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"abstract": "Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Data mining is a fast-growing area. The first Knowledge Discovery in Databases Workshop was held in August 1989, in conjunction with the 1989 International Joint Conference on Artificial Intelligence, and this workshop series became the International Conference on Knowledge Discovery and Data Mining (KDD) in 1995. In 2003, there were a total of 15 data mining conferences. These 15 conferences do not include various artificial intelligence (AI), statistics and database conferences (and their workshops) that also solicited and accepted data mining related papers, such as IJCAI, ICML, ICTAI, COMPSTAT, AI & Statistics, SIGMOD, VLDB, ICDE, and CIKM. Among various data mining conferences, KDD and ICDM (the IEEE International Conference on Data Mining) are arguably (or unarguably) the two premier ones in the field. ICDM was established in 2000, sponsored by the IEEE Computer Society, and had its first annual meeting in 2001. This talk will review the topics of interest from ICDM from an AI perspective, and analyze common topics in data mining and AI, including key AI ideas that have been used in both data mining and machine learning. We will also discuss two current research projects on (1) user-centered agents for biological information exploration on the Web, and (2) dynamic classifier selection in dealing with streaming data. Both projects apply data mining techniques for intelligent analysis of large volumes of data.",
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"title": "2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
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"abstract": "Cognitive load (CL) when using Virtual Reality (VR) requires more experimental inputs, especially to determine how VR affects human psychophysiology depending on the task. Classifying humans’ physiological variations in a controlled setup is essential. We randomly assigned 92 participants to three experimental conditions: control, stereoscopy, and dual-task. Participants fulfilled a rest-baseline period, a Stroop task (25 congruent, 25 incongruent words), and a NASA-TLX questionnaire. We recorded behavioral and physiological data from eye-tracking(ET), electrocardiogram(ECG), and electrodermal activity (EDA). NASA-TLX scores of control and stereoscopy were statistically different with dual-task conditions. We used NASA-TLX scores to create three classes and train a CL classifier based on physiological variations. We deployed linear models penalized with the L1 norm to select the most relevant features correlated with subjective CL levels. The ECG sensor provided the most selected features compared to EDA and ET. We compared SVM, Logistic Regression, and Gradient boosting classifier models. The Gradient boosting method with 87.23% accuracy and an 87.13% F1 score is the most performant. Future works will try to compare such an approach with stressful stimuli.",
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"content": "Cognitive load (CL) when using Virtual Reality (VR) requires more experimental inputs, especially to determine how VR affects human psychophysiology depending on the task. Classifying humans’ physiological variations in a controlled setup is essential. We randomly assigned 92 participants to three experimental conditions: control, stereoscopy, and dual-task. Participants fulfilled a rest-baseline period, a Stroop task (25 congruent, 25 incongruent words), and a NASA-TLX questionnaire. We recorded behavioral and physiological data from eye-tracking(ET), electrocardiogram(ECG), and electrodermal activity (EDA). NASA-TLX scores of control and stereoscopy were statistically different with dual-task conditions. We used NASA-TLX scores to create three classes and train a CL classifier based on physiological variations. We deployed linear models penalized with the L1 norm to select the most relevant features correlated with subjective CL levels. The ECG sensor provided the most selected features compared to EDA and ET. We compared SVM, Logistic Regression, and Gradient boosting classifier models. The Gradient boosting method with 87.23% accuracy and an 87.13% F1 score is the most performant. Future works will try to compare such an approach with stressful stimuli.",
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"abstract": "Proposes a new approach to the automatic generation of triangular irregular networks (TINs) from dense terrain models. We have developed and implemented an algorithm based on the greedy principle used to compute minimum-link paths in polygons. Our algorithm works by taking greedy cuts (\"bites\") out of a simple closed polygon that bounds the yet-to-be triangulated region. The algorithm starts with a large polygon, bounding the whole extent of the terrain to be triangulated, and works its way inward, performing at each step one of three basic operations: ear cutting, greedy biting, and edge splitting. We give experimental evidence that our method is competitive with current algorithms and has the potential to be faster and to generate many fewer triangles. Also, it is able to keep the structural terrain fidelity at almost no extra cost in running time and it requires very little memory beyond that for the input height array.",
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"content": "Proposes a new approach to the automatic generation of triangular irregular networks (TINs) from dense terrain models. We have developed and implemented an algorithm based on the greedy principle used to compute minimum-link paths in polygons. Our algorithm works by taking greedy cuts (\"bites\") out of a simple closed polygon that bounds the yet-to-be triangulated region. The algorithm starts with a large polygon, bounding the whole extent of the terrain to be triangulated, and works its way inward, performing at each step one of three basic operations: ear cutting, greedy biting, and edge splitting. We give experimental evidence that our method is competitive with current algorithms and has the potential to be faster and to generate many fewer triangles. Also, it is able to keep the structural terrain fidelity at almost no extra cost in running time and it requires very little memory beyond that for the input height array.",
