Reviewerly/exHarmony huggingface dataset:
Multi-Minimax: A New AI Paradigm for Simultaneously-Played Multi-player Games

The best reported methods for turn-based multi-player game playing AI algorithms include Max$$^n$$, Paranoid and Best Reply Search. All of these methods make decisions by modelling the game by assuming that there is some predetermined play ordering for the players. While this is meaningful for ordered turn-based games, there are a host of scenarios where the players need not be constrained to make their moves in such a manner. Little research has been done for turn-based games of this kind such as financial games that involve buying and selling on the stock market in no specific order (For games with shared resources (e.g., financial games) or simultaneously-played move games, one could alternatively consider multi-player AI algorithms to be those that treat the game with each opponent as a separate game. This is currently open.). In this paper, we shall present and test a new algorithm for multi-player game playing on a game which does not require a fixed sequential play ordering. The game that we have used to demonstrate this is the multi-player Snake Game, also referred to as a “Light Bike” game which is a turn-based game requiring simultaneous moves at every turn. Our newly-proposed scheme, the Multi-Minimax, along with the Added Pruning method, performs better when compared to the similar AI strategies examined in this paper. Additionally, among all the algorithms that did not use the proposed pruning, Multi-Minimax performs the best. We can conclude that, at the least, under certain conditions in the area of multi-player game playing AI, similar results can be replicated with these newly proposed Added Pruning and Multi-Minimax methods. As far as we know, the results presented here are of a pioneering sort, and we are unaware of any comparable results.
Automated Intelligent Diagnostic Procedure for Chronic Leukemia

Abstract Due to growing statistics and the important role of early diagnosis for chronic Leukemia, an automated intelligent diagnostic procedure for chronic Leukemia is needed. This paper presents an automated procedure for this diagnostic system which consisted of four main stages; namely Image Segmentation, Feature Extraction, Feature Selection and Classification. Colour Thresholding and Gradient Edge Detection were applied to segment the nucleus of white blood cells from the image. A total of 548 cells nuclei were extracted from 100 images and used to develop and validate the proposed procedure. Three feature selection algorithms and three classifiers were analyzed and evaluated in order to obtain a reliable and robust automated diagnostic procedure. Based on the results, the ReliefF algorithm was selected and applied for feature selection, the method yields the best accuracy of 98.1% for testing accuracy. Besides, the selected classifier was Multilayer Perceptron (MLP) network trained by Levenberg Marquardt (LM) algorithm.
Group’s psychological potential as a condition for the effective education of foreign students in Russian university

The paper discusses the results of an empirical study of the influence of the socio-psychological characteristics of academic student group upon the success of the educational activities of foreign students in Russian universities using the example of students studying in universities of the city of Kursk. The dependence between the nature of the leading attitudes towards educational and professional activities of foreign students at the university is shown on the level of development of the student academic group, which also acts as a subject of psychological assistance to foreign students who find themselves in new ethnocultural conditions.
On professional training of bachelor of economics to entrepreneurship in contemporary conditions

One of the most significant problems of modern pedagogy is Bachelor of Economics (B. Ec.) training to entrepreneurship. This article examines to what extent this issue is studied in the Russian and foreign scientific literature; identifies the key-problems existing in the system of modern professional education in the field of the B. Ec. training to entrepreneurship. The definition of “entrepreneurial activity” is analyzed from the pedagogical, sociological and economic perspectives. The basic university-based means, methods and forms of the B. Ec. training to entrepreneurship are studied; the necessity of their use in the pedagogical training is substantiated. The research concerns the issue of activating innovation entrepreneurial activity of B. Ec. students. The research results aimed at identifying the entrepreneurial activity of students are described in detail. Based on the data studied, a model of optimal business interaction of B. Ec. students and university is presented. The article also provides practical recommendations directed at increasing the entrepreneurial activity of students at university
Classification of provinces in Indonesia based on 2014 presidential election participation levels using fuzzy c-means

Abstract The presidential and vice presidential elections held every five years is a benchmark for democracy in Indonesia. However, the expected level of participation cannot be achieved. Election socialization as an effort to increase the level of participation in each province is different because the level of participation in each province is also varied. Therefore, there is a need of regional grouping based on the level of electoral participation in order to facilitate the electoral socialization process. This study uses fuzzy c-means with two attributes, namely the number of voters and the number of voting rights users. All provinces in Indonesia are classified into two clusters. If the provincial participation rate is below the national participation standard (69.58%) then the province is considered to have a low participation rate and vice versa. Therefore, the first cluster constitutes provinces with high electoral participation rate and the second cluster constitutes provinces with low electoral participation rate. The results of the classification are 9 provinces in first cluster and 24 provinces in the second cluster.
Subscription; Remedy for Cyber Debris!?

IoT devices that are left out on the Internet without administration have been at risk of cyberattacks. Unfortunately, the case of malware “Mirai” made this concern a reality. In this paper we discuss such devices as “Cyber Debris” and propose the solution of this problem. Consumer IoT devices have a particularly high risk of becoming Cyber Debris. It is because consumers do not have the enough ability to manage these devices. To solve this problem of consumer responsibility, we focus on the provision of IoT devices by subscription: IoT (Sub). We show how IoT(Sub) is effective against Cyber Debris compared to sold devices: IoT(Sale). The conditions for facilitating IoT (Sub) are analyzed. We examine UK policies from the perspective of deploying IoT (Sub). Finally conclusions and further research are proposed.
Remote sensing image retrieval method based on improved fast search and find of density peaks—bag of visual words model

Bag of visual words (BoVW) model has been widely used in content-based image retrieval for its good performance. The k-means clustering algorithm plays an important role in BoVW, but there are still some problems that need to be solved. Many methods are proposed to improve the clustering performance, such as fast search and find of density peaks (FSFDP), which has been successfully applied in image classification or image retrieval for its high efficiency but suffers from some limitations when it is applied to remote sensing image retrieval. A remote sensing image retrieval method based on an improved FSFDP-BoVW model is proposed. A feature selection method combining a chi-square test and a term frequency-inverse document frequency is proposed to select the features that can best represent image content and reduce the redundancy of remote sensing image features; a natural nearest neighbor algorithm is utilized to redefine the density of data points to reduce the interference of human subjectivity in FSFDP. Experimental results show that the proposed method can effectively reduce the time spent in constructing a codebook and improve the retrieval accuracy.
Top Tree Compression of Tries

We present a compressed representation of tries based on top tree compression [ICALP 2013] that works on a standard, comparison-based, pointer machine model of computation and supports efficient prefix search queries. Namely, we show how to preprocess a set of strings of total length n over an alphabet of size $$\sigma$$ into a compressed data structure of worst-case optimal size $$O(n/\log _\sigma n)$$ that given a pattern string P of length m determines if P is a prefix of one of the strings in time $$O(\min (m\log \sigma ,m + \log n))$$ . We show that this query time is in fact optimal regardless of the size of the data structure. Existing solutions either use $$\Omega (n)$$ space or rely on word RAM techniques, such as tabulation, hashing, address arithmetic, or word-level parallelism, and hence do not work on a pointer machine. Our result is the first solution on a pointer machine that achieves worst-case o(n) space. Along the way, we develop several interesting data structures that work on a pointer machine and are of independent interest. These include an optimal data structures for random access to a grammar-compressed string and an optimal data structure for a variant of the level ancestor problem.
NICT’s Machine Translation Systems for CCMT-2019 Translation Task

This paper describes the NICT’s neural machine translation systems for Chinese$$\leftrightarrow $$English directions in the CCMT-2019 shared news translation task. We used the provided parallel data augmented with a large quantity of back-translated monolingual data to train state-of-the-art NMT systems. We then employed techniques that have been proven to be most effective, such as fine-tuning, and model ensembling, to generate the primary submissions of Chinese$$\leftrightarrow $$English translation tasks.
New Educational Practice As a Modification of the Social Campaign «Children for Children»

The article draws attention to the fact that in the context of an increase in the pace of development of society, requirements for the younger generation, conditions should be created for the formation of children’s experience of cognitive and social activity. The features of the new educational practice, which appeared in the framework of the social campaign «Children for Children» in additional education at school, are revealed. The pedagogical results and eff ects of conducting classes by individual young children for peers in the system of additional education at school are noted. The importance of pedagogical support is indicated.
Convergence Analysis of an SLF-NMU Algorithm for Non-negative Latent Factor Analysis on a High-Dimensional and Sparse Matrix

Non-negative latent factor (NLF) models have been frequently applied to information extraction, pattern recognition, and community detection. An NLF model can well represent a high-dimensional and sparse (HiDS) matrix of non-negative data and efficiently acquire useful knowledge from it. A single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is highly efficient to build NLF model. However, its convergence ability on such a matrix is still unveiled in theory. This paper presents the convergence property of an SLF-NMU algorithm. We theoretically prove that it can guarantee its convergence to a Karush-Kuhn-Tucher (KKT) stationary point. Empirical studies on two HiDS matrices from practical applications indicate that an SLF-NMU algorithm make an NLF model to converge at a relatively steady state.
A New Cyclic Spatial Attention Module for Convolutional Neural Networks

