nils-herrmann/clean_scirepeval huggingface dataset:
Sequential and Unsupervised Document Authorial Clustering Based on Hidden Markov Model

Document clustering groups documents of certain similar characteristics in one cluster. Document clustering has shown advantages on organization, retrieval, navigation and summarization of a huge amount of text documents on Internet. This paper presents a novel, unsupervised approach for clustering single-author documents into groups based on authorship. The key novelty is that we propose to extract contextual correlations to depict the writing style hidden among sentences of each document for clustering the documents. For this purpose, we build an Hidden Markov Model (HMM) for representing the relations of sequential sentences, and a two-level, unsupervised framework is constructed. Our proposed approach is evaluated on four benchmark datasets, widely used for document authorship analysis. A scientific paper is also used to demonstrate the performance of the approach on clustering short segments of a text into authorial components. Experimental results show that the proposed approach outperforms the state-of-the-art approaches.
Border Gateway Protocol Anomaly Detection Using Failure Quality Control Method

Border Gateway Protocol (BGP) is the de-facto inter-domain routing protocol used across thousands of Autonomous Systems (AS) joined together in the Internet. Security has been a major issue for BGP. Nevertheless, BGP suffers from serious threats even today, like Denial of Service (DoS) attack and misconfiguration of routing information. BGP is one of the complex routing protocols and hard to configure against malicious attacks. However, it is important to detect such malicious activities in a network, which could otherwise cause problems for availability of services in the Internet. In this paper we use the Failure Quality Control (FQC), a technique to detect anomaly packets in the network for real time intrusion detection.
A New Universal Generating Function Method for Estimating the Novel Multiresource Multistate Information Network Reliability

In this article, we introduce a special novel multistate network that permits multiresource to be transmitted from the source node to multiple targets simultaneously without satisfying the flow conservation law. This network is called the multiresource multistate information network (MMIN). The one-to-many-targets (i.e. one-to-all-target-subset) reliability problem of the MMIN is considered next under limited cost and capacity constraints. A straightforward, exact algorithm derived from the universal generating function method (UGFM) is developed for this new problem. The correctness and computational complexity of the proposed UGFM will be analysed and proven. One example is given to illustrate how MMIN reliability is evaluated using the proposed UGFM.
Concurrent edge detection with Spiral Architecture on Linux

Edge detection is an essential tool in image analysis. It is a process of identifying significant discontinuities in light intensities in order to form an outline of the object of interest. Spiral Architecture provides powerful computational power as a method of image data representation. Gaussian multi-scale theory was employed as the mathematical model for edge detection. The parallel processing algorithm was implemented in the form of a master-slave model. Spiral Architecture enables an image to be uniformly partitioned and distributed to slave processors after spiral multiplication. Concurrent processing is facilitated by inter-process communication and socket interface programming tools of Linux operating system.
Estimation of Internal and External Parameters for Camera Calibration Using 1D Pattern

Camera calibration is to estimate the intrinsic and extrinsic parameters of a camera. Most of object-based calibration methods used 3D or 2D pattern. A novel and more flexible 1D object-based calibration was introduced only a couple of years ago, but merely for estimation of intrinsic parameters. The estimation of extrinsic papers is essential when multiple cameras are involved for simultaneously taking images from different view angles and when the knowledge of relative locations between the cameras is required. Though it is relatively simple using 2D or 3D calibration pattern, the estimation of extrinsic parameters is not obvious using 1D pattern. In this paper, we will perform a 1D camera calibration involving both intrinsic and extrinsic parameters.
Moving vehicle detection based on an improved interframe difference and a Gaussian model

For moving vehicle detection, this paper presents an algorithm on the basis of an improved interframe differential algorithm and an improved Gaussian model. Firstly, according to a statistical histogram, an interesting region is extracted. Through a mean algorithm, an initial background model is established. The interesting region is divided into several blocks by a self-adaptive method. Secondly, according to an improved interframe difference algorithm, the interesting region is separated roughly. On the basis of these steps, we utilize an improved Gaussian model to separate the rough results precisely. At last, the results are processed by double-threshold background subtracting. Experimental results show this algorithm can detect moving vehicles rapidly and accurately.
Detection of Denial-of-Service Attacks Based on Computer Vision Techniques

