nils-herrmann/clean_scirepeval huggingface dataset:
Extraction of rules from artificial neural networks for nonlinear regression

Neural networks (NNs) have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful as function approximators because they do not require prior knowledge of the input data distribution and they have been shown to be universal approximators. In many applications, it is desirable to extract knowledge that can explain how Me problems are solved by the networks. Most existing approaches have focused on extracting symbolic rules for classification. Few methods have been devised to extract rules from trained NNs for regression. This article presents an approach for extracting rules from trained NNs for regression. Each rule in the extracted rule set corresponds to a subregion of the input space and a linear function involving the relevant input attributes of the data approximates the network output for all data samples in this subregion. Extensive experimental results on 32 benchmark data sets demonstrate the effectiveness of the proposed approach in generating accurate regression rules.
A minimal neural network for target searching and danger avoidance by smell

Research on sensory-motor coordination has attracted an increasing amount of interests recently. However, these works have been focused almost exclusively on visuomotor coordination. Research on olfactory-motor coordination, on the other hand, has been lacking. This paper contributes to the study of olfactory-motor coordination by devising a minimal neural network that allows a simulated creature to search for food and to avoid danger by smell.
Invariant and perceptually consistent texture mapping for content-based image retrieval

Texture is an important visual feature for content-based image retrieval. An ideal content-based retrieval system should compare images in its database with the query in a manner that is consistent with human's perception of visual similarity. Moreover, texture matching should be invariant to texture scale and orientation because the same texture can appear in the images in varying scales and orientations. In practice, however, texture similarity computed using computational texture features is not necessarily consistent with human's perception. This paper presents a method of mapping texture features into a texture space that is scale and orientation invariant, and at the same time, consistent with human's perception. Test results show that this method achieves a better retrieval performance than methods that are not invariant and not perceptually consistent.
Automatic identification of Frankfurt plane and mid-sagittal plane of skull

Automatic identification of the Frankfurt plane (FP) and mid-sagittal plane (MSP) of a human skull is a very important task. These planes are used to define important landmarks on the skull for surgery planning and guidance, forensic reconstruction, and anthropological and archaeological studies. At present, there is no known method for automatic identification of FP. Existing methods identify FP by fitting a plane over manually marked landmark points. On the other hand, existing automatic methods identify MSP by looking for a symmetric plane that divides the skull into the left and right sides. Unfortunately, this approach is inaccurate because extensive anthropological studies show that there is significant lateral asymmetry in the skulls. This paper proposes an automatic, accurate, and robust method for identifying FP and MSP of skulls. It registers a template skull model with known landmarks to a target skull to locate the landmarks, FP, and MSP on the target skull. Then, it iteratively refines the landmark locations, FP and MSP according to their medical definitions. Test results show that the proposed algorithm is more robust and accurate than symmetry-based methods. Moreover, it can also be applied to partial skull models of real patients.
3D-2D spatiotemporal registration for sports motion analysis

Computer systems are increasingly being used for sports training. Existing sports training systems either require expensive 3D motion capture systems or do not provide intelligent analysis of userpsilas sports motion. This paper presents a framework for affordable and intelligent sports training systems for general users that require only single camera to record the userpsilas motion. Sports motion analysis is formulated as a 3D-2D spatiotemporal motion registration problem. A novel algorithm is developed to perform spatiotemporal registration of the expertpsilas 3D reference motion and a performerpsilas 2D input video, thereby computing the deviation of the performerpsilas motion from the expertpsilas motion. The algorithm can effectively handle ambiguous situations in a single video such as depth ambiguity of body parts and partial occlusion. Test results show that, despite using only single video, the algorithm can compute 3D posture errors that reflect the performerpsilas actual motion error.
Adaptive histograms and dissimilarity measure for texture retrieval and classification

Histogram-based dissimilarity measures are extensively used for content-based image retrieval. In an earlier paper, we proposed an efficient weighted correlation dissimilarity measure for adaptive-binning color histograms. Compared to existing fixed-binning histograms and dissimilarity measures, adaptive histograms together with weighted correlation produce the best overall performance in terms of high accuracy, small number of bins, no empty bin, and efficient computation for image classification and retrieval. This paper follows up on the study of adaptive histograms by applying them to texture classification, retrieval, and clustering. Adaptive histograms are generated from the amplitude of the discrete Fourier transform of images. Extensive comparisons with well-known texture features and dissimilarity measures show that, again, adaptive histograms and weighted correlation produce good overall performance.
Associating text and graphics for scientific chart understanding

