number, the thermal front always lags behind the fluid front and the instability is dominated by the viscosity variation due to concentration.",
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"normalizedAbstract": "The nonlinear simulation of interfacial instabilities in miscible displacements in porous media often requires sophisticated numerical algorithms as well as very fine spatial and temporal resolutions. In this study, Hartley transform based Pseudo-spectral method is used to simulate time evolution of thermo-viscous fingers in rectilinear geometry. The problem is formulated using continuity equation, Darcy?s law, and volume-averaged forms of convection-diffusion equation for mass and energy balance. The numerical code is validated against established results for isothermal displacements. The effects of exponential dependence of viscosity on concentration and temperature, Lewis number, and porosity on the stability of the thermo-viscous flow are examined. It has been generally observed that at practical values of porosity and Lewis number, the thermal front always lags behind the fluid front and the instability is dominated by the viscosity variation due to concentration.",
"fno": "28130006",
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"affiliation": "University of Calgary, AB, Canada",
"fullName": "M. N. Islam",
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"affiliation": "University of Calgary, AB, Canada",
"fullName": "J. Azaiez",
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"abstract": "The on-line spoof fingers detection algorithm presented here adopting ridge tracing features as input to Support Vector Machine (SVM) classifier. The wavelet transforms the coefficients of ridge data along ridges into sequences. The derived information relates to geometrical distribution of pores on ridges. As a result, it is more effective to distinguish pore information between spoof and live fingers. The features of pore information on several ridges are inputted to typical SVM system to evaluate the proposed system. The results with benchmark database show the improvement on spoof finger detection.",
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"title": "21st International Symposium on High Performance Computing Systems and Applications (HPCS'07)",
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"title": "Nonlinear Simulation of Thermo-viscous Fingering in Miscible Displacements in Porous Media",
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"abstract": "The nonlinear simulation of interfacial instabilities in miscible displacements in porous media often requires sophisticated numerical algorithms as well as very fine spatial and temporal resolutions. In this study, Hartley transform based pseudo-spectral method is used to simulate time evolution of thermoviscous fingers in rectilinear geometry. The problem is formulated using continuity equation, Darcy's law, and volume-averaged forms of convection-diffusion equation for mass and energy balance. The numerical code is validated against established results for isothermal displacements. The effects of exponential dependence of viscosity on concentration and temperature, Lewis number, and porosity on the stability of the thermo-viscous flow are examined. It has been generally observed that at practical values of porosity and Lewis number, the thermal front always lags behind the fluid front and the instability is dominated by the viscosity variation due to concentration.",
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{
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"content": "The nonlinear simulation of interfacial instabilities in miscible displacements in porous media often requires sophisticated numerical algorithms as well as very fine spatial and temporal resolutions. In this study, Hartley transform based pseudo-spectral method is used to simulate time evolution of thermoviscous fingers in rectilinear geometry. The problem is formulated using continuity equation, Darcy's law, and volume-averaged forms of convection-diffusion equation for mass and energy balance. The numerical code is validated against established results for isothermal displacements. The effects of exponential dependence of viscosity on concentration and temperature, Lewis number, and porosity on the stability of the thermo-viscous flow are examined. It has been generally observed that at practical values of porosity and Lewis number, the thermal front always lags behind the fluid front and the instability is dominated by the viscosity variation due to concentration.",
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"normalizedAbstract": "The nonlinear simulation of interfacial instabilities in miscible displacements in porous media often requires sophisticated numerical algorithms as well as very fine spatial and temporal resolutions. In this study, Hartley transform based pseudo-spectral method is used to simulate time evolution of thermoviscous fingers in rectilinear geometry. The problem is formulated using continuity equation, Darcy's law, and volume-averaged forms of convection-diffusion equation for mass and energy balance. The numerical code is validated against established results for isothermal displacements. The effects of exponential dependence of viscosity on concentration and temperature, Lewis number, and porosity on the stability of the thermo-viscous flow are examined. It has been generally observed that at practical values of porosity and Lewis number, the thermal front always lags behind the fluid front and the instability is dominated by the viscosity variation due to concentration.",
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"affiliation": "University of Calgary, AB, Canada",
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"title": "Hand posture recognition using finger geometric feature",
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"abstract": "Hand posture recognition (HPR) plays an important role in human-computer interaction (HCI) since it is one of the most common and natural ways of communication among human beings. Different fingers often represent different meanings which will attract more attentions in HPR research. Based on finger geometric feature and its classification, we develop a HPR system that can tell its posture on possible fingers. We explore kinematic constraints of the hand with forearm to extract finger geometric features which are translation, rotation and scale invariant. We first search hand components with the help of skeleton, and then order them into a serial arrangement according to either left hand or right hand and extract the geometric features among fingers, palm and forearm, finally those features are used in SVM classification for HPR. Our method can recognize twelve different types of hand postures for both hands respectively. Experiments under different illumination conditions and different scenes demonstrate the effectiveness and efficiency of the proposed method.",
"abstracts": [
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"content": "Hand posture recognition (HPR) plays an important role in human-computer interaction (HCI) since it is one of the most common and natural ways of communication among human beings. Different fingers often represent different meanings which will attract more attentions in HPR research. Based on finger geometric feature and its classification, we develop a HPR system that can tell its posture on possible fingers. We explore kinematic constraints of the hand with forearm to extract finger geometric features which are translation, rotation and scale invariant. We first search hand components with the help of skeleton, and then order them into a serial arrangement according to either left hand or right hand and extract the geometric features among fingers, palm and forearm, finally those features are used in SVM classification for HPR. Our method can recognize twelve different types of hand postures for both hands respectively. Experiments under different illumination conditions and different scenes demonstrate the effectiveness and efficiency of the proposed method.",
