Reviewerly/exHarmony huggingface dataset:
VEDAS: an efficient GPU alternative for store and query of large RDF data sets

Abstract Resource Description Framework (RDF) is commonly used as a standard for data interchange on the web. The collection of RDF data sets can form a large graph which consumes time to query. It is known that modern Graphic Processing Units (GPUs) can be employed to execute parallel programs in order to speedup the running time. In this paper, we propose a novel RDF data representation along with the query processing algorithm that is suitable for GPU processing. Since the main challenges of GPU architecture are the limited memory sizes, the memory transfer latency, and the vast number of GPU cores. Our system is designed to strengthen the use of GPU cores and reduce the effect of memory transfer. We propose a representation consists of indices and column-based RDF ID data that can reduce the GPU memory requirement. The indexing and pre-upload filtering techniques are then applied to reduce the data transfer between the host and GPU memory. We add the index swapping process to facilitate the sorting and joining data process based on the given variable and add the pre-upload step to reduce the size of results’ storage, and the data transfer time. The experimental results show that our representation is about 35% smaller than the traditional NT format and 40% less compared to that of gStore. The query processing time can be speedup ranging from 1.95 to 397.03 when compared with RDF3X and gStore processing time with WatDiv test suite. It achieves speedup 578.57 and 62.97 for LUBM benchmark when compared to RDF-3X and gStore. The analysis shows the query cases which can gain benefits from our approach.
MODERN FINTECH DIRECTIONS IN THE BANKING SECTOR

Introduction. One of the factors behind structural changes in Ukraine's financial market is the increasing use of the Internet and mobile devices to provide financial services. It can be seen now that radical changes in technology have affected not only the information sphere but also the economic sphere. The current technological revolution has also had a significant impact, including on the banking sector's infrastructure, which is associated with increased automation in bank operations and greater customer focus. For the most part, such transformations in the industry are associated with the need to reduce costs, improve the security of financial transactions, and ensure that the service industry is responsive to an ever-evolving society. The purpose is to determine trends directions of research of new financial technologies in the banking industry. Methods. The theoretical and methodological basis of the article is the scientific works of domestic and foreign scientists and specialists in the theory of banking innovation and financial technologies. In the course of the research, such general scientific methods of cognition were used as: theoretical generalization, comparison, and systematization (in the study of the essence of the concepts of “financial technology”, “fintech”, “artificial intelligence”, “open banking”, “blockchain”; definition of types of financial technologies); observation (during the study of implemented fintech in banking institutions); graphical method – for visual presentation of analysis results. Results. The essence of the concept of “financial technology” is disclosed in the article. Modern fintech trends in Ukraine and in the world are analyzed. Also, presents the most successful examples of the use of financial technologies in the banking sector in recent years. In addition, a number of areas for future research in the field of financial technology, which are currently very promising in the banking sector and could be launched in Ukraine and worldwide are offered. Conclusions. Given the significant interbank competition, the problem of developing financial technologies in the provision of financial services by banking institutions has become particularly urgent. In addition, the improvement of financial technologies is not a simple matter and should be multifaceted in order to ensure the efficiency and expected performance of banks. Therefore, it is necessary to improve not only financial technologies, financial services and products, but also ways of providing them to clients of banking institutions, communication of bank employees with clients, advertising, promotion of modern financial technologies, etc. Financing, all kinds of consulting services and payments can all be considered as one of the most promising areas for future research in financial technology.
Pengujian Sistem Informasi Parkir PT KISP Berbasis Desktop dengan Metode Black-Box

PT KISP is a market in the Ciledug area, which requires a parking application, it is hoped that this parking implementation can solve the problem of user needs, namely helping the parking system to be more effective and efficient in terms of processing data and information. With this application, it is hoped that it will make it easier for users to make transactions that are easy and in a short time. To design and implement a reliable parking application that is protected from damage or errors, comprehensive testing is needed, such as the functional test, performance test and user interface test aspects, so testing is needed. Testing parking applications based on black box, software testing is useful to ensure whether the actual results match the expected results and to ensure the application is not damaged. Based on the black box testing that has been carried out, it includes input, process and output in the parking application.
Bounds on Causal Effects and Application to High Dimensional Data

This paper addresses the problem of estimating causal effects when adjustment variables in the back-door or front-door criterion are partially observed. For such scenarios, we derive bounds on the causal effects by solving two non-linear optimization problems, and demonstrate that the bounds are sufficient. Using this optimization method, we propose a framework for dimensionality reduction that allows one to trade bias for estimation power, and demonstrate its performance using simulation studies.
Query Expansion for Slovak to Bulgarian Language Machine Translation using Parallel Search

