Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Network-Based Super-Resolution Considering Quantitative and Perceptual Quality

In recent years, the common algorithms for image super-resolution based on deep learning have been increasingly successful, but there is still a large gap between the results generated by each algorithm and the ground-truth. Even some algorithms that are dedicated to image perception produce more textures that do not exist in the original image, and these artefacts also affect the visual perceptual quality of the image. We believe that in the existing perceptual-based image super-resolution algorithm, it is necessary to consider Super-Resolution (SR) image quality, which can restore the important structural parts of the original picture. This paper mainly improves the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) algorithm in the following aspects: adding a shallow network structure, adding the dual attention mechanism in the generator and the discriminator, including the second-order channel mechanism and spatial attention mechanism and optimizing perceptual loss by adding second-order covariance normalization at the end of feature extractor. The results of this paper ensure image perceptual quality while reducing image distortion and artefacts, improving the perceived similarity of images and making the images more in line with human visual perception.
Weighted Based Frequent and Infrequent Pattern Mining Model for Real-time E-Commerce Databases

In modern system e-commerce is developing in fast and it makes the availability of resources and services on the internet colorful.In today's e-commerce world day by day the data is increasing tremendously and this data should be used effectively.Data mining techniques produce useful knowledge for decision makers from high dimensional databases.Association rule mining is a used in e-commerce data analysis to realize cross selling and patterns generated can be used as recommendation system.Numerous models have been studied in both frequent as well as infrequent pattern mining in marketing applications which have some unsolved issues yet.A novel weighted based frequent and infrequent pattern mining model for real time e-commerce databases is proposed to find weighted based frequent and infrequent patterns from large data bases.Here weighted infrequent ranking measure is used to filter the infrequent product from the frequent associations.In this model a real-time e-commerce application is designed for pattern extraction process.This model is implemented in Java on real time e-commerce database (flip cart database).This model generates weighted based frequent and infrequent patterns based on user selected feature product in e-commerce database.This model is also implemented on distributed market database (training database), cloud database and medical database.
Using WHY-type Question-Answer Pairs to Improve Implicit Causal Relation Recognition

Implicit causal relation recognition aims to identify the causal relation between a pair of arguments. It is a challenging task due to the lack of conjunctions and the shortage of labeled data. In order to improve the identification performance, we come up with an approach to expand the training dataset. On the basis of the hypothesis that there inherently exists causal relations in WHY-type Question-Answer (QA) pairs, we utilize WHY-type QA pairs for the training set expansion. In practice, we first collect WHY-type QA pairs from the Knowledge Bases (KBs) of the reading comprehension tasks, and then convert them into narrative argument pairs by Question-Statement Conversion (QSC). In order to alleviate redundancy, we use active learning (AL) to select informative samples from the synthetic argument pairs. The sampled synthetic argument pairs are added to the Penn Discourse Treebank (PDTB), and the expanded PDTB is used to retrain the neural network-based classifiers. Experiments show that our method yields a performance gain of 2.42% F 1-score when AL is used, and 1.61% without using.
Implementation of Behavior Recognition Based on Machine Vision

Since the beginning of the 21st century, with the rapid development of the global economy and the rapid advancement of science and technology, real-time monitoring of human behavior through video surveillance has become a common security measure. However, most of them have a series of problems such as fast calculation speed, low recognition accuracy and delay. In this paper, the moving target detection is performed on the video sequence frame, and the moving target is tracked and analyzed. Through the related algorithms to complete the recognition of the behavior of the target human body and the determination of normal and abnormal behavior, it has broad application prospects in the future.
Towards Machine Learning for Enhanced Maths Teaching to the Blind

This summary refers to a complete research paper. In 2010, the World Health Organization (WHO) estimated that 19 million children under the age of 15 were visually impaired, with about 39 million blind and 246 million people with severe or moderate vision loss. These numbers suggest the potential size and potential impact of the visually impaired on a day-today basis. The situation has seen some improvement in recent years with increasing access to formal education, from primary school to higher education. As a result, the demand for innovative, technology-based assistance tools that enhance the user experience and the quality of education has increased. This work takes a step towards bridging a technological gap with the design and validation of a system of artificial neural networks, called as Deep Neural Networks (DNNs), capable of identifying the main Cartesian curves of the mathematics curriculum. The Cartesian set comprises 6 degrees of rational algebraic curves, in addition to a total of 42 conic curves, for this paper will be presented two sets of curves, parabolas and ellipses. The development of this gave some methodological steps. First, relevant artificial neural networks were investigated considering the needs of the user and the space of the problem - computer efficiency, loss rate and precision of the generated models used as selection metrics for each neural network tested. The selected network models were InceptionV3, MobileNETV2, VGG16, and VGG19. With average accuracy of 94% and 20% of mean loss, the selected network for the application was VGG16.
A Study of Perceptual Quality Assessment for Stereoscopic Image Retargeting

