nils-herrmann/clean_scirepeval huggingface dataset:
A Four-State Markov Model Based on Measurements for Evaluating the Packet-level Performance of VANET

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). Understanding and modeling packet error in the VANET is especially relevant to the design and analysis of higher layer wireless communication protocols. In this paper, the analysis of the packet error characteristics based on measurements in live VANET in different scenarios is shown form which Traces-4 are selected for further analysis. Based on the statistical dependence between burstlengths and gaplengths, we present a packet-level Markov (PLM) model which is capable of describing the measured statistics properly. The parameters of the model are obtained from the packet error statistical characteristics. Finally, it is demonstrated that both the accuracy and the efficiency of the proposed PLM model outperform other popular Markov models.
Acceleration and accuracy improvement of FEM computation by using FETI-DP and BI hybrid algorithm

This paper proposes a new hybrid algorithm, which is referred to as FETI-DP-BI, to solve antenna array radiation and scattering problems in electromagnetic simulation. Our mathematical treatment is based on the combination of the dual-primal finite element tearing and interconnecting (FETI-DP) and boundary integral (BI) methods. Decomposed interior FE domains are glued together by the Robin transmission condition (TC) using two Lagrange multipliers, while they communicate with the exterior BI region through the Dirichlet-Neumann or Robin boundary condition (BC). Such a hybridization exhibits an excellent numerical scalability and accuracy, especially when equipped with preconditioners based on absorbing boundary condition (ABC).
Satisfactory Design of IIR Digital Filter Based on Chaotic Mutation Particle Swarm Optimization

Based on Chaotic Mutation Particle Swarm Optimization (CPSO), this paper proposed a novel method for satisfactory design of digital IIR filters. First, chaotic mutation was used to improve the performance of standard PSO to avoid premature convergence. Then, the “shrinking” strategy was introduced to raise its local search ability. Finally the CPSO algorithm was used to optimize the IIR digital filter numerator and denominator coefficients. Simulation results show that the proposed method has the advantages of more stability, higher optimizing precision and strong global searching ability. The CPSO is a promising approach for satisfactory design of IIR filters.
A Small High-Temperature Superconducting Maglev Propeller System Model

A small high-temperature superconducting maglev propeller system model has been designed and constructed. It mainly consists of a permanent-magnet track, a superconducting maglev vehicle, two linear motors, an automatic controlling electric circuit, and a spring. The linear motors can be powered by a single- or three-phase power supply. It can be used to do many experiments in different fields of science and technology for polytechnic instruction. Some experiments such as the effect of capacitive load in the electrocircuit, the voltage of the power supply, and the initial moving speed of the maglev model on the total mechanical work of the vehicle, which is done by the linear motors in single-phase power supply, have been carried out. Results will be discussed in detail.
Implementation of the Second-Order ABC in the FETI-DPEM Method for 3D EM Problems

The electromagnetic dual-primal finite element tearing and interconnecting (FETI-DPEM) method, originally equipped with the first-order absorbing boundary condition (ABC), is combined with the second-order ABC implemented using auxiliary variables to enhance the accuracy of electromagnetic scattering and radiation simulations. When the second-order ABC surface is partitioned by domain decomposition, it is necessary to enforce an extra continuity condition on the auxiliary variables representing a continuous surface divergence for the electric field at the junctions of the subdomain interfaces and the truncation surface. A simple approach to enforce such a continuity is proposed without altering the overall framework of the FETI-DPEM method. Numerical examples are provided to demonstrate the accuracy improvement due to the use of the second-order ABC.
Unsupervised Active Learning Based on Hierarchical Graph-Theoretic Clustering

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.
Learning activity patterns using fuzzy self-organizing neural network

Activity understanding in visual surveillance has attracted much attention in recent years. In this paper, we present a new method for learning patterns of object activities in image sequences for anomaly detection and activity prediction. The activity patterns are constructed using unsupervised learning of motion trajectories and object features. Based on the learned activity patterns, anomaly detection and activity prediction can be achieved. Unlike existing neural network based methods, our method uses a whole trajectory as an input to the network. This makes the network structure much simpler. Furthermore, the fuzzy set theory based method and the batch learning method are introduced into the network learning process, and make the learning process much more efficient. Two sets of data acquired, respectively, from a model scene and a campus scene are both used to test the proposed algorithms. Experimental results show that the fuzzy self-organizing neural network (fuzzy SOM) is much more efficient than the Kohonen self-organizing feature map (SOFM) and vector quantization in both speed and accuracy, and the anomaly detection and activity prediction algorithms have encouraging performances.
Discriminant Tracking Using Tensor Representation with Semi-supervised Improvement

