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"abstract": "A novel method of robust skeletonization based on the Voronoi diagram of boundary points, which is characterized by correct Euclidean metries and inherent preservation of connectivity, is presented. The regularization of the Voronoi medial axis (VMA) in the sense of H. Blum's (1967) prairie fire analogy is done by attributing to each component of the VMA a measure of prominence and stability. The resulting Voronoi skeletons appear largely invariant with respect to typical noise conditions in the image and geometric transformations. Hierarchical clustering of the skeleton branches, the so-called skeleton pyramid, leads to further simplification of the skeleton. Several applications demonstrate the suitability of the Voronoi skeleton to higher-order tasks such as object recognition.<>",
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"content": "A novel method of robust skeletonization based on the Voronoi diagram of boundary points, which is characterized by correct Euclidean metries and inherent preservation of connectivity, is presented. The regularization of the Voronoi medial axis (VMA) in the sense of H. Blum's (1967) prairie fire analogy is done by attributing to each component of the VMA a measure of prominence and stability. The resulting Voronoi skeletons appear largely invariant with respect to typical noise conditions in the image and geometric transformations. Hierarchical clustering of the skeleton branches, the so-called skeleton pyramid, leads to further simplification of the skeleton. Several applications demonstrate the suitability of the Voronoi skeleton to higher-order tasks such as object recognition.<>",
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"title": "On the Properties of Morphological Skeletons of Discrete Binary Image Using Double Structuring Elements",
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"abstract": "This paper discusses the properties of the morphological skeletons of a discrete binary image using double structuring elements. Skeletons are generated under the main structuring element and the sub structuring element. The original image can be reconstructed using one of the structuring elements as the conventional skeleton by a single structuring element. Selecting a structuring element as the subset of other one, a characteristic pattern of the image can be generated as the skeleton subsets. Among them are a unique nested skeleton and a shape representing skeleton having skeletal line component and total shape one. The conventional type, skeleton can be obtained either by setting the two structuring be same or by the intersection of the two skeletons by two sets of double structuring elements. We discuss some theorems, control generation of the skeleton, and the related morphological skeletons with interesting examples",
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"content": "This paper discusses the properties of the morphological skeletons of a discrete binary image using double structuring elements. Skeletons are generated under the main structuring element and the sub structuring element. The original image can be reconstructed using one of the structuring elements as the conventional skeleton by a single structuring element. Selecting a structuring element as the subset of other one, a characteristic pattern of the image can be generated as the skeleton subsets. Among them are a unique nested skeleton and a shape representing skeleton having skeletal line component and total shape one. The conventional type, skeleton can be obtained either by setting the two structuring be same or by the intersection of the two skeletons by two sets of double structuring elements. We discuss some theorems, control generation of the skeleton, and the related morphological skeletons with interesting examples",
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"title": "Straight skeletons for binary shapes",
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"abstract": "This paper reviews the concept of straight skeletons, which is well known in computational geometry, and applies it to binary shapes that are used in vision-based shape and object recognition. We devise a novel algorithm for computing discrete straight skeletons from binary input images, which is based on a polygonal approximation of the input shape and a hybrid method that combines continuous and discrete geometry. In our experiments, we analyze the potential of straight skeletons in shape recognition, by comparing their performance with medial-axis based shock graphs on the Kimia shape databases. Our discrete straight skeleton algorithm is not only outperforming typical skeleton algorithms in terms of computational complexity, it also delivers surprisingly good results in its straightforward application to shape recognition.",
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"content": "This paper reviews the concept of straight skeletons, which is well known in computational geometry, and applies it to binary shapes that are used in vision-based shape and object recognition. We devise a novel algorithm for computing discrete straight skeletons from binary input images, which is based on a polygonal approximation of the input shape and a hybrid method that combines continuous and discrete geometry. In our experiments, we analyze the potential of straight skeletons in shape recognition, by comparing their performance with medial-axis based shock graphs on the Kimia shape databases. Our discrete straight skeleton algorithm is not only outperforming typical skeleton algorithms in terms of computational complexity, it also delivers surprisingly good results in its straightforward application to shape recognition.",
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"affiliation": "Institute for Theoretical Computer Science, Graz University of Technology, Austria",
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"affiliation": "Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Austria",
