nils-herrmann/clean_scirepeval huggingface dataset:
Understanding Scenery Quality: A Visual Attention Measure and Its Computational Model

Travel photos record tourists' experiences and attentions when visiting a place. We question if they embed any untapped indices, subconsciously created by the tourists, for measuring the scenery quality? By analyzing thousands of such photos and inspired by the psychological theory of "broaden-and-build", our study reveals a strong inclination of taking panoramic photos at high rating outdoor tourist spots. Thus, this preference can be a supplementary measure of indexing the scenery quality. However, the task of recognizing panoramic photos is nontrivial. In this paper, we propose a visual attention inspired computational model to address this issue, which mimics human perceptual and cognitive mechanisms by a focus model and a scale model. The experiments on a newly created dataset demonstrate a remarkable performance of our proposal, along with its effectiveness in measuring scenery quality also verified by 10 high rating outdoor spots and 2 lower rating ones from across the world.
Fractional Differential Systems: A Fuzzy Solution Based on Operational Matrix of Shifted Chebyshev Polynomials and Its Applications

In this paper, a new formula of fuzzy Caputo fractional-order derivatives  $(0 < v \leq 1)$ in terms of shifted Chebyshev polynomials is derived. The proposed approach introduces a shifted Chebyshev operational matrix in combination with a shifted Chebyshev tau technique for the numerical solution of linear fuzzy fractional-order differential equations. The main advantage of the proposed approach is that it simplifies the problem alike in solving a system of fuzzy algebraic linear equations. An approximated error bound between the exact solution and the proposed fuzzy solution with respect to the number of fuzzy rules and solution errors is derived. Furthermore, we also discuss the convergence of the proposed method from the fuzzy perspective. Experimentally, we show the strength of the proposed method in solving a variety of fractional differential equation models under uncertainty encountered in engineering and physical phenomena (i.e., viscoelasticity, oscillations, and resistor–capacitor (RC) circuits). Comparisons are also made with solutions obtained by the Laguerre polynomials and the fractional Euler method.
Deep-plant: Plant identification with convolutional neural networks

This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England. To gain intuition on the chosen features from the CNN model (opposed to a `black box' solution), a visualisation technique based on the deconvolutional networks (DN) is utilized. It is found that venations of different order have been chosen to uniquely represent each of the plant species. Experimental results using these CNN features with different classifiers show consistency and superiority compared to the state-of-the art solutions which rely on hand-crafted features.
GMM-QNT hybrid framework for vision-based human motion analysis

The understanding of human behaviour in video is a challenging task in that the same behaviour might have several different meanings depending upon the scene and task context in which it is performed. While human seem to perform scene interpretations without effort, this is a formidable and yet unsolved task for artificial vision systems. One of the main reasons is that there exists a gap between low-level vision at signal level and high-level representation of activities at symbolic level. In this paper, we present an intelligent connection framework using Gaussian Mixture Model-based clustering (GMM) to bridge the low-level vision data and the Qualitative Normalised Templates (QNT) - a symbolic representation for human motion based on fuzzy qualitative robot kinematics, which could link the former with domain-dependent scenarios. The proposed method has been applied to the recognition of eight types of human motions and an empirical comparison with fuzzy hidden Markov-based human motion recognition system.
A fuzzy qualitative approach to human motion recognition

The understanding of human motions captured in image sequences pose two main difficulties which are often regarded as computationally ill-defined: 1) modelling the uncertainty in the training data, and 2) constructing a generic activity representation that can describe simple actions as well as complicated tasks that are performed by different humans. In this paper, these problems are addressed from a direction which utilises the concept of fuzzy qualitative robot kinematics. First of all, the training data representing a typical activity is acquired by tracking the human anatomical landmarks in an image sequences. Then, the uncertainty arise when the limitations of the tracking algorithm are handled by transforming the continuous training data into a set of discrete symbolic representations - qualitative states in a quantisation process. Finally, in order to construct a template that is regarded as a combination ordered sequence of all body segments movements, robot kinematics, a well-defined solution to describe the resulting motion of rigid bodies that form the robot, has been employed. We defined these activity templates as qualitative normalised templates, a manifold trajectory of unique state transition patterns in the quantity space. Experimental results and a comparison with the hidden Markov models have demonstrated that the proposed method is very encouraging and shown a better successful recognition rate on the two available motion databases.
HGO-CNN: Hybrid generic-organ convolutional neural network for multi-organ plant classification