Visual tracking has witnessed growing methods in object representation, which is crucial to robust tracking. The dominant mechanism in object representation is using image features encoded in a vector as observations to perform tracking, without considering that an image is intrinsically a matrix, or a 2^nd-order tensor. Thus approaches following this mechanism inevitably lose a lot of useful information, and therefore cannot fully exploit the spatial correlations within the 2D image ensembles. In this paper, we address an image as a 2^nd-order tensor in its original form, and find a discriminative linear embedding space approximation to the original nonlinear sub manifold embedded in the tensor space based on the graph embedding framework. We specially design two graphs for characterizing the intrinsic local geometrical structure of the tensor space, so as to retain more discriminant information when reducing the dimension along certain tensor dimensions. However, spatial correlations within a tensor are not limited to the elements along these dimensions. This means that some part of the discriminant information may not be encoded in the embedding space. We introduce a novel technique called semi-supervised improvement to iteratively adjust the embedding space to compensate for the loss of discriminant information, hence improving the performance of our tracker. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.
Motion from point matches: multiplicity of solutions

The authors study the multiplicity of solutions of the motion problems. Given n point matches between two frames, an effort is made to determine the number of solution to the motion problem. It is shown that the maximum number of solutions is ten when five-point matches are available. Two approaches are used. The first attempts to recover the motion parameters by studying the essential matrix. A natural extension of this is to use algebraic geometry to characterize the set of possible essential matrixes. The authors present some results based on this approach. The second one, based on projective geometry, dates from the previous century. The authors show that the two approaches are compatible and yield the same result. They then describe a computer implementation of the second approach that uses MAPLE, a language for symbolic computation. The program allows the exact computation of the solutions for any configuration of five points.<<ETX>>
Occlusion Reasoning for Tracking Multiple People

Occlusion reasoning is one of the most challenging issues in visual surveillance. In this letter, we propose a new approach for reasoning about occlusions between multiple people. In our approach, occlusion relationships between people are explicitly defined and deduction of the occlusion relationships is integrated into the whole tracking framework. The prior knowledge is supplied by a set of models which include a 2-D elliptical shape model, a spatial-color mixture of Gaussians appearance model, and a motion model with constant velocity. An observation likelihood function is constructed based on the similarity between the observations and the object appearance models with given states. The occlusion relationships are deduced from the current states of the objects and the current observations, using the observation likelihood function. The previous occlusion relationships are not required for deducing the current occlusion relationships. The problem of tracking and occlusion reasoning for more than two people is formulated mathematically, and a solution is proposed based on particle filtering. Experimental results on several real video sequences from indoor and outdoor scenes show the effectiveness of our approach.
A survey on visual surveillance of object motion and behaviors

Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance.
Graph-Embedding-Based Learning for Robust Object Tracking

Object tracking is viewed as a two-class “one-versus-rest” classification problem, in which the sample distribution of the target over a short period of time is approximately Gaussian while the background samples are often multimodal. Based on these special properties, we propose a graph-embedding-based learning method, in which the topology structures of graphs are carefully designed to reflect the properties of the sample distributions. This method can simultaneously learn the subspace of the target and its local discriminative structure against the background. Moreover, a heuristic negative sample selection scheme is adopted to make the classification more effective. In applications to tracking, the graph-embedding-based learning is incorporated into a Bayesian inference framework cascaded with hierarchical motion estimation, which significantly improves the accuracy and efficiency of the localization. Furthermore, an incremental updating technique for the graphs is developed to capture the changes in both appearance and illumination. Experimental results demonstrate that, compared with the two state-of-the-art methods, the proposed tracking algorithm is more efficient and effective, particularly in dynamically changing and cluttered scenes.
Towards the Automatic Reconstruction of Dendritic Trees using Particle Filters

