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"abstract": "Existing radiometric compensation methods for projector-camera systems have been shown to produce compensated colours which are inconsistent to a human viewer. In this paper, a novel radiometric compensation method for projector-camera systems and textured surfaces is introduced based on the human visual system (HVS) colour response. The proposed method can extend established compensation methods to produce colours which are human-perceived to be correct (egocentric modelling). As a result, this method performs radiometric compensation which is not only consistent and precise, but also produces images which are visually accurate to an external colour reference. This method is shown to produce generally the lowest average radiometric compensation error when compared to compensation performed using only the response of a camera, demonstrated through quantitative analysis of compensated colours, and supported by qualitative results.",
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"abstract": "In this paper, a technique is presented for CT image analysis and visualization of the bronchial airways. The technique provides a non-invasive way to examine the interior of the bronchial tubes and to detect various properties of the tubes such as abnormal morphology caused by foreign objects stuck in the airways or some other disease. The input to the procedure are chest images obtained by spiral computed tomography (CT). 3-D neural network-based segmentation of CT images is performed to extractthe airways. The resulting 3-D binary volume representing the location of the airways is thinned to extract the medial axis of the bronchial tubes. The marching cube algorithm is used to perform a triangulation of the airway surface. The extracted axis and the triangulated surface is converted to Virtual Reality Modeling Language (VRML) format. The virtual environment in VRML format is then used for inspection and visualization of the bronchial tube interior.",
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"content": "In this paper, a technique is presented for CT image analysis and visualization of the bronchial airways. The technique provides a non-invasive way to examine the interior of the bronchial tubes and to detect various properties of the tubes such as abnormal morphology caused by foreign objects stuck in the airways or some other disease. The input to the procedure are chest images obtained by spiral computed tomography (CT). 3-D neural network-based segmentation of CT images is performed to extractthe airways. The resulting 3-D binary volume representing the location of the airways is thinned to extract the medial axis of the bronchial tubes. The marching cube algorithm is used to perform a triangulation of the airway surface. The extracted axis and the triangulated surface is converted to Virtual Reality Modeling Language (VRML) format. The virtual environment in VRML format is then used for inspection and visualization of the bronchial tube interior.",
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"affiliation": "University of Zagreb",
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"affiliation": "University of Zagreb",
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"affiliation": "University of Zagreb",
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"affiliation": "University of Zagreb",
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"abstract": "The management of distributed applications and systems (MANDAS) project addresses problems arising in the management applications. The MANDAS information repository (MIR) provides database support for the management applications and supports their integration into a single management environment. We examine the problem of distributed applications management to extract the requirements for an MIR. Based on the requirements, we present an information model for distributed applications management and outline a prototype MIR developed for the MANDAS project.",
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"content": "The management of distributed applications and systems (MANDAS) project addresses problems arising in the management applications. The MANDAS information repository (MIR) provides database support for the management applications and supports their integration into a single management environment. We examine the problem of distributed applications management to extract the requirements for an MIR. Based on the requirements, we present an information model for distributed applications management and outline a prototype MIR developed for the MANDAS project.",
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"abstract": "In the traditional Spatial Decision Support System (SDSS), most of the GIS systems only take the database system as the driving core, provide the raw data and the information from limited spatial processing. It can not give substantive solutions to satisfy the weak-structure system in most decision support applications. Moreover, models in SDSS often get the wrong analytical results because of lacking the corresponding spatial parameters et al., and the analytical results are poor visualization for the users. In this paper, we presented the concept of Model-blade base based on object-oriented and the realization principle, combining with the enhanced generalized repository and GIS database researches indicated that taking model-blade as the core, generalized knowledge as the drive, the new Intelligent Spatial Decision Support System(ISDSS) can integrate the model-blade base, the generalized repository and the GIS database technology into GIS, realize the seamless integration of them.finally,we using the Hazardous Chemical Leak(HCL) model-blade and the generalized knowledge(such as historical cases of hazardous chemical leak, expert knowledge & experiences corresponding to hazardous chemical leak and data mined from GIS database) as an example to demonstrate the feasibilities and advances of ISDSS.",
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"abstract": "With the continuous improvement of China’s social and economic level, people’s aesthetic ability is also constantly improving. At present, the full application of computer graphics and illustration art in the process of commercial design is positively helpful to improve the level of commercial design. In order to fully grasp the practical points of computer graphic illustration art in commercial design, we need to conduct research from the main role of computer graphic illustration art in the process of commercial design. At the same time, we want to analyze the challenges faced by computer graphic illustration art in the business process, and explore the practical points of illustration art in commercial design. Only in this way can the creative level of computer graphic illustration art be improved and the long-term development of the illustration art market can be promoted.",
