Reviewerly/exHarmony huggingface dataset:
Aspects of Studying Self-Adjustment of Mental States of the Employees of Internal Affairs Agencies of Ukraine within Professional Activity

The article is focused on the research of self-regulation of mental states that arise in the employees of law enforcement agencies of Ukraine during the performance of professional activity in the conditions of extreme situations. The relevance of the research is currently increasing due to the need to increase resilience and overcome difficult life situations that often occur in the modern world.&#x0D; It has been determined that the professional activity of the employees of law enforcement agencies of Ukraine has its features, which require not only the professional competence of a specialist, but also certain personal qualities, with adaptation to extreme situations.&#x0D; The concepts of mental self-regulation, self-regulation and general ability to self-regulation have been analyzed. The basic levels of self-regulation related to professional activity (psycho-physiological, socio-psychological, psychological, spiritual) and corresponding self-regulatory abilities (emotional and psycho-physiological states, activity regulation, personal self-regulation, higher self-regulation ability) have been highlighted. They often occur while law enforcement officers perform their professional duties and lead to the emergence of negative mental states.&#x0D; The concept and main characteristics of an extreme situation have been characterized; self-regulation regulatory systems have been listed, which at the same time act as levels of self-regulation that ensures the effectiveness of professional activities of police officers in extreme situations.&#x0D; The author has studied methods of psychological influence (autogenic training and its variants, naive methods of self-regulation, the simplest methods of self-regulation, ideomotor training and neuro-linguistic programming), which are necessary for solving the set task.&#x0D; The author of the article describes how to possess various techniques of mental self-regulation, which allow to optimize the mental states and behavior of police officers in extreme situations.&#x0D; The conducted analysis of the aspect of studying self-regulation of mental states of police officers in professional activities to extreme situations demonstrates the possession of techniques of mental self-regulation and different levels and abilities of self-regulation, which allow to optimize the mental state and behavior of police officers.
Machine’s Statistical Parsing and Human’s Cognitive Preference for Garden Path Sentences

We focus in this article on the comparison between machine statistical parsing and human cognitive preference when dealing with the semantic circuit of garden path sentences. Stanford parser and 126 Chinese college students are introduced to discuss the parsing of garden path sentences. Machine has the ability to successfully parse sentences based on statistical information. If the statistical result is coincident with answer, an excellent parsing result comes into being. Otherwise, ambiguous effect and processing breakdown may occur. Human beings have their cognitive preference. When they are required to parse garden path sentences, some are confused by ambiguity just as machine does. However, some people, mostly belonging to high span readers with more linguistic experience, have special ability to disambiguate the sentences after backtracking and reanalyzing. We draw a conclusion that human beings sometimes parse some types of sentences better than machine does.
Model based clustering using finite mixtures of multivariate geometric skew normal distribution

The non-normal distributions for finite mixture model techniques to clustering have been well developed and much used. Particularly, in case of finite mixture models, the component distributions are skewed for multivariate data. It is shown that clustering approach to finite mixture models analyzes the data for asymmetric behavior and heavy tails. In this paper, clustering using multivariate geometric skew normal mixture models has been discussed. The Expectation Maximization (EM) is used to compute maximum likelihood estimates for finite mixture of multivariate geometric skew normal mixture models. Bayesian Information Criterion and Akaike Information Criterion are used for model selection. Eigen value decomposition of covariance matrix are considered and compared to each other. This clustering approach is illustrated with the help of simulated and real life datasets where comparisons are drawn with other mixture models.
The Second International Conference on the global ecological problems within the Fifth International Forum “The Book. Culture. Education. Innovations” – “Crimea–2019”

Traditionally (for 20 years in a row) the issues of ecological education have been being discussed at the forum. As of present time, the section “Ecological information and ecological culture” held since 2018 within the framework of the International Conference on the global ecological problems, makes an important and highly demanded professional site for librarians’ exchanging their experience in the area. The conference program comprises papers, round tables, master classes. In 2019, the focus has been made on the innovative instruments of ecological education. The review of events and individual papers enables to get the idea of the libraries’ activities, to absorb new ideas, to find partners and like-minded associates. In 2019, for the first time, the special program for 12–17 year-olds was organized, namely “The school site: What does quality living mean? (on the UN sustainable development goals)”. The leaning program and games were offered to the winners of and participants in the essay and research contest, as well as for anyone interested. The Crimean schoolchildren took part in the event. The review also comprises references and links to multiple sources of the detailed and complete information with the conference subject scope.
A Collaborative Filtering Methods Based on Expected Utility

