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"abstract": "Illumination is rarely constant in intensity or color throughout a scene. Multiple light sources with different spectra-sun and sky, direct and interreflected light-are the norm. Nonetheless, almost all color constancy algorithms assume that the spectrum of the incident illumination remains constant across the scene. We assume the converse, that illumination does vary, in developing a new algorithm for color constancy. Rather than creating difficulties, varying illumination is in fact a very powerful constraint. Indeed tests of our algorithm using real images of an office scene show excellent results.",
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"title": "Cosmology on the Blue Waters Early Science System",
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"abstract": "The submitted visualization represents work performed by the Enzo PRAC team lead by Brian O'Shea on the Blue Waters Early Science system. A relatively small test calculation was performed followed by several much larger AMR cosmological runs. The overall scientific goal was to understand how galaxies in the early Universe (the first billion years or so after the Big Bang) grow and evolve, in several statistically-dissimilar environments. Specifically, we looked at a region that is statistically over dense (substantially more galaxies than the average per volume), under dense (the opposite), of average mean density, and, finally, a region that will make a Milky Way-like galaxy at the present day. For each calculation, we used two separate formulations for our sub grid models of star formation and feedback. The simulation visualized in these movies represents the \"average\" calculations, which are the most statistically comparable to galaxies that have been observed with the Hubble Space Telescope and large ground-based telescopes. The visualization were done using the VisIt volume renderer. The analysis was completely performed on the Blue Waters Early science system. Volume renderings of density and temperature are presented. Each of these simulations was substantially larger than any previous Enzo AMR calculation ever run (as well as larger than any other AMR cosmological simulation ever done). By the end of the run, the calculations have several billion unique resolution elements on six levels of adaptive mesh.",
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"content": "A novel interactive virtual needle insertion simulation is presented. Simulation models are based upon planar tissue deformations and forces measured during needle insertion experiments. Users are able to control a three-degree-of-freedom virtual needle as it penetrates a linear elastostatic tissue model, while experiencing steering torques and lateral needle forces through a planar haptic interface. Efficient numerical computation techniques permit fast simulation of relatively complex two-dimensional and three-dimensional environments at haptic control rates.",
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"abstract": "Surgical procedures requiring needle navigation assistance suffer from complicated hand-eye coordination and are mentally demanding. Augmented reality (AR) can help overcome these issues. How-ever, only an insufficient amount of fundamental research has focused on the design and hardware selection of such AR needle navigation systems. This work contributes to this research area by presenting a user study (n=24) comparing three state-of-the-art navigation concepts displayed by an optical see-through head-mounted display and a stereoscopic projection system. A two-dimensional glyph visualization resulted in higher targeting accuracy but required more needle insertion time. In contrast, punctures guided by a three-dimensional see-through vision concept were less accurate but faster and were favored in a qualitative interview. The third concept, a static representation of the correctly positioned needle, showed too high target errors for clinical accuracy needs. This concept per-formed worse when displayed by the projection system. Besides that, no meaningful differences between the evaluated AR display devices were detected. User preferences and use case restrictions, e.g., sterility requirements, seem to be more crucial selection criteria. Future work should focus on improving the accuracy of the see-through vision concept. Until then, the glyph visualization is recommended.",
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"content": "Surgical procedures requiring needle navigation assistance suffer from complicated hand-eye coordination and are mentally demanding. Augmented reality (AR) can help overcome these issues. How-ever, only an insufficient amount of fundamental research has focused on the design and hardware selection of such AR needle navigation systems. This work contributes to this research area by presenting a user study (n=24) comparing three state-of-the-art navigation concepts displayed by an optical see-through head-mounted display and a stereoscopic projection system. A two-dimensional glyph visualization resulted in higher targeting accuracy but required more needle insertion time. In contrast, punctures guided by a three-dimensional see-through vision concept were less accurate but faster and were favored in a qualitative interview. The third concept, a static representation of the correctly positioned needle, showed too high target errors for clinical accuracy needs. This concept per-formed worse when displayed by the projection system. Besides that, no meaningful differences between the evaluated AR display devices were detected. User preferences and use case restrictions, e.g., sterility requirements, seem to be more crucial selection criteria. Future work should focus on improving the accuracy of the see-through vision concept. Until then, the glyph visualization is recommended.",
