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"title": "2022 2nd International Conference on Computer Graphics, Image and Virtualization (ICCGIV)",
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"doi": "10.1109/ICCGIV57403.2022.00043",
"title": "Ellipsoidal ray casting algorithm",
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"abstract": "The Digital Earth (DE) offers a visualization platform via a reference ellipsoid (RE). However, for global volume data on the DE, ray casting algorithm (RCA) faces the challenges of poor performance, earth curvature and incorrect volume rendering due to ignoring earth occlusion. To address the issues, RCA is refactored as ellipsoidal ray casting algorithm (ERCA), which emphasizes the following aspects: ellipsoid- texture space logic mapping, ellipsoidal trilinear interpolation, ray sampling optimization operators, distinguishable transfer function, and proxy ellipsoidal geometry. In particular, a new curved multi-scale ellipsoidal volume grid (EVG) is proposed, which uniformly represents local or global volume data and supports logical mapping between ellipsoidal space and texture space. Ray sampling is optimized with a novel set of culling operators: proxy geometry culling, earth occlusion culling and ellipsoidal bounding box culling, which remove a large number of invalid sampling points to ensure high performance and eliminate correct rendering of ERCA. Experimental results show that ERCA is valid. The ellipsoid-based volume rendering framework can help humans gain insight into 3D scalar data overlaid on the DE.",
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"content": "The Digital Earth (DE) offers a visualization platform via a reference ellipsoid (RE). However, for global volume data on the DE, ray casting algorithm (RCA) faces the challenges of poor performance, earth curvature and incorrect volume rendering due to ignoring earth occlusion. To address the issues, RCA is refactored as ellipsoidal ray casting algorithm (ERCA), which emphasizes the following aspects: ellipsoid- texture space logic mapping, ellipsoidal trilinear interpolation, ray sampling optimization operators, distinguishable transfer function, and proxy ellipsoidal geometry. In particular, a new curved multi-scale ellipsoidal volume grid (EVG) is proposed, which uniformly represents local or global volume data and supports logical mapping between ellipsoidal space and texture space. Ray sampling is optimized with a novel set of culling operators: proxy geometry culling, earth occlusion culling and ellipsoidal bounding box culling, which remove a large number of invalid sampling points to ensure high performance and eliminate correct rendering of ERCA. Experimental results show that ERCA is valid. The ellipsoid-based volume rendering framework can help humans gain insight into 3D scalar data overlaid on the DE.",
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"Ellipsoidal Bounding Box Culling",
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"Ellipsoidal Space",
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"affiliation": "North China Institute of Computing Technology (NCI),Battlefield Environment Department,Beijing,China",
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"affiliation": "North China Institute of Computing Technology (NCI),Battlefield Environment Department,Beijing,China",
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{
"affiliation": "China People’s Liberation Army National Defense University,Joint Operations Academy,Beijing,China",
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"title": "2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)",
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"doi": "10.1109/LDAV.2018.8739224",
"title": "SpRay: Speculative Ray Scheduling for Large Data Visualization",
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"abstract": "Figure 1:All images rendered with our speculative ray tracing technique. First three columns: a massive channel-flow turbulence DNS dataset. Last two columns: an RM fluid instability dataset and an Enzo Astrophysics AMR dataset (left and right). The number of triangles from left to right: DNS2 (1.8 billion), DNS1-side and DNS1-back (0.9 billion), RM (108 million), and Enzo (8 million). Five images on the bottom row show ambient occlusion shading, and the rest show three-bounce path tracing. For all datasets, 32 samples per pixel were used to render images at 1024×1024 resolution, and one diffuse ray and 16 shadow rays were generated at every hit point. Using Stampede2 Skylake at the Texas Advanced Computing Center, each node with 192 GB memory, at least eight nodes are required to render the DNS dataset with speculation enabled.With modern supercomputers offering petascale compute capability, scientific simulations are now producing terascale data. For comprehensive understanding of such large data, ray tracing is becoming increasingly important for 3D-rendering in visualization due to its inherent ability to convey physically realistic visual information to the user. Implementing efficient parallel ray tracing systems on supercomputers while maximizing locality and parallelism is challenging because of the overhead incurred by ray communication across the cluster of compute nodes and data loading from storage. To address the problem, reordering rendering computations by means of ray batching and scheduling has been proposed to temporarily avoid inherent dependencies in the rendering computations and amortize the cost of expensive data moving operations over ray batches. In this paper, we introduce a novel speculative ray scheduling method that builds upon this insight but radically changes the approach to resolving dependencies by allowing redundant computations to a certain extent. To evaluate the method, we measure the performance of different implementations for both out-of-core and in situ rendering setups. Results show that compared to a well-known scheduling method, our approach on ambient occlusion and path tracing achieves up to 2.3× speedup for the scenes comprising up to billions of triangles extracted from terascale scientific data.",
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"content": "Figure 1:All images rendered with our speculative ray tracing technique. First three columns: a massive channel-flow turbulence DNS dataset. Last two columns: an RM fluid instability dataset and an Enzo Astrophysics AMR dataset (left and right). The number of triangles from left to right: DNS2 (1.8 billion), DNS1-side and DNS1-back (0.9 billion), RM (108 million), and Enzo (8 million). Five images on the bottom row show ambient occlusion shading, and the rest show three-bounce path tracing. For all datasets, 32 samples per pixel were used to render images at 1024×1024 resolution, and one diffuse ray and 16 shadow rays were generated at every hit point. Using Stampede2 Skylake at the Texas Advanced Computing Center, each node with 192 GB memory, at least eight nodes are required to render the DNS dataset with speculation enabled.With modern supercomputers offering petascale compute capability, scientific simulations are now producing terascale data. For comprehensive understanding of such large data, ray tracing is becoming increasingly important for 3D-rendering in visualization due to its inherent ability to convey physically realistic visual information to the user. Implementing efficient parallel ray tracing systems on supercomputers while maximizing locality and parallelism is challenging because of the overhead incurred by ray communication across the cluster of compute nodes and data loading from storage. To address the problem, reordering rendering computations by means of ray batching and scheduling has been proposed to temporarily avoid inherent dependencies in the rendering computations and amortize the cost of expensive data moving operations over ray batches. In this paper, we introduce a novel speculative ray scheduling method that builds upon this insight but radically changes the approach to resolving dependencies by allowing redundant computations to a certain extent. To evaluate the method, we measure the performance of different implementations for both out-of-core and in situ rendering setups. Results show that compared to a well-known scheduling method, our approach on ambient occlusion and path tracing achieves up to 2.3× speedup for the scenes comprising up to billions of triangles extracted from terascale scientific data.",