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"abstract": "In the future, robots may perform cooperative tasks with humans in daily life. In this paper, the authors focus on a hand-over motion as an example of cooperative work between a human and a robot, and propose an algorithm which enables a robot to perform a human-like motion. First the authors analyze trajectories and velocity patterns of a hand-over motion performed by two humans. The experimental results show that a receiver's motion during hand-over has some typical characteristics. The authors then confirm that a human-like motion can be generated using these characteristics. Finally, the authors plan the robot's motion considering these results. Initially, two kinds of potential fields are used to generate a motion command which leads the robot along a trajectory similar to that followed by the human. In addition, more precise motion is considered at the end of the hand-over operation to guarantee accurate positioning and to soften the shock of contact. Simulation results show the validity of the proposed method.",
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"content": "In the future, robots may perform cooperative tasks with humans in daily life. In this paper, the authors focus on a hand-over motion as an example of cooperative work between a human and a robot, and propose an algorithm which enables a robot to perform a human-like motion. First the authors analyze trajectories and velocity patterns of a hand-over motion performed by two humans. The experimental results show that a receiver's motion during hand-over has some typical characteristics. The authors then confirm that a human-like motion can be generated using these characteristics. Finally, the authors plan the robot's motion considering these results. Initially, two kinds of potential fields are used to generate a motion command which leads the robot along a trajectory similar to that followed by the human. In addition, more precise motion is considered at the end of the hand-over operation to guarantee accurate positioning and to soften the shock of contact. Simulation results show the validity of the proposed method.",
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"affiliation": "Dept. of Mech. Aeronaut. Eng., Tohoku Univ., Sendai, Japan",
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"affiliation": "Dept. of Mech. Aeronaut. Eng., Tohoku Univ., Sendai, Japan",
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"abstract": "Traditional dead reckoning schemes predict a player's position by assuming that players move with constant force or velocity. However, because player movement is rarely linear in nature, using linear prediction fails to produce an accurate result. Among existing dead reckoning methods, only few focus on improving prediction accuracy via genuinely non-traditional methods for predicting the path of a player. In this paper, we propose a new prediction method based on play patterns. We implemented a 2D top-down multiplayer online game to act as a test harness that we used to collect play data from 44 experienced players. From the data for half of these players, we extracted play patterns, which we used to create our dead reckoning algorithm. A comparative evaluation proceeding from an extensive set of simulations (using the other half of our play data) suggests that our EKB algorithm yields more accurate predictions than the IEEE standard dead reckoning algorithm and the recent “Interest Scheme” algorithm.",
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"abstract": "A robust VAR-based (vector autoregressive) model is introduced for motion prediction in 3D hand tracking. This dynamic VAR motion model is learned in an online manner. The kinematic structure of the hand is accounted for in the form of constraints when solving for the parameters of the VAR model. Also integrated into the motion prediction model are adaptive weights that are optimised according to the reliability of past predictions. Experiments on synthetic and real video sequences show a substantial improvement in tracking performance when the robust VAR motion model is used. In fact, utilising the robust VAR model allows the tracker to handle fast out-of-plane hand movement with severe self-occlusion.",
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"abstract": "The provably NP-hard problem of finding optimal piecewise linear approximation for images is extended from 1D curve fitting to 2D surface fitting by a dual-agent algorithm. The results not only yield a storage-efficient codec for range, or intensity, images but also a surface triangulation technique to generate succinct, accurate and visually pleasant 3D visualization model. Comparing with the traditional piecewise linear image coding (PLIC) algorithms, triangulation of a range image is more adaptive due to conformity of the shape, orientation and size of triangles with the image contents. The triangularization algorithm presented here differs from previous approaches in that it strives to minimize the total number of triangles (or vertices) needed to approximate the image surface while keeping the deviation of any intensity value to within a prescribed error tolerance. Unlike most methods of bottom-up triangularization, which could bog down before any mesh simplification even begins, this algorithm fits the surface in a top-down manner, avoiding the generation of most unnecessary triangles. When combined with an efficient 3D triangulation-encoding scheme, the algorithm achieves compact code length with guaranteed error bound, thus providing a more faithful representation of all image features. A variety of benchmark test images have been experimented and compared.",
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"abstract": "This paper presents a new indexing-based approach to fingerprint identification. Central to the proposed approach is the idea of associating a unique topological structure with the fingerprint minutiae using the Delaunay triangulation. This allows for choosing more \"meaningful\" minutiae groups (i.e., triangles) during indexing, preserves index selectivity, reduces memory requirements without sacrificing recognition accuracy, and improves recognition time. Specifically, assuming N minutiae per fingerprint on the average, the proposed approach considers only O(N) minutiae triangles during indexing or recognition. This compares favorably to O(N3), the number of triangles usually considered by other approaches, leading to significant memory savings and improved recognition time. Besides their small number, the minutiae triangles we used for indexing have good discrimination power since, among all possible minutiae triangles, they are the only ones satisfying the properties of the Delaunay triangulation. As a result, index selectivity is preserved and indexing can be implemented in a low-dimensional space. Some key characteristics of the Delaunay triangulation are (i) it is unique (assuming no degeneracies), (ii) can be computed efficiently in O(NlogN) time, and (iii) noise or distortions affect it only locally. The proposed approach has been tested on a database of 300 fingerprints (10 fingerprints from 30 persons), demonstrating good performance.",
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"abstract": "This paper presents an architecture for interactive storytelling using state-of-the-art-technology in natural language processing, speech synthesis and 3D-character animation. A conversational 3D-character is used to tell nonlinear stories interactively. Our research focuses on multimodal conversation that combines verbal-vocal with nonverbal-nonvocal natural language. We present a framework for synchronizing speech with facial movements, gesture and body posture by combining findings from linguistics and psychology. The interaction process incorporates the parsing of a textual natural language utterance, the emotion-based triggering of a textual response and its vocal and visual performance - speech synthesis linked with lip-synchronous generation of facial expressions and gestures.",
