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"abstract": "We propose a transformation invariant public fragile watermarking scheme for 3D model authentication. The proposed scheme embeds watermarks in a subset a model's faces to keep them in a predefined relationship with neighboring vertices so that any changes will ruin the relationship between the mark faces and neighboring vertices. The proposed scheme has the following advantages: It is invariant to translation, rotation, and uniform scaling operations. These operations do not change the integrity of the original model and should not be regarded as malicious attacks. The proposed method is public— the receiver does not need the original model and watermarks for authentication. It can locate changed regions. Distortion caused by the watermark embedding is controlled by the key settings according to the users' precision requirement. Experimental results show the scheme's effectiveness.",
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"abstract": "A new efficient biorthogonal wavelet analysis based on the √3 subdivision is proposed in the paper by using the lifting scheme. Since the √3 subdivision is of the slowest topological refinement among the traditional triangular subdivisions, the multiresolution analysis based on the √3 subdivision is more balanced than the existing wavelet analyses on triangular meshes, and accordingly offers more levels of detail for processing polygonal models. In order to optimize the multiresolution analysis process, the new wavelets, no matter whether they are interior or on boundaries, are orthogonalized with the local scaling functions based on a discrete inner product with subdivision masks. Because the wavelet analysis and synthesis algorithms are actually composed of a series of local lifting operations, they can be performed in linear time. The experiments demonstrate the efficiency and stability of the wavelet analysis for both closed and open triangular meshes with √3 subdivision connectivity. The √3-subdivision-based biorthogonal wavelets can be used in many applications such as progressive transmission, shape approximation, multiresolution editing and rendering of 3D geometric models.",
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"content": "A new efficient biorthogonal wavelet analysis based on the √3 subdivision is proposed in the paper by using the lifting scheme. Since the √3 subdivision is of the slowest topological refinement among the traditional triangular subdivisions, the multiresolution analysis based on the √3 subdivision is more balanced than the existing wavelet analyses on triangular meshes, and accordingly offers more levels of detail for processing polygonal models. In order to optimize the multiresolution analysis process, the new wavelets, no matter whether they are interior or on boundaries, are orthogonalized with the local scaling functions based on a discrete inner product with subdivision masks. Because the wavelet analysis and synthesis algorithms are actually composed of a series of local lifting operations, they can be performed in linear time. The experiments demonstrate the efficiency and stability of the wavelet analysis for both closed and open triangular meshes with √3 subdivision connectivity. The √3-subdivision-based biorthogonal wavelets can be used in many applications such as progressive transmission, shape approximation, multiresolution editing and rendering of 3D geometric models.",
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"title": "√3-Subdivision-Based Biorthogonal Wavelets",
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"abstract": "Abstract—This paper presents an event-driven approach that efficiently detects collisions among multiple ballistic spheres moving in the 3D space. Adopting a hierarchical uniform space subdivision scheme, we are able to trace the trajectories of spheres and their time-varying spatial distribution. We identify three types of events to detect the sequence of all collisions during our simulation: collision, entering, and leaving. The first type of events is due to actual collisions, and the other two types occur when spheres move from subspace to subspace in the space. Tracing all such events in the order of their occurring times, we are able to avoid fixed time step simulation. When the size of the largest sphere is bounded by a constant multiple of that of the smallest, it takes Z_$O\\left( {\\bar n_c\\ \\log\\ n+\\overline n_e\\ \\log\\ n} \\right)$_Z time with O(n) space after O(n log n) time preprocessing to simulate n moving spheres, where Z_$\\overline n_c$_Z and Z_$\\overline n_e$_Z are the number of actual collisions and that of entering and leaving events during the simulation, respectively. Since Z_$\\overline n_e$_Z depends on the size of subspaces, we modify the collision model from kinetic theory for molecular gas to determine the subspace sizes for the space subdivision scheme, that minimize simulation time. Experimental results show that collision detection can be done in linear time in n over a large range.",