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"abstract": "Model compression is considered to be an effective way to reduce the implementation cost of deep neural networks (DNNs) while maintaining the inference accuracy. Many recent studies have developed efficient model compression algorithms and implementations in accelerators on various devices. Protecting integrity of DNN inference against fault attacks is important for diverse deep learning enabled applications. However, there has been little research investigating the fault resilience of DNNs and the impact of model compression on fault tolerance. In this work, we consider faults on different data types and develop a simulation framework for understanding the fault resiliency of compressed DNN models as compared to uncompressed models. We perform our experiments on two common DNNs, LeNet-5 and VGG16, and evaluate their fault resiliency with different types of compression. The results show that binary quantization can effectively increase the fault resilience of DNN models by 10000x for both LeNet5 and VGG16. Finally, we propose software and hardware mitigation techniques to increase the fault resiliency of DNN models.",
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"content": "Model compression is considered to be an effective way to reduce the implementation cost of deep neural networks (DNNs) while maintaining the inference accuracy. Many recent studies have developed efficient model compression algorithms and implementations in accelerators on various devices. Protecting integrity of DNN inference against fault attacks is important for diverse deep learning enabled applications. However, there has been little research investigating the fault resilience of DNNs and the impact of model compression on fault tolerance. In this work, we consider faults on different data types and develop a simulation framework for understanding the fault resiliency of compressed DNN models as compared to uncompressed models. We perform our experiments on two common DNNs, LeNet-5 and VGG16, and evaluate their fault resiliency with different types of compression. The results show that binary quantization can effectively increase the fault resilience of DNN models by 10000x for both LeNet5 and VGG16. Finally, we propose software and hardware mitigation techniques to increase the fault resiliency of DNN models.",
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"abstract": "Information Retrieval (IR) methods are commonly based on words, these methods allow the user to formulate a query through keywords. However, there are situations where the user has only one example document and based on this example it is needed to recover the most similar documents in a collection. This paper proposes an IR method that receives as input a query document and retrieves the k most similar documents to the query document using a representation based on Maximal Frequent Sequences (MFSs). Our method is tested and compared against the IR model based on bag of words, the experimental results show that the proposed method obtains good performance in contrast to the results obtained by the IR model based on bag of words.",
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"content": "Information Retrieval (IR) methods are commonly based on words, these methods allow the user to formulate a query through keywords. However, there are situations where the user has only one example document and based on this example it is needed to recover the most similar documents in a collection. This paper proposes an IR method that receives as input a query document and retrieves the k most similar documents to the query document using a representation based on Maximal Frequent Sequences (MFSs). Our method is tested and compared against the IR model based on bag of words, the experimental results show that the proposed method obtains good performance in contrast to the results obtained by the IR model based on bag of words.",
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"abstract": "DDI is an important part of drug-related research and pharmacovigilance. Extracting DDI information from scientific literature has become a low-cost and highly reliable way. Currently, existing works are all sentence-level DDI relation extraction. In fact, the entity relationship is often expressed by multiple sentences. Moreover, the sentence-level DDI relation extraction also causes a large amount of redundancy in the whole dataset with increasing in negative instance data. In this study, we propose a document-level DDI relation extraction method based on document-entity embedding. Our method performs special processing on the DDI Extraction 2013 for the first time, in order to calculate document-level relation extraction. For obtaining document-level entity information, we propose a document-entity embedding method to integrate the information of all same drugs in the same article. The experimental results show that the processing of DDI Extraction 2013 dataset is reasonable. In addition, the proposed method has achieved good performance on document-level DDI dataset, and the best F1 score is 62.51%. This is the first time that DDI Extraction 2013 has been processed into a document-level dataset, and document-level DDI relation extraction has been realized.",
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"content": "DDI is an important part of drug-related research and pharmacovigilance. Extracting DDI information from scientific literature has become a low-cost and highly reliable way. Currently, existing works are all sentence-level DDI relation extraction. In fact, the entity relationship is often expressed by multiple sentences. Moreover, the sentence-level DDI relation extraction also causes a large amount of redundancy in the whole dataset with increasing in negative instance data. In this study, we propose a document-level DDI relation extraction method based on document-entity embedding. Our method performs special processing on the DDI Extraction 2013 for the first time, in order to calculate document-level relation extraction. For obtaining document-level entity information, we propose a document-entity embedding method to integrate the information of all same drugs in the same article. The experimental results show that the processing of DDI Extraction 2013 dataset is reasonable. In addition, the proposed method has achieved good performance on document-level DDI dataset, and the best F1 score is 62.51%. This is the first time that DDI Extraction 2013 has been processed into a document-level dataset, and document-level DDI relation extraction has been realized.",
