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"normalizedAbstract": "We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing platform. We then analyze what human interpretable image properties can have an impact on visual search difficulty, and how accurate are those properties for predicting difficulty. Next, we build a regression model based on deep features learned with state of the art convolutional neural networks and show better results for predicting the groundtruth visual search difficulty scores produced by human annotators. Our model is able to correctly rank about 75% image pairs according to their difficulty score. We also show that our difficulty predictor generalizes well to new classes not seen during training. Finally, we demonstrate that our predicted difficulty scores are useful for weakly supervised object localization (8% improvement) and semi-supervised object classification (1% improvement).",
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"abstract": "Previous studies suggest that physiological effects of mental effort as manipulated trough cognitive task difficulty differ from effects of mental effort as manipulated trough a visuomotor task such as lane keeping in simulated driving. Most notably, heart rate increases with mental effort in the former but not in the latter task. EEG seems to be indicative of mental effort in both cases. In previous research [1], Brouwer and colleagues examined effects of mental effort as manipulated in a cognitive (memory) task on a range of physiological signals. In the present research we examine the same types of physiological signals using the same kind of analysis in a visuomotor (simulated driving) task. In this case, mental effort was manipulated using wide and narrow lanes. Effects of task difficulty on both subjective mental effort and behavioral variables were comparable across tasks. Effect of task difficulty was replicated for respiration frequency and to some extent for EEG alpha activity. However, in contrast to the cognitive task [1], skin conductance and heart rate related variables were not significantly affected by task difficulty in the current visuomotor task. We argue that differences in visual attention and cerebral energy demand between the types of tasks may be at the basis of this.",
"abstracts": [
{
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"content": "Previous studies suggest that physiological effects of mental effort as manipulated trough cognitive task difficulty differ from effects of mental effort as manipulated trough a visuomotor task such as lane keeping in simulated driving. Most notably, heart rate increases with mental effort in the former but not in the latter task. EEG seems to be indicative of mental effort in both cases. In previous research [1], Brouwer and colleagues examined effects of mental effort as manipulated in a cognitive (memory) task on a range of physiological signals. In the present research we examine the same types of physiological signals using the same kind of analysis in a visuomotor (simulated driving) task. In this case, mental effort was manipulated using wide and narrow lanes. Effects of task difficulty on both subjective mental effort and behavioral variables were comparable across tasks. Effect of task difficulty was replicated for respiration frequency and to some extent for EEG alpha activity. However, in contrast to the cognitive task [1], skin conductance and heart rate related variables were not significantly affected by task difficulty in the current visuomotor task. We argue that differences in visual attention and cerebral energy demand between the types of tasks may be at the basis of this.",
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"normalizedAbstract": "Previous studies suggest that physiological effects of mental effort as manipulated trough cognitive task difficulty differ from effects of mental effort as manipulated trough a visuomotor task such as lane keeping in simulated driving. Most notably, heart rate increases with mental effort in the former but not in the latter task. EEG seems to be indicative of mental effort in both cases. In previous research [1], Brouwer and colleagues examined effects of mental effort as manipulated in a cognitive (memory) task on a range of physiological signals. In the present research we examine the same types of physiological signals using the same kind of analysis in a visuomotor (simulated driving) task. In this case, mental effort was manipulated using wide and narrow lanes. Effects of task difficulty on both subjective mental effort and behavioral variables were comparable across tasks. Effect of task difficulty was replicated for respiration frequency and to some extent for EEG alpha activity. However, in contrast to the cognitive task [1], skin conductance and heart rate related variables were not significantly affected by task difficulty in the current visuomotor task. We argue that differences in visual attention and cerebral energy demand between the types of tasks may be at the basis of this.",
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"title": "2014 IEEE 3rd International Conference on Serious Games and Applications for Health (SeGAH)",
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"title": "The effect of different difficulty adaptation strategies on enjoyment and performance in a serious game for memory training",
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"abstract": "Among the approaches used to make serious games and learning environments enjoyable, two important ones are 1) adapting task difficulty to match user performance, which leverages the theories of zone of proximal development and flow and 2) providing a high degree of control and choice, which uses constructs from self-determination theory. However, it is not clear whether the two approaches lead to similar results or whether focusing on performance may lead to suboptimal enjoyment. This is relevant since, in addition to being performance-oriented, serious games and learning environments also need to be enjoyable to be effective. To address this issue, we evaluated two kinds of difficulty adaptation in a simple memory training game: one adaptation based solely on difficulty-performance matching and the other based solely on providing a high degree of control/choice. Our results suggest that neither approach is optimal: players enjoyed themselves the most in the control/choice mode, but performed the best in the difficulty-performance matching mode. Player behavior in the game suggests that a trade-off between maximizing performance and maximizing enjoyment could be achieved by a combination of the two approaches. However, long-term studies would be required to determine whether such a combination could indeed be effective.",
"abstracts": [
{
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"content": "Among the approaches used to make serious games and learning environments enjoyable, two important ones are 1) adapting task difficulty to match user performance, which leverages the theories of zone of proximal development and flow and 2) providing a high degree of control and choice, which uses constructs from self-determination theory. However, it is not clear whether the two approaches lead to similar results or whether focusing on performance may lead to suboptimal enjoyment. This is relevant since, in addition to being performance-oriented, serious games and learning environments also need to be enjoyable to be effective. To address this issue, we evaluated two kinds of difficulty adaptation in a simple memory training game: one adaptation based solely on difficulty-performance matching and the other based solely on providing a high degree of control/choice. Our results suggest that neither approach is optimal: players enjoyed themselves the most in the control/choice mode, but performed the best in the difficulty-performance matching mode. Player behavior in the game suggests that a trade-off between maximizing performance and maximizing enjoyment could be achieved by a combination of the two approaches. However, long-term studies would be required to determine whether such a combination could indeed be effective.",