Abstract In recent years, there has been a variety of improved collaborative filtering methods, which promote accuracy of recommendation by analysing too much user or item information like individual characteristics, application scenarios and so on. This increases the time complexity of the algorithm and leads to low efficiency of recommendation. Therefore, how to improve the quality of recommendation through analysing a small amount of effective information has attracted more attention in the research of collaborative filtering methods. This paper proposes a collaborative filtering method based on expected utility from the perspective of the economic significance of user rating. Firstly, the method establishes the corresponding similarity criterion and calculates the similarity between users or items by scoring expectation. Then, the nearest neighbour set is calculated and the final prediction is got. Finally, our method is validated by the experimental results using movielens-1m data set. The results show that the collaborative filtering method proposed in this paper is simpler and more feasible compared with the traditional IBCF, KNN and other mainstream methods, and can improve the efficiency and quality effectively of recommendation only using less information. This work has significance both in theoretical research and practical application value.
Classification using proximity catch digraphs

We employ random geometric digraphs to construct semi-parametric classifiers. These data-random digraphs belong to parameterized random digraph families called proximity catch digraphs (PCDs). A related geometric digraph family, class cover catch digraph (CCCD), has been used to solve the class cover problem by using its approximate minimum dominating set and showed relatively good performance in the classification of imbalanced data sets. Although CCCDs have a convenient construction in $${\mathbb {R}}^d$$, finding their minimum dominating sets is NP-hard and their probabilistic behaviour is not mathematically tractable except for $$d=1$$. On the other hand, a particular family of PCDs, called proportional-edge PCDs (PE-PCDs), has mathematically tractable minimum dominating sets in $${\mathbb {R}}^d$$; however their construction in higher dimensions may be computationally demanding. More specifically, we show that the classifiers based on PE-PCDs are prototype-based classifiers such that the exact minimum number of prototypes (equivalent to minimum dominating sets) is found in polynomial time on the number of observations. We construct two types of classifiers based on PE-PCDs. One is a family of hybrid classifiers that depends on the location of the points of the training data set, and another type is a family of classifiers solely based on class covers. We assess the classification performance of our PE-PCD based classifiers by extensive Monte Carlo simulations, and compare them with that of other commonly used classifiers. We also show that, similar to CCCD classifiers, our classifiers tend to be robust to the class imbalance in classification as well.
Methodology for Model-Based Fuzzy Kalman Filter Design via Singular Spectral Analysis of Experimental Data

This chapter presents a methodology for designing of fuzzy Kalman filter (FKF) via spectral decomposition of the experimental data. The adopted methodology consists in the parametric estimation of local state-space linear submodels of a fuzzy model of the dynamic system, by means of a fuzzy algorithm based on least squares, as well as in the estimation of FKF gains from the fuzzy model, using the parallel and distributed compensation (PDC) method. The partitioning of experimental data is performed by the fuzzy C-Means (FCM) clustering algorithm, for the definition of the rule base as well as the nonlinear FKF characteristic. Considering the PDC method, the Kalman gains in the consequent of each FKF rule are updated as a function of the unobservable components resulting from the spectral decomposition of noisy experimental data. Computational and experimental results illustrate the good performance of the methodology presented when compared to relevant approaches from the literature.
TagNet: Tag Out the Value Sequence of SQL Statement

In order to assist person who doesn't know how to write SQL to access the data in a relation database, using a deep neural architecture to translate the natural language to SQL has recently been extensively studied. Previous work suffers from the complexity of where clause, since the number of conditions is totally random and predicting the value of condition is a sequence-to-sequence problem. We follow the slot filling idea, and introduce a model called TagNet. First of all, we innovatively propose a task attention mechanism. It takes the relativity of tasks into consideration for attention mechanism. Secondly, we use type embedding of each token of question and each column to enhance the representation for value prediction. Thirdly, in the task of predicting WHERE VALUE, we propose a tag decoder. It output a sequence of equal length compared with input. It consists of two tokens:<;QUE>, <;VAL>, indicating the corresponding token of input is whether or not a value token. We evaluate out model on WikiSQL, and compared to our baseline-SQLNet, we gain an absolute 7.6% increase on logic form accuracy and 6.3% increase on execution accuracy.
The Differentiated Didactic Approach to Teaching the Arts (D.D.A.T.A.) in Special Education and Positive Psychology

