Reviewerly/exHarmony huggingface dataset:
Determining Manufacturing Condition Range Using a Causal Quality Model and Deep Learning

This study proposes a method to improve the calculation of probabilities in a Bayesian network. The aim of the Bayesian network in this study is to specify the manufacturing condition range needed to meet a specified level of product quality. In the manufacturing process, workers need to operate within manufacturing condition range to improve product yield. Conventionally, quality standards are established for each manufacturing process, and workers operate according to the tolerances determined by these standards. However, these efforts are not very effective because it is not clear which tolerances actually reduce the failure rate. In addition, product yield is not improved. We develop a method that uses deep learning to estimate the conditional probabilities in Bayesian network modeling. Using the proposed method, the AUC of the model increases from 0.91 to 0.99, indicating that this approach can be used for specifying the tolerances of manufacturing conditions.
Generalized matroid matching

The matroid parity problem is known to be unsolvable in polynomial time in the general case and is polynomial for linearly represented matroids. Its generalization asks if there exists an independent set in a matroid, intersecting k-tuples in a given way. The known polynomial, non-polynomial and NP-hard special cases are summarized and some open cases are mentioned.
The Establishment of University Teachers' Knowledge Connotation Reconstruction Model Based on Big Data

To improve the knowledge management and evaluation ability of college teachers, in this paper, the reconstruction of college teachers' knowledge connotation information based on big data, and a method based on Lyapunov exponent spectrum feature extraction for college teachers' knowledge connotation information reconstruction under the big data environment is proposed. The feature analysis method is used to reconstruct the phase space of college teachers' knowledge connotation information. In the reconstructed phase space, the grid region clustering algorithm is used to carry out the fuzzy clustering processing of college teachers' knowledge connotation information, and the Lyapunov exponent spectrum is extracted from the cluster knowledge connotation information. The feature is to use the particle swarm filtering method to suppress the false data interference and realize the optimization of the knowledge content information of college teachers in the big data environment. The simulation results show that the method can be used to reconstruct the knowledge content of college teachers under the big data environment. The university students' knowledge connotation information big data regular knowledge, the knowledge connotation reconstruction based on spectral feature quantity analysis, the data clustering is better, and the accurate mining performance of college teachers' knowledge connotation big data is improved.
The Universality of the Kalman Filter

The universality of the Kalman filtering can be found in the control theory. The Kalman filter has found its applications in sophisticated autonomous systems and smart products, which are attributed to its realization in a single complex chip. In this chapter, considering the Kalman filter from the perspective of conditional characteristic function evolution and Itô calculus, three Kalman filtering theorems and their formal proof are developed. Most notably, this chapter reveals the following: (1) Kalman filtering equations are a consequence of the ‘evolution of conditional characteristic function' for the linear stochastic differential system coupled with the linear discrete measurement system. (2) The Kalman filtering is a consequence of the ‘stochastic evolution of conditional characteristic function' for the linear stochastic differential system coupled with the linear continuous measurement system. (3) The structure of the Kalman filter remains invariant under two popular stochastic interpretations, the Itô vs Stratonovich.
Reasoning by a Bipolar Argumentation Framework for PROLEG

We develop a system allowing lawyers and law school students to analyze court judgments. We describe a transformation from the logic programming language PROLEG to a bipolar argumentation framework (BAF) and the legal reasoning involved. Legal knowledge written in a PROLEG program is transformed into a BAF, in which the structure of argumentation in a judgment is clear. We describe two types of reasoning by the BAF: clarification of the entire structure and causality of arguments, and identification of the required evidence, and we show its applications on legal reasoning.
Providing multi-user in NCL with userAgent and UserProfile

This proposal consists of adding a new language module named Users to provide multi-user support in NCL, proposing three new NCL elements: userBase, userAgent and userProfile. user-Base is used for declaring user and their profiles. userAgent identifies a user and userProfile represents a description of the user’s profile. Each userAgent is associated with only one userProfile, and together, they allow representing a unique user. The user description with its specific characteristics can be loaded from a profile specified with the Resource Description Framework (RDF) standard, for example.
Water Resources Mapping for Firefighters in Kotamadya Yogyakarta Based on Android