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"abstract": "Researchers developing implementations of distributed graph analytic algorithms require graph generators that yield graphs sharing the challenging characteristics of real-world graphs (small-world, scale-free, heavy-tailed degree distribution) with efficiently calculable ground-truth solutions to the desired output. Reproducibility for current generators [1] used in benchmarking are somewhat lacking in this respect due to their randomness: the output of a desired graph analytic can only be compared to expected values and not exact ground truth. Nonstochastic Kronecker product graphs [2] meet these design criteria for several graph analytics. Here we show that many flavors of triangle participation can be cheaply calculated while generating a Kronecker product graph. Given two medium-sized scale-free graphs with adjacency matrices A and B, their Kronecker product graph has adjacency matrix C = A ⊗ B. Such graphs are highly compressible: |E| edges are represented in O(|E|1/2) memory and can be built in a distributed setting from small data structures, making them easy to share in compressed form. Many interesting graph calculations have worst-case complexity bounds O(|E|p) and often these are reduced to O(|E|p/2) for Kronecker product graphs, when a Kronecker formula can be derived yielding the sought calculation on C in terms of related calculations on A and B. We focus on deriving formulas for triangle participation at vertices, tC, a vector storing the number of triangles that every vertex is involved in, and triangle participation at edges, ΔC, a sparse matrix storing the number of triangles at every edge. When factors A and B are undirected, C is also undirected. In the case when both factors have no self loops we show tC = 2tA ⊗ tB, ΔC = ΔA ⊗ ΔB. Moreover, we derive the respective formulas when A and B have self loops, which boosts the triangle counts for the associated vertices/edges in C. We additionally demonstrate strong assumptions on B that allow the truss decomposition of C to be derived cheaply from the truss decomposition of A. We extend these results and show Kronecker formulas for triangle participation in both directed graphs and undirected, vertex-labeled graphs. In these classes of graphs each vertex / edge can participate in many different types of triangles.",
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"doi": "10.1109/FOCS52979.2021.00045",
"title": "Covering Polygons is Even Harder",
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"abstract": "In the Minimum Convex Cover (MCC) problem, we are given a simple polygon <tex>Z_$\\mathcal{P}$_Z</tex> and an integer <tex>Z_$k$_Z</tex>, and the question is if there exist <tex>Z_$k$_Z</tex> convex polygons whose union is <tex>Z_$\\mathcal{P}$_Z</tex>. It is known that MCC is NP-hard [Culberson & Reckhow: Covering polygons is hard, FOCS 1988/Journal of Algorithms 1994] and in <tex>Z_$\\exists \\mathbb{R}$_Z</tex> [O'Rourke: The complexity of computing minimum convex covers for polygons, Allerton 1982]. We prove that MCC is <tex>Z_$\\exists \\mathbb{R}$_Z</tex>-hard, and the problem is thus <tex>Z_$\\exists \\mathbb{R}$_Z</tex>-complete. In other words, the problem is equivalent to deciding whether a system of polynomial equations and inequalities with integer coefficients has a real solution. If a cover for our constructed polygon exists, then so does a cover consisting entirely of triangles. As a byproduct, we therefore also establish that it is <tex>Z_$\\exists \\mathbb{R}$_Z</tex>-complete to decide whether <tex>Z_$k$_Z</tex> triangles cover a given polygon. The issue that it was not known if finding a minimum cover is in N P has repeatedly been raised in the literature, and it was mentioned as a “long-standing open question” already in 2001 [Eidenbenz & Widmayer: An approximation algorithm for minimum convex cover with logarithmic performance guarantee, ESA 2001/SIAM Journal on Computing 2003]. We prove that assuming the widespread belief that <tex>Z_$\\mathsf{NP}\\neq\\exists \\mathbb{R}$_Z</tex>, the problem is not in N P. An implication of the result is that many natural approaches to finding small covers are bound to give suboptimal solutions in some cases, since irrational coordinates of arbitrarily high algebraic degree can be needed for the corners of the pieces in an optimal solution.",
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"content": "In the Minimum Convex Cover (MCC) problem, we are given a simple polygon <tex>$\\mathcal{P}$</tex> and an integer <tex>$k$</tex>, and the question is if there exist <tex>$k$</tex> convex polygons whose union is <tex>$\\mathcal{P}$</tex>. It is known that MCC is NP-hard [Culberson & Reckhow: Covering polygons is hard, FOCS 1988/Journal of Algorithms 1994] and in <tex>$\\exists \\mathbb{R}$</tex> [O'Rourke: The complexity of computing minimum convex covers for polygons, Allerton 1982]. We prove that MCC is <tex>$\\exists \\mathbb{R}$</tex>-hard, and the problem is thus <tex>$\\exists \\mathbb{R}$</tex>-complete. In other words, the problem is equivalent to deciding whether a system of polynomial equations and inequalities with integer coefficients has a real solution. If a cover for our constructed polygon exists, then so does a cover consisting entirely of triangles. As a byproduct, we therefore also establish that it is <tex>$\\exists \\mathbb{R}$</tex>-complete to decide whether <tex>$k$</tex> triangles cover a given polygon. The issue that it was not known if finding a minimum cover is in N P has repeatedly been raised in the literature, and it was mentioned as a “long-standing open question” already in 2001 [Eidenbenz & Widmayer: An approximation algorithm for minimum convex cover with logarithmic performance guarantee, ESA 2001/SIAM Journal on Computing 2003]. We prove that assuming the widespread belief that <tex>$\\mathsf{NP}\\neq\\exists \\mathbb{R}$</tex>, the problem is not in N P. An implication of the result is that many natural approaches to finding small covers are bound to give suboptimal solutions in some cases, since irrational coordinates of arbitrarily high algebraic degree can be needed for the corners of the pieces in an optimal solution.",