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"abstract": "Three potential problems exist in multifingered hands. The impact forces that result during each instant of grasping a rigid object can affect the functioning of the finger tip sensor. A hand with hard fingers cannot securely grasp objects that have uneven surfaces due to the poor conformability of the fingers. Repetitive strains are induced into the fingers throughout manipulation task. Carefully chosen materials-plastic, rubber sponge, a fine powder, a paste, and a gel-were experimentally compared for their ability to overcome these three problems. Results showed that sponge is the most suitable and plastic is the least suitable for the application. For practical reasons, however, the gel was a good compromise over the sponge. It is recommended that future robotic hands constitute a soft finger or at least fingers with soft tips, constructed out of carefully chosen materials.<>",
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"content": "Three potential problems exist in multifingered hands. The impact forces that result during each instant of grasping a rigid object can affect the functioning of the finger tip sensor. A hand with hard fingers cannot securely grasp objects that have uneven surfaces due to the poor conformability of the fingers. Repetitive strains are induced into the fingers throughout manipulation task. Carefully chosen materials-plastic, rubber sponge, a fine powder, a paste, and a gel-were experimentally compared for their ability to overcome these three problems. Results showed that sponge is the most suitable and plastic is the least suitable for the application. For practical reasons, however, the gel was a good compromise over the sponge. It is recommended that future robotic hands constitute a soft finger or at least fingers with soft tips, constructed out of carefully chosen materials.<>",
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"abstract": "Thanks to its strong capacity to immerse users in virtual worlds, virtual reality can elicit various emotions with diverse environments. This aspect of virtual reality makes it an interesting and powerful tool in many fields, such as entertainment with scenarios based on a strong emotional implication, training in particular for social or communication skills, or even medical therapy with phobia or addiction treatment. However, in virtual reality the participant lives the experience through an avatar, and feels the emotion according to what happens to this avatar.This paper discusses the link between embodiment and emotional implication in virtual reality. In particular, we looked at how emotions and the sense of embodiment are correlated in virtual reality. Through an experiment, we demonstrate that the sense of embodiment is strongly correlated with the emotional experience of the virtual environment. The sense of embodiment is increased when the virtual scenarios make the participants feel strong emotions, whether those emotions are positive or negative. We also show that emotions mainly affect two sub-components of embodiment: the appearance and response sub-scales.",
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"abstract": "Serious Games (SGs) have been widely adopted in cultural settings and museums, especially the last five years. It is expected that SG can link directly the museum content with target user groups and can fulfill their educational needs. This paper aims to present a systematic mapping study on modern museum gaming technologies and applications. More specifically, focus is given on the use of Serious Games (SGs) by the visitors during exhibition navigation and exploration of museum's cultural and educational content. Major analysis criteria and review findings include game goals, purpose of use, scope, user acceptance and educational effectiveness. A pool of 48 quality literature publications were set under examination. It was found that most solutions were game-based than play-based and that both genres had educational benefits on offer. Also, the frequency of relevant publications per year was increasing from year 2009 up to 2015. Regarding scope, studied applications were split almost equally among education, culture and arts, while the overwhelming majority were targeted to the general public and not teachers, students or parents specifically. Those findings are of particular importance for museum curators, digital cultural content creators, pedagogical engineers and museum interface designers.",
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"title": "Learning game players by an evolutionary approach using pairwise comparison without prior knowledge",
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"abstract": "There are many studies to learn an artificial game player or game strategy automatically or by unsupervised learning. However, it is still difficult to make a good player without using prior knowledge such as a heuristic player to play with, game records, and so on. One of representative methods for unsupervised learning of players is evolutionary learning approaches. In this study, an evolutionary approach with using pairwise comparison of two players is proposed to learn Othello players under the condition that the players only know the rules of the game. In order to solve a highly dynamic and unstable problem of learning players, Differential Evolution (DE) is adopted because DE adopts pairwise comparison and has been successfully applied to a highly uncertain and unstable problem of optimizing human preference. In our proposed method, players are randomly generated and form a population. Each player in the population is perturbed by DE operations and a child player is created. As pairwise comparison, the child plays against the parent player. The winner becomes a survivor. The population is replaced by the survivors. Players are evolved by repeating these processes. Computer simulation of Othello games is performed and it is shown that the method can generate good players who can win a standard heuristic player.",
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"content": "There are many studies to learn an artificial game player or game strategy automatically or by unsupervised learning. However, it is still difficult to make a good player without using prior knowledge such as a heuristic player to play with, game records, and so on. One of representative methods for unsupervised learning of players is evolutionary learning approaches. In this study, an evolutionary approach with using pairwise comparison of two players is proposed to learn Othello players under the condition that the players only know the rules of the game. In order to solve a highly dynamic and unstable problem of learning players, Differential Evolution (DE) is adopted because DE adopts pairwise comparison and has been successfully applied to a highly uncertain and unstable problem of optimizing human preference. In our proposed method, players are randomly generated and form a population. Each player in the population is perturbed by DE operations and a child player is created. As pairwise comparison, the child plays against the parent player. The winner becomes a survivor. The population is replaced by the survivors. Players are evolved by repeating these processes. Computer simulation of Othello games is performed and it is shown that the method can generate good players who can win a standard heuristic player.",
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"title": "Towards a Construction and Validation of a Serious Game Product Quality Model",