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"affiliation": "Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zurich, Switzerland",
"fullName": "Aniket Nagle",
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"affiliation": "Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland",
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"affiliation": "Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland",
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"abstract": "In Online Education System (OES), it is popular to assess examinee by providing exercises with different difficulty. So Exercise Difficulty Prediction (EDP) is a fundamental task of OES, such as medicine examination and school assignment. In view of the rich semantic information embedded in exercise which contains texts and images, it is hard to represent exercise and predict its difficulty. In this paper, we propose a novel Bayesian inference-based Exercise Difficulty Prediction (BEDP) framework to predict difficulty of visual-textual exercise. In BEDP, given exercises with texts, images and difficulty label, we first apply a pretrained Deep Multimodal Embedding model to obtain the unified multimodal representations of each exercise. Then we propose a Bayesian inference-based Softmax Regression classifier to predict the difficulty of exercise. Variational Inference (VI) is applied for approximately solve probabilistic model with a joint distribution of observations and latent variables. By experimenting on two self-collected dataset, we demonstrate the effectiveness of our proposed framework on predicting the difficulty of exercise, and conduct convergence diagnostics for Bayesian inference.",
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"content": "In Online Education System (OES), it is popular to assess examinee by providing exercises with different difficulty. So Exercise Difficulty Prediction (EDP) is a fundamental task of OES, such as medicine examination and school assignment. In view of the rich semantic information embedded in exercise which contains texts and images, it is hard to represent exercise and predict its difficulty. In this paper, we propose a novel Bayesian inference-based Exercise Difficulty Prediction (BEDP) framework to predict difficulty of visual-textual exercise. In BEDP, given exercises with texts, images and difficulty label, we first apply a pretrained Deep Multimodal Embedding model to obtain the unified multimodal representations of each exercise. Then we propose a Bayesian inference-based Softmax Regression classifier to predict the difficulty of exercise. Variational Inference (VI) is applied for approximately solve probabilistic model with a joint distribution of observations and latent variables. By experimenting on two self-collected dataset, we demonstrate the effectiveness of our proposed framework on predicting the difficulty of exercise, and conduct convergence diagnostics for Bayesian inference.",
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"abstract": "3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.",
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"content": "3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.",
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"affiliation": "Princeton University, USA",
"fullName": "Zhirong Wu",
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"affiliation": "Princeton University, USA",
"fullName": "Shuran Song",
"givenName": "Shuran",
"surname": "Song",
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"affiliation": "Massachusetts Institute of Technology, USA",
"fullName": "Aditya Khosla",
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"affiliation": "Princeton University, USA",
"fullName": "Fisher Yu",
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{
"affiliation": "Princeton University, USA",
"fullName": "Linguang Zhang",
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"affiliation": "Chinese University of Hong Kong, China",
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"affiliation": "Princeton University, USA",
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"abstract": "Most objects are composed of parts which have a semantic meaning. Ahandle can have many different shapes and can be present in quite different objects, but there is only one semantic meaning to a handle, which is \"a part that is designed especially to be grasped by the hand\". We introduce here a novel 3D algorithm named CPS for the decomposition of objects into their semantically meaningful parts. These meaningful parts are learned from experiments where robot grasps different objects. Objects are represented in compositional graph hierarchy where their parts are represented as the relationship between subparts, which are in turn represented based on the relationships between small adjacent regions. Unlike other compositional approaches, our method relies on learning semantically meaningful parts which are learned from grasping experience. This compositional part representation provides generalization for part segmentation. We evaluated our method in this respect, by training ion one dataset and evaluating it on another. We achieved on average78% part overlap accuracy for segmentation of novel part instances.",
"abstracts": [
{
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"content": "Most objects are composed of parts which have a semantic meaning. Ahandle can have many different shapes and can be present in quite different objects, but there is only one semantic meaning to a handle, which is \"a part that is designed especially to be grasped by the hand\". We introduce here a novel 3D algorithm named CPS for the decomposition of objects into their semantically meaningful parts. These meaningful parts are learned from experiments where robot grasps different objects. Objects are represented in compositional graph hierarchy where their parts are represented as the relationship between subparts, which are in turn represented based on the relationships between small adjacent regions. Unlike other compositional approaches, our method relies on learning semantically meaningful parts which are learned from grasping experience. This compositional part representation provides generalization for part segmentation. We evaluated our method in this respect, by training ion one dataset and evaluating it on another. We achieved on average78% part overlap accuracy for segmentation of novel part instances.",
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"normalizedAbstract": "Most objects are composed of parts which have a semantic meaning. Ahandle can have many different shapes and can be present in quite different objects, but there is only one semantic meaning to a handle, which is \"a part that is designed especially to be grasped by the hand\". We introduce here a novel 3D algorithm named CPS for the decomposition of objects into their semantically meaningful parts. These meaningful parts are learned from experiments where robot grasps different objects. Objects are represented in compositional graph hierarchy where their parts are represented as the relationship between subparts, which are in turn represented based on the relationships between small adjacent regions. Unlike other compositional approaches, our method relies on learning semantically meaningful parts which are learned from grasping experience. This compositional part representation provides generalization for part segmentation. We evaluated our method in this respect, by training ion one dataset and evaluating it on another. We achieved on average78% part overlap accuracy for segmentation of novel part instances.",