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"normalizedAbstract": "In the Minimum Convex Cover (MCC) problem, we are given a simple polygon - and an integer -, and the question is if there exist - convex polygons whose union is -. It is known that MCC is NP-hard [Culberson & Reckhow: Covering polygons is hard, FOCS 1988/Journal of Algorithms 1994] and in - [O'Rourke: The complexity of computing minimum convex covers for polygons, Allerton 1982]. We prove that MCC is --hard, and the problem is thus --complete. In other words, the problem is equivalent to deciding whether a system of polynomial equations and inequalities with integer coefficients has a real solution. If a cover for our constructed polygon exists, then so does a cover consisting entirely of triangles. As a byproduct, we therefore also establish that it is --complete to decide whether - triangles cover a given polygon. The issue that it was not known if finding a minimum cover is in N P has repeatedly been raised in the literature, and it was mentioned as a “long-standing open question” already in 2001 [Eidenbenz & Widmayer: An approximation algorithm for minimum convex cover with logarithmic performance guarantee, ESA 2001/SIAM Journal on Computing 2003]. We prove that assuming the widespread belief that -, the problem is not in N P. An implication of the result is that many natural approaches to finding small covers are bound to give suboptimal solutions in some cases, since irrational coordinates of arbitrarily high algebraic degree can be needed for the corners of the pieces in an optimal solution.",
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"affiliation": "University of Copenhagen,BARC",
"fullName": "Mikkel Abrahamsen",
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"title": "Contour Detection in Unstructured 3D Point Clouds",
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"abstract": "We describe a method to automatically detect contours, i.e. lines along which the surface orientation sharply changes, in large-scale outdoor point clouds. Contours are important intermediate features for structuring point clouds and converting them into high-quality surface or solid models, and are extensively used in graphics and mapping applications. Yet, detecting them in unstructured, inhomogeneous point clouds turns out to be surprisingly difficult, and existing line detection algorithms largely fail. We approach contour extraction as a two-stage discriminative learning problem. In the first stage, a contour score for each individual point is predicted with a binary classifier, using a set of features extracted from the point's neighborhood. The contour scores serve as a basis to construct an overcomplete graph of candidate contours. The second stage selects an optimal set of contours from the candidates. This amounts to a further binary classification in a higher-order MRF, whose cliques encode a preference for connected contours and penalize loose ends. The method can handle point clouds > 107 points in a couple of minutes, and vastly outperforms a baseline that performs Canny-style edge detection on a range image representation of the point cloud.",
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"content": "We describe a method to automatically detect contours, i.e. lines along which the surface orientation sharply changes, in large-scale outdoor point clouds. Contours are important intermediate features for structuring point clouds and converting them into high-quality surface or solid models, and are extensively used in graphics and mapping applications. Yet, detecting them in unstructured, inhomogeneous point clouds turns out to be surprisingly difficult, and existing line detection algorithms largely fail. We approach contour extraction as a two-stage discriminative learning problem. In the first stage, a contour score for each individual point is predicted with a binary classifier, using a set of features extracted from the point's neighborhood. The contour scores serve as a basis to construct an overcomplete graph of candidate contours. The second stage selects an optimal set of contours from the candidates. This amounts to a further binary classification in a higher-order MRF, whose cliques encode a preference for connected contours and penalize loose ends. The method can handle point clouds > 107 points in a couple of minutes, and vastly outperforms a baseline that performs Canny-style edge detection on a range image representation of the point cloud.",
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"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
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"title": "Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points",
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"abstract": "We propose a method for human activity recognition from RGB data that does not rely on any pose information during test time, and does not explicitly calculate pose information internally. Instead, a visual attention module learns to predict glimpse sequences in each frame. These glimpses correspond to interest points in the scene that are relevant to the classified activities. No spatial coherence is forced on the glimpse locations, which gives the attention module liberty to explore different points at each frame and better optimize the process of scrutinizing visual information. Tracking and sequentially integrating this kind of unstructured data is a challenge, which we address by separating the set of glimpses from a set of recurrent tracking/recognition workers. These workers receive glimpses, jointly performing subsequent motion tracking and activity prediction. The glimpses are soft-assigned to the workers, optimizing coherence of the assignments in space, time and feature space using an external memory module. No hard decisions are taken, i.e. each glimpse point is assigned to all existing workers, albeit with different importance. Our methods outperform the state-of-the-art on the largest human activity recognition dataset available to-date, NTU RGB+D, and on the Northwestern-UCLA Multiview Action 3D Dataset.",
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{