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"abstract": "With the increasing popularity of digital documents in the information era, large scale document-term matching is critical for document similarity tasks. However, the traditional automatic term matching method is mainly from the perspective of classification, which ignores the macroscopic and ambiguous characteristics of domain terms. This paper proposes a document-term matching method based on information retrieval (IR-DTM). We utilized an automatic data collection method, which can effectively alleviate the problem of insufficient data and greatly reduce the manual workload. Then, we realized semantic matching between macro terms and micro documents by extending the words in terms, thereby effectively improving the accuracy of document-term matching. Experimental results show that our method can perform effective automatic matching of domain terms and case documents. The effectiveness of IR-DTM is evaluated by R@1, R@5, and R@10. The R@10 percentage points on PolProv1.0, EcoProv1.0, and TechProv1.0 datasets can reach 84.6%, 92.5% and 93.2% respectively.",
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"content": "With the increasing popularity of digital documents in the information era, large scale document-term matching is critical for document similarity tasks. However, the traditional automatic term matching method is mainly from the perspective of classification, which ignores the macroscopic and ambiguous characteristics of domain terms. This paper proposes a document-term matching method based on information retrieval (IR-DTM). We utilized an automatic data collection method, which can effectively alleviate the problem of insufficient data and greatly reduce the manual workload. Then, we realized semantic matching between macro terms and micro documents by extending the words in terms, thereby effectively improving the accuracy of document-term matching. Experimental results show that our method can perform effective automatic matching of domain terms and case documents. The effectiveness of IR-DTM is evaluated by R@1, R@5, and R@10. The R@10 percentage points on PolProv1.0, EcoProv1.0, and TechProv1.0 datasets can reach 84.6%, 92.5% and 93.2% respectively.",
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"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences,Beijing,China",
"fullName": "Jinchao Feng",
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"affiliation": "School of Cyber Security, Nanjing University of Science and Technology,Nanjing,China",
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"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences,Beijing,China",
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"abstract": "Social networking services (SNSs) have become the main avenue, where people speak their thoughts. Accordingly, we can explore people’s thoughts by analyzing topics in SNS. When do topics change? Do they ever come back? What do people mainly talk about? In this study, we design and propose a novel visual analytics system to answer these interesting questions. We abstract the topic per unit time as a point in a two-dimensional space through document embedding and dimensionality reduction techniques and provide supplemented charts that represent words appearing at a certain time and the time-series change of word occurrence over the entire period. We employ a novel text visualization technique, called semantic preserving word bubbles, to visualize words at a certain time. In addition, we demonstrate the effectiveness of our system using Twitter data about early COVID-19 trends in Japan. We propose our system to help users to explore and understand transitions in posted contents on SNS.",
"abstracts": [
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"content": "Social networking services (SNSs) have become the main avenue, where people speak their thoughts. Accordingly, we can explore people’s thoughts by analyzing topics in SNS. When do topics change? Do they ever come back? What do people mainly talk about? In this study, we design and propose a novel visual analytics system to answer these interesting questions. We abstract the topic per unit time as a point in a two-dimensional space through document embedding and dimensionality reduction techniques and provide supplemented charts that represent words appearing at a certain time and the time-series change of word occurrence over the entire period. We employ a novel text visualization technique, called semantic preserving word bubbles, to visualize words at a certain time. In addition, we demonstrate the effectiveness of our system using Twitter data about early COVID-19 trends in Japan. We propose our system to help users to explore and understand transitions in posted contents on SNS.",
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"fullName": "Tiandong Xiao",
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"doi": "10.1109/VIS.2005.63",
"title": "Interpolation And Visualization For Advected Scalar Fields",