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"abstract": "A Serious Game (SG) is a game for purposes other than mere entertainment. SGs are currently in widespread use and their popularity has begun to increase steadily. The number of users of these systems is also growing day-by-day, signifying that their social impact is very high; it is precisely for this reason that SG quality evaluation is of the utmost importance. The principal objective of our long-term research, initiated one year and a half ago, is therefore to define and validate a quality model adapted specifically to SGs that has been agreed on by experts and is useful in practice, in order to allow SG designers and developers to ensure, evaluate and improve the quality of the SGs they build from the early stages of its development. The main goal of this paper is to present the construction processes of a preliminary version of the SG Quality Model (QSGame-Model) adapted from the ISO/IEC 25010 standard, which is a product quality model that can be applied to any kind of SG and will be validated in the near future.",
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"normalizedAbstract": "Most of the current literature research on electronic user consumption behavior believes that game payment behavior mainly comes from “digital identity” in the game, and this identity agree mainly depends on the time spent by users in games. The more time users spend building this digital identity, the more willing they are to pay for games. In game design, game companies will also focus on how to promote game users to stay online longer in the game through the improvement of game quality. After the rise of eSports, electronic games are no longer the only medium between companies and users. DOTA2 “interactive compendium” is a new medium used in games to communicate with users. The interactive compendium exists on the basis of The International DOTA2 Championships(TI), which has a paid impact on users who do not play games. This study takes the DOTA2 “cloud player” group as the research object, and analyzes the main factors affecting their consumption through surveys, especially factors outside the game, explore how the “digital identity” of users is preserved and continues to function. Which proves that users may continue to pay for games even if they no longer use game products, or even pay more, but a new medium is required.",
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"fullName": "Haonan Yang",
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"affiliation": "University of China,School of eSports Communication,Nanjing,China",
"fullName": "Xiao Gong",
"givenName": "Xiao",
"surname": "Gong",
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"title": "Developing a Virtual Reality Serious Game to Recreational Therapy Using iPlus Methodology",
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"abstract": "Serious Games (SGs) are used in various areas, such as training, advertising, health fields and communications. Within the subset of serious games that appeal to health care systems, we propose a game that focuses on mental health treatment. In order to ensure that an SG meets a user's expectations, it must be designed using the appropriate methodology for the context, which is user-centered and includes experts in disability, pedagogy and game design. The objective of this research work is to present the construction of a virtual reality serious game for Recreational therapy as a study case of iPlus, a methodology for the design of SGs, which is based on the previous idea. iPlus allows the conception of educational SGs, based on a participative, user-centered approach.",
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"title": "The Construction and Impact of Mobile Game Players Community: A Study on “Honor of Kings”",
"normalizedTitle": "The Construction and Impact of Mobile Game Players Community: A Study on “Honor of Kings”",
"abstract": "With the development of the mobile Internet and smart devices, mobile games have become important ways for people to entertain and socialize daily. Mobile games players socialize online beyond gender, class, age, and cultural background. Meanwhile, they form player communities dominated by them in the game playing. This paper takes the mobile game “Honor of Kings” (HoK) as an example. It uses case study, questionnaire and interview methods to study the in-game and out-of-game information dissemination behaviors of its players that influence player communities’ construction. It is found that players mainly build online and off-line communities through in-game and out-of-game information expression, reception, and group identity. However, due to the limitation of the community circles, players receive information in a “one-dimension” and fragmented manner, the instability of the game community increases.",
"abstracts": [
{
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"content": "With the development of the mobile Internet and smart devices, mobile games have become important ways for people to entertain and socialize daily. Mobile games players socialize online beyond gender, class, age, and cultural background. Meanwhile, they form player communities dominated by them in the game playing. This paper takes the mobile game “Honor of Kings” (HoK) as an example. It uses case study, questionnaire and interview methods to study the in-game and out-of-game information dissemination behaviors of its players that influence player communities’ construction. It is found that players mainly build online and off-line communities through in-game and out-of-game information expression, reception, and group identity. However, due to the limitation of the community circles, players receive information in a “one-dimension” and fragmented manner, the instability of the game community increases.",
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"fno": "425400a617",
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"affiliation": "Wuhan Qingchuan University,School of Media and Arts,Wuhan,China",
"fullName": "Gu Wang",
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"abstract": "Ray tracing efficiently models complex illumination effects to improve visual realism in computer graphics. Typical modern GPUs use wide SIMD processing, and have achieved impressive performance for a variety of graphics processing including ray tracing. However, SIMD efficiency can be reduced due to the divergent branching and memory access patterns that are common in ray tracing codes. This paper explores an alternative approach using MIMD processing cores custom-designed for ray tracing. By relaxing the requirement that instruction paths be synchronized as in SIMD, caches and less frequently used area expensive functional units may be more effectively shared. Heavy resource sharing provides significant area savings while still maintaining a high MIMD issue rate from our numerous light-weight cores. This paper explores the design space of this architecture and compares performance to the best reported results for a GPU ray tracer and a parallel ray tracer using general purpose cores. We show an overall performance that is six to ten times higher in a similar die area.",
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"title": "Characteristics of Induced Potential of Electromagnetic Flow Meter",
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"abstract": "The basic principle of EMF (electromagnetic flow meter) is the Faraday electromagnetic induction law. An induced voltage EMF for measuring flow rate of single phase in pipes has been used successfully in many industrial areas. But the measurement of volumetric flow rate of multiphase flow is not understood poor, especially the measurement of the continuous phase velocity profile in multiphase flows where the continuous phase is electrical conductive. This paper tries to investigate the characteristic of induced electric potential or potential drop, which can reflect the velocity distribution of the conductive continuous phase in the flow pipe. A predefined fluid velocity distribution, vz=5+200y, that means the component z of fluid velocity varies with the coordinate value of axis y and the components x and y of the fluid velocity are 0, was applied in the investigation. The numerical simulation model with geometry and physical preconditions was built. The analysing methodology adopting multiple virtual electrodes that could be arranged at any position of the internal circumference of fluid pipe to meet the requirement of signals picking for induced potential was proposed. Finally, the characteristics of induced potential that varies with the electrodes position are analysed clearly in different figures",