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"abstract": "In this paper, we present a novel and robust spline approximation algorithm given a noisy symmetric positive definite (SPD) tensor field. Such tensor fields commonly arise in the field of Medical Imaging in the form of Diffusion Tensor (DT) MRI data sets. We develop a statistically robust algorithm for constructing a tensor product of B-splines - for approximating and interpolating these data - using the Riemannian metric of the manifold of SPD tensors. Our method involves a two step procedure wherein the first step uses Riemannian distances in order to evaluate a tensor spline by computing a weighted intrinsic average of diffusion tensors and the second step involves minimization of the Riemannian distance between the evaluated spline curve and the given data. These two steps are alternated to achieve the desired tensor spline approximation to the given tensor field. We present comparisons of our algorithm with four existing methods of tensor interpolation applied to DT-MRI data from fixed heart slices of a rabbit, and show significantly improved results in the presence of noise and outliers. We also present validation results for our algorithm using synthetically generated noisy tensor field data with outliers. This interpolation work has many applications e.g., in DT-MRI registration, in DT-MRI Atlas construction etc. This research was in part funded by the NIH ROI NS42075 and the Department of Radiology, University of Florida.",
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"abstract": "Modern applications such as Internet traffic, telecommunication records, and large-scale social networks generate massive amounts of data with multiple aspects and high dimensionalities. Tensors (i.e., multi-way arrays) provide a natural representation for such data. Consequently, tensor decompositions such as Tucker become important tools for summarization and analysis.One major challenge is how to deal with high-dimensional, sparse data. In other words, how do we compute decompositions of tensors where most of the entries of the tensor are zero. Specialized techniques are needed for computing the Tucker decompositions for sparse tensors because standard algorithms do not account for the sparsity of the data. As a result, a surprising phenomenon is observed by practitioners: Despite the fact that there is enough memory to store both the input tensors and the factorized output tensors, memory overflows occur during the tensor factorization process. To address this intermediate blowup problem, we propose Memory-Efficient Tucker (MET). Based on the available memory, MET adaptively selects the right execution strategy during the decomposition. We provide quantitative and qualitative evaluation of MET on real tensors. It achieves over 1000X space reduction without sacrificing speed; it also allows us to work with much larger tensors that were too big to handle before. Finally, we demonstrate a data mining case-study using MET.",
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"title": "Tensor Rank: Some Lower and Upper Bounds",
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"abstract": "The results of Strassen~\\cite{strassen-tensor} and Raz~\\cite{raz} show that good enough tensor rank lower bounds have implications for algebraic circuit/formula lower bounds. We explore tensor rank lower and upper bounds, focusing on explicit tensors. For odd d, we construct field-independent explicit 0/1 tensors T:[n]^d\\to\\mathbb{F} with rank at least 2n^{\\lfloor d/2\\rfloor}+n-\\Theta(d\\lg n). This improves the lower-order terms in known lower bounds for any odd d\\ge 3. We also explore a generalization of permutation matrices, which we denote permutation tensors. We show, by applying known counting lower bounds, that there exist order-3 permutation tensors with super-linear rank as well as order-Z_$d$_Z permutation tensors with high rank. We also explore a natural class of permutation tensors, which we call group tensors. For any group G, we define the group tensor T_G^d:G^d\\to\\mathbb{F}, by T_G^d(g_1,\\ldots,g_d)=1Z_$ iff $_Zg_1\\cdots g_d=1_G. We give two upper bounds for the rank of these tensors. The first uses representation theory and works over ``large'' fields Z_$\\mathbb{F}, showing (among other things) that \\rank_\\mathbb{F}(T_G^d)\\le |G|^{d/2}. In the case that d=3, we are able to show that \\rank_\\mathbb{F}(T_G^3)\\le O(|G|^{\\omega/2})\\le O(|G|^{1.19}), where $_Z\\omegaZ_$ is the exponent of matrix multiplication. The next upper bound uses interpolation and only works for abelian G, showing that over any field \\mathbb{F}$_Z that Z_$\\rank_\\mathbb{F}(T_G^d)\\le O(|G|^{1+\\lg d}\\lg^{d-1}|G|). In either case, this shows that many permutation tensors have far from maximal rank, which is very different from the matrix case and thus eliminates many natural candidates for high tensor rank. We also explore monotone tensor rank. We give explicit 0/1 tensors T:[n]^d\\to\\mathbb{F} that have tensor rank at most $_Zdn$ but have monotone tensor rank exactly n^{d-1}. This is a nearly optimal separation.",
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"content": "The results of Strassen~\\cite{strassen-tensor} and Raz~\\cite{raz} show that good enough tensor rank lower bounds have implications for algebraic circuit/formula lower bounds. We explore tensor rank lower and upper bounds, focusing on explicit tensors. For odd d, we construct field-independent explicit 0/1 tensors T:[n]^d\\to\\mathbb{F} with rank at least 2n^{\\lfloor d/2\\rfloor}+n-\\Theta(d\\lg n). This improves the lower-order terms in known lower bounds for any odd d\\ge 3. We also explore a generalization of permutation matrices, which we denote permutation tensors. We show, by applying known counting lower bounds, that there exist order-3 permutation tensors with super-linear rank as well as order-$d$ permutation tensors with high rank. We also explore a natural class of permutation tensors, which we call group tensors. For any group G, we define the group tensor T_G^d:G^d\\to\\mathbb{F}, by T_G^d(g_1,\\ldots,g_d)=1$ iff $g_1\\cdots g_d=1_G. We give two upper bounds for the rank of these tensors. The first uses representation theory and works over ``large'' fields $\\mathbb{F}, showing (among other things) that \\rank_\\mathbb{F}(T_G^d)\\le |G|^{d/2}. In the case that d=3, we are able to show that \\rank_\\mathbb{F}(T_G^3)\\le O(|G|^{\\omega/2})\\le O(|G|^{1.19}), where $\\omega$ is the exponent of matrix multiplication. The next upper bound uses interpolation and only works for abelian G, showing that over any field \\mathbb{F}$ that $\\rank_\\mathbb{F}(T_G^d)\\le O(|G|^{1+\\lg d}\\lg^{d-1}|G|). In either case, this shows that many permutation tensors have far from maximal rank, which is very different from the matrix case and thus eliminates many natural candidates for high tensor rank. We also explore monotone tensor rank. We give explicit 0/1 tensors T:[n]^d\\to\\mathbb{F} that have tensor rank at most $dn$ but have monotone tensor rank exactly n^{d-1}. This is a nearly optimal separation.",
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"affiliation": "Alberta of University, Canada",
