Reviewerly/exHarmony huggingface dataset:
Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration

Abstract The image restoration (IR) technique is a part of image processing to improve the quality of an image that is affected by noise and blur. Thus, IR is required to attain a better quality of image. In this paper, IR is performed using linear regression-based support vector machine (LR-SVM). This LR-SVM has two steps: training and testing. The training and testing stages have a distinct windowing process for extracting blocks from the images. The LR-SVM is trained through a block-by-block training sequence. The extracted block-by-block values of images are used to enhance the classification process of IR. In training, the imperfections on the image are easily identified by setting the target vectors as the original images. Then, the noisy image is given at LR-SVM testing, based on the original image restored from the dictionary. Finally, the image block from the testing stage is enhanced using the hybrid Laplacian of Gaussian (HLOG) filter. The denoising of the HLOG filter provides enhanced results by using block-by-block values. This proposed approach is named as LR-SVM-HLOG. A dataset used in this LR-SVM-HLOG method is the Berkeley Segmentation Database. The performance of LR-SVM-HLOG was analyzed as peak signal-to-noise ratio (PSNR) and structural similarity index. The PSNR values of the house and pepper image (color image) are 40.82 and 36.56 dB, respectively, which are higher compared to the inter- and intra-block sparse estimation method and block matching and three-dimensional filtering for color images at 20% noise.
The virtual symbol world in the Homo educandus informational space miscellaneous

The urgency of the article is due to the trends that are clearly traceable today in the cultural and educational space of the higher school, namely, the significant changes in the ideological orientations of student youth that are associated with the emergence of "postmodern" consciousness. This situation is connected with the formation of the information society. The formation of the personality, its motives, values, behavioral skills occurs in the convergent space of the collision of the virtual and real world. The article presents the results of the analysis and examination of the functioning of the modern virtual symbolic world as a component of the informational and cultural-educational spaces of the individuals. The role of the virtual (computer) world and the tendencies of its influence on the cultural developmental factors of the development of Homo educancy are outlined. It was discovered that virtual (computer) reality as a component of the symbolic world offers a person wide opportunities for information exchange and communication in the "space of freedom", but at the same time there are traces when it is transformed on its simulacrum from the symbolic space that exists in real space and time. The simulacrum is deprived of value and sacred meanings, creating separate elements of perception and understanding of information. The authors substantiate the leading principles of the cultural orientation of the virtual world and identified the negative consequences of violating the axiological norms of the use of virtual reality. As a conclusion, a wide range of opportunities offered by the virtual symbolic world is proposed, accompanied by philosophical and pedagogical guidelines and norms based on human dimension. This will be facilitated by the search for approaches, principles, methods, techniques and means of strengthening the symbolic cultural potential of the virtual world that fills the modern information space of Homo educandus in order to maximally bring it closer to the cultural and educational space.
DISCOVERY LEARNING (DL) STRATEGY ON STUDENTS READING COMPREHENSION IN REPORT TEXT

This research entitled “Discovery Learning strategy on Grade Eleven Students’ Reading Comprehension in Report text at SMA Negeri 6 Karawang” principally aims to investigate whether or not DL strategy significantly affects on Students’Reading Comprehension. Research method used in this research was quasi experimental, quantitative and the design was randomized post-test only control group design.To collect data the writer used instruments in the form of objective, multiple choice 30 question items for sample class out of 60 questions items that have been tried out in IPS class on grade eleven at SMA Negeri 6 Karawang. The scores obtained from the try-out class were used to analyze the validity, reliability, discrimination power and difficulty index of each question items in the instruments.&#x0D; The sample of this research was grade eleven students in IPS class consisting of 60 students. The students is divided into two classes, 30 students in experimental class thought using DLstrategy and 30 in control class thought without DL strategy that are taken from the population using cluster random technique.The analysis using SPSS-based One Way Anova Formula produced significance (Sig.) value of 0.002, since the significance value was smaller than (&lt;) Alpha 0.05, H0 is rejected and HAis accepted. It means that DL Strategy has significant effect on students’ reading comprehension.
Distance Based Edge Linking (DEL) for Character Recognition

This article proposes an minimum distance based edge linking algorithms for handwritten character images. Improvement of performance for machine recognition is challenging task due to noise and degraded input images. In the proposed system we enhance the recognition rate of object reconstruction for broken edges by using edge linking. Such edges of objects are reconstructed by using novel Distance based Edge Linking (DEL) approach. Developed new benchmark approach is fill the gaps between nearest edge segment of Binary image map (BIM). We obtain state-of-art performance of proposed system on character recognition (CR) using two datasets MNIST and ISI.
A Regularized Convolutional Neural Network for Semantic Image Segmentation