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"id": "12OmNyLiuAD",
"doi": "10.1109/ITCS.2010.102",
"title": "Processing Method of Optical Grating Scale Output Signal",
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"abstract": "When designing the water level measuring instrument, incremental optical grating scale is used as position sensor. Since the water level must be measured repeatedly and continuously, cumulative error of measuring is the critical issue. Using asynchronous sequential logic circuit-level design approach, abstracting signal timing diagram from various moments of changing direction, designing of incentive state and output function expression and writing the expression to a programmable device, a grating scale processing circuit can be constructed. The water level measuring instrument based on this circuit is running perfectly on the model of Yangtze River flood proofing, indicating no cumulative errors are produced. The device can also be used for optical encoder position measurement and other measurements.",
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"keywords": [
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"fullName": "Xiwei Yang",
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"title": "2013 11th East-West Design and Test Symposium (EWDTS)",
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"doi": "10.1109/EWDTS.2013.6673206",
"title": "Lower bound of error in AOA based passive source localization using single moving platform",
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"abstract": "Error assessment is one of the great considerations in any position-finding (PF) system. In this paper it is assumed that a single moving sensor measures the Angle Of Arrival (AOA) of the signals emitted by a stationary source. These AOA measurements are used to find true position of the source. Regarding such a scenario, source estimated location error is analyzed and a lower bound of error is extracted regarding Cramer-Rao criteria.",
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"content": "Error assessment is one of the great considerations in any position-finding (PF) system. In this paper it is assumed that a single moving sensor measures the Angle Of Arrival (AOA) of the signals emitted by a stationary source. These AOA measurements are used to find true position of the source. Regarding such a scenario, source estimated location error is analyzed and a lower bound of error is extracted regarding Cramer-Rao criteria.",
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"authors": [
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"affiliation": "Sharif, University of Technology, Iran",
"fullName": "F. Hejazi",
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"affiliation": "Amirkabir University of Technology, Iran",
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"affiliation": "Sharif, University of Technology, Iran",
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"title": "Proceedings 1992 IEEE International Conference on Robotics and Automation",
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"doi": "10.1109/ROBOT.1992.220167",
"title": "A tactile sensing method for employing force/torque information through insensitive probes",
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"abstract": "The authors propose an object shape detection system using a force/torque sensor and a flexible/inflexible insensitive probe. A tactile sensing method which makes it possible to estimate a contact position on the probe only by measuring contact force has been developed. Though the probe does not have any sensitive devices. the method derives the contact position from force information measured by the force/torque sensor. When the probe bends according to the applied force, the method compensates for the probe deflection by estimating its transformation based on force/torque sensor information. It has been applied to a manipulator system, and object shape detection experiments have been conducted using a cylindrical probe. Shapes of 3D objects have successfully been reproduced.<>",
"abstracts": [
{
"abstractType": "Regular",
"content": "The authors propose an object shape detection system using a force/torque sensor and a flexible/inflexible insensitive probe. A tactile sensing method which makes it possible to estimate a contact position on the probe only by measuring contact force has been developed. Though the probe does not have any sensitive devices. the method derives the contact position from force information measured by the force/torque sensor. When the probe bends according to the applied force, the method compensates for the probe deflection by estimating its transformation based on force/torque sensor information. It has been applied to a manipulator system, and object shape detection experiments have been conducted using a cylindrical probe. Shapes of 3D objects have successfully been reproduced.<>",
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"Robots",
"Spatial Variables Measurement",
"Tactile Sensors",
"Torque Measurement",
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"authors": [
{
"affiliation": "NTT Telecommun. Field Syst. R&D Center, Ibaraki, Japan",
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"affiliation": "NTT Telecommun. Field Syst. R&D Center, Ibaraki, Japan",
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"abstract": "At present, the manual inspection efficiency of large-diameter pipe fittings is low. A high-efficiency automatic measurement system for industrial robots using line laser sensors was proposed. This system has realized the accurate measurement of the inner diameter of the pipe fittings. According to the geometric relationship among pipe fittings, line laser sensors, and industrial robots, a measurement mathematical model was constructed. Among them, a posture analysis method based on projection of three-dimensional contour data was proposed to solve the installation angle of the line laser sensor; the least square method was used to solve the exact solution of the six-axis rotation axis; the Levenberg-Marquardt algorithm was used to fit the ellipse to avoid the singular value problem in the solution process. The system error of the measurement principle was analyzed, and the incremental compensation method was used to construct the system error compensation model to solve the influence of the position and attitude error of the laser sensor on the measurement accuracy. A rigorous calibration and acquisition methods were used to ensure that the engineering system accurately implements the measurement design principle. The experimental results show that the measurement error of the line laser large-diameter pipe robot measurement system designed in this paper was less than 0.025%.",
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"content": "At present, the manual inspection efficiency of large-diameter pipe fittings is low. A high-efficiency automatic measurement system for industrial robots using line laser sensors was proposed. This system has realized the accurate measurement of the inner diameter of the pipe fittings. According to the geometric relationship among pipe fittings, line laser sensors, and industrial robots, a measurement mathematical model was constructed. Among them, a posture analysis method based on projection of three-dimensional contour data was proposed to solve the installation angle of the line laser sensor; the least square method was used to solve the exact solution of the six-axis rotation axis; the Levenberg-Marquardt algorithm was used to fit the ellipse to avoid the singular value problem in the solution process. The system error of the measurement principle was analyzed, and the incremental compensation method was used to construct the system error compensation model to solve the influence of the position and attitude error of the laser sensor on the measurement accuracy. A rigorous calibration and acquisition methods were used to ensure that the engineering system accurately implements the measurement design principle. The experimental results show that the measurement error of the line laser large-diameter pipe robot measurement system designed in this paper was less than 0.025%.",