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"abstract": "The importance of triangle meshes with a special kind of connectivity, the so-called subdivision connectivity is still growing. Therefore it is important to develop efficient algorithms for converting a given mesh with arbitrary connectivity into one with subdivision connectivity. In this paper we have focused on 2-manifold triangle meshes with boundary and no holes. We discuss the importance of a parametrization with minimal distortion for the process of remeshing. Based on the concept of most isometric parametrizations we have developed a remeshing algorithm for the given class of triangle meshes. A series of examples shows the advantages of our approach.",
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"abstract": "A segmentation algorithm for image content analysis is presented. We assume that the textures in a video scene can be labeled subjectively relevant or irrelevant. Relevant textures are defined as containing subjectively meaningful details, while irrelevant textures can be seen as image content with less important subjective details. We apply this idea to video coding using a texture analyzer and a texture synthesizer. The texture analyzer (encoder side) identifies the texture regions with unimportant subjective details and generates side information for the texture synthesizer (decoder side), which in turn inserts synthetic textures at the specified locations. The focus of this paper is the texture analyzer, which uses multiple MPEG-7 descriptors simultaneously for similarity estimation. The texture analyzer is based on a split and merge segmentation approach. Its current implementation yields an identification rate of up to 96% and an average gain of up to 10% compared to single descriptor usage.",
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"normalizedAbstract": "We propose a new fast height-field rendering method for Image-Based Lighting. Our approach approximates the environmental illumination using spherical radial basis functions (SRBFs). We approximate the integrand of the rendering equation as a sum of products of the SRBFs and the visibility function. Then we solve the integral by approximating the visibility function of each SRBF as the fraction visible above the height-field. This fraction is precomputed into a 2-Dtexture, as a function of the SRBF size and its angle to the horizon. We use two different approximations of the horizon. The first, called a horizon map, can render more accurate self occlusion. The second, called a cone map, needs only a single RGBA 2D texture but can only render rough self-shadowing. Our approach can quickly render shading and self-shadowing of a height-field from all-frequency environmental lighting.",
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"abstract": "A plenoptic function is a parameterized function describing the flow of light in space, and has served as a key idea in building some of the recent image based rendering systems. The paper presents a new representation scheme, called a spherical light field, of the plenoptic function, that is based on spheres. While methods using spherical coordinates are thought to require substantially more computation than those using planar or cylindrical coordinates, we show that spheres can also be used efficiently in representing and resampling the flow of light. Our image based rendering algorithm is different from the previous systems, the light field and lumigraph, in that it is an \"object space\" algorithm that can be easily embedded into the traditional polygonal rendering system. Our method is easily accelerated by 3D graphics boards that support the primitive functionality, such as viewing and smooth shading. In addition, we introduce an encoding scheme based on wavelets for compression of the huge data resulting from sampling of the spherical light field. The proposed technique can be easily adapted to compress the light field and lumigraph data, and offers as high compression ratios as the previous methods. Furthermore, it naturally creates a multi resolutional representation of the light flow that can be exploited effectively in the future applications. We show how to access the compressed data efficiently using a modified significance map and an incremental decoding technique, and report experimental results on several test data sets.",
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"affiliation": "Dept. of Comput. Sci., Sogang Univ., Seoul, South Korea",
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"abstract": "Severe hail, or spherical ice precipitation over 1 inch in diameter, has caused billions of dollars in damage to crops, buildings, automobiles, and aircraft. Accurate predictions of severe hail with enough lead time can allow people to mitigate some hail damage by sheltering themselves and their vehicles and by rerouting their aircraft. Current pinpoint forecasts of severe hail rely on detection of hail in existing storms with radar-based methods. Predictions beyond an hour are limited to probabilistic predictions over larger areas based on expected environmental conditions. This paper describes a technique that could increase the accuracy of severe hail forecasts by incorporating output from an ensemble of storm scale numerical weather prediction models into a spatiotemporal relational data mining model that would produce probabilistic predictions of severe hail. The spatiotemporal relational framework represents the ensemble output as a network of storm objects connected by spatial relationships. Composites of the ensemble data show spatial biases in the placement of severe and non severe hail storms. The spatiotemporal relational model performs significantly better at discriminating between severe and non-severe hail compared to a traditional data mining model of the same type. Variable importance rankings show results physically consistent with previous studies and highlight the importance of the relational data.",