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"abstract": "Doppler radars are useful facilities for weather forecasting. The data sampled by using Doppler radars are used to measure the distributions and densities of rain drops, snow crystals, hail stones, or even insects in the atmosphere. In this paper, we propose to build up a graphics-based software system for visualizing Doppler radar data. In the system, the reflectivity data gathered by using Doppler radars are post-processed to generate virtual cloud images which reveal the densities of precipitation in the air. An optical flow based method is adopted to compute the velocities of clouds, advected by winds. Therefore, the movement of clouds is depicted. The cloud velocities are also used to interpolate reflectivities for arbitrary time steps. Therefore, the reflectivities at any time can be produced. Our system composes of three stages. At the first stage, the raw radar data are re-sampled and filtered to create a multiple resolution data structure, based on a pyramid structure. At the second stage, a numeric method is employed to compute cloud velocities in the air and to interpolate radar reflectivity data at given time steps. The radar reflectivity data and cloud velocities are displayed at the last stage. The reflectivities are rendered by using splatting methods to produce semi-transparent cloud images. Two kinds of media are created for analyzing the reflectivity data. The first kind media consists of a group of still images of clouds which displays the distribution and density of water in the air. The second type media is a short animation of cloud images to show the formation and movement of the clouds. To show the advection of clouds, the cloud velocities are displayed by using two dimensional images. In these images, the velocities are represented by arrows and superimposed on cloud images. To enhance image quality, gradients and diffusion of the radar data are computed and used in the rendering process. Therefore the cloud structures are better portrayed. In order to achieve interactive visualization, our system is also comprised with a view-dependent visualization module. The radar data at far distance are rendered in lower resolutions, while the data closer to the eye position is rendered in details.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Doppler radars are useful facilities for weather forecasting. The data sampled by using Doppler radars are used to measure the distributions and densities of rain drops, snow crystals, hail stones, or even insects in the atmosphere. In this paper, we propose to build up a graphics-based software system for visualizing Doppler radar data. In the system, the reflectivity data gathered by using Doppler radars are post-processed to generate virtual cloud images which reveal the densities of precipitation in the air. An optical flow based method is adopted to compute the velocities of clouds, advected by winds. Therefore, the movement of clouds is depicted. The cloud velocities are also used to interpolate reflectivities for arbitrary time steps. Therefore, the reflectivities at any time can be produced. Our system composes of three stages. At the first stage, the raw radar data are re-sampled and filtered to create a multiple resolution data structure, based on a pyramid structure. At the second stage, a numeric method is employed to compute cloud velocities in the air and to interpolate radar reflectivity data at given time steps. The radar reflectivity data and cloud velocities are displayed at the last stage. The reflectivities are rendered by using splatting methods to produce semi-transparent cloud images. Two kinds of media are created for analyzing the reflectivity data. The first kind media consists of a group of still images of clouds which displays the distribution and density of water in the air. The second type media is a short animation of cloud images to show the formation and movement of the clouds. To show the advection of clouds, the cloud velocities are displayed by using two dimensional images. In these images, the velocities are represented by arrows and superimposed on cloud images. To enhance image quality, gradients and diffusion of the radar data are computed and used in the rendering process. Therefore the cloud structures are better portrayed. In order to achieve interactive visualization, our system is also comprised with a view-dependent visualization module. The radar data at far distance are rendered in lower resolutions, while the data closer to the eye position is rendered in details.",
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"affiliation": "National Taiwan Ocean University",
"fullName": "Shyh-Kuang Ueng",
"givenName": "Shyh-Kuang",
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"affiliation": "National Taiwan Ocean University",
"fullName": "Sheng-Chuan Wang",
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"affiliation": "University of Szeged, Hungary",
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"abstract": "Despite recent successes in hand pose estimation, there yet remain challenges on RGB-based 3D hand pose estimation (HPE) under hand-object interaction (HOI) scenarios where severe occlusions and cluttered backgrounds exhibit. Recent RGB HOI benchmarks have been collected either in real or synthetic domain, however, the size of datasets is far from enough to deal with diverse objects combined with hand poses, and 3D pose annotations of real samples are lacking, especially for occluded cases. In this work, we propose a novel end-to-end trainable pipeline that adapts the hand-object domain to the single hand-only domain, while learning for HPE. The domain adaption occurs in image space via 2D pixel-level guidance by Generative Adversarial Network (GAN) and 3D mesh guidance by mesh renderer (MR). Via the domain adaption in image space, not only 3D HPE accuracy is improved, but also HOI input images are translated to segmented and de-occluded hand-only images. The proposed method takes advantages of both the guidances: GAN accurately aligns hands, while MR effectively fills in occluded pixels. The experiments using Dexter-Object, Ego-Dexter and HO3D datasets show that our method significantly outperforms state-of-the-arts trained by hand-only data and is comparable to those supervised by HOI data. Note our method is trained primarily by hand-only images with pose labels, and HOI images without pose labels.",
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"content": "Despite recent successes in hand pose estimation, there yet remain challenges on RGB-based 3D hand pose estimation (HPE) under hand-object interaction (HOI) scenarios where severe occlusions and cluttered backgrounds exhibit. Recent RGB HOI benchmarks have been collected either in real or synthetic domain, however, the size of datasets is far from enough to deal with diverse objects combined with hand poses, and 3D pose annotations of real samples are lacking, especially for occluded cases. In this work, we propose a novel end-to-end trainable pipeline that adapts the hand-object domain to the single hand-only domain, while learning for HPE. The domain adaption occurs in image space via 2D pixel-level guidance by Generative Adversarial Network (GAN) and 3D mesh guidance by mesh renderer (MR). Via the domain adaption in image space, not only 3D HPE accuracy is improved, but also HOI input images are translated to segmented and de-occluded hand-only images. The proposed method takes advantages of both the guidances: GAN accurately aligns hands, while MR effectively fills in occluded pixels. The experiments using Dexter-Object, Ego-Dexter and HO3D datasets show that our method significantly outperforms state-of-the-arts trained by hand-only data and is comparable to those supervised by HOI data. Note our method is trained primarily by hand-only images with pose labels, and HOI images without pose labels.",
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"abstract": "Non-rigid registration of 3D facial surfaces is a crucial step in a variety of applications. Outliers, i.e., features in a facial surface that are not present in the reference face, often perturb the registration process. In this paper, we present a novel method which registers facial surfaces reliably also in the presence of huge outlier regions. A cost function incorporating several channels (red, green, blue, etc.) is proposed. The weight of each point of the facial surface in the cost function is controlled by a weight map, which is learned iteratively. Ideally, outliers will get a zero weight so that their disturbing effect is decreased. Results show that with an intelligent initialization the weight map improves the registration results considerably.",
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"abstract": "The technique presented solves the problem of texture segmentation in two steps. In the first, a textured image is divided into small squares (20*20 in this case) and a hierarchical clustering algorithm related to a choice of ultrametric distances is used to obtain an initial segmentation. In the second step, the texture boundaries are improved using a recursive procedure with vertical and horizontal segments of smaller and smaller size, converging towards the correct texture boundaries.<>",
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"abstract": "Acquiring realistic texture details for 3D models is important in 3D reconstruction. However, the existence of geometric errors, caused by noisy RGB-D sensor data, always makes the color images cannot be accurately aligned onto reconstructed 3D models. In this paper, we propose a global-to-local correction strategy to obtain more desired texture mapping results. Our algorithm first adaptively selects an optimal image for each face of the 3D model, which can effectively remove blurring and ghost artifacts produced by multiple image blending. We then adopt a non-rigid global-to-local correction step to reduce the seaming effect between textures. This can effectively compensate for the texture and the geometric misalignment caused by camera pose drift and geometric errors. We evaluate the proposed algorithm in a range of complex scenes and demonstrate its effective performance in generating seamless high fidelity textures for 3D models.",
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"abstract": "Aiming at the problem of different kinds of noise in 3D point cloud data, we propose a point cloud denoising method based on noise classification. This algorithm first divides noise in point cloud data into inner points and outer points, and uses radius filtering and statistical filtering to remove the outer points. Then, normal and curvature information of point cloud are estimated by the principal component analysis. At the same time, curvature information is introduced into the bilateral filtering factor to improve the existing algorithm. Ultimately, we smooth inner points mixed in point cloud by utilizing the modified algorithm. Comparing the improved algorithm with the bilateral filtering algorithm on the bunny, horse and dragon model, experimental results indicate that the maximum error and the average error are reduced. The algorithm in this paper makes models' features maintained better while models are smoothed.",
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"affiliation": "Communication University of China,School of Information and Communication Engineering,Beijing,China,100024",
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"abstract": "A direct phase-resolved simulation tool for nonlinear ocean surface wave-field evolution, which is named SNOW (simulation of nonlinear ocean wave-fields), is applied to under the dynamics and to obtain a reliable prediction of realistic ocean waves. Unlike the phase-averaged approach, SNOW solves the primitive Euler equation, preserves the phases of the wave-field during its nonlinear evolution, and accounts for the effects of the complex physical processes (such as wave breaking and wind input) in a direct physics-based way. Therefore, the detailed descriptions of the free surface and the kinematics/dynamics of the wave-field are obtained with SNOW. In this work, we focus on the understanding of the characteristics and occurrence statistics of rogue wave events and the development and validation of a novel capability for deterministic prediction of such wave events in realistic ocean environments. Specifically, our study shows that the inclusion of nonlinear wave-wave interactions is critical for the prediction of rogue wave occurrence especially in sea states with relatively narrow wave spectrum bandwidth and narrow directional spreading angle. Moreover, it is found that nonlinear coupling between swell and wind waves in crossing sea states can significantly increase the occurrence of rogue wave events. In addition, we demonstrate and validate the capability of phased-resolved reconstruction and forecasting of realistic ocean wave-field evolution by the use of hybrid radar/wave-probe sensed wave data with SNOW simulations.",
