nils-herrmann/clean_scirepeval huggingface dataset:
Statistical mesh distributions for 3D object topology

In this paper we introduce several statistical distributions to analyze the topological properties of 3D graphics. The proposed statistical measures include the mesh degree, the mesh assortativity, the mesh clustering coefficient, and the mesh geodesic distance distributions. These probabilistic distributions provide useful information about the way 3D mesh models are connected. Illustrating experimental results show the effectiveness of the proposed measures in quantifying the topological features of 3D objects.
A topological variational model for image singularities

Image singularities are prominent landmarks and their detection, recognition, and classification is a crucial step in image processing and computer vision. Such singularities carry important information for further operations, such as image registration, shape analysis, motion estimation, and object recognition. We propose a topological gradient descent flow for image singularities. The approach is expressed in the higher order variational framework as a minimizer of a variational integral involving the gradient and the Hessian matrix of the height function defined on a manifold. We demonstrate through numerical simulations the power of the proposed technique in preserving image singularities.
Nonlinear image filtering: trade-off between optimality and practicality

The high sensitivity of many specific filters to an accurate modeling of the noise that is to be removed led us to investigate the existence of a new class of filters using the theory of robust estimation. The latter class includes a large number of filters whose optimality when given a specific noise distribution is attained by merely adjusting weights. We also show that a convex combination of the mean and relaxed median filters exhibits many good properties. Some deterministic and asymptotic properties are studied, and comparisons with other filtering schemes are performed. Experimental results showing a much improved performance of the proposed filters in the presence of mixed Gaussian and heavy-tailed noise are analyzed and illustrated.
A Bayesian approach for software quality prediction

Many statistical algorithms have been proposed for software quality prediction of fault-prone and non fault-prone program modules. The main goal of these algorithms is the improvement of software development processes. In this paper, we introduce a new software prediction algorithm. Our approach is purely Bayesian and is based on finite Dirichlet mixture models. The implementation of the Bayesian approach is done through the use of the Gibbs sampler. Experimental results are presented using simulated data, and a real application for software modules classification is also included.
Nonlinear image filtering in a mixture of Gaussian and heavy-tailed noise

Inspired by robust estimation, nonlinear denoising methods combining the mean, the median, and the LogCauchy filters are proposed. Some statistical and asymptotic properties are studied, and comparisons with other nonlinear filtering schemes are performed. Experimental results showing a much improved performance of the proposed filters in the presence of Gaussian and heavy-tailed noise are analyzed and illustrated.
Structural risk minimization using nearest neighbor rule

We present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem. We propose a fast reference set thinning algorithm on the training data set similar to a support vector machine approach. We then show that the nearest neighbor rule based on the reduced set implements the structural risk minimization principle, in a manner, which does not involve selection of a convenient feature space. Simulation results on real data indicate that this method significantly reduces the computational cost of the conventional support vector machines, and achieves a nearly comparable test error performance.
An active contour model for image segmentation: A variational perspective

Image segmentation is a crucial step in computer vision, medical imaging and image processing. There has been recently an interest in a nonlinear partial differential equation based approach, motivated by a more systematic approach for image segmentation. In this paper, a novel active contour model expressed in terms of an energy functional is formulated in a calculus of variations framework. The key idea behind the proposed technique is to segment objects from the background of images that may have non-uniform brightness because of illumination. Simulation results showing a much improved performance of the proposed method in image segmentation are analyzed and illustrated.
Incremental Line Tangent Space Alignment Algorithm

In this paper an incremental version of line tangent space alignment (LTSA) is proposed on the basis of incremental locally linear embedding (LLE) generalizations and the subsequent incremental Hessian locally linear embedding (HLLE). The main goal of this algorithm is to reduce the dimensionality of high-dimension manifolds into a lower dimension representation such that the significant characteristics of the dataset are preserved while adapting to newly added points arriving to the dataset. Experimental results are performed to verify how the new projection of points, along with the additional points, produces a good fit to the original manifold.
Fast Surface Denoising Using Finite Volumes of the Dual Mesh