Subjective and objective perceptual quality assessment for stereoscopic retargeted images is a fundamentally important issue in stereoscopic image retargeting (SIR) which has not been deeply investigated. Here, a stereoscopic image retargeting quality assessment (SIRQA) database is proposed to study the perceptual quality of different stereoscopic retargeted images. To construct the database, we collect 720 stereoscopic retargeted images generated by eight representative SIR methods. The perceptual quality (mean opinion scores, MOS) of each stereoscopic retargeted image is subjectively rated by 30 viewers. For objective assessment, several publicly available quality evaluation metrics are tested on the database. Experimental results show that there is a large room for improving the accuracy of objective quality assessment in SIRQA by comprehensively considering geometric distortion, content loss and stereoscopic perceptual quality.
Improved 3D imaging and measurement with fringe projection structured light field

In optical three-dimensional imaging and measurement based on fringe projection, wrapped phases may cause a problem of ambiguity, which can be overcome by phase unwrapping generally. In this paper, a method for absolute phase unwrapping using light-field imaging is reported. In a recorded light field under structured illumination, i.e., a structured light field, a wrapped phase-encoded field was retrieved and resampled in diverse image planes associated with several possible fringe orders in a measurement volume. By leveraging phase consistency constraint, the resampled wrapped phase-encoded field correct fringe orders could be determined to unwrap phase without any additional encoding information.
Genetic algorithms and multiprocessor task scheduling: A systematic literature review

This paper reports a systematic review of the literature about genetic algorithms applied to the multiprocessor task scheduling problem. After defining a protocol with the main rules of this review, the research was performed considering journal papers published between 1990 and 2018. At the end of this process, 37 works were recovered and analyzed. By performing a meta-analysis, a variety of information was extracted and summarized, including impact factor, Eigenfactor score, scenarios considered, optimization metrics, volume of citations, and others.
A Novel Path Planning Algorithm Based on Q-learning and Adaptive Exploration Strategy

In an unknown environment, how to plan a path is a fundamental problem for agents. In this paper, we propose an improved reinforcement learning algorithm, called adaptive exploration Q-learning algorithm (AEQ), to solve path planning problem. Firstly, to ensure an agent learns autonomously in the process of trial and error when the agent knows nothing about the environment, AEQ chooses Q-learning algorithms to improve. Secondly, AEQ utilizes an adaptive exploration strategy that aims at speeding up the algorithm’s convergence. The adaptive exploration strategy dynamically adjusts the exploration factor according to various situations so that the agent explores the environment sufficiently and makes full use of the environment information. The experimental results show that the agent successfully reaches the goal without collision by AEQ. Besides, compared with the classical Q-learning algorithm and SARSA algorithm, AEQ improves the convergence speed and reduces the convergence time.
Resource’s Richness of Personality

resource’s richness of the personality.The following methods are applied: psychological survey using suchtechniques as personal’s resource questionnaire, personality’s psychologicalresourcefulness questionnaire, diagnostics of existential resources’ indicatorsquestionnaire, virtue and strength of character questionnaire, crisis copingquestionnaire, psychology and personality questionnaire of uncertainty. Methodsof mathematical and statistical analysis such as multivariate analysis, onefactorvariance analysis, cluster analysis, classification analysis, and correlationanalysis have been applied. The study is based on the Bayer model, which turnsthe problem into a solution in which it will be solved.The results of the research: according to the multivariate analysis (factorweight &gt; 0,70), which cumulatively explained 57,1% of the variance in thestudy group, it was found that factor 1 (44,3%) contains such components of resource’s richness as psychological survival resources, psychologicalresourcefulness, psychological well-being, coherence, existential resources,strength of character. The content of the second factor (13%) are the indicatorsof personal resourcefulness and intolerance for uncertainty. Systemic reflectionprevails over quasi-reflection and introspective reflection in individuals withhigh levels of resource’s richness. Changes in the level of resource’s richness aremost due to uncertainty tolerance (rank 100). The inter-correlation betweenpsychological resourcefulness and other components of resource’s richness is theclosest one. Uncertainty intolerance is the most closely related to psychologicalresourcefulness, the components of which are the interpretative codes of thesignificance of an event to man. Psychological survival resources are in onecluster with coherence. The ability of a person to use consciously availableown resources is conditioned by the level of his systemic reflection, which isincorporated into the resource’s richness through psychological resourcefulness.Conclusions. It is not important how many psychological resources a personhas, but whether, he or she knows about them and whether they can applythem according to their life situation. Resource’s richness is characterized as areflexively conditioned ability of a person to self-transformation.
Deep Reinforcement Learning with Smooth Policy