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"normalizedAbstract": "Figure 1:All images rendered with our speculative ray tracing technique. First three columns: a massive channel-flow turbulence DNS dataset. Last two columns: an RM fluid instability dataset and an Enzo Astrophysics AMR dataset (left and right). The number of triangles from left to right: DNS2 (1.8 billion), DNS1-side and DNS1-back (0.9 billion), RM (108 million), and Enzo (8 million). Five images on the bottom row show ambient occlusion shading, and the rest show three-bounce path tracing. For all datasets, 32 samples per pixel were used to render images at 1024×1024 resolution, and one diffuse ray and 16 shadow rays were generated at every hit point. Using Stampede2 Skylake at the Texas Advanced Computing Center, each node with 192 GB memory, at least eight nodes are required to render the DNS dataset with speculation enabled.With modern supercomputers offering petascale compute capability, scientific simulations are now producing terascale data. For comprehensive understanding of such large data, ray tracing is becoming increasingly important for 3D-rendering in visualization due to its inherent ability to convey physically realistic visual information to the user. Implementing efficient parallel ray tracing systems on supercomputers while maximizing locality and parallelism is challenging because of the overhead incurred by ray communication across the cluster of compute nodes and data loading from storage. To address the problem, reordering rendering computations by means of ray batching and scheduling has been proposed to temporarily avoid inherent dependencies in the rendering computations and amortize the cost of expensive data moving operations over ray batches. In this paper, we introduce a novel speculative ray scheduling method that builds upon this insight but radically changes the approach to resolving dependencies by allowing redundant computations to a certain extent. To evaluate the method, we measure the performance of different implementations for both out-of-core and in situ rendering setups. Results show that compared to a well-known scheduling method, our approach on ambient occlusion and path tracing achieves up to 2.3× speedup for the scenes comprising up to billions of triangles extracted from terascale scientific data.",
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"title": "2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA)",
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"title": "A client-based visual analytics framework for large spatiotemporal data under architectural constraints",
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"abstract": "A primary aim of visual analytics is to provide end-users interactive and scalable environments to facilitate their decision making tasks. Researchers have often utilized several server-client solutions to support interactive data exploration (e.g., building the data cube, parallelizing data processing). However, these solutions can suffer from scalability issues especially in the absence of adequate computation functionality provided by servers. Organizational policies can also prohibit the transfer of data to external data servers because of security or budgetary concerns; thereby, severely limiting the capability of the visual analytic systems. Therefore, in this paper, we propose an interactive client-based visual analytics framework for large-scale spatiotemporal data. The proposed framework follows a sampling based incremental visual analysis approach to sustain the real-time responsiveness, meanwhile, with affordable computation resources in a client machine. General sampling methods [34] preprocess the entire dataset to build data indexing, which can bring the client unaffordable computation overhead. Instead, our framework proposes a novel data management model, using the spatiotemporal clustering pattern to predictively organize and sample data based on historical data acquisition activities. We demonstrate the capabilities and usefulness of our framework by applying it on crime data and Twitter data. We also conduct several experimental evaluations to determine the efficacy of our framework.",
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"content": "A primary aim of visual analytics is to provide end-users interactive and scalable environments to facilitate their decision making tasks. Researchers have often utilized several server-client solutions to support interactive data exploration (e.g., building the data cube, parallelizing data processing). However, these solutions can suffer from scalability issues especially in the absence of adequate computation functionality provided by servers. Organizational policies can also prohibit the transfer of data to external data servers because of security or budgetary concerns; thereby, severely limiting the capability of the visual analytic systems. Therefore, in this paper, we propose an interactive client-based visual analytics framework for large-scale spatiotemporal data. The proposed framework follows a sampling based incremental visual analysis approach to sustain the real-time responsiveness, meanwhile, with affordable computation resources in a client machine. General sampling methods [34] preprocess the entire dataset to build data indexing, which can bring the client unaffordable computation overhead. Instead, our framework proposes a novel data management model, using the spatiotemporal clustering pattern to predictively organize and sample data based on historical data acquisition activities. We demonstrate the capabilities and usefulness of our framework by applying it on crime data and Twitter data. We also conduct several experimental evaluations to determine the efficacy of our framework.",
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"abstract": "Throughout many scientific and engineering fields, including control theory, quantum mechanics, advanced dynamics, and network theory, a great many important applications rely on the spectral decomposition of matrices. Traditional methods such as the power iteration method, Jacobi eigenvalue method, and QR decomposition are commonly used to compute the eigenvalues and eigenvectors of a square and symmetric matrix. However, these methods suffer from certain drawbacks: in particular, the power iteration method can only find the leading eigen-pair (i.e., the largest eigenvalue and its corresponding eigenvector), while the Jacobi and QR decomposition methods face significant performance limitations when facing with large scale matrices. Typically, even producing approximate eigenpairs of a general square matrix requires at least O(N<sup>3</sup>) time complexity, where N is the number of rows of the matrix. In this work, we exploit the newly developed memristor technology to propose a low-complexity, scalable memristorbased method for deriving a set of dominant eigenvalues and eigenvectors for real symmetric non-negative matrices. The time complexity for our proposed algorithm is O(N<sup>2</sup>/Δ) (where Δ governs the accuracy). We present experimental studies to simulate the memristor-supporting algorithm, with results demonstrating that the average error for our method is within 4%, while its performance is up to 1.78X better than traditional methods.",
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"abstract": "The authors characterize the eigenvalues and eigenvectors of covariance matrices that arise in direction finding scenarios with multiple parameters such as azimuth, elevation and, in some applications, also range (multi-D scenarios). They build upon work by H. Lee (1992) for closely-spaced signals with a single directional parameter (1-D). In multi-D, the limiting (small signal spacing) eigenvalues and eigenvectors can be ascertained from a sequence of constant low-rank matrices N/sub k/ expressed in terms of the generic arrival vector, its spatial derivatives, the source configuration, and the source covariances. The limiting eigenvalues are proportional to delta omega /sup 2(k-1)/, where delta omega is the maximum spacing between sources and k epsilon (1,. . .m). It is shown that for a given number of sources m decreases as parameter dimension increases, hence covariance matrix conditioning is improved in multi-D relative to 1-D settings. The results are applicable to analysis of detection and parameter estimation algorithms in multi-D applications.",
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"title": "A Modification of Eigenvalues to Compensate Estimation Errors of Eigenvectors",