In this paper we present a surface denoising technique using the vertex-centered finite volume method in conjunction with the mesh covariance fractional anisotropy. The approach is computationally fast and able to effectively remove undesirable noise while preserving prominent geometric features of a 3D mesh surface such as curved surface regions, sharp edges, and fine details. Extensive experimental results on various 3D models demonstrate the effectiveness of the proposed iterative algorithm, which yields satisfactory output results in just one single iteration.
Reconstruction of reflectance spectra using robust nonnegative matrix factorization

In this correspondence, we present a robust statistics-based nonnegative matrix factorization (RNMF) approach to recover the measurements in reflectance spectroscopy. The proposed algorithm is based on the minimization of a robust cost function and yields two equations updated alternatively. Unlike other linear representations, such as principal component analysis, the RNMF technique is resistant to outliers and generates nonnegative-basis functions, which balance the logical attractiveness of measurement functions against their physical feasibility. Experimental results on a spectral library of reflectance spectra are presented to illustrate the much improved performance of the RNMF approach
An Information-Theoretic Approach to Georegistration of Digital Elevation Maps

Georegistration of digital elevation maps is a vital step in fusing sensor data. In this paper, we present an entropic registration method using Morse singularities. The core idea behind our proposed approach is to encode an elevation map into a set of Morse singular points. Then an information-theoretic dissimilarity measure between the Morse features of the target and the reference maps is maximized to bring the elevation data into alignment. We also show that maximizing this divergence measure leads to minimizing the total length of the joint minimal spanning tree of both elevation data maps. Illustrating experimental results are presented to show the robustness and the georegistration accuracy of the proposed approach.
PDE-based Smoothing for 3D Mesh Quality Improvement

In all real applications, measurements are perturbed by noise. In the course of acquiring, transmitting or processing 3D models, the noise-induced degradation needs to be removed before further processing of the 3D data. In this paper, we propose a vertex-based anisotropic diffusion for 3D mesh denoising by solving a discrete partial differential equation. The core idea behind our proposed technique is to use geometric insight in helping construct an efficient and fast 3D mesh smoothing strategy to fully preserve the geometric structure of the data. Illustrating experimental results demonstrate a much improved performance of the proposed approach in comparison with existing methods currently used in 3D mesh smoothing
Probabilistic shape descriptor for triangulated surfaces

The importance of shape recognition is increasing rapidly in the field of computer graphics and multimedia communication because it is difficult to process information efficiently without its recognition. In this paper, we present a 3D object recognition approach based on a global geodesic measure. The key idea behind our methodology is to represent an object by a probabilistic shape descriptor that measures the global geodesic distance between two arbitrary points on the surface of an object. The geodesic distance has the advantage to be able to capture the intrinsic geometric structure of the data. Object matching can then be carried out by an information-theoretic dissimilarity measure calculations between geodesic shape distributions.
Feature-Based Registration of Digital Elevation Data

We present a feature-based registration method using Morse singularities and information theory. The main idea behind our proposed framework is to encode a 3D terrain digital elevation map into a set of Morse critical points. Then an entropic dissimilarity measure between the Morse features of the target and the reference maps is maximized to bring the elevation data into alignment. We also show that maximizing this divergence measure leads to minimizing the total length of the joint minimal spanning tree between features of the misaligned elevation maps. Illustrative experimental results clearly show the much improved performance and the georegistration accuracy of the proposed technique
Nonrigid Image Registration Using an Entropic Similarity

In this paper, we propose a nonrigid image registration technique by optimizing a generalized information-theoretic similarity measure using the quasi-Newton method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between the fixed and moving 3-D image pairs. To achieve a compromise between the nonrigid registration accuracy and the associated computational cost, we implement a three-level hierarchical multiresolution approach such that the image resolution is increased in a coarse to fine fashion. Experimental results are provided to demonstrate the registration accuracy of our approach. The feasibility of the proposed method is demonstrated on a 3-D magnetic resonance data volume and also on clinically acquired 4-D CT image datasets.
Affinity-based probabilistic reasoning and document clustering on the WWW

The World Wide Web (WWW) has become one of the fastest growing applications on the Internet today. More and more information sources have linked online through WWW, but finding information on the WWW is also a great challenge. For most users, the information retrieved is not well organized and the access time is considered high on the WWW currently. Therefore, there is a need to develop a good mechanism to organize and manage the tremendous size and various kinds of information to facilitate the functionality of a search engine for information retrieval on the WWW. In response to such a demand we propose a Markov Model Mediator (MMM) mechanism which employs affinity based data mining techniques to organize and manage the information sources so that the most relevant documents are clustered together to achieve higher recall and precision values for information retrieval on the WWW.
A novel rate-based hop by hop congestion control algorithm