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"abstract": "To obtain the fairing surface model of the loss part of detective skull rapidly, a new modeling method on skull defect repair is proposed in this paper. This method is to adopt the surface reconstruction algorithm to construct the local model of defective skull based on the sub-pixel cube model. The hole’s feature on the surface is automatically identified by adopting the dihedral angle criteria algorithm, and two three-dimensional polygons are generated by searching the boundary edge of the hole. The polygon is triangulated based on the minimum area principle, and the mesh is refined based on the shortest edge principle. The weighted umbrella-operator is applied to control the curvature transformation of the patching mesh for smoothing it, and the fairing surface patch is generated. Then, the prosthesis model of defective skull is generated by Boolean calculation between every two key models. The modeling efficiency and precision are too higher.",
"abstracts": [
{
"abstractType": "Regular",
"content": "To obtain the fairing surface model of the loss part of detective skull rapidly, a new modeling method on skull defect repair is proposed in this paper. This method is to adopt the surface reconstruction algorithm to construct the local model of defective skull based on the sub-pixel cube model. The hole’s feature on the surface is automatically identified by adopting the dihedral angle criteria algorithm, and two three-dimensional polygons are generated by searching the boundary edge of the hole. The polygon is triangulated based on the minimum area principle, and the mesh is refined based on the shortest edge principle. The weighted umbrella-operator is applied to control the curvature transformation of the patching mesh for smoothing it, and the fairing surface patch is generated. Then, the prosthesis model of defective skull is generated by Boolean calculation between every two key models. The modeling efficiency and precision are too higher.",
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"normalizedAbstract": "To obtain the fairing surface model of the loss part of detective skull rapidly, a new modeling method on skull defect repair is proposed in this paper. This method is to adopt the surface reconstruction algorithm to construct the local model of defective skull based on the sub-pixel cube model. The hole’s feature on the surface is automatically identified by adopting the dihedral angle criteria algorithm, and two three-dimensional polygons are generated by searching the boundary edge of the hole. The polygon is triangulated based on the minimum area principle, and the mesh is refined based on the shortest edge principle. The weighted umbrella-operator is applied to control the curvature transformation of the patching mesh for smoothing it, and the fairing surface patch is generated. Then, the prosthesis model of defective skull is generated by Boolean calculation between every two key models. The modeling efficiency and precision are too higher.",
"fno": "3583a568",
"keywords": [
"Skull Defect Repair",
"Sub Pixel Cube",
"Reconstruction",
"Mesh Refinement And Fairing",
"Prosthesis Model"
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"affiliation": null,
"fullName": "Fei You",
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"fullName": "Yuan Yao",
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"proceeding": {
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"title": "2004 IEEE International Conference on Multimedia and Expo (ICME)",
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"doi": "10.1109/ICME.2004.1394143",
"title": "Regular 3D mesh reconstruction based on cylindrical mapping",
"normalizedTitle": "Regular 3D mesh reconstruction based on cylindrical mapping",
"abstract": "Complete 3D surface models of real objects can be obtained by integrating several range images, each representing a different view of the object. The mesh generated for these models have irregular connectivity in general. Several remeshing techniques have been proposed to approximate the irregular mesh models with regular meshes, which have several advantages in several applications. We present a prototype of a system to generate regular 3D models of real objects with a simple setup of a single 3D scanner and a turntable. Multiple scans representing different parts of the object surface are mapped on a 2D plane, which facilitates the processes of smooth integration, hole filling and regularization. The complete 3D surface model is represented like a 2D image, on which existing and new signal and image processing techniques can be applied easily.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Complete 3D surface models of real objects can be obtained by integrating several range images, each representing a different view of the object. The mesh generated for these models have irregular connectivity in general. Several remeshing techniques have been proposed to approximate the irregular mesh models with regular meshes, which have several advantages in several applications. We present a prototype of a system to generate regular 3D models of real objects with a simple setup of a single 3D scanner and a turntable. Multiple scans representing different parts of the object surface are mapped on a 2D plane, which facilitates the processes of smooth integration, hole filling and regularization. The complete 3D surface model is represented like a 2D image, on which existing and new signal and image processing techniques can be applied easily.",
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"normalizedAbstract": "Complete 3D surface models of real objects can be obtained by integrating several range images, each representing a different view of the object. The mesh generated for these models have irregular connectivity in general. Several remeshing techniques have been proposed to approximate the irregular mesh models with regular meshes, which have several advantages in several applications. We present a prototype of a system to generate regular 3D models of real objects with a simple setup of a single 3D scanner and a turntable. Multiple scans representing different parts of the object surface are mapped on a 2D plane, which facilitates the processes of smooth integration, hole filling and regularization. The complete 3D surface model is represented like a 2D image, on which existing and new signal and image processing techniques can be applied easily.",
"fno": "01394143",
"keywords": [
"Solid Modelling",
"Mesh Generation",
"Optical Scanners",
"Regular 3 D Mesh Reconstruction",
"Cylindrical Mapping",
"Real Object 3 D Surface Models",
"Range Image Integration",
"Irregular Mesh Connectivity",
"Remeshing Techniques",
"3 D Scanner",
"Turntable",
"2 D Plane Object Surface Mapping",
"Hole Filling",
"Regularization",
"Signal Processing",
"Image Processing",
"Mesh Generation",
"Image Coding",
"Shape",
"Image Reconstruction",
"Surface Reconstruction",
"Prototypes",
"Filling",
"Topology"
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"authors": [
{
"affiliation": "Univ. of Kitakyushu, Japan",
"fullName": "I.R. Khan",
"givenName": "I.R.",
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"affiliation": "Univ. of Kitakyushu, Japan",
"fullName": "M. Okuda",
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{
"affiliation": "Univ. of Kitakyushu, Japan",
"fullName": "S. Takahashi",
