Reviewerly/exHarmony huggingface dataset:
Three-Player Wasserstein GAN via Amortised Duality

We propose a new formulation for learning generative adversarial networks (GANs) using optimal transport cost (the general form of Wasserstein distance) as the objective criterion to measure the dissimilarity between target distribution and learned distribution. Our formulation is based on the general form of the Kantorovich duality which is applicable to optimal transport with a wide range of cost functions that are not necessarily metric. To make optimising this duality form amenable to gradient-based methods, we employ a function that acts as an amortised optimiser for the innermost optimisation problem. Interestingly, the amortised optimiser can be viewed as a mover since it strategically shifts around data points. The resulting formulation is a sequential min-max-min game with 3 players: the generator, the critic, and the mover where the new player, the mover, attempts to fool the critic by shifting the data around. Despite involving three players, we demonstrate that our proposed formulation can be trained reasonably effectively via a simple alternative gradient learning strategy. Compared with the existing Lipschitz-constrained formulations of Wasserstein GAN on CIFAR-10, our model yields significantly better diversity scores than weight clipping and comparable performance to gradient penalty method.
Discussion on the Ways of Librarianship Development in Russia at the All-Russian Library Congress — 2019

The article presents an overview of the activities of the All-Russian Library Congress — the 24th Annual Conference of the Russian Library Association (RLA), which was held on May 11—17, 2019 in Tula, the Library Capital of Russia in 2019. The theme of the Congress is “Concept of Development and Strategic Objectives of Librarianship in Russia”. The authors note the expansion of international participation in the Congress’ 2019, the increase of the number of special and training events, the increasing integration and interaction of various sections of the RBA. The Congress approved the main provisions of the draft Concept of librarianship development in Russia, adopted the RLA Manifesto “Library is the Humanistic Stronghold of the Nation” and approved the draft revision of supplemented “Manual of Library Services for Children in Russia”. The authors consider the events of the Congress — two plenary sessions, section meetings, School of Acquisitions Librarian, School of Library Blogger, which became the place for discussions on a wide range of questions, the 20th Exhibition of publishing products, new information technologies, goods and services, as well as about 530 reports. The article emphasizes the importance of participation of libraries in the “Culture” National project and the role of the Program on modernization of municipal libraries. The Congress discussed the legal framework of library work; project activities; digital environment of libraries; social partnership with institutions of culture, education and tourism; issues of standardization, etc. The article highlights the election of Vice-presidents and Board members of the RLA, as well as presents the procedural decisions taken: on the establishment of the RBA Foundation, on the need to develop draft professional standards for the leading directions of library activities, etc.
The Effectiveness Of Using Comic Strips To Increase Students’ Reading Comprehension For The Eight Grade Of SMPN 1 Pundong

Abstract─ This research was aimed to find out whether the use of comic strip was more effective than the use of conventional media in increasing students' reading comprehension.This research was quasi-experimental research which involved two groups, there were the experimental group and the control group.The samples were three classes of the eight grade students of SMPN 1 Pundong, Bantul, Yogyakarta.The variables of the research were comic strip as the independent variable, while the dependent variable was students' reading comprehension.The data collection used a pre-test and post-test.Data analysis was used ANCOVA.The result of the research shows that the use of comic strip was more effective than the use of conventional media in students' reading comprehension by the results of LSD test with a mean difference of 0.265 and a significance of p 0.00 was lower than 0.05.
An Ellipse Fitting with PSO for Automatic Reading Recognition of Pointer Instruments

Automatic reading recognition technology of pointer instruments based on computer vision is important for the development of industrial information technology. An ellipse fitting with Particle Swarm Optimization (PSO) for automatic reading recognition is designed to realize the pointer line detection and angle calculation for circular pointer instruments in this paper. Firstly, in order to eliminate the non-instrument arc segment in the instrument edge image, the concavities and convexities of the panel contour are classified by the gradient calculation of the edge image for the detected instrument; and then the residual elliptic arc is fit by PSO to overcome some adverse effects such as light, environment and noise on the contour detection image and to realize rapid smooth fitting for the instrument panel contour. Finally, the interment reading is determined by the measurement range and the angle between pointer straight line and scale line, according to the processed results of scale zone and pointer zone. Experimental results indicated that the proposed method is to realize fast and accurate reading detection of the circular pointer instrument without dependence on visual shooting angle.
Aggrandized Random Forest to Detect the Credit Card Frauds

