nils-herrmann/clean_scirepeval huggingface dataset:
Analysis on influence factors of the reliability of electric power communication networks

With the operation of the electric power network, the influence degree of stress, aging, deformation, bending and other factors on the reliability of the networks is found to have practical significance. The data of network status are large and independent, so we can correlate these independent data by means of subjective. In this paper, we combine the subjective weighting method and the objective weighting method, and invite six selected professionals to evaluate the decimal scoring method, then after dimensionless processing, we get the entropy relation grade of the influencing factors by using the grey relation entropy, finally we obtain the primary and secondary relationship of the influences on the reliability of the network. Results show that in the study of reliability influencing factors of electric power network, the grey relation entropy is a simple, practical and ideal method, and it provides an effective basis for the modeling of the reliability of electric power optical fiber communication networks.
Block-Based Image Compression With Parameter-Assistant Inpainting

This correspondence presents an image compression approach that integrates our proposed parameter-assistant inpainting (PAI) to exploit visual redundancy in color images. In this scheme, we study different distributions of image regions and represent them with a model class. Based on that, an input image at the encoder side is divided into featured and non-featured regions at block level. The featured blocks fitting the predefined model class are coded by a few parameters, whereas the non-featured blocks are coded traditionally. At the decoder side, the featured regions are restored through PAI relying on both delivered parameters and surrounding information. Experimental results show that our method outperforms JPEG in featured regions by an average bit-rate saving of 76% at similar perceptual quality levels.
Gaining Big Picture Awareness through an Interconnected Cross-Layer Situation Knowledge Reference Model

In both military operations and the commercial world, cyber situation awareness (SA) is a key element of mission assurance. Due to the needs for mission damage and impact assessment and asset identification (and prioritization), cyber SA is beyond intrusion detection and attack graph analysis. In this paper, we propose a cross-layer situation knowledge reference model (SKRM) to address the unique cyber SA needs of real-world missions. SKRM provides new insight on how to break the "stovepipes" created by isolated situation knowledge collectors and gain comprehensive level big picture awareness. Through a concrete case study, we show that SKRM is the key enabler for two SA capabilities beyond intrusion detection and aintrusionttack graph analysis. The potentials and the current limitations of SKRM and SKRM-enabled analysis are also discussed.
A user adaptive scheduling multiple access protocol

A multiple access control (MAC) protocol - user adaptive scheduling multiple access (UASMA) protocol is proposed in this paper, which can perfectly schedule the packet transmission according to the exact number of active mobile terminals (MTs) determined by self-organizing algorithm, and adjust the number of packets sending by one node in one frame properly. UASMA protocol employs an especial frame structure, which makes it possible to allocate channel resource to uplink and downlink traffic according o their respective service requirements. And the concept of referenced-frame-length is proposed, which can ensure the frame length various in a certain range, and consequently increase the utilization of the channel greatly. Meanwhile, UASMA protocol uses an efficient collision resolution algorithm to guarantee that active MTs can access the channel rapidly. Finally, the performance of UASMA protocol is evaluated by simulation and compared with UPMA protocol, the simulation results show that the former has better performance than the latter.
Moving object tracking with feature learning and inheriting

Moving object tracking with discriminative model is very popular in recent years, which focuses on online selecting highly informative features to maximize the separability between object and background. An adapted particle filter tracker with online learning and inheriting discriminative model is proposed in this paper. Top-ranked discriminative features are selected into appearance model by Online Weighted Multiple Instance Learning, and some stable features are inherited to subsequent frames. This discriminative appearance model with feature learning and feature inheriting helps particle filter to evaluate particle more effectively, and particle filter can make online learning to generate a better representative set of training examples. Experiments on challenging video sequences show that our method achieved competitive results compared with state-of-the-art online learning-based discriminative tracking approaches.
Surrogate models for user's evaluations base on weighted support vector machine in IGAs

Interactive genetic algorithms (IGAs) are effective methods of tackling optimization problems involving qualitative indices by incorporating a user's evaluations into traditional genetic algorithms. The problem of user fatigue resulting from the user's evaluations, however, has a negative influence on the performance of these algorithms. Substituting the user's evaluations with various surrogate models is beneficial to alleviate user fatigue. Previous studies, however, have not taken full advantage of information provided by samples obtained earlier when constructing or updating these models. We focus on the issue of user fatigue in this study, and present a novel method of effectively alleviating user fatigue by substituting the user's evaluations with a weighted support vector machine (WSVM) and by incorporating it with the mechanism of transfer learning. The proposed method is applied to the fashion evolutionary design system and compared with previous effective IGAs. The experimental results confirm the advantage of the proposed method in both alleviating user fatigue and improving the precision of the surrogate model.
Feature-based image set compression