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"content": "Severe hail, or spherical ice precipitation over 1 inch in diameter, has caused billions of dollars in damage to crops, buildings, automobiles, and aircraft. Accurate predictions of severe hail with enough lead time can allow people to mitigate some hail damage by sheltering themselves and their vehicles and by rerouting their aircraft. Current pinpoint forecasts of severe hail rely on detection of hail in existing storms with radar-based methods. Predictions beyond an hour are limited to probabilistic predictions over larger areas based on expected environmental conditions. This paper describes a technique that could increase the accuracy of severe hail forecasts by incorporating output from an ensemble of storm scale numerical weather prediction models into a spatiotemporal relational data mining model that would produce probabilistic predictions of severe hail. The spatiotemporal relational framework represents the ensemble output as a network of storm objects connected by spatial relationships. Composites of the ensemble data show spatial biases in the placement of severe and non severe hail storms. The spatiotemporal relational model performs significantly better at discriminating between severe and non-severe hail compared to a traditional data mining model of the same type. Variable importance rankings show results physically consistent with previous studies and highlight the importance of the relational data.",
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"title": "STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction",
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"abstract": "Although many video prediction methods have obtained good performance in low-resolution (64∼128) videos, predictive models for high-resolution (512∼4K) videos have not been fully explored yet, which are more meaningful due to the increasing demand for high-quality videos. Compared with low-resolution videos, high-resolution videos contain richer appearance (spatial) information and more complex motion (temporal) information. In this paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for high-resolution video prediction. On the one hand, we propose a Spatiotemporal Encoding-Decoding Scheme to preserve more spatiotemporal information for high-resolution videos. In this way, the appearance details for each frame can be greatly preserved. On the other hand, we design a Residual Predictive Memory (RPM) which focuses on modeling the spatiotemporal residual features (STRF) between previous and future frames instead of the whole frame, which can greatly help capture the complex motion information in high-resolution videos. In addition, the proposed RPM can supervise the spatial encoder and temporal encoder to extract different features in the spatial domain and the temporal domain, respectively. Moreover, the proposed model is trained using generative adversarial networks (GANs) with a learned perceptual loss (LP-loss) to improve the perceptual quality of the predictions. Experimental results show that STRPM can generate more satisfactory results compared with various existing methods.",
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"affiliation": "School of Computer Science and Technology, University of Chinese Academy of Sciences,Beijing,China",
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"affiliation": "School of Computer Science and Technology, University of Chinese Academy of Sciences,Beijing,China",
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"affiliation": "Chinese Academy of Meteorological Sciences, China",
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"affiliation": "Chinese Academy of Meteorological Sciences, China",
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"abstract": "Traffic flow prediction is of great importance in traffic management and public safety, but is challenging due to the complex spatial-temporal dependencies as well as temporal dynamics. Existing work either focuses on traditional statistical models, which have limited prediction accuracy, or relies on black-box deep learning models, which have superior prediction accuracy but are hard to interpret. In contrast, we propose a novel interpretable spatiotemporal deep learning model for traffic flow prediction. Our main idea is to model the physics of traffic flow through a number of latent Spatio-Temporal Potential Energy Fields (ST-PEFs), similar to water flow driven by the gravity field. We develop a Wind field Decomposition (WD) algorithm to decompose traffic flow into poly-tree components so that ST-PEFs can be established. We then design a spatiotemporal deep learning model for the ST-PEFs, which consists of a temporal component (modeling the temporal correlation) and a spatial component (modeling the spatial dependencies). To the best of our knowledge, this is the first work that make traffic flow prediction based on ST-PEFs. Experimental results on real-world traffic datasets show the effectiveness of our model compared to the existing methods. A case study confirms our model interpretability.",
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"content": "Traffic flow prediction is of great importance in traffic management and public safety, but is challenging due to the complex spatial-temporal dependencies as well as temporal dynamics. Existing work either focuses on traditional statistical models, which have limited prediction accuracy, or relies on black-box deep learning models, which have superior prediction accuracy but are hard to interpret. In contrast, we propose a novel interpretable spatiotemporal deep learning model for traffic flow prediction. Our main idea is to model the physics of traffic flow through a number of latent Spatio-Temporal Potential Energy Fields (ST-PEFs), similar to water flow driven by the gravity field. We develop a Wind field Decomposition (WD) algorithm to decompose traffic flow into poly-tree components so that ST-PEFs can be established. We then design a spatiotemporal deep learning model for the ST-PEFs, which consists of a temporal component (modeling the temporal correlation) and a spatial component (modeling the spatial dependencies). To the best of our knowledge, this is the first work that make traffic flow prediction based on ST-PEFs. Experimental results on real-world traffic datasets show the effectiveness of our model compared to the existing methods. A case study confirms our model interpretability.",
