nils-herrmann/clean_scirepeval huggingface dataset:
Efficient implementation of DVR's control

This paper reports the detailed implementation of a three-phase dynamic voltage restorer (DVR). Such devices have gained popularity among the PWM-based compensation series devices. Two cases are studied: balanced and unbalanced voltage sags, which are the most frequent disturbances. For the latter case, a modified delta rule is used to transform the three-phase voltage into positive, negative, and zero sequences. The model of the LC filter is explained and a Software Phase Locked Loop (SPLL) based on Trans-vector-type is developed. DVR's simulations are provided. Hardware implementation employs a fixed point Digital Signal Processor and power electronics devices, as well as an SPLL. Experimental results on voltage sags have been dealt with to corroborate the adequate DVR performance.
Optical gas sensor based on an Android application for real-time, reconfigurable spectroscopic data analysis

We present an easily deployable distributed system for gas sensing and analysis based on an Android application functioning as a real-time comparator between the measurements of a molecular spectroscopy sensor and an online spectroscopic database. The application wirelessly reads via Bluetooth the optical spectrum captured by a tunable laser gas sensor and compares it to a simulated spectrum performing molecule identification and concentration estimation. The simulations are computed in real-time using spectroscopic parameters retrieved from online databases providing a high degree of flexibility and adaptability. The experimental validation of the performance of the system has been carried out using a specially designed Wavelength Modulation Spectroscopy (WMS) instrument, spectroscopic data from HITRAN and methane as the target gas.
AuDy: Automatic Dynamic Least-Weight Balancing for Stream Workloads Scalability

Previews research works focus in the separation of data processing issues, heavy database queries and stream data processing over high-data-rate (CEP). In many contexts high rate data streams and database query processing work together, for information correlation, data merging, aggregation with big quantities of previous data. In our work we mention these as Stream-DB workloads. A solution for any database engine is to parallelize the load though many machines or cores. However nodes can still overload. We propose an automated method for scalability and balancing of Stream-DB workloads, called AuDy. This mechanism integrates overload (re)scheduling, automated elasticity, shedding policies, admission control and alerts when more resources are required, to provide total balanced operations avoiding overload problems.
Towards generic fitting using multiple features Discriminative Active Appearance Models

A solution for Discriminative Active Appearance Models is proposed. The model consists in a set of descriptors which are covariances of multiple features evaluated over the neighborhood of the landmarks whose locations are governed by a Point Distribution Model (PDM). The covariance matrices are a special set of tensors that lie on a Riemannian manifold, which make it possible to measure the dissimilarity and to update them, imposing the temporal appearance consistency. The discriminative fitting method produce patch response maps found by convolution around the current landmark position. Since the minimum of the responce map isn't always the correct solution due to detection ambiguities, our method finds candidates to solutions based on a mean-shift algorithm, followed by an unsupervised clustering technique used to locate and group the candidates. A mahalanobis based metric is used to select the best solution that is consistent with the PDM. Finally the global PDM optimization step is performed using a weighted least-squares warp update, based on the Lucas Kanade framework. The weights were extracted from a landmark matching score statistics. The effectiveness of the proposed approach was evaluated on unseen data on the challenging Talking Face video sequence, demonstrating the improvement in performance.
Biomechanical Properties of Breast Tissue

The present thesis studies the mechanical behaviour of the breast tissue for a better understanding of its function, with special attention to breast skin elasticity. The skin elasticity is important for studying the effect of plastic and oncoplastic surgery techniques for breast reconstruction as well as for the design of cosmetic breast implants. An experimental set was built and an intensive testing program was conducted to obtain the elastic properties of the breast skin. The mechanical properties of the skin were determined with a non-invasive, in vivo suction skin elasticity meter (Cutometer SEM 575, Courage & Khazaka Electronic GmbH, Germany). The mechanical parameters analyzed were firmness (Uf) and elasticity (Ur/Uf). Several key points of the breast anatomy were investigated. Variations in the mechanical properties of the skin were analyzed with respect to: (1) Age, (2) hormonal status, (3) different breast size, (4) different body mass index, (5) breast region. Thus, it verifies whether there are differences in the values of elasticity and firmness due to the several variables under analysis. This study concluded that the age and number of samples can influence some of the results, requiring further future work.
A modular reinforcement-based neural controller for a three-link manipulator

