Reviewerly/exHarmony huggingface dataset:
EASY ENSEMMBLE WITH RANDOM FOREST TO HANDLE IMBALANCED DATA IN CLASSIFICATION

Imbalanced data might cause some issues in problem definition level, algorithm level, and data level. Some of the methods have been developed to overcome this issue, one of state-of-the-art method is Easy Ensemble. Easy Ensemble was claimed can improve model performance to classify minority class, and overcome the deficiency of random under- sampling. In this paper we discussed the implementation of Easy Ensemble with Random Forest Classifiers to handle imbalance problem in credit scoring case. This combination method is implemented in two datasets which taken from data science competition website, finhacks.id and kaggle.com with class proportion within majority and minority is 70:30 and 94:6. The results showed that resampling with Easy Ensemble can improve Random Forest classifier performance upon minority class. Recall on minority class increased significantly after the resampling. Before resampling, the recall on minority class for the first dataset (finhacks.id) was 0.49, and increased to 0.82 after the resampling. Similar results were obtained for the second data set (kaggle.com), where the recall for the minority class was increased from just 0.14 to 0.73.
Audiovisual quality of live music streaming over mobile networks using MPEG-DASH

The MPEG-DASH protocol has been rapidly adopted by most major network content providers and enables clients to make informed decisions in the context of HTTP streaming, based on network and device conditions using the available media representations. A review of the literature on adaptive streaming over mobile shows that most emphasis has been on adapting the video quality whereas this work examines the trade-off between video and audio quality. In particular, subjective tests were undertaken for live music streaming over emulated mobile networks with MPEG-DASH. A group of audio/video sequences was designed to emulate varying bandwidth arising from network congestion, with varying trade-off between audio and video bit rates. Absolute Category Rating was used to evaluate the relative impact of both audio and video quality in the overall Quality of Experience (QoE). One key finding from the statistical analysis of Mean Opinion Scores (MOS) results using Analysis of Variance indicates that providing reduced audio quality has a much lower impact on QoE than reducing video quality at similar total bandwidth situations. This paper also describes an objective model for audiovisual quality estimation that combines the outcomes from audio and video metrics into a joint parametric model. The correlation between predicted and subjective MOS was computed using several outcomes (Pearson and Spearman correlation coefficients, Root Mean Square Error (RMSE) and epsilon-insensitive RMSE). The obtained results indicate that the proposed approach is a viable solution for objective audiovisual quality assessment in the context of live music streaming over mobile network.
Topological Sort for Sentence Ordering.

Sentence ordering is the task of arranging the sentences of a given text in the correct order. Recent work using deep neural networks for this task has framed it as a sequence prediction problem. In this paper, we propose a new framing of this task as a constraint solving problem and introduce a new technique to solve it. Additionally, we propose a human evaluation for this task. The results on both automatic and human metrics across four different datasets show that this new technique is better at capturing coherence in documents.
Mechanisms to Develop Trust between Educational Process Subjects in the Society of Risk and Uncertainty

The paper aims to reveal mechanisms to develop trust between educational process subjects in the society of risk and uncertainty. The authors describe the basic approaches to understanding trust, consider its role in the process of interaction of educational process subjects and determine levels, criteria of interpersonal depersonified, interpersonal personified and institutional trust, identify pedagogical conditions contributing to increasing the level of trust between educational process subjects. Scientific originality of the study involves describing mechanisms to develop trust taking into account modern tendencies in educational sphere. The conducted empirical research justifies importance of confidential relations between educational process subjects to raise quality of education and to overcome misunderstanding in the society of risk and uncertainty.
Seismic horizon detection using Convolutional Neural Networks

Summary This article extends the category of artificial intelligence algorithms by applying Deep Reinforcement Learning (DRL) for reservoir production optimization based on NVP objective. This work concerns with waterflooding optimization problem to maximize the economic returned by intelligently adjusting the injection rates. Also, allowing the DRL system to observe the raw data as a distribution of primary variables (pressure and saturation).
Research on Library Personalized Recommendation System Based on Restricted Boltzmann Machine

At present, most recommendation systems in libraries are content-based and collaborative filtering-based, but these recommendation systems ignore the deep-seated characteristics of readers' personalized information. In order to improve the function of Library recommendation system, this paper proposes a library recommendation system based on restricted Boltzmann machine and collaborative filtering algorithm, and simulates the performance of the algorithm. The results show that the proposed algorithm has good application effect.
Video Prediction via Example Guidance