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"abstract": "With the emergence of AI(artificial intelligence), it is becoming more and more critical for organizations to utilize it to their advantage. However, organizations that possess a decent amount of data might not have the technical competence to perform machine learning, and vice versa. Hence, it is reasonable for the two kinds of organizations to work together to realize the value of the data. With the increasing concern over data privacy, regulations such as GDPR(General Data Protection Regulation) prevent an organization from sharing data with another unless the data is processed to the point that the individuals in the data are not identifiable. Various ways of data anonymization have been proposed and developed, including the ones that utilize neural networks to achieve the goal, like AE, VAE, and GAN. With the addition of a differential privacy framework like TensorFlow Privacy, privacy can be guaranteed, but data still needs to be usable after privacy protection measures are deployed. The present study aims to integrate TensorFlow Privacy into the synthetic data generation process and evaluate its usefulness for daily use in the industries. Since TensorFlow Privacy brings a provable privacy guarantee to synthetic data, the present study focuses on the evaluation of data utility. TensorFlow is widely used for machine learning in the industry and academically. TensorFlow Privacy, which is also developed by Google, can prove to be a valuable addition to the synthetic data generation pipeline. The result shows that VAE with TensorFlow Privacy 1) generates synthetic data with good data utility in most cases in terms of descriptive statistics and machine learning classification tasks, and 2) The customizable TensorFlow Privacy parameters work as intended in terms of privacy-utility trade-off.",
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"content": "With the emergence of AI(artificial intelligence), it is becoming more and more critical for organizations to utilize it to their advantage. However, organizations that possess a decent amount of data might not have the technical competence to perform machine learning, and vice versa. Hence, it is reasonable for the two kinds of organizations to work together to realize the value of the data. With the increasing concern over data privacy, regulations such as GDPR(General Data Protection Regulation) prevent an organization from sharing data with another unless the data is processed to the point that the individuals in the data are not identifiable. Various ways of data anonymization have been proposed and developed, including the ones that utilize neural networks to achieve the goal, like AE, VAE, and GAN. With the addition of a differential privacy framework like TensorFlow Privacy, privacy can be guaranteed, but data still needs to be usable after privacy protection measures are deployed. The present study aims to integrate TensorFlow Privacy into the synthetic data generation process and evaluate its usefulness for daily use in the industries. Since TensorFlow Privacy brings a provable privacy guarantee to synthetic data, the present study focuses on the evaluation of data utility. TensorFlow is widely used for machine learning in the industry and academically. TensorFlow Privacy, which is also developed by Google, can prove to be a valuable addition to the synthetic data generation pipeline. The result shows that VAE with TensorFlow Privacy 1) generates synthetic data with good data utility in most cases in terms of descriptive statistics and machine learning classification tasks, and 2) The customizable TensorFlow Privacy parameters work as intended in terms of privacy-utility trade-off.",
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"title": "swFLOW: A Dataflow Deep Learning Framework on Sunway TaihuLight Supercomputer",
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"abstract": "Deep learning technology is widely used in many modern fields and a number of deep learning models and software frameworks have been proposed. However, it is still very difficult to process deep learning tasks efficiently on traditional high performance computing (HPC) systems with specialized architectures such as Sunway TaihuLight. In this paper, we propose swFLOW: a TensorFlow-based dataflow deep learning framework on Sunway TaihuLight. Based on the performance analysis results on convolutional neural network (CNN), we optimize the convolution layer, reduce the data layout transpose operation and get 10.42x speedup compared to single management processing element (MPE) version. As for distributed training, we use elastic averaging stochastic gradient descent (EASGD) algorithm to reduce communication and use data prefetch to avoid data fetch being a performance bottleneck. On 512 processes, we get a parallel efficiency of 81.01% with communication period τ = 8. Limited by the maximal executable batch size, the current performance of swFLOW is far from optimal. It is very necessary to further optimize using technology like remote direct memory access (RDMA) and model parallelism.",
"abstracts": [
{
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"content": "Deep learning technology is widely used in many modern fields and a number of deep learning models and software frameworks have been proposed. However, it is still very difficult to process deep learning tasks efficiently on traditional high performance computing (HPC) systems with specialized architectures such as Sunway TaihuLight. In this paper, we propose swFLOW: a TensorFlow-based dataflow deep learning framework on Sunway TaihuLight. Based on the performance analysis results on convolutional neural network (CNN), we optimize the convolution layer, reduce the data layout transpose operation and get 10.42x speedup compared to single management processing element (MPE) version. As for distributed training, we use elastic averaging stochastic gradient descent (EASGD) algorithm to reduce communication and use data prefetch to avoid data fetch being a performance bottleneck. On 512 processes, we get a parallel efficiency of 81.01% with communication period τ = 8. Limited by the maximal executable batch size, the current performance of swFLOW is far from optimal. It is very necessary to further optimize using technology like remote direct memory access (RDMA) and model parallelism.",
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"affiliation": "University of Delaware",
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"affiliation": "University of Science and Technology of China",
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"affiliation": "University of Delaware",
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"content": "More and more applications use microservice architecture. Protecting the reliability of the microservice system is very important for the stable operation of applications. However, the complexity of microservice systems poses a great challenge to operation and maintenance. Researchers have proposed a series of anomaly detection algorithms, which can automatically detect the anomalies of cloud systems in time. However, for the microservice system with a complex spatial structure, there is no effective method to represent the fine-grained features of the internal metric level of the microservice. To solve this problem, we propose a fine-grained metric-level spatial feature graph TopoMetrics and use a spatiotemporal neural network STAD to obtain the spatiotemporal features of microservices, which can accurately detect the anomalies of complex microservices. We compare STAD with the most advanced algorithms in three open microservice workloads. The experimental results show that the average precision of STAD is significantly higher than that of the most advanced baseline method.",
