Reviewerly/exHarmony huggingface dataset:
DISTRIBUTED VERSION CONTROL SYSTEM AS A TOOL OF PROJECT ACTIVITY ON INFORMATICS LESSONS AT SECONDARY SCHOOL

The article discusses methodological aspects of project activity using distributed version control system at secondary school on informatics lessons. Distributed version control system is considered as a learning tool, as a way of enriching the content of the basic course of informatics and as a tool for organizing and filling out the school information educational environment.
Stimulating Interest in School Physics with the Help of Kids’ Toys

The article shows the role of a scientific toy in the methodology of teaching physics and describes graphic examples in various sections. The significance of homemade toys is highlighted, examples of such toys are also given.
Estimation of Parameters of Non-Stationary Information Processes Using Wavelet Analysis

The paper proposes an algorithm for estimation the parameters of information processes based on a wavelet analysis apparatus suitable for non-stationary and non-Markov control processes. Comparative analysis with algorithms based on the Fourier transform and the fast Fourier transform is carried out. The analysis shows that the proposed algorithm improves the accuracy of processing complex and non-stationary processes. For processing a variable frequency signal the error of the results obtained using the synthesized algorithm is 20% lower than when using the fast Fourier transform and 10% lower than using the Fourier transform.
A Way to Understand the Features of Deep Neural Networks by Network Inversion

New variants of Deep Neural Networks (DNN) have been proposed continuously in recent years, and have led to impressive performance in a wide range of fields such as computer vision, natural language processing, and recommender systems. However, DNN are often criticized by the lack of interpretability. This paper proposes a network inversion method to understand the features extracted by DNN. DNN can be considered as a kind of transformation. When studying the characteristics and the features of a transformation, the inverse transformation is often involved. By comparing the inverted signal with the original one, better understanding of the features and properties of transformation can be achieved. In this paper, it has been found that the features extracted by a dimension-reduction layer in a DNN are essentially the special solution of the layer’s constraint equations, and the linear combination of the general solutions is neglected by the layer. This find-out should help to understand the structure and function of a DNN. The experiments in this paper showed the importance of this find-out.
System to Support Multidimensional Assessment of Human Functioning

Considering the importance of the assessment of human function, the present paper reports a research study that aimed to develop an information system to support a comprehensive set of clinical instruments. Following the requirements analysis, the system was implementing according the Model-View-Controller architecture. The system allows the selection of the most appropriate assessment instruments for a given patient the registration of the assessments carried out, as well as the access to consolidated information for correct decision-making, and was evaluated using quantitative and qualitative methods. The quantitative evaluation consisted in a usability assessment using the Post-Study System Usability Questionnaire, while the qualitative evaluation consisted in a focus group to determine the perceived utility of the developed system to support the care of older adults. The results show the potential of the system to support the care of older adults.
Inference Trees, Competitive Models, and Sparse Big Data Heuristics

Novel predictive modeling analytics techniques with decision trees on competitive models with big data heuristics are presented in brief. Spanning trees are applied to focus on plan goal models that can be processed with splitting agent trees, The chapter is based on agent plan computing where the interaction amongst heterogeneous computing resources is via objects, multiagent AI and agent intelligent languages. Modeling, objectives, and planning issues are examined at an agent planning. Further developing the techniques for model discovery and prediction planning first author developed from the preceding chapters we examine inference decision trees with application to big data areas. Based on the first authors past decade on the preceding chapters, agent computing techniques are applied to present precise decision strategies on multiplayer games with only perfect information between agent pairs on a vector spanning models. We brief on how sparse matrices enable efficient computability on big date heuristics. Predictive analytics on “big data” are presented with new admissibility criteria. Tree computing grammar algebras graphs are a basis for realizing tree goal planning.
On the Period mod m of Polynomially-Recursive Sequences: a Case Study.

Many polynomially-recursive sequences have a periodic behavior mod m. In this paper, we analyze the period mod m of a class of second-order polynomially-recursive sequences. Starting with a problem originally coming from an enumeration of avoiding pattern permutations, we give a generalization which appears to be linked with nice number theory notions (the Carmichael function, algebraic integers, Wieferich primes).
Designing Information Systems of Mobile Phone Catalog Product by Web-based

Abstract Macromedia Dreamweaver 8.0 programming language is an application program that can edit HTML visually and manage websites and pages. This study aims to apply the PHP programming language with Macromedia Dreamweaver 8.0 to display mobile phone catalog information and increase the knowledge and capabilities of researchers regarding the website through internet media. The research method used is identification, formalization, implementation, and testing that aims to maximize the final results. The final result of designing information systems of mobile phone catalog product by Web-based is to apply the website to facilitate information retrieval and selling mobile phones online.
Security Challenges and Suggested Solutions for e-Health Information in Modern Society

