Reviewerly/exHarmony huggingface dataset:
Aspect term extraction and optimized deep fuzzy clustering-based inverted indexing for document retrieval

Finding good relevant documents for query optimization is a well-known difficulty in the field of document retrieval. This paper develops a novel approach, named Exponential Aquila Optimizer (EAO)-based Deep Fuzzy Clustering for retrieving the documents. The proposed technique effectively finds the relevant documents and tries to understand the relationship among the documents and queries in terms of the significance of documents for query optimization. Here, the Deep Fuzzy Clustering is employed for performing cluster-based inverted indexing where the Training procedure of Deep Fuzzy Clustering is done using the developed optimization algorithm, named EAO. Meanwhile, the developed EAO is newly designed by the incorporation of EWMA and AO. In addition, complex query matching is done using the Tversky index for the user-based queries, such as multigram queries and semantic queries. On the other hand, the RV coefficient is accomplished for performing query optimization for relevant document retrieval. The proposed technique achieves better performance in terms of the performance metrics, like precision, recall, and F-measure with the maximum precision of 1, maximum recall of 0.956, and maximum F-measure of 0.977, respectively.
Multiple Object Tracking through Background Learning

This paper discusses about the new approach of multiple object tracking relative to background information. The concept of multiple object tracking through background learning is based upon the theory of relativity, that involves a frame of reference in spatial domain to localize and/or track any object. The field of multiple object tracking has seen a lot of research, but researchers have considered the background as redundant. However, in object tracking, the background plays a vital role and leads to definite improvement in the overall process of tracking. In the present work an algorithm is proposed for the multiple object tracking through background learning. The learning framework is based on graph embedding approach for localizing multiple objects. The graph utilizes the inherent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple objects. The proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation measures. It is observed that our proposed algorithm gives better performance.
Visual Business Analytics: Using the Example of a Call Center

In this article we will examine an approach to the analysis of semi-structured log data using the example of a call center as a subsection of a central corporate service center. In such data all events of a caller passing through the routing are stored. Consequently, it is possible to trace more precisely what the customer experiences during his call. However, this information is only available in semi-structured form. A little-known approach in Anglo-Saxon literature, the Visual Business Analytics (VBA), represents a holistic concept for achieving added value from semi-structured data. In the VBA, data is initially transformed and structured and then prepared for analysis purposes. The goal is to derive recommendations for supporting management decisions in a call center. In the further course, the development of the approaches of information representation is examined first, then the VBA is presented and applied to the log protocols in a call center. Finally, other call center applications of VBA are considered, and an outlook is given on other industries in which the use of VBA offers advantages.
Online English Teaching System under the Background of Epidemic Situation Based on Intelligent Feature Recognition Technology

In order to improve the effect of online English teaching in the context of the epidemic, this paper combines intelligent feature recognition technology to carry out an online English teaching system in the context of the epidemic and greatly reduces the number of variants by selecting mutation operators with excellent performance. Moreover, in this paper, the mutation adequacy and the number of mutation operators are regarded as two objective functions, and the selection problem of mutation operators is generated into a two-stage optimization problem, and the above problems are solved by a genetic algorithm. In addition, this paper sorts the mutation branches and preferentially covers the mutation branches of the mutants corresponding to the mutation operators with higher mutation scores. The experimental study shows that the online English teaching system based on intelligent feature recognition technology proposed in this paper meets the actual needs of online English teaching in the context of the epidemic.
Autoencoder-based Local Descriptor for Target Recognition in Infrared Images

Target recognition systems consist of two stages: feature extraction and classification. To improve the performance of these systems, we require high-quality features. The features extracted from RGB images are generally rich in information, which is not the case for infrared images. The processing of this later is challenging, mainly due to their properties of low contrast, competitive background and lack of edges. Traditional hand-crafted feature extractors are limited by these challenges. In this work, an autoencoder-based local descriptor is explored for target recognition in infrared images. Firstly, we generate the local descriptors using a convolutional autoencoder. Next, a global representation is built from these local descriptors, based on the bag of visual words model. Finally, we fed the global descriptor to a classifier. The evaluation of this approach is carried out on two thermal datasets: FLIR thermal starter dataset and VAIS dataset.
Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints

Due to the open nature of WIFI connection, it is exposing its private information to the attackers. Traditional WIFI security methods are no longer able to meet the current security needs, and more and more wireless-side physical layer security solutions provide solutions, among which RF fingerprinting is an endogenous security technology with potential. Constructing an effective and accurate method to identify WIFI devices that steal information is a difficulty that today’s society needs to face. The main problem is not only that the recognition accuracy is difficult to improve but also the problem of data shortage. In this paper, we first construct a large-scale WIFI real-world measurement dataset. Next, we use PSD and bispectrum features, as well as complex ResNet schemes for WIFI device identification experiments, and compare and analyze them from multiple perspectives. The experimental results show that the proposed algorithm can achieve up to 97% recognition accuracy among 100 devices. Moreover, when the SNR is 0 dB, the complex ResNet method can still achieve 78% recognition accuracy among 100 devices. Finally, this paper summarizes the experimental analysis of the measured dataset and discusses the open issues related to this area.
Monotonic Gaussian regularization of attention for robust automatic speech recognition

The Attention-based Encoder–Decoder (AED) models are one of the most popular models for Automatic Speech Recognition (ASR). However, instability can occur in AED with problems such as incorrect insertions or word repetitions due to the violation of the inherent monotonic alignment property. To address these problems, we propose a monotonic Gaussian regularization method to guide the attention training, where the guiding map is depicted as a sequence of Gaussian distributions with monotonically moving centers. Experiments show our method reduces the insertion error rate by a relative 7% on the HKUST dataset, relative 20% and 16% on two large industrial datasets, and a relative 21% on an out-of-domain test set. The overall Character Error Rates (CERs) are all reduced at the same time, indicating that the model’s recognition ability is well maintained. Therefore, our proposed method improves model performance by enhancing monotonic alignment, and provides better robustness.
Depth Estimation of Monocular PCB Image Based on Self-Supervised Convolution Network

To improve the accuracy of using deep neural networks to predict the depth information of a single image, we proposed an unsupervised convolutional neural network for single-image depth estimation. Firstly, the network is improved by introducing a dense residual module into the encoding and decoding structure. Secondly, the optimized hybrid attention module is introduced into the network. Finally, stereo image is used as the training data of the network to realize the end-to-end single-image depth estimation. The experimental results on KITTI and Cityscapes data sets show that compared with some classical algorithms, our proposed method can obtain better accuracy and lower error. In addition, we train our models on PCB data sets in industrial environments. Experiments in several scenarios verify the generalization ability of the proposed method and the excellent performance of the model.
Identification and Analysis of Priority Department Topics of Student’s Scientific Research

The article deals with the use of modern information tools at the Department of Control and Intelligent Technologies of the National Research University "MPEI" for organizing "feedback" with students, which involves conducting and analyzing online surveys on the choice of topics for Educational Research Work. It is noted that the choice of topics by students is directly related to their subsequent employment. This allows, based on the analysis of the survey results, to identify the training courses and areas of scientific research that are most in demand in practice and, in the opinion of students, are useful in professional activities (employment in their specialty). The retrospective data covers the period from 2015 to 2019 and contains 107 questionnaires. The work identifies the most popular department topics, builds their ratings, analyzes the change in student preferences over time, examines the relationship between topics. The analysis showed that the Department of Control and Intelligent Technologies has a group of Student’s Scientific Research directions that are most in demand by students. Also, topics that over time have ceased to be indicated by students as priorities and by now have significantly lost their relevance from the point of view of their usefulness in employment in their specialty are identified. It was found out which topics have stable connections with each other and often fall into the questionnaires together. The results of the analysis enable the department to forecast demand and prepare human resources to meet the increasing number of applications in popular areas. The research carried out makes it possible to automate the process of assigning students to the Student’s Scientific Research and choosing the most suitable scientific advisor. The proposed approach provides the department with "feedback", on the basis of which it is possible to correct the topics of the Student’s Scientific Research, update, adjust (and expand) courses that contribute to the employment of graduates and provide knowledge that is in demand in the modern labor market.
THE DIDACTIC-PEDAGOGICAL TRAINING OF TEACHERS: CONFIGURATIONS, THEORETICAL BRANDS AND REPERCUSSIONS