Detection of Denial-of-Service (DoS) attacks has attracted researchers since 1990s. A variety of detection systems has been proposed to achieve this task. Unlike the existing approaches based on machine learning and statistical analysis, the proposed system treats traffic records as images and detection of DoS attacks as a computer vision problem. A multivariate correlation analysis approach is introduced to accurately depict network traffic records and to convert the records into their respective images. The images of network traffic records are used as the observed objects of our proposed DoS attack detection system, which is developed based on a widely used dissimilarity measure, namely Earth Mover's Distance (EMD). EMD takes cross-bin matching into account and provides a more accurate evaluation on the dissimilarity between distributions than some other well-known dissimilarity measures, such as Minkowski-form distance Lp and X2 statistics. These unique merits facilitate our proposed system with effective detection capabilities. To evaluate the proposed EMD-based detection system, ten-fold cross-validations are conducted using KDD Cup 99 dataset and ISCX 2012 IDS Evaluation dataset. The results presented in the system evaluation section illustrate that our detection system can detect unknown DoS attacks and achieves 99.95 percent detection accuracy on KDD Cup 99 dataset and 90.12 percent detection accuracy on ISCX 2012 IDS evaluation dataset with processing capability of approximately 59,000 traffic records per second.
A Comparison on Histogram Based Image Matching Methods

Using colour histogram as a stable representation over change in view has been widely used for object recognition. In this paper, three newly proposed histogram-based methods are compared with other three popular methods, including conventional histogram intersection (HI) method, Wong and Cheung's merged palette histogram matching (MPHM) method, and Gevers' colour ratio gradient (CRG) method. These methods are tested on vehicle number plate images for number plate classification. Experimental results disclose that, the CRG method is the best choice in terms of speed, and the GWHI method can give the best classification results. Overall, the CECH method produces the best performance when both speed and classification performance are concerned.
Small target detection using an optimization-based filter

Small target detection is a critical problem in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some challenges remained, e.g. cloud edges and horizontal lines are likely to cause false alarms. This paper proposes a novel method using an optimization-based filter to detect infrared small target in heavy clutter. First, we design a certain pixel area as active area. Second, a weighted quadratic cost function is performed in the active area. Finally, a filter based on statistics of active area is derived from the cost function. Our method could preserve heterogeneous area, meanwhile, remove target region. Experimental results show our method achieves satisfied performance in heavy clutter.
A probability-dynamic Particle Swarm Optimization for object tracking

Particle Swarm Optimization has been used in many research and application domain popularly since its development and improvement. Due to its fast and accurate solution searching, PSO has become one of the high potential tools to provide better outcomes to solve many practical problems. In image processing and object tracking applications, PSO also indicates to have good performance in both linear and non-linear object moving pattern, many scientists conduct development and research to implement not only basic PSO but also improved methods in enhancing the efficiency of the algorithm to achieve precise object tracking orbit. This paper is aim to propose a new improved PSO by comparing the inertia weight and constriction factor of PSO. It provides faster and more accurate object tracking process since the proposed algorithm can inherit some useful information from the previous solution to perform the dynamic particle movement when other better solution exists. The testing experiments have been done for different types of video, results showed that the proposed algorithm can have better quality of tracking performance and faster object retrieval speed. The proposed approach has been developed in C++ environment and tested against videos and objects with multiple moving patterns to demonstrate the benefits with precise object similarity.
Generalized local N-ary patterns for texture classification

Local Binary Pattern (LBP) has been well recognised and widely used in various texture analysis applications of computer vision and image processing. It integrates properties of texture structural and statistical texture analysis. LBP is invariant to monotonic gray-scale variations and has also extensions to rotation invariant texture analysis. In recent years, various improvements have been achieved based on LBP. One of extensive developments was replacing binary representation with ternary representation and proposed Local Ternary Pattern (LTP). This paper further generalises the local pattern representation by formulating it as a generalised weight problem of Bachet de Meziriac and proposes Local N-ary Pattern (LNP). The encouraging performance is achieved based on three benchmark datasets when compared with its predecessors.
Visual tracking via graph-based efficient manifold ranking with low-dimensional compressive features

In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes, and the goal of tracking is to search the unlabeled sample that is the most relevant with existing labeled nodes by manifold ranking algorithm. Meanwhile, we adopt non-adaptive random projections to preserve the structure of original image space, and a very sparse measurement matrix is used to efficiently extract low-dimensional compressive features for object representation. Furthermore, spatial context is used to improve the robustness to appearance variations. Experimental results on some challenging video sequences show the proposed algorithm outperforms six state-of-the-art methods in terms of accuracy and robustness.
Error concealment for H.264/AVC using regression modeling digest of technical papers