As the development of the Internet continues, congestion control has become a big issue to the computer network society. Most congestion control schemes fall into two categories, end-to-end and hop-by-hop schemes. We propose a novel hop-by-hop algorithm that originates from a classical traffic control algorithm. The experimental results show that our proposed algorithm can achieve short delays and quick responses to the congestion situations and cause no packet loss. It can also minimize the bandwidth requirement and achieve a very high buffer usage level for nodes along the transmission path.
Clustering-based binary-class classification for imbalanced data sets

In this paper, we propose a new clustering-based binary-class classification framework that integrates the clustering technique into a binary-class classification approach to handle the imbalanced data sets. A binary-class classifier is designed to classify a set of data instances into two classes; while the clustering technique partitions the data instances into groups according to their similarity to each other. After applying a clustering algorithm, the data instances within the same group usually have a higher similarity, and the differences among the data instances between different groups should be larger. In our proposed framework, all negative data instances are first clustered into a set of negative groups. Next, the negative data instances in each negative group are combined with all positive data instances to construct a balanced binary-class data set. Finally, subspace models trained on these balanced binary-class data sets are integrated with the subspace model trained on the original imbalanced data set to form the proposed classification model. Experimental results demonstrate that our proposed classification framework performs better than the comparative classification approaches as well as the subspace modeling method trained on the original data set alone.
Biological Image Temporal Stage Classification via Multi-layer Model Collaboration

In current biological image analysis, the temporal stage information, such as the developmental stage in the Drosophila development in situ hybridization images, is important for biological knowledge discovery. Such information is usually gained through visual inspection by experts. However, as the high-throughput imaging technology becomes increasingly popular, the demand for labor effort on annotating, labeling, and organizing the images for efficient image retrieval has increased tremendously, making manual data processing infeasible. In this paper, a novel multi-layer classification framework is proposed to discover the temporal information of the biological images automatically. Rather than solving the problem directly, the proposed framework uses the idea of ``divide and conquer'' to create some middle level classes, which are relatively easy to annotate, and to train the proposed subspace-based classifiers on the subsets of data belonging to these categories. Next, the results from these classifiers are integrated to improve the final classification performance. In order to appropriately integrate the outputs from different classifiers, a multi-class based closed form quadratic cost function is defined as the optimization target and the parameters are estimated using the gradient descent algorithm. Our proposed framework is tested on three biological image data sets and compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed middle-level classes and the proper integration of the results from the corresponding classifiers are promising for mining the temporal stage information of the biological images.
Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method

The public health infrastructure delivers proper health care services as part of the basic needs of the general population. The health care system in the United States is rapidly changing in order to provide a better and convenient healthcare system to the public. Unfortunately, this comprehensive expand has also given rise to healthcare frauds in recent years where losses surge up to $1.8 billion in the country. Organizations such as the Center for Medicare Services (CMS) have started providing accesses to comprehensive medical big data to promote the identification of healthcare frauds as an important research topic. In this paper, we will use the Patient Rule Induction Method (PRIM) based bump hunting method to identify the spaces of higher modes and masses to indicate the peak anomalies in the CMS 2014 dataset. By applying our framework, we can find a way to observe anomalies, which can be attributed to frauds in legal medical practices or other interesting insights in the CMS dataset. This will enable us to characterize the attribute space and explain the events incurring losses to the medicare/medicaid program. The proposed framework is compared with several methods to illustrate the efficiency and effectiveness of the proposed framework for fraud detection.
Enhancing Concept Detection by Pruning Data with MCA-Based Transaction Weights