Classification of plants based on a multi-organ approach is very challenging. Although additional data provides more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the degree of complexity of the problem. Existing approaches focus mainly on generic features for species classification, disregarding the features representing the organs. In fact, plants are complex entities sustained by a number of organ systems. In our approach, we exploit the PlantClef2015 benchmark, and introduce a hybrid generic-organ convolutional neural network (HGO-CNN), which takes into account both organ and generic information, combining them using a new feature fusion scheme for species classification. We show that our proposed method outperforms the state-of-the-art results.
Text detection via edgeless Stroke Width Transform

Text detection in scene images has gained widespread interests. A notable work, which is the Stroke Width Transform (SWT), has been attracting much interests due to its simplicity and efficiency. However, the SWT has difficulty in situations such as blur, low contrast, and illumination change images since it highly relies on the outcome from the edge detector. In this paper, a novel method is proposed to obtain stroke width image without the edge detectors. In particular, we replace the edge detector algorithm with the Extremal Regions (ERs) and propose a novel weighted Markov Random Field (MRF) method with three properties to construct a finer stroke width image. Experiment results on ICDAR datasets and a comparison with the state-of-the-art methods have shown the efficiency of the proposed method.
Fuzzy Qualitative Human Motion Analysis

This paper proposes a fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms. First, a data-quantization process is proposed to relax the computational complexity suffered from visual tracking algorithms. Second, a novel human motion representation, i.e., qualitative normalized template, is developed in terms of the fuzzy qualitative robot kinematics framework to effectively represent human motion. The human skeleton is modeled as a complex kinematic chain, and its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional hidden Markov model (HMM) and fuzzy HMM (FHMM) shows that the proposed approach consistently outperforms both HMMs in human motion recognition.
FTFBE: A numerical approximation for fuzzy time-fractional Bloch equation

Fractional calculus has a long successful history of 300 years, as it able to model natural phenomena states more accurately than the differential equations of integer order. With this, it plays an important role in variant disciplines. Recently, variant fractional models for the Bloch equations have been proposed, however, effective numerical methods for the fractional Bloch equation (FBE) are still in the infancy stage. In this paper, we extend the time-fractional Bloch equation (TFBE) to fuzzy field under the generalized Caputo differentiability, such that these extensions have natural relationship between crisp. For this purpose, we adopted the fractional Adams-Bashforth-Moulton (FABM) type predictorcorrector method, and introduced a new variant - the fuzzy fractional ADM (FFABM) to find the numerical solution. In this case, a new theorem concerning the error of our proposed FFADM method is also presented. Finally, the capability of the newly developed numerical methods is demonstrated in a fuzzy fractional-order problem, and it achieves satisfactorily in terms of numerical stability.
GrCS: Granular Computing-Based Crowd Segmentation

Crowd segmentation is important in serving as the basis for a wide range of crowd analysis tasks such as density estimation and behavior understanding. However, due to interocclusions, perspective distortion, clutter background, and random crowd distribution, localizing crowd segments is technically a very challenging task. This paper proposes a novel crowd segmentation framework-based on granular computing (GrCS) to enable the problem of crowd segmentation to be conceptualized at different levels of granularity, and to map problems into computationally tractable subproblems. It shows that by exploiting the correlation among pixel granules, we are able to aggregate structurally similar pixels into meaningful atomic structure granules. This is useful in outlining natural boundaries between crowd and background (i.e., noncrowd) regions. From the structure granules, we infer the crowd and background regions by granular information classification. GrCS is scene-independent and can be applied effectively to crowd scenes with a variety of physical layout and crowdedness. Extensive experiments have been conducted on hundreds of real and synthetic crowd scenes. The results demonstrate that by exploiting the correlation among granules, we can outline the natural boundaries of structurally similar crowd and background regions necessary for crowd segmentation.
Human posture classification using hybrid Particle Swarm Optimization