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"abstract": "In statistical pattern recognition, parameters of distributions are usually estimated from training samples. It is well known that shortage of training samples causes estimation errors, which reduce recognition accuracy. By studying estimation errors of eigenvalues, various methods of avoiding recognition accuracy reduction have been proposed. However, estimation errors of eigenvectors have not been considered enough. In this paper, we investigate estimation errors of eigenvectors to show these errors are another factor of recognition performance reduction. We propose a new method for modifying eigenvalues in order to reduce bad influence caused by estimation errors of eigenvectors. Effectiveness of the method is shown by experimental results.",
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"content": "In statistical pattern recognition, parameters of distributions are usually estimated from training samples. It is well known that shortage of training samples causes estimation errors, which reduce recognition accuracy. By studying estimation errors of eigenvalues, various methods of avoiding recognition accuracy reduction have been proposed. However, estimation errors of eigenvectors have not been considered enough. In this paper, we investigate estimation errors of eigenvectors to show these errors are another factor of recognition performance reduction. We propose a new method for modifying eigenvalues in order to reduce bad influence caused by estimation errors of eigenvectors. Effectiveness of the method is shown by experimental results.",
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"fno": "07502378",
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"abstract": "In this paper, we present an implementation of the block power method based on Spark, the big data processing framework, to approximate the dominant eigenvalues and eigenvectors of a large, sparse matrix in distributed computing environment. To take advantage of graph-parallel computation in Spark, we employ the property graph with specially defined vertex and edge types to represent the sparse matrix and the associated block matrix together. Graph operations are then performed on the constructed property graph to efficiently carry out the iteration and decomposition steps of the block power method in parallel. The numerical results on a Markov chain application for modeling stochastic luminal Calcium release site are provided to demonstrate the effectiveness and scalability of the block power method implementation.",
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"abstract": "Spectral graph theory gives a useful approach to analyzing network structure based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights. However, in large scale and complex social networks, since it is difficult to know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we consider a method for indirectly determining a Laplacian matrix from its eigenvalues and eigenvectors. As the first step, our prior study proposed a method for estimating eigenvalues of a Laplacian matrix by using the resonance of oscillation dynamics on networks with no a priori information about the network structure, and showed the effectiveness of this method. In this paper, we propose a method for estimating the eigenvectors of a Laplacian matrix by once again using the resonance of oscillation dynamics on networks.",
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"content": "Understanding the inner workings of deep neural networks (DNNs) is essential to provide trustworthy artificial intelligence techniques for practical applications. Existing studies typically involve linking semantic concepts to units or layers of DNNs, but fail to explain the inference process. In this paper, we introduce neural architecture disentanglement (NAD) to fill the gap. Specifically, NAD learns to disentangle a pre-trained DNN into sub-architectures according to independent tasks, forming information flows that describe the inference processes. We investigate whether, where, and how the disentanglement occurs through experiments conducted with handcrafted and automatically-searched network architectures, on both object-based and scene-based datasets. Based on the experimental results, we present three new findings that provide fresh insights into the inner logic of DNNs. First, DNNs can be divided into sub-architectures for independent tasks. Second, deeper layers do not always correspond to higher semantics. Third, the connection type in a DNN affects how the information flows across layers, leading to different disentanglement behaviors. With NAD, we further explain why DNNs sometimes give wrong predictions. Experimental results show that misclassified images have a high probability of being assigned to task sub-architectures similar to the correct ones. Our code is available at https://github.com/hujiecpp/NAD.",
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"affiliation": "Xiamen University,MAC Lab, School of Informatics",
"fullName": "Jie Hu",
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"fullName": "Liujuan Cao",
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"surname": "Cao",
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"affiliation": "Xiamen University,MAC Lab, School of Informatics",
"fullName": "Tong Tong",
"givenName": "Tong",
"surname": "Tong",
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{
"affiliation": "University of Chinese Academy of Sciences",
"fullName": "Qixiang Ye",
"givenName": "Qixiang",
"surname": "Ye",
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{
"affiliation": "Xiamen University,MAC Lab, School of Informatics",
"fullName": "Shengchuan Zhang",
"givenName": "Shengchuan",
"surname": "Zhang",
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{
"affiliation": "Tencent Youtu Lab",
"fullName": "Ke Li",
"givenName": "Ke",
"surname": "Li",
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"affiliation": "Tencent Youtu Lab",
"fullName": "Feiyue Huang",
"givenName": "Feiyue",
"surname": "Huang",
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"affiliation": "Inception Institute of Artificial Intelligence",
"fullName": "Ling Shao",
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"surname": "Shao",
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"affiliation": "Xiamen University,MAC Lab, School of Informatics",
"fullName": "Rongrong Ji",
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"surname": "Ji",
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"abstract": "Systematic techniques to improve quality of deep neural networks (DNNs) are critical given the increasing demand for practical applications including safety-critical ones. The key challenge comes from the little controllability in updating DNNs. Retraining to fix some behavior often has a destructive impact on other behavior, causing regressions, i.e., the updated DNN fails with inputs correctly handled by the original one. This problem is crucial when engineers are required to investigate failures in intensive assurance activities for safety or trust. Search-based repair techniques for DNNs have potentials to tackle this challenge by enabling localized updates only on “responsible parameters” inside the DNN. However, the potentials have not been explored to realize sufficient controllability to suppress regressions in DNN repair tasks. In this paper, we propose a novel DNN repair method that makes use of the training history for judging which DNN parameters should be changed or not to suppress regressions. We implemented the method into a tool called Neurecover and evaluated it with three datasets. Our method outperformed the existing method by achieving often less than a quarter, even a tenth in some cases, number of regressions. Our method is especially effective when the repair requirements are tight to fix specific failure types. In such cases, our method showed stably low rates (<2 %) of regressions, which were in many cases a tenth of regressions caused by retraining.",
"abstracts": [