The paper presents results of the application of a statistical approach for Slovak to Bulgarian language machine translation. It uses Information Retrieval inspired search techniques and employs sever alalgorithmic steps of parallel statistical search with query expansion in Slovak-Bulgarian EUROPARL 7 Corpus using the Sketch Engine software and its scoring. The search includes the generation of concordances,collocations, word sketch differences, word sketches, and thesauri of the studied keyword (query) by using a statistical scoring, which is regarded as intermediate (inter-lingual) semantic standard presentation by means of which the studied keyword (from the source language) is mapped together with its possible translation equivalents (onto the target language. The results present the study of adjectival collocabillity in both Slovak and Bulgarian language from the corpus of political speech texts outlining the standard semantic relations based on the evaluation of statistical scoring. Finally, the advantages and shortcomings of the approach are discussed.
RWNE: A Scalable Random-Walk based Network Embedding Framework with Personalized Higher-order Proximity Preserved

Higher-order proximity preserved network embedding has attracted increasing attention. In particular, due to the superior scalability, random-walk-based network embedding has also been well developed, which could efficiently explore higher-order neighborhoods via multi-hop random walks. However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved. In this paper, to address the above issues, we present a general scalable random-walk-based network embedding framework, in which random walk is explicitly incorporated into a sound objective designed theoretically to preserve arbitrary higher-order proximity. Further, we introduce the random walk with restart process into the framework to naturally and effectively achieve personalized-weighted preservation of proximities of different orders. We conduct extensive experiments on several real-world networks and demonstrate that our proposed method consistently and substantially outperforms the state-of-the-art network embedding methods.
Deep Learning Features for Discriminating Between Benign and Malignant Microcalcification Lesions

Accurate diagnosis of microcalcification (MC) lesions in mammograms as benign or malignant is a challenging clinical task. In this study we investigate the potential discriminative power of deep learning features in MC lesion diagnosis. We consider two types of deep learning networks, of which one is a convolutional neural network developed for MC detection and the other is a denoising autoencoder network. In the experiments, we evaluated both the separability between malignant and benign lesions and the classification performance of image features from these two networks using Fisher's linear discriminant analysis on a set of mammographic images. The results demonstrate that the deep learning features from the MC detection network are most discriminative for classification of MC lesions when compared to both features from the autoencoder network and traditional handcrafted texture features.
SVP-CF: Selection via Proxy for Collaborative Filtering Data

We study the practical consequences of dataset sampling strategies on the performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often taken in a naive or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting users or items with many interactions. As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance -- masking performance deficiencies in algorithms or altering the relative performance of algorithms, as compared to models trained on the complete dataset. Following this observation, this paper makes the following main contributions: (1) characterizing the effect of sampling on algorithm performance, in terms of algorithm and dataset characteristics (e.g. sparsity characteristics, sequential dynamics, etc.); and (2) designing SVP-CF, which is a data-specific sampling strategy, that aims to preserve the relative performance of models after sampling, and is especially suited to long-tail interaction data. Detailed experiments show that SVP-CF is more accurate than commonly used sampling schemes in retaining the relative ranking of different recommendation algorithms.
Smooth augmented Lagrangian method for twin bounded support vector machine

&lt;p style='text-indent:20px;'&gt;In this paper, we propose a method for solving the twin bounded support vector machine (TBSVM) for the binary classification. To do so, we use the augmented Lagrangian (AL) optimization method and smoothing technique, to obtain new unconstrained smooth minimization problems for TBSVM classifiers. At first, the augmented Lagrangian method is recruited to convert TBSVM into unconstrained minimization programming problems called as AL-TBSVM. We attempt to solve the primal programming problems of AL-TBSVM by converting them into smooth unconstrained minimization problems. Then, the smooth reformulations of AL-TBSVM, which we called AL-STBSVM, are solved by the well-known Newton's algorithm. Finally, experimental results on artificial and several University of California Irvine (UCI) benchmark data sets are provided along with the statistical analysis to show the superior performance of our method in terms of classification accuracy and learning speed.&lt;/p&gt;
Bayesian functional graphical models