This paper presents our recent work that aims at associating the recognition results of textual and graphical information contained in the scientific chart images. Text components are first located in the input image and then recognized using OCR. On the other hand, the graphical objects are segmented and form high level symbols. Both logical and semantic correspondence between text and graphical symbols are identified. The association of text and graphics allows us to capture the semantic meaning carried by scientific chart images in a more complete way. The result of scientific chart image understanding is presented using XML documents.
Continuous-spaced action selection for single- and multi-robot tasks using cooperative extended Kohonen maps

Action selection is a central issue in the design of behavior-based control architectures for autonomous mobile robots. This paper presents an action selection framework based on an assemblage of self-organizing neural networks called Cooperative Extended Kohonen Maps. This framework encapsulates two features that significantly enhance a robot's action selection capability: self-organization in the continuous state and action spaces to provide smooth, efficient and fine motion control; action selection via the cooperation and competition of Extended Kohonen Maps so that more complex motion tasks can be achieved. Qualitative and quantitative comparisons for both single- and multi-robot motion tasks show that our framework can provide better action selection than do action superposition methods.
Reactive, distributed layered architecture for resource-bounded multi-robot cooperation: application to mobile sensor network coverage

This paper describes a reactive, distributed layered architecture for cooperation of multiple resource-bounded robots, which is utilized in mobile sensor network coverage. In the upper layer, a dynamic task allocation scheme self-organizes the robot coalitions to track efficiently in separate regions. It uses the concepts of ant behavior to self-regulate the regional distributions of robots in proportion to that of the targets to be tracked in the changing environment. As a result, the adverse effects of task interference between robots are minimized and sensor network coverage is improved. In the lower layer, the robots use self-organizing neural networks to coordinate their target tracking within a region. Quantitative comparisons with other tracking strategies such as static sensor placements, potential fields, and auction-based negotiation show that our approach can provide better coverage and greater flexibility in responding to environmental changes.
Adaptive binning and dissimilarity measure for image retrieval and classification

Color histogram is an important part of content-based image retrieval systems. It is a common understanding that histograms that adapt to images can represent their color distributions more efficiently than histograms with fixed binnings. However, among existing dissimilarity measures, only the Earth Mover's Distance can compare histograms with different binnings. This paper presents a detailed quantitative study of fixed and adaptive binnings and the corresponding dissimilarity measures. An efficient dissimilarity measure is proposed for comparing histograms with different binnings. Extensive test results show that adaptive binning and dissimilarity produce the best overall performance, in terms of good accuracy, small number of bins, no empty bin, and efficient computation, compared to existing fixed binning schemes and dissimilarity measures.
Rapid 3D model acquisition from images of small objects

This paper describes a system for rapid acquisition of 3D models of objects for use in applications such as CAD and VR. The system consists of an image capturing platform, which comprises a PC-controlled turntable and CCD camera, and associated computer vision and graphics algorithms for 3D model acquisition. An algorithm for accurately recovering 3D points from image sequences is described. Experiments performed on synthetic and real data show that the algorithm is accurate in recovering the coordinates of 3D points and is robust against noise in 2D feature location and 3D object rotation. Results of applying the system to acquiring 3D models of real objects are illustrated.
An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks

Self-organizing feature maps such as extended Kohonen maps (EKMs) have been very successful at learning sensorimotor control for mobile robot tasks. This letter presents a new ensemble approach, cooperative EKMs with indirect mapping, to achieve complex robot motion. An indirect-mapping EKM self-organizes to map from the sensory input space to the motor control space indirectly via a control parameter space. Quantitative evaluation reveals that indirect mapping can provide finer, smoother, and more efficient motion control than does direct mapping by operating in a continuous, rather than discrete, motor control space. It is also shown to outperform basis function neural networks. Furthermore, training its control parameters with recursive least squares enables faster convergence and better performance compared to gradient descent. The cooperation and competition of multiple self-organized EKMs allow a nonholonomic mobile robot to negotiate unforeseen, concave, closely spaced, and dynamic obstacles. Qualitative and quantitative comparisons with neural network ensembles employing weighted sum reveal that our method can achieve more sophisticated motion tasks even though the weighted-sum ensemble approach also operates in continuous motor control space.
Human body posture refinement by nonparametric belief propagation

