Reviewerly/exHarmony huggingface dataset:
Semantic Property Graph for Scalable Knowledge Graph Analytics

Graphs are a natural and fundamental representation of describing the activities, relationships, and evolution of various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two of the most used data models to encode information in a graph. Both models are similar in terms of using basic graph elements such as nodes and edges but differ in terms of modeling approach, expressibility, serialization, and target applications. RDF is a flexible data exchange model for expressing information about entities but it tends to a have high memory footprint and inefficient storage, which does not make it a natural choice to perform scalable graph analytics. In contrast, LPG has gained traction as a reliable model in performing scalable graph analytic tasks such as sub-graph matching, network alignment, and real-time knowledge graph query. It provides efficient storage, fast traversal, and flexibility to model various real-world domains. At the same time, the LPGs lack the support of a formal knowledge representation such as an ontology to provide automated knowledge inference. We propose Semantic Property Graph (SPG) as a logical projection of reified RDF into LPG model. SPG continues to use RDF ontology to define type hierarchy of the projected graph and validate it against a given ontology. We present a framework to convert reified RDF graphs into SPG using two different computing environments. We also present cloud-based graph migration capabilities using Amazon Web Services.
Promising Educational Technologies for Professional Training

Comprehensive changes in Russian education are aimed at meeting the requirements of the modern economy and society. At the same time, the main vector in this area is the coordination of the content and component composition of professional training with the demands of the labor market and with the urgent need to increase the availability of high-quality educational services. Public experience demonstrates that objective reality makes significant adjustments to the professional training and education. Therefore, teachers should acknowledge and reflect in their professional activities all the new requirements presented by society in a timely manner. In the context of professional education, intensive search is carried out for adequate forms and methods to improve the effectiveness of education. In this regard, the practical value of simulation methods and technologies, aimed at revealing the creative potential of a future specialist, their individual and professional properties and the ability to function successfully in the new economic conditions, is increasing. Educational technologies as mechanisms for the joint activity of the teacher and students are aimed at the development of certain skills and abilities, as well as personal and professional development. The teacher should correctly apply educational technologies and methods to provide quality training for competent professionals. The variety of these methods is proposed to encourage intrinsic motivation, a lively interest of students in learning and self-development. The competencies of a qualified specialist and the criteria that he/she must meet have become the basis for creating a generalized concept of a professional. Along with the individual properties of the student, the foundation of the concept is knowledge and skills in the areas provided by the general and special components of the Federal State Educational Standards. In the framework of professional education, the main place in the educational process should be occupied by active educational technologies and methods, focused on intensifying the cognitive activities of students, supporting creative initiative and abilities and development of intellectual needs.
RAM Encryption Mechanism without Hardware Support

The RAM encryption encrypts the data on memory to prevent data leakage from an adversary to eavesdrop the memory space of the target program. In this paper, we discuss the feasibility of software based RAM encryption and clarify that it is possible to be secure against so-called semi-honest adversaries under some additional and reasonable constraints. In addition, we tentatively embed our RAM encryption mechanism to SHA-256 hash function. The performance evaluation results are also reported in this paper.
The Design Of "Stratega": A General Strategy Games Framework

Stratega, a general strategy games framework, has been designed to foster research on computational intelligence for strategy games. In contrast to other strategy game frameworks, Stratega allows to create a wide variety of turn-based and real-time strategy games using a common API for agent development. While the current version supports the development of turn-based strategy games and agents, we will add support for real-time strategy games in future updates. Flexibility is achieved by utilising YAML-files to configure tiles, units, actions, and levels. Therefore, the user can design and run a variety of games to test developed agents without specifically adjusting it to the game being generated. The framework has been built with a focus of statistical forward planning (SFP) agents. For this purpose, agents can access and modify game-states and use the forward model to simulate the outcome of their actions. While SFP agents have shown great flexibility in general game-playing, their performance is limited in case of complex state and action-spaces. Finally, we hope that the development of this framework and its respective agents helps to better understand the complex decision-making process in strategy games. Stratega can be downloaded at: https://github.research.its.qmul.ac.uk/eecsgameai/Stratega
Diffraction model of a laser speckle interferometer for measuring micro-displacements of objects with scattering surface