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"content": "Sina Weibo is the most popular microblog service in China and it can provide abundant information about netizens' attitudes and opinions to those events which are exposed on the Internet. However, it is difficult to know the characteristics of internet public opinions, such as the evolution of users' focus over time, spatio-temporal distribution of users participating in event comments, weibo retweet relation, etc. To fully understand those, we propose a visual analytic system of Weibo Event, short for WeiboViz, which can be mainly divided into four subparts: fundamental information visualization, spatio-temporal distribution visualization, keywords and entities visualization, weibo retweet relation visualization. A case study of \"'Pseudomonas aeruginosa' exceeded in the Master Kong You Yue drinking water\" demonstrates the effectiveness of the proposed system for the exploration and understanding of weibo data about specific event.",
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"content": "Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks such as object recognition. They are gaining huge importance in recent times but are computationally intensive. Typically trained on massive datasets, two-dimensional CNNs are used for image classification and recognition purposes and consume huge computational time. For applications like human action recognition involving video inputs, their 3D counterparts termed as 3D convolutional neural networks (3D-CNNs) are employed. Scaling up the computations to support large datasets and accelerating the training on these models for high performance has been the need of the hour especially in 3D deep learning models since the extended connectivity of CNN in the time domain takes huge time for training the model. Also there is a need to look at the model parameters and hyper parameters that determine both the computational performance as well as the accuracy of the deep neural network. Accelerators such as Graphics Processing Units (GPUs) and multi-cores provide a means for speeding up the training of CNNs and achieve higher performance by parallelizing the training of these models by taking advantage of data and model parallelism. In this work we use multi-core CPUs and GPUs to scale-up the training of 3D-CNNs. We achieve a faster implementation as well as report how various network parameters affect the performance of the model thereby providing recommendations on initializing the values of the same. The code scales up well on multi-cores and GPUs, with a speedup of 10x on CPUs and achieves almost 12x on GPUs compared to the serial version. Our work infers that 3D-CNN code scales up best on CPUs when the convolution step is implemented with a highly parallel FFT based approach, thereby achieving the performance comparable to GPUs using OpenMP.",
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"abstract": "Deep convolutional neural networks (CNNs) have proven highly effective for visual recognition, where learning a universal representation from activations of convolutional layer plays a fundamental problem. In this paper, we present Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep architecture that quantizes the local activations of convolutional layer in a deep generative model, by training them in an end-to-end manner. To incorporate FV encoding strategy into deep generative models, we introduce Variational Auto-Encoder model, which steers a variational inference and learning in a neural network which can be straightforwardly optimized using standard stochastic gradient method. Different from the FV characterized by conventional generative models (e.g., Gaussian Mixture Model) which parsimoniously fit a discrete mixture model to data distribution, the proposed FV-VAE is more flexible to represent the natural property of data for better generalization. Extensive experiments are conducted on three public datasets, i.e., UCF101, ActivityNet, and CUB-200-2011 in the context of video action recognition and fine-grained image classification, respectively. Superior results are reported when compared to state-of-the-art representations. Most remarkably, our proposed FV-VAE achieves to-date the best published accuracy of 94.2% on UCF101.",
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"content": "Social networking services specifically Facebook has recently shown exponential growth in its number of users, which attracted researchers to investigate its educational potentials. While various studies have investigated learners' attitudes towards incorporating Facebook in classrooms, limited attention has been paid to examine learners' level of knowledge and motivation involved in online learning using Facebook. Therefore, this study proposes a new designed Facebook learning tool with various applied design strategies to enhance the learning process in classrooms. Thirty five learners, from a public University in Tunisia, participated in a quasi-experiment to validate this designed Facebook learning tool. The findings highlight that this tool has had a positive impact on the learners' level of knowledge regarding learning \"game development\" course. Besides, learners found this tool useful, interesting and safe, resulting in better engagement and a competitive attitude.",
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"Computer Aided Instruction",
"Computer Games",
"Computer Science Education",
"Human Factors",
"Social Networking Online",
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"Public University",
"Tunisia",
"Game Development Course",
"Facebook Learning Tool",
"Learning Process",
"Applied Design Strategies",
"Online Learning",
"Social Networking Services",
"Classrooms",
"Facebook",
"Tools",
"Games",
"Education",
"Learning Systems",
"Privacy",
"Online Social Networks",
"Social Presence",
"Facebook",
"Online Learning"
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"fullName": "Manal Abdo Farhan Saif",
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"fullName": "Ahmed Tlili",
"givenName": "Ahmed",
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"fullName": "Fathi Essalmi",
"givenName": "Fathi",
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{
"affiliation": null,
"fullName": "Mohamed Jemni",
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"doi": "10.1109/FIE.2013.6684915",
"title": "Adding social elements to game-based learning - An exploration",
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"abstract": "Game-based learning is to present the instruction by games in learning, with the main purpose of triggering learners' motives instead of instructing the courses. Thus, increasing learning motive by game-based learning becomes a common instructional strategy to enhance learning achievement. However, it is not easy to design interesting games combined with courses. In 2011, Echeverria proposed a design to combine characteristics of games with elements of courses by matching the virtual scenarios in games with proper courses. However, in the past game-based learning, students were gathered in regular places for several times of game-based learning. Students' learning was limited by time and space. Therefore, for students' game-based learning at any time and in any places, based on theories of design elements of online community game Aki Järvinen, this study treats Facebook as the platform of games. The development by online community game is easier, faster and cheaper than traditional video games. In 2006, Facebook allowed API program of the third party. Therefore, by Facebook, this study provides the platform for students to learn in social lives to explore students' activities in online community games. Questionnaire survey is conducted to find out if the design of non-single user game is attractive for students to participate in game-based learning.",