With the rapid increase in the amount of multimedia data, the researches on semantic information retrieval are facing a very challenging problem - the number of positive data instances with the target concept/object/event compared with the number of negative data instances without the target concept/object/event is much smaller, which is also called the data imbalance issue. Therefore, one of the popular topics in multimedia information processing and retrieval is data pruning, a technique that can automatically identify and prune the data instances from the training data set so that the pruned data set is able to enhance the performance of model learning, classification, and concept detection. In this paper, a novel data pruning framework which gives each transaction a weight based on multiple correspondence analysis (MCA) is proposed. These transaction weights are used as the measure for pruning the training data set. Meanwhile, the testing data set could be weighted and pruned as well so that the computational cost is reduced not only when building the model but also when applying the classifiers. Experimenting with 18 high-level concepts and the benchmark (both balanced and imbalanced) data sets from TRECVID, our proposed framework achieves promising results to enhance the concept detection performance of three well-known classifiers commonly used for concept detection.
Object tracking and multimedia augmented transition network for video indexing and modeling

S.C. Chen et al. (1999) proposed a multimedia augmented transition network (ATN) model, together with its multimedia input strings, to model and structure video data. This multimedia ATN model was based on an ATN model that had been used within the artificial intelligence (AI) arena for natural-language understanding systems, and its inputs were modeled by multimedia input strings. The temporal and spatial relations of semantic objects were captured by an unsupervised video segmentation method called the SPCPE (simultaneous partitioning and class parameter estimation) algorithm, and they were modeled by the multimedia input strings. However, the segmentation method used was not able to identify objects that are overlapped together within video frames. The identification of overlapped objects is a great challenge. For this purpose, a backtrack-chain-update-split algorithm is developed in this paper that identifies the split segment (object) and uses this information in the current frame to update the previous frames in a backtrack-chain manner. The proposed split algorithm provides more accurate temporal and spatial information of the semantic objects for video indexing.
Video scene change detection method using unsupervised segmentation and object tracking

In order to manage the growing amount of video information efficiently, a video scene change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the scene change detection to organize the video content. In this paper, we present an effective scene change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate scene change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter

The rise in popularity of social interacting websites such as Facebook, Twitter, and Snapchat has been challenged by the upsurge of unwelcomed and troubling bodies on these systems. This includes spam senders, malware systems, and other content contaminators. It is noted that highly automated accounts with 450 tweets per day produced almost 18% of entire Twitter circulation in the 2016 U.S. Presidential election. It is also observed that those disruptive systems called bots are inclined more towards circulating negative news than positive information. This paper introduces a novel framework named Associative Affinity Factor Analysis (AAFA) designed for stance detection and bot identification. Using AAFA, the proposed framework identifies real people from bots and detects the stance in bipolar affinities. The 2016 U.S. Presidential election campaign was used as a test use case because of its significant and unique counter-factual properties. The results show that our proposed AAFA framework achieves high accuracy when compared to several existing state-of-theart methods.
An Integrated and Interactive Video Retrieval Framework with Hierarchical Learning Models and Semantic Clustering Strategy

In this research, we propose an integrated and interactive framework to manage and retrieve large scale video archives. The video data are modeled by a hierarchical learning mechanism called HMMM (hierarchical Markov model mediator) and indexed by an innovative semantic video database clustering strategy. The cumulated user feedbacks are reused to update the affinity relationships of the video objects as well as their initial state probabilities. Correspondingly, both the high level semantics and user perceptions are employed in the video clustering strategy. The clustered video database is capable of providing appealing multimedia experience to the users because the modeled multimedia database system can learn the user's preferences and interests interactively
Spatiotemporal vehicle tracking: the use of unsupervised learning-based segmentation and object tracking

In this paper, a framework for spatiotemporal vehicle tracking using unsupervised learning-based segmentation and object tracking is presented. An adaptive background learning and subtraction method is proposed and applied to two real-traffic video sequences to obtain more accurate spatiotemporal information on the vehicle objects. As demonstrated in the experiments, almost all vehicle objects are successfully identified through this framework.
Message from Program Co-Chairs

Welcome to the proceedings of the 11 IEEE International Conference on Information Reuse and Integration (IEEE IRI 2010) in Las Vegas, Nevada, USA. It has been our privilege as Program Co-Chairs to organize this impressive event. Information Reuse and Integration (IRI) aims at maximizing the reuse of information by creating simple, rich, and reusable knowledge representations and consequently explores strategies for integrating this knowledge into legacy systems. IRI plays a pivotal role in the capture, representation, maintenance, integration, validation, and extrapolation of information; and applies both information and knowledge for enhancing decision-making in various application domains.