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"content": "Abstract—This paper presents an event-driven approach that efficiently detects collisions among multiple ballistic spheres moving in the 3D space. Adopting a hierarchical uniform space subdivision scheme, we are able to trace the trajectories of spheres and their time-varying spatial distribution. We identify three types of events to detect the sequence of all collisions during our simulation: collision, entering, and leaving. The first type of events is due to actual collisions, and the other two types occur when spheres move from subspace to subspace in the space. Tracing all such events in the order of their occurring times, we are able to avoid fixed time step simulation. When the size of the largest sphere is bounded by a constant multiple of that of the smallest, it takes $O\\left( {\\bar n_c\\ \\log\\ n+\\overline n_e\\ \\log\\ n} \\right)$ time with O(n) space after O(n log n) time preprocessing to simulate n moving spheres, where $\\overline n_c$ and $\\overline n_e$ are the number of actual collisions and that of entering and leaving events during the simulation, respectively. Since $\\overline n_e$ depends on the size of subspaces, we modify the collision model from kinetic theory for molecular gas to determine the subspace sizes for the space subdivision scheme, that minimize simulation time. Experimental results show that collision detection can be done in linear time in n over a large range.",
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"abstract": "This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to as TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor. Specifically, a stable three-order tensor is first constructed from the normalized image, so as to enhance the robustness of our TD hashing. A popular TD algorithm, called Tucker decomposition, is then exploited to decompose the three-order tensor into a core tensor and three orthogonal factor matrices. As the factor matrices can reflect intrinsic structure of original tensor, hash construction with the factor matrices makes a desirable discrimination of the TD hashing. To examine these claims, there are 14,551 images selected for our experiments. A receiver operating characteristics (ROC) graph is used to conduct theoretical analysis and the ROC comparisons illustrate that the TD hashing outperforms some state-of-the-art algorithms in classification performance between the robustness and discrimination.",
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"content": "Intelligent transportation systems are a key component in smart cities, and the estimation and prediction of the spatiotemporal traffic state is critical to capture the dynamics of traffic congestion, i.e., its generation, propagation and mitigation, in order to increase operational efficiency and improve livability within smart cities. And while spatiotemporal data related to traffic is becoming common place due to the wide availability of cheap sensors and the rapid deployment of IoT platforms, the data still suffer some challenges related to sparsity, incompleteness, and noise which makes the traffic analytics difficult. In this article, we investigate the problem of missing data or noisy information in the context of real-time monitoring and forecasting of traffic congestion for road networks in a city. The road network is represented as a directed graph in which nodes are junctions (intersections) and edges are road segments. We assume that the city has deployed high-fidelity sensors for speed reading in a subset of edges; and the objective is to infer the speed readings for the remaining edges in the network; and to estimate the missing values in the segments for which sensors have stopped generating data due to technical problems (e.g., battery, network, etc.). We propose a tensor representation for the series of road network snapshots, and develop a regularized factorization method to estimate the missing values, while learning the latent factors of the network. The regularizer, which incorporates spatial properties of the road network, improves the quality of the results. The learned factors, with a graph-based temporal dependency, are then used in an autoregressive algorithm to predict the future state of the road network with a large horizon. Extensive numerical experiments with real traffic data from the cities of Doha (Qatar) and Aarhus (Denmark) demonstrate that the proposed approach is appropriate for imputing the missing data and predicting the traffic state. It is accurate and efficient and can easily be applied to other traffic datasets.",
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"abstract": "Turbines are essential components of jet planes and power plants. Therefore, their efficiency and service life are of central engineering interest. In the case of jet planes or thermal power plants, the heating of the turbines due to the hot gas flow is critical. Besides effective cooling, it is a major goal of engineers to minimize heat transfer between gas flow and turbine by design. Since it is known that splat events have a substantial impact on the heat transfer between flow and immersed surfaces, we adapt a splat detection and visualization method to a turbine cascade simulation in this case study. Because splat events are small phenomena, we use a direct numerical simulation resolving the turbulence in the flow as the base of our analysis. The outcome shows promising insights into splat formation and its relation to vortex structures. This may lead to better turbine design in the future.",