This paper presents a modular neural controller that learns goal-oriented obstacle-avoiding motion strategies for a sensor-based three-link planar robot arm. It acquires these strategies through reinforcement learning from local sensory data. The controller has two reinforcement-based modules: a module for negotiating obstacles and a module for moving to the goal. Both modules generate actions that are interpreted with regard to a goal vector in the robot joint space. A differential inverse kinematics (DIV) module is used to obtain such a goal vector. The DIV module is based on the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics in polar coordinates. The controller achieves a satisfactory performance quite rapidly and shows good generalization capabilities in the face of new environments.
Optimal bit allocation for FGS coding

A new bit allocation scheme for fine-granular-scalability (FGS) video coding is proposed. Differing from traditional bit allocation schemes, the focus is on understanding the relationship between rate-distortion analysis and nonzero binary-scaled coefficients (NZBC) in the bitplane coding. An optimal strategy for NZBC coding is then derived and experimental results are given for a comparison of the new scheme with uniform bit allocation.
Region-Based Saliency Detection and Its Application in Object Recognition

The objective of this paper is twofold. First, we introduce an effective region-based solution for saliency detection. Then, we apply the achieved saliency map to better encode the image features for solving object recognition task. To find the perceptually and semantically meaningful salient regions, we extract superpixels based on an adaptive mean shift algorithm as the basic elements for saliency detection. The saliency of each superpixel is measured by using its spatial compactness, which is calculated according to the results of Gaussian mixture model (GMM) clustering. To propagate saliency between similar clusters, we adopt a modified PageRank algorithm to refine the saliency map. Our method not only improves saliency detection through large salient region detection and noise tolerance in messy background, but also generates saliency maps with a well-defined object shape. Experimental results demonstrate the effectiveness of our method. Since the objects usually correspond to salient regions, and these regions usually play more important roles for object recognition than background, we apply our achieved saliency map for object recognition by incorporating a saliency map into sparse coding-based spatial pyramid matching (ScSPM) image representation. To learn a more discriminative codebook and better encode the features corresponding to the patches of the objects, we propose a weighted sparse coding for feature coding. Moreover, we also propose a saliency weighted max pooling to further emphasize the importance of those salient regions in feature pooling module. Experimental results on several datasets illustrate that our weighted ScSPM framework greatly outperforms ScSPM framework, and achieves excellent performance for object recognition.
Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework. By incorporating the similarity preserving term into the objective of sparse coding, our proposed Laplacian sparse coding can alleviate the instability of sparse codes. Furthermore, we propose a Hypergraph Laplacian sparse coding (HLSc), which extends our Laplacian sparse coding to the case where the similarity among the instances defined by a hypergraph. Specifically, this HLSc captures the similarity among the instances within the same hyperedge simultaneously, and also makes the sparse codes of them be similar to each other. Both Laplacian sparse coding and Hypergraph Laplacian sparse coding enhance the robustness of sparse coding. We apply the Laplacian sparse coding to feature quantization in Bag-of-Words image representation, and it outperforms sparse coding and achieves good performance in solving the image classification problem. The Hypergraph Laplacian sparse coding is also successfully used to solve the semi-auto image tagging problem. The good performance of these applications demonstrates the effectiveness of our proposed formulations in locality and similarity preservation.
Learning to combine multi-resolution spatially-weighted co-occurrence matrices for image representation

Bag-of-Words is widely used to describe images for image classification. However, this approach is limited because the spatial relation over visual words is not well exploited and also it is difficult to generate a single comprehensive vocabulary. In this paper, we propose novel effective schemes to handle these two issues. First, we propose a structure propagation technique to build more reasonable co-occurrence matrices of visual words to exploit the spatial information, which assigns a higher weight to the co-occurrence over two patches that lie in the same object part. Second, we build the multiple-histogram representation over hierarchical vocabularies to avoid the ambiguity of single vocabulary, and particularly present a learning approach to combine the multiple histograms to integrate both within-vocabulary and cross-vocabulary information. We evaluate our proposed method using the Princeton sports event dataset. Compared to the state-of-the-art results, our proposed approach has shown promising results.
Multi-layer group sparse coding — For concurrent image classification and annotation