Accurate human body posture refinement from single or multiple images is essential in many applications. Two main causes of difficulty to solve the refinement problem are high degree freedom of human body and self-occlusion. One of the most recent algorithms is nonparametric belief propagation (NBP) that solves the problem in a lower dimensional state space. However, it is difficult to handle self-occlusion. This paper presents an NBP-based algorithm that can refine body posture even in self-occlusion case, which has been shown by experimental results. The experimental results also show that our algorithm can accurately refine body posture even if the initial posture has large difference from the true posture.
Generating rules from trained network using fast pruning

Before symbolic rules are extracted from a trained neural network, the network is usually pruned so as to obtain more concise rules. Typical pruning algorithms require retraining the network which incurs additional cost. This paper presents FERNN, a fast method for extracting rules from trained neural networks without network re-training. Given a fully connected trained feedforward network, FERNN first identifies the relevant hidden units by computing their information gains. Next, it identifies relevant connections from the input units to the relevant hidden units by checking the magnitudes of their weights. Finally, FERNN generates rules based on the relevant hidden units and weights. Our experimental results show that the size and accuracy of the tree generated are comparable to those extracted by another method which prunes and retrains the network.
A Cellular Neural Network as a Principal Component Analyzer

In this paper, A configuration of Cellular Neural Network (CNN) is introduced to implement Principal Component Analysis (PCA). CNN is a parallel computing paradigm. Many researchers considered it as the next generation universal machine and developed so-called CNN universal chips. Based on the capability of CNN, an alternative PCA implementation named Principal Component Analyzing Cellular Neural Network (PCACNN) is proposed. PCA is used to reduce the dimensions of a given dataset in order to extract the principal information of the given dataset. In decades, many researchers presented their investigations based on PCA in order to improve the performance and/or to attack some open issues in specific fields. In this paper, PCA is implemented based on the architecture and capabilities of CNN. Consequently, the computing performance of PCA can be improved as long as the CNN architecture can be realized.
A neural network for attentional spotlight

The architecture and dynamics of a neural network that generates an attentional spotlight are discussed. The network has three layers. The desired position of the spotlight is indicated by activating one of the input units. The activity of the unit represents the desired size of the spotlight. A shunting feedback network serves as the output layer and produces a spotlight of the desired shape. The spotlight has a fixed maximum size (determined by network parameters) which corresponds to the largest amount of information that a vision system can process in parallel. The spotlight generation network was simulated numerically using a forward Euler method.<<ETX>>
An improved neural network for segmenting objects' boundaries in real images

An important task in object recognition is to first identify the boundaries of the objects in the input image. Several neural networks have been proposed to perform edge detection and boundary segmentation. Among them, Grossberg and Mingolla's (1985) boundary contour system (BCS) seems promising because it is able to complete missing object boundaries. Although BCS has been shown to work well on synthetic and silhouette images, we found that it has some shortcomings when applied to real images. This paper presents an improved version of BCS for handling the shortcomings.
Autonomic mobile sensor network with self-coordinated task allocation and execution

This paper describes a distributed layered architecture for resource-constrained multirobot cooperation, which is utilized in autonomic mobile sensor network coverage. In the upper layer, a dynamic task allocation scheme self-organizes the robot coalitions to track efficiently across regions. It uses concepts of ant behavior to self-regulate the regional distributions of robots in proportion to that of the moving targets to be tracked in a nonstationary environment. As a result, the adverse effects of task interference between robots are minimized and network coverage is improved. In the lower task execution layer, the robots use self-organizing neural networks to coordinate their target tracking within a region. Both layers employ self-organization techniques, which exhibit autonomic properties such as self-configuring, self-optimizing, self-healing, and self-protecting. Quantitative comparisons with other tracking strategies such as static sensor placements, potential fields, and auction-based negotiation show that our layered approach can provide better coverage, greater robustness to sensor failures, and greater flexibility to respond to environmental changes
Enhancing the reactive capabilities of integrated planning and control with Cooperative Extended Kohonen Maps