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"abstract": "The fusion of Pedestrian Dead Reckoning (PDR) and Radio frequency(RF) technologies with complementary advantages has been broadly applied in modern Indoor Positioning System (IPS). In this paper, a novel Hybrid Pedestrian Positioning System (HPPS) based on two UWB anchors and IMU measurements is proposed to achieve accurate and stable tracking in long distance travelling indoor. Unlike other existing works, HPPS does not require prior knowledge of a initial position to start with. Pedestrian initial position is estimated by First Reliable Step Identification algorithm based on UWB ranging measurements and pedestrian step length. Afterwards, the system iteratively update the position using Incremental Position Estimation algorithm aided by gyroscope. HPPS not only effectively reduces position error caused by sensor bias and step detection error, but also adapts to different attachments (handheld, in the pocket, etc.) and moving modes. As evaluated with four walking experiments, HPPS performs accurate long distance tracking and achieves a maximum of 89% improvement on Average Position Error compared with typical PDR algorithm.",
"abstracts": [
{
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"content": "The fusion of Pedestrian Dead Reckoning (PDR) and Radio frequency(RF) technologies with complementary advantages has been broadly applied in modern Indoor Positioning System (IPS). In this paper, a novel Hybrid Pedestrian Positioning System (HPPS) based on two UWB anchors and IMU measurements is proposed to achieve accurate and stable tracking in long distance travelling indoor. Unlike other existing works, HPPS does not require prior knowledge of a initial position to start with. Pedestrian initial position is estimated by First Reliable Step Identification algorithm based on UWB ranging measurements and pedestrian step length. Afterwards, the system iteratively update the position using Incremental Position Estimation algorithm aided by gyroscope. HPPS not only effectively reduces position error caused by sensor bias and step detection error, but also adapts to different attachments (handheld, in the pocket, etc.) and moving modes. As evaluated with four walking experiments, HPPS performs accurate long distance tracking and achieves a maximum of 89% improvement on Average Position Error compared with typical PDR algorithm.",
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"normalizedAbstract": "The fusion of Pedestrian Dead Reckoning (PDR) and Radio frequency(RF) technologies with complementary advantages has been broadly applied in modern Indoor Positioning System (IPS). In this paper, a novel Hybrid Pedestrian Positioning System (HPPS) based on two UWB anchors and IMU measurements is proposed to achieve accurate and stable tracking in long distance travelling indoor. Unlike other existing works, HPPS does not require prior knowledge of a initial position to start with. Pedestrian initial position is estimated by First Reliable Step Identification algorithm based on UWB ranging measurements and pedestrian step length. Afterwards, the system iteratively update the position using Incremental Position Estimation algorithm aided by gyroscope. HPPS not only effectively reduces position error caused by sensor bias and step detection error, but also adapts to different attachments (handheld, in the pocket, etc.) and moving modes. As evaluated with four walking experiments, HPPS performs accurate long distance tracking and achieves a maximum of 89% improvement on Average Position Error compared with typical PDR algorithm.",
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"keywords": [
"Distance Measurement",
"Gyroscopes",
"Indoor Navigation",
"Indoor Radio",
"Inertial Navigation",
"Iterative Methods",
"Mobile Radio",
"Pedestrians",
"Position Measurement",
"Radio Tracking",
"Ultra Wideband Communication",
"Average Position Error",
"First Reliable Step Identification Algorithm",
"IMU Measurements",
"Incremental Position Estimation Algorithm",
"Indoor Long Distance Travelling",
"Indoor Positioning System",
"Long Distance Tracking",
"Pedestrian Dead Reckoning",
"Pedestrian Step Length",
"Radiofrequency Technologies",
"UWB Ranging Measurements",
"UWB PDR Hybrid Pedestrian Positioning System",
"Legged Locomotion",
"Dead Reckoning",
"Estimation",
"Position Measurement",
"Length Measurement",
"Distance Measurement",
"Gyroscopes",
"Pedestrian Dead Reckoning",
"UWB Technology",
"Indoor Navigation",
"Hybrid System",
"Step Detection"
],
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"affiliation": "Research Institute of China Telecom,Research Institute of Mobile and Terminal Technology,Guangzhou,China",
"fullName": "Sijie Niu",
"givenName": "Sijie",
"surname": "Niu",
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"affiliation": "Research Institute of China Telecom,Research Institute of Mobile and Terminal Technology,Guangzhou,China",
"fullName": "Zirong Chen",
"givenName": "Zirong",
"surname": "Chen",
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"affiliation": "Zhengzhou University,School of Cyberspace Security,Zhengzhou,China",
"fullName": "Yunbo Zhang",
"givenName": "Yunbo",
"surname": "Zhang",
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{
"affiliation": "Research Institute of China Telecom,Research Institute of Mobile and Terminal Technology,Guangzhou,China",
"fullName": "Peng Cao",
"givenName": "Peng",
"surname": "Cao",
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{
"affiliation": "Research Institute of China Telecom,Research Institute of Mobile and Terminal Technology,Guangzhou,China",
"fullName": "Xianfei Zhu",
"givenName": "Xianfei",
"surname": "Zhu",
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{
"affiliation": "Research Institute of China Telecom,Research Institute of Mobile and Terminal Technology,Guangzhou,China",
"fullName": "Tao Pang",
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"surname": "Pang",
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"affiliation": "Research Institute of China Telecom,Research Institute of Mobile and Terminal Technology,Guangzhou,China",
"fullName": "Yujie Liang",
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"title": "Computer Simulation Research on the Evaluation of Star Point Positioning Accuracy of Star Sensor",
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"abstract": "The star point positioning accuracy of the star sensor is an important indicator for evaluating the measurement accuracy of the star sensor. However, the star point positioning accuracy index cannot be directly measured by laboratory or outside experiments. In this paper, a method for indirect evaluation of star positioning accuracy of star sensors is proposed. Firstly, the definition of the star diagonal distance of the star sensor is given, and the imaging model of the star sensor is established. On this basis, the formula for transmitting the position error of star point is derived. A large number of simulation calculations are carried out by computer simulation method, and the quantitative relationship between standard deviation of Star diagonal distance and that of star position is obtained. The results show that under the condition of high-precision calibration of the focal length, there is a linear relationship between the standard deviation of star diagonal distance and that of star point position, and the standard deviation of star diagonal distance is about 1.42 times that of star point position. Therefore, star diagonal distance error of the star sensor can be measured and calculated through laboratory or outside experiments, and then star position error can be calculated with the method in this paper, so as to realize the evaluation of star positioning accuracy.",