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"abstract": "In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations. On one hand, 3D tracking by detection has shown robust use in the context of interaction (Kinect) and surface tracking. On the other hand, volumetric representations have recently been proven efficient both for building 3D features and for addressing the 3D tracking problem. We leverage these benefits by unifying both families of approaches into a single, fully volumetric tracking-by-detection framework. We use a centroidal Voronoi tessellation (CVT) representation to compactly tessellate shapes with optimal discretization, construct a feature space, and perform the tracking according to the correspondences provided by trained random forests. Our results show improved tracking and training computational efficiency and improved memory performance. This in turn enables the use of larger training databases than state of the art approaches, which we leverage by proposing a cross-tracking subject training scheme to benefit from all subject sequences for all tracking situations, thus yielding better detection and less overfitting.",
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"content": "In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations. On one hand, 3D tracking by detection has shown robust use in the context of interaction (Kinect) and surface tracking. On the other hand, volumetric representations have recently been proven efficient both for building 3D features and for addressing the 3D tracking problem. We leverage these benefits by unifying both families of approaches into a single, fully volumetric tracking-by-detection framework. We use a centroidal Voronoi tessellation (CVT) representation to compactly tessellate shapes with optimal discretization, construct a feature space, and perform the tracking according to the correspondences provided by trained random forests. Our results show improved tracking and training computational efficiency and improved memory performance. This in turn enables the use of larger training databases than state of the art approaches, which we leverage by proposing a cross-tracking subject training scheme to benefit from all subject sequences for all tracking situations, thus yielding better detection and less overfitting.",
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"abstract": "Nonlinear deformations in fingerprint images, arising from the elasticity of the skin as well as the pressure and movement of the finger during image acquisition, lead to difficulties in establishing a match between multiple impressions acquired from the same finger. One solution to this problem is to estimate and remove the relative deformations prior to the matching stage. In this paper, these relative deformations are represented as an average deformation model based on minutiae locations and orientations using 2-D thin plate splines (TPS). The estimated average deformation is used to pre-distort a template prior to matching it with a query image in a verification task. Experimental results show that the use of minutiae locations and orientations to estimate the deformation leads to a more representative deformation model than using minutiae locations only. An index of deformation based on the bending energy is also proposed to select templates with the least variability in the deformations. The EER goes down by ap 1.1% when we incorporate minutiae orientation information and use the template selection strategy.",
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"abstract": "Study of ship deformation accumulation for a long time is of practical significance. According to the initial-boundary value problem of the deformation of the ship, large deviation theory gives the deformation time series convergence theorem, which deduces the convergence rate formula of discrete deformation. The convergence speed of the actual example is attached.",
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"abstract": "Abstract: In this paper, we present a new approach for the extraction of brain tumor boundary in a series of 2D MR image slices. The shape and position of tumor in one slice could be assumed to be similar to that in its neighboring slices. Using this correlation between consecutive images, the initial plan applied for each slice is extracted from the resulting boundary of the previous slice. The tumor boundary is located using region and contour deformation, which tolerates a rough initial plan. Therefore, only one coarse manual initial plan is required for the whole series of MR image slices. Performance of our approach is evaluated on MR image set. Comparisons with manual tracing show the accuracy and effectiveness of our approach.",
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"content": "Abstract: In this paper, we present a new approach for the extraction of brain tumor boundary in a series of 2D MR image slices. The shape and position of tumor in one slice could be assumed to be similar to that in its neighboring slices. Using this correlation between consecutive images, the initial plan applied for each slice is extracted from the resulting boundary of the previous slice. The tumor boundary is located using region and contour deformation, which tolerates a rough initial plan. Therefore, only one coarse manual initial plan is required for the whole series of MR image slices. Performance of our approach is evaluated on MR image set. Comparisons with manual tracing show the accuracy and effectiveness of our approach.",
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"affiliation": "The University of Hong Kong",
"fullName": "Albert K.W. Law",
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"affiliation": "The University of Hong Kong",
"fullName": "H. Zhu",
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"abstract": "Arbitrary shape text detection is a challenging task due to the high complexity and variety of scene texts. In this work, we propose a novel adaptive boundary proposal network for arbitrary shape text detection, which can learn to directly produce accurate boundary for arbitrary shape text without any post-processing. Our method mainly consists of a boundary proposal model and an innovative adaptive boundary deformation model. The boundary proposal model constructed by multi-layer dilated convolutions is adopted to produce prior information (including classification map, distance field, and direction field) and coarse boundary proposals. The adaptive boundary deformation model is an encoder-decoder network, in which the encoder mainly consists of a Graph Convolutional Network (GCN) and a Recurrent Neural Network (RNN). It aims to perform boundary deformation in an iterative way for obtaining text instance shape guided by prior information from the boundary proposal model. In this way, our method can directly and efficiently generate accurate text boundaries without complex post-processing. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. Code is available at the website: https://github.com/GXYM/TextBPN.",
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"abstract": "In order to solve the problem that the boundary of gingiva is wrinkled after deformation in orthodontic simulation, we designed a simulation method in a wrapping manner for virtual gingiva based on the traditional Mass-spring model. Firstly, the transition boundary point between teeth and gums was selected as the active point in the deformation process, and the position of the active point was determined by wrapping it on the tooth surface after the correction operation. Then, in order to solve the problem of missing attachment point when the position of the active point is determined, we added a segment of tooth root based on the method of tooth contour curve fitting. Finally, we used the sum of surface curvature values to compare the experimental results. The experimental results show that the proposed method can effectively alleviate the problem of boundary wrinkles after gingival deformation in the case of large correction range, and improve the authenticity of the virtual orthodontic system.",