The present research focused on the configuration of the subjects Didactics, Applied Didactics, Teaching Practice and Supervised Internship in the Curricular Pedagogical Projects of the degrees at the Federal University of Acre, in order to identify the movement of debates, disputes and redefinitions of the curricular designs.For that, a documental research was carried out, having as source the curriculum available on the university's website.The theoretical framework used was the authors: DIMAS (2008), GATTI (2015), LIBÂNEO (2015) and PIMENTA (2015).As a result of this research, we highlight that this cycle of curricular reforms was carried out in 2006 and 2008, with curricula updates from 2014 onwards.degree rather than an epistemological discussion about training conceptions and practices.There was a predominance of relativization about the nature of pedagogical subjects and the knowledge produced by the area of Education about the subjects and processes of school education.
Efficient game theoretic approach to dynamic graph partitioning

As a building block in many graph-based applications, graph partitioning aims to divide a graph into smaller parts of roughly equal size, and meanwhile, minimize the number of cutting edges. Existing solutions for graph partitioning are mainly designed for static graphs and are not appropriate for many dynamic graphs in real-world scenarios, including social networks, knowledge graphs, and web graphs. Although there is an incremental method, called IncKGGGP, proposed to efficiently deal with dynamic graphs, it can only be deployed on top of a specific batch partitioning algorithm, called KGGGP, which inherently impairs the final partitioning quality. To alleviate these issues, in this paper, we propose a novel Edge-Cut Partitioning approach based on Game theory for dynamic graphs (ECPG). Generally, ECPG is equipped with the following two nice properties. (1) High effectiveness. It can cope with dynamic graphs on top of any initial partitioning result, and then, achieve higher-quality results by choosing more effective static algorithms. (2) High efficiency. By exerting the advantage of game theory, ECPG can not only assign updated vertices into desirable partitions efficiently based on a low time complexity function but also can reduce redundant computations by reusing existing partitioning results. We also prove that there exists a Nash equilibrium in ECPG. From experimental results over several real-world graphs, it demonstrates that ECPG significantly outperforms the existing algorithms by up to one order of magnitude with comparable partitioning quality.
CVFont: Synthesizing Chinese Vector Fonts via Deep Layout Inferring

Abstract Creating a high‐quality Chinese vector font library, which can be directly used in real applications is time‐consuming and costly, since the font library typically consists of large amounts of vector glyphs. To address this problem, we propose a data‐driven system in which only a small number (about 10%) of Chinese glyphs need to be designed. Specifically, the system first automatically decomposes those input glyphs into vector components. Then, a layout prediction module based on deep neural networks is applied to learn the layout style of input characters. Finally, proper components are selected to assemble the glyph of each unseen character based on the predicted layout to build the font library that can be directly used in computers and smart mobile devices. Experimental results demonstrate that our system synthesizes high‐quality glyphs and significantly enhances the producing efficiency of Chinese vector fonts.
Singular Value Decomposition and Manifold Regulation-based Multi-label Classification

In multi-label classification, an instance may contain multiple labels simultaneously, so it is applied widely in many aspects such as product recommendation, biological function prediction and document annotation. However, the high-dimensional problem of feature space and sparseness problem of label space bring great challenges to multi-label classification. To solve these problems, this paper proposes a Singular Value Decomposition and Manifold Regulation-based Multi-label Classification (SDMR) framework. In this framework, the label space is transformed into latent label space by singular value decomposition (SVD). Then an improved principal component analysis method based on manifold regularization (PCAM) is proposed, which can find a few effective features to maximize the dependence between low-dimensional features and latent labels. Finally, a powerful multi-label classifier is learned from low-dimensional spaces. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on ten real-world multi-label data sets. Compared with the traditional multi-label classification algorithms, the proposed algorithm through dual spaces reduction can achieve better classification performance.
Generating predicate suggestions based on the space of plans: an example of planning with preferences