"abstracts": [
{
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"content": "The star point positioning accuracy of the star sensor is an important indicator for evaluating the measurement accuracy of the star sensor. However, the star point positioning accuracy index cannot be directly measured by laboratory or outside experiments. In this paper, a method for indirect evaluation of star positioning accuracy of star sensors is proposed. Firstly, the definition of the star diagonal distance of the star sensor is given, and the imaging model of the star sensor is established. On this basis, the formula for transmitting the position error of star point is derived. A large number of simulation calculations are carried out by computer simulation method, and the quantitative relationship between standard deviation of Star diagonal distance and that of star position is obtained. The results show that under the condition of high-precision calibration of the focal length, there is a linear relationship between the standard deviation of star diagonal distance and that of star point position, and the standard deviation of star diagonal distance is about 1.42 times that of star point position. Therefore, star diagonal distance error of the star sensor can be measured and calculated through laboratory or outside experiments, and then star position error can be calculated with the method in this paper, so as to realize the evaluation of star positioning accuracy.",
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"normalizedAbstract": "The star point positioning accuracy of the star sensor is an important indicator for evaluating the measurement accuracy of the star sensor. However, the star point positioning accuracy index cannot be directly measured by laboratory or outside experiments. In this paper, a method for indirect evaluation of star positioning accuracy of star sensors is proposed. Firstly, the definition of the star diagonal distance of the star sensor is given, and the imaging model of the star sensor is established. On this basis, the formula for transmitting the position error of star point is derived. A large number of simulation calculations are carried out by computer simulation method, and the quantitative relationship between standard deviation of Star diagonal distance and that of star position is obtained. The results show that under the condition of high-precision calibration of the focal length, there is a linear relationship between the standard deviation of star diagonal distance and that of star point position, and the standard deviation of star diagonal distance is about 1.42 times that of star point position. Therefore, star diagonal distance error of the star sensor can be measured and calculated through laboratory or outside experiments, and then star position error can be calculated with the method in this paper, so as to realize the evaluation of star positioning accuracy.",
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"affiliation": "Key laboratory of Science and Technology on Space Optoelectronic Precision Measurement, CAS, University of Chinese Academy of Sciences,Chengdu,China",
"fullName": "Zhuang Yuan",
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"affiliation": "Key laboratory of Science and Technology on Space Optoelectronic Precision Measurement, CAS, Institute of Optics and Electronics, CAS,Chengdu,China",
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"affiliation": "Key laboratory of Science and Technology on Space Optoelectronic Precision Measurement, CAS, University of Chinese Academy of Sciences,Chengdu,China",
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"title": "Evaluation of hand and stylus based calibration for optical see-through head-mounted displays using leap motion",
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"content": "Next generation OST HMDs promise the inclusion of a variety of integrated and on-board sensors. In particular, hand tracking cameras, such as the Leap Motion, show potential for facilitating intuitive OST calibration procedures accessible to researchers, developers, and novice users alike. In this work, we evaluate hand and stylus based OST calibration utilizing tracking data from a Leap Motion. Our findings show that performance of both methods is comparable to results from prior studies using standard environment-centric methods. Also, while our hand based calibration improved through the use of more contextual reticle designs, calibrations performed with a stylus yielded the most accurate and precise results over all.",
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"normalizedAbstract": "Next generation OST HMDs promise the inclusion of a variety of integrated and on-board sensors. In particular, hand tracking cameras, such as the Leap Motion, show potential for facilitating intuitive OST calibration procedures accessible to researchers, developers, and novice users alike. In this work, we evaluate hand and stylus based OST calibration utilizing tracking data from a Leap Motion. Our findings show that performance of both methods is comparable to results from prior studies using standard environment-centric methods. Also, while our hand based calibration improved through the use of more contextual reticle designs, calibrations performed with a stylus yielded the most accurate and precise results over all.",
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"authors": [
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"affiliation": "Mississippi State University",
"fullName": "Kenneth R. Moser",
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"fullName": "J. Edward Swan",
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"abstract": "Recent advances in 3D reconstruction and tracking technologies have made it possible to volumetrically capture human body and performance at real time. In the field of human-computer interaction, however, no works have been reported on the user study made with such volumetric capture avatars. This paper investigates how the volumetric capture avatar affects users' sense of social presence in immersive virtual environments. In our experiments, the volumetric capture avatar of an actor is compared with the actor captured in 2D video and another 3D avatar obtained by pre-scanning the actor. The experiment results show that users have the highest sense of social presence with the volumetric capture avatar when performing dynamic tasks whereas they have higher sense of social presence with the volumetric capture avatar and 2D video than with the pre-scanned avatar when performing static tasks. These imply that the emerging volumetric capture techniques can be an attractive tool for mixed reality, telepresence, and many other 3D applications.",
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"abstract": "Urban traffic congestion is a major nuisance for residents' daily commute, but few studies have focused on the effective combination between urban traffic condition visualization and hybrid traffic travel route recommendation. In this paper, the visualization exploration of urban transportation patterns is realized by multilayer complex transportation networks, which are constructed by taxi transportation network, bike-sharing transportation network, and urban transportation community network. Based on multilayer complex traffic networks, a genetic algorithm modified by A * search algorithm is used to generate multi-modal travel routes. The case studies prove that this method can effectively reduce the time cost of commuting between congested areas by generating hybrid traffic routes.",