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"content": "Systematic techniques to improve quality of deep neural networks (DNNs) are critical given the increasing demand for practical applications including safety-critical ones. The key challenge comes from the little controllability in updating DNNs. Retraining to fix some behavior often has a destructive impact on other behavior, causing regressions, i.e., the updated DNN fails with inputs correctly handled by the original one. This problem is crucial when engineers are required to investigate failures in intensive assurance activities for safety or trust. Search-based repair techniques for DNNs have potentials to tackle this challenge by enabling localized updates only on “responsible parameters” inside the DNN. However, the potentials have not been explored to realize sufficient controllability to suppress regressions in DNN repair tasks. In this paper, we propose a novel DNN repair method that makes use of the training history for judging which DNN parameters should be changed or not to suppress regressions. We implemented the method into a tool called Neurecover and evaluated it with three datasets. Our method outperformed the existing method by achieving often less than a quarter, even a tenth in some cases, number of regressions. Our method is especially effective when the repair requirements are tight to fix specific failure types. In such cases, our method showed stably low rates (<2 %) of regressions, which were in many cases a tenth of regressions caused by retraining.",
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"abstract": "Deep neural networks (DNNs) have already achieved great success in a lot of application areas and brought profound changes to our society. However, it also raises new security problems, among which how to protect the intellectual property (IP) of DNNs against infringement is one of the most important yet very challenging topics. To deal with this problem, recent studies focus on the IP protection of DNNs by applying digital watermarking, which embeds source information and/or authentication data into DNN models by tuning network parameters directly or indirectly. However, tuning network parameters inevitably distorts the DNN and therefore surely impairs the performance of the DNN model on its original task regardless of the degree of the performance degradation. It has motivated the authors in this paper to propose a novel technique called pooled membership inference (PMI) so as to protect the IP of the DNN models. The proposed PMI neither alters the network parameters of the given DNN model nor fine-tunes the DNN model with a sequence of carefully crafted trigger samples. Instead, it leaves the original DNN model unchanged, but can determine the ownership of the DNN model by inferring which mini-dataset among multiple mini-datasets was once used to train the target DNN model, which differs from previous arts and has remarkable potential in practice. Experiments also have demonstrated the superiority and applicability of this work.",
"abstracts": [
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"abstractType": "Regular",
"content": "Deep neural networks (DNNs) have already achieved great success in a lot of application areas and brought profound changes to our society. However, it also raises new security problems, among which how to protect the intellectual property (IP) of DNNs against infringement is one of the most important yet very challenging topics. To deal with this problem, recent studies focus on the IP protection of DNNs by applying digital watermarking, which embeds source information and/or authentication data into DNN models by tuning network parameters directly or indirectly. However, tuning network parameters inevitably distorts the DNN and therefore surely impairs the performance of the DNN model on its original task regardless of the degree of the performance degradation. It has motivated the authors in this paper to propose a novel technique called pooled membership inference (PMI) so as to protect the IP of the DNN models. The proposed PMI neither alters the network parameters of the given DNN model nor fine-tunes the DNN model with a sequence of carefully crafted trigger samples. Instead, it leaves the original DNN model unchanged, but can determine the ownership of the DNN model by inferring which mini-dataset among multiple mini-datasets was once used to train the target DNN model, which differs from previous arts and has remarkable potential in practice. Experiments also have demonstrated the superiority and applicability of this work.",
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"normalizedAbstract": "Deep neural networks (DNNs) have already achieved great success in a lot of application areas and brought profound changes to our society. However, it also raises new security problems, among which how to protect the intellectual property (IP) of DNNs against infringement is one of the most important yet very challenging topics. To deal with this problem, recent studies focus on the IP protection of DNNs by applying digital watermarking, which embeds source information and/or authentication data into DNN models by tuning network parameters directly or indirectly. However, tuning network parameters inevitably distorts the DNN and therefore surely impairs the performance of the DNN model on its original task regardless of the degree of the performance degradation. It has motivated the authors in this paper to propose a novel technique called pooled membership inference (PMI) so as to protect the IP of the DNN models. The proposed PMI neither alters the network parameters of the given DNN model nor fine-tunes the DNN model with a sequence of carefully crafted trigger samples. Instead, it leaves the original DNN model unchanged, but can determine the ownership of the DNN model by inferring which mini-dataset among multiple mini-datasets was once used to train the target DNN model, which differs from previous arts and has remarkable potential in practice. Experiments also have demonstrated the superiority and applicability of this work.",
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"affiliation": "Shanghai University,Shanghai,China,200444",
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"title": "A Search-Based Testing Framework for Deep Neural Networks of Source Code Embedding",
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"abstract": "Over the past few years, deep neural networks (DNNs) have been continuously expanding their real-world applications for source code processing tasks across the software engineering domain, e.g., clone detection, code search, comment generation. Although quite a few recent works have been performed on testing of DNNs in the context of image and speech processing, limited progress has been achieved so far on DNN testing in the context of source code processing, that exhibits rather unique characteristics and challenges.In this paper, we propose a search-based testing framework for DNNs of source code embedding and its downstream processing tasks like Code Search. To generate new test inputs, we adopt popular source code refactoring tools to generate the semantically equivalent variants. For more effective testing, we leverage the DNN mutation testing to guide the testing direction. To demonstrate the usefulness of our technique, we perform a large-scale evaluation on popular DNNs of source code processing based on multiple state-of-the-art code embedding methods (i.e., Code2vec, Code2seq and CodeBERT). The testing results show that our generated adversarial samples can on average reduce the performance of these DNNs from 5.41% to 9.58%. Through retraining the DNNs with our generated adversarial samples, the robustness of DNN can improve by 23.05% on average. The evaluation results also show that our adversarial test generation strategy has the least negative impact (median of 3.56%), on the performance of the DNNs for regular test data, compared to the other methods.",
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"content": "Over the past few years, deep neural networks (DNNs) have been continuously expanding their real-world applications for source code processing tasks across the software engineering domain, e.g., clone detection, code search, comment generation. Although quite a few recent works have been performed on testing of DNNs in the context of image and speech processing, limited progress has been achieved so far on DNN testing in the context of source code processing, that exhibits rather unique characteristics and challenges.In this paper, we propose a search-based testing framework for DNNs of source code embedding and its downstream processing tasks like Code Search. To generate new test inputs, we adopt popular source code refactoring tools to generate the semantically equivalent variants. For more effective testing, we leverage the DNN mutation testing to guide the testing direction. To demonstrate the usefulness of our technique, we perform a large-scale evaluation on popular DNNs of source code processing based on multiple state-of-the-art code embedding methods (i.e., Code2vec, Code2seq and CodeBERT). The testing results show that our generated adversarial samples can on average reduce the performance of these DNNs from 5.41% to 9.58%. Through retraining the DNNs with our generated adversarial samples, the robustness of DNN can improve by 23.05% on average. The evaluation results also show that our adversarial test generation strategy has the least negative impact (median of 3.56%), on the performance of the DNNs for regular test data, compared to the other methods.",