Fire extinguishing system in placing reservoirs there are still many officers who do not know the location so that they can implement computerized technology so that firefighters can work to maximize the information technology made by the author well. Mobile-based reservoir mapping system application developed using Geofence method. The main purpose for this application that officers can easily find the location of the reservoir closest to the location of the fire and where officers can also find out the location of the reservoir distance from the fire. The accuracy of testing results with Google maps where the accuracy is very precise to the location of the water source or reservoir. Testing using a smartphone comparison from Android 4.1 Jelly Bean version to Android 7.0 Nougat can run with distance results in the application using google maps, get the same results and where there is a test version of Android V4.4 Kitkat that did not work because RAM does not support this application. Testing in the form of a black box method where this method tests every function contained and the test is functioning and running as smoothly as possible where the system is functioning properly with success rate 100%.
Constructing Features of Competence-Oriented Specialist Models Based on Tutoring Integrated Expert Systems Technology

The scientific and technological experience of the development and use of tutoring integrated expert systems and the creation of a single ontological space of knowledge and skills for the automated construction of competence-oriented models of specialists in the field of methods and technologies of artificial intelligence in the direction of training “Software engineering” obtained at the Department of Cybernetics of NRNU MEPhI is analyzed.
Learning via AI Dolls: Creating Self-Active Learning for Children

This research aimed to propose an artificial intelligence (AI) chatbot mobile application for pre-school children engaging in active learning processes. The research tools Microsoft Bot Framework and Azure Bot Service were used to create an AI chatbot doll (AIBD) prototype. With this AIBD, the players simply dragged and dropped items in an intelligent bot builder that could create characters with different identities using a set of customized doll items, for example, genders, dresses, shoes, or flowers. During play, children could either learn about what colors, apparel, languages, or music they liked or communicate their attitudes or thoughts to the dolls. Consequently, the AIBD also learned and collected personal data, such as players’ personalities, emotions, attitudes, or behaviors. Finally, the data were stored in a private cloud repository, where only authorized parents could access the reports. In our findings, the average correlation between the capability of the AIBD’s learning performance and children’s active learning processes was high (support value equal to 0.791 and confidence value equal to 0.853). Moreover, the children playing with the AIBD could not only develop their emotions but they also saw large improvements in their ideas generation.
STUDENT ATTITUDE TO DEMOGRAPHIC SITUATION IN RUSSIA

Purpose of the study: Since the new generation is the main entity of material and social benefits’ renewal process of any state, the article aims to identify the students’ attitude to the demographic situation in Russia. It is the younger generation of different age groups that is the main labor unit of the state population. Relevant is the fact that the issues of demography are a global challenge of self-organizing society. The purpose of the article is to identify the students’ attitude to the demographic situation in Russia, as well as to consider various models of reproductive behavior. Methodology: The leading methods for the study of this problem is the survey method, which allows to make a qualitative analysis of the characteristics of the students’ attitude to the demographic situation in Russia and to establish the subjective position of students regarding demographic development. Results: The article reveals that the following administrative and legal measures contribute to the improvement of the demographic situation in the country (legislative acts regulating the marriage age, divorce, attitude to abortion and contraception, the status of mother and children in the divorce, working women’s labor regime); economic measures (paid leave, various benefits at child’s birth, preferential credit conditions if necessary, tax and housing benefits); educational measures (formation of public opinion, the core and standards of demographic behavior, determination of attitude to religious norms, traditions and customs, family planning policy, sexual education of youth). The authors established a high level of awareness of students about the demographic situation in the country; the presence of anxiety about this situation. Applications of this study: The data obtained in the work can be used in sociology, social psychology, political science, as well as for further theoretical development of this issue. Novelty/Originality of this study: The study revealed a high level of awareness of young people about the demographic situation in the country, certain anxiety about this situation. However, young people are not in a hurry to participate in the solution of demographic problems, referring mainly to the difficulties of material nature. This indicates the need for more careful consideration of measures of state support for young families, taking into account the research of their opinion on this matter.
Extensible Visualizations of Ontologies in OWLGrEd

OWLGrEd is a visual editor for OWL 2.0 ontologies that combines UML class diagram notation and textual OWL Manchester syntax for expressions. We review the basic OWLGrEd options for ontology presentation customization and consider the framework of OWLGrEd extensions that enables introducing rich use-case specific functionality to the editor. A number of available OWLGrEd extensions offering rich ontology management features to their end-users are described, as well.
Pedagogical maintenance of algorithmic system of federal state educational standard results’ monitoring in educational establishment