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"affiliation": "Beihang University, China",
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"abstract": "A review system is a valuable component in E-commerce applications. Existing centralized review systems are prone to review frauds and manipulations. A decentralized, immutable and auditable blockchain system is a natural fit to prevent fraudulent and manipulated reviews. We develop and implement ReviewChain, a decentralized review system using Ethereum smart contract. ReviewChain validates new reviews by querying the identity of purchasers from a supply chain system. We assume that the supply chain system is based on blockchain technology. We develop and implement a mechanism to tie two disparate Ethereum based blockchain networks. Ethereum smart contract code is limited and cannot make external procedure calls. Therefore, we introduce multi-blockchain gateway nodes called notaries. Our proposed smart contracts and notaries allow us to interface two blockchains effectively. We run ReviewChain and discuss how various design parameters impact the system performance.",
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"content": "Current blockchains often are designed with one use case in mind, such as currency transfer in Bitcoin or record storage in Namecoin. While application-specific blockchains are powerful tools for fulfilling their niche use cases, there are blockchains like Ethereum that have a set of use cases comprising a superset of the use cases of many blockchains. These generalized blockchains are powerful tools for extending decentralization to new use cases without designing entirely new blockchains. This paper considers tradeoffs in the design and implementation of blockchain systems that intend to deliver general functionality and examines various current and Merged-mining, fragmentation, the network effect, scalability, and generalized blockchain approaches are all considered and put into context. Bitcoin, Ethereum, Cardano, and Rootstock are considered in various aspects of the current limitations, and potential future solutions to problems noted. This paper weighs the chances of success of various generalized blockchain paradigms, including the network effect scalability and potential client-side use. We conclude that the network effect likely favors a Bitcoin/sidechain solution while scalability favors a direct Ethereum solution. The likelihood of success of mainstream adoption of any single decentralized application appears to favor an Ethereum solution.",
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"affiliation": "SimCenter and Dept. of Computer Science & Engineering, University of Tennessee at Chattannooga, Chattanooga, Tennessee",
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"doi": "10.1109/Cybermatics_2018.2018.00207",
"title": "Astraea: A Decentralized Blockchain Oracle",
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"abstract": "The public blockchain was originally conceived to process monetary transactions in a peer-to-peer network while preventing double-spending. It has since been extended to numerous other applications including execution of programs that exist on the blockchain called “smart contracts.” Smart contracts have a major limitation, namely they only operate on data that is on the blockchain. Trusted entities called oracles attest to external data in order to bring it onto the blockchain but they do so without the robust security guarantees that blockchains generally provide. This has the potential to turn oracles into centralized points-of-failure. To address this concern, this paper introduces Astraea, a decentralized oracle based on a voting game that decides the truth or falsity of propositions. Players fall into two roles: voters and certifiers. Voters play a low-risk/low-reward role that is resistant to adversarial manipulation while certifiers play a high-risk/high-reward role so they are required to play with a high degree of accuracy. This paper also presents a formal analysis of the parameters behind the system to measure the probability of an adversary with bounded funds being able to successfully manipulate the oracle's decision, that shows that the same parameters can be set to make manipulation arbitrarily difficult-a desirable feature for the system. Further, this analysis demonstrates that under those conditions a Nash equilibrium exists where all rational players are forced to behave honestly.",
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"content": "The public blockchain was originally conceived to process monetary transactions in a peer-to-peer network while preventing double-spending. It has since been extended to numerous other applications including execution of programs that exist on the blockchain called “smart contracts.” Smart contracts have a major limitation, namely they only operate on data that is on the blockchain. Trusted entities called oracles attest to external data in order to bring it onto the blockchain but they do so without the robust security guarantees that blockchains generally provide. This has the potential to turn oracles into centralized points-of-failure. To address this concern, this paper introduces Astraea, a decentralized oracle based on a voting game that decides the truth or falsity of propositions. Players fall into two roles: voters and certifiers. Voters play a low-risk/low-reward role that is resistant to adversarial manipulation while certifiers play a high-risk/high-reward role so they are required to play with a high degree of accuracy. This paper also presents a formal analysis of the parameters behind the system to measure the probability of an adversary with bounded funds being able to successfully manipulate the oracle's decision, that shows that the same parameters can be set to make manipulation arbitrarily difficult-a desirable feature for the system. Further, this analysis demonstrates that under those conditions a Nash equilibrium exists where all rational players are forced to behave honestly.",