In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics. In this work, we propose a simple yet effective framework that can efficiently predict plausible future states. The key insight is that the potential distribution of a sequence could be approximated with analogous ones in a repertoire of training pool, namely, expert examples. By further incorporating a novel optimization scheme into the training procedure, plausible predictions can be sampled efficiently from distribution constructed from the retrieved examples. Meanwhile, our method could be seamlessly integrated with existing stochastic predictive models; significant enhancement is observed with comprehensive experiments in both quantitative and qualitative aspects. We also demonstrate the generalization ability to predict the motion of unseen class, i.e., without access to corresponding data during training phase.
Amortized Population Gibbs Samplers with Neural Sufficient Statistics

We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling. APG samplers construct high-dimensional proposals by iterating over updates to lower-dimensional blocks of variables. We train each conditional proposal by minimizing the inclusive KL divergence with respect to the conditional posterior. To appropriately account for the size of the input data, we develop a new parameterization in terms of neural sufficient statistics. Experiments show that APG samplers can train highly structured deep generative models in an unsupervised manner, and achieve substantial improvements in inference accuracy relative to standard autoencoding variational methods.
Critical thinking in STEM (science, technology, engineering, and mathematics)

espanolEl proposito de este estudio es determinar el lugar del pensamiento critico en la formacion de futuros maestros. Para llevar a cabo el estudio, se organizo una encuesta a 200 estudiantes de licenciatura a tiempo completo de la Universidad Pedagogica Estatal Herzen de Rusia. Todos los estudiantes estudian en ciencias naturales. El estudio analizo varias tareas integradas que pueden estar dirigidas al desarrollo del pensamiento critico. Segun los resultados, el pensamiento critico juega un papel importante en el proceso educativo, que debe tenerse en cuenta en la formacion de futuros docentes. EnglishThe purpose of this study is to determine the place of critical thinking in the training of future teachers. To conduct the study, a survey of 200 full-time bachelor students of the Herzen State Pedagogical University of Russia was organized. All students study in Natural Sciences. The study analyzed several integrated tasks that can be aimed at the development of critical thinking. According to the results, critical thinking plays an important role in the educational process, which must be taken into account in the training of future teachers.
Scientific School at higher education institution: professional formation of a researcher

In the modern education system, strategies for improving the scientific potential of universities are being developed, in light of which issues related to streamlining the work of scientific schools in universities and large-scale involvement of students in research of scientific schools are being updated. The phenomenon of a scientific school is considered as an effective mobile reserve on the way to increase the scientific component in a university. Attention is focused on the ambiguity of the concept of a scientific school and the lack of a unified conceptual approach in relation to the definition of this concept. Emphasis is placed on philosophical studies of the phenomenon of the scientific school. The criteria of success of a scientific school are analyzed. Particular attention is paid to the continuity of generations in a scientific school and the importance of personal contacts between teacher and student. A scientific school will cease to exist if it is not replenished with new personnel, the cultivation of young scientists is the most important task of modern higher education. A significant difference between the scientific school and the research team is revealed. The value of a scientific school in the professional development of a researcher is shown on the example of the M. A. Rozov’s theory of social relay races. It is concluded that scientific schools are an integral part of education and the successful existence of scientific schools requires further study and an integrated science-research approach.
Web-to-Voice Transfer for Product Recommendation on Voice

While product recommendation algorithms on the Web are well-supported by a vast amount of interaction data, the same is not true on Voice. A promising approach to mitigate the issue is transfer learning, i.e., transferring the knowledge of customers' shopping behaviors learned from their shopping activities on the Web to Voice. Such a Web-to-Voice transfer is challenging due to customers' distinct shopping behaviors on Voice: customers are inclined to purchase more low-consideration products and are more likely to purchase certain products repeatedly. This paper presents TransV, a novel Web-to-Voice neural transfer network that allows for effective transfer of customers' shopping patterns from the Web to Voice, while taking into account customers' distinct purchase patterns on Voice. Our method extends the state-of-the-art self-attention neural architecture with a multi-level tri-factorization neural component, which allows to explicitly capture the similarity and dissimilarity of customers' shopping patterns on the Web and Voice. To model repeated purchases, TransV adopts a recency-based copy mechanism that considers the impact of the recency of historical purchases on customers' behavior of repeated purchases. Extensive validation on multiple real-world datasets, including two cross-platform datasets from Amazon.com and Amazon Alexa, shows that our method is able to improve voice-based recommendation substantially by 26.8% as compared with non-transfer learning methods.
RECONFIGURABLE WHEEL-LEGGED ROBOT