This paper presents a novel error concealment algorithm for H.264/AVC, using a regression model constructed according to the spatial relationship between the block locations and their local motion activities. The experimental results show that the proposed algorithm achieves significant PSNR improvement over the existing methods. Moreover, the implementation of the proposed algorithm is very simple and therefore it can be readily applied to video applications running on various consumer electronic devices, including mobile devices.
Deformable Template Matching Using Proposal-Based Best-Buddies Similarity

We propose a new method for template matching based on the Best-Buddies Similarity (BBS) measure. Our method is able to match objects with large difference in size and hence achieves a deformable template matching. In addition, compared with the original method for template matching based on the BBS, our method significantly cuts down on the computation time. The fast and deformable template matching is implemented by measuring the BBS of only potential areas instead of all positions in an image. The potential areas, which can have different size from the given template, are found by a proposal generation based on edge priors and a selective search among the obtained proposals. The results from the experiments conduct-ed on a challenging dataset demonstrate that our method out-performs the state-of-the-art methods in terms of accuracy.
Graph-Based Text Segmentation Using a Selected Channel Image

This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two polarity trend. According to the observation that the histogram distributions of the respective colour channel images are usually different from each other, we select the colour channel image with the histogram having the biggest distance between the two main peaks, which represents the main foreground colour strength and background colour strength respectively. The peak distance is estimated by the mean-shift procedure performed on each individual channel image. Then, a graph model is constructed on a selected channel image to segment the text image into foreground and background. The proposed method is tested on a public database, and its effectiveness is demonstrated by the experimental results.
Refined Gaussian Weighted Histogram Intersection and Its Application in Number Plate Categorization

This paper proposes a refined Gaussian weighted histogram intersection for content-based image matching and applies the method for number plate categorization. Number plate images are classified into two groups based on their colour similarities with the model image of each group. The similarities of images are measured by the matching rates between their colour histograms. Histogram intersection (HI) is used to calculate the matching rates of histograms. Since the conventional histogram intersection algorithm is strictly based on the matching between bins of identical colours, the final matching rate could easily be affected by colour variation caused by various environment changes. In our recent paper, a Gaussian weighted histogram intersection (GWHI) algorithm has been proposed to facilitate the histogram matching via taking into account matching of both identical colours and similar colours. The weight is determined by the distance between two colours. When applied to number plate categorization, the GWHI algorithm demonstrates to be more robust to colour variations and produces a classification with much lower intra-class distance and much higher inter-class distance than previous HI algorithms. However, the processing speed of this GWHI method is still not satisfying. In this paper, the GWHI method is further refined, where a colour quantization method is utilized to reduce the number of colours without introducing apparent perceptual colour distortion. New experimental results demonstrate that using the refined GWHI method, image categorization can be done more efficiently
Color Distribution Information for the Reduced-Reference Assessment of Perceived Image Quality

Reduced-reference systems can predict in real-time the perceived quality of images for digital broadcasting, only requiring that a limited set of features, extracted from the original undistorted signals, is transmitted together with the image data. This paper uses descriptors based on the color correlogram, analyzing the alterations in the color distribution of an image as a consequence of the occurrence of distortions, for the reduced reference data. The processing architecture relies on a double layer at the receiver end. The first layer identifies the kind of distortion that may affect the received signal. The second layer deploys a dedicated prediction module for each type of distortion; every predictor yields an objective quality score, thus completing the estimation process. Computational-intelligence models are used extensively to support both layers with empirical training. The double-layer architecture implements a general purpose image quality assessment system, not being tied up to specific distortions and, at the same time, it allows us to benefit from the accuracy of specific, distortion-targeted metrics. Experimental results based on subjective quality data confirm the general validity of the approach.
Towards Individual QoE for Multiparty Videoconferencing