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"fullName": "Maryam Vahdat Pour",
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"affiliation": "Kyushu University,Department of Information Science and Electrical Engineering,Japan",
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"abstract": "Tachie painting is an emerging digital portrait art form that shows a character in a standing pose. Automatic generation of a Tachie picture from a real photo would facilitate many creation tasks. However, it is non-trivial to represent Tachie’s artistic styles and establish a delicate mapping from the real-world image domain to the Tachie domain. Existing approaches generally suffer from inaccurate style transformation and severe structure distortion when applied to Tachie style transfer. In this paper, we propose the first approach for Tachie stylization of portrait photographs. Based on the unsupervised CycleGAN framework, we design two novel loss functions to emphasize lines and tones in the Tachie style. Furthermore, we design a character-enhanced stylization framework by introducing an auxiliary body mask to better preserve the global body structure. Experiment results demonstrate the robustness and better generation capability of our method in Tachie stylization from photos in a wide range of poses, even trained on a small dataset.",
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"abstract": "Neural style transfer (NST) is a technique based on deep learning that preserves the content of an image and converts its style to a target style. In recent years, NST has been widely used to generate new artworks based on existent styles to promote cultural communication. However, there is little research that considers the protection of copyright during the generation of stylised images. To this end, we propose an end-to-end protectable style transfer network based on steganography, called PSTNet. This network, including a pair of encoder and decoder, takes a content image and copyright information as input. The encoder embeds copyright information directly into the input content image and render the content image in a specific style. When the copyright needs to be verified, only the corresponding decoder can extract copyright information correctly. Furthermore, an elaborated designed noise layer is added between the encoder and decoder to improve the robustness of the copyright protection method. Experiments show that the protectable stylised images generated by PSTNet have significant visual effects and the undetectability of copyright information is proved by steganalysis. In addition, our method is robust enough that the copyright of generated stylised images can still be proved even after spreading on real social networks. We hope this work will raise awareness of the protection of artworks created by NST.",
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"content": "Neural style transfer (NST) is a technique based on deep learning that preserves the content of an image and converts its style to a target style. In recent years, NST has been widely used to generate new artworks based on existent styles to promote cultural communication. However, there is little research that considers the protection of copyright during the generation of stylised images. To this end, we propose an end-to-end protectable style transfer network based on steganography, called PSTNet. This network, including a pair of encoder and decoder, takes a content image and copyright information as input. The encoder embeds copyright information directly into the input content image and render the content image in a specific style. When the copyright needs to be verified, only the corresponding decoder can extract copyright information correctly. Furthermore, an elaborated designed noise layer is added between the encoder and decoder to improve the robustness of the copyright protection method. Experiments show that the protectable stylised images generated by PSTNet have significant visual effects and the undetectability of copyright information is proved by steganalysis. In addition, our method is robust enough that the copyright of generated stylised images can still be proved even after spreading on real social networks. We hope this work will raise awareness of the protection of artworks created by NST.",
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"normalizedAbstract": "Neural style transfer (NST) is a technique based on deep learning that preserves the content of an image and converts its style to a target style. In recent years, NST has been widely used to generate new artworks based on existent styles to promote cultural communication. However, there is little research that considers the protection of copyright during the generation of stylised images. To this end, we propose an end-to-end protectable style transfer network based on steganography, called PSTNet. This network, including a pair of encoder and decoder, takes a content image and copyright information as input. The encoder embeds copyright information directly into the input content image and render the content image in a specific style. When the copyright needs to be verified, only the corresponding decoder can extract copyright information correctly. Furthermore, an elaborated designed noise layer is added between the encoder and decoder to improve the robustness of the copyright protection method. Experiments show that the protectable stylised images generated by PSTNet have significant visual effects and the undetectability of copyright information is proved by steganalysis. In addition, our method is robust enough that the copyright of generated stylised images can still be proved even after spreading on real social networks. We hope this work will raise awareness of the protection of artworks created by NST.",
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"title": "Chameleon: Image Style Transfer Based on Image Classification Networks",
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"abstract": "In recent years, deep neural networks enabled computers to extract features of images; this stimulated interest in image style transfer. Since the first NST algorithm, many schemes are proposed to accelerate the process. Yet, these methods more or less sacrifice the visual quality of the output. In this paper, we analyze two precursive algorithms and generalize the results. We propose an Image Style Transfer method based on Image Classification Networks. With Chameleon, we accelerate the runtime by 11 times and we show that general CNN techniques would improve the stylization process. Our work provides novel insights into the classification capacity of CNNs and demonstrates the potential of NST algorithms.",
"abstracts": [
{
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"content": "In recent years, deep neural networks enabled computers to extract features of images; this stimulated interest in image style transfer. Since the first NST algorithm, many schemes are proposed to accelerate the process. Yet, these methods more or less sacrifice the visual quality of the output. In this paper, we analyze two precursive algorithms and generalize the results. We propose an Image Style Transfer method based on Image Classification Networks. With Chameleon, we accelerate the runtime by 11 times and we show that general CNN techniques would improve the stylization process. Our work provides novel insights into the classification capacity of CNNs and demonstrates the potential of NST algorithms.",
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"affiliation": "NYU Shanghai USTC,China",
"fullName": "Haobo Li",
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"affiliation": "USTC,School of Computer Science,China",