Accurate identification and categorization of different types of human postures is a vital element of a real time video-based surveillance system. In this paper, we present an approach known as the hybrid Particle Swarm Optimization (PSO+K) to classify human postures into their respective clusters. The PSO algorithm is used to search for possible optimal solution from the solution space. Then the results of the PSO are used as initial cluster centroids of the K-Means for further refinement to find the final optimal solution. Experimental results from the algorithm are compared with the K-Means and the conventional PSO algorithm using our posture dataset and the result shows that PSO+K produces better accuracies compared to other algorithms.
Crowd Saliency Detection via Global Similarity Structure

It is common for CCTV operators to overlook interesting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveillance. This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure. The global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Ranking is then performed on the global similarity structure to identify a set of extrem a. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrem a in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.
Early human actions detection using BK sub-triangle product

Humans have the natural capabilities to perceive and anticipate actions of objects they interact with, including incidents happen within their neighborhood. These days, this important aspect of human perception has been widely incorporated in the computer vision framework to perform human action detection task. However, little attention is paid to the problem of detecting ongoing human actions as early as possible, which is crucial in a number of important applications ranging from video surveillance to health-care. In this paper, we propose a framework for detecting ongoing human actions as early as possible, i.e. detecting an action as soon as it begins, but before it completes. This is make possible with the used of Fuzzy Bandler and Kohout's sub-triangle product (BK subproduct) inference mechanism, utilizing the fuzzy capabilities in handling the arisen uncertainties during the human action recognition stage for a reliable decision making. Experimental results on publicly available dataset illustrate the effectiveness of the proposed method.
A Fusion Approach for Efficient Human Skin Detection

A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.
ArtGAN: Artwork synthesis with conditional categorical GANs

This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ArtGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces. The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated images) to the generator from the discriminator. With the feedback from the label information, the generator is able to learn faster and achieve better generated image quality. Empirically, we show that the proposed ArtGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.
Unveiling contrast in darkness

At nighttime, reduced visibility could cause foreground and background images to appear to blend together. However, ambient light is always present in the natural environment, and as a consequence, it creates some contrast in darkness. In this paper, we formulate a visual analytic method that automatically unveils the contrast of dark images (i.e. nighttime), revealing the "hidden" contents. We utilize the traits of image representations obtained from computer vision techniques through a learning based inversion algorithm, eliminating the reliance on night vision camera and at the same time minimizing the need for human intervention (i.e. manual fine-tuning the gamma correction using Adobe Photoshop software). Experiments using the new Malaya Pedestrian in the Dark (MyPD) dataset that we have collected from the website Flickr, and in comparison with conventional methods such as image integral and gamma correction, it shows the efficacy of the proposed method. Additionally, we show the potential of this framework in applications that could benefit public safety.
Fuzzy set and multi descriptions property

Multi descriptions property is a fundamental property on membership functions of fuzzy sets, which has not been attended so far. The property suggests that not all membership functions of fuzzy sets can be viewed as a simple number in the range of 0 and 1. In fact, membership function of an element in a fuzzy set should be viewed as the sum of strength of the element that show each attribute which describes the fuzzy set. Therefore, fuzzy sets operation such as union, intersection and subsethood measure need to be revised. By adopting multi descriptions property, this paper introduced a set of improvement based on Checklist Paradigm for these operations for type-1 fuzzy sets. Since the multi descriptions property may also appear in type-2 fuzzy sets, we proposed a method for the subsethood measurement, namely Representative Method. This Representative Method provides simple and fast approximate measurements for subsethood.
Generalised approximate equalities based on rough fuzzy sets & rough measures of fuzzy sets