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"content": "Turbines are essential components of jet planes and power plants. Therefore, their efficiency and service life are of central engineering interest. In the case of jet planes or thermal power plants, the heating of the turbines due to the hot gas flow is critical. Besides effective cooling, it is a major goal of engineers to minimize heat transfer between gas flow and turbine by design. Since it is known that splat events have a substantial impact on the heat transfer between flow and immersed surfaces, we adapt a splat detection and visualization method to a turbine cascade simulation in this case study. Because splat events are small phenomena, we use a direct numerical simulation resolving the turbulence in the flow as the base of our analysis. The outcome shows promising insights into splat formation and its relation to vortex structures. This may lead to better turbine design in the future.",
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"content": "We introduce a 3D tree modeling technique that utilizes examples of real trees to enhance tree creation with realistic structures and fine-level details. In contrast to previous works that use smooth generalized cylinders to represent tree branches, our method generates realistic looking tree models with complex branching geometry by employing an exemplar database consisting of real-life trees reconstructed from scanned data. These trees are sliced into representative parts (denoted as tree-cuts), representing trunk logs and branching structures. In the modeling process, tree-cuts are positioned in space in an intuitive manner, serving as efficient proxies that guide the creation of the complete tree. Allometry rules are taken into account to ensure reasonable relations between adjacent branches. Realism is further enhanced by automatically transferring geometric textures from our database onto tree branches as well as by guided growing of foliage. Our results demonstrate the complexity and variety of trees that can be generated with our method within few minutes. We carry a user study to test the effectiveness of our modeling technique.",
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"abstract": "Coding schemes for the quantization of line drawings that outperform basic chain codes are investigated. First, subpixel accuracy reconstruction is obtained by a simple linear filtering of the chain code points, which achieves a factor-of-three to-four reduction in average reconstruction distortion for smooth curves. Second, the basic chain encoding schemes are generalized to a multipath-tree-searched encoding scheme. A variation of the (M,L)-algorithm is used to maintain M contending chain code paths in storage and choose the best path from these. Over a wide variety of source curves, tree-searched chain coding results in nearly an order of magnitude reduction in average reconstruction distortion over smoother chain codes. The performance improvement for curves is obtained with only a slight increase in bit rate over basic chain codes.",
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"content": "Coding schemes for the quantization of line drawings that outperform basic chain codes are investigated. First, subpixel accuracy reconstruction is obtained by a simple linear filtering of the chain code points, which achieves a factor-of-three to-four reduction in average reconstruction distortion for smooth curves. Second, the basic chain encoding schemes are generalized to a multipath-tree-searched encoding scheme. A variation of the (M,L)-algorithm is used to maintain M contending chain code paths in storage and choose the best path from these. Over a wide variety of source curves, tree-searched chain coding results in nearly an order of magnitude reduction in average reconstruction distortion over smoother chain codes. The performance improvement for curves is obtained with only a slight increase in bit rate over basic chain codes.",
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"abstract": "Tree modeling has been extensively studied in computer graphics. Recent advances in the development of high-resolution sensors and data processing techniques are extremely useful for collecting 3D datasets of real-world trees and generating increasingly plausible branching structures. The rapid development of versatile acquisition platforms allows us to capture multi-view images and scanned data that can be used for guided 3D tree modeling. In this paper, we carry out a comprehensive review of the state-of-the-art methods for the 3D modeling of botanical tree geometry by taking input data from real scenarios. A wide range of studies has been proposed following different approaches. To summarize the most relevant contributions, these have been classified into three categories: (1) procedural reconstruction, (2) geometry-based extraction, which is also divided into three groups (thinning, clustering and spanning tree refinement), and (3) image-based modeling. In addition, we describe other approaches aimed at improving the reconstruction process by adding additional features to achieve a realistic appearance of the tree models. Thus, we provide an overview of the most effective procedures to assist researchers in the photorealistic modeling of trees in geometry and appearance. The article concludes with remarks and trends for promising research opportunities in 3D tree modeling using real-world data.",
