Reviewerly/exHarmony huggingface dataset:
Geriatric Disease Reasoning Based on Knowledge Graph

The lack of health care for ageing has become one of China's most serious challgengs. The main work of this paper is building a database of a geriatric knowledge graph and proposing three inference rules based on Bayesian algorithm, which can effectively help the elderly to understand their health better and find out the abnormal condition as soon as possible. At the same time, it can assist doctors make auxiliary medical decisions and improve the cure rate. This article introduced a complete process of building a knowledge graph, from schema structure design to data acquisition, and processing the data until it fits the standard. Before applying to disease reasoning, we imported knowledge data into the Neo4j graph database to make full use of the inference flexibility and accuracy of the knowledge graph.
Development of information security in the “Internet of Things”

The Internet of Things, or IoT, refers to billions of physical devices around the world that are connected to the Internet for data collection and sharing. With cheap processors and wireless networks, it is possible that everything will be part of the IoT - from the tablet to the plane. This adds artificial intelligence to devices, allowing them to communicate without human intervention and combining the digital and physical worlds. The problem of constructing mechanisms for protecting multi-agent robotic systems from attacks by embedded intruder robots is considered. A class of so-called “soft” attacks using message interception, generation and transmission of misinformation to a team of robots, as well as carrying out other actions that do not have identifiable signs of the invasion of intruder robots, is considered.A theoretical security model for multi-agent robotic systems is proposed, which is based on the zonal security model and the police station model for distributed computing systems. The main idea underlying the proposed subject-object model of access control is that in addition to the entities of the subject and object, a logically independent entity - the police station - is introduced into the information system; this entity, in accordance with the concept of the appeal security monitor, has the function of checking legitimacy access and/or integrity of transactions spatially distributed within the region of subjects and objects. Thus, it is proposed to design an initially homogeneous multi-agent system as a heterogeneous one, in which, in addition to executing agents, there are agents designed exclusively for solving security tasks: identification and authentication, access control, key generation and distribution, and agents’ location analysis. In order to solve the last task, the region is divided into several zones, for which zonal and interzonal security procedures are introduced.The model efficiency is illustrated by an example of its use when constructing the protection mechanism of the classical iterative task of distributing the robots’ force assignment for several purposes. At that, the order of interaction of agents with the police stations of their zone and the implementation of the interzonal security policy are shown.
Improved Fuzzy Clustering using Ensemble based Differential Evolution for Remote Sensing Image

Identification of homogeneous regions in a satellite image is essentially the clustering of pixels in intensity space. Importantly remote sensing image like satellite images contain varieties of land cover types. Some of the covers are significantly large areas whereas some are relatively smaller regions. Therefore, automatically detecting such wide varying areas is a challenging task. Hence, this fact motivated us to propose an improved clustering technique viz. Ensemble based Differential Evolution for Fuzzy Clustering (EDEFC). For this purpose, very recently developed three variants of differential evolution (DE) are used in order to perform the clustering with different set of solutions. As a result, better clustering solution yields from the ensemble of DEs by exhaustive exploration of search space. The proposed EDEFC technique is applied on two numeric remote sensing datasets and Indian Remote Sensing (IRS) satellite image of Kolkata. The results of the EDEFC are shown quantitatively and visually by comparing with eight other clustering techniques. Moreover, the statistical significance test has also been performed in order to judge the superiority of EDEFC.
Image Classification using Shifted Legendre-Fourier Moments and Deep Learning

Image classification using deep learning has been of great interest to researchers in recent years due to the increasing use of digital images in various fields. In this paper, we present a new image classification system using image moments and deep learning. The proposed system is derived by combining Shifted Legendre-Fourier moment's invariants and convolutional neural network (CNN). This method allows us to obtain an image classification model with high accuracy using small image datasets during the training phase. The validity of this proposed method has been provided under different transformations.
Application and Research of Heterogeneous Computing in Ethereum Consensus Algorithm

This paper researches the consensus mechanism of Ethereum, focusing on the analysis and research of the consensus algorithm. The consensus algorithm is the kernel of Ethereum implementation, which is the most computationally intensive, time-consuming and occupies much memory. The concrete implementation architecture and method of heterogeneous operation are analyzed and implemented on the Xilinx ZCU106 platform. Finally, the verification content has been obtained.
A retrospective analysis of the interrelated development of science and higher education in the world