Despite the many significant advances made in robot motion research, few works have focused on the tight integration of high-level deliberative planning with reactive control at the lowest level. In particular, the real-time performance of existing integrated planning and control architectures is still not optimal because the reactive control capabilities have not been fully realized. This paper aims to enhance the low-level reactive capabilities of integrated planning and control with Cooperative Extended Kohonen Maps for handling complex, unpredictable environments so that the work-load of the high-level planner can be consequently eased. The enhancements include fine, smooth motion control, execution of more complex motion tasks such as overcoming unforeseen concave obstacles and traversing between closely spaced obstacles, and asynchronous execution of behaviors.
Integrated planning and control of mobile robot with self-organizing neural network

Despite the many significant advances made in robotics research, few works have focused on the tight integration of task planning and motion control. Most integration works involve the task planner providing discrete commands to the low-level controller, which performs kinematics and control computations to command the motor and joint actuators. This paper presents a framework of the integrated planning and control for mobile robot navigation. Unlike existing integrated approaches, it produces a sequence of checkpoints instead of a complete path at the planning level. At the motion control level, a neural network is trained to perform motor control that moves the robot from one checkpoint to the next. This method allows for a tight integration between high-level planning and low-level control, which permits real-time performance and easy modification of motion path while the robot is enroute to the goal position.
3D model retrieval with morphing-based geometric and topological feature maps

Recent advancement in 3D digitization techniques have prompted the need for 3D object retrieval. Our method of comparing 3D objects for retrieval is based on 3D morphing. It computes, for each 3D object, two spatial feature maps that describe the geometry and topology of the surface patches on the object, while preserving the spatial information of the patches in the maps. The feature maps capture the amount of effort required to morph a 3D object into a canonical sphere, without performing explicit 3D morphing. Fourier transforms of the feature maps are used for object comparison so as to achieve invariant retrieval under arbitrary rotation, reflection, and non-uniform scaling o the objects. Experimental results show that our method of retrieving 3D models is very accurate, achieving a precision of above .086 even at a recall rate of 1.0.
A spatiotemporal memory that separates "what" and "when"

To process a spatiotemporal pattern, a short-term memory (STM) is needed to maintain the pattern over time. In general, a STM for storing a temporal sequence of input items should possess characteristics such as acting as a first-in-first-out buffer, storing multiple items of the same type, and encoding interval-invariant and rate-invariant representations of the input items. Some of these characteristics are lacking in existing STM models. This article presents a spatiotemporal STM that possesses all the above characteristics. It is achieved using a novel method of organizing the STM by separating the storage of spatial and temporal information.
Automatic breast cancer grading of histopathological images

Breast cancer grading of histopathological images is the standard clinical practice for the diagnosis and prognosis of breast cancer development. In a large hospital, a pathologist typically handles 100 grading cases per day, each consisting of about 2000 image frames. It is, therefore, a very tedious and time-consuming task. This paper proposes a method for automatic computer grading to assist pathologists by providing second opinions and reducing their workload. It combines the three criteria in the Nottingham scoring system using a multi-resolution approach. To our best knowledge, there is no existing work that provide complete grading according to the Nottingham criteria.
Flip-avoiding interpolating surface registration for skull reconstruction

Reconstruction of skulls from defective models is a very important and challenging task in craniofacial surgery, forensics, and anthropology. Existing methods typically reconstruct approximating surfaces that regard corresponding points on the target skull as soft constraints, thus incurring non-zero error even for non-defective parts and high overall reconstruction error. This paper proposes a novel method that non-rigidly registers an interpolating surface that regards corresponding target points as hard constraints, thus achieving low reconstruction error. To overcome the shortcoming of interpolating surface, a flip-avoiding method is used to detect and exclude conflicting hard constraints that would otherwise cause surface patches to flip. Comprehensive test results show that our method is more accurate than existing methods and it is robust against severe outliers such as radiation artifacts in CT due to dental implants.
Color segmentation and figure-ground segregation of natural images

To recognize the objects in an image and to understand the image content, a computer system has to first separate the foreground objects from the background. Image segmentation and figure-ground segregation are, therefore, essential for computer image understanding. This paper describes a system called OLAG (Object-LAyer Grouping) for image segmentation and figure-ground segregation. OLAG consists of several incremental refinement steps which use colour and other visual cues such as size and compactness for grouping the image pixels. It produces, as an end result, a set of layers each containing an object or object part. Figure and ground relationships among the objects are inferred, giving their relative depths. It is shown that interesting and useful segmentation results can be obtained from the system.