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"content": "In order to solve the problem that the boundary of gingiva is wrinkled after deformation in orthodontic simulation, we designed a simulation method in a wrapping manner for virtual gingiva based on the traditional Mass-spring model. Firstly, the transition boundary point between teeth and gums was selected as the active point in the deformation process, and the position of the active point was determined by wrapping it on the tooth surface after the correction operation. Then, in order to solve the problem of missing attachment point when the position of the active point is determined, we added a segment of tooth root based on the method of tooth contour curve fitting. Finally, we used the sum of surface curvature values to compare the experimental results. The experimental results show that the proposed method can effectively alleviate the problem of boundary wrinkles after gingival deformation in the case of large correction range, and improve the authenticity of the virtual orthodontic system.",
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"abstract": "In text mining, Latent Semantic Analysis (LSA) is the popular method to reduce the dimension of document vectors. Since LSA produces a set of topics by statistical information, the meaning of each topic is not clear.We proposed a method to reduce the dimension by clustering the words in the documents. This method produces a set of clusters of words instead of topics. Using Word2vec to vectorize the words, the mean vector of the cluster is calculated, which shows the meaning of the cluster.In this paper, we show the dimensionality reduction and the meaning of the generated clusters by word cloud, on document classification problem with a subset of BBC Dataset.",
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"affiliation": "National Institute of Technology (KOSEN),Dept. of Electrical and Computer Engineering,Gifu College Gifu,Japan",
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"abstract": "The fluid scheduling model allows for schedules in which an individual task may be assigned a fraction of a processor at each time instant. These assignments are subject to the constraints that no fraction exceeds one and the sum of all the assigned fractions do not exceed the sum of the computing capacities of all the processors at any instant. An algorithm, MC-Fluid, has recently been proposed for scheduling systems of mixed-criticality implicit-deadline sporadic tasks under the fluid scheduling model. MC-Fluid has been shown to have a speedup bound no worse than (1 + √5)/2 or ≈ 1.618 for scheduling dual-criticality systems. We derive here a simplified variant of MC-Fluid called MCF, that has run-time linear in the number of tasks. We prove that this simplified variant has a speedup bound no worse than 4/3 for dual-criticality systems, and show that this implies that MC-Fluid, too, has a speedup bound no worse than 4/3. We know from prior results in uniprocessor mixed-criticality scheduling that no algorithm may have a speedup bound smaller than 4/3, allowing us to conclude that MCF and MC-Fluid are in fact speedup-optimal for dual-criticality scheduling.",
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"abstract": "Generating 3D digital representations of plants is indispensable for researchers to gain a detailed understanding of plant dynamics. Emerging high-throughput plant phenotyping techniques can capture plant point clouds that, however, often contain imperfections and make it a changeling task to generate accurate 3D reconstructions. We present an end-to-end pipeline to reconstruct surfaces from point clouds of maize and rice plants. In particular, we propose a two-step clustering approach to accurately segment the points of each individual plant component according to maize and rice properties. We further employ surface fitting and edge fitting to ensure the smoothness of resulting surfaces. Realistic visualization results are obtained through post-processing, including texturing and lighting. Our experimental study has explored the parameter space and demonstrated the effectiveness of our pipeline for high-throughput plant phenotyping.",
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"affiliation": "Department of Computer Science and Engineering",
"fullName": "Feiyu Zhu",
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"affiliation": "Department of Biological Systems Engineering",
"fullName": "Suresh Thapa",
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"affiliation": "Department of Computer Science and Engineering",
"fullName": "Tiao Gao",
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"affiliation": "Department of Biological Systems Engineering",
"fullName": "Yufeng Ge",
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"affiliation": "Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA",
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"abstract": "Plant phenotyping is the task of measuring plant attributes. We term 'length phenotyping' the task of measuring the length of a part of interest of a plant. The recent rise of low cost RGB-D sensors, and accurate deep networks, provides new opportunities for length phenotyping. In this paper we present a general technique for measuring length, based on three stages: object detection, point of interest identification, and a 3D measurement phase. We address object detection and interest point identification by training network models for each task, and use robust de-projection for the 3D measurement stage. We apply our method to two real world tasks: measuring the height of a banana tree, and measuring the length, width, and aspect ratio of banana leaves in potted plants. Our results indicate satisfactory measurement accuracy, with less than 10% deviation in all measurements. The two tasks were solved using the same pipeline with minor adaptations, indicating the general potential of the method.",
"abstracts": [
{
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"content": "Plant phenotyping is the task of measuring plant attributes. We term 'length phenotyping' the task of measuring the length of a part of interest of a plant. The recent rise of low cost RGB-D sensors, and accurate deep networks, provides new opportunities for length phenotyping. In this paper we present a general technique for measuring length, based on three stages: object detection, point of interest identification, and a 3D measurement phase. We address object detection and interest point identification by training network models for each task, and use robust de-projection for the 3D measurement stage. We apply our method to two real world tasks: measuring the height of a banana tree, and measuring the length, width, and aspect ratio of banana leaves in potted plants. Our results indicate satisfactory measurement accuracy, with less than 10% deviation in all measurements. The two tasks were solved using the same pipeline with minor adaptations, indicating the general potential of the method.",
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"normalizedAbstract": "Plant phenotyping is the task of measuring plant attributes. We term 'length phenotyping' the task of measuring the length of a part of interest of a plant. The recent rise of low cost RGB-D sensors, and accurate deep networks, provides new opportunities for length phenotyping. In this paper we present a general technique for measuring length, based on three stages: object detection, point of interest identification, and a 3D measurement phase. We address object detection and interest point identification by training network models for each task, and use robust de-projection for the 3D measurement stage. We apply our method to two real world tasks: measuring the height of a banana tree, and measuring the length, width, and aspect ratio of banana leaves in potted plants. Our results indicate satisfactory measurement accuracy, with less than 10% deviation in all measurements. The two tasks were solved using the same pipeline with minor adaptations, indicating the general potential of the method.",
"fno": "250600c609",
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"Image Sensors",
"Neural Nets",
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"authors": [
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"affiliation": "Ben Gurion University, Israel",
"fullName": "Adar Vit",
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{
"affiliation": "Ben Gurion University, Israel",
"fullName": "Guy Shani",
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{
"affiliation": "Ben Gurion University, Israel",
"fullName": "Aharon Bar-Hillel",
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"title": "2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE)",
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"title": "Numerical Simulation Method for Three-Dimensional Flow Field of Multistage Axial Flow Compressor",
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"abstract": "Numerical simulation method (NSM) as an important technique of computational inside fluid machinery complex blade tip clearance leakage flow has been proposed. Clearance leakage and end wall boundary layer and the blade boundary layer interaction makes the tip clearance flow in showing 3D vortex characteristics due to the clearance leakage. Although the geometric size of blade tip clearance compared with the whole flow passage is very small, but it is on the blade passage nearly 20% regional flows, the tip leakage flow will result in the reduction of the tip near the work efficiency, resulting in channel blockage and local losses increase, therefore the correct prediction of the tip leakage flow phenomenon has important significance on further improve the performance of axial flow compressor rotor and expand the stable working range of the rotor.",
"abstracts": [
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"content": "Numerical simulation method (NSM) as an important technique of computational inside fluid machinery complex blade tip clearance leakage flow has been proposed. Clearance leakage and end wall boundary layer and the blade boundary layer interaction makes the tip clearance flow in showing 3D vortex characteristics due to the clearance leakage. Although the geometric size of blade tip clearance compared with the whole flow passage is very small, but it is on the blade passage nearly 20% regional flows, the tip leakage flow will result in the reduction of the tip near the work efficiency, resulting in channel blockage and local losses increase, therefore the correct prediction of the tip leakage flow phenomenon has important significance on further improve the performance of axial flow compressor rotor and expand the stable working range of the rotor.",
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"fno": "2628a220",
"keywords": [
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"Channel Flow",
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"title": "Analysis of vibration characteristics of the centrifugal pump base on CFD",
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"abstract": "Marine centrifugal pump is one of the important factors that cause ship vibration, so the research on centrifugal pump vibration is particularly important. In this paper, in order to study the flow-induced vibration characteristics of centrifugal pump, a virtual prototype model of the centrifugal pump is established in finite element method. The theory of Computational fluid dynamics (CFD) is utilized to analyze the inner flow field of the centrifugal pump, and steady-state calculation and transient calculation of the flow field are completed. The fluid forces acquired from the transient calculation results of flow field are used as the excitation loads for the virtual prototype model of the centrifugal pump. The fluid-solid coupling model of the centrifugal pump is built, and the vibration acceleration of the centrifugal pump's foot is calculated, and the flow-induced vibration characteristics of the centrifugal pump are obtained. The data of the centrifugal pump measured in the experiment are consistent with the simulation results. This study provide reference for the fluid-solid coupling method in centrifugal pump fluid field prediction and flow-induced vibration research.",
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"content": "Marine centrifugal pump is one of the important factors that cause ship vibration, so the research on centrifugal pump vibration is particularly important. In this paper, in order to study the flow-induced vibration characteristics of centrifugal pump, a virtual prototype model of the centrifugal pump is established in finite element method. The theory of Computational fluid dynamics (CFD) is utilized to analyze the inner flow field of the centrifugal pump, and steady-state calculation and transient calculation of the flow field are completed. The fluid forces acquired from the transient calculation results of flow field are used as the excitation loads for the virtual prototype model of the centrifugal pump. The fluid-solid coupling model of the centrifugal pump is built, and the vibration acceleration of the centrifugal pump's foot is calculated, and the flow-induced vibration characteristics of the centrifugal pump are obtained. The data of the centrifugal pump measured in the experiment are consistent with the simulation results. This study provide reference for the fluid-solid coupling method in centrifugal pump fluid field prediction and flow-induced vibration research.",
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"abstract": "We introduce global regularities in the 2.5D building modeling problem, to reflect the orientation and placement similarities between planar elements in building structures. Given a 2.5D point cloud scan, we present an automatic approach that simultaneously detects locally fitted plane primitives and global regularities. While global regularities are extracted by analyzing the plane primitives, they adjust the planes in return and effectively correct local fitting errors. We explore a broad variety of global regularities between 2.5D planar elements including both planer roof patches and planar facade patches. By aligning planar elements to global regularities, our method significantly improves the model quality in terms of both geometry and human judgement.",
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"content": "In many clinical applications, interactive exploration of three dimension medical data sets is required, but the huge amount of computational time and storage space needed for rendering do not allow the visualization of large medical data sets by now. In this paper we present a new algorithm for rendering large medical data sets on standard PC. The key technique is a new representation method of large medical dataset based on an efficient octree data structure. First, the input data is converted into a compressed hierarchical octree representation. Then, a template-based raycasting scheme using shear-warp factorization for expediting parallel viewing of volume datasets is employed. The algorithm exploits the uniform shape, orientation and size of the “nonempty” octree blocks by building templates for ray/block intersections in the case of raycasting. Experiment results display that high quality human body interior images of a large dataset can be generated by the proposed approach on a standard PC platform fast.",
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"abstract": "We have developed a programming template to implement divide and conquer algorithms on MIMD computers. The template is based on the parallel divide and conquer function of Z.G. Mou and P. Hudak (1988). We explore the programmability and performance of this approach by solving some well known numerical problems on a shared memory multiprocessor and a multicomputer. A byproduct of this work is a new parallel algorithm for solving tridiagonal systems of equations.",
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"abstract": "A simple formal model of distributed mutual exclusion algorithms is introduced and a new class of algorithms is derived using the divide and conquer paradigm. A number of metrics reflecting certain realistic concerns about the distributed mutual exclusion algorithms are proposed and several relationships among them are identified. It is shown that the divide and conquer paradigm significantly simplifies the derivation of algorithms with desired values for these metrics. The new class of algorithms is compared to the voting algorithms.",
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"abstract": "In this paper, the index space of the (n /spl times/ n)-matrix multiply-add problem C = C + A/spl middot/B is represented as a 3D n /spl times/ n /spl times/ n torus. All possible time-scheduling functions to activate the computation and data rolling inside the 3D torus index space are determined. To maximize efficiency when solving a single problem, we mapped the computations into the 2D n /spl times/ n toroidal array processor. All optimal 2D data allocations that solve the problem in n multiply-add-roll steps are obtained. The well known Cannon's algorithm is one of the resulting allocations. We used the optimal data allocations to describe all variants of the GEMM operation on the 2D toroidal array processor. By controling the data movement, the transposition operation is avoided in 75% of the GEMM variants. However, only one matrix transpose is needed for the remaining 25%. Ultimately, we described four versions of the GEMM operation covering the possible layouts of the initially loaded data into the array processor.",
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"content": "For the registration of large image distortion of some special places, the non-rigid registration is more accurate and necessary compared with the rigid registration. In this paper, a kind of non-rigid medical image registration algorithm based on the thin-plate spline is presented and discussed in detail. The hybrid energy formula is presented. In the process of registration, the thin-plate spline algorithm is proposed to deal with the whole image registration. In order to reduce and locate the registration region, the iterativeness of simulated annealing algorithm is proposed. In the local pending registration area, mutual information method is used and the local distortion is adjusted to resolve the incomplete match caused by the lack of characteristic points, which do help to get smoother image and better effect of non-rigid image registration. The dynamic outlier removal method is used to deal with outlier and make sure the accurate transformation function. In addition, some empirical data was modified for the purpose of getting better effect. It was proved by the experiments that the non-rigid medical image registration made a better registration effect for the large image distortion.",
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"title": "Influence of Multiple Cutouts on the Buckling of Large-Scale Thin-Walled Cylindrical Shells of Desulphurizing Tower under Wind Loading",
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"abstract": "The desulphurizing tower used in the power plant is the key environmental protection facility to eliminate the air pollution. Multiple cutouts are opened on the tower wall because of the technology requirement. The presence of multiple cutouts will influence the stability of tower under wind loading. On the project background of the large-scale thin-walled cylindrical shells of a practical desulphurizing tower, the investigations are conducted into the buckling mode and buckling capacity of the large-scale thin-walled cylindrical shells with multiple circular cutouts under wind loading by nonlinear finite element methods. The influences a reinvestigated of the cutout position and the number of cutouts on the buckling behavior of cylindrical shells under wind loading. The comparison is conducted between the buckling response of the cylinder with a single cutout and that of the cylinder with multiple cutouts. On the basis of comparison results, the proposal is produced for the buckling check against wind loading of large-scale thin-walled cylindrical shells structure as desulphurizing tower.",
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"content": "The desulphurizing tower used in the power plant is the key environmental protection facility to eliminate the air pollution. Multiple cutouts are opened on the tower wall because of the technology requirement. The presence of multiple cutouts will influence the stability of tower under wind loading. On the project background of the large-scale thin-walled cylindrical shells of a practical desulphurizing tower, the investigations are conducted into the buckling mode and buckling capacity of the large-scale thin-walled cylindrical shells with multiple circular cutouts under wind loading by nonlinear finite element methods. The influences a reinvestigated of the cutout position and the number of cutouts on the buckling behavior of cylindrical shells under wind loading. The comparison is conducted between the buckling response of the cylinder with a single cutout and that of the cylinder with multiple cutouts. On the basis of comparison results, the proposal is produced for the buckling check against wind loading of large-scale thin-walled cylindrical shells structure as desulphurizing tower.",
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"affiliation": null,
"fullName": "Dengfeng Wang",
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"fullName": "Yongjiu Shi",
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"title": "2011 IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises",
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"title": "A Semi-Analytical Approach for the Contact Dynamics of Ancient Columns Modeled as Rigid Blocks",
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"abstract": "The problem of conservation and protection of architectural heritage against dynamic inputs such as the case of earthquakes has represented for years an important task in the field of structural engineering with the aim of describing and understanding the behaviour of monumental structures. This work deals with monumental structures made of large and heavy stone blocks, simply supported to each other, a typical technology used to build the Greek temples. The plane dynamics of a rigid rectangular block simply supported on a horizontally moving ground, with friction and unilateral constraints at ground contact, is modeled by means of a new semi-analytical approach for non-smooth contact dynamics recently developed by one of the authors [1]. The dynamics - formulated as a contact problem governed by Signorini's and Coulomb's laws - is converted into an equivalent problem of static balance by making use of specific projective techniques in the configurations space. The procedure is then applied to a numerical model, so that a code can be implemented where the dynamics of the block is fully modeled according to the various possible mechanisms considering both phases of smooth and non-smooth contact dynamics, moreover normal and tangential contact reactions during smooth dynamics, and corresponding impulses during non-smooth, are estimated at each step. Either extended or single point contacts are then possible with consequent sliding, rocking or sliding-rocking modes, so as the possibility of impacts and also of free dynamics when contact is lost. Analyses are conduced and then compared to corresponding cases previously treated in literature, in order to validate the proposed numerical method for the rocking mode. Moreover, dynamic evolution of the block is represented graphically in order to observe different configurations at each instant of time. The goal is to understand the dynamic behaviour of the block under several input intensities in order to prevent excessive sliding or overturning and to localize with sufficient precision the position of the block after the earthquake.",