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"affiliation": "Department of Electrical and Computer Engineering, University of Toronto",
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"affiliation": "Department of Electrical and Computer Engineering, University of Toronto",
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"affiliation": "Department of Computer Science, University of Toronto",
"fullName": "Andreas Veneris",
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"affiliation": "Department of Electrical and Computer Engineering, University of Toronto",
"fullName": "Zissis Poulos",
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"affiliation": "Department of Electrical and Computer Engineering, University of Toronto",
"fullName": "Neil Veira",
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"affiliation": "Department of Informatics, Athens University of Economics and Business",
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"abstract": "Blockchain based smart contracts are computer programs that encode an agreement between non-trusting participants. Smart contracts are executed on a blockchain system if specified conditions are met, without the need of a trusted third party. Blockchains and smart contracts have received increasing and booming attention in recent years, also in academic circles. We carry out a systematic mapping study of all peer-reviewed technology-oriented research in smart contracts. Our interest is twofold, namely to provide a survey of the scientific literature and to identify academic research trends and uptake. We only focus on peer-reviewed scientific publications, in order to identify how academic researchers have taken up smart contract technologies and established scientific outputs. We obtained all research papers from the main scientific databases, and using the systematic mapping method arrived at 188 relevant papers. We classified these papers into six categories, namely, security, privacy, software engineering, application, performance & scalability and other smart contract related topics. We found that the majority of the papers falls into the applications (about 64%) and software engineering (21%) categories. Compared to our 2017 survey [1], we observe that the number of relevant articles has increased about eightfold and shifted considerably towards applications of smart contracts.",
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{
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"content": "Blockchain based smart contracts are computer programs that encode an agreement between non-trusting participants. Smart contracts are executed on a blockchain system if specified conditions are met, without the need of a trusted third party. Blockchains and smart contracts have received increasing and booming attention in recent years, also in academic circles. We carry out a systematic mapping study of all peer-reviewed technology-oriented research in smart contracts. Our interest is twofold, namely to provide a survey of the scientific literature and to identify academic research trends and uptake. We only focus on peer-reviewed scientific publications, in order to identify how academic researchers have taken up smart contract technologies and established scientific outputs. We obtained all research papers from the main scientific databases, and using the systematic mapping method arrived at 188 relevant papers. We classified these papers into six categories, namely, security, privacy, software engineering, application, performance & scalability and other smart contract related topics. We found that the majority of the papers falls into the applications (about 64%) and software engineering (21%) categories. Compared to our 2017 survey [1], we observe that the number of relevant articles has increased about eightfold and shifted considerably towards applications of smart contracts.",
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"normalizedAbstract": "Blockchain based smart contracts are computer programs that encode an agreement between non-trusting participants. Smart contracts are executed on a blockchain system if specified conditions are met, without the need of a trusted third party. Blockchains and smart contracts have received increasing and booming attention in recent years, also in academic circles. We carry out a systematic mapping study of all peer-reviewed technology-oriented research in smart contracts. Our interest is twofold, namely to provide a survey of the scientific literature and to identify academic research trends and uptake. We only focus on peer-reviewed scientific publications, in order to identify how academic researchers have taken up smart contract technologies and established scientific outputs. We obtained all research papers from the main scientific databases, and using the systematic mapping method arrived at 188 relevant papers. We classified these papers into six categories, namely, security, privacy, software engineering, application, performance & scalability and other smart contract related topics. We found that the majority of the papers falls into the applications (about 64%) and software engineering (21%) categories. Compared to our 2017 survey [1], we observe that the number of relevant articles has increased about eightfold and shifted considerably towards applications of smart contracts.",
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"title": "Bitcoin Blockchain Transactions Visualization",