The biggest challenge in image set compression is how to efficiently remove the set redundancy among images as well as the redundancy inside a single image. Different from all the previous schemes, in this paper we are the first to propose a generic image set compression scheme which removes the set redundancy based on local features in addition to luminance values. The SIFT (Scale Invariant Feature Transform) descriptor which characterizes an image region invariant to scale and rotation is utilized in our scheme to measure and further enhance the correlation among images. Given an image set, we build a minimal cost prediction structure according to the SIFT-based prediction measure between images. We also utilize a SIFT-based global transformation to enhance the correlation between two images by aligning them to each other in terms of both geometry and intensity. The set redundancy and image redundancy are both further reduced by block-based motion estimation and rate-distortion optimal mechanism proposed in HEVC. Experimental results show that our new feature based scheme always produces the best result regardless the image set's properties.
Multi-Point Disturbance Detection and High-Precision Positioning of Polarization-Sensitive Optical Time-Domain Reflectometry

Compared with its counterparts, polarization sensitive optical time-domain reflectometry (P-OTDR) has two typical problems in terms of multi-point disturbance detection and high precision positioning, which restrict its practical applications. In this paper, we propose a novel method by using two-dimensional (2-D) image processing and statistical clustering. In place of the traditional spatial or temporal signal analysis way, the idea employs the temporally and the spatially uneven/inconsistent evolving patterns induced by the multiple individual events, and the proposed method is carried out to accumulate the temporally differentiated OTDR traces and then build up a 2-D temporal-spatial evolving graph, by using edge detection and automatic clustering to distinguish different disturbing points with precise locations. Through series of multi-point disturbance tests, the proposed method has been proved to have better performance than the conventional direct differentiation method and the fast Fourier transform spectrum analysis.
Comparisons between the one-loop and two-loop solutions for improving the coding efficiency of FGS

As the MPEG core experiments have shown, the current FGS video coding scheme suffers from a 2/spl sim/3dB coding efficiency loss compared with the non-scalable single-layer coding for a wide range of bit rates. This is mainly due to the open-loop enhancement structure that ensures drifting free coding. Two categories of schemes are proposed to address this issue with the same basic idea, ie, introducing some enhanced reference layers into the motion compensation loop. In the one-loop solution, several enhancement bit planes are directly introduced to the motion compensation loop of the base layer. On the other hand, the two-loop solution establishes another motion compensation loop at the enhancement layer. There are many different characteristics with these two solutions. This paper analyzes the one-loop and two-loop solutions in detail from different respects, including coding efficiency, drifting errors, flexibility, and complexity. In the end, the experimental results at the same conditions verify the differences between the one-loop and two-loop solutions.
SIFT-Based Image Compression

This paper proposes a novel image compression scheme based on the local feature descriptor - Scale Invariant Feature Transform (SIFT). The SIFT descriptor characterizes an image region invariantly to scale and rotation. It is used widely in image retrieval. By using SIFT descriptors, our compression scheme is able to make use of external image contents to reduce visual redundancy among images. The proposed encoder compresses an input image by SIFT descriptors rather than pixel values. It separates the SIFT descriptors of the image into two groups, a visual description which is a significantly sub sampled image with key SIFT descriptors embedded and a set of differential SIFT descriptors, to reduce the coding bits. The corresponding decoder generates the SIFT descriptors from the visual description and the differential set. The SIFT descriptors are used in our SIFT-based matching to retrieve the candidate predictive patches from a large image dataset. These candidate patches are then integrated into the visual description, presenting the final reconstructed images. Our preliminary but promising results demonstrate the effectiveness of our proposed image coding scheme towards perceptual quality. Our proposed image compression scheme provides a feasible approach to make use of the visual correlation among images.
An Effective Shape-Texture Weighted Algorithm for Multi-view Face Tracking in Videos

In this paper, an effective face tracking algorithm based on the combination of shape and texture information is proposed. The edge map is used to represent the shape of a face, while the texture information is characterized by the local binary pattern (LBP). As the face patterns to be tracked in consecutive frames are highly correlated, an accurate tracking can be achieved by searching for the shortest weighted feature distance between the face pattern and the possible face candidates. The weights of the shape and texture can be adapted for real-time tracking. Both the edge map and the LBP can, to a certain extent, alleviate the illumination effect. Moreover, skin-color-like objects will not be falsely tracked as a face. Our proposed algorithm complements the AdaBoost face detection algorithm to form a multi-view face-tracking system. Experimental results show that our algorithm can track faces in varying poses (tilted or rotated) in real time.
A very low bit-rate video coding algorithm by focusing on moving regions

Block-based motion estimation and compensation are the most popular techniques for video coding. As the shape and the structure of an object in a picture are arbitrary, the performance of such conventional block-based methods may not be satisfactory. In this paper, a very low bit-rate video coding algorithm focussing on moving regions is proposed. The objective is to improve the coding performance, which gives better subjective and objective quality than that of the conventional coding methods at the same bit rate. We predefined eight patterns to represent the moving regions in a macroblock. The patterns are then used for motion estimation and compensation to reduce the prediction errors. Furthermore, in order to increase the compression performance, the residual errors of a macroblock are rearranged into a block with no significant increase of high order DCT coefficients. As a result, both the prediction efficiency and the compression efficiency are improved.
Hybrid learning framework for web information retrieval

Machine learning techniques have been considered a very promising solution to Web information retrieval, which is based on the ranking of the relevance of samples to a query input. However, the connotation of labeling in ranking is quite different from that in classification. Specifically, the labeling of samples for ranking is usually incomplete, i.e. only a part of samples are labeled. In order to remedy this methodological gap, in this paper we propose a hybrid learning framework, called fuzzy-label learning, which consists of two layers. First, we utilize a label-propagation algorithm to estimate those labels of unlabeled samples by their neighborhoods. Second, we adopt RankBoost on the samples with fuzzy labels. Experiments with five-fold cross-validation using the Letor benchmark datasets show that the proposed hybrid learning framework can definitively improve the search performance achieved by the RankBoost algorithm for Web information retrieval.
Global face reconstruction for face hallucination using orthogonal canonical correlation analysis

In this paper, a global face reconstruction framework for face hallucination is proposed to globally reconstruct a high-resolution (HR) version of a face from an input low-resolution (LR) face, based on learning from LR-HR face pairs using orthogonal canonical correlation analysis (orthogonal CCA). In our proposed algorithm, face images are first represented using principal component analysis (PCA). CCA with the orthogonality property is then employed to maximize the correlation between the PCA coefficients of the LR and the HR face pairs so as to improve the hallucination performance. The original CCA does not own the orthogonality property, which is crucial for information reconstruction. In this paper, we utilize an orthogonal variant of CCA, which has been proven by experiments to achieve a better performance than the original CCA in terms of global face reconstruction.
Automatic generation of moment invariants and the use of higher order moments for character recognition

An automatic approach for the generation of moment invariants is presented. By making use of this approach, a complete set of moment invariants is generated, many of which have never been reported in the literature. This approach will guarantee not to miss any possible expression that may be useful for pattern recognition. The generated set of moment invariants, which include higher order moments, is used for the recognition of English alphabets. The results of the authors' study on selecting the best set of weighting factors for invariant moments are included. The recognition rates have improved to over 98.8%.<<ETX>>
A Novel Example-Based Super-Resolution Approach Based on Patch Classification and the KPCA Prior Model

In this paper, we propose a novel example-based super-resolution method to hallucinate high-resolution images from low-resolution images. As example-based super-resolution is a kind of learning process, how to learn effectively from training samples is essential to the quality of the reconstructed images. In our algorithm, a classification process is firstly employed to construct a well-organized patch database. Then, the KPCA prior model is used for each class to infer the high-resolution output. Since the training samples or patches are divided into numerous classes, the variations among the patches in each class or cluster are therefore greatly reduced. In addition, KPCA can capture the high-order statistics in those training samples, which makes the learning process even more powerful. Experiments show that the proposed algorithm can provide a high quality for image super- resolution reconstruction.
High-Resolution Face Verification Using Pore-Scale Facial Features