This article looks through the current problem of the modern educational system – the need of pedagogical maintenance of algorithmic system of monitoring of FSES requirements in educational establishment in order to deduce and fetch out the results of pedagogical activity. The author analyses the system of quality evaluation of education, used in schools, points the possible forms and types of formalisation of FSES requirements’ results in educational establishment. The article also suggests to use different ways to get the information for the purpose of evaluation and diagnostics of the quality of education.
A Method for Block Compression of Images Based on Data-hiding

Abstract The existing image block compression method does not reduce the amount of data after the image block processing. For the image with large amount of raw data, such as high-resolution remote sensing images, the image compression will consume more hardware resources and cause great pressure on the compression performance. Aiming at this situation, a method for block compressed of images based on data-hiding is proposed. The reference image block and similar image blocks are judged by the similarity of image blocks. The number of similar image blocks is hidden in the reference image block by data-hiding, and only the reference image blocks are subjected to JPEG2000 compression. The experimental results show that the method reduces the amount of data before image compression by 1/3 and increase the compression ratio of image compression by 1.5 times.
Towards an Ontology for Supporting Dynamic Reconfiguration of IoT Applications

In the last years, several ontologies have been developed in the Internet of Things (IoT) domain to specify intelligent environments. Where, environment descriptions include both the software services, that expose the functionalities of devices; and resources, i.e. software components that control physical sensors and actuators, hosted on devices that sense or change the state of objects in the environment. This work proposes an ontological semantic network that supports the development of IoT applications that establish, at runtime, the binding with device services that allow them to fulfill their functionalities. This is done by enhancing device service descriptions with properties that allow inferring relationships among the attributes of the environment that an application is designed to control and the environment properties that device services are able to control. The applicability of the proposed model is shown through the population of the ontological network where an application establishes at runtime the binding among its functionalities and device services.
Elimination of Machine Translation Errors in English Language Transformation

To improve the level of automation and intelligence of English language transformation in machine translation, a method of machine translation error elimination based on deep learning and feature extraction of language transformation error is proposed. The semantic correlation detection model of error exclusion in English language conversion translation is constructed by using the differentiated semantic modification method, and the semantic tree of error exclusion in English language transformation translation is built by means of grammar analysis. The semantic similarity feature of English language transformation is extracted. According to the different combinations of semantic similarity, the semantic allocation and machine translation error feature analysis in English language transformation are carried out. The tree topic word list of English language conversion is established by means of deep learning method, and the sentence structure of English language transformation is adjusted according to the semantic modification target in the tree topic word list. In order to eliminate the errors in translation of English language conversion and the registration of topic words, the optimal semantic correlation feature of each clause is calculated, and the deep learning algorithm is used to automatically optimize the errors in translation of English language conversion. The simulation results show that the accuracy of the proposed approach is high and the relevance of translation calibration is strong.
Document Clustering by Relevant Terms: An Approach

In this work, a document clustering based on relevant terms into an untagged medical text corpus approach is presented. To achieve this, to create a list of documents containing each word is necessary. Then, for relevant term extraction, the frequency of each term is obtained in order to compute the word weight into the corpus and into each document. Finally, the clusters are built by mapping using main concepts from an ontology and the relevant terms (only subjects), assuming that if two words appear in the same documents these words are related. The obtained clusters have a category corresponding to ontology concepts, and they are measured with cluster from K-Means (assuming the k-Means cluster were well formed) using the Overlap Coefficient and obtaining 70% of similarity among the clusters.
A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification

Citizen science is becoming mainstream in a wide variety of real-world applications in astronomy or bioinformatics, in which, for example, classification tasks by experts are very time consuming. These projects engage amateur volunteers that are tasked to manually classify unannotated examples. As a result, we obtain a larger volume of labelled data that, however, contains a great level of uncertainty due to the wide range of expertise of the volunteers. Handling that inherent uncertainty is key to building robust and fast machine learning models that maximise the outcome of citizen science projects. In this work, we introduce a preliminary approach that first transforms the original results from a citizen science project to handle the uncertainty, and then uses this as input to a fuzzy k-nearest neighbour classifier. We leverage citizen science results in such a way that it naturally speeds up the learning and classification phases of the fuzzy classifier, and improves the classification performance. As a case study, we will focus on the Galaxy Zoo project that consisted of galaxy image classification. Our experimental results show that an appropriate use of citizen science data enables a faster and more robust classification using the fuzzy k-nearest neighbour classifier.
Determination of the Central Pattern Generator Parameters by a Neuro-Fuzzy Evolutionary Algorithm