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"content": "A direct phase-resolved simulation tool for nonlinear ocean surface wave-field evolution, which is named SNOW (simulation of nonlinear ocean wave-fields), is applied to under the dynamics and to obtain a reliable prediction of realistic ocean waves. Unlike the phase-averaged approach, SNOW solves the primitive Euler equation, preserves the phases of the wave-field during its nonlinear evolution, and accounts for the effects of the complex physical processes (such as wave breaking and wind input) in a direct physics-based way. Therefore, the detailed descriptions of the free surface and the kinematics/dynamics of the wave-field are obtained with SNOW. In this work, we focus on the understanding of the characteristics and occurrence statistics of rogue wave events and the development and validation of a novel capability for deterministic prediction of such wave events in realistic ocean environments. Specifically, our study shows that the inclusion of nonlinear wave-wave interactions is critical for the prediction of rogue wave occurrence especially in sea states with relatively narrow wave spectrum bandwidth and narrow directional spreading angle. Moreover, it is found that nonlinear coupling between swell and wind waves in crossing sea states can significantly increase the occurrence of rogue wave events. In addition, we demonstrate and validate the capability of phased-resolved reconstruction and forecasting of realistic ocean wave-field evolution by the use of hybrid radar/wave-probe sensed wave data with SNOW simulations.",
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"normalizedAbstract": "A direct phase-resolved simulation tool for nonlinear ocean surface wave-field evolution, which is named SNOW (simulation of nonlinear ocean wave-fields), is applied to under the dynamics and to obtain a reliable prediction of realistic ocean waves. Unlike the phase-averaged approach, SNOW solves the primitive Euler equation, preserves the phases of the wave-field during its nonlinear evolution, and accounts for the effects of the complex physical processes (such as wave breaking and wind input) in a direct physics-based way. Therefore, the detailed descriptions of the free surface and the kinematics/dynamics of the wave-field are obtained with SNOW. In this work, we focus on the understanding of the characteristics and occurrence statistics of rogue wave events and the development and validation of a novel capability for deterministic prediction of such wave events in realistic ocean environments. Specifically, our study shows that the inclusion of nonlinear wave-wave interactions is critical for the prediction of rogue wave occurrence especially in sea states with relatively narrow wave spectrum bandwidth and narrow directional spreading angle. Moreover, it is found that nonlinear coupling between swell and wind waves in crossing sea states can significantly increase the occurrence of rogue wave events. In addition, we demonstrate and validate the capability of phased-resolved reconstruction and forecasting of realistic ocean wave-field evolution by the use of hybrid radar/wave-probe sensed wave data with SNOW simulations.",
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"abstract": "The detection of ocean waves can be used to monitor ocean and to forecast wind-storms upon the ocean surface. The paper presents a method for extracting wave direction and wavelength of ocean waves from a sea image. Based on the assumption that the power spectrum of an ocean wave can be thought of as a thin plane consisting of discrete particles, the wave direction is on the axis direction which minimizes the moment of inertia. By finding the maximum amplitude of mean spectrum within a neighborhood along this axis direction, the location of the main wave and its wavelength can be determined immediately. The method is tested on an air photo of the East Sea of China.<>",
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"abstract": "In Ocean of Things, information prediction is an important part of marine data processing. Due to a variety of marine acquisition devices, the distribution of marine information is discrete. The marine area is wide, and the resolution of the collected data is small, which causes the IoT device to accurately calculate the information of a small sea area. Processing discrete and sparse data is the main job of marine information processing equipment. The main purpose of this paper is to predict the information of the blind area based on the collected data. In order to increase the resolution of sea surface information, the availability of marine data in the Internet of Things is improved. This paper proposes a method for predicting information on marine blind spots. This method is based on the improved machine learning algorithm FCM. This method is used to predict sea surface temperature and improve the resolution of sea surface data. Finally, this paper has done a number of simulation experiments, based on different methods to predict the sea surface temperature. The experimental results are discussed and the performance of the proposed method is analyzed.",
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"content": "In Ocean of Things, information prediction is an important part of marine data processing. Due to a variety of marine acquisition devices, the distribution of marine information is discrete. The marine area is wide, and the resolution of the collected data is small, which causes the IoT device to accurately calculate the information of a small sea area. Processing discrete and sparse data is the main job of marine information processing equipment. The main purpose of this paper is to predict the information of the blind area based on the collected data. In order to increase the resolution of sea surface information, the availability of marine data in the Internet of Things is improved. This paper proposes a method for predicting information on marine blind spots. This method is based on the improved machine learning algorithm FCM. This method is used to predict sea surface temperature and improve the resolution of sea surface data. Finally, this paper has done a number of simulation experiments, based on different methods to predict the sea surface temperature. The experimental results are discussed and the performance of the proposed method is analyzed.",