Convolutional neural networks (CNNs) show outstanding performance in many image processing problems, such as image recognition, object detection and image segmentation. Semantic segmentation is a very challenging task that requires recognizing, understanding what's in the image in pixel level. Though the state of the art has been greatly improved by CNNs, there is no explicit connections between prediction of neighbouring pixels. That is, spatial regularity of the segmented objects is still a problem for CNNs. In this paper, we propose a method to add spatial regularization to the segmented objects. In our method, the spatial regularization such as total variation (TV) can be easily integrated into CNN network. It can help CNN find a better local optimum and make the segmentation results more robust to noise. We apply our proposed method to Unet and Segnet, which are well established CNNs for image segmentation, and test them on WBC, CamVid and SUN-RGBD datasets, respectively. The results show that the regularized networks not only could provide better segmentation results with regularization effect than the original ones but also have certain robustness to noise.
Visual Analysis of Charge Flow Networks for Complex Morphologies

Abstract In the field of organic electronics, understanding complex material morphologies and their role in efficient charge transport in solar cells is extremely important. Related processes are studied using the Ising model and Kinetic Monte Carlo simulations resulting in large ensembles of stochastic trajectories. Naive visualization of these trajectories, individually or as a whole, does not lead to new knowledge discovery through exploration. In this paper, we present novel visualization and exploration methods to analyze this complex dynamic data, which provide succinct and meaningful abstractions leading to scientific insights. We propose a morphology abstraction yielding a network composed of material pockets and the interfaces, which serves as backbone for the visualization of the charge diffusion. The trajectory network is created using a novel way of implicitly attracting the trajectories to the skeleton of the morphology relying on a relaxation process. Each individual trajectory is then represented as a connected sequence of nodes in the skeleton. The final network summarizes all of these sequences in a single aggregated network. We apply our method to three different morphologies and demonstrate its suitability for exploring this kind of data.
Dubins Waypoint Navigation of Small-Class Unmanned Aerial Vehicles

This paper considers a variation on the Dubins path problem and proposes an improved waypoint navigation (WN) algorithm called Dubins waypoint navigation (DWN).Based on the Dubins path problem, an algorithm is developed that is updated in real-time with a horizon of three waypoints.The purpose of DWN is to overcome a problem that we find in existing WN for small-class fixed-wing unmanned aerial vehicles (UAV) of not accurately reaching waypoints.This problem results at times in high overshoot and, in the presence of wind disturbances, it can cause a vehicle to miss the waypoint and swirl around it.To prevent this, the DWN creates "new waypoints" that are in the background, called turning points.Examples illustrate the improvement of the performance of WN achieved using the DWN algorithm in terms of the targeting of waypoints while reducing fuel and time.
Sociology of Students: A Theoretical Perspective and Research Practice

Higher school as a social institution aims at the formation of the most valuable resource of the XXI century that human capital is. Students are now an influential group of young people, and taking into consideration students’ “advanced” status, its social activity (including network) and socio-political and socio-economic potential, we can assume that it is the student youth, which de- termines the youth “agenda”. This makes constant research of students relevant and important, both at the theoretical and academic level of sociological science and at the level of applied knowledge focused on the constant monitoring of social well-being, lifestyle, values, opinions and activity of students in specific universities and in a whole country. Students are active participants of economic and social life; they have a significant political potential. The authors present the results of the monitoring of social well-being of first-year students at Kazan national research technological University. The method applied was a questionnaire study with the use of electronic resource. The article also dwells on the current state and trends of student studies, as well as the processes of their institutionalization. The authors emphasize the importance of sociological surveys of student life conducted with the help of student sociological labs.
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression

Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work we focus on robotics problems with a budget of only 10-20 trials. This is a very challenging setting even for data-efficient approaches like Bayesian optimization (BO), especially when optimizing higher-dimensional controllers. Simulated trajectories can be used to construct informed kernels for BO. However, previous work employed supervised ways of extracting low-dimensional features for these. We propose a model and architecture for a sequential variational autoencoder that embeds the space of simulated trajectories into a lower-dimensional space of latent paths in an unsupervised way. We further compress the search space for BO by reducing exploration in parts of the state space that are undesirable, without requiring explicit constraints on controller parameters. We validate our approach with hardware experiments on a Daisy hexapod robot and an ABB Yumi manipulator. We also present simulation experiments with further comparisons to several baselines on Daisy and two manipulators. Our experiments indicate the proposed trajectory-based kernel with dynamic compression can offer ultra data-efficient optimization.
SEMANTIC MAPPING STRATEGY ON STUDENTS’ VOCABULARY LEARNING RESULT