From the collection of supervised machine learning technique, an ensemble procedure is used in Random Forest.In the arena of Data mining, there is an excellent claim for machine learning techniques.Random Forest has tremendous latent of becoming a widespread technique for forthcoming classifiers as its performance has been found analogous with ensemble techniques bagging and boosting.In the present work we have proposed an algorithm, Aggrandized Random Forest to detect fraud from credit card transactions/ATM transactions with high accuracy considering both balanced and imbalanced dataset, comparatively to the defined classification algorithm Random Forest in Data mining.
eCall for Heavy Goods Vehicles

eCall is the European in-vehicle emergency call system. The functionality required for an in-vehicle emergency call service for heavy goods vehicles (HGVs) is largely similar to eCall for cars and vans. The specific requirements for HGVs include communicating information about the cargo carried by the vehicle to the public safety answering point (PSAP) and finding a suitable triggering mechanism for automated eCall for HGVs. The paper summarises the requirements for HGV eCall and drafts three architectural solutions for providing the PSAP access to information on cargo carried by the vehicle: a multi-party approach, a proxy approach, and a distributed system with authentication and authorisation of the HGV Information Service (HGV IS) and PSAP by trusted third parties. Finally, the paper examines the possibilities for interoperability with Next Generation eCall (NG-eCall) and ERA-GLONASS. The challenges to implementing the service and the needs for further research are also reviewed briefly.
A path planning method with specification of curvatures of boundary conditions based on clothoidalal curve elements

Path planning is essential for the autonomous driving or advanced driver assistance systems. Efficiency and ride comfort of vehicles is achieved by path planning using smooth reference paths where there is no discontinuity. If we want to ensure the continuity at the connecting points of sequentially generated paths, it is necessary that the curvature at the endpoints of each paths can be specified. In this research, the method to generate such a smooth path is proposed. The proposed paths are composed of three clothoid curves, which have same length and are connected with no discontinuity of curvatures and tangent angles. It is shown that the proposed method can generate a path in at most a few iterations and that the path can be easily tracked by using curvature information. A discussion of preferable combinations of path generating method and path tracking method is also given.
Probability, coherent belief and coherent belief changes

This paper is about the statics and dynamics of belief states that are represented by pairs consisting of an agent’s credences (represented by a subjective probability measure) and her categorical beliefs (represented by a set of possible worlds). Regarding the static side, we argue that the latter proposition should be coherent with respect to the probability measure and that its probability should reach a certain threshold value. On the dynamic side, we advocate Jeffrey conditionalisation as the principal mode of changing one’s belief state. This updating method fits the idea of the Lockean Thesis better than plain Bayesian conditionalisation, and it affords a flexible method for adding and withdrawing categorical beliefs. We show that it fails to satisfy the traditional principles of Inclusion and Preservation for belief revision and the principle of Recovery for belief withdrawals, as well as the Levi and Harper identities. We take this to be a problem for the latter principles rather than for the idea of coherent belief change.
Modelling of Software Producer and Customer Interaction: Nash Equilibrium

The article aims to simulate the process of interaction between software provider and his client. The authors consider the problem of information support of foreign economic activity. For successful integration of regions into the system of world economic relations, it is necessary to provide exporters and importers with modern software. The problem is the mismatch between the strategies of the software provider and the client. An additional complication is the volatility of the parties’ behaviour. Both the provider and the user of the software must constantly review their strategy of behavior. To find the optimal variant of interaction, the authors calculated the Nash equilibrium.
CONTROL AUTOMATION OF MARITIME UNMANNED COMPLEX WITH A GROUP OF AUTONOMOUS UNDERWATER VEHICLES