Task planning in human-robot environments tends to be particularly complex as it involves additional uncertainty introduced by the human user. Several plans, entailing few or various differences, can be obtained to solve the same given task. To choose among them, the usual least-cost plan criteria is not necessarily the best option, because here, human constraints and preferences come into play. Knowing these user preferences is very valuable to select an appropriate plan, but the preference values are usually hard to obtain. In this context, we propose the Space-of-Plans-based Suggestions (SoPS) algorithms that can provide suggestions for some planning predicates, which are used to define the state of the environment in a task planning problem where actions modify the predicates. We denote these predicates as suggestible predicates, of which user preferences are a particular case. The first algorithm is able to analyze the potential effect of the unknown predicates and provide suggestions to values for these unknown predicates that may produce better plans. The second algorithm is able to suggest changes to already known values that potentially improve the obtained reward. The proposed approach utilizes a Space of Plans Tree structure to represent a subset of the space of plans. The tree is traversed to find the predicates and the values that would most increase the reward, and output them as a suggestion to the user. Our evaluation in three preference-based assistive robotics domains shows how the proposed algorithms can improve task performance by suggesting the most effective predicate values first.
Model‐based risk assessment evaluation

Abstract Risk assessment (RA) belongs to the core parts of an organizational risk management process. The primary goals of a RA are the identification, estimation and prioritization of risk(s) to organizational operations. The existing quantitative analysis of RA is focusing on the risk factors, that is, threat or vulnerability. However, the quantitative metrics of the assessment have not been formally defined and modeled yet. Consequently, it is essential to define a formal model and relevant security metrics for RA within an organization. In this study, we will use a complete lattice to analyze risk factors. Moreover, a formal model will be defined to describe the RA process. The reachability of the model will be discussed. Quantitative metrics will be defined to provide insight on the risk estimation. The model can be expressed using colored Petri nets. This study can assist chief information security officers analyze security risks in their organizations and help them balance the security budget in their organizations.
Difficulty level estimation of mathematics problems using machine learning

The aim of this research is to design an automatic system that can estimate the difficulty level of mathematics problems in the way that is similar to human judgment. This system helps reduce the teacher workload in the question bank construction and also helps students who want to practice the problems with varieties of difficulty levels for self-learning. Our system started with extracting features from the mathematics problem directly and then using machine-learning algorithms to estimate the difficulty level so that the desired value is consistent with the estimation made by human experts. The designed system extracts feature from the mathematics problems by simulating the human calculation process and counting the number of applying formulas during the optimal path of the problem-solving process, then the features are used for training naive Bayes, neural network, regression, and support vector machine (SVM). The comparative result from the 4 model prediction on the differential calculus dataset shows that the regression model is the best predictor, where the mean absolute error between the machine learning predicted value and the labels from human experts is approximately 0.57 level out of 1 - 5 scales.
Fourier and Wavelet Transforms

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
Using fractal interpolation over complex network modeling of deep texture representation

Convolutional neural networks have been a funda-mental model in computer vision in the last years. Nevertheless, specifically in the analysis of texture images, the use of that model as a feature extractor rather than trained from scratch or extensively fine tuned has demonstrated to be more effective. In this scenario, such deep features can also benefit from further advanced analysis that can provide more meaningful representation than the direct use of feature maps. A successful example of such procedure is the recent use of visibility graphs to analyze deep features in texture recognition. It has been found that models based on complex networks can quantify properties such as periodicity, randomness and chaoticity. All those features demonstrated usefulness in texture classification. Inspired by this context, here we propose an alternative modeling based on complex networks to leverage the effectiveness of deep texture features. More specifically, we employ recurrence matrices of the neural activation at the penultimate layer. Moreover, the importance of complexity attributes, such as chaoticity and fractality, also instigates us to associate the complex networks with a fractal technique. More precisely, we complement the complex network representation with the application of fractal interpolation over the degree distribution of the recurrence matrix. The final descriptors are employed for texture classification and the results are compared, in terms of accuracy, with classical and state-of-the-art approaches. The achieved results are competitive and pave the way for future analysis on how such complexity measures can be useful in deep learning-based texture recognition.
Performance analysis of gradient based image edge detection

Edge detection is a dynamic area of research and it gives support to higher level image analysis. Edge detection is a method of image processing used to make points in a digital image with discontinuities, sharp modifiers in the image brightness. Edge detection methods used in this work are Sobel, Prewitt, Roberts, Laplacian of Gaussian and Canny. The performance evaluation measures such as accuracy and specificity calculated from the Berkeley Segmentation dataset. Edge detection is used for object detection which offers various applications like medical field, industrial field, biometrics etc.