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"affiliation": "NYU Shanghai USTC,China",
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"abstract": "The novel COVID-19 pandemic has posed unprecedented challenges to the society and the health sector all over the globe. Here, we present a new network-based methodology to analyze COVID-19 data measures and its application on a real dataset. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar dataset, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/",
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"title": "Forecasting the COVID-19 Pandemic in Bangladesh Using ARIMA Model",
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"abstract": "The effects of the coronavirus disease in 2019 are visible in every corner of the globe. The public health system is mostly affected, and the economic and social crises are also increasing day by day. Due to the widespread nature and the unavailability of drugs or vaccines for this pandemic, it is urgent to predict the COVID-19 infected cases to handle the situation more efficiently. Time series prediction is a crucial technique of the machine learning domain to deal with the issue. This research aims to predict the number of daily confirmed COVID-19 cases for a successful time. To forecast COVID-19 instances in Bangladesh, we use the Autoregressive Integrated Moving Average (ARIMA) model. The experimental results show that the estimated best models are: ARIMA(3,1,0) with drift, ARIMA(3,1,2) with drift, ARIMA(5,1,0) perform significant predictions on three different kinds of COVID-19 datasets.",
"abstracts": [
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"content": "The effects of the coronavirus disease in 2019 are visible in every corner of the globe. The public health system is mostly affected, and the economic and social crises are also increasing day by day. Due to the widespread nature and the unavailability of drugs or vaccines for this pandemic, it is urgent to predict the COVID-19 infected cases to handle the situation more efficiently. Time series prediction is a crucial technique of the machine learning domain to deal with the issue. This research aims to predict the number of daily confirmed COVID-19 cases for a successful time. To forecast COVID-19 instances in Bangladesh, we use the Autoregressive Integrated Moving Average (ARIMA) model. The experimental results show that the estimated best models are: ARIMA(3,1,0) with drift, ARIMA(3,1,2) with drift, ARIMA(5,1,0) perform significant predictions on three different kinds of COVID-19 datasets.",
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"affiliation": "Patuakhali Science and Technology University,Department of Computer Science and Information Technology,Bangladesh",
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"affiliation": "Patuakhali Science and Technology University,Department of Computer and Communication Engineering,Bangladesh",
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"abstract": "In order to solve nonlinear, non-stationary and complex problem with the time series in practical production and life, a multiple regression model for time series analysis is used in this paper. By introducing the principle of multiple regression, the multivariate time series analysis model not only overcome random factors of the time series, but also consider the many factors affecting the development of things, so as to improve forecasting accuracy and increase the reliability of predictio. For illustration, an example of a business forecast is utilized to show the feasibility of the multivariate time series analysis model in solving nonlinear, non-stationary and complex problem with the time series in practical production and life. Empirical results show that using the model in the case of known factors, combined with experimental data, can effective forecast for corporate earnings. This multivariate time series analysis model effective solution to the nonlinear time series, non-stationary and complex issues, so as to provide decision-making basis with an accurate quantitative and intuitive for decision makers.",
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"abstract": "The advent of the big data era has led to the explosive growth of multivariate and multi-channel time series data. Multivariate time series, because of their high dimensional and Spatio-temporal correlation characteristics, make it difficult for classical statistical approaches to effectively model and efficiently handle the Spatio-temporal characteristics among data. Therefore, a multivariate time series prediction model based on a graph convolutional neural network is proposed better to explore the spatial relationships between multivariate time series and predict multivariate time series more accurately. The spatial correlation between each monitoring point in the data monitoring system is extracted using the graph convolutional neural network, and the temporal features in the sequences are processed using GRU. Finally, it is normalized. Experiments prove that the GCN-GRU model has higher prediction accuracy and better results than other commonly used prediction methods.",
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"content": "The advent of the big data era has led to the explosive growth of multivariate and multi-channel time series data. Multivariate time series, because of their high dimensional and Spatio-temporal correlation characteristics, make it difficult for classical statistical approaches to effectively model and efficiently handle the Spatio-temporal characteristics among data. Therefore, a multivariate time series prediction model based on a graph convolutional neural network is proposed better to explore the spatial relationships between multivariate time series and predict multivariate time series more accurately. The spatial correlation between each monitoring point in the data monitoring system is extracted using the graph convolutional neural network, and the temporal features in the sequences are processed using GRU. Finally, it is normalized. Experiments prove that the GCN-GRU model has higher prediction accuracy and better results than other commonly used prediction methods.",
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"affiliation": "School of Mathematics & Computer Science Wuhan Polytechnic University,Wuhan,Hubei",
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"title": "2019 IEEE International Congress on Internet of Things (ICIOT)",
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"doi": "10.1109/ICIOT.2019.00024",
"title": "ActSen - AI-Enabled Real-Time IoT-Based Ergonomic Risk Assessment System",
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"abstract": "Musculoskeletal Disorders (MSDs) are injuries and disorders that affect the human body's movement or musculoskeletal system. There are three primary ergonomic MSD risk factors, High task repetition, Forceful exertions and Repetitive awkward postures. Exposure to these workplace risk factors fatigue the worker's body beyond their ability to recover, leading to MSD. A variety of ergonomic risk assessment tools have been developed such as Rodgers Muscle Fatigue Analysis to help to evaluate the risk of MSD so that early intervention can be applied to prevent the development of an MSD. However, ergonomic risk assessment tools are usually carried out using subjective observational methods, which require a field expert performing a time-consuming analysis of the postures on site. Monitoring workers under staged environment and high manpower cost make observational methods impractical and not accurate to conduct ergonomic risk assessment especially for dynamic and nonroutine work. In this paper, ActSen, a real-time ergonomic risk assessment system is proposed. ActSen leverages on the recent development of embedded and Artificial Intelligent (AI) technologies. ActSen can continuously (a) acquire workers activities/postures data using various sensors, (b) process, classify and tabulate the workers movements using AI algorithms, (c) conduct real ergonomic risk assessment based on the detected activities/postures, and (d) output to interactive dashboard to facilitate smart scheduling and provide assistance when needed.",