We develop a Bayesian graphical modeling framework for functional data for correlated multivariate random variables observed over a continuous domain. Our method leads to graphical Markov models for functional data which allows the graphs to vary over the functional domain. The model involves estimation of graphical models that evolve functionally in a nonparametric fashion while accounting for within-functional correlations and borrowing strength across functional positions so contiguous locations are encouraged but not forced to have similar graph structure and edge strength. We utilize a strategy that combines nonparametric basis function modeling with modified Bayesian graphical regularization techniques, which induces a new class of hypoexponential normal scale mixture distributions that not only leads to adaptively shrunken estimators of the conditional cross-covariance but also facilitates a thorough theoretical investigation of the shrinkage properties. Our approach scales up to large functional datasets collected on a fine grid. We show through simulations and real data analysis that the Bayesian functional graphical model can efficiently reconstruct the functionally-evolving graphical models by accounting for within-function correlations.
Formation of universal competencies of undergraduates during development of the plot of web-quest

The problem and the aim of the study. The use of innovative technologies for the development of basic personal qualities and supra-subject competencies is a priority direction for the development of the international educational environment. Web-quest is one of the applications of the gamification technology in didactics. The authors suggest that when designing a web-quest, special attention should be paid to working with the formulation of the web-quest plot as an important factor in the formation of universal competencies of undergraduates. Research methods. To determine the sequence of training actions in the development of the plot, the principles of designing the game environment of the quest, the measurement procedures, the system-activity and competence approaches were used. The experiment involved 20 students of Vyatka State University (50% boys and 50% girls) in the specialty 44.04.01 Teacher education. Informatization of education (Master's degree level). Triggers, macros, and hyperlinks are used to implement transitions. The web quest is hosted on the OneDrive. Statistical methods: G-sign test, multiple correlation analysis. Results. During the experiment undergraduates learned to identify and design the elements of the plot; to arrange events in a chronological order of presentation; to establish a logical connection between the facts; to transform the plot into a storyline and back. The empirical value of Gemp=2&lt;Gcr=3 (for p=0.01) obtained in the estimation procedure proves that the changes in the system are not random. The coefficient (0&lt;R1,2,3...N=0.86&lt;1) is calculated, confirming the positive correlation of universal competencies with undergraduates performance. The evaluation of its statistical significance was done (Femp0.01=14.47&gt;Fcrit0.01=5.29). In conclusion we summarize the problems connected with the development on the plot of the web-quest (presentation of facts in chronological order, author's detachment, etc.) and offer recommendations for their resolution: graphic and verbal visualization, numbering of events, the use of well-known literary plots.
Using Augmented Reality in Different BIM Workflows

Building Information Modeling is an increasingly common process for managing the entire lifecycle of a building - from design and planning, through the construction phase, to operation and maintenance. The result of this process is a building information model with all the generated data and information about the construction process that can be used in a variety of different end-user scenarios. One such use of the model is in a number of different augmented reality applications. Augmented reality technology is being used to bridge the gap between the digital and real worlds and is rapidly becoming an essential part of modern building data modeling design workflows. The chapter provides an overview of building data modeling and the current state of the art in the use of augmented reality in various user scenarios of building data modeling and explores various challenges that need to be addressed for the adoption of augmented reality technology in architecture, engineering, and construction in general.
북한 「경제연구」로 분석한 경제정책 변화: 텍스트 마이닝 접근법 (Economic Policy Changes in North Korea: A Text Mining Approach to Economic Research)