The recent advancement in Information and Communication Technology (ICT) has undoubtedly improved services in all sectors in the world. Specifically, Information Technology (IT) has led to a very vital innovation in the health sector called electronic health (e-Health). In order to optimize full and excellent benefits of this innovation, its implementation in a cloud-based environment is important. However, with noticeable and numerous benefits inherent from e-Health in a cloud computing, its full utilization is still hampered by challenges of security and privacy. The Internet of Things (IoT) which is considered as a connection of various smart objects through network has unfolded many opportunities in many areas particularly in the healthcare sector. However, the introduction of IoT services in electronic health applications has resulted to increase fear and concerns of security and privacy. In this paper, we focused on extensive review of current and existing literatures of various approaches and mechanisms being used to handle security and privacy related matters in e-Health. Strengths and weaknesses of some of these approaches were enunciated. The literature review was carried out after selecting over 110 original articles and figured out several models adopted in their solutions. After comparing models used, we arrived at the reviewed articles. Reviewed articles were narrowed down to the current number because of similarity observed in the models adopted by some researchers. Also, we give an acceptable and standard definition of e-Health. An effort was made to classify cloud-based models. Security and privacy requirements as recommended by Health Insurance Portability and Accountability Act (HIPAA) were also provided. Remarks and recommendations were made regarding the review process and future directions on security and privacy of e-Health in cloud computing. Finally, we proposed a secured architecture for electronic health that could guarantee efficiency, reliability, and regulated access framework to health information. The architecture, though is currently under implementation, will yield the objective for which it is designed for. Its full-scale deployment will undoubtedly guarantee security of classified and confidential information.
A Blockchain Review: A Comparative Study Between Public Key Infrastructure and Identity Based Encryption

Blockchain is inventive technology that allows passing information without the need for third party or transaction cost. It offers solid properties such as decentralization, transparency, and immutability which make it desirable to adopt in sense of sharing data and maintain its security. However, with the emergence of blockchain technology, new issues have arisen with regards to security in the blockchain authentication and infrastructure. This paper focusing on understanding blockchain and what limitation and challenges adhered with it. Moreover, it reviews and analyzes the different authentication methods and discuss the integration of Identity-based encryption (IBE) and how does it differ than public key infrastructure (PKI) to determine the best method.
Visit Results Reporting System from External Parties in Adira Finance Using CodeIgniter

Abstract The Palembang branch of Adira Finance has its own procedures in handling its overdue customers, namely customers who do not make installment payments, based on the time of late payment. One of the measures taken is to collaborate with third parties to make visits to overdue customers. This study aims to build a system that can be used by third parties for conveying information in the form of results of visits that have been made to overdue customers. System development is carried out by prototype method to obtain the results of a system that suits the needs of its users. The results of the study are a system that can be used by three types of users, namely admin, external party, and executor. The system can be used to divide the assignment of customer visits to the external, and each external can continue the assignment to the executor. Both external and executor can report the results of their visit to the system.
Multivariate Linear Models: General

This chapter introduces the basic theory for linear models with more than one dependent variable.
The Koala Part-of-Speech Tagset for Written Swedish

We present the Koala part-of-speech tagset for written Swedish. The categorization takes the Swedish Academy grammar (SAG) as its main starting point, to fit with the current descriptive view on Swedish grammar. We argue that neither SAG, as is, nor any of the existing part-of-speech tagsets, meet our requirements for a broadly applicable categorization. Our proposal is outlined and compared to the other descriptions, and motivations for both the tagset as a whole as well as decisions about individual tags are discussed.
A Blind Coupon Mechanism Enabling Veto Voting over Unreliable Networks

A Blind Coupon Mechanism (BCM) allows spreading of alerts quietly and quickly over unreliable networks. The BCM primitive ensures that alerts are efficiently broadcast while the nature of these signals are securely hidden. However, current BCM proposals are limited to indicating a single bit of information and also lack natural privacy properties. In this paper, we develop a new BCM solution that allows the transmission of several alerts privately and in one shot. This extension perfectly suits a new kind of applications, that is (absolute) veto voting over unreliable networks, in which multiple decisions are reached using only simple peer-to-peer communications. Our enhanced BCM efficiently supports the spread of votes over unreliable networks while hiding whether these votes contain any or several vetoes. We prove our BCM solution secure and illustrate its use for veto voting protocols in limited communication infrastructures.
Novel Indexing of Cyclic Codes With Run-Length Applications