The selection of appropriate parameters of a Central Pattern Generator (CPG) facilitates its use on applications in robotics. A Central Pattern Generator is class of oscillator represented by a system of n differential equations of first order with m parameters, finding those m parameters to match a specific behavior is a complex problem to solve. For the development of applications in robotics, such as locomotion, the CPG have been applied as a decentralized control system. In this work, a Neuro-Fuzzy Evolutionary procedure is proposed to determine the parameters of a CPG for a specific cases of 2, 3, 4, 5 and 6 neurons. The developed methodology is presented in detail. The proposed algorithm guarantees a given response in the frequency and amplitude into the ranges required.
Training lightweight network from scratch for efficient object detection in aerial images

Object detection in aerial images plays an important role for a wide range of applications. Although many efforts have been done in the last decade, it is still an active and challenging problem because of the highly complex backgrounds and the large variations in the visual appearance of objects caused by viewpoint variation, occlusion, illumination, etc. Recently, many object detectors based on deep learning demonstrate the great advantages for significantly improving the detection performance in aerial images. However, the most accuracy neural networks usually have hundreds of layers and thousands of channels, thus requiring huge computation and memory consumption. Besides, the state-of-the-art object detectors are usually fined-tuned from the models pretrained on classification dataset ImageNet, which limits the modification of network architecture and also leads to learning bias because of the different domains. In this paper we trained a lightweight convolutional neural network from scratch to perform object detection in aerial images. When designing the lightweight network, Concatenated Rectified Linear Units (CReLU) and depthwise separable convolution operation were employed to reduce the computation cost and model size. When training the lightweight network from scratch, we employ Group Normalization (GN) in each convolution layer, which makes smoother optimization landscape and has more stable gradients. A serial of ablation experiments is conducted on the recently published large-scale Dataset for Object detection in Aerial images (DOTA), and the results show that the proposed object detection methods with lightweight network trained from scratch achieves competitive performance but has smaller model size and lower computation cost.
Generalized Construction of Compression Function to Build a Cryptographic Hash

A Cryptographic hash is called one way function where it receives and returns data in an arbitrary and fixed size respectively. It has certain applications like digital signatures, message authentication-code, and password verification. Primarily, block-cipher-based compression function is used to build a cryptographic hash. Typically, three variants of block-cipher are available under the double block-length compression function (DBL), namely (n, n), (n, 2n) and lightweight-cipher. Interestingly, many schemes have been proposed independently based on the above types of block-cipher types. However, these constructions are suitable case to case such as short message, long message, and variable message. Secondly, the communication networks are complex and depend on various types of resource constrained devices which are useful under IoT, cloud, and grid networks. These devices have certain limitations such as limited processing, and storage capabilities. Therefore, security solution for these devices should be platform independent and generalized. However, there is a lack of general construction of compression function (based on block-cipher) to build a cryptographic hash. Therefore, we propose a generalized construction of compression function that supports all types of block-cipher and can run under a single platform. Moreover, the proposed scheme outperforms state-of-the-art works in terms of security and efficiency rate.
Autonomous Robot Navigation with Fuzzy Logic in Uncontrolled Environments

The “tourist in an unfamiliar village” navigation model seeks to simulate how a human would navigate with minimal information about their surroundings. By implementing this model and using Continuous Transmitted Frequency Modulated (CTFM) ultrasonic sensors, along with more advanced localization and mapping algorithms, a more efficient form of autonomous navigation can be created. Through utilizing the CTFM ultrasonic sensor echo data analysis techniques and combining them with Simultaneous Localization and Mapping (SLAM) methodology, it is theorized that the mobile robot will be able to identify obstacles, as well as characteristics and features in the environment .
Digital Camera in Movement Tracking on FPGA Board DE2

This project deals with the problem of detecting objects in real-time using the Altera DE2- FPGA to store and manipulate images. The detection algorithm was the object selected to subtract the background. The search reveals the sequence of the image or video, and this paper suggests a new way to detect a moving object based on the background subtraction. Background subtraction is one of the simplest algorithms to detect the object in an image. There are many high complexity accessories that produce better results. The main objective of this project is to track certain parts and detect realtime movement and build the basic work execution that can be used to support and strengthen future projects. In order to achieve the proposed objectives, a specific motion tracking is detected based on a particular color and part of the image by the subtraction algorithm, specify the motion location to store and manipulate the image.