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"title": "Rumor Spreading with Cross Propagation in Multilayer Social Networks",
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"abstract": "Social networks have achieved great success by making social interaction online. Malicious users, whereas, make use of social networks to spread rumors. Recent researches manifest that the union of social applications can reach better efficiency. Unfortunately, novel security threats are emerging when malicious users take advantage of this opportunity to spread rumors from a single social network to multilayer networks. In this paper, concerning the cross propagation in the multilayer social networks, the S2IR2 model is therefore proposed to analyze the rumor spreading. The rumor spreading process is molded by mean-field equations. With the simulations of various parameters, the numerical results give the performances of propagation speed and range. For restraining the rumor spreading, the security strategies are concluded.",
"abstracts": [
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"content": "Social networks have achieved great success by making social interaction online. Malicious users, whereas, make use of social networks to spread rumors. Recent researches manifest that the union of social applications can reach better efficiency. Unfortunately, novel security threats are emerging when malicious users take advantage of this opportunity to spread rumors from a single social network to multilayer networks. In this paper, concerning the cross propagation in the multilayer social networks, the S2IR2 model is therefore proposed to analyze the rumor spreading. The rumor spreading process is molded by mean-field equations. With the simulations of various parameters, the numerical results give the performances of propagation speed and range. For restraining the rumor spreading, the security strategies are concluded.",
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"abstract": "The excellent landscape of urban public space can lead to rich urban life, but the current situation of China's construction shows some shortcomings, which is not conducive to the proceduralization and systematization of the landscape design process. The landscape of urban public space reflects modern people's values, aesthetics and interests. However, there are still many problems in the planning and design of urban public space landscape, and there are many unsatisfactory landscape designs. The application of computer technology in urban landscape construction can make landscape construction more standardized and scientific, from early geological verification to preliminary scheme research to later simulated environment design. As the carrier of people's social activities, public space has emerged some urgent problems. The research on the layout and mode of urban point public space is conducive to solve the current problems. This paper introduces the key technology of computer virtual technology, and provides some reference for improving the development efficiency of urban point public space landscape.",
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"content": "The excellent landscape of urban public space can lead to rich urban life, but the current situation of China's construction shows some shortcomings, which is not conducive to the proceduralization and systematization of the landscape design process. The landscape of urban public space reflects modern people's values, aesthetics and interests. However, there are still many problems in the planning and design of urban public space landscape, and there are many unsatisfactory landscape designs. The application of computer technology in urban landscape construction can make landscape construction more standardized and scientific, from early geological verification to preliminary scheme research to later simulated environment design. As the carrier of people's social activities, public space has emerged some urgent problems. The research on the layout and mode of urban point public space is conducive to solve the current problems. This paper introduces the key technology of computer virtual technology, and provides some reference for improving the development efficiency of urban point public space landscape.",
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"abstract": "Despite the importance of conducting systematic literature reviews (SLRs) for identifying the research gaps in software engineering (SE) research, SLRs are a complex, multi-stage, and time-consuming process if performed manually. Conducting an SLR in line with the guidelines and practice in the SE domain requires considerable effort and expertise. The objective of this SLR is to identify and classify text-mining techniques and tools that can help facilitate SLR activities. This study also investigates the adoption of text-mining (TM) techniques to support SLR in the SE domain. We performed a mixed search strategy to identify relevant studies published from January 1, 2004, to December 31, 2016. We shortlisted 32 papers into the final set of relevant studies published in the SE, medicine and social science disciplines. The majority of the text-mining techniques attempted to support the study selection stage. Only 12 out of the 14 studies in the SE domain applied text-mining techniques, focusing primarily on facilitating the search and study selection stages. By learning from the experience of applying TM techniques in clinical medicine and social science fields, we believe that SE researchers can adopt appropriate SLR automation strategies for use in the SE field.",
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"content": "Despite the importance of conducting systematic literature reviews (SLRs) for identifying the research gaps in software engineering (SE) research, SLRs are a complex, multi-stage, and time-consuming process if performed manually. Conducting an SLR in line with the guidelines and practice in the SE domain requires considerable effort and expertise. The objective of this SLR is to identify and classify text-mining techniques and tools that can help facilitate SLR activities. This study also investigates the adoption of text-mining (TM) techniques to support SLR in the SE domain. We performed a mixed search strategy to identify relevant studies published from January 1, 2004, to December 31, 2016. We shortlisted 32 papers into the final set of relevant studies published in the SE, medicine and social science disciplines. The majority of the text-mining techniques attempted to support the study selection stage. Only 12 out of the 14 studies in the SE domain applied text-mining techniques, focusing primarily on facilitating the search and study selection stages. By learning from the experience of applying TM techniques in clinical medicine and social science fields, we believe that SE researchers can adopt appropriate SLR automation strategies for use in the SE field.",
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"abstract": "Continuous delivery and rapidly changing requirements in agile environments force the developers to put non-functional requirements (NFRs) on halt till maintenance phase. However, neglecting NFRs during prioritization phase may lead to inaccurate estimations for software projects resulting in high maintenance cost and failures. The subjective and uncertain nature of non-functional requirements makes them unfit to be prioritized using conventional prioritization methods. Although the existing literature reports on inadequate consideration given to NFRs prioritization, still no comprehensive systematic effort has been done to report the limitations and evaluation mechanisms of existing NFRs prioritization approaches. Requirements engineering society lacks a broad understanding of NFRs prioritization approaches and the challenges which need to be overcome. Therefore, we aim to investigate (i) the existing NFR prioritization techniques and their validation mechanisms, (ii) the role of Artificial Intelligence (AI) in NFRs prioritization, and (iii) the limitations of existing NFRs prioritization techniques. For this, we reviewed the literature published from 2008 till present and extracted 30 studies. The results reveal twenty-five NFRs prioritization techniques out of which only three are AI based. The major limitations we have come across are that most of the NFRs prioritization techniques are not scalable to large datasets, inter-dependencies between functional requirements (FRs) and NFRs are ignored, and the uncertainties associated with NFRs are not considered at all. However, the literature suggests that AI-based techniques and Fuzzy logic may be used to solve issues such as uncertainties i.e. ambiguities, vagueness, and subjective opinions of stakeholders. This review adds to the existing body of knowledge on NFRs and motivates the practitioners to focus on the NFR prioritization by highlighting the limitations of the existing methods.",