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"content": "Nowadays, fuzz testing has significantly expedited the vulnerability discovery of Linux kernel. Security analysts use the manifested error behaviors to infer the exploitability of one bug and thus prioritize the patch development. However, only using an error behavior in the report, security analysts might underestimate the exploitability of the kernel bug because it could manifest various error behaviors indicating different exploitation potentials. In this work, we conduct an empirical study on multiple error behaviors of kernel bugs to understand 1) the prevalence of multiple error behaviors and the possible impact of multiple error behaviors towards the exploitation potential; 2) the factors that manifest multiple error behaviors with different exploitation potential. We collected <italic>all the fixed kernel bugs</italic> reported on Syzbot from September 2017 to January 2022, including 3,352 bug reports. We observed that multiple error behaviors manifested by kernel bugs are prevalent in the real world, and more error behaviors help unveil the exploitability of kernel bugs. Then we organized Linux kernel experts to analyze a sample of kernel bug dataset (484 bug reports, unique 162 bugs) and identified 6 key contributing factors to the mutiple error behaviors. Finally, based on the empirical findings, we propose an object-driven fuzzing technique to explore all possible error behaviors that a kernel bug might bring about. To evaluate the utility of our proposed technique, we implement our fuzzing tool <monospace>GREBE</monospace> and apply it to 60 real-world Linux kernel bugs. On average, <monospace>GREBE</monospace> could manifest 2+ additional error behaviors for each of the kernel bugs. For 26 kernel bugs, <monospace>GREBE</monospace> discovers higher exploitation potential. We report to kernel vendors some of the bugs – the exploitability of which was wrongly assessed and the corresponding patch has not yet been carefully applied – resulting in their rapid patch adoption.",
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"abstract": "Search rank fraud, the fraudulent promotion of products hosted on peer-review sites, is driven by expert workers recruited online, often from crowdsourcing sites. In this paper we introduce the <italic>fraud de-anonymization</italic> problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in peer-review sites. We collect and study data from crowdsourced search rank fraud jobs, and survey the capabilities and behaviors of 58 search rank fraud workers recruited from 6 crowdsourcing sites. We collect a gold standard dataset of Google Play user accounts attributed to 23 crowdsourced workers and analyze their fraudulent behaviors in the wild. We propose <sc>Dolos</sc>, a fraud de-anonymization system that leverages traits and behaviors we extract from our studies, to attribute detected fraud to crowdsourcing site workers, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by <italic>different</italic> workers, and use stylometry and supervised learning to attribute them to crowdsourcing site profiles. <sc>Dolos</sc> correctly identified the owners of 95 percent of fraud worker-controlled communities, and uncovered fraud workers who promoted as many as 97.5 percent of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, <sc>Dolos</sc> identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts. <sc>Dolos</sc> significantly outperformed adapted dense subgraph detection and loopy belief propagation competitors, on two new coverage scores that measure the quality of detected community partitions.",
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"abstract": "<italic>Motivation:</italic> The prediction of drug-target interaction (DTI) from heterogeneous biological data is critical to predict drugs and therapeutic targets for known diseases such as tumor and bowel disease. The study of DTI based on drug representation learning can strengthen or integrate our knowledge of pharmacological and chemical phenomena. Therefore, there is a strong motivation to develop effective methods that can detect these potential drug-target interactions. <italic>Results:</italic> We have developed a novel Knowledge-Enhanced Multi-View framework (KEMV) to predict unknown DTIs from pharmacological data and chemical data on a large scale. The proposed method consists of two steps: (i) learning more comprehensive drug representations via the proposed multi-view attention mechanism, which bridges pharmacological and chemical information, and interactively summarizes the attention values depending on varying interactions between different pairs of drug features. (ii) predicting unknown drug-target interactions based on the drug and target representations. The method is tested on real-world dataset KEGG with three classes of important drug–target interactions involving enzymes, ion channels, and G-protein-coupled receptors. Our framework is proven to uncover potential DTIs with scientific evidences explaining the mechanism of the interactions through the processing of high-dimensional, heterogeneous, and sparse drug data. <italic>Availability:</italic> The originality of the proposed method lies in the attentive integration of pharmacological and chemical information for representation of drug candidate compounds and the prediction of drug-target interaction toward drug discovery in a unified framework. Our results are reproducible and and code is available at: <uri>https://github.com/YuanKQ/DTI-Prediction</uri>.",
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