Korean Abstract: 본고에서는 텍스트 마이닝 등 인공지능 분석 기법을 활용하여 북한 학술지 「경제연구」의 제목에서 유추할 수 있는 북한 경제정책 변화를 분석해 보았다. 「경제연구」에 수록된 논문을 김일성, 김정일, 김정은 시기로 구분하여, 각 통치자별 경제정책의 특징을 일관성과 변화 관점에서 추적하였다. 통치자의 정책적 관심사를 반영한다고 볼 수 있는 「경제연구」에 게재된 논문에 대한 심층 분석 결과는 김일성, 김정일, 김정은으로 이어지는 통치자 관점에서 경제정책의 핵심적인 방향과 당면 과제의 변화를 잘 포착하고 있다. 분석 결과, ‘주체의 경제이론’에 기반한 ‘사회주의 경제강국건설’을 위한 ‘경제사업’에 대한 ‘국가의 전략적 관리’는 일관되게 강조되는 원칙으로 고수하면서도, 대내외 환경변화에 대응한 차별화된 정책 방향의 변화도 관찰되었다. 또한 통치자 시기별로 논문 주제의 분포가 크게 다르게 나타나며 주제의 범위나 방향도 최고통치자에 따라 많은 차이가 나타났다. 「경제연구」등 북한 문헌에 대한 연구는 북한 내부에서 일어나고 있는 정책의 변화를 잘 반영하고 있으므로 대북정책 수립에 기여할 것으로 기대된다.English Abstract: This paper analyzes changes in North Korea’s economic policy according to the North Korean journal, Economic Research, through various analysis techniques of text mining. The paper included in Economic Research is divided into periods according to ruler－Kim Il-sung, Kim Jong-il, and Kim Jong-un, and tracks the consistency of and changes in each ruler’s economic policies. The analysis showed a change in policy direction that was differentiated in response to changes in the internal and external environments while adhering to the principle of consistent emphasis on national strategic management for the “socialist economic construction of a powerful country” based on the economic theory of Juche. The in-depth analysis of the paper published in Economic Research, which reflects the policy interests of the rulers, captures changes in the core direction and challenges of economic policy from the perspective of rulers. The number and the variety of the thesis topics is more obvious in Kim Jong-un compared to the former highest rulers. The study of North Korean literature, including Economic Research, is expected to contribute to the establishment of North Korea’s policy as it reflects the change in policy taking place inside the country.
Spatial distribution of invasive species: an extent of occurrence approach

Abstract Ecological Risk Assessment faces the challenge of determining the impact of invasive species on biodiversity conservation. Although many statistical methods have emerged in recent years in order to model the evolution of the spatio-temporal distribution of invasive species, the notion of extent of occurrence, formally defined by the International Union for the Conservation of Nature, has not been properly handled. In this work, a novel and flexible reconstruction of the extent of occurrence from occurrence data will be established from nonparametric support estimation theory. Mathematically, given a random sample of points from some unknown distribution, we establish a new data-driven method for estimating its probability support S in general dimension. Under the mild geometric assumption that S is $$r-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>r</mml:mi> <mml:mo>-</mml:mo> </mml:mrow> </mml:math> convex, the smallest $$r-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>r</mml:mi> <mml:mo>-</mml:mo> </mml:mrow> </mml:math> convex set which contains the sample points is the natural estimator. A stochastic algorithm is proposed for determining an optimal estimate of r from the data under regularity conditions on the density function. The performance of this estimator is studied by reconstructing the extent of occurrence of an assemblage of invasive plant species in the Azores archipelago.
Play as a Player in Design. Rethinking a ‘Curious Practice’ for Co-designing Public Space

‘Move the Neighbourhood’ is a research project experimenting with co-designing playable installations for a public green space in Copenhagen through a design-based collaboration between children and design-researchers. We employed a co-design process to investigate whether deconstructing the rules for both play and design could trigger new ways of thinking about playable spaces. The aim was to test a participatory process in order to identify what might be meaningful in relation to both play and designing for play, along a spectrum ranging from rules to collaborative improvisation. Our fieldwork cultivated what Haraway calls ‘response-ability’ in a ‘curious practice’ that explores the unanticipated in the collaboration between children and designers. The metaphor of a ‘jelly cake’ from play-research was used to illustrate the messiness of play and to frame the discussion on collaborative design. We see play as a serious co-player that evokes collective worlds through productive, messy fields of action, and enables actors to engage in the co-design of playable public space. In this article, we investigate how play can create agency, spark imagination and open up practices in both artistic and academic processes. Drawing on Barad’s concept of ‘intra-action’, we investigate design/play as a dynamic engine for exploring collaborative design practices as a dialogue between art, play and co-design.
fGOT: Graph Distances Based on Filters and Optimal Transport

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph distance. It is an optimal transport based distance which drives graph comparison through the probability distribution of filtered graph signals. This creates a highly flexible distance, capable of prioritising different spectral information in observed graphs, offering a wide range of choices for a comparison metric. We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem. We then propose a new approximate cost function that circumvents many computational difficulties inherent to graph comparison and permits the exploitation of fast algorithms such as mirror gradient descent, without grossly sacrificing the performance. We finally propose a novel algorithm derived from a stochastic version of mirror gradient descent, which accommodates the non-convexity of the alignment problem, offering a good trade-off between performance accuracy and speed. The experiments on graph alignment and classification show that the flexibility gained through filter graph distances can have a significant impact on performance, while the difference in speed offered by the approximation cost makes the framework applicable in practical settings.