Reviewerly/exHarmony huggingface dataset:
A Logical Neural Network Structure With More Direct Mapping From Logical Relations

Logical relations widely exist in human activities. Human use them for making judgement and decision according to various conditions, which are embodied in the form of \emph{if-then} rules. As an important kind of cognitive intelligence, it is prerequisite of representing and storing logical relations rightly into computer systems so as to make automatic judgement and decision, especially for high-risk domains like medical diagnosis. However, current numeric ANN (Artificial Neural Network) models are good at perceptual intelligence such as image recognition while they are not good at cognitive intelligence such as logical representation, blocking the further application of ANN. To solve it, researchers have tried to design logical ANN models to represent and store logical relations. Although there are some advances in this research area, recent works still have disadvantages because the structures of these logical ANN models still don't map more directly with logical relations which will cause the corresponding logical relations cannot be read out from their network structures. Therefore, in order to represent logical relations more clearly by the neural network structure and to read out logical relations from it, this paper proposes a novel logical ANN model by designing the new logical neurons and links in demand of logical representation. Compared with the recent works on logical ANN models, this logical ANN model has more clear corresponding with logical relations using the more direct mapping method herein, thus logical relations can be read out following the connection patterns of the network structure. Additionally, less neurons are used.
Exploring The Robustness Of Digital Watermarking Algorithms Based On Transform Function And Machine Learning

The robustness of digital watermarking algorithms is a critical factor for analysing the security of digital multimedia. The increasing utility of digital multimedia in every filed area of business needed copyright protection. Copyright protection is the intergluteal property of digital assets. The strength of robustness used various algorithms and methods in current research trends of digital data security. This paper explores the robustness of digital image watermarking using transform function and machine learning. The machine learning-based digital watermarking techniques uplift the strength of security. The machine learning algorithm's primary issue is the transformation and mapping of digital image data for the conversion of digital image data using the process of transform function. The utility of the transform function is very high in the area of image processing. The transform function is directly applied in image compression, pattern recognition, and many more. In digital watermarking, the transform function provides security and compression of data—the combination of transform function and machine learning born a new dimension of digital image watermarking. The analysis of watermarking algorithms used MATLAB software and reputed image data based on empirical parameters for performance.
한국어 학습자를 위한 생활문화교육 세부항목 연구

It is necessary that cultural education in integrated textbooks is implemented for Korean language learners who need to acquire the basic cultural background for adapting to Korean life, as well as functional education of language. The cultural factors are extracted, analyzed, and compared from selected integrated textbooks which are utilized as the main channel of actual cultural education, and the most frequent living culture items are intensively examined and compared with the cultural domain of Internationally Accepted Standard Model . In addition, as conducting a survey and analyzing its results on the preference and demand for cultural items focusing on Korean language learners who are studying at the international exchange centers of three universities, the complement factors are presented which are expected to be reflected in the 'Standard Model' and the integrated Korean textbooks of universities. Also, this study aims to suggest the detailed items of living culture, as designing them to be proper to the needs of international students.
THE RELATIONSHIP BETWEEN SELF-REGULATED LEARNING AND PARENT’S ATTENTION WITH MATHEMATICS LEARNING OUTCOMES IN STUDENTS CLASS XI

Many factors determine student learning outcomes. Self-regulated learning and parent’s attention are some of the factors suspected that relate to student learning outcomes. This study aims to determine the presence or absence of a positive relationship between and significant relationship among the self-regulated learning and parent’s attention with learning achievement mathematics students class Vocational High School (SMK) 1 Bantul in the first semester of the academic year 2018/2019. The populations of this study were all students of class XI consisting of 6 classes with 188 students. Samples were taken using a random sampling technique. The data collection techniques used the non-test technique by a questionnaire and test. The test research instruments were: validity tests, different power tests, reliability tests, and independence tests. The writer uses product-moment correlation analysis and multiple linear regression analysis to analyze the data. The results of the study showed that there was a positive and significant correlation between: self-regulated learning and parent’s attention to the mathematics learning outcomes with R 2 = 0.6869, coefficient determine 0,4718 and F count = 12.0598  F table = 3.3541, with RC = , RC  =  and EC = , EC  = .
Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda Networks