"abstracts": [
{
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"content": "Game-based learning is to present the instruction by games in learning, with the main purpose of triggering learners' motives instead of instructing the courses. Thus, increasing learning motive by game-based learning becomes a common instructional strategy to enhance learning achievement. However, it is not easy to design interesting games combined with courses. In 2011, Echeverria proposed a design to combine characteristics of games with elements of courses by matching the virtual scenarios in games with proper courses. However, in the past game-based learning, students were gathered in regular places for several times of game-based learning. Students' learning was limited by time and space. Therefore, for students' game-based learning at any time and in any places, based on theories of design elements of online community game Aki Järvinen, this study treats Facebook as the platform of games. The development by online community game is easier, faster and cheaper than traditional video games. In 2006, Facebook allowed API program of the third party. Therefore, by Facebook, this study provides the platform for students to learn in social lives to explore students' activities in online community games. Questionnaire survey is conducted to find out if the design of non-single user game is attractive for students to participate in game-based learning.",
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"normalizedAbstract": "Game-based learning is to present the instruction by games in learning, with the main purpose of triggering learners' motives instead of instructing the courses. Thus, increasing learning motive by game-based learning becomes a common instructional strategy to enhance learning achievement. However, it is not easy to design interesting games combined with courses. In 2011, Echeverria proposed a design to combine characteristics of games with elements of courses by matching the virtual scenarios in games with proper courses. However, in the past game-based learning, students were gathered in regular places for several times of game-based learning. Students' learning was limited by time and space. Therefore, for students' game-based learning at any time and in any places, based on theories of design elements of online community game Aki Järvinen, this study treats Facebook as the platform of games. The development by online community game is easier, faster and cheaper than traditional video games. In 2006, Facebook allowed API program of the third party. Therefore, by Facebook, this study provides the platform for students to learn in social lives to explore students' activities in online community games. Questionnaire survey is conducted to find out if the design of non-single user game is attractive for students to participate in game-based learning.",
"fno": "06684915",
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"Positron Emission Tomography",
"Facebook",
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"affiliation": "Dept. of Electron. Eng., Chung Yuan Christian Univ., Taoyuan, Taiwan",
"fullName": "Chien-Hung Lai",
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"affiliation": "Dept. of Inf. & Comput. Eng., Chung Yuan Christian Univ., Taoyuan, Taiwan",
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{
"proceeding": {
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"doi": "10.1109/IC3.2018.8530525",
"title": "Invitation or Bait? Detecting Malicious URLs in Facebook Events",
"normalizedTitle": "Invitation or Bait? Detecting Malicious URLs in Facebook Events",
"abstract": "With 2.2 billion monthly active users, Facebook is the most popular Online Social Network. Given its huge popularity and diverse features such as pages, events, groups etc., it is potentially the most attractive platform for cybercriminals to launch various attacks. In this paper, we study the role of Facebook Events in disseminating malicious URLs in the network. Here, we focus our analysis on Facebook Events which are created by Facebook Pages. The existing services like Web of Trust (WOT) and other blacklists follow crowdsourcing models. Thus, malicious URLs can only be detected once they are widespread on the network and has done significant damage. Therefore, we train a supervised machine learning model on our labeled dataset to create an efficient classifier for automatic detection of malicious Facebook events, independent of blacklists and third-party reputation services. Our model is able to classify malicious events with 75% accuracy using Support Vector Machine. To the best of our knowledge, this is the first paper to study the presence of malicious URLs on Facebook Events.",
"abstracts": [
{
"abstractType": "Regular",
"content": "With 2.2 billion monthly active users, Facebook is the most popular Online Social Network. Given its huge popularity and diverse features such as pages, events, groups etc., it is potentially the most attractive platform for cybercriminals to launch various attacks. In this paper, we study the role of Facebook Events in disseminating malicious URLs in the network. Here, we focus our analysis on Facebook Events which are created by Facebook Pages. The existing services like Web of Trust (WOT) and other blacklists follow crowdsourcing models. Thus, malicious URLs can only be detected once they are widespread on the network and has done significant damage. Therefore, we train a supervised machine learning model on our labeled dataset to create an efficient classifier for automatic detection of malicious Facebook events, independent of blacklists and third-party reputation services. Our model is able to classify malicious events with 75% accuracy using Support Vector Machine. To the best of our knowledge, this is the first paper to study the presence of malicious URLs on Facebook Events.",
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"normalizedAbstract": "With 2.2 billion monthly active users, Facebook is the most popular Online Social Network. Given its huge popularity and diverse features such as pages, events, groups etc., it is potentially the most attractive platform for cybercriminals to launch various attacks. In this paper, we study the role of Facebook Events in disseminating malicious URLs in the network. Here, we focus our analysis on Facebook Events which are created by Facebook Pages. The existing services like Web of Trust (WOT) and other blacklists follow crowdsourcing models. Thus, malicious URLs can only be detected once they are widespread on the network and has done significant damage. Therefore, we train a supervised machine learning model on our labeled dataset to create an efficient classifier for automatic detection of malicious Facebook events, independent of blacklists and third-party reputation services. Our model is able to classify malicious events with 75% accuracy using Support Vector Machine. To the best of our knowledge, this is the first paper to study the presence of malicious URLs on Facebook Events.",