Any cyclic code over a finite field can be considered as a sum of some non-degenerate irreducible cyclic codes. We use the traditional trace function representation of irreducible cyclic codes to suggest a similar representation to any cyclic code. This representation suggests an indexing for codewords of the cyclic code. Specifically, each codeword is identified by a triplet IA, RA and QA. The relationship between these triplets was studied for codewords that are cyclic shifts of each other. We introduce a set S(C) whose elements correspond to subsets that each contains all the cyclic shifts of a codeword. As an application to the proposed indexing, we take advantage of the trace function linearity to explore codewords with a specific run-length. Because the run-length is invariant to cyclic shifts, it suffices to explore elements of S(C) that match codewords with a specific run-length. Instead of searching at the code for these codewords, the problem is turned into solving a system of linear equations.
On the multi-dimensional modal logic of substitutions

Abstract We prove completeness, interpolation, decidability and an omitting types theorem for certain multi-dimensional modal logics where the states are not abstract entities but have an inner structure. The states will be sequences. Our approach is algebraic addressing varieties generated by complex algebras of Kripke semantics for such logics. The algebras dealt with are common cylindrification free reducts of cylindric and polyadic algebras. For finite dimensions, we show that such varieties are finitely axiomatizable, have the super amalgamation property, and that the subclasses consisting of only completely representable algebras are elementary, and are also finitely axiomatizable in first order logic. Also their modal logics have an N P complete satisfiability problem. Analogous results are obtained for infinite dimensions by replacing finite axiomatizability by finite schema axiomatizability.
Resolving Conflict of Interests and Recommending Expert Reviewers for Academic Publications Using Linked Open Data

Scholarly peer review is a process of evaluating the suitability of a research work for publication judged by qualified researchers. A professional peer review process ensures the quality of the produced scientific research work. However, there are two main challenges to achieve professional peer review: (1) selecting reviewers with similar competences as the authors (peers) of a submitted research work and (2) resolving any Conflict of Interest (Col) between reviewers and authors. Currently, to solve the first challenge, editors and conferences organizers select reviewers manually. Similarly, the current solution of the second challenge is that authors and reviewers are asked to manually declare any CoI. Such a manual solution is error-prone, waste time, and tedious for reviewers, authors, editors, and organizers. To address the aforementioned two challenges, we have developed a novel recommender system that (1) suggests expert reviewers and (2) resolves any CoI between the recommended reviewers and the author(s) of a submitted paper. To develop our recommender system, we utilized the DBLP citation network database represented as Linked Open Data. To select candidate reviewers who are expert in the topic of a submitted paper without CoIs, we use Latent Dirichlet Allocation (LDA) topic modeling to extract the topics researchers are working on and the topics of a submitted paper, then our system executes a SPARQL query that returns the best candidate reviewers. Finally, our system executes another SPARQL query that detects any CoIs between the candidate reviewers and the authors of a submitted paper and hence excludes them. Our experimental evaluations corroborate the ability or our system to recommend expert reviewers without CoIs.
Learning Case-Based Reasoning Solutions by Association Rules Approach

Case-Based Reasoning (CBR) is amply used as a method to solve problems in many domains. It involves retrieval and use of past cases to solve a new one. However, using past cases to solve a new one by this methodology is weighing a great deal. In this paper, we present our approach for reducing the search space solution through association rules and focus only interesting cases that meet only these rules. The reduced search space is then used by CBR to compute a solution for the new problem. Through this work, we aim to propose an approach that combines Association Rules and CBR to improve searching solution for similar cases. Thereafter, we test our approach by using real-life datasets.
Overview on 3D reconstruction from images

In recent years, the world of computer graphics has made tremendous progress. Several 3D model visualization techniques have emerged and have been introduced in the hardware's. The machines on which are realized the 3D visualization have also evolved. We do not need expensive computers to see a world in 3D; a simple computer can do the trick.
An Efficient Hybrid Encryption Scheme for Large Genomic Data Files