The Differentiated Didactic Approach to Teaching the Arts (D.D.A.T.A) is a modern teaching approach in the field of Special Education.Its main goal is to teach in a differentiated manner Music and Dance to artistic ensembles such as an orchestra or dance group comprising mentally disabled members.It has been implemented and taken shape successfully within the framework of the clinical work carried out by Dr. Ioannis Makris on a sample of 100 individuals with mental disabilities during the period Oct 2012-Jan 2020.The main reason the D.D.A.T.A. model has been so successful does not rely solely on the differentiating teaching itself.It also hinges on being able to create the appropriate positive psychology for mentally disabled individuals with a view to empowering them to attend the training program suitable for them.The following were observed during the implementation of D.D.A.T.A.: the mental and physical health of the mentally disabled participants improved and was reinforced; their various strengths were further developed and honed; their ability to forge and/or sustain interpersonal relationships improved; and the personal goals they had set were accomplished.All of the areas in which improvement was observed constitute key goals of Positive Psychology.Thus, the goal of the present study is to examine how Positive Psychology relates to the D.D.A.T.A. model which is addressed to people with mental disabilities.The group under study is the Ichochroma (Timbre) Orchestra which was established by Dr. Ioannis Makris.Within the framework of inclusive didactics, the orchestra has successfully and harmoniously collaborated with both professional and amateur choirs at concerts and festivals.
ОСНОВНЫЕ ФУНКЦИИ УЧИТЕЛЯ-БАКАЛАВРА СОВРЕМЕННОЙ ШКОЛЫ

The high professionalism of the teacher invariably remains the decisive criterion for the fulfillment of his social functions. Teacher didactic training is a basic professional quality. The question of the effectiveness of the organization of the process of vocational guidance in the light of the problems under consideration can be successfully resolved on the basis of determining the basic pedagogical abilities and skills that graduates must be formed to successfully overcome the difficulties encountered in the educational process. Before describing the basic abilities in the activities of a young teacher, it is necessary to highlight the functions of his activities, which are discussed in the article, which highlights the methodology for studying the level of didactic knowledge, skills of graduates of a pedagogical university.
Improving ECDLP Computation in Characteristic 2

Pollard rho and its parallelized variants are at present known as the best generic algorithms for computing discrete logarithms in groups of elliptic curves over finite fields. The $$r+h$$-mixed walk, one of the variant parallelized rho method in characteristic 2, is expected to have r times point addition operations and h times point halving operations. We observe that by reducing the randomness but increasing the ratio of h/r, the overall efficiency for parallelized rho method can be improved. Hence, we try to find the best ratio to get the best overall efficiency for parallelized rho method. And then, we provide an optimal configuration with the best overall efficiency for the parallelized rho method. Our experiments show that the optimal configuration can improve the overall efficiency of ECC2-79 by about $$36\%$$. Further, we give algorithms to improve the efficiency of basic operations in $$\mathbb {F}_{2^{131}}$$ and estimate that the optimal configuration can improve the overall efficiency of ECC2-131 by about $$39\%$$.
A Secure and Credible Supply Chain System Based on Blockchain

In traditional supply chain systems, a centralized server is utilized to store information and a series of IoT devices are applied to manage and upload information. However, the server may be attacked easily and the IoT devices may forge the information, which result in a single point of failure, incorrect information problem and security challenges. To solve these problems, we design a distributed, secure and credible system by exploiting blockchain technology and game theory. The information in our system is consistent after consensus process, the stored information can’t be forged and the devices are motivated to behave honestly. The performance analysis demonstrates that our proposed system provides satisfied security and the numerical results illustrate that our award and punishment scheme can offer remarkable incentives for IoT devices to upload correct messages while keeping the system credible.
Characterizing Architectural Drifts of Adaptive Systems

An adaptive system (AS) evaluates its own behavior and changes it when the evaluation indicates that the system is not accomplishing what it is intended to do, or when better functionality or performance is possible. MAPE-K is a reference model that prescribes the adaptation mechanism of ASs by means of high-level abstractions such as Monitors, Analyzers, Planners and Executors and the relationships among them. Since the abstractions and the relationships provided by MAPE-K are generic, other reference models were proposed focusing on providing lower level abstractions to support software engineers in a more suitable way. However, after the analysis of seven representative ASs, we realized the abstractions prescribed by the existing reference models are not properly implemented, thus leading to architectural drifts. Therefore, in this paper we characterized three of these drifts by describing them with a template and showing practical examples. The three architectural drifts of ASs are Scattered Reference Inputs, Mixed Executors and Effectors, and Obscure Alternatives. We expect that by identifying and characterizing these drifts, we can help software architects improve their design and, as a consequence, increase the reliability of this type of systems.
Household waste management in Sukoharjo Village, Ngaglik District, Sleman Regency, Yogyakarta Special Region