On the basis of diffraction transformations of an optical wave field a mathematical model for the formation of speckle modulated interference patterns and signals at the output of a speckle interferometer is developed, which allows us to identify their properties and quantitative parameters. Speckle interferometers based on a Michelson arrangement are considered, where objects with scattering surfaces are used instead of mirrors in the reference and object arms. Results of numerical simulation of speckle modulated interference patterns on the basis of diffraction transformations of wave fields in an interferometer are discussed. Simulated images obtained at the output of the interferometer when focusing laser beams on the scattering surfaces of the controlled and reference objects are considered. Experimental results of using a speckle interferometer with a digital matrix photodetector for measuring the temperature micro-displacements of an object with a scattering surface and a quantitative comparison of experimental data with the results obtained by a numerical experiment using a diffraction model of a speckle interferometer are presented.
Advanced Graph-Based Deep Learning for Probabilistic Type Inference

Dynamically typed languages such as JavaScript and Python have emerged as the most popular programming languages in use. Important benefits can accrue from including type annotations in dynamically typed programs. This approach to gradual typing is exemplified by the TypeScript programming system which allows programmers to specify partially typed programs, and then uses static analysis to infer the remaining types. However, in general, the effectiveness of static type inference is limited and depends on the complexity of the program's structure and the initial type annotations. As a result, there is a strong motivation for new approaches that can advance the state of the art in statically predicting types in dynamically typed programs, and that do so with acceptable performance for use in interactive programming environments. Previous work has demonstrated the promise of probabilistic type inference using deep learning. In this paper, we advance past work by introducing a range of graph neural network (GNN) models that operate on a novel type flow graph (TFG) representation. The TFG represents an input program's elements as graph nodes connected with syntax edges and data flow edges, and our GNN models are trained to predict the type labels in the TFG for a given input program. We study different design choices for our GNN models for the 100 most common types in our evaluation dataset, and show that our best two GNN configurations for accuracy achieve a top-1 accuracy of 87.76% and 86.89% respectively, outperforming the two most closely related deep learning type inference approaches from past work -- DeepTyper with a top-1 accuracy of 84.62% and LambdaNet with a top-1 accuracy of 79.45%. Further, the average inference throughputs of those two configurations are 353.8 and 1,303.9 files/second, compared to 186.7 files/second for DeepTyper and 1,050.3 files/second for LambdaNet.
Understanding Boolean Function Learnability on Deep Neural Networks.

Computational learning theory states that many classes of boolean formulas are learnable in polynomial time. This paper addresses the understudied subject of how, in practice, such formulas can be learned by deep neural networks. Specifically, we analyse boolean formulas associated with the decision version of combinatorial optimisation problems, model sampling benchmarks, and random 3-CNFs with varying degrees of constrainedness. Our extensive experiments indicate that: (i) regardless of the combinatorial optimisation problem, relatively small and shallow neural networks are very good approximators of the associated formulas; (ii) smaller formulas seem harder to learn, possibly due to the fewer positive (satisfying) examples available; and (iii) interestingly, underconstrained 3-CNF formulas are more challenging to learn than overconstrained ones. Source code and relevant datasets are publicly available (this https URL).
GINet: Graph Interaction Network for Scene Parsing

Recently, context reasoning using image regions beyond local convolution has shown great potential for scene parsing. In this work, we explore how to incorporate the linguistic knowledge to promote context reasoning over image regions by proposing a Graph Interaction unit (GI unit) and a Semantic Context Loss (SC-loss). The GI unit is capable of enhancing feature representations of convolution networks over high-level semantics and learning the semantic coherency adaptively to each sample. Specifically, the dataset-based linguistic knowledge is first incorporated in the GI unit to promote context reasoning over the visual graph, then the evolved representations of the visual graph are mapped to each local representation to enhance the discriminated capability for scene parsing. GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph. We perform full ablation studies to demonstrate the effectiveness of each component in our approach. Particularly, the proposed GINet outperforms the state-of-the-art approaches on the popular benchmarks, including Pascal-Context and COCO Stuff.
Analysis of employees’ social and psychological component indicators in the structure of higher education institution image