It is expedient to perform underwater search operations on large water areas using a group of autonomous self-propelled underwater vehicles. However, with a large distance to the search areas, the sea transition (from one point to the other) of the underwater vehicles requires high energy costs. This leads to the necessity to use heavy-duty underwater vehicles, which determines the high cost of the search operation. The transport of underwater vehicles is proposed to be carried out with an unmanned surface vessel, equipped with actuators for the automatic release of a group of vehicles under water and receiving on board after the end of the underwater mission. The maritime unmanned complex consisting of an unmanned surface vessel and a group of autonomous underwater vehicles on its board forms a new type of marine robotics, the complete automation of which is an actual scientific and technical task. For its implementation, the underlying (basic) automation technology of the marine search underwater mission has been developed as the theoretical basis for the development of the generalized structure of the complex automatic control system. Ten implementation stages of the underlying technology are formulated and the analysis of their automation features with the use of modern methods in the field of marine robotics is performed. Automation of the underlying technology stages involves the transfer of the vessel to a given water area, the automatic release (launch) of the group of underwater vehicles and their coordinated motion to the search area, the search operations and the return to the unmanned surface vessel, as well as the recovery of the vessel to the base. The generalized requirements for automatic control systems constituting the maritime unmanned complex at each stage of its functioning are provided. The spiral trajectory of waiting for the motion of the underwater vehicles at the group formation stages, for the search operation execution and after its completion, is proposed. For the spatial motion of the autonomous underwater vehicle as an agent of the group, the automatic control system was improved by introducing the blocks of the “Navigation Situation Model” and the “Navigation Threat Identifier, which make it impossible for emergency collision with the neighboring underwater vehicles of the group and disintegrate the group due to the data communication loss between them.
Enhanced MP3 transmission over Wi-Fi and LTE networks using unequal error protection and varying frequency transforms

Wi-Fi and LTE are commonly used for the transmission of high bandwidth data and multimedia over the internet. Providing a good quality of service in the transmission of such data over these wireless channels is challenging due to channel impairments such as noise and fading. This paper proposes three enhanced transmission schemes for audio over Wi-Fi and LTE. The first proposed scheme exploits the unequal importance of the bits generated by an MP3 codec to offer different levels of protection to them during transmission by mapping the important bits on prioritized QAM constellation bit positions. The second and third schemes use the statistical distribution of source symbols to map the bits of the encoded symbols to an SQAM constellation. For the second scheme, only systematic bits from the encoder are mapped onto the SQAM constellation. While for the third scheme, both the systematic and parity bits are mapped onto the SQAM constellation. A comparative analysis with different frequency transforms have been done. The simulation results show that the proposed schemes increase the system performance by 1–20 dB in segmented signal to noise ratio (SSNR). Using DWT further increases the gains up to 90 dB for 16-QAM at rate ½ as compared to FFT.
Comprehensive evaluation of usability at the mobile end interface

Research on interface usability has always been qualitative. This thesis conducted a quantitative research on the evaluation of usability of mobile terminal interface adopting Analytic Hierarchy Process in Management and Fuzzy Evaluation Theory in Fuzzy Mathematics. Firstly, according to the factors influencing users' evaluation of usability of mobile terminal, an evaluation index framework of mobile application was established. Then, according to the fuzzy comprehensive decision based on the data of questionnaire survey, a comprehensive evaluation of usability of mobile application was made. Finally, a detailed elaboration on the practice and application of this evaluation method was offered with the example of a tourism mobile application.
Influence of an Instructional Strategy on the Attitudes of University Professors toward Distance Education

The purpose of the study was to determine the influence of an instructional strategy for the use of DOKEOS, Course Management System (CMS) on the attitudes of professors toward distance education. The study was carried out in the Faculty of Accounting and Administration of a Mexican Public University. The research design was non-experimental with a group of twenty-five professors who were selected through purposive sampling. A questionnaire, which was an attitude scale toward the use of an administration system of online courses, was administered to participants before and after the implementation of an instructional strategy on the use of DOKEOS. Afterwards, usage statistics was examined between weeks three and six. Data analysis included calculation frequency, median, mode and standard deviation. The significant differences between the median of the pre-tests and post-tests were analyzed, and then the t-test was administered. Results showed that the instructional strategy contributed to a more positive attitude of professors toward distance education.
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks

As deep learning is showing unprecedented success in medical image analysis tasks, the lack of sufficient medical data is emerging as a critical problem. While recent attempts to solve the limited data problem using Generative Adversarial Networks (GAN) have been successful in generating realistic images with diversity, most of them are based on image-to-image translation and thus require extensive datasets from different domains. Here, we propose a novel model that can successfully generate 3D brain MRI data from random vectors by learning the data distribution. Our 3D GAN model solves both image blurriness and mode collapse problems by leveraging alpha-GAN that combines the advantages of Variational Auto-Encoder (VAE) and GAN with an additional code discriminator network. We also use the Wasserstein GAN with Gradient Penalty (WGAN-GP) loss to lower the training instability. To demonstrate the effectiveness of our model, we generate new images of normal brain MRI and show that our model outperforms baseline models in both quantitative and qualitative measurements. We also train the model to synthesize brain disorder MRI data to demonstrate the wide applicability of our model. Our results suggest that the proposed model can successfully generate various types and modalities of 3D whole brain volumes from a small set of training data.
Efficient Regularization Parameter Selection for Latent Variable Graphical Models via Bi-Level Optimization