The 3D reconstruction of a Golgi-stained dendritic tree from a serial stack of images captured with a transmitted light bright-field microscope is investigated. Modifications to the boot-strap filter are discussed such that the tree structure may be estimated recursively as a series of connected segments. The tracking performance of the bootstrap particle filter is compared against Differential Evolution, an evolutionary global optimisation method, both in terms of robustness and accuracy. It is found that the particle filtering approach is significantly more robust and accurate for the data considered.
The eleventh IEEE international workshop on visual surveillance

The application area of visual surveillance is an important challenge for computer vision scientists. As in previous years, the large number of high quality submissions reflects the continuing attention it attracts from research groups across the globe. In this Eleventh workshop in the series, some shifts in focus, compared with previous workshops, can be observed. In the previous workshop, for example, approximately half of the papers accepted for publication proposed innovative processes to segment the foreground (moving objects) from the background (residual scene). This year, only four (from forty-one) papers address this issue.
A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
Graph Based Discriminative Learning for Robust and Efficient Object Tracking

Object tracking is viewed as a two-class 'one-versus-rest' classification problem, in which the sample distribution of the target is approximately Gaussian while the background samples are often multimodal. Based on these special properties, we propose a graph embedding based discriminative learning method, in which the topology structures of graphs are carefully designed to reflect the properties of the sample distributions. This method can simultaneously learn the subspace of the target and its local discriminative structure against the background. Moreover, a heuristic negative sample selection scheme is adopted to make the classification more effective. In tracking procedure, the graph based learning is embedded into a Bayesian inference framework cascaded with hierarchical motion estimation, which significantly improves the accuracy and efficiency of the localization. Furthermore, an incremental updating technique for the graphs is developed to capture the changes in both appearance and illumination. Experimental results demonstrate that, compared with two state-of-the-art methods, the proposed tracking algorithm is more efficient and effective, especially in dynamically changing and clutter scenes.
A swarm intelligence based searching strategy for articulated 3D human body tracking

This paper proposes an annealed particle swarm optimization based particle filter algorithm for articulated 3D human body tracking. In our algorithm, a sampling covariance and an annealing factor are incorporated into the velocity updating equation of particle swarm optimization (PSO). The sampling covariance and the annealing factor are initiated with appropriate values at the beginning of the PSO iteration, and ‘annealing’ is carried out at reasonable steps. Experiments with multi-camera walking sequences from the Brown dataset show that: 1) the proposed tracker can effectively alleviate the problem of inconsistency between the image likelihood and the true model; 2) the tracker is also robust to noise and body self-occlusion.
Recognition of Pornographic Web Pages by Classifying Texts and Images

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages
Salient feature point detection for image matching

A saliency based feature point detector is proposed, based on a decision-theoretic formulation of saliency. The saliency of an image region is defined to be the Kullback-Leibler (K-L) divergence between the conditional probability density function (pdf) for the matching regions and a background pdf. These pdfs are modeled by elliptically symmetric distributions (ESDs). We improve the ESD models by reducing the number of parameters without any significant degradation in the modeling of image regions. Experimental results from the Middlebury stereo dataset show that the accuracy of estimates of saliency is increased and fewer computations are required. It is also verified that the saliency of a region can be viewed as a measurement of how suitable the region is for image matching. In the Middlebury stereo dataset, salient regions are dense, and a promising matching rate is achieved.
Robust detection of degenerate configurations for the fundamental matrix

New methods are reported for the detection of multiple solutions (degeneracy) when estimating the fundamental matrix, with specific emphasis on robustness in the presence of data contamination (outliers). The fundamental matrix can be used as a first step in the recovery of structure from motion. If the set of correspondences is degenerate then this structure cannot be accurately recovered and many solutions will explain the data equally well. It is essential that we are alerted to such eventualities. However, current feature matchers are very prone to mismatching, giving a high rate of contamination within the data. Such contamination can make a degenerate data set appear non degenerate, thus the need for robust methods becomes apparent. The paper presents such methods with a particular emphasis on providing a method that will work on real imagery and with an automated (non perfect) feature detector and matcher. It is demonstrated that proper modelling of degeneracy in the presence of outliers enables the detection of outliers which would otherwise be missed. Results using real image sequences are presented. All processing, point matching, degeneracy detection and outlier detection is automatic.<<ETX>>
Semantic-Based Surveillance Video Retrieval

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene