Reviewerly/exHarmony huggingface dataset:
Depth-Preserving Real-Time Arbitrary Style Transfer.

Style transfer is the process of rendering one image with some content in the style of another image, representing the style. Recent studies of Liu et al. (2017) show that traditional style transfer methods of Gatys et al. (2016) and Johnson et al. (2016) fail to reproduce the depth of the content image, which is critical for human perception. They suggest to preserve the depth map by additional regularizer in the optimized loss function, forcing preservation of the depth map. However these traditional methods are either computationally inefficient or require training a separate neural network for each style. AdaIN method of Huang et al. (2017) allows efficient transferring of arbitrary style without training a separate model but is not able to reproduce the depth map of the content image. We propose an extension to this method, allowing depth map preservation by applying variable stylization strength. Qualitative analysis and results of user evaluation study indicate that the proposed method provides better stylizations, compared to the original AdaIN style transfer method.
PRECISE DISPARITY ESTIMATION FOR NARROW BASELINE STEREO BASED ON MULTISCALE SUPERPIXELS AND PHASE CORRELATION

Abstract. With the rapid development of subpixel matching algorithms, the estimation of image shifts with an accuracy of higher than 0.05 pixels is achieved, which makes the narrow baseline stereovision possible. Based on the subpixel matching algorithm using the robust phase correlation (PC), in this work, we present a novel hierarchical and adaptive disparity estimation scheme for narrow baseline stereo, which consists of three main steps: image coregistration, pixel-level disparity estimation, and subpixel refinement. The Fourier-Mellin transform with subpixel PC is used to co-register two input images. Then, the pixel-level disparities are estimated in an iterative manner, which is achieved through multiscale superpixels. The pixel-level PC is performed with the window sizes and locations adaptively determined according to superpixels, with the disparity values calcualted. Fast weighted median filtering based on edge-aware filter is adopted to refine the disparity results. At last, the accurate disparities are calculated via a robust subpixel PC method. The combination of multiscale superpixel hierarchy, adaptive determination of the window size and location of correlation, fast weighted median filtering and subpixel PC make the proposed scheme be able to overcome the issues of either low-texture areas or fattening effect. Experimental results on a pair of UAV images and the comparison with the fixed-window PC methods, the iterative scheme with fixed variation strategy, and a sophisticated implementation using global optimization demonstrate the superiority and reliability of the proposed scheme.
MIFO

Data Analytics Frameworks encourage sharing of clusters for execution of mixed workloads by promising fairness and isolation along with high performance and resource utilization. However, concurrent query executions on such shared clusters result in increased queue and resource waiting times for queries affecting their overall performance. MIFO is a dataflow aware scheduling policy that mitigates the impacts due to queue and resource contentions by reducing the waiting times for queries near completion. We present heuristics that exploit query semantics to proactively trigger MIFO-based allocations in a workload. Our experiments on Apache Spark using TPCDS benchmark show that compared to a FAIR policy, MIFO provides an improved mean response time, reduced makespan of the workload and average speedup between 1.2x-2.7x in highly concurrent setting with only a momentary deviation in fairness.
Review on gamification in children computer interaction (CCI) for persona modelling

Human Computer Interaction (HCI) plays an important role in connecting humans and computers. Many studies conducted to find better alternatives to improve communication between humans and computers. Various frameworks, catalogue and models revised to complement the lack of existing ideas. The growing technology is increasingly being used by not only adults but also children. However, many applications developed do not fully emphasize the use of HCI suitable for children. Thus, Children Computer Interaction (CCI) created to meet the specific needs of children. Yet, there are still many CCI weaknesses being improved to overcome various problems from time to time. One of the ideas presented is through gamification, which is fun and enjoyable in accordance with the nature of the children. Still, the use of gamification is not as simple as adding some game elements into children's apps, but wider to ensure success in achieving the objectives of the developed application. One way that matter is through the use of user-centered design-persona model. So, this paper reviewed the use of current HCI/CCI, gamification and modified the previously proposed design principles in HCI for children into interview questions for data collection which will be analyzed later to create persona model for future work.
Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation

This research was conducted to solve the out-of-vocabulary problem caused by Uyghur spelling errors in Uyghur–Chinese machine translation, so as to improve the quality of Uyghur–Chinese machine translation. This paper assesses three spelling correction methods based on machine translation: 1. Using a Bilingual Evaluation Understudy (BLEU) score; 2. Using a Chinese language model; 3. Using a bilingual language model. The best results were achieved in both the spelling correction task and the machine translation task by using the BLEU score for spelling correction. A maximum F1 score of 0.72 was reached for spelling correction, and the translation result increased the BLEU score by 1.97 points, relative to the baseline system. However, the method of using a BLEU score for spelling correction requires the support of a bilingual parallel corpus, which is a supervised method that can be used in corpus pre-processing. Unsupervised spelling correction can be performed by using either a Chinese language model or a bilingual language model. These two methods can be easily extended to other languages, such as Arabic.
Density-based clustering with constraints

In this paper we present our ic-NBC and ic-DBSCAN algorithms for data clustering with constraints. The algorithms are based on density-based clustering algorithms NBC and DBSCAN but allow users to incorporate background knowledge into the process of clustering by means of instance constraints. The knowledge about anticipated groups can be applied by specifying the so-called must-link and cannot-link relationships between objects or points. These relationships are then incorporated into the clustering process. In the proposed algorithms this is achieved by properly merging resulting clusters and introducing a new notion of deferred points which are temporarily excluded from clustering and assigned to clusters based on their involvement in cannot-link relationships. To examine the algorithms, we have carried out a number of experiments. We used benchmark data sets and tested the efficiency and quality of the results. We have also measured the efficiency of the algorithms against their original versions. The experiments prove that the introduction of instance constraints improves the quality of both algorithms. The efficiency is only insignificantly reduced and is due to extra computation related to the introduced constraints.
Investigating Embodied Music Expression Through the Leap Motion: Experimentations in Educational and Clinical Contexts

This paper describes a computer-based approach to foster embodied musical expression. The proposed system captures user’s hand gestures through a Leap Motion controller, and the signals thus generated are sent to a software tool that converts movements into music notes. When applied to an educational or a rehabilitation context, this solution may foster the development of communication and motor skills by using free-hand interaction. The conceptual framework was experimented in two contexts: technologically-augmented music lessons in a primary school, and music therapy sessions for the rehabilitation of physically and intellectually impaired people.
An Improvement of the VDSR Network for Single Image Super-Resolution by Truncation and Adjustment of the Learning Rate Parameters

Abstract A problem of single image super-resolution is considered, where the goal is to recover one high-resolution image from one low-resolution image. Whereas this problem has been successfully solved so far by the known VDSR network, such an approach still cannot give an overall beneficial effect compared to bicubic interpolation. This is so due to the fact that the image reconstruction quality has been estimated separately by three subjective factors. Moreover, the original VDSR network consisting of 20 convolutional layers is apparently not optimal by its depth. This is why here those factors are aggregated, and the network performance is deemed by a single estimator. Then the depth is tried to be decreased (truncation) along with adjusting the learning rate drop factor. Finally, a plausible improvement of the VDSR network is confirmed. The best truncated network, performing by almost 3.2 % better than bicubic interpolation, occupies less memory space and is about 1.44 times faster than the original VDSR network for images of a medium size.
A study of using syntactic cues in short-text similarity measure

Short-text semantic similarity is an essential technique of natural language search and is widely used in social network analysis and opinion mining to find unknown knowledge. Such similarity measures usually measure short texts with 10-20 words. Similar to spoken utterances, short texts do not necessarily follow formal grammatical rules. The limited information contained in short texts and their syntactic and semantic flexibility make similarity measures difficult. Therefore, this study designed and tested a part-of-speech-based short-text similarity algorithm to solve those problems. The effects of evaluating different parts of speech are thoroughly discussed. The proposed algorithm achieved the best performance using word measures corresponding to different parts of speech.
Applications of MaxSAT in Data Analysis

We highlight important real-world optimization problems arising from data analysis and machine learning, representing somewhat atypical applications of SAT-based solver technology, to which the SAT community could focus more attention on. To address the problem of current lack of heterogeneity in benchmark sets available for evaluating MaxSAT solvers, we provide a benchmark library of MaxSAT instances encoding different data analysis and machine learning problems. By doing so, we also advocate extending MaxSAT solvers to accept real-valued weights for soft clauses as input via the presented problem domains in which the use of real-valued costs plays an integral role.
Establishing, Developing, and Sustaining a Community of Data Champions

Supporting good practice in Research Data Management (RDM) is challenging for higher education institutions, in part because of the diversity of research practices and data types across disciplines. While centralised research data support units now exist in many universities, these typically possess neither the discipline-specific expertise nor the resources to offer appropriate targeted training and support within every academic unit. One solution to this problem is to identify suitable individuals with discipline-specific expertise that are already embedded within each unit, and empower these individuals to advocate for good RDM and to deliver support locally. This article focuses on an ongoing example of this approach: the Data Champion Programme at the University of Cambridge, UK. We describe how the Data Champion programme was established; the programme’s reach, impact, strengths and weaknesses after two years of operation; and our anticipated challenges and planned strategies for maintaining the programme over the medium- and long-term.
Fake Product Review Monitoring System

In the current scenario, the data on the web is growing exponentially.Social media is generating a large amount of data such as reviews, comments, and customer's opinions on a daily basis.This huge amount of user generated data is worthless unless some mining operations are applied to it.As there are a number of fake reviews so opinion mining technique should incorporate Spam detection to produce a genuine opinion.Nowadays, there are a number of people using social media opinions to create their call on shopping for product or service.Opinion Spam detection is an exhausting and hard problem as there are many faux or fake reviews that have been created by organizations or by the people for various purposes.They write fake reviews to mislead readers or automated detection system by promoting or demoting target products to promote them or to degrade their reputations.The proposed technique includes Ontology, Geo location and IP address tracking, Spam words Dictionary using Naïve Bayes, Brand only review detection and tracking account used.
Improving Mongolian-Chinese Neural Machine Translation with Morphological Noise

For the translation of agglutinative language such as typical Mongolian, unknown (UN-K) words not only come from the quite restricted vocabulary, but also mostly from misunderstanding of the translation model to the morphological changes.In this study, we introduce a new adversarial training model to alleviate the UNK problem in Mongolian→Chinese machine translation.The training process can be described as three adversarial sub models (generator, value screener and discriminator), playing a win-win game.In this game, the added screener plays the role of emphasizing that the discriminator pays attention to the added Mongolian morphological noise 1 in the form of pseudo-data and improving the training efficiency.The experimental results show that the newly emerged Mongolian→Chinese task is state-of-the-art.Under this premise, the training time is greatly shortened.
Towards Improved Real-Time Observability of Behind-Meter PhotoVoltaic Systems

The stochasticity in energy supply introduced by increased PV penetration in grid distribution systems can lead to frequent and sudden supply-demand imbalances, thereby causing voltage and frequency fluctuations. Enhanced observability, in real-time, of grid attributes such as real and reactive power flows, power injections/consumption and voltages is becoming increasingly essential to ensure smooth grid operation under these new large-scale PV integration scenarios. Two significant challenges prevent grid operators from achieving this objective: 1) Computational complexity of quasi-static time-series simulations, and 2) Partial observability of behind-meter grid assets. In this work, we take a step towards this objective by enhancing the observability of smart inverter equipped PV systems located behind AMI meters. Smart inverters convert the DC output of PV systems into real and reactive power, the ratio of which is governed by a power factor setting. This in turn is governed by the real-time voltage at its terminal, as per a pre-set volt/var curve. While it is possible to infer the power factor setting - thereby, real and reactive power injection of the PV system - at each time interval by quasi-static time series simulations, such methods are not scalable to large grid sizes. We propose to reduce the reliance on simulations by developing a deep neural network based data-driven model which predicts the time dependent power factors of the PV systems in the grid. The model uses a small amount of simulated data to learn the dependence of power factor on 1) load and apparent PV power injection (root mean square of real and reactive power) data, and 2) AMI net-load data (for BTM PV assets). Our results show that our model can predict power factors with mean absolute error of less than 6.2% under full observability scenario and under 8.6% under the partial observability scenario. Thus, our data-driven model can reduce the amount of simulations required to compute real and reactive injections from PV systems in the distribution system with or without complete information.
International University Network Internet Project of Integrated Engineering Education