Videoconferencing is becoming an essential part in everyday life. The visual channel allows us for interactions that were not possible over audio-only communication systems, such as the telephone. However, being a de-facto  over-the-top service, the quality of the delivered videoconferencing experience is subject to variations, depending on network conditions. Videoconferencing systems adapt to network conditions by changing, for example, encoding bit rate of the video. For this adaptation not to hamper the benefits related to the presence of a video channel in the communication, it needs to be optimized according to a measure of the quality of experience (QoE) as perceived by the user. The latter is highly dependent on the ongoing interaction and individual preferences, which have hardly been investigated so far. In this paper, we focus on the impact that video quality has on conversations that revolve around objects that are presented over the video channel. To this end, we conducted an empirical study where groups of four people collaboratively build a Lego model over a videoconferencing system. We examine the requirements for such a task by showing when the interaction, measured by visual and auditory cues, changes depending on the encoding bit rate and loss. We then explore the impact that prior experience with the technology and affective state have on QoE of participants. We use these factors to construct predictive models that double the accuracy compared to a model based on the system factors alone. We conclude with a discussion of how these factors could be applied in real-world scenarios.
Co-occurrence Matrixes for the Quality Assessment of Coded Images

Nonlinear issues complicate the modeling of the user-perceived quality of compressed images, especially when setting up feature-based objective methods. The research described in this paper indicates that the co-occurrence matrix and its features can be effective descriptors, embedding relevant information with respect to the perceptual phenomenon of visual quality. The paper first proposes a statistical approach to correlating objective quantities with subjective empirical evidence, then applies such a protocol to features associated with co-occurrence matrixes. Models from computational intelligence eventually support the actual quality-prediction task. Experimental results on real benchmarks confirm the general validity of the approach.
1Mbps is enough: Video quality and individual idiosyncrasies in multiparty HD video-conferencing

Most video platforms deliver HD video in high bitrate encoding. Modern video-conferencing systems are capable of handling HD streams, but using multiparty conferencing, average internet connections in the home are on their bandwidth limit. For properly managing the encoding bitrate in videoconferencing, we must know what is the minimum bitrate requirement to provide users an acceptable experience, and what is the bitrate level after which QoE saturates?. Most available subjective studies in this area used rather dated technologies. We report on a multiparty study on video quality with HD resolution. We tested different encoding bitrates (256kbs, 1024kbs and 4096kbs) and packet loss rates (0, 0.5%) in groups of 4 participants with a scenario based on the ITU building blocks task. We discuss the influence of group interaction and individual idiosyncrasies based on different mixed models, and look at covariates engagement and enjoyment as further explanatory factors. We found that 256kbs is still sufficient to provide a fair overall experience, but video quality is noticed to be poor. On the higher bitrate end, most people will not perceive the difference between 1024kbs and 4096kbs, considering in both cases the quality to be close to excellent. Independent on bitrate, packet loss has a small but significant impact, quantifiable in, on average, less than half a point difference on a 5-point ITU scale.
How to apply spatial saliency into objective metrics for JPEG compressed images?

This paper investigates how saliency obtained from eye-tracking data can be integrated into objective metrics for JPEG compressed images. The objective metrics used in this paper are both based on features, locally extracted from the images and serving as input to a neural network for the overall quality prediction. We compare various weighting functions to combine saliency with these objective metrics, taking into account the possible distraction due to artifacts that might affect the quality judgment. Experimental results indicate that including saliency into objective metrics in an appropriate way can further enhance their performance.
Beauty is in the scale of the beholder: Comparison of methodologies for the subjective assessment of image aesthetic appeal

A first step towards creating automatic measures of image aesthetic appeal is understanding its appreciation via subjective testing. Nevertheless, reliably setting up such tests appears to be challenging, as aesthetic appeal is proven to be influenced by a number of subjective factors. In this paper we investigate four scale types for aesthetic appeal rating and assess their ability to provide general quantification of aesthetic appeal, as well as repeatable judgment across experiments. We asked 24 users to assess a representative image set constructed to uniformly cover a wide range of aesthetic appeal. Our experiments show that the Absolute Category Rating (ACR) 5-point scale provides the most consistent ratings across participants, which are also repeatable across different experiments.
Maximal-discrepancy bounds for regularized classifiers

Regularized Classifiers such as SVM or RLS are among the most used and successful classifiers in machine learning. The theory and the empirical evaluation of the associate generalization bounds are of paramount importance; bounds based on the Maximal-Discrepancy approach proved quite effective. The paper shows an efficient, iterative procedure to evaluate Maximal-Discrepancy bounds for this kind of classifiers. Empirical results on UCI datasets show that this approach can attain tighter bounds to the run-time classification error.