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"content": "We propose a method for human activity recognition from RGB data that does not rely on any pose information during test time, and does not explicitly calculate pose information internally. Instead, a visual attention module learns to predict glimpse sequences in each frame. These glimpses correspond to interest points in the scene that are relevant to the classified activities. No spatial coherence is forced on the glimpse locations, which gives the attention module liberty to explore different points at each frame and better optimize the process of scrutinizing visual information. Tracking and sequentially integrating this kind of unstructured data is a challenge, which we address by separating the set of glimpses from a set of recurrent tracking/recognition workers. These workers receive glimpses, jointly performing subsequent motion tracking and activity prediction. The glimpses are soft-assigned to the workers, optimizing coherence of the assignments in space, time and feature space using an external memory module. No hard decisions are taken, i.e. each glimpse point is assigned to all existing workers, albeit with different importance. Our methods outperform the state-of-the-art on the largest human activity recognition dataset available to-date, NTU RGB+D, and on the Northwestern-UCLA Multiview Action 3D Dataset.",
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"content": "Protein is the major component of the organism. It has a unique three-dimensional structure determined by its amino acid sequence. A concave (pocket) on the surface of a protein is known to be the best target for a drug to react. We started analyzing how\" drug ability\" of proteins related to the location of amino acids in a pocket. For as tarter of the study, this paper presents a visualization tool for distance analysis between pockets and the amino acid residue. Provided that a protein surface is described by a triangular mesh, this tool first identifies pockets on the protein surface, specifies the deepest point and outer loops of the pocket, and calculates distances between atoms of an amino acid residue and the deepest point or the outer loops of the pocket. The tool then visualizes the statistics of the distance calculation results by poly line charts and the distribution by scatter plots. This paper proposes a biological interpretation of the visualization results.",
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"abstract": "During the last two decades a wide variety of advanced methods for the visual exploration of large data sets have been proposed. For most of these techniques user interaction has become a crucial element, since there are many situations in which a user or an analyst has to select the right parameter settings from among many or select a subset of the available attribute space for the visualization process, in order to construct valuable visualizations that provide insight, into the data and reveal interesting patterns. The right choice of input parameters is often essential, since suboptimal parameter settings or the investigation of irrelevant data dimensions make the exploration process more time consuming and may result in wrong conclusions. In this paper we propose a novel method for automatically determining meaningful parameter- and attribute settings based on the information content of the resulting visualizations. Our technique called Pixnostics, in analogy to Scagnostics (Wilkinson et al., 2005), automatically analyses pixel images resulting from diverse parameter mappings and ranks them according to the potential value for the user. This allows a more effective and more efficient visual data analysis process, since the attribute/parameter space is reduced to meaningful selections and thus the analyst obtains faster insight into the data. Real world applications are provided to show the benefit of the proposed approach",
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"abstract": "From a user perspective both security and power consumption during video transmissions are critical. On the one hand, assuming an eavesdropper on the wireless link, the transmitted video should be encrypted so that the unauthorized user is not able to reconstruct it. On the other hand, depending on the strength and the type of cryptographic method used during information exchange, there is a corresponding power consumption overhead. Since encryption protocols are oblivious to the wireless channel conditions, all packets are encrypted regardless of the probability of them getting lost. However, if the application layer protocol received feedback from the link layer on the channel losses, the protocol could decide how much encryption effort is actually needed so that the eavesdropper cannot reconstruct the video clip successfully. To an eavesdropper packets lost due to interference or encrypted packets do not make any difference; both are invisible to the passive attacker. In this paper, we map the percentage of lost/encrypted packets to an objective video quality metric (PSNR). We also create a look-up table through experiments on real multi-hop video transmission traffic, which allows the application layer to adjust the amount of encryption performed.",
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"affiliation": "Department of Electrical and Computer Engineering, Villanova University, PA 19085, USA",
"fullName": "Christopher Mansour",
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"affiliation": "Department of Electrical and Computer Engineering, Villanova University, PA 19085, USA",
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"abstract": "Although we can interactively rotate a 3D projected high-dimensional geometry and observe its dynamic changes, this traditional visualization method is limited and highly sensitive to the choice of viewing direction. Parallel-coordinates plots supplement this visualization scenario by providing statistical analysis of the geometry for distinct pairs of co-dimensions. Such analysis results in visual signatures that embed geometric structures such as symmetry, and thus allows us to overview the status of the missing dimensions while exploring the projected geometry. This paper presents a blue-noise sampling approach for efficient construction of continuous parallel-coordinates plots of high-dimensional geometric surfaces defined by mathematical equations. We employ the parallel-coordinates plots with the embedded visual signatures to assist the interactive exploration of high-dimensional geometries, typically for 2-manifold embedded in 4-space. While we interactively explore the 3D projected geometry, we can observe dynamic changes on its visual signature. Various geometric properties can also be identified and visualized. Moreover, we can interactively brush the plots, and see their counterparts in the 3D projection. Assorted geometric properties such as curvature can further be used to enhance the visual signature.",