We present a multi-layer group sparse coding framework for concurrent image classification and annotation. By leveraging the dependency between image class label and tags, we introduce a multi-layer group sparse structure of the reconstruction coefficients. Such structure fully encodes the mutual dependency between the class label, which describes the image content as a whole, and tags, which describe the components of the image content. Then we propose a multi-layer group based tag propagation method, which combines the class label and subgroups of instances with similar tag distribution to annotate test images. Moreover, we extend our multi-layer group sparse coding in the Reproducing Kernel Hilbert Space (RKHS) which captures the nonlinearity of features, and further improves performances of image classification and annotation. Experimental results on the LabelMe, UIUC-Sport and NUS-WIDE-Object databases show that our method outperforms the baseline methods, and achieves excellent performances in both image classification and annotation tasks.
Regularized Feature Reconstruction for Spatio-Temporal Saliency Detection

Multimedia applications such as image or video retrieval, copy detection, and so forth can benefit from saliency detection, which is essentially a method to identify areas in images and videos that capture the attention of the human visual system. In this paper, we propose a new spatio-temporal saliency detection framework on the basis of regularized feature reconstruction. Specifically, for video saliency detection, both the temporal and spatial saliency detection are considered. For temporal saliency, we model the movement of the target patch as a reconstruction process using the patches in neighboring frames. A Laplacian smoothing term is introduced to model the coherent motion trajectories. With psychological findings that abrupt stimulus could cause a rapid and involuntary deployment of attention, our temporal model combines the reconstruction error, regularizer, and local trajectory contrast to measure the temporal saliency. For spatial saliency, a similar sparse reconstruction process is adopted to capture the regions with high center-surround contrast. Finally, the temporal saliency and spatial saliency are combined together to favor salient regions with high confidence for video saliency detection. We also apply the spatial saliency part of the spatio-temporal model to image saliency detection. Experimental results on a human fixation video dataset and an image saliency detection dataset show that our method achieves the best performance over several state-of-the-art approaches.
Semantic Retrieval with Enhanced Matchmaking and Multi-Modality Ontology

This paper describes a semantic retrieval system that allows matchmaking with ranked output and the use of multi-modality ontology to retrieve animal images. Our multi-modality ontology, which integrates both image features and text information, is extended to provide a ranking mechanism. Ranking is calculated from correlation in each modality and is used to refine the semantic matchmaking result. To benchmark our results, we use the top 200 images of Google Image Search for each category to do the experimental comparison. Google Image Search claims to be the most comprehensive on the Web, with billions of images indexed and available for viewing. For different categories of animals in the canine family, we found averages of about 60% of top 200 images are correct images. Google returns even more false results outside this range. Therefore any bigger image set will become meaningless in our experiment. The medium size of the data set is fine since we are testing the retrieval performance on web images and concerned mainly with the precision of top retrievals. We believe the canine domain is challenging as demonstrated by the visual variance of objects and backgrounds. Twenty animal categories, containing animal images and corresponding web pages, are collected to form a systematic animal family. Results show that we can classify perceptually close animal species which share similar appearances as we can infer their hidden relationships from the canine family graph. By assigning a ranking to the semantic relationships we show unequivocal evidence that our improved model achieves good accuracy.
Artistes Management System

The Artistes Management System (AMS) is an intranet application designed and developed for the broadcasting industry. AMS captures all the artistes' information and maintains artistes' dossiers, both in English as well as in Chinese. It also automates the artistes' booking system and captures and maintains the artistes' schedules. Other key functions of AMS include the generation of statistical reports and the maintenance of the system's user profiles and access levels. The system entails a minimum installation of additional software on client workstations, as it is Web-based and employs a central server with a multiple-client concept.
QoS measurement issues with DAML-QoS ontology