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"title": "2013 IEEE Applied Imagery Pattern Recognition Workshop: Sensing for Control and Augmentation (AIPR 2013)",
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"doi": "10.1109/AIPR.2013.6749307",
"title": "Stressed vegetation identification by SAR time series as an indicator of slope instability in Mississippi River levee segments",
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"abstract": "Surface vegetation reflects various characteristics of the soil on which it grows. Vegetation type and growth rate differences were observed between recently cracked surfaces and stable soil on earthen levees along the lower Mississippi River. We attempted to directly characterize the levee surface beneath the vegetation cover using X-band synthetic aperture radar from TerraSAR-X. Due to its short wavelength, however, most of the backscatter is from the vegetation rather than the soil. Hence a time-series of the SAR imagery was made over the time when vegetation growth and biomass were at their lowest and the multi-temporal radar backscatter pattern was used to identify healthy and stressed vegetation growing over stable and unstable (subject to slump slides) levee segments. Field data showed that vegetation was most vigorous over healthy or repaired levee surfaces and poorest over areas with surface cracks. Three time series of the HH and VV and HH-VV images were made for 6 dates over a 7 month period. Correlation with field data and analysis revealed that imagery from October through February was most effective in identifying the target vegetation differences. In this time span the differences in vigor of the vegetation were greatest between healthy levee surfaces and those with cracks on them (which could lead to potential levee failures). The 3 time series were classified independently using field derived training polygons and the stressed vegetation class was extracted from it. More than 90% of the known slump slides were identified by the classification but a significant number of `false positives' resulted.",
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"content": "Surface vegetation reflects various characteristics of the soil on which it grows. Vegetation type and growth rate differences were observed between recently cracked surfaces and stable soil on earthen levees along the lower Mississippi River. We attempted to directly characterize the levee surface beneath the vegetation cover using X-band synthetic aperture radar from TerraSAR-X. Due to its short wavelength, however, most of the backscatter is from the vegetation rather than the soil. Hence a time-series of the SAR imagery was made over the time when vegetation growth and biomass were at their lowest and the multi-temporal radar backscatter pattern was used to identify healthy and stressed vegetation growing over stable and unstable (subject to slump slides) levee segments. Field data showed that vegetation was most vigorous over healthy or repaired levee surfaces and poorest over areas with surface cracks. Three time series of the HH and VV and HH-VV images were made for 6 dates over a 7 month period. Correlation with field data and analysis revealed that imagery from October through February was most effective in identifying the target vegetation differences. In this time span the differences in vigor of the vegetation were greatest between healthy levee surfaces and those with cracks on them (which could lead to potential levee failures). The 3 time series were classified independently using field derived training polygons and the stressed vegetation class was extracted from it. More than 90% of the known slump slides were identified by the classification but a significant number of `false positives' resulted.",
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"normalizedAbstract": "Surface vegetation reflects various characteristics of the soil on which it grows. Vegetation type and growth rate differences were observed between recently cracked surfaces and stable soil on earthen levees along the lower Mississippi River. We attempted to directly characterize the levee surface beneath the vegetation cover using X-band synthetic aperture radar from TerraSAR-X. Due to its short wavelength, however, most of the backscatter is from the vegetation rather than the soil. Hence a time-series of the SAR imagery was made over the time when vegetation growth and biomass were at their lowest and the multi-temporal radar backscatter pattern was used to identify healthy and stressed vegetation growing over stable and unstable (subject to slump slides) levee segments. Field data showed that vegetation was most vigorous over healthy or repaired levee surfaces and poorest over areas with surface cracks. Three time series of the HH and VV and HH-VV images were made for 6 dates over a 7 month period. Correlation with field data and analysis revealed that imagery from October through February was most effective in identifying the target vegetation differences. In this time span the differences in vigor of the vegetation were greatest between healthy levee surfaces and those with cracks on them (which could lead to potential levee failures). The 3 time series were classified independently using field derived training polygons and the stressed vegetation class was extracted from it. More than 90% of the known slump slides were identified by the classification but a significant number of `false positives' resulted.",
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"Vegetation Mapping",
"Soil",
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"affiliation": "Geosystems Res. Inst., Mississippi State Univ., Starkville, MS, USA",
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"affiliation": "Geosystems Res. Inst., Mississippi State Univ., Starkville, MS, USA",
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"affiliation": "Geosystems Res. Inst., Mississippi State Univ., Starkville, MS, USA",
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"proceeding": {
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"title": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
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"abstract": "Tasks coupled in an in situ workflow may not process data at the same speed, potentially causing overflows in the communication channel between them. To prevent this problem, software infrastructures for in situ workflows usually impose a strict FIFO policy that has the side-effect of slowing down faster tasks to the speed of the slower ones. This may not be the desired behavior; for example, a scientist may prefer to drop older data in the communication channel in order to visualize the latest snapshot of a simulation. In this paper, we present Manala, a flexible flow control library designed to manage the flow of messages between a producer and a consumer in an in situ workflow. Manala intercepts messages from the producer, stores them, and selects the message to forward to the consumer depending on the flow control policy. The library is designed to ease the creation of new flow control policies and buffering mechanisms. We demonstrate with three examples how changing the flow control policy between tasks can influence the performance and results of scientific workflows. The first example focuses on materials science with LAMMPS and a synthetic diffraction analysis code. The second example is an interactive visualization scenario with Gromacs as the producer and Damaris/Viz as consumer. Our third example studies different strategies to perform an asynchronous checkpoint with Gromacs.",