This research entitled “The Effect of Semantic Mapping Strategy on Students’ Vocabulary Learning Resultat the Grade Ten of SMK Pusaka 1 in Academic Year of 2017/2018” principally aims to investigate whether or not Semantic Mapping Strategy significantly affects on Students’ Vocabulary Learning Result. The research samples were taken using cluster random sampling, with the total number of samples being 72 students. The location of this research was SMKPusaka 1 Jakarta. The research methodology adopted was true experimental method, with the research design being randomized post-test only control group design. To collect the data, the students were given objective test, numbering 30 items. The research data were analyzed descriptively and inferentially. The findings indicated that there was significant difference of post-test score between the students who received Semantic Mapping Strategy and students who did not receive Semantic Mapping Strategy. It can be seen from the data analysis using ANOVA that showed Fobserved (7.801) is higher than the critical value of Ftable (at the significant level of 0.05 and df = 1/70), namely 3.96. It means that Semantic Mapping Strategy has significant effect on Students’ Vocabulary Learning Result.
“Improving The Students’ Writing Narrative Text by Applying Cooperative Learning Type Student Teams Achievement Divisions (STAD)”

This research aimed to analyze the student teams achievement divisions (STAD) technique of cooperative learning approach to improve writing narrative text. This study was a quantitative method and pre-experimental design in this research,the subject of the study was implemented in RPL 1 class tenth grade students of SMK Mahardhika Batujajar west Bandung in the 2018- 2019 academic years as sample. The data were obtained from several sources including the data analysis of pretestand posttest, then the dataanalysis of descriptive statistics and N-Gain. The results of this reserach that student teams achievement division(STAD) technique is effective in teaching writing narrative text data from classroom observation and students texts show the improvement in students writing skill in writing narrative text. The students write a good narrative text with clear generic structures and can understood the linguistic. It could be seen from the average of posttest results was 78.87 and the data avarege of pretest results in class research was 41.17 the difference score pretest and postest showed the STAD technique can improve students ability writing narrative text.Key Words: Cooperative Learning, STAD, Writing, Narrative text.
Motion modeling of a non-holonomic wheeled mobile robot based on trajectory tracking control

Trajectory tracking is a problem of emphasis for the mobile robot. In this study, a coordinate transformation method was used to build a kinematic model of the wheeled mobile robot. A traditional proportional-integral-derivative control method was researched and improved by combining it with a neural network. A neural network proportional-integral-derivative trajectory tracking control method was thus designed, and a simulation experiment was performed using Simulink. The results show that in circular trajectory tracking control, the maximum errors of the X axis, Y axis, and θ were approximately 2.1 m, 2.3 m, and 0.4 rad, respectively, and that the system remained stable after running for 10 s. In straight-line trajectory tracking control, the maximum errors of the X axis, Y axis, and θ were approximately −0.8 m, 1.3 m, and 0.3 rad, respectively, and the system remained stable after running for 8 s. The error was relatively small, and the effect of trajectory tracking control was good. The studied method had good performance in terms of wheeled mobile robot trajectory tracking control and is worthy of further promotion and application.
Hybrid Fuzzy C-Means Using Bat Optimization and Maxi-Min Distance Classifier

Fuzzy c-means (FCM) is a frequently used clustering method because of its efficiency, simplicity and easy implementation. Major drawbacks of FCM are sensitivity to initialization and local convergence problem. To overcome the drawbacks, the proposed method describes a hybrid FCM using Bat optimization and Maxi-min classifier. Maxi-min classifier is used to decide the count of clusters and then pass that count to randomized fuzzy c-means algorithm, which improves the performance. Bat optimization is a global optimization method used for solving many optimization problems due to its high convergence rate. Two popular datasets from kaggle are used to show the comparison between proposed technique and the fuzzy c means algorithm in terms of performance. Experiment results showing that the proposed technique is efficient and the results are encouraging.
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case