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"content": "Although we can interactively rotate a 3D projected high-dimensional geometry and observe its dynamic changes, this traditional visualization method is limited and highly sensitive to the choice of viewing direction. Parallel-coordinates plots supplement this visualization scenario by providing statistical analysis of the geometry for distinct pairs of co-dimensions. Such analysis results in visual signatures that embed geometric structures such as symmetry, and thus allows us to overview the status of the missing dimensions while exploring the projected geometry. This paper presents a blue-noise sampling approach for efficient construction of continuous parallel-coordinates plots of high-dimensional geometric surfaces defined by mathematical equations. We employ the parallel-coordinates plots with the embedded visual signatures to assist the interactive exploration of high-dimensional geometries, typically for 2-manifold embedded in 4-space. While we interactively explore the 3D projected geometry, we can observe dynamic changes on its visual signature. Various geometric properties can also be identified and visualized. Moreover, we can interactively brush the plots, and see their counterparts in the 3D projection. Assorted geometric properties such as curvature can further be used to enhance the visual signature.",
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"abstract": "Despite the substantial progress of deep models for crack recognition, due to the inconsistent cracks in varying sizes, shapes, and noisy background textures, there still lacks the discriminative power of the deeply learned features when supervised by the cross-entropy loss. In this paper, we propose the geometry-aware guided loss (GAGL) that enhances the discrimination ability and is only applied in the training stage without extra computation and memory during inference. The GAGL consists of the feature-based geometry-aware projected gradient descent method (FGA-PGD) that approximates the geometric distances of the features to the class boundaries, and the geometry-aware update rule that learns an anchor of each class as the approximation of the feature expected to have the largest geometric distance to the corresponding class boundary. Then the discriminative power can be enhanced by minimizing the distances between the features and their corresponding class anchors in the feature space. To address the limited availability of related benchmarks, we collect a fully annotated dataset, namely, NPP2021, which involves inconsistent cracks and noisy backgrounds in real-world nuclear power plants. Our proposed GAGL outperforms the state of the arts on various benchmark datasets including CRACK2019, SDNET2018, and our NPP2021.",
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"content": "Despite the substantial progress of deep models for crack recognition, due to the inconsistent cracks in varying sizes, shapes, and noisy background textures, there still lacks the discriminative power of the deeply learned features when supervised by the cross-entropy loss. In this paper, we propose the geometry-aware guided loss (GAGL) that enhances the discrimination ability and is only applied in the training stage without extra computation and memory during inference. The GAGL consists of the feature-based geometry-aware projected gradient descent method (FGA-PGD) that approximates the geometric distances of the features to the class boundaries, and the geometry-aware update rule that learns an anchor of each class as the approximation of the feature expected to have the largest geometric distance to the corresponding class boundary. Then the discriminative power can be enhanced by minimizing the distances between the features and their corresponding class anchors in the feature space. To address the limited availability of related benchmarks, we collect a fully annotated dataset, namely, NPP2021, which involves inconsistent cracks and noisy backgrounds in real-world nuclear power plants. Our proposed GAGL outperforms the state of the arts on various benchmark datasets including CRACK2019, SDNET2018, and our NPP2021.",
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"normalizedAbstract": "Despite the substantial progress of deep models for crack recognition, due to the inconsistent cracks in varying sizes, shapes, and noisy background textures, there still lacks the discriminative power of the deeply learned features when supervised by the cross-entropy loss. In this paper, we propose the geometry-aware guided loss (GAGL) that enhances the discrimination ability and is only applied in the training stage without extra computation and memory during inference. The GAGL consists of the feature-based geometry-aware projected gradient descent method (FGA-PGD) that approximates the geometric distances of the features to the class boundaries, and the geometry-aware update rule that learns an anchor of each class as the approximation of the feature expected to have the largest geometric distance to the corresponding class boundary. Then the discriminative power can be enhanced by minimizing the distances between the features and their corresponding class anchors in the feature space. To address the limited availability of related benchmarks, we collect a fully annotated dataset, namely, NPP2021, which involves inconsistent cracks and noisy backgrounds in real-world nuclear power plants. Our proposed GAGL outperforms the state of the arts on various benchmark datasets including CRACK2019, SDNET2018, and our NPP2021.",
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"affiliation": "Shenzhen University,Shenzhen,China",
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"title": "PGDP5K: A Diagram Parsing Dataset for Plane Geometry Problems",