The article discusses the process of forming scientifi c and educational centers in a historical context.Science and education have been developed in interconnection since ancient times.Changes in one area have always entailed changes in another.All these transformations were naturally refl ected in the architectural and planning appearance of educational objects.Initially, universities did not have their own buildings, and classes were held in rented premises.At the same time, students and teachers tried to settle near the classrooms.Over time, this led to the formation of colleges, university complexes, campuses, and later entire university cities.When considering examples of university complexes in various cities and countries, the main fundamental components of modern scientifi c and educational centers were highlighted.Based on this, it was concluded that at present the university is a space of education, science and communications, the joint work of researchers, specialists and students.
Cement pavement performance evaluation based on the discrete Hopfield neural network

Because of the deficiency of the index of cement pavement performance evaluation and the defect of the evaluation method in the specification, the performance of the pavement is comprehensively evaluated by seven optimized indexes and grading standards that reflect functional performance and structure of the pavement. Because the discrete Hopfield neural network is available with simple construction procedure, less training samples, and strong objectivity.The DHNN is constructed by MATLAB to evaluate the performance of test pavement. The ideal cement pavement performance grading evaluation index matrix and 6 places unclassified of test pavement performance evaluation index matrix are input to the neural network then the evaluation result is obtained after simulating and learning. Finally, comparing the result of the DHNN with the fuzzy complex matter element method and the nonlinear fuzzy method, it is proved that the discrete Hopfield neural network evaluation method is reliable.
Structural design of anthropomorphic robot vision system

Abstract Anthropomorphic robot is one of the latest research directions in the field of intelligent robot research. It is an intelligent robot with similar shape and humanity. It has mobile function, operation function, sensing function, memory and autonomy, and can realize human-computer interaction. Robot technology has always been a hotspot of technology that people pay attention to. Because of the diverse development of robots, machine vision systems are gradually being established. The machine vision system realizes the functions of collecting images, analyzing images, acquiring information, processing images and others by simulating the human eye. It is a comprehensive technology integrating automation and computer. The article deeply studies the hardware composition and image processing technology of machine vision system and gives an example study on the application of machine vision in various fields. It also analyzes the current development status of machine vision system in China and looks forward to the bright future of machine vision.
Expert system for corn plant disease diagnosis with the breadth-first search method

Abstract Corn is one of the most important carbohydrate-producing food plants in the world, besides wheat and rice. But farmers often experience crop failures caused by disease attacks on corn plants. Some diseases that interfere with the productivity of maize plants are downy mildew, leaf spot disease, leaf rust disease, dwarf virus disease, midrib rot, stem rot and swollen burns. If the disease in corn plants is not known quickly, then the handling will be too late so that the plant will not develop, stop producing even the corn plants can die. This can lead to reduced yields and also cause crop failure. To overcome this problem, an expert system application for diagnosing maize diseases was made by adopting expert knowledge into the system using the breadth first search method that provides information about plant diseases and can diagnose symptoms of plant diseases, especially corn plants, while providing a solution to overcome them, which can later be used to reduce or reduce the risk of damage plant.
Hydrogen Sulfide Exsposure Coverage for Residents’ Health Risk at Sukawinata Landfill

Abstract This study aims was to assess environmental health risk of hydrogen sulfide gases exposure for residents living around Sukawinatan Landfill. Methods: This study was descriptive research and used quantitative analysis method. The approach used was Environmental Health and Risk Assessment. Sample in this research was 100 respondents who were divided two radiuses, 300 meters and 600 meters away from the landfills, 50 respondents for each radius. Purposive Sampling was conducted to select respondents. Air sampling was conducted to assess hydrogen sulfide concentration around landfill. Data was collected through interview using questionnaires, weighing weight and assessment of hydrogen sulfide concentration used impinger. Data analysis was conducted using univariate and risk analysis. Result: The study found that the average of hydrogen sulfide concentration in radius 300 and 600 meters were 0, 0017 mg/m 3 and 0, 0023 mg/m3 which were not exceed the threshold based on State Minister of The Environment decision Number 50 in 1996. Then, we still found that the residents remained at risk with RQ ≥ 1. The risk analysis resulted that there were 38% residents in 300 meters and 30% residents in 600 meter radius had RQ ≥ 1. Discussion and Conclusion: Though, the concentration of Hydrogen sulfide was still below the threshold but the residents living around Sukawinata landfill would be at risk to be exposed by Hydrogen sulfide which is dangerous for their health. Relocation of sukawinata landfill should be conducted since it is not eligible for landfill area anymore.
A new text-based w-distance metric to find the perfect match between words