The transfer of educational technologies of the OECD countries for higher technical education in the CIS requires significant financial and material resources. It is advisable to unite pedagogical teams and the laboratory base of universities that train specialists in related specialties using the Internet. The international scientific and educational network Internet project “Synergy” has been implemented with the support of the concern “Festo” (Germany). The universities directly involved in the regular educational activities of the project are NIU Moscow Power Engineering Institute, Peter the Great St. Petersburg Polytechnic University, the Baltic State Technical University “VOENMECH” named after D.F. Ustinova, Omsk State Technical University and Karaganda State Technical University. The laboratories of the project participants are equipped with modern training stands and multimedia tools for conducting on-line courses and laboratory work in the remote access mode produced by the leading domestic and foreign companies including world leaders in the field of automation and robotics Festo, Siemens, Mitsubishi Auto Electrician. Regular online training has been organized with the use of exchange, composite and embedded module courses. The experience gained in the process of project implementation became the basis for the development of a new interdisciplinary program for the specialized magistracy as “Robotics. Control systems” for the Kazakhstan industry. The international university network project “Synergy” can become the basis for the practical implementation of the principles of the Bologna process, taking into account the specifics of the CIS countries.
Performance evaluation and Nash equilibrium of a cloud architecture with a sleeping mechanism and an enrollment service

Cloud computing makes it possible for application providers to provide services seamlessly and application users to receive services adaptively. By offering services that give users an initial experience, application providers can usually attract more users. This research proposes a type of sleeping mechanism-based cloud architecture where an experience service and an enrollment service are provided on one virtual machine (VM). Accordingly, we model the cloud architecture as a queue with an asynchronous multi-vacation and a selectable extra service. We also analyze the queueing model in the steady state by constructing a three-dimensional Markov chain. Following this, we evaluate the system performance of the proposed cloud architecture based on the energy conservation level of the system and the mean delay of the visitors who select the enrollment service. Moreover, we study the Nash equilibrium strategy of visitors by building an individual welfare function, and develop an improved intelligent search algorithm to investigate the socially optimal strategy of visitors. Aiming to achieve a social optimum, we formulate a pricing policy with a reasonable enrollment fee.
A Comparative Analysis of Machine Learning Techniques for Spam Detection

Data Science is an emerging multidisciplinary field which employs algorithms, processes, scientific methods to extract information and insights in various forms which is both structures and unstructured much similar to data mining and prediction analysis.Advertisement and bulk emails, also called as spam, makes an estimate of 62% of the Worldwide internet traffic.Since 1978, when first unwanted mail was sent, technology have advanced but still the detection of spams remains a chronophagous and big budget problem in the field of mathematical sciences.The current study evaluates the effectiveness and efficiency of various machine learning techniques which include K-NN, Decision tree, random forest, Naive Bayes and SVM for spam detection.A data set comprising of 962 emails containing both genuine emails and spams has been used in this study.Some deep learning techniques for classification of spams is also suggested for better performance.
NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector

This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019.We developed a system that predicts whether an English news article follows a hyperpartisan argumentation.Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019.The Paparazzo system, in which we use tri-grams of words and hepta-grams of characters, officially ranks thirteen with an accuracy of 0.747.Another system of ours, which utilizes trigrams of words, tri-grams of characters, trigrams of part-of-speech, syntactic dependency sub-trees, and named-entity recognition tags, achieved an accuracy of 0.787 and is proposed after the deadline of Task 4.
Practice-Oriented Direction of Professional Preparation of Future Teachers of Primary Education in the Context of European Integration Processes

The article reveals the essence of practice-oriented training of future teachers of primary education on the basis of the implementation of contextual learning technology.An express analysis of the phenomenon of pedagogical thinking was made, certifying that it personifies the level of development of mental, cognitive, creative, and research abilities of the future teacher of primary education, his operational and technological skills, which consist in finding solutions to any problem situation; in shaping pedagogical thinking of future teachers of primary education a special role was played by pedagogical situations that were solved with the help of practice-oriented tasks (pedagogical tasks).In the process of experimental learning, we specifically modeled pedagogical situations that were solved using a system of practice-oriented tasks represented by three groups: general pedagogical, didactic-methodical, and reflexive-design.No less important for the development of pedagogical thinking of future teachers of primary education during practical and laboratory classes was the use of interactive teaching methods, namely: brainstorming, discussion, debate, various types of training, etc.The active introduction of contextual learning technology, based on the practice-oriented activity of teachers, first of all, influenced the formation of a new pedagogical thinking and provided for an understanding of the value-attitude to the future profession, and an awareness of one's own capabilities that determine professional growth paths.