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"content": "Musculoskeletal Disorders (MSDs) are injuries and disorders that affect the human body's movement or musculoskeletal system. There are three primary ergonomic MSD risk factors, High task repetition, Forceful exertions and Repetitive awkward postures. Exposure to these workplace risk factors fatigue the worker's body beyond their ability to recover, leading to MSD. A variety of ergonomic risk assessment tools have been developed such as Rodgers Muscle Fatigue Analysis to help to evaluate the risk of MSD so that early intervention can be applied to prevent the development of an MSD. However, ergonomic risk assessment tools are usually carried out using subjective observational methods, which require a field expert performing a time-consuming analysis of the postures on site. Monitoring workers under staged environment and high manpower cost make observational methods impractical and not accurate to conduct ergonomic risk assessment especially for dynamic and nonroutine work. In this paper, ActSen, a real-time ergonomic risk assessment system is proposed. ActSen leverages on the recent development of embedded and Artificial Intelligent (AI) technologies. ActSen can continuously (a) acquire workers activities/postures data using various sensors, (b) process, classify and tabulate the workers movements using AI algorithms, (c) conduct real ergonomic risk assessment based on the detected activities/postures, and (d) output to interactive dashboard to facilitate smart scheduling and provide assistance when needed.",
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"affiliation": "Nanyang Polytechnic",
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"title": "Risk Assessment Model of College Students' New Ventures Based on Big Data Analysis and Fuzzy Evaluation",
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"abstract": "In recent decades, the number of newly-employed people in China has continued to grow. At the same time, with the expansion of universities, the number of university graduates has also grown linearly. Based on the employment pressures of society and college graduates, the state clearly puts forward the slogan of promoting employment through entrepreneurship, and college students have become the focal point for responding to this slogan. This paper proposes a fuzzy comprehensive evaluation model and method for college students' online entrepreneurship risk. The research status of college students' entrepreneurship risk assessment at home and abroad is analyzed, and the reasons for the high error rate of College Students' entrepreneurship risk assessment are found. Then the model of risk assessment is established based on the support vector machine of large data analysis method. Finally, the effectiveness and superiority of the algorithm are tested through specific simulation and comparison experiments, which provided a scientific basis for the college students' online venture risk aversion strategies.",
"abstracts": [
{
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"content": "In recent decades, the number of newly-employed people in China has continued to grow. At the same time, with the expansion of universities, the number of university graduates has also grown linearly. Based on the employment pressures of society and college graduates, the state clearly puts forward the slogan of promoting employment through entrepreneurship, and college students have become the focal point for responding to this slogan. This paper proposes a fuzzy comprehensive evaluation model and method for college students' online entrepreneurship risk. The research status of college students' entrepreneurship risk assessment at home and abroad is analyzed, and the reasons for the high error rate of College Students' entrepreneurship risk assessment are found. Then the model of risk assessment is established based on the support vector machine of large data analysis method. Finally, the effectiveness and superiority of the algorithm are tested through specific simulation and comparison experiments, which provided a scientific basis for the college students' online venture risk aversion strategies.",
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"normalizedAbstract": "In recent decades, the number of newly-employed people in China has continued to grow. At the same time, with the expansion of universities, the number of university graduates has also grown linearly. Based on the employment pressures of society and college graduates, the state clearly puts forward the slogan of promoting employment through entrepreneurship, and college students have become the focal point for responding to this slogan. This paper proposes a fuzzy comprehensive evaluation model and method for college students' online entrepreneurship risk. The research status of college students' entrepreneurship risk assessment at home and abroad is analyzed, and the reasons for the high error rate of College Students' entrepreneurship risk assessment are found. Then the model of risk assessment is established based on the support vector machine of large data analysis method. Finally, the effectiveness and superiority of the algorithm are tested through specific simulation and comparison experiments, which provided a scientific basis for the college students' online venture risk aversion strategies.",
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"authors": [
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"affiliation": "Chongqing Vocational Institute of Engineering, Chongqing, China",
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"abstract": "The next point-of-interest (POI) recommendation is indispensable in enhancing the richness of users’ lives and helping service providers achieve more economic earnings. Recurrent Neural Network (RNN) based methods are remarkable in learning users’ long-term or short-term behavioral dependencies. However, existing RNN-based methods lack sufficient interaction with their contexts, and at the same time, ignore the importance of non-consecutive POIs with different degrees for understanding users’ behaviors. In order to solve these problems, we propose a novel Spatio-Temporal model based on mogrifier LSTM and attention network (named STMLA) for next POI recommendation. The STMLA model builds a parallel structure to process the users’ check-in sequences through the mogrifier LSTM and the multi-head attention network, which can achieve better contextual interaction while selectively considering nonconsecutive factors with different degrees of significance. Our STMLA algorithm explicitly integrates temporal and spatial information to capture users’ long-term and short-term preferences, incorporating spatial information to build the Location-Saltant algorithm. Through extensive experiments on several real-world datasets, we demonstrate that our model outperforms the existing state-of-the-art methods in the next POI recommendation task.",
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"content": "The next point-of-interest (POI) recommendation is indispensable in enhancing the richness of users’ lives and helping service providers achieve more economic earnings. Recurrent Neural Network (RNN) based methods are remarkable in learning users’ long-term or short-term behavioral dependencies. However, existing RNN-based methods lack sufficient interaction with their contexts, and at the same time, ignore the importance of non-consecutive POIs with different degrees for understanding users’ behaviors. In order to solve these problems, we propose a novel Spatio-Temporal model based on mogrifier LSTM and attention network (named STMLA) for next POI recommendation. The STMLA model builds a parallel structure to process the users’ check-in sequences through the mogrifier LSTM and the multi-head attention network, which can achieve better contextual interaction while selectively considering nonconsecutive factors with different degrees of significance. Our STMLA algorithm explicitly integrates temporal and spatial information to capture users’ long-term and short-term preferences, incorporating spatial information to build the Location-Saltant algorithm. Through extensive experiments on several real-world datasets, we demonstrate that our model outperforms the existing state-of-the-art methods in the next POI recommendation task.",