Models based on attention mechanisms have shown unprecedented speech recognition performance. However, they are computationally expensive and unnecessarily complex for keyword spotting, a task targeted to small-footprint devices. This work explores the application of Lambda networks, an alternative framework for capturing long-range interactions without attention, for the keyword spotting task. We propose a novel \textit{ResNet}-based model by swapping the residual blocks by temporal Lambda layers. Furthermore, the proposed architecture is built upon uni-dimensional temporal convolutions that further reduce its complexity. The presented model does not only reach state-of-the-art accuracies on the Google Speech Commands dataset, but it is 85% and 65% lighter than its Transformer-based (KWT) and convolutional (Res15) counterparts while being up to 100 times faster. To the best of our knowledge, this is the first attempt to explore the Lambda framework within the speech domain and therefore, we unravel further research of new interfaces based on this architecture.
A Fuzzy Post-project Evaluation Approach for Security Video Surveillance System

Video surveillance is an essential component of the public security system. The security video surveillance system is a powerful means to prevent violence and crimes, and it is closely coupled with the construction of smart cities. A post-project evaluation is an evaluation of a project's actions and outcomes after its completion. Post-project evaluation can scientifically and objectively evaluate the construction effectiveness of video surveillance system at a certain stage. Utilizing post-project evaluation can find out the causes of success or failure to make recommendations for the construction of a security video surveillance system in the next stage. Therefore, we propose a fuzzy post-project evaluation approach for the security video surveillance system in a real-world community. The fuzzy theory and fuzzy multi-level evaluation method are applied. The evaluation result demonstrates that the proposed approach is practically applicable to real-world security video surveillance systems.
Machine Learning on Graphs, from Graph Topology to Applocations

Contemporary data analytics applications on graphs frequently operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, somewhat than serving as prior knowledge which aids the problematic solution. Part III of this monograph starts by a comprehensive account of ways to study the pertinent graph topology, reaching from the humblest case where the physics of the problem previously suggest a likely graph structure, through to general cases where the graph structure is to be learned from the data experiential on a graph.
Survey on Text Transformation using Bi-LSTM in Natural Language Processing with Text Data

Communication using text can be understood by human beings as language plays a vital role in their life. Computers need to understand such human readable languages in order to explore documents in a wider manner. For such cases, machine learning algorithms as well as natural language processing algorithms can be used. Computers understand the human readable language and convert them as machine readable form. Replication of data stands as a most challenging problem in document exploration as the document may contain many repetitive words. The system makes use of classifier named LSTM to remove the words appearing frequently in the entire document. Firstly the text is transformed using NLP techniques followed by feature extraction and finally Bi-LSTM is used to classify the text data. LSTM mainly focus on reduction of repetitive words from the entire document and also maintains the integrity of the document. Bi-Lstm is an effective method to connect two different independent RNN together that helps to follow the sequence of statement front and back path every now and then.Multi directional ways of running the code will help in future prediction by saving the sentence for future purpose also.When the independent hidden states are joined it will be preserved for future and also past search. Although the word count is reduced, content of the word document is maintained in the LSTM method which stands as a major advantage. The experimental results showcase the efficacy of the system.
The Effect of Innovation Spirit on the Performance of Service Providers

The study targeted the study of effect of applying Emirati methods with its pivots, Information Technology and National Innovation Strategy for creativity when providing service to government tourism agencies from the point of view of administrators on the quality of services after applying those methods from the clients' point of view, The Study followed the descriptive analytical approach, and the research was conducted by a group of tourism agencies in the Emirates, and those agencies were randomly chosen through the ideal bag, and the study sample consisted of the administrators of the tourist agencies and their number reached 100 respondents who were chosen by the restricted sample according to the nature of the respondents and the other sample of Clients also numbered 100 equally with the number of administrators and to collect study data two questionnaires were prepared, the first for administrators and the second directed to clients With tourist agencies, it is oh What the study found from the results of the existence of the effect of the Information Technology application when providing the service with government tourism agencies from the point of view of administrators on the quality of services after the application of those methods from the customers' point of view and the existence of the impact of the application of the National Innovation Strategy when providing the service with government tourism agencies from the point of view of administrators on Quality of services after applying these methods from the customer's point of view.
Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition

Human gesture recognition has drawn much attention in the area of computer vision. However, the performance of gesture recognition is always influenced by some gesture-irrelevant factors like the background and the clothes of performers. Therefore, focusing on the regions of hand/arm is important to the gesture recognition. Meanwhile, a more adaptive architecture-searched network structure can also perform better than the block-fixed ones like Resnet since it increases the diversity of features in different stages of the network better. In this paper, we propose a regional attention with architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace the fixed Inception modules with the automatically rebuilt structure through the network via Neural Architecture Search (NAS), owing to the different shape and representation ability of features in the early, middle, and late stage of the network. It enables the network to capture different levels of feature representations at different layers more adaptively. Meanwhile, we also design a stackable regional attention module called dynamic-static Attention (DSA), which derives a Gaussian guidance heatmap and dynamic motion map to highlight the hand/arm regions and the motion information in the spatial and temporal domains, respectively. Extensive experiments on two recent large-scale RGB-D gesture datasets validate the effectiveness of the proposed method and show it outperforms state-of-the-art methods. The codes of our method are available at: https://github.com/zhoubenjia/RAAR3DNet.
Training in the sphere of computational informatics as a condition of developing the informational-computational competence of a would-be informatics teacher

The huge computational resources of the modern computers are used mainly to solve the problems arising in various applied spheres during mathematical modeling of real objects. These issues are researched in computational informatics. A would-be informatics teacher must have knowledge of it, master the methods of numerical modeling, and be able to teach it to schoolchildren, broadening their knowledge about the possible spheres of using a computer. The informational-computational competence, manifested as the ability and preparedness of a would-be informatics teacher to solve computational problems on a computer and to teach schoolchildren to do it, is one of the most important components of their professional competence. To identify the structure and content of the informational-computational competence of a would-be informatics teacher and to develop the methodology of their training in the sphere of computational informatics ensuring its development. Competence and context approaches to education are used, as well as the concept of funding pedagogical education, as the most effective approaches to developing the informational-computational competence of a would-be informatics teacher. The research result is the structural-logical model of the development the informational-computational competence of a would-be informatics teacher during their training in the sphere of computational informatics and the teaching methodology developed on its basis. Implementation of the proposed methodology in teaching computational informatics can provide a would-be informatics teacher with the necessary knowledge and facilitate the development of the informational-computational competence.
The Fate of the Epistolary Heritage of the Veliky Ustyug Merchant Family Buldakovs of the Late 18th – the First Quarter of the 19th Century

The article presents the history of the epistolary heritage of the Buldakov merchant family. The object of the research is letters of M. M. Buldakov, a representative of the Veliky Ustyug merchants, the leading director of the Russian-American company. These documents contain valuable information on the history of Russian merchants in the last quarter of the 18th – first third of the 19th century and the activities of the Russian-American company. Similar to all sources of epistolary genre, their distinctive feature is that they were created without additional edits and censorship and thereby recorded historical reality at first hand, while retaining the style and language characteristic of the period. The main problem faced by historians is patchy location of M. M. Buldakov’s letters. Currently, only a part of them, stored in the Vologda State Historical-Architectural and Art Museum-Reserve, has been published and introduced into scientific use. The documents scattered across other archives are yet to be identified and examined. The purpose of this study is to determine the true volume of the Buldakovs' epistolary heritage, to understand the reasons for its fragmentation, and to trace the movement of documents from one owner to another. This is the first attempt to restore the path of epistolaries from the pre-revolutionary family archive to modern archives, which determines the scientific novelty of the work. The methodological basis of the research is principles of historicism, consistency, and objectivity. This makes it possible to consolidate the scattered information and facts testifying to the movement of M. M. Buldakov’s papers. The analytical and synthetic method of comparing data provides an opportunity to see the general picture of letters location, while concrete-historical method allows us to describe the facts in historical sequence. We managed to identify three large collections of letters: in the Russian State Archive of Ancient Acts, in the Archive of the Foreign Policy of the Russian Empire of the Historical and Documentary Department of the Ministry of Foreign Affairs, and in the Vologda State Historical, Architectural and Art Museum-Reserve. The results of this study provide a unified picture of the epistolaries location in different archives, which, in turn, provides an opportunity for historians and archivists to continue their work on identifying and introducing into scientific use of documents necessary for further study of the history of Russian merchants and the Russian-American company activities.
Sampling distributions, or why exploratory analyses are not enough