With the rapid development of genomic sequencing technology, the cost of obtaining personal genomic data and analyzing it effectively has been gradually reduced. The analysis and utilization of genomic data have gradually come into the public view, the privacy leakage of genomic data has aroused the attention of researchers. Genomic data has unique format and a large amount of data, but the existing genetic privacy protection schemes often fail to consider security, availability and efficiency together. In this paper, we analyzed widely used genomic data file formats and designed a hybrid encryption scheme for large genomic data files. Firstly, we designed a key agreement protocol based on RSA asymmetric cryptography. Secondly, we used AES symmetric encryption to encrypt the genomic data by optimizing the packet processing of files and multithreading encryption, and improved the usability by assisting the computing platform with key management. Software implementation indicates that the scheme can be applied to the secure transmission of genomic data in the network environment and provide an efficient encryption method for the privacy protection of genomic data.
Extraction of Numerical data from Categorical Data Set and Artificial Neural Networks

In this study, the need for numerical data which is required for the machine learning techniques is considered. In order to classify a categorical data by multilayer perceptron, which is one of the well-known artificial neural network structure, the content of the data is digitalized. While the numerical equivalent of the data content is created at categorical level, the protection of the information it carries has been an important issue in terms of achieving effective results and has been studied a lot. Here, a binary-defined transformation is applied to the categorical data. Afterwards, a guide vector is implemented to protect the relation at its own business context. The guide vector implicitly conserves positional relation of the corresponding dataset at the categorical level. Hence, a significant dimensional reduction is accomplished in this new dataset obtained after the digitalization of the original dataset. The classification performance of newly extracted features is validated with multilayer perceptron structure and successful results are observed. In this study, the actual data extracted from the enterprise resource planning (ERP) system is used as the main data source. The underlying methods of modeling and data processing are implemented in MATLAB and Python programming languages.
Substantiation of the backpropagation technique via the Hamilton—Pontryagin formalism for training nonconvex nonsmooth neural networks

Introduction.The machine learning problem consists of the identification of parameters of a neural network model, e.g., neural weights, using a set of input-output observations.The training task is formulated as a task of minimizing some smooth loss functional (empirical risk), which measures the average forecast error of the neural network model.Methods of training (identification) of large neural network models are discussed in many articles and books [1, 2].To train deep (i.e., multilayer) neural networks, the stochastic gradient method and its modifications are mainly used [2, 3], being adopted from the theory of stochastic approximation and stochastic programming, since only they are practically applicable for training such networks.The stochastic gradient of a risk functional is a random vector whose mathematical expectation approximates the gradient of a target functional, and the stochastic gradient descent method is an iterative one for changing the desired model parameters in the direction of the stochastic (anti-) gradient.To solve smooth neural network training problems, the BackProp method is widely used [1, 4], i.e. a special method for calculating the gradients of a target functional with respect to
The Reseptive and Productive Learning Models Based on Folktales for Learning Indonesian as a Foreign Language

Abstract This research is intended to describe and explain reseptive and productive learning models based on folktales for Learning Indonesian as a Foreign Language (Bahasa Indonesia untuk Penutur Asing/BIPA). The research method used here is the qualitative descriptive method, using the content analysis technique. Data was collected from observation implementation of learning with the reseptive and productive models in the class, Indonesian students as a Foreign Language, and documents. The validity of data was verified through the triangulation method. Data analysis used the interactive analysis technique pioneered by Miles and Hubermann. The research results indicate that the appropriate learning model for introducing folktales to Learning Indonesian as a Foreign Language students is the receptive and productive models, which can be applied to improve the four language skills. The four language skills include listening, reading, speaking, and writing. The receptive learning model consists of the interest stage, enjoyment stage, and response stage. The productive learning model consists of writing stories, writing simple drama script, and roleplaying. This learning model consists of several methods through which foreign students can learn Indonesian actively, creatively, and enjoyably.
Research on wireless robot path planning under edge computing considering multistep searching and inflection points

Abstract The wireless robot path planning is a hot research topic in the fields of automatic control and wireless communication. With the help of the edge computing model, the robot can implement location‐aware and positioning to effectively explore the direction and avoid barriers with less computation time. In this paper, an improved ant colony optimization algorithm is proposed to wireless robot path planning. First, the grid method to build a formal map for the robot environment is introduced, by which we can accurately simulate the barriers and paths environment. Second, the multistep path‐planning method is proposed to optimize the path length of robot walking and improve the quality of the solution. Third, to make the wireless robot route smoother, the inflection point parameter is integrated into the probability formula. All these related data are collected and processed at the edge node with cooperative communication. The way of edge computing makes root robustness and stability. Besides, the pheromone strength coefficient Q is reduced to increase the convergence speed of the proposed algorithm. Finally, the experiment results show that our method can obtain a better path planning for the wireless robot, compared with the traditional algorithms.