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"abstract": "Diagram parsing is an important foundation for geometry problem solving, attracting increasing attention in the field of intelligent education and document image understanding. Due to the complex layout and between-primitive relationship, plane geometry diagram parsing (PGDP) is still a challenging task deserving further research and exploration. An appropriate dataset is critical for the research of PGDP. Although some datasets with rough annotations have been proposed to solve geometric problems, they are either small in scale or not publicly available. The rough annotations also make them not very useful. Thus, we propose a new large-scale geometry diagram dataset named PGDP5K and a novel annotation method. Our dataset consists of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types. Different from previous datasets, our PGDP5K dataset is labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships. What is more, combined with above annotations and geometric prior knowledge, it can generate intelligible geometric propositions automatically and uniquely. We performed experiments on PGDP5K and IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves only 66.07% F1 value. This shows that PGDP5K presents a challenge for future research. Our dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-PGDP5K/.",
"abstracts": [
{
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"content": "Diagram parsing is an important foundation for geometry problem solving, attracting increasing attention in the field of intelligent education and document image understanding. Due to the complex layout and between-primitive relationship, plane geometry diagram parsing (PGDP) is still a challenging task deserving further research and exploration. An appropriate dataset is critical for the research of PGDP. Although some datasets with rough annotations have been proposed to solve geometric problems, they are either small in scale or not publicly available. The rough annotations also make them not very useful. Thus, we propose a new large-scale geometry diagram dataset named PGDP5K and a novel annotation method. Our dataset consists of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types. Different from previous datasets, our PGDP5K dataset is labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships. What is more, combined with above annotations and geometric prior knowledge, it can generate intelligible geometric propositions automatically and uniquely. We performed experiments on PGDP5K and IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves only 66.07% F1 value. This shows that PGDP5K presents a challenge for future research. Our dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-PGDP5K/.",
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"normalizedAbstract": "Diagram parsing is an important foundation for geometry problem solving, attracting increasing attention in the field of intelligent education and document image understanding. Due to the complex layout and between-primitive relationship, plane geometry diagram parsing (PGDP) is still a challenging task deserving further research and exploration. An appropriate dataset is critical for the research of PGDP. Although some datasets with rough annotations have been proposed to solve geometric problems, they are either small in scale or not publicly available. The rough annotations also make them not very useful. Thus, we propose a new large-scale geometry diagram dataset named PGDP5K and a novel annotation method. Our dataset consists of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types. Different from previous datasets, our PGDP5K dataset is labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships. What is more, combined with above annotations and geometric prior knowledge, it can generate intelligible geometric propositions automatically and uniquely. We performed experiments on PGDP5K and IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves only 66.07% F1 value. This shows that PGDP5K presents a challenge for future research. Our dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-PGDP5K/.",
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"authors": [
{
"affiliation": "Beijing Jiaotong University,School of Electronic Information Engineering,Beijing,China",
"fullName": "Yihan Hao",
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"affiliation": "Institute of Automation of Chinese Academy of Science,National Laboratory of Pattern Recognition (NLPR),Beijing,China",
"fullName": "Mingliang Zhang",
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{
"affiliation": "Institute of Automation of Chinese Academy of Science,National Laboratory of Pattern Recognition (NLPR),Beijing,China",
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"abstract": "Twitter is widely used by businesses to communicate with and obtain feedback from their customers, almost in real time. Automated analysis is necessary to deal with the large volumes of tweets in a timely manner, and an insightful classification is a first step in this analysis. This paper presents an ensemble method that combines classifier outputs by including the individual probability distributions with an additional learning layer, as opposed to an arithmetic combination of weighted probabilities. Besides using probabilities generated by individual classifiers, we also show that the use of tweet vectors helps with the ensemble learning step. In addition, we have described a mapping method to employ a cloud taxonomy service as an additional classifier. We include LLDA, Naive Bayes, and the cloud taxonomy service for the ensemble method, and have applied our methods on a real industry tweet dataset. The proposed ensemble model is able to outperform several widely used probability based ensemble methods, i.e., Weighted Sum and Product of Experts (PoE). The ensemble model is also more adaptive than previous models in handling the variations in the probability distributions output from the individual classifiers. In addition to the algorithms used and the improved results, this paper's contribution is in insights from the application of models on a unique, real dataset.",