The paper deals with the reconfigurable robot design.A robot should be able to locomote via using wheels on a smooth terrain and locomote via using legs on a rough terrain or if necessary also cross an obstacle.The transformation between legs and wheels is a suitable concept to an unknown environment with obstacles.Wheeled locomotion is better from the viewpoint of energy consumption.A design of a wheeled mode of the robot was the first part of the design.The design of a quarter model of the mobile robot in a legged mode has been also made.A position vector of an end point (wheel) has been determined via using the Denavit-Hartenberg principle.The realised model is used mainly for an experimental study of the reconfigurable robot designed principle.A locomotion strategy of the robot has been proposed.
Detecting Responsive Web Design Bugs with Declarative Specifications

Responsive Web Design (RWD) is a concept that is born from the need to provide users with a positive and intuitive experience, no matter what device they use. Complex Cascading Style Sheets (CSS) are used in RWD to smoothly change the appearance of a website based on the window width of the device being used. The paper presents an automated approach for testing these dynamic web applications, where a combination of dynamic crawling and back-end testing is used to automatically detect RWD bugs.
Mimic Defense System Security Analysis Model

In this article, the mimic defense technology based on dynamic heterogeneous redundancy architecture is studied, and the mimic defense system is described formally to improve the proactive defense capability of information systems and key equipment in the network. A method integrating dynamic feature, heterogeneous feature and redundant feature that carries out security analysis of mimic defense system by probabilistic analysis is proposed for the lack of effective security assessment method about mimic defense system. Vali-dated by simulation experiments simulating the attacker and the mimic defense system, this model can calculate the safety of the mimic defense system according to the attack factor, heterogeneity degree, dynamic transformation and other related factors. According to the research results, the proposed model is of certain guiding significance for helping designers to construct mimic defense system.
Learning Interpretable Representations with Informative Entanglements

Learning interpretable representations in an unsupervised setting is an important yet a challenging task. Existing unsupervised interpretable methods focus on extracting independent salient features from data. However they miss out the fact that the entanglement of salient features may also be informative. Acknowledging these entanglements can improve the interpretability, resulting in extraction of higher quality and a wider variety of salient features. In this paper, we propose a new method to enable Generative Adversarial Networks (GANs) to discover salient features that may be entangled in an informative manner, instead of extracting only disentangled features. Specifically, we propose a regularizer to punish the disagreement between the extracted feature interactions and a given dependency structure while training. We model these interactions using a Bayesian network, estimate the maximum likelihood parameters and calculate a negative likelihood score to measure the disagreement. Upon qualitatively and quantitatively evaluating the proposed method using both synthetic and real-world datasets, we show that our proposed regularizer guides GANs to learn representations with disentanglement scores competing with the state-of-the-art, while extracting a wider variety of salient features.
Teaching Using ICT in an International Perspective: Towards Modern Pedagogy

Digital technologies have become an essential part of the education policy in most European countries. ICT implementation into teaching and learning entails considerable potential to encourage or trigger changes in the field of education, particularly in the work of teachers, which leads to thinking about the so-called modern pedagogy. The aim of the study is, therefore, to explore and explain how ICT-related topics are formulated in the education policy of three European countries. Another goal is to describe how Czech, Slovak and Polish teachers use and perceive digital technologies in their work. We use analysis of educational policy documents of selected countries and analysis of three international comparative surveys as well. It is possible to say that in the educational policy of the three countries there is a visible deviation from the traits of traditional pedagogy, with an emphasis on the development of the skills component of learning, in particular on key competences. The teachers themselves are on an imaginary path from traditional to modern pedagogy, in which the possibilities of digital technologies would be used more and more efficiently. It should be teaching, that is more focused on pupils’ active participation, and learning not confined to classroom space.
Rataplan