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"authors": [
{
"affiliation": "Department of Computer Science, Jaypee Institute of Information Technology, Noida, India",
"fullName": "Sonu Gupta",
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{
"affiliation": "Department of Computer Science, National Institute of Technology Delhi New, Delhi, India",
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"abstract": "Nowadays, Facebook is considered as one of the most popular social networking sites and its engagement in our life has increased dramatically. Although Facebook is the most used social networking website among university students and lecturers, it has not been exploited for educational purposes. The purpose of this research was to examine whether Facebook can be used for educational purposes in Iraqi universities as a virtual learning environment. For this purpose, two surveys and a case study have been conducted. The results showed that Facebook is the most preferred online environment for education, in which 57% of the students have spent significant time on Facebook. In addition, 81.2% of the lecturers agreed on using Facebook as an educational environment, and 55.4% of lecturers indicated that Facebook can sometimes be exploited by lecturers and students as an educational environment. Based on the results, a new model on Facebook has been suggested that can be used by lecturers and students in order to share announcements and lectures as well as students can also ask and discuss subjects with their lecturers by using extra features such as high light, announcements, understand and don't understand.",
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"abstract": "The spread of information on Facebook and Twitter is much more efficient than on traditional social media platforms. For word-of-mouth (WOM) marketing, social media have become a rich information source for companies or scholars to design models to examine this repository and mine useful insights for marketing strategies. However, social media language is relatively short and contains special words and symbols. Most natural language processing (NLP) methods focus on processing formal sentences and are not well-suited to such short messages. In this study we propose a novel sentiment analysis framework based on deep learning models to extract sentiment from social media. We collect data from which we compile a dataset. After processing these special terms, we seek to establish a semantic dataset for further research. The extracted information will be useful for many future applications. The experimental data have been obtained by crawling several social media platforms.",
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"affiliation": "National Taipei University of Technology,Department of Information and Finance Management,Taipei,Taiwan",
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"abstract": "Deep Learning models have demonstrated significant performance on different tasks such as computer vision, natural language processing, etc. In recent years, these models have also achieved remarkable progress in Intrusion Detection Systems. However, the mechanism of these models is often hard to understand, especially for researchers in the domain of network security. In this paper, we propose a visual analytics system for interpretable deep learning based intrusion detection. During the design of this visual analytics system, we follow the requirements and features of explainable artificial intelligence for users in the domain of network security. The system allows users to select the best parameters to construct the model, to better understand the role of neurons in a deep learning model, to select instances and explore the detection mechanism of the model on these instances. We present multiple use cases to demonstrate the effectiveness of our system.",
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"content": "A new general dimension reduction framework based on similar and dissimilar metric learning is proposed in this paper which allows us to exploit the geometry of data to reduce the data dimension for classification and visualization. The general formulation can unify the existing dimension reduction algorithms within a common framework. Furthermore, this metric learning framework can be used as a general platform for developing new dimension reduction algorithms. By utilizing this framework as a tool, we propose a novel supervised dimension reduction algorithm named sub-manifold preserving analysis (SMPA) in which the intrinsic sub-manifold structure will be preserved while the margin of interclass will be separated. Experimental evidences show that performance of our proposed SMPA algorithm is better than other algorithms.",
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"normalizedAbstract": "A new general dimension reduction framework based on similar and dissimilar metric learning is proposed in this paper which allows us to exploit the geometry of data to reduce the data dimension for classification and visualization. The general formulation can unify the existing dimension reduction algorithms within a common framework. Furthermore, this metric learning framework can be used as a general platform for developing new dimension reduction algorithms. By utilizing this framework as a tool, we propose a novel supervised dimension reduction algorithm named sub-manifold preserving analysis (SMPA) in which the intrinsic sub-manifold structure will be preserved while the margin of interclass will be separated. Experimental evidences show that performance of our proposed SMPA algorithm is better than other algorithms.",
"fno": "04761130",
"keywords": [
"Data Reduction",
"Data Visualisation",
"Geometry",
"Learning Artificial Intelligence",
"Pattern Classification",
"Similar Metric Learning",
"Supervised Dimension Reduction Framework",
"Sub Manifold Preserving Analysis",
"Data Visualization",
"Data Classification",
"Dissimilar Metric Learning",
"Geometry",
"Data Reduction",
"Algorithm Design And Analysis",
"Data Visualization",
"Machine Learning Algorithms",
"Principal Component Analysis",
"Linear Discriminant Analysis",
"Embedded Computing",
"Laplace Equations",
"Symmetric Matrices",
"Mathematics",
"Sun"
],
"authors": [
{
"affiliation": "School of Mathematics and Computational Science, Sun Yat-Sen University, China, 510275",
"fullName": "Chunyuan Lu",
"givenName": "Chunyuan",
"surname": "Lu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Mathematics and Computational Science, Sun Yat-Sen University, China, 510275",
"fullName": "Guocan Feng",
"givenName": "Guocan",
"surname": "Feng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Informatics, University of Bradford, BD7 1DP, UK",
"fullName": "Jianmin Jiang",
"givenName": "Jianmin",
"surname": "Jiang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Computer and Information Science, Northeastern University, Boston, MA, USA, 02115",
"fullName": "Patrick Wang",
"givenName": "Patrick",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2008-12-01T00:00:00",
"pubType": "proceedings",
"pages": "",
"year": "2008",
"issn": "1051-4651",
"isbn": "978-1-4244-2174-9",
"notes": null,
"notesType": null,
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