The mechanism of attention mechanisms is ubiquitous in the biological world. In order to make the convolutional neural network improve the model recognition ability and robustness, this paper proposes a lightweight and efficient Cyclic Spatial Attention Module (CSAM) for convolutional neural networks. CSAM is dedicated to making neural networks more effective in screening useful features. It generates a attention map by spatial information filtering according to the output features of the current convolutional layer. Then the attention maps are multiplied to the input feature maps for adaptive feature refinement, and combined with the residual and batch normalization techniques to return to the current convolution layer. CSAM is lightweight and can be directly added to traditional convolutional networks for end-to-end training. We evaluate network performance on classical benchmark datasets cifar10 and GTSRB. Experiments show that this module can effectively improve the performance of convolutional neural networks. And CSAM has obvious advantages over the popular attention modules for convolutional neural networks in recent years.
An Automatic Base Expression Selection Algorithm Based on Local Blendshape Model

In order to give a virtual human rich and realistic facial expression in the film production process, a good blendshape model is needed. But selecting and capturing base expressions for blendshape model requires a lot of manual work, time and effort, and the model also lacks expressiveness. A method for automatically selecting a set of base expressions from a sequence of facial motions is proposed in this paper. In this method, the Procrustes analysis is used to estimate the difference between face meshes and determine the composition of the base expressions. And the base expressions are used to build a local blendshape model which can enhance expressiveness. The results of reconstructing facial expressions by the local blendshape model are shown in this paper. By this method, the base expressions can be automatically selected from the expression sequence, reducing the manual operation.
Reducing dimension of parametric space based on the approximation of components for interpolation of multidimensional signals

Abstract In this paper, we address the problem of parametric space dimension reduction in the interpolation of multidimensional signals task. We develop adaptive parameterized interpolation algorithms for multidimensional signals. We perform a dimension reduction of the parameter space to reduce the complexity of optimizing such algorithms. The dependences of the samples inside the signal sections and between the signal sections are taken into account in various ways to reduce the dimension. We consider the dependencies between the signal sections through the approximation algorithm for the sections. We take into account the sample dependencies inside sections due to an adaptive parameterized interpolation algorithm. As a result, we solve the optimization problem of an adaptive interpolator in the parameter space of lower dimension for each signal section separately. To study the effectiveness of adaptive interpolators, we perform computational experiments using real-world multidimensional signals. Experimental results showed that the proposed interpolator improves the efficiency of the compression method up to 10% compared with the prototype algorithm.
TecnoAgrico: a new approach for crop scale agricultural DSSs

In this work is described the approach used, in the frame of a Rural Development Programme project named TecnoAgrico, to develop the modelling core of a crop-scale multi purposes and multi species agricultural DSS. An innovative mixed System Dynamics/data science methodology and a modular architecture are proposed.
Implementing the project for building professionally significant qualities of bachelors of economics

The research subject is building professionally significant qualities of Bachelors of Economics. The aim of the work is to show the possibilities of the project’s management in building professionally significant qualities of Bachelors of Economics. The novelty of the research is conditioned by rapid changes at today’s labor market within digital economy, which makes HEIs to respond actively through training highly marketable professionals. The article contains a description of the project aimed to solve the problem. The article presents the matrix of professionally significant qualities. The matrix base is made up of qualities-relations, individual and personal qualities, special abilities and qualities, social and personal qualities. The contents of these clusters correlate with soft skills, accumulating the requirements of digital economy, which is manifested in the articulation of critical thinking, teamwork skills, client focus, etc. On implementating the project, the goal was achieved by following a particular system mechanism – a hidden curriculum, that is a curriculum superstructure. To implement the project there were used technologies of portfolio and individual trajectory of the student’s professional development as mechanisms of control and self-control. Diagnostic tests proved the effectiveness of the project. Diagnostics revealed a great dynamics in professional skills and socially and individually significant abilities and qualities. As post-project activities, it is planned to dessiminate the practice for implementing bachelor’s programmes in Management, Human Resource Management, Trading, State and Municipal Management.
Research on the Comment Emotion Based Program Satisfaction Model