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"content": "Tasks coupled in an in situ workflow may not process data at the same speed, potentially causing overflows in the communication channel between them. To prevent this problem, software infrastructures for in situ workflows usually impose a strict FIFO policy that has the side-effect of slowing down faster tasks to the speed of the slower ones. This may not be the desired behavior; for example, a scientist may prefer to drop older data in the communication channel in order to visualize the latest snapshot of a simulation. In this paper, we present Manala, a flexible flow control library designed to manage the flow of messages between a producer and a consumer in an in situ workflow. Manala intercepts messages from the producer, stores them, and selects the message to forward to the consumer depending on the flow control policy. The library is designed to ease the creation of new flow control policies and buffering mechanisms. We demonstrate with three examples how changing the flow control policy between tasks can influence the performance and results of scientific workflows. The first example focuses on materials science with LAMMPS and a synthetic diffraction analysis code. The second example is an interactive visualization scenario with Gromacs as the producer and Damaris/Viz as consumer. Our third example studies different strategies to perform an asynchronous checkpoint with Gromacs.",
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"Libraries",
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"fullName": "Matthieu Dreher",
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"abstract": "Computer security has become a central focus in the information age. Though enormous effort has been expended on ensuring secure computation, software exploitation remains a serious threat. The software attack surface provides many avenues for hijacking; however, most exploits ultimately rely on the successful execution of a control-flow attack. This pervasive diversion of control flow is made possible by the pollution of control flow structure with attacker-injected runtime data. Many control-flow attacks persist because the root of the problem remains: runtime data is allowed to enter the program counter. In this paper, we propose a novel approach: Control-Data Isolation. Our approach provides protection by going to the root of the problem and removing all of the operations that inject runtime data into program control. While previous work relies on CFG edge checking and labeling, these techniques remain vulnerable to attacks such as heap spray, read, or GOT attacks and in some cases suffer high overheads. Rather than addressing control-flow attacks by layering additional complexity, our work takes a subtractive approach; subtracting the primary cause of contemporary control-flow attacks. We demonstrate that control-data isolation can assure the integrity of the programmer's CFG at runtime, while incurring average performance overheads of less than 7% for a wide range of benchmarks.",
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"content": "Computer security has become a central focus in the information age. Though enormous effort has been expended on ensuring secure computation, software exploitation remains a serious threat. The software attack surface provides many avenues for hijacking; however, most exploits ultimately rely on the successful execution of a control-flow attack. This pervasive diversion of control flow is made possible by the pollution of control flow structure with attacker-injected runtime data. Many control-flow attacks persist because the root of the problem remains: runtime data is allowed to enter the program counter. In this paper, we propose a novel approach: Control-Data Isolation. Our approach provides protection by going to the root of the problem and removing all of the operations that inject runtime data into program control. While previous work relies on CFG edge checking and labeling, these techniques remain vulnerable to attacks such as heap spray, read, or GOT attacks and in some cases suffer high overheads. Rather than addressing control-flow attacks by layering additional complexity, our work takes a subtractive approach; subtracting the primary cause of contemporary control-flow attacks. We demonstrate that control-data isolation can assure the integrity of the programmer's CFG at runtime, while incurring average performance overheads of less than 7% for a wide range of benchmarks.",
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"affiliation": "University of Michigan",
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"affiliation": "University of California - Berkeley",
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"affiliation": "University of Michigan",
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"affiliation": "University of Michigan",
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"title": "Detection of slump slides on earthen levees using polarimetric SAR imagery",
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"abstract": "Key results are presented of an extensive project studying the use of synthetic aperture radar (SAR) as an aid to the levee screening process. SAR sensors used are: (1) The NASA UAVSAR (Uninhabited Aerial Vehicle SAR), a fully polarimetric L-band SAR capable of sub-meter ground sample distance; and (2) The German TerraSAR-X radar satellite, also multi-polarized and featuring 1-meter GSD, but using an X-band carrier. The study area is a stretch of 230 km of levees along the lower Mississippi River. The L-band measurements can penetrate vegetation and soil somewhat, thus carrying some information on soil texture and moisture which are relevant features to identifying levee vulnerability to slump slides. While X-band does not penetrate as much, its ready availability via satellite makes multitemporal algorithms practical. Various feature types and classification algorithms were applied to the polarimetry data in the project; this paper reports the results of using the Support Vector Machine (SVM) and back-propagation Artificial Neural Network (ANN) classifiers with a combination of the polarimetric backscatter magnitudes and texture features based on the wavelet transform. Ground reference data used to assess classifier performance is based on soil moisture measurements, soil sample tests, and on site visual inspections.",
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"abstract": "This paper explores anomaly detection algorithms to detect vulnerabilities on Mississippi river levees using remotely sensed Synthetic Aperture Radar (SAR) data. Earthen levees protect large areas of populated and cultivated land in the United States. One sign of potential levee failure is the occurrence of landslides due to slope instabilities. Such slides could lead to further erosion and through seepage during high water events. This research seeks to design a system that is capable of performing automated target recognition tasks using radar data to detect problem areas on earthen levees. Polarimetric SAR data is effective for detecting such phenomena. In this research, we analyze the ability of different polarization channels in detecting landslides with different frequency bands of synthetic aperture radar data using anomaly detection algorithms. The two SAR datasets used in this study are: (1) the X-band satellite-based radar data from DLR's TerraSAR-X satellite, and (2) the L-band airborne radar data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The RX anomaly detector, an unsupervised classification algorithm, was implemented to detect anomalies on the levee. The discrete wavelet transform (DWT) is used for feature extraction. The algorithm was tested with both the L-band and X-band SAR data and the results demonstrate that landslide detection using L-band radar data has better accuracy compared to the X-band data based on the detection of true positives.",
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