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"abstract": "Blockchains were originally used to support the Bitcoin cryptocurrency and now entire software ecosystems are being supported by blockchains. Despite their widespread use, not much is known about how peers in the bitcoin blockchain network use the system. We present visualizations to help in identifying some patterns in the usage of bitcoin blockchain supported technologies. In this paper, we observe the bitcoin transaction continent-wise via a visualization of the locations from where peers in the bitcoin blockchain network were making their transactions, using WebGL technology. We analyzed regional bitcoin blockchain usage patterns by observing their clusters formation over time. We also presented a pattern of how the value of the bitcoin changes over time.",
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"content": "Blockchains were originally used to support the Bitcoin cryptocurrency and now entire software ecosystems are being supported by blockchains. Despite their widespread use, not much is known about how peers in the bitcoin blockchain network use the system. We present visualizations to help in identifying some patterns in the usage of bitcoin blockchain supported technologies. In this paper, we observe the bitcoin transaction continent-wise via a visualization of the locations from where peers in the bitcoin blockchain network were making their transactions, using WebGL technology. We analyzed regional bitcoin blockchain usage patterns by observing their clusters formation over time. We also presented a pattern of how the value of the bitcoin changes over time.",
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"affiliation": "Department of Computer Science, University of Saskatchewan, Saskatoon, Canada",
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"title": "Evolution of code clone ratios throughout development history of open-source C and C++ programs",
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"content": "A code clone is a fragment of code which is duplicated throughout the source code of a project. Code clones have been shown to make a project less maintainable because all code clones will share potential bugs and problems. Unlike other code clone research, this study analyzes the code clone ratios over the entire development lifetime of three open-source projects written in C/C++ to understand code clone growth in software over development and potential developer habits which could affect this growth. The study utilizes CCFinderX and Git to detect clone metrics across development history. The results from each project show very low, stable ratios across development history, with the code clone ratios only fluctuating greatly during the beginning of development mostly and very little refactoring occurring. This study goes further into the potential cause of low ratios and different fluctuations at different periods of development.",
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"affiliation": "Graduate School of Information Science and Technology, Osaka University Osaka, Japan",
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"normalizedAbstract": "Source code histories are commonly used by developers and researchers to reason about how software evolves. Through a survey with 42 professional software developers, we learned that developers face significant mismatches between the output provided by developers' existing tools for examining source code histories and what they need to successfully complete their historical analysis tasks. To address these shortcomings, we propose CodeShovel, a tool for uncovering method histories that quickly produces complete and accurate change histories for 90% methods (including 97% of all method changes) outperforming leading tools from both research (e.g, FinerGit) and practice (e.g., IntelliJ / git log). CodeShovel helps developers to navigate the entire history of source code methods so they can better understand how the method evolved. A field study on industrial code bases with 16 industrial developers confirmed our empirical findings of CodeShovel's correctness, low runtime overheads, and additionally showed that the approach can be useful for a wide range of industrial development tasks.",
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"abstract": "Facial expressions are important information that reflects human emotions. Recognizing dynamic expressions of students in class, 8 kinds of emotions are selected for application: positive emotions: “happy”; negative emotions: “disgust, Sadness, doubts, contempt, anger”; neutral emotion: “focus, surprise”. [1] In this design, the classroom performance scoring system in normal hours is split into four functions: wireless network list acquisition and verification, face recognition, emotion analysis, and scoring record storage. On this basis, SVM and Softmax are used. Facial expression recognition and a data storage database is designed to realize the function of an intelligent scoring system for classroom performance points. To solve this problem, an expression recognition method combining pyramid convolutional neural network and attention mechanism is proposed.",
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"affiliation": "Huaiyin Institute of Technology,Faculty of Computer&Software Engineering,Huaian,China",
"fullName": "Boxuan Zhang",
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"affiliation": "Huaiyin Institute of Technology,Faculty of Computer&Software Engineering,Huaian,China",
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"affiliation": "Huaiyin Institute of Technology,Faculty of Computer&Software Engineering,Huaian,China",
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"affiliation": "Huaiyin Institute of Technology,Faculty of Computer&Software Engineering,Huaian,China",
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"affiliation": "The University of Tokyo",
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