Deep reinforcement learning (RL) has achieved great empirical successes in various domains. However, the large search space of neural networks requires a large amount of data, which makes the current RL algorithms not sample efficient. Motivated by the fact that many environments with continuous state space have smooth transitions, we propose to learn a smooth policy that behaves smoothly with respect to states. We develop a new framework -- \textbf{S}mooth \textbf{R}egularized \textbf{R}einforcement \textbf{L}earning ($\textbf{SR}^2\textbf{L}$), where the policy is trained with smoothness-inducing regularization. Such regularization effectively constrains the search space, and enforces smoothness in the learned policy. Moreover, our proposed framework can also improve the robustness of policy against measurement error in the state space, and can be naturally extended to distribubutionally robust setting. We apply the proposed framework to both on-policy (TRPO) and off-policy algorithm (DDPG). Through extensive experiments, we demonstrate that our method achieves improved sample efficiency and robustness.
Motivational Teaching Strategies within Saudi University EFL Classrooms: How to Improve Students’ Achievement?

Motivation is essential because it highly contributes to achievement. Teachers need to motivate their students in order to develop a positive outcome. Research on motivation for foreign language learning has shown that teachers should be aware of their actions and behaviors in classroom because it is very likely that they can demotivate learners. This study explores how motivational strategies used by English as a foreign language (EFL) teachers could increase the motivation of students to learn English as a foreign language. Participants included five EFL teachers along with one hundred first-year female students. The students completed a survey while the teachers took part in an interview. Both measures aimed to explore the attitudes of both teachers and students towards motivation in the foreign language classroom. Results showed that the most used motivational strategies were: resources that satisfy the students’ needs and interests, group work, active participation in class, and praises and rewards. The findings of this study can contribute to teachers’ knowledge of the motivational strategies that work best for students especially those that are pertinent to the Saudi context to improve their current teaching methods and can contribute also to further research on motivation.&#x0D;
Synthetic aperture radar image change detection based on Kalman filter and nonlocal means filter in the nonsubsampled shearlet transform domain

An unsupervised change-detection method specifically oriented to improve the change-detection accuracy of synthetic aperture radar (SAR) images is proposed. This method has four main steps. First, a preprocessing method is proposed based on the Kalman filter. We concentrate on reducing the false detection or missing detection rate caused by only using a single filter. Second, the difference image containing explicit information about the changed region is generated based on a standard log-ratio operator. Third, an improved nonsubsampled shearlet transform (NSST) algorithm based on the nonlocal means (NLM) filter is proposed. Simultaneously, we decompose the difference image into low-frequency and high-frequency subbands by the improved NSST algorithm. In particular, in order to effectively preserve the detailed information while denoising, the NLM filter is used to suppress the noise of the high-frequency subbands. Fourth, the final difference image is obtained by the fuzzy C-means algorithm, which is selected because of its high clustering performance and wide range of applications. Experiments conducted on three real datasets of images demonstrate the effectiveness of the proposed method.
High value and low-cost virtual reality healthcare professional education: proceedings of a roundtable workshop

This article was migrated. The article was marked as recommended. Introduction The following represents proceedings of an online roundtable workshop on high value and low-cost virtual reality healthcare professional education. The purpose of the workshop was to seek the views of professionals from different sectors and different countries on moving toward high value and low-cost virtual reality education. The workshop was comprised of eight delegates with an interest in this subject. The delegates were from Europe, the Middle East, and Australia. They represented different sectors - including clinical medicine, educational economics, health professional education, simulation, technology, evidence-based methodologies, and industry. Themes The following themes emerged from the workshop: the challenge of thinking about the cost of virtual reality from the points of view of the payer and the user; the core need to define the context of use of virtual reality; the absolute need to define the purpose of a virtual reality programme; the recognition of the growing opportunity of multiplayer virtual reality; the need to exploit the unique properties of virtual reality; the importance of realising that there are already various forms of virtual reality available and these can achieve different outcomes at different costs; the need to integrate virtual reality into the rest of the curriculum; and the various forms of cost analysis that might be suitable for evaluating the cost and outcomes of virtual reality. Conclusions In the long-term, a growing body of evidence that is based on original research and systematic reviews will help us decide what is high value and low-cost virtual reality in healthcare professional education. However, a strategic approach is needed to ensure that the original research concentrates on the right topics that will yield the most value to education decision-makers and to other related stakeholders. We feel that non-hierarchical interdisciplinary roundtable discussions are an effective means of planning strategy.
New Techniques for Searching Differential Trails in Keccak