In an attempt to incorporate user knowledge in order to decide about the equality of sets, the concepts of approximate equalities using rough sets were introduced. These notions have been generalised in several ways and very recently [1] extended four types of approximate equalities using rough fuzzy sets instead of only rough sets. To be precise, a concept of leveled approximate equality was introduced and properties were studied. In this paper we extend this work with case studies to illustrate the applications of the concepts and compare them respectively. We also introduce and discuss the rough measures of basic sets, fuzzy sets and interpret four types of approximate equalities in terms of the accuracy measure as well as rough measures. The analysis had provided a clear distinguish notion in terms of the measures.
Fuzzy qualitative complex actions recognition

Understanding actions is a complex issue in many aspects. However, most of the literature on action recognition deals with only simple actions. In this paper, we proposed the fuzzy qualitative robot kinematics framework to complex actions over time, e.g. walk then run and over the body, walk while wave hand etc. The human limbs is modelled as articulated rigid bodies and its motion is represented by a series of such models in terms of time. With this, we eventually converted the human motion analysis into a conventional robotic problem which has been well studied. Experimental results has shown that the action model built in this manifold offers few advantages. e.g. handles the tradeoffs in the off-the-shelf tracking algorithm and avoid using generative model where the size of the training data typically goes as the square of the number of states.
A quad tree based method for blurred and non-blurred video text frames classification through quality metrics

Blur is a common artifact in video, which adds more complexity to text detection and recognition. To achieve good accuracies for text detection and recognition, this paper suggests a new method for classifying blurred and non-blurred frames in video. We explore quality metrics, namely, BRISQUE, NRIQA, GPC and SI, in a new way for classification. We estimate the values of these metrics with the help of predefined samples called reference values. To widen the difference between metric values for better classification, we introduce scaling factors as a non-linear sigmoidal function, which considers the metric of each current frame and its reference and results in templates. Based on the characteristics of metrics, the proposed method finds a relationship between the metrics to derive rules for classification. To classify the frame containing local blur, we explore quad tree division with classification rules which divide non-blurred blocks to identify local blur. We use standard databases, namely, ICDAR 2013, ICDAR 2015 and YVT videos for experimentation, and evaluate the proposed method in terms of text detection and recognition rates given by text detection and binarization methods before and after classification.
Fuzzy-rough based decision system for gait adopting instance selection

A fuzzy rough set theory based gait decision system is presented for diagnosis of fall risk. Distracted walking is investigated. Gait cycles of thirty young participants were monitored while they walked across a walkway at a self-selected pace and also while being distracted by performing a task alongside walking. Results shows distracted walking being similar to impaired walking of an elderly person. The aim is to adopt instance selection methods to eliminate redundant or noisy sample from training data thus preventing the system from detecting erroneous gait patterns or deviations. Decision systems have great potential in medical informatics to serve as diagnostic tools. Results show that instance selection improves the efficacy of various classifiers in detecting distracted gait. The model is capable of assessing gait based on the easily obtainable features and categorizing them in terms of fall risk. This will help to identify individuals at risk in fall prevention management.
Toward the existence of solutions of fractional sequential differential equations with uncertainty

The main study of this paper is focused on the solutions of a class of fuzzy sequential fractional differential equations in the form of (<sub>0</sub>D<sub>x</sub><sup>β</sup>y)'(x) = b(x)y(x), where (<sub>0</sub>D<sub>x</sub><sup>β</sup>y)(x) is the fuzzy Riemann-Liouville derivative of order β ∈ (0, 1). On this subject, a new fuzzy complete metric space is introduced. Finally, we proof the existence and uniqueness of our solution using the contraction principle.
A New Approach to Feature Selection in Handwritten Farsi/Arabic Character Recognition

Feature extraction and feature selection are very important steps in pattern recognition systems. However, finding an optimal, effective, and robust feature set is usually a difficult task. In this paper, with the use of a comprehensive study on offline handwritten Farsi/Arabic digit recognition systems, a set of well-known features were extracted. Then, by employing one- and two-dimensional spectrum diagrams for standard deviation and minimum to maximum distributions, an optimal subset of initial features set was selected automatically. Experimental results, according to one of the biggest standard handwritten Farsi digit datasets, the HODA, had shown 95.70% accuracy with the proposed method. The achieved results showed a salient improvement in system precision in comparison to using other state-of-the-art approaches.