With the continuous development of Internet, personalized demands for TV programs are getting higher and higher. The current program evaluation is short of users' emotional dimension, in this paper, we proposed users' evaluation information based the program satisfaction model. Our research collected the data of users' comments, combined psychology model and text sentiment analysis, and then used the analysis results to calculate satisfaction of the program. The results of experiment showed that the accuracy of the satisfaction curve fitting was 85.50%-96.08%, which confirms the effectiveness of the method we proposed.
Kernel-target Alignment Based Multiple Kernel One-class Support Vector Machine

One-class support vector machine is a hot research topic in the domain of machine learning. It is currently widely used to deal with one-class classification problems or classification problems of class imbalance data, and has good performance in many practical applications. A key problem of one-class support vector machine is the selection of its kernel function and parameters, which has a vital impact on the final performance of the classifier. At present, there is no unified method for how to select the appropriate kernel function and its parameters. In order to solve this problem, the multiple kernel method is introduced into the one-class support vector machine. i.e., a combined kernel is used to replace a single kernel in the one-class support vector machine, where the combined kernel is obtained by weighted summation of several basic kernels, and the kernel weight is calculated by the kernel-target alignment. The experimental results on UCI database show that this method can effectively save training time and solve the selection of kernel parameter and its parameters based on high classification performance.
System Environment and Context-Awareness

Referring to the previous chapter, whenever a group of entities (actor-roles) collectively realize a goal, we consider them to belong to a system. What has not been discussed in the mentioned chapter nevertheless is adaptability—we observe that in their behavior, humans are adapting every minute and every second to what is happening around (a human would often do this intuitively), and for this reason it makes sense considering this issue with regard to systems and especially enterprise systems (and EIS) which are human-driven. An adaptable system has the ability to adjust to new conditions [1]. An essential feature of adaptable systems is context-awareness [2]—this is adjusting the system behavior depending on the situation at hand (context state) [3]. As studied in [2], context-aware systems are all about adjusting “something” to the context state; however, what is adjusted differs: (1) Some context-aware systems optimize system-internal processes based on the context state at hand [4, 5], for example, regulating the electro-consumption of home appliances for the sake of keeping the overall building consumption within some boundaries. (2) Other context-aware systems maximize the user-perceived effectiveness of delivered services, by providing different service variants depending on the situation of the user [6], for example, treating a distantly monitored patient in one way when his/her condition is normal and in another way in case of emergency. (3) Still other context-aware systems are about offering value sensitivity when the society demands so [7], for example, in the case of supporting judiciary processes, different levels of transparency are to be provided to different categories of stakeholders. We do not claim exhaustiveness with regard to those three context-awareness perspectives. At the same time, as studied in [2], those three perspectives “cover” a broad range of currently relevant applications, especially as it concerns real-life (business) processes. For this reason, we will elaborate and discuss those perspectives in the first section of the current chapter. Then in Sect. 3.2 we will conceptualize context-awareness (considering it in general). In Sect. 3.3, we will consider the operationalization of context-awareness—by means of context-aware applications. In Sect. 3.4, we will address the potentials of (statistical) data analysis [8] with regard to context-aware applications. Finally, in Sect. 3.5 we will briefly discuss the relevance of classification (and decision trees, in particular) as a prediction “instrument” [9] in the cases when sensing technology [10] is inapplicable.
Methods for the selection of parameters and structure of the neural network model

After the problem formulation in the form of functional optimization and choosing the type of basis functions, it is necessary to select an algorithm for finding the decomposition coefficients of the approximate neural network solution to the problem (1.1) according to the basis chosen and the method of the neural network restructuring. If it is necessary, the type of elements of the basis can also be fitted. The network structure justification implies the choice of the basis elements, the number of layers, and the number of elements in every layer. The selected algorithms should allow efficient parallel and distributed implementation. In this chapter, we create a complex of methods and algorithms for finding neural network dependence y = f(x, w) according to heterogeneous data. We suppose this data can be specified, using differential equations or other relationships. We focus on finding the structure of the function f in conjunction with searching the weight vector w. In the overwhelming majority of publications dedicated to the neural networks application in the field of mathematical modeling problem, only finding the weights is implemented without selecting a structure, leaving the most important question of choosing a structure for imperfect user intuition. Our experience has shown that the implementation of evolutionary algorithms for the network structure selection according to the problem being solved allows us to obtain a significantly more compact model without loss of modeling accuracy. Reducing the size of the model (the number of layers and the number of neurons, etc.) speeds up the calculations for the model and improves its predictive properties.