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"abstract": "Information visualization focuses on the use of visual means for exploring non-visual information. While free-form text is a rich, common source of information, visualization of text is a challenging problem since text is inherently non-spatial. This paper explores the use of implicit surface models for visualizing text. We describe several techniques for text visualization that aid in understanding document content and document relationships. A simple method is defined for mapping document content to shape. By comparing the shapes of multiple documents, global content similarities and differences may be noted. In addition, we describe a visual clustering method in which documents are arranged in 3D based upon similarity scoring. Documents deemed closely related blend together as a single connected shape. Hence, a document corpus becomes a collection of shapes that reflect inter-document relationships. These techniques provide methods to visualize individual documents as well as corpus meta-data. We then combine the two techniques to produce transparent clusters enclosing individual document shapes. This provides a way to visualize both local and global contextual information. Finally, we elaborate on several potential applications of these methods.",
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"abstract": "systems, general-and special-purpose search engines. Instead of choosing between e.g. Google, Bing and Baidu, a user may want to get integrated results from all three search engines (and more) via a single search query. The user may also want results orsuggestions to questions implied by, but not explicitly stated, in his query. Ideally, the combined results from different searchengines will be relevance-ranked, taking into account each user's individual preferences. There exist \"expert systems\" thatintegrate results or recommendations from multiple different websites and/or other search engines -- e.g., the meta-search engines for finding the best flights and airfares. However, these meta-search engines do not (i) relevance-rank on behalf of the end-user, (ii) learn over time, which websites/individual search engines are most trustworthy and relevant to a particular user, (iii) maintain a quality assurance model of the individual sources of information or recommendation that they harvest, or (iv) create sub-queries or new queries based on inference of the user intent, and not merely what the user has explicitly asked for. We propose a unified framework to address all these issues. In particular, our goal is to enable the end-user to seamlessly obtain integrated expertise from a variety of sources, so that those recommendations are ranked based on both (a) the user's preferences and (b) different individual sources' of recommendation reputation and trustworthiness. Our vision for achieving these goals is to have a decentralized, transparent market-place of search engines, recommenders and knowledge bases, where the burdens of integrating, ranking and evaluating quality of different knowledge sources are taken off the end-user.",
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"abstract": "The results of protein search engines depend mainly upon a set of parameters that adjust the searching space. One of the most effective parameters is the peptide mass window tolerance (w). Most of the current search engines use a constant user-defined value for this parameter. As an alternative option, Comet search engine designers proposed a statistical technique to estimate the best tolerance window for an input spectra file. However, this technique sometimes fails in picking a value, may set the parameter to a value that results in a loss of many correct matches, and is available only for one type of mass; namely ppm. In this paper, we propose to use particle swarm optimization (PSO) to improve the coverage of search engines by picking the optimal value for this influential parameter to maximize PSMs. Our results show that this biologically-inspired algorithm can be utilized to find peptide mass window tolerance values that facilitate Comet to increase peptide spectra matches, resulting in improved peptide identification. We also show experimental evidence that an open search (i.e., wide tolerance window) does not always optimize spectra matching using the current search engines and that narrow tolerance windows improve the coverage of protein search engines.",
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"abstract": "We can distinguish two types of web search engines: general use ones that index and search all the web, and site-specific ones that are provided by individual websites for local searching. A comparison of the effectiveness of the two types allows search engine users to choose the right engine and organizations to decide whether they should develop their own search software or purchase the search function as a service. We evaluate the performance of two general purpose search engines and 10 site-specific ones. The criteria we used are precision and relative recall. We entered 20 queries in each website’s search engine and evaluated the first 10 links. According to the results, Google is in most cases the most efficient search engine.??However, in some cases general purpose search engines do not index the website’s content as well as a site-specific engine.",
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"title": "Palestinian International Conference on Information and Communication Technology (PICICT)",
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"title": "How Web Applications Complement Search Engines?",