The article presents the analysis of social and psychological component indicators of academic employees in the structure of higher education institution (university) image. As a part of comprehensive study we determined the internal structure of a higher education institution image according the results of factor and correlation analysis which includes five factors: professional, social-psychological, motivational, emotional-volitional and individual-psychological. This approach gave a possibility to characterize the peculiarities of each factor, published in scientific articles, influence on the image of the university. Some of the factors have been stydied previously and we could find scientific publications on these issues. At the same time the results of the social and psychological component research have not been published yet but have a significant scientific interest since they characterize a sample of 285 representatives of the rectorate, dean's offices, heads of departments and academic staff of the university. We found out that each category of respondents has its own peculiarities regarding the level of satisfaction with quality of life, subjective satisfaction or not satisfaction with social or personal recognition, material and social security, level of personal authority. We were able to find the dominant type of university employees’ attitude towards people in interaction: authoritarianism, egoism, aggression, suspicion, subordination, dependence, friendliness and altruism. The results of this study can be used for individual diagnosis and appropriate correction of each category of the respondents to have a positive impact on the image of the university.
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model

This paper addresses the problem of unsupervised approach of credit card fraud detection in unbalanced dataset using the ARIMA model. The ARIMA model is fitted on the regular spending behaviour of the customer and is used to detect fraud if some deviations or discrepancies appear. Our model is applied to credit card datasets and is compared to 4 anomaly detection approaches such as K-Means, Box-Plot, Local Outlier Factor and Isolation Forest. The results show that the ARIMA model presents a better detecting power than the benchmark models.
Semi-parametric estimation of the variogram scale parameter of a Gaussian process with stationary increments

We consider the semi-parametric estimation of the scale parameter of the variogram of a one-dimensional Gaussian process with known smoothness. We suggest an estimator based both on quadratic variations and the moment method. We provide asymptotic approximations of the mean and variance of this estimator, together with asymptotic normality results, for a large class of Gaussian processes. We allow for general mean functions, provide minimax upper bounds and study the aggregation of several estimators based on various variation sequences. In extensive simulation studies, we show that the asymptotic results accurately depict the finite-sample situations already for small to moderate sample sizes. We also compare various variation sequences and highlight the efficiency of the aggregation procedure.
Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation.

Machine learning models trained in one domain perform poorly in the other domains due to the existence of domain shift. Domain adaptation techniques solve this problem by training transferable models from the label-rich source domain to the label-scarce target domain. Unfortunately, a majority of the existing domain adaptation techniques rely on the availability of target-domain data, and thus limit their applications to a small community across few computer vision problems. In this paper, we tackle the challenging zero-shot domain adaptation (ZSDA) problem, where target-domain data is non-available in the training stage. For this purpose, we propose conditional coupled generative adversarial networks (CoCoGAN) by extending the coupled generative adversarial networks (CoGAN) into a conditioning model. Compared with the existing state of the arts, our proposed CoCoGAN is able to capture the joint distribution of dual-domain samples in two different tasks, i.e. the relevant task (RT) and an irrelevant task (IRT). We train CoCoGAN with both source-domain samples in RT and dual-domain samples in IRT to complete the domain adaptation. While the former provide high-level concepts of the non-available target-domain data, the latter carry the sharing correlation between the two domains in RT and IRT. To train CoCoGAN in the absence of target-domain data for RT, we propose a new supervisory signal, i.e. the alignment between representations across tasks. Extensive experiments carried out demonstrate that our proposed CoCoGAN outperforms existing state of the arts in image classifications.
Entangled states in supersymmetric quantum mechanics

We study entanglement of spin degrees of freedom with continuous one in supersymmetric (SUSY) quantum mechanics. Concurrence is determined by mean value of spin and is calculated explicitly for SUSY states. We show that eigenstates of supercharges are maximally entangled. As an example the entanglement of atom state with photon state and SUSY in Jaynes-Cummings model are considered.
HO$$\pi $$ in Coq

We present a formalization of HO $$\pi $$ in Coq, a process calculus where messages carry processes. Such a higher-order calculus features two very different kinds of binder: process input, similar to $$\lambda $$ -abstraction, and name restriction, whose scope can be expanded by communication. For the latter, we compare four approaches to represent binders: locally nameless, de Bruijn indices, nominal, and Higher-Order Abstract Syntax. In each case, we formalize strong context bisimilarity and prove it is compatible, i.e., closed under every context, using Howe’s method, based on several proof schemes we developed in a previous paper.
Dangers of IP Camera - An Observational Study on Peeping