While Exploratory Data Analysis able to summarize variables and give us invaluable insights into our data, these summaries are not enough to make decisions. This is because the random aspect of data collection results in inherent randomness in our statistics. In this chapter, we look at the concepts of statistics as random variables and quantifying that variability. We focus on the idea of the sampling distribution, the probability distribution for a sample statistic. Particularly important is the Central Limit Theorem, a statistical theorem that results in the sampling distribution for our sample mean and sample proportion being a Normal distribution.
Ethics and AI Innovation

The Fourth Industrial Revolution has astonishing potential to solve many of humanity’s problems, but it has also brought about an array of new threats. The challenge is to find a way to mitigate the negatives of the Revolution without impairing the extraordinary potential of AI to accelerate all areas of human development. AI ethics offers a possible basis for doing so by providing a set of aspirational ideals as to the role of AI, rather than a minimum standard for compliance which is likely to become increasingly irrelevant. Throughout history, humans have adapted and adjusted to the technologies of the time and though the integration of AI into all human experience and decision-making will come to be seen as normal and taken for granted, there will still be a number of profound ethical choices that must be made. Implementing ethical AI will require a multi-modal and co-regulatory approach. There are a variety of existing approaches but some common principles have emerged. These provide a framework for action.&#x0D;
A logical framework with a graph meta-language

We conjecture that the relative unpopularity of logical frameworks among practitioners is partly due to their complex meta-languages, which often demand both programming skills and theoretical knowledge of the meta-language in question for them to be fruitfully used. We present ongoing work on a logical framework with a meta-language based on graphs. A simpler meta-language leads to a shallower embedding of the object language, but hopefully leads to easier implementation and usage. A graph-based logical framework also opens up interesting possibilities in time and space performance by using heavily optimized graph databases as backends and by proof compression algorithms geared towards graphs. Deductive systems can be specified using simple domain-specific languages built on top of the graph database's query language. There is support for interactive (through a web-based interface) and semiautomatic (following user-defined tactics specified by a domain-specific language) proof modes. We have so far implemented nine systems for propositional logic, including Fitch-style and backward-directed Natural Deduction systems for intuitionistic and classical logic (with the classical systems reusing the rules of the intuitionistic ones), and a Hilbert-style system for the K modal logic.
PGST: a Polyglot Gender Style Transfer method

Recent developments in Text Style Transfer have led this field to be more highlighted than ever. The task of transferring an input's style to another is accompanied by plenty of challenges (e.g., fluency and content preservation) that need to be taken care of. In this research, we introduce PGST, a novel polyglot text style transfer approach in the gender domain, composed of different constitutive elements. In contrast to prior studies, it is feasible to apply a style transfer method in multiple languages by fulfilling our method's predefined elements. We have proceeded with a pre-trained word embedding for token replacement purposes, a character-based token classifier for gender exchange purposes, and a beam search algorithm for extracting the most fluent combination. Since different approaches are introduced in our research, we determine a trade-off value for evaluating different models' success in faking our gender identification model with transferred text. To demonstrate our method's multilingual applicability, we applied our method on both English and Persian corpora and ended up defeating our proposed gender identification model by 45.6% and 39.2%, respectively. While this research's focus is not limited to a specific language, our obtained evaluation results are highly competitive in an analogy among English state of the art methods.
Creating High Temperature Ferroelectrics by Using Ternary Components with Limited Solubility

High temperature ferroelectrics are needed for actuator applications ranging from drilling on the surface of Venus to fuel modulation in jet engines. Machine learning in previous work has dramatically reduced search space and produced new candidates for experimentation. It is possible to create high temperature piezoelectrics by changing the end member of ternary systems. The starting point was near the morphotropic phas boundary between BiScO3(BS) and PbTiO3 (PT) (38%/62%). The third end member was suggested by machine learning to create a high Tc with limited solubility; BiCo0.88Fe0.12O3 (BCF).