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"normalizedAbstract": "The next point-of-interest (POI) recommendation is indispensable in enhancing the richness of users’ lives and helping service providers achieve more economic earnings. Recurrent Neural Network (RNN) based methods are remarkable in learning users’ long-term or short-term behavioral dependencies. However, existing RNN-based methods lack sufficient interaction with their contexts, and at the same time, ignore the importance of non-consecutive POIs with different degrees for understanding users’ behaviors. In order to solve these problems, we propose a novel Spatio-Temporal model based on mogrifier LSTM and attention network (named STMLA) for next POI recommendation. The STMLA model builds a parallel structure to process the users’ check-in sequences through the mogrifier LSTM and the multi-head attention network, which can achieve better contextual interaction while selectively considering nonconsecutive factors with different degrees of significance. Our STMLA algorithm explicitly integrates temporal and spatial information to capture users’ long-term and short-term preferences, incorporating spatial information to build the Location-Saltant algorithm. Through extensive experiments on several real-world datasets, we demonstrate that our model outperforms the existing state-of-the-art methods in the next POI recommendation task.",
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"affiliation": "Chongqing University of Technology,School of Artificial Intelligence,Chongqing,China",
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"affiliation": "Chongqing University of Technology,School of Artificial Intelligence,Chongqing,China",
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"affiliation": "Chongqing University of Technology,School of Artificial Intelligence,Chongqing,China",
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"affiliation": "Chongqing University of Technology,School of Artificial Intelligence,Chongqing,China",
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"affiliation": "Beijing University of Posts and Telecommunications",
"fullName": "Weihong Wu",
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"abstract": "Experts combating wildlife trafficking manually sift through articles about seizures and arrests, which is time consuming and make identifying trends difficult. We apply natural language processing techniques to automatically extract data from reports published by the Eco Activists for Governance and Law Enforcement (EAGLE). We expanded Python spaCy's pre-trained pipeline and added a custom named entity ruler, which identified 15 fully correct and 36 partially correct events in 15 reports against an existing baseline, which did not identify any fully correct events. The extracted wildlife trafficking events were inserted to a database. Then, we created visualizations to display trends over time and across regions to support domain experts. These are accessible on our website, Wildlife Trafficking in Africa.",
"abstracts": [
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"content": "Experts combating wildlife trafficking manually sift through articles about seizures and arrests, which is time consuming and make identifying trends difficult. We apply natural language processing techniques to automatically extract data from reports published by the Eco Activists for Governance and Law Enforcement (EAGLE). We expanded Python spaCy's pre-trained pipeline and added a custom named entity ruler, which identified 15 fully correct and 36 partially correct events in 15 reports against an existing baseline, which did not identify any fully correct events. The extracted wildlife trafficking events were inserted to a database. Then, we created visualizations to display trends over time and across regions to support domain experts. These are accessible on our website, Wildlife Trafficking in Africa.",
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"abstract": "The world immediately studied Coronavirus Disease 2019 (COVID-19) and raced towards finding the cure and developing an effective treatment. An automated approach is needed to discover drug candidates and provide those data to facilitate clinical trials in saving time and only focusing on the candidates which potentially become the cure for COVID-19. We propose the Drug Candidates for the Prevention of COVID-19 (DCPC) Database. DCPC Database provides a list of candidates of potential drugs for the prevention of COVID-19 based on disease-drug associations which are automatically discovered from biomedical literature. DCPC database is an integrative structural database, which involves a chemical database repository, such as PubChem and DrugBank to ensure that drug compound candidates have a standard representation of compounds. The database provides keyword-chosen categories and a determination of minimum supported articles for search, a list of drug candidates in the sorted table followed by the detail for each candidate, and a download feature. The keyword category consists of three keywords, they are Chinese herbal compounds, Indian medicinal plants, and Indian medicinal plants & diabetic treatment herbs. Each candidate links to an article in the biomedical literature and to a page of the compound structure visualization. DCPC is freely available at https://dcpc.brin.go.id/dcpc/.",
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"Patient Treatment",
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"affiliation": "Universitas Brawijaya,Faculty of Medicine,Department of Mental Health Nursing,Indonesia",
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"affiliation": "National Formosa University,Department of Computer Science and Information Engineering,Taiwan",
"fullName": "Chien-Hung Huang",
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"affiliation": "Asia University,Department of Bioinformatics and Medical Engineering,Taiwan",
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"abstract": "Recent developments in the fields of medicinal chemistry and computer science have made possible to perform rational drug design, helping reduce the cost and time required to discover new therapeutic agents. Particularly, quantitative structure-activity relationship (QSAR) studies have been widely employed to correlate molecular structure to biological activity. Such analyses take into account a set of chemical compounds encoded by numerical descriptors and biological data for generating mathematical models capable to predict biological activity values for structurally similar unknown compounds. Although a huge amount of chemical and biological data has become publicly available, there is an increasing need for integrating and sharing the data from various repositories in order to derive a reusable domain knowledge base. Attempting to overcome such limitation, this article proposes an ontology for QSAR studies, named OntoQSAR, which describes the major concepts in these analyses, like methods used to obtain chemical descriptors and biological properties of chemical compounds. Since the compounds of interest in these studies must be subjected to the same methods for obtaining the necessary numerical descriptors, the developed ontology will allow that information to be reused in other related researches.",
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"content": "Recent developments in the fields of medicinal chemistry and computer science have made possible to perform rational drug design, helping reduce the cost and time required to discover new therapeutic agents. Particularly, quantitative structure-activity relationship (QSAR) studies have been widely employed to correlate molecular structure to biological activity. Such analyses take into account a set of chemical compounds encoded by numerical descriptors and biological data for generating mathematical models capable to predict biological activity values for structurally similar unknown compounds. Although a huge amount of chemical and biological data has become publicly available, there is an increasing need for integrating and sharing the data from various repositories in order to derive a reusable domain knowledge base. Attempting to overcome such limitation, this article proposes an ontology for QSAR studies, named OntoQSAR, which describes the major concepts in these analyses, like methods used to obtain chemical descriptors and biological properties of chemical compounds. Since the compounds of interest in these studies must be subjected to the same methods for obtaining the necessary numerical descriptors, the developed ontology will allow that information to be reused in other related researches.",
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"fullName": "Rafael G. Silveira",
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"affiliation": "University of Sao Paulo, Brazil",
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