Existing research on information security for IP cameras has been primarily focused on issues with authentication or malware, but not on the peeping method itself. How cyber peeping is conducted in real world can further help in strengthening defenses accordingly and spread more awareness about dangers of IP camera. In this research, we observed peeps by setting up a honeypot using decoy cameras in two scenarios. First, where background information (handwritten URL and ID/password bait) can be read by humans. Second, simulating a living-room in a home environment. As a result, many examples of peeping into the decoy cameras were confirmed in reality. Also, a rapid increase in peeping (over 20, 000 times/day) was seen after a decoy camera's feed got posted on a well-known website, showing a large scale peeping danger also exists due to such websites. The results of this study were used in several TV programs to show the dangers of using IP cameras over a national broadcasting station and also were directly shared with IP camera vendors, resulting in the improvement of IP camera security. Therefore, we believe that this study can further help in improving the security and awareness on the dangers associated with IP cameras.
Visualisasi Algoritma sebagai Sarana Pembelajaran K-Means Clustering

Algorithm Visualization (AV) is often used in computer science to represents how an algorithm works. Educators believe that visualization can help students to learn difficult algorithms. In this paper, we put our interest in visualizing one of Machine Learning (ML) algorithms. ML algorithms are used in various fields. Some of the algorithms are used to classify, predict, or cluster data. Unfortunately, many students find that ML algorithms are hard to learn since some of these algorithms include complicated mathematical equations. We hope this research can help computer science students to understand K-Means Clustering in an easier way.
Predicting Credit Card Delinquencies: An Application of Deep Neural Networks

The objective of this paper is 2-fold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called deep neural network), an emerging artificial intelligence technology, in credit risk domain. With a real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client’s personal characteristics and the spending behaviors. Compared to machine learning algorithms of logistic regression, naive Bayes, traditional artificial neural network, and decision tree, deep neural network has a better overall predictive performance with the highest F scores and AUC. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.
Improving Semantic Dependency Parsing with Syntactic Features

We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees. Our empirical results show that only gold-standard synta ...
Tax Knowledge Graph for a Smarter and More Personalized TurboTax

Most knowledge graph use cases are data-centric, focusing on representing data entities and their semantic relationships. There are no published success stories to represent large-scale complicated business logic with knowledge graph technologies. In this paper, we will share our innovative and practical approach to representing complicated U.S. and Canadian income tax compliance logic (calculations and rules) via a large-scale knowledge graph. We will cover how the Tax Knowledge Graph is constructed and automated, how it is used to calculate tax refunds, reasoned to find missing info, and navigated to explain the calculated results. The Tax Knowledge Graph has helped transform Intuit's flagship TurboTax product into a smart and personalized experience, accelerating and automating the tax preparation process while instilling confidence for millions of customers.
Analysis of Job Importance and Job Performance in Dietitians by Kindergarten Establishment Type

The purpose of this study was to compare and analyze job importance and job performance of dietitians by kindergarten establishment type. The survey was conducted from July 2 to 19, 2019. A total of 152 responses were used for final analysis (18 public independent, 96 public attached, and 38 private kindergartens). The questionnaire consisted of two parts. The first part consisted of questions asking for respondents’ general characteristics and workplace characteristics, whereas the second part consisted of items measuring job importance and job performance of kindergarten dietitians. Descriptive analysis, One-way Anova, Wilcoxon signed-rank test, and paired t-test were used for statistical analysis. Finally, IPA (Importance-Performance Analysis) was conducted to analyze the importance and performance of kindergarten dietitian’s jobs. The findings of the study show that there were differences in dietitians’ duties depending on kindergarten establishment type. There were items that differed significantly by establishment type in terms of job importance and job performance of kindergarten dietitians. In addition, the difference between job importance and job performance showed significant differences between kindergarten establishment type. Moreover, the IPA results show that ‘service management’ item was located in a different quadrant for all types of establishments. This study offers the basis for kindergarten policy in terms of foodservice management, and suggests further research on the kindergarten foodservice environment for each type of establishment.