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"abstract": "Search engines use content and link cues when searching for relevant pages that meet with the user query. They depend on clustering hypothesis and clustering link hypothesis as a strategy for search. There is a serious need for designing models that could reorganize the content of the web dynamically to meet users' needs. As search engines don't satisfy the user's need for complete and recently updated information, the researchers must design models depending on new concepts and paradigms to improve the coverage and recency of search engines. This work will focus on models used new concepts and paradigms for search, like the emergence concept as a stigmergy mechanism, the multi-agents system and Web Ants. Using the synthetic pheromone in Web Ants, the documents in Content Usage Ants model are labeled using the keywords not only in the content of documents in the content space and usage space, but also in the header and links of the web pages. The documents will have more weight if the words are found in the title and the link of the pages. This weight will be used to calculate the content similarity measurements in both spaces and compare the results to complement search engines. We will also make an overview of existing literature regarding intelligent semantic search engines. An overview of how the whole system of a search engine works is provided. and an overview of some models that been used to improve the way in which search engines work like: Content Usage Ants , Web comb models and other semantic web models.",
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"abstract": "Search engines are essential actors for web browsing. We analyze here the economic competition between search engines earning money from ad word auctions. We develop a two-level game where at the largest time scale search engines decide which allocation rule to implement, between revenue-based and bid-based, and at the lowest time-scale advertisers decide how to split their advertising budget between the two search engines, depending on the benefits this will bring to them. The game at the largest time scale is solved using backward induction, the search engines anticipating the reactions of advertisers. We describe the advertisers best strategies and show how to determine, depending on parameters, an equilibrium on the ranking rule strategy for search engines, this may explain Yahoo!'s move to switch from bid-based to revenue-based ranking to follow Google's strategy.",
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"title": "2009 IEEE International Conference on Semantic Computing",
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"title": "If You Ask Nicely, I will Answer: Semantic Search and Today's Search Engines",
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"abstract": "Today's search engines are still very sensitive to the way queries are constructed. In some occasions, equivalent but slightly different forms of a query lead to completely different results. However, popular queries with only one right answer seem to be generally well served by search engines, which generally return the correct answer among their top 10 search results. Internet's redundancy of information and the recent proliferation of user generated content helps search engines to remain almost entirely keyword oriented and still robustly handle equivalent versions of queries. In this paper we propose a family of metrics to evaluate the semantical invariance of a given search engine, and we report experimental results for well-known engines such as Google, Yahoo!, Live and Ask.com, as well as for new semantic search engines like Hakia and Cuil.",
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"fullName": "Alessio Signorini",
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"abstract": "Many websites induce the browser to send network traffic in response to user input events. This includes websites with autocomplete, a popular feature on search engines that anticipates the user's query while they are typing. Websites with this functionality require HTTP requests to be made as the query input field changes, such as when the user presses a key. The browser responds to input events by generating network traffic to retrieve the search predictions. The traffic emitted by the client can expose the timings of keyboard input events which may lead to a keylogging side channel attack whereby the query is revealed through packet inter-arrival times. We investigate the feasibility of such an attack on several popular search engines by characterizing the behavior of each website and measuring information leakage at the network level. Three out of the five search engines we measure preserve the mutual information between keystrokes and timings to within 1% of what it is on the host. We describe the ways in which two search engines mitigate this vulnerability with minimal effects on usability.",
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{
"proceeding": {
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"title": "2018 IEEE International Conference on Data Mining Workshops (ICDMW)",
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"groupId": "1001620",
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"article": {
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"title": "Time Series Analysis for Bitcoin Transactions: The Case of Pirate@40's HYIP Scheme",
"normalizedTitle": "Time Series Analysis for Bitcoin Transactions: The Case of Pirate@40's HYIP Scheme",
"abstract": "Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a \"static\" manner and none of them analyzes transaction history \"dynamically\", i.e. without taking into account the \"time variation of how Bitcoin is transferred\". The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a \"static\" manner and none of them analyzes transaction history \"dynamically\", i.e. without taking into account the \"time variation of how Bitcoin is transferred\". The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a \"static\" manner and none of them analyzes transaction history \"dynamically\", i.e. without taking into account the \"time variation of how Bitcoin is transferred\". The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.",
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"Fraud",
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"Feature Extraction",
"Time Series Analysis",
"Investment",
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"Anomaly Detection",
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{
"affiliation": null,
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"fullName": "Tomoaki Ohtsuki",
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{
"affiliation": null,
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],
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{
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"abstractUrl": "/proceedings-article/icdcs/2020/700200b031/1rsiIodIyRO",
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