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"content": "Continuous delivery and rapidly changing requirements in agile environments force the developers to put non-functional requirements (NFRs) on halt till maintenance phase. However, neglecting NFRs during prioritization phase may lead to inaccurate estimations for software projects resulting in high maintenance cost and failures. The subjective and uncertain nature of non-functional requirements makes them unfit to be prioritized using conventional prioritization methods. Although the existing literature reports on inadequate consideration given to NFRs prioritization, still no comprehensive systematic effort has been done to report the limitations and evaluation mechanisms of existing NFRs prioritization approaches. Requirements engineering society lacks a broad understanding of NFRs prioritization approaches and the challenges which need to be overcome. Therefore, we aim to investigate (i) the existing NFR prioritization techniques and their validation mechanisms, (ii) the role of Artificial Intelligence (AI) in NFRs prioritization, and (iii) the limitations of existing NFRs prioritization techniques. For this, we reviewed the literature published from 2008 till present and extracted 30 studies. The results reveal twenty-five NFRs prioritization techniques out of which only three are AI based. The major limitations we have come across are that most of the NFRs prioritization techniques are not scalable to large datasets, inter-dependencies between functional requirements (FRs) and NFRs are ignored, and the uncertainties associated with NFRs are not considered at all. However, the literature suggests that AI-based techniques and Fuzzy logic may be used to solve issues such as uncertainties i.e. ambiguities, vagueness, and subjective opinions of stakeholders. This review adds to the existing body of knowledge on NFRs and motivates the practitioners to focus on the NFR prioritization by highlighting the limitations of the existing methods.",
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"normalizedAbstract": "Continuous delivery and rapidly changing requirements in agile environments force the developers to put non-functional requirements (NFRs) on halt till maintenance phase. However, neglecting NFRs during prioritization phase may lead to inaccurate estimations for software projects resulting in high maintenance cost and failures. The subjective and uncertain nature of non-functional requirements makes them unfit to be prioritized using conventional prioritization methods. Although the existing literature reports on inadequate consideration given to NFRs prioritization, still no comprehensive systematic effort has been done to report the limitations and evaluation mechanisms of existing NFRs prioritization approaches. Requirements engineering society lacks a broad understanding of NFRs prioritization approaches and the challenges which need to be overcome. Therefore, we aim to investigate (i) the existing NFR prioritization techniques and their validation mechanisms, (ii) the role of Artificial Intelligence (AI) in NFRs prioritization, and (iii) the limitations of existing NFRs prioritization techniques. For this, we reviewed the literature published from 2008 till present and extracted 30 studies. The results reveal twenty-five NFRs prioritization techniques out of which only three are AI based. The major limitations we have come across are that most of the NFRs prioritization techniques are not scalable to large datasets, inter-dependencies between functional requirements (FRs) and NFRs are ignored, and the uncertainties associated with NFRs are not considered at all. However, the literature suggests that AI-based techniques and Fuzzy logic may be used to solve issues such as uncertainties i.e. ambiguities, vagueness, and subjective opinions of stakeholders. This review adds to the existing body of knowledge on NFRs and motivates the practitioners to focus on the NFR prioritization by highlighting the limitations of the existing methods.",
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"abstract": "Traditional machine learning approaches are plagued with problems for practical use in operational cyber security. The class of unsupervised learning algorithms called tensor decompositions provide a new approach for analyzing network traffic data that avoids these traditional problems. Tensors are a natural representation for multidimensional data as an array with arbitrary dimensions. Tensor decompositions factor the data into components, each of which represents a different pattern of activity from within the original data.We use ENSIGN, a tensor decomposition toolbox developed by Reservoir Labs, in the security operations center for the SCinet network at SC15 - The International Conference for High Performance Computing, Networking, Storage and Analysis. ENSIGN integrates naturally into an operational cyber security framework by extracting anomalous patterns of network traffic. In this paper, we present two case studies highlighting specific actionable results: one, discovering an external attacker and tracing the evolution of the attack over time, and the other, extracting an example of data exfiltration that the actor disguised as DNS activity and cleanly separating it from normal DNS activity. Through proof-of-concept experiments at SC15, we successfully demonstrate concrete and practical use of ENSIGN and make a critical step forward towards delivering an integrated tensor analysis engine for network security.",
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"content": "Traditional machine learning approaches are plagued with problems for practical use in operational cyber security. The class of unsupervised learning algorithms called tensor decompositions provide a new approach for analyzing network traffic data that avoids these traditional problems. Tensors are a natural representation for multidimensional data as an array with arbitrary dimensions. Tensor decompositions factor the data into components, each of which represents a different pattern of activity from within the original data.We use ENSIGN, a tensor decomposition toolbox developed by Reservoir Labs, in the security operations center for the SCinet network at SC15 - The International Conference for High Performance Computing, Networking, Storage and Analysis. ENSIGN integrates naturally into an operational cyber security framework by extracting anomalous patterns of network traffic. In this paper, we present two case studies highlighting specific actionable results: one, discovering an external attacker and tracing the evolution of the attack over time, and the other, extracting an example of data exfiltration that the actor disguised as DNS activity and cleanly separating it from normal DNS activity. Through proof-of-concept experiments at SC15, we successfully demonstrate concrete and practical use of ENSIGN and make a critical step forward towards delivering an integrated tensor analysis engine for network security.",
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