We present Rataplan, a robust and resilient pixel-based approach for linking multi-modal proxies to automated sequences of actions in graphical user interfaces (GUIs). With Rataplan, users demonstrate a sequence of actions and answer human-readable follow-up questions to clarify their desire for automation. After demonstrating a sequence, the user can link a proxy input control to the action which can then be used as a shortcut for automating a sequence. Alternatively, output proxies use a notification model in which content is pushed when it becomes available. As an example use case, Rataplan uses keyboard shortcuts and tangible user interfaces (TUIs) as input proxies, and TUIs as output proxies. Instead of relying on available APIs, Rataplan automates GUIs using pixel-based reverse engineering. This ensures our approach can be used with all applications that offer a GUI, including web applications. We implemented a set of important strategies to support robust automation of modern interfaces that have a flat and minimal style, have frequent data and state changes, and have dynamic viewports.
Prediction of user interest fluctuation using fuzzy neural networks in web search

Purpose The user interest in content searching in the web will be changed over by time. Design/methodology/approach The system is in need to find the content of user over the temporal aspects. Findings So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important. Research limitations/implications So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important. Practical implications In this work, fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios. Social implications In this proposed work, both the long-term and short-term interest are evaluated using the specialized user interface designed to retrieve the user interest based on the user searching activities. Originality/value This work also categorizes the future needs of users using this proposed system.
Penerapan Metode Simpleks Untuk Meningkatkan Keuntungan Produksi

Oma Corry's business is a small-scale bread business in Sorong City, West Papua Province. One of the problems faced by Oma Corry Enterprises is the problem of calculating or determining the maximum profit gained every day from production activities. To calculate the benefits of Bread and Flour Bread and Babengka still use a calculator and write in books so it is less efficient. Whereas now the calculation of maximum profits must be completed quickly and precisely, so we need a system that can provide convenience in calculating profits. To achieve this goal, the simplex method is chosen for the manual calculation process and the POM-QM for Windows tool is used to speed up the calculation process. The result is a maximum profit of IDR 494,000 per day and a more efficient calculation process
Learning to Stop While Learning to Predict

There is a recent surge of interest in designing deep architectures based on the update steps in traditional algorithms, or learning neural networks to improve and replace traditional algorithms. While traditional algorithms have certain stopping criteria for outputting results at different iterations, many algorithm-inspired deep models are restricted to a ``fixed-depth'' for all inputs. Similar to algorithms, the optimal depth of a deep architecture may be different for different input instances, either to avoid ``over-thinking'', or because we want to compute less for operations converged already. In this paper, we tackle this varying depth problem using a steerable architecture, where a feed-forward deep model and a variational stopping policy are learned together to sequentially determine the optimal number of layers for each input instance. Training such architecture is very challenging. We provide a variational Bayes perspective and design a novel and effective training procedure which decomposes the task into an oracle model learning stage and an imitation stage. Experimentally, we show that the learned deep model along with the stopping policy improves the performances on a diverse set of tasks, including learning sparse recovery, few-shot meta learning, and computer vision tasks.
Access and Use of Mobile Phone in Daily Life Activities by Rural Women of Gilgit-Baltistan, Pakistan

This study aims to investigate the access to a mobile phone, usage pattern, and its impact on mountainous rural women of two districts, i.e., Hunza and Nagar districts of Gilgit-Baltistan, Pakistan. To attain the objective of the research, the researchers have employed various statistic methods, and data were collected through a questionnaire from 190 respondents in the study area (200 respondents were selected). Initially, Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests were used for sampling adequacy, and factor analysis technique was used to explain correlations among multiple outcomes. The results revealed that 80% of women in the Hunza and Nagar districts own mobile phones (access) and 63% have good skills of mobile phone usage for a variety of purposes. Moreover, 56.4% of women use mobile phones in their daily life activities; however, 23.6% disagreed with the statements. On the contrary, the results show that 71.8% women use mobile phones for security purposes. Therefore, the study recommends that effective use of the mobile phone in daily activities of mountainous rural women can be one of the effective strategies to boost their confidence level and feeling of security. Finally, socioeconomic development of the area is possible by providing technical skills related to mobile phone business to the unskilled women of the two districts.
3D Surface Painting in VR using Force Feedback

In this paper, we propose haptic interfaces based on force feedback to provide a physical painting experience to virtual reality users.Through this system, the user can create surface-based painting holding a haptic stylus, while utilizing both visual feedback from the worn HMD and haptic feedback during painting.In particular, the haptic interfaces simulate the physical interaction between painting brush and painting, which can help to improve the spatial perception of users and compensate for visual feedback.This can reduce laborious drawing works to repeatedly paint strokes and therefore yield a better painting performance.As a result, users can experience more effective and realistic VR painting with this system.