Latent variable graphical models are an extension of Gaussian graphical models that decompose the precision matrix into a sparse and a low-rank component. These models can be learned with theoretical guarantees from data via a semidefinite program. This program features two regularization terms, one for promoting sparsity and one for promoting a low rank. In practice, however, it is not straightforward to learn a good model since the model highly depends on the regularization parameters that control the relative weight of the loss function and the two regularization terms. Selecting good regularization parameters can be modeled as a bi-level optimization problem, where the upper level optimizes some form of generalization error and the lower level provides a description of the solution gamut. The solution gamut is the set of feasible solutions for all possible values of the regularization parameters. In practice, it is often not feasible to describe the solution gamut efficiently. Hence, algorithmic schemes for approximating solution gamuts have been devised. One such scheme is Benson's generic vector optimization algorithm that comes with approximation guarantees. So far Benson's algorithm has not been used in conjunction with semidefinite programs like the latent variable graphical Lasso. Here, we develop an adaptive variant of Benson's algorithm for the semidefinite case and show that it keeps the known approximation and run time guarantees. Furthermore, Benson's algorithm turns out to be practically more efficient for the latent variable graphical model than the existing solution gamut approximation scheme on a wide range of data sets.
PECULIAR FEATURES OF COMMUNICATION IN THE SPORTS INTERNET DISCOURSE

The present article is devoted to the analysis of Internet discourse represented on web-sites of the British rugby teams. The aim of the research is identification of communicative situation features in the sport Internet discourse. The following tasks were fulfilled in order to achieve the aim: investigating peculiar features of language representation that is typical of a referential situation (an event, in this case a sports competition) and the analysis of communicative discursive situation that takes place during transmission of the message covering this event. The articles from the news sections of the web-sites were used as the source of empirical data.&#x0D; The research focuses on representation of a socially important event in the sports discourse a sports match. The constituents of a situational model (Participants, their Actions, the Conditions) were singled out by means of the analysis of an extract belonging to the studied discourse. The analysis was carried out with the help of the traditional model of a news item: Event Who, What, Where, When, Why, How. The analysis of linguistic representation of a situational model revealed that the event is performed on a macro- and a micro-levels. That aspect depends on the authors focus either on the match, on the team as a whole or on the specific aspects of the event. The linguistic representation of both levels is demonstrated in the tables and illustrated by the relevant examples.&#x0D; The following methods were used during the research: descriptive method, contextual-semantic method, method of content analysis as well as the method of discourse analysis. The peculiarities of communicative processes in the sports Internet-discourse were analyzed with the use of Hymes pattern S-P-E-A-K-I-N-G. The study of such elements as Setting and Scene, Participants, Ends, Act of Sequence, Genres is presented in the article.
Query Time Optimization Using Hungarian Algorithm

The huge amount of data that is committed to the databases, takes more time, than the time that is estimated which further creates trouble for database transactions. To untighten this process, data needs to be clustered based on common attributes, to commit data faster. Databases like Hadoop have structures like MapReduce, which are incompetent to handle the process alone. As a result, the accumulation of these transactions eventually slows down the process as well as adversely affects the performance of the application that creates havoc at the user end. The proposed system puts forth the idea of decomposing this data to hyper graphs based on correlating features between them. Furthermore, the use of Hungarian and Genetic Algorithm, achieves optimized transaction time.
Tree based frequent and rare pattern mining techniques: a comprehensive structural and empirical analysis

Pattern mining has emerged as a compelling field of research over the years due to its extensive popularity in several application domains. Researchers in this field have contributed ample endeavors, encompassing different issues encountered during pattern generation. The patterns are either extracted based on a level-wise exploration of the search space or by employing efficient tree data structures. Tree based approaches show remarkable performance over the former ones on various grounds and are therefore regarded as the most prominent techniques of pattern mining. A precise and impartial analysis of these eminent techniques is necessary to widen the scope of effective pattern mining using the notion of tree data structures. This paper is therefore an attempt to provide a comparative scrutiny of the fundamental tree based pattern mining techniques through performance analysis based on several decisive parameters. The paper provides a structural classification of the widely referenced tree based pattern mining techniques in four categories and an analytical comparison of these techniques using benchmark real and synthetic datasets. Through this empirical study, an endeavor has been made to enable the researchers identify the factors affecting the performance of the most well-known techniques of pattern mining: the tree based approaches.