With the industry's efforts in promoting the use of Web services, a huge number of Web services are being developed for the Web. To improve the service provider's competition advantage, it is necessary to support QoS-aware service discovery mechanism as well as the corresponding measurement system. In this paper we present a QoS measurement framework based on the DAML-QoS ontology, together with the measurement code generator. The DAML-QoS ontology is designed to specify QoS constraints for Web services. It works as a complement to DAML-S to provide QoS-aware service discovery. This paper focuses on its measurement framework to check the service provider's runtime compliance with the advertised service level. The code generator helps to generate the measurement code according to the DAML-QoS specification. This ontology level specification tries to reduce the service's integration complexity and achieve better interoperability, automation and extensibility
Anomaly region detection and localization in metal surface inspection

Visual inspection and identification of anomalies are important steps in the manufacturing process. A data set containing images of metallic components at different orientations and lighting conditions have been used for this experiment. Our aim is to find the anomaly in an image by subtracting it from an equivalent reference image with no anomalies. This is challenging due to the availability of multiple and slightly changing viewpoints for the reference image. The anomalies can only be detected in the residue obtained by using an identical reference image with no anomalies in the subtraction process. The proposed system is based on finding the best reference from a pool of similar images. A method for ranking the images based on similarity is also introduced. The proposed method scales well for high contrast images with dark colored anomalies.
Adaptive hierarchical multi-class SVM classifier for texture-based image classification

In this paper, we present a new classification scheme based on support vector machines (SVM) and a new texture feature, called texture correlogram, for high-level image classification. Originally, SVM classifier is designed for solving only binary classification problem. In order to deal with multiple classes, we present a new method to dynamically build up a hierarchical structure from the training dataset. The texture correlogram is designed to capture spatial distribution information. Experimental results demonstrate that the proposed classification scheme and texture feature are effective for high-level image classification task and the proposed classification scheme is more efficient than the other schemes while achieving almost the same classification accuracy. Another advantage of the proposed scheme is that the underlying hierarchical structure of the SVM classification tree manifests the interclass relationships among different classes.
Global motion compensated key frame extraction from compressed videos

A key frame extraction approach, based on change detection of DC images extracted from compressed video, is proposed in this paper. We define a simple pixel change map that captures additional information in a frame with respect to its adjacent frames. Since global motion contributes to pixel changes, falsely indicating the presence of key frames, it is compensated by adaptively filtering the pixel change map using a modified version of the least mean square (LMS) algorithm. The prediction errors thus obtained are used to subsequently select the key frames. The key frames are selected so that the cumulative prediction error is partitioned into equal amounts in each segment. The entire procedure is computationally simple and flexible. Experimental results illustrate the good performance of the proposed algorithm.
Sparse Representation With Kernels

Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.
Coefficient thresholding and optimized selection of the Lagrangian multiplier for nonreference frames in H.264 video coding

A new strategy to select the Lagrangian multiplier for nonreference frames is proposed, with the objective to maximize the average PSNR. The selection is based on the observation that nonreference frames should be optimized using a Lagrangian multiplier equivalent to the negative slope of the global R-D curve. This curve is not known a priori and a simple approximation is presented. Further, a new criterion for transform coefficient thresholding is proposed based on the current frame type and Lagrangian optimization. The new scheme is shown to improve the PSNR between 0.35 and 1.12 dB compared to the H.264 Test Model. The average improvement for all sequences is 0.63 dB, or equivalently a bit rate reduction of 11%.
Efficient image retrieval using MPEG-7 descriptors

In this paper, a new method to calculate the similarity among images using dominant color descriptor is discussed. Using earth mover's distance (EMD), better retrieval results can be obtained compared with those obtained from the original MPEG-7 reference software (XM) [Text of ISO/IEC 15938-6/FDIS Information Technology-Multimedia content description interface-Part 6: Reference Software]. To further improve the retrieval accuracy, texture information from edge histogram descriptor is added. In order to reduce the retrieval time, two different methods which can prune the images far from the query image are discussed. One is the lower bound of EMD, while the other is the M-tree index based on EMD distance. Experiments show that the lower bound is easier to implement and more efficient than the M-tree.