Abstract Waste is an item that is considered to be unused and disposed of by the previous owner / user, but actually from the dumped part of the waste it can still be used if managed with the right procedure. The problem faced today is handling waste without going through the right management process. The community disposed of all the waste produced through the officers, buried in the garden or disposed of it wildly. This is found in Sleman Regency in general and in Sukoharjo Village specifically. This study aims to find out the characteristics of the waste management community in Sukoharjo Village; explain waste management in Sukoharjo Village; and examine the differences in waste management by the community in Sukoharjo Indah Housing with Banjarsari Hamlet . The method used in this study is a survey method. Survey is an investigation by using questionnaires for collecting data. Data collection was carried out by observation and interview of 76 respondents. The results, waste management model differences in the two regions due to the differences in household characteristics, such as income level and the land ownership to burn trash. On the other hand, the trash also requires management to avoid direct way disposed to landfill, by making use reduction, reuse, and recycling. However, waste management in both areas is reach the phase of reduction. This is due to the lack of socialization of waste management in the two regions, especially in Banjarsari Hamlet RT 06 surveyed had never held socialization.
Improved Integral Attack on Generalized Feistel Cipher

Division property is a generalized integral property proposed by Todo in Eurocrypt 2015. Utilizing automated tools such as SAT and MILP, the complexity to search for integral distinguisher by division property was greatly reduced. Based on division property and automated tools, Derbez et al. obtained a 10-round integral distinguisher of RECTANGLE by considering the linear transformation of the input and output state bits of the cipher, which is one round longer than known integral distinguishers. In this paper, we further consider improved integral attack on block ciphers with Generalized Feistel Structure (GFS cipher) by considering the linear transformation of the S-boxes. Taking the 16-branch GFS cipher with 4-bit S-boxes as an example, using this improved method, we can increase the round of integral distinguishers by one round for many S-boxes. The result implies that ability to resist this improved integral attack should also be considered when designing corresponding GFS ciphers.
A Method for Pedestrian Intrusion Detection of Railway Perimeter Based on HOG and SVM

In order to ensure the safety of railway operation, it has become urgent to strengthen the detection and protection of railway perimeter safety. In this paper, a method for pedestrian intrusion detection of railway perimeter based on histograms of oriented gradients (HOG) plus support vector machine (SVM) is proposed. By establishing the perimeter intrusion image sample set of the railway scene, Gaussian filtering is performed on the sample image to obtain the training set of positive and negative samples, and then, the HOG features are extracted for training. Finally, the field image is used to test this algorithm. The results show that the proposed algorithm is better than other algorithms, such as the inter-frame difference method and the mixed Gaussian background modeling method in the detection of pedestrian intrusion detection of railway perimeter. This paper describes in detail the above process and puts forward the deficiencies for further research in the future.
Semantic Equivalent Relation Phrase Extraction for RDF Predicates

Relation phrase mapping can fill the gap between the relation phrase and the RDF predicates. It can be widely used in question/answer system to help the general users ask questions in natural language over an RDF knowledge base. Relation phrase mapping is one of the most important steps to mine the semantic meaning of natural language questions. Instance extraction has been well studied by some literal work whereas the semantic meaning for relation phrase has the same importance. The gAnswer system has conducted a paraphrase dictionary to fill the gap between the relation phrase and the RDF predicates. However, the precision of the paraphrase dictionary is unsatisfactory for natural language question answer. In this paper, we adopt the structure of gAnswer and present a method to build a more accurate paraphrase dictionary using vector space model. The result confirms that our method can generate a large number of relation phrase and improve the precision at the same time.
Online course: Introduction to accounts

This online course looks at accounts in their simplest form and aims to demystify the language of accounts.
Iterative Differential Evolution with Real Parameter Encoding

Evolutionary algorithms are a sub-discipline of artificial intelligence to solve various real-world problems. These algorithms are based on the Darwinian principle of evolution and so is the name evolutionary algorithm. Differential Evolution (DE) algorithm is a kind of evolutionary algorithms which are used for optimizing a problem mostly for real-valued functions. It uses random solutions and creates new solutions from the previous or existing solutions. This population based algorithm applies three operators namely selection, crossover and, mutation. In this work, a new strategy has been developed to improve the performance of the basic DE algorithm. Also, the resultant performance is compared to other optimization algorithms which show that modified DE is performing better than other existing algorithms.