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"abstract": "Visual exploration of large and complex 3D flow fields is critically important for understanding many aero- and hydro-dynamical systems that dominate various physical and natural phenomena in the world. In this paper, we introduce the FlowGraph, a novel compound graph representation that organizes streamline clusters and spatial regions hierarchically for occlusion-free and controllable visual exploration. Our approach works with any seeding strategies as long as the domain is well covered and important flow features are captured. By transforming a flow field to a graph representation, we enable observation and exploration of the relationships among streamline clusters, spatial regions and their interconnection in the transformed space. The FlowGraph not only provides a visual mapping that abstracts streamline clusters and spatial regions in various levels of detail, but also serves as a navigation tool that guides flow field exploration and understanding. Through brushing and linking in conjunction with the spatial streamline view, we demonstrate the effectiveness of FlowGraph with several visual exploration and comparison tasks that can not be well accomplished using the streamline view alone. As occlusion and clutter are almost ubiquitous in 3D flows, the FlowGraph represents a promising direction for enhancing our ability to understand large and complex flow field data.",
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"abstract": "Software development increasingly depends on libraries and frameworks to increase productivity and reduce time-to-market. Despite this fact, we still lack techniques to assess developers expertise in widely popular libraries and frameworks. In this paper, we evaluate the performance of unsupervised (based on clustering) and supervised machine learning classifiers (Random Forest and SVM) to identify experts in three popular JavaScript libraries: facebook/react, mongodb/node-mongodb, and socketio/socket.io. First, we collect 13 features about developers activity on GitHub projects, including commits on source code files that depend on these libraries. We also build a ground truth including the expertise of 575 developers on the studied libraries, as self-reported by them in a survey. Based on our findings, we document the challenges of using machine learning classifiers to predict expertise in software libraries, using features extracted from GitHub. Then, we propose a method to identify library experts based on clustering feature data from GitHub; by triangulating the results of this method with information available on Linkedin profiles, we show that it is able to recommend dozens of GitHub users with evidences of being experts in the studied JavaScript libraries. We also provide a public dataset with the expertise of 575 developers on the studied libraries.",
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"abstract": "This paper surveys benchmarking principles, machine learning devices including GPUs, FPGAs, and ASICs, and deep learning software frameworks. It also reviews these technologies with respect to benchmarking from the perspectives of a 6-metric approach to frameworks and an 11-metric approach to hardware platforms. Because MLPerf is a benchmark organization working with industry and academia, and offering deep learning benchmarks that evaluate training and inference on deep learning hardware devices, the survey also mentions MLPerf benchmark results, benchmark metrics, datasets, deep learning frameworks and algorithms. We summarize seven benchmarking principles, differential characteristics of mainstream AI devices, and qualitative comparison of deep learning hardware and frameworks.",
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"abstract": "Mixed reality applications which must provide visual coherence between synthetic and real objects need relighting solutions for both: synthetic objects have to match lighting conditions of their real counterparts, while real surfaces need to account for the change in illumination introduced by the presence of an additional synthetic object. In this paper we present a novel relighting solution called Delta Voxel Cone Tracing to compute both direct shadows and first bounce mutual indirect illumination. We introduce a voxelized, pre-filtered representation of the combined real and synthetic surfaces together with the extracted illumination difference due to the augmentation. In a final gathering step this representation is cone-traced and superimposed onto both types of surfaces, adding additional light from indirect bounces and synthetic shadows from anti-radiance present in the volume. The algorithm computes results at interactive rates, is temporally coherent and to our knowledge provides the first real-time rasterizer solution for mutual diffuse, glossy and perfect specular indirect reflections between synthetic and real surfaces in mixed reality.",
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"abstract": "A common way to evaluate the performance of a system is to compare the algorithmic outputs with ground truth to identify divergences in the system's performance and discover the errors it is prone to. In the absence of such ground truth or as a follow-on to performance evaluation, performance analysis at the algorithmic level can provide developers insight into performance singularities. Such performance singularity identification and testing provides real-time meta-data that allows developers to understand the impact of singularities on the overall performance of the system. As an example of the concepts developed in this paper, we present a navigation solution based on image registration algorithms and the methodology used for the identification and testing of performance singularities of this algorithm.",
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"fullName": "Chris Scrapper",
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"doi": "10.1109/CCBD.2014.33",
"title": "Signal Singularity Detection Based on the Hermitian Wavelet for Fault Diagnosis",