We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of $K$ classes and lie in the $d$-dimensional Euclidean space. Previous works have left open the challenge of designing efficient algorithms with finite mistake bounds when the data is linearly separable by a margin $\gamma$. In this work, we take a first step towards this problem. We consider two notions of linear separability: strong and weak. 1. Under the strong linear separability condition, we design an efficient algorithm that achieves a near-optimal mistake bound of $O\left( K/\gamma^2 \right)$. 2. Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$. Our algorithm is based on kernel Perceptron, which is inspired by the work of (Klivans and Servedio, 2008) on improperly learning intersection of halfspaces.
Nonlinear Prediction of Multidimensional Signals via Deep Regression with Applications to Image Coding

Deep convolutional neural networks (DCNN) have enjoyed great successes in many signal processing applications because they can learn complex, non-linear causal relationships from input to output. In this light, DCNNs are well suited for the task of sequential prediction of multidimensional signals, such as images, and have the potential of improving the performance of traditional linear predictors. In this research we investigate how far DCNNs can push the envelop in terms of prediction precision. We propose, in a case study, a two-stage deep regression DCNN framework for nonlinear prediction of two-dimensional image signals. In the first-stage regression, the proposed deep prediction network (PredNet) takes the causal context as input and emits a prediction of the present pixel. Three PredNets are trained with the regression objectives of minimizing l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> norms of prediction residuals, respectively. The second-stage regression combines the outputs of the three PredNets to generate an even more precise and robust prediction. The proposed deep regression model is applied to lossless predictive image coding, and it outperforms the state-of-the-art linear predictors by appreciable margin.
One-Class Order Embedding for Dependency Relation Prediction

Learning the dependency relations among entities and the hierarchy formed by these relations by mapping entities into some order embedding space can effectively enable several important applications, including knowledge base completion and prerequisite relations prediction. Nevertheless, it is very challenging to learn a good order embedding due to the existence of partial ordering and missing relations in the observed data. Moreover, most application scenarios do not provide non-trivial negative dependency relation instances. We therefore propose a framework that performs dependency relation prediction by exploring both rich semantic and hierarchical structure information in the data. In particular, we propose several negative sampling strategies based on graph-specific centrality properties, which supplement the positive dependency relations with appropriate negative samples to effectively learn order embeddings. This research not only addresses the needs of automatically recovering missing dependency relations, but also unravels dependencies among entities using several real-world datasets, such as course dependency hierarchy involving course prerequisite relations, job hierarchy in organizations, and paper citation hierarchy. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the prediction accuracy as well as to gain insights using the learned order embedding.
Characteristic vector and weight distribution of a linear code

We develop an algorithm for computing the weight distribution of a linear $[n,k]$ code over a finite field $\mathbb{F}_q$. We represent the codes by their characteristic vector with respect to a given generator matrix and a special type of a generator matrix of the k-dimensional simplex code. This characteristic vector is the input data of our algorithms. The complexity of the presented algorithms is O(kq^k).
ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks

Artificial Neural Networks (ANNs) have demonstrated remarkable utility in various challenging machine learning applications. While formally verified properties of their behaviors are highly desired, they have proven notoriously difficult to derive and enforce. Existing approaches typically formulate this problem as a post facto analysis process. In this paper, we present a novel learning framework that ensures such formal guarantees are enforced by construction. Our technique enables training provably correct networks with respect to a broad class of safety properties, a capability that goes well-beyond existing approaches, without compromising much accuracy. Our key insight is that we can integrate an optimization-based abstraction refinement loop into the learning process and operate over dynamically constructed partitions of the input space that considers accuracy and safety objectives synergistically. The refinement procedure iteratively splits the input space from which training data is drawn, guided by the efficacy with which such partitions enable safety verification. We have implemented our approach in a tool (ART) and applied it to enforce general safety properties on unmanned aviator collision avoidance system ACAS Xu dataset and the Collision Detection dataset. Importantly, we empirically demonstrate that realizing safety does not come at the price of much accuracy. Our methodology demonstrates that an abstraction refinement methodology provides a meaningful pathway for building both accurate and correct machine learning networks.
Dynamic Measurement Scheduling for Event Forecasting using Deep RL.

Imagine a patient in critical condition. What and when should be measured to forecast detrimental events, especially under the budget constraints? We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed measurements. We learn our policy to be dynamically dependent on the patient's health history. To scale our framework to exponentially large action space, we distribute our reward in a sequential setting that makes the learning easier. In our simulation, our policy outperforms heuristic-based scheduling with higher predictive gain and lower cost. In a real-world ICU mortality prediction task (MIMIC3), our policies reduce the total number of measurements by $31\%$ or improve predictive gain by a factor of $3$ as compared to physicians, under the off-policy policy evaluation.