Keccak-f is the permutation used in the NIST SHA-3 hash function standard. Inspired by the previous exhaustive differential trail search methods by Mella et al. at ToSC 2017, we introduce in this paper new algorithms to cover 3-round trail cores with propagation weight at least 53, up from the previous best weight 45. To achieve the goal, the concept of ideal improvement assumption is proposed to construct theoretical representative of subspaces so as to efficiently cover the search space of 3-round trail cores with at least one out-Kernel α state. Of particular note is that the exhaustiveness in 3-round trail core search of at least one out-Kernel α is only experimentally verified. With the knowledge of all 3-round trail cores of weight up to 53, lower bounds on 4/5/6-round trails are tightened to 56/58/108, from the previous 48/50/92, respectively.
Analisis Hidrologi untuk Penentuan Metode Intensitas Hujan di Wilayah Kecamatan Bogor Barat, Kota Bogor

&lt;p&gt;Rain intensity is defined as the rainfall falls in per unit time, expressed in mm/hours. Rain determined to get the discharge flood plans in sub-districts west Bogor, Bogor. Discharge flood plan needed to take into account the drainage channel would be designed again in districts west Bogor having problems. The study aimed to determine the intensity of rainfall in west Bogor as the basic determination of the drainage channel in west Bogor. Research carried out by changing the maximum daily rainfall to rain intensity. Methods used the method Bell Tanimoto, Van Breen, and Hasper Der Weduwen which is a statistical method and produce the intensity of rain. The amount of rain obtained will be substituted into formula Talbot, Sherman, and Ishiguro, and then compared to the early rain intensity. The selection method determined based on the value of the smallest standard deviation. The result of this study showed that the method chosen to determine the intensity of rain is Van Breen Method with Talbot Formula. &lt;/p&gt;
Optimal choice of supervised techniques for MR image classification

Magnetic Resonance Imaging (MRI) is a modern, robust method that uses in the detection of various medical problems.In this research work, a trial is used to attempt for the detection of tumour in pancreas MR images.An automated classifier is used for detection of tumour in MR images and avoids the drawbacks of MRI.This automated classifiers can detect automatically, either the MR image is affected or not affected.Features are extracted from MR images using second order statistics approach and are classified by two techniques Support Vector Machine (SVM) and Extreme Learning Machine (ELM).SVM approach has high classification accuracy (96%) which is higher than ELM, while ELM performs faster compared to SVM.
Face Recognition through Machine Learning of Periocular region

Face Recognition through Machine Learning of Periocular region - written by Ch Sai Chandana , K Damodhar Rao , Dr. Prasanta Kumar Sahoo published on 2020/03/28 download full article with reference data and citations
Global Optimization of Graph Filters with Multiple Shift Matrices

Using graphs to represent data sets that reside on irregular and complex structures can bring special advantages. Graph signal processing (DSP G ) converts traditional DSP operators, such as time shift, linear filters and Fourier transform, from time and frequency domain to the graph domain. In machine learning applications, DSP G provides an approach for semi-supervised classification. Different from conventional graph-filter-based classifiers, we propose a new graph filter with multiple graph shift matrices that can provide better classification performance when the feature quality is uneven. To solve the resulting non-convex problem, a tight and efficient convex relaxation approach is introduced. Through a branch and bound optimization method, we can find the mapping from the optimum relaxed parameter set to original parameter set, which technically provides the globally optimum solution. Simulation experiments corroborate our results.
Automating Common Data Science Matrix Transformations

Programming languages such as R or Python are commonplace in data science projects. However, transforming data is usually tricky and the composition of the right primitives (using the appropriate libraries) to get the most elegant code transformation is not always easy. In this paper, we present the first system that is able to automatically synthesise program snippets in R given an input data matrix and an output matrix, partially filled by the user representing the required transformation. We use the type information given by the dimensions of the matrix primitives (and other constraints) to reduce the combinatorial explosion of primitive compositions. We test the performance of our approach with a set of artificial data and real examples from Stack Overflow questions.
On relationships between imbalance and overlapping of datasets

The paper deals with problems that imbalanced and overlapping datasets often en- counter. Performance indicators as accuracy, precision and recall of imbalanced data sets, both with and without overlapping, are discussed and compared with the same performance indicators of balanced datasets with overlapping. Three popular classification algorithms, namely, Decision Tree, KNN (k-Nearest Neighbors) and SVM (Support Vector Machines) classifiers are analyzed and compared.