Face recognition methods, which usually represent face images using holistic or local facial features, rely heavily on alignment. Their performances also suffer a severe degradation under variations in expressions or poses, especially when there is one gallery per subject only. With the easy access to high-resolution (HR) face images nowadays, some HR face databases have recently been developed. However, few studies have tackled the use of HR information for face recognition or verification. In this paper, we propose a pose-invariant face-verification method, which is robust to alignment errors, using the HR information based on pore-scale facial features. A new keypoint descriptor, namely, pore-Principal Component Analysis (PCA)-Scale Invariant Feature Transform (PPCASIFT)-adapted from PCA-SIFT-is devised for the extraction of a compact set of distinctive pore-scale facial features. Having matched the pore-scale features of two-face regions, an effective robust-fitting scheme is proposed for the face-verification task. Experiments show that, with one frontal-view gallery only per subject, our proposed method outperforms a number of standard verification methods, and can achieve excellent accuracy even the faces are under large variations in expression and pose.
Fast super-resolution based on weighted collaborative representation

Recently, collaborative representation (CR) has been proposed as an l2-norm least-square solution for image super-resolution with significantly less computation than the l1-norm version of the Sparse-Coding-based Super-Resolution (ScSR) without any sacrifice in terms of image quality. In this paper we propose a novel weighted collaborative representation (WCR) instead of the original CR model for single image super-resolution. Our proposed method can achieve more than a 0.2~0.3 dB gain without requiring any additional cost compared to the original CR model. Moreover, we devise a hierarchical-clustering KD-tree searching scheme which can reduce the computational complexity on searching part in our WCR model from O(n) to O(n1/m), where n is the atom count and m is the number of layers, without any compromise of image quality.
Rotation- and scale-invariant texture classification using slide matching of the gabor feature

This paper proposes a set of efficient algorithms for rotation- and scale-invariant texture classification. This set is based on the well established Gabor feature. A circular sum of the Gabor feature elements belonging to the same scale is proposed to reduce the effect of rotation, while a slide matching of augmented scales is proposed to address the effect of scaling. The resulting feature vector is more compact, and the distance measure requires less computation. Experimental results indicate that this algorithm is effective for classifying texture images under different scales and rotations.
Locating head boundary by snakes

Snakes is an active contour model for representing image contours. This active contour model is tailor-made for representing the head boundary in a head-and-shoulders image. The effect of the parameters for snakes is investigated by locating head boundary and a best set of the parameters is suggested. A fast algorithm based on the greedy algorithm for active contour modelling is also presented. This fast algorithm has performance comparable to the greedy algorithm and has the execution time reduced by about 30% on the average.<<ETX>>
Using artificial neural network to predict mortality of radical cystectomy for bladder cancer

Surgical removal of bladder, i.e. radical cystectomy, is a standard treatment option for muscle invasive bladder cancer. Unfortunately, the treatment is associated with significant morbidities and mortalities. Many studies have been conducted to predict the morbidities and mortalities of radical cystectomy based on statistical analysis. In this paper, an artificial neural network is employed to predict 5-year mortality of radical cystectomy. The clinico-pathological data from a urology unit of a district hospital in Hong Kong were used to train and test the model. The outcome of the surgery was computed by an artificial neural network based on the risk factors identified by a conventional statistical method. It was found that the best overall accuracy of the neural network model was 77.8% and the 5-year mortality predicted by the model was comparable to that achieved by conventional statistical methods. The results of this study reflect that artificial intelligence has great development potential in medicine.
Saliency analysis based on multi-scale wavelet decomposition

In this paper, an efficient saliency analysis method based on multi-scale wavelet analysis is proposed, which can be used for car detection in intelligent transportation applications. In our method, saliency regions are considered as abnormal parts in a normal background; the wavelet theory is then used to detect these abnormal parts. Compared with other wavelet-based methods, our method need not perform inverse wavelet transformation, which is time-consuming. Besides, the use of multi-scale wavelet analysis can eliminate the drawbacks of the traditional center-surround methods, which have difficulties in detecting salient regions far away from object boundaries. Furthermore, a saliency prior process is adopted in our method, which can enhance the saliency map. Experimental results show that our method can achieve excellent results in terms of receiver operating characteristic (ROC) curve, the area under the curve (AUC) score, and visual performance, as compared to other state-of-the-art methods.
Transform-based fine-coarse vector quantization

Fine-coarse vector quantization (FCVQ) is a fast method for searching an unstructured vector quantization codebook. This approach imposes slight distortion to the image and requires substantially large storage. The storage requirement restricts the dimension of the vector blocks. The FCVQ is designed in the transform domain (FCVQ-TB) to provide a possible solution to the problem of memory requirement. Experiments are given to evaluate the performance of the FCVQ-TB.<<ETX>>