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"abstract": "On Big Data analysis for fault diagnosis, health monitoring & fault tolerance of rockets and spacecrafts in aerospace industry, as the local anomaly induced signals tend to have singularity, this paper presents a guideline to employ the time-scale amplitude and phase diagrams based on the Hermitian wavelet transform to identify signal singularities. Firstly, the principle of signal singularity detection based on wavelet transform is formulated. Secondly, the definitions, characteristics, and expressions of the Hermitian wavelet transform is studied. Finally, simulations are carried out to verify the proposed algorithms.",
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"fullName": "Peng Li",
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"title": "2009 IEEE Conference on Computer Vision and Pattern Recognition",
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"doi": "10.1109/CVPR.2009.5206490",
"title": "Curvature and singularity driven diffusion for oriented pattern enhancement with singular points",
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"abstract": "Oriented patterns, e.g. fingerprints, consist of smoothly varying flow-like patterns, together with important singular points (i.e. cores and deltas) where the orientation changes abruptly. Gabor filters and anisotropic diffusion methods have been widely used to enhance oriented patterns. However, none of them can well cope with regions of varying curvatures or regions surrounding singular points. By incorporating the ridge curvatures and the singularities into the diffusion model, we propose a new diffusion method to better exploit the global characteristics of oriented patterns. Specifically, we first locate the singular points, and regularize the estimated orientation field by using a singularity driven nonlinear diffusion process. We then enhance the oriented patterns by applying an oriented diffusion process which is driven by the curvature and singularity. Experiments on synthetic data and real fingerprint images validated that the proposed method is capable of consistently enhancing oriented patterns while well preserving the ridge structures in singular regions.",
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"content": "Oriented patterns, e.g. fingerprints, consist of smoothly varying flow-like patterns, together with important singular points (i.e. cores and deltas) where the orientation changes abruptly. Gabor filters and anisotropic diffusion methods have been widely used to enhance oriented patterns. However, none of them can well cope with regions of varying curvatures or regions surrounding singular points. By incorporating the ridge curvatures and the singularities into the diffusion model, we propose a new diffusion method to better exploit the global characteristics of oriented patterns. Specifically, we first locate the singular points, and regularize the estimated orientation field by using a singularity driven nonlinear diffusion process. We then enhance the oriented patterns by applying an oriented diffusion process which is driven by the curvature and singularity. Experiments on synthetic data and real fingerprint images validated that the proposed method is capable of consistently enhancing oriented patterns while well preserving the ridge structures in singular regions.",
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"normalizedAbstract": "Oriented patterns, e.g. fingerprints, consist of smoothly varying flow-like patterns, together with important singular points (i.e. cores and deltas) where the orientation changes abruptly. Gabor filters and anisotropic diffusion methods have been widely used to enhance oriented patterns. However, none of them can well cope with regions of varying curvatures or regions surrounding singular points. By incorporating the ridge curvatures and the singularities into the diffusion model, we propose a new diffusion method to better exploit the global characteristics of oriented patterns. Specifically, we first locate the singular points, and regularize the estimated orientation field by using a singularity driven nonlinear diffusion process. We then enhance the oriented patterns by applying an oriented diffusion process which is driven by the curvature and singularity. Experiments on synthetic data and real fingerprint images validated that the proposed method is capable of consistently enhancing oriented patterns while well preserving the ridge structures in singular regions.",
"fno": "05206490",
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"affiliation": "Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Hong Kong",
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"title": "2008 International Symposium on Computer Science and Computational Technology (ISCSCT)",
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"doi": "10.1109/ISCSCT.2008.122",
"title": "An Improved Fingerprint Singular Point Detection Algorithm Based on Continuous Orientation Field",
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"abstract": "It is very important to detect singular points (core and delta) accurately and reliably for classification and matching of fingerprint. In this paper, an improved method for singularity detection in fingerprint images, which based on continuous orientation field, is proposed to improve accuracy of the position and reliability of the singularity. Firstly, the blocks which may contain singularities are detected by computing the Poincar",
"abstracts": [
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"content": "It is very important to detect singular points (core and delta) accurately and reliably for classification and matching of fingerprint. In this paper, an improved method for singularity detection in fingerprint images, which based on continuous orientation field, is proposed to improve accuracy of the position and reliability of the singularity. Firstly, the blocks which may contain singularities are detected by computing the Poincar",
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"fullName": "Ting Tang",
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