The k-NN algorithm is an instance-based learning algorithm which is widely used in the data mining applications. The core engine of the k-NN algorithm is the distance/similarity function. The performance of the k-NN algorithm varies with the selection of distance function. The traditional distance/ similarity functions in k-NN do not perfectly handle the mix-mode words such as when one string has multiple substrings/words. For example, a two-word string of “Employee Name”, a one-word string of “Name” or more than one word such as, “Name of Employee”. This ambiguity is faced by different distance/similarity functions causing difficulties in finding the perfect match of words. To improve the perfect-match calculation functionality in the traditional k-NN algorithm, a new similarity distance metric is developed and named as word-distance (w-distance). The perfect match will help us to identify the exact required value. The proposed w-distance is a hybrid of distance and similarity in nature because it is to handle dissimilarity and similarity features of strings at the same time. The simulation results showed that w-distance has a better impact on the performance of the k-NN algorithm as compared to the Euclidean distance and the cosine similarity.
Exploring Variability in IoT Data for Human Activity Recognition

Human Activity Recognition (HAR) is a well-studied scientific area that has gained much traction with the rise of Internet of Things (IoT). Despite the interest in HAR for a wide spectrum of domains (technological, medical, etc.) only a few works exist, which study the variability in IoT data. To correctly perceive this variability, it is essential to dynamically model the evolving context of daily-life activities. Additionally, it is required to reduce the calculation cost of HAR, which is crucial for security and real-time applications. For the purpose of dynamically modeling, three context-aware approaches are formalized along with a context-free baseline. This study demonstrates improvements in terms of both of accuracy and calculation cost by considering variability in IoT data; our experimental study on real datasets reduced calculation cost by 20% while increasing accuracy by 20%.
Improving Load Forecasting and Renewable Energy Management for Green Computing using FIS, PSO and Catfish

The development and design of energy systems as an integrated part of achieving future 100% Renewable Energy (RE). Therefore, renewable energy systems are investigated and comparatively assessed to solve global energy related issues in a sustainable manner. One of the objective of this work is to investigate the renewable energy systems. Therefore, an optimized Fuzzy Interference System (FIS) is proposed for short term forecasting intervals, which considers time, temperature, pressure and relative humidity. It helps to minimize the operational costs of energy sources. Further, FIS, particle swarm optimization (PSO) and Catfish algorithms have been applied on these parameters to calculate the %error between actual and forecasted load. The simulation results showed that the %error between actual and forecasted load lies under ±3%, ±5% and ±6% for FIS, PSO and Catfish algorithms respectively. The proposed model is also compared with M. Rizwan et. al [75] where the relative error was 6%. The results showed that the proposed model has great potential for practical application in power systems.
The Effect of Accumulation on the Characteristics of Video Information Systems in Various Detection Modes

The effect of the accumulation time on the generalized signal-to-noise ratio of video information systems in various modes is considered. It is stated the signal-to-noise ratio does not depend on the accumulation time at high background level in random signal detection and background noise limited modes. The signal-to-noise ratio linearly depends on the accumulation time for the general and dark-noise limited modes.
Research on image matching algorithm based on improved SIFT UAV

Abstract Aiming at the low accuracy of UAV image registration algorithm, and the complex calculation of SIFT algorithm feature descriptor and the low time efficiency, an image matching algorithm based on improved SIFT is proposed. Firstly, the image of UAV is preprocessed, and the feature point operator is detected by Shi-Tomasi corner, then is extracted by SIFT feature vector, and the dimension of feature vector is reduced by PCA. Then the nearest neighbor method is used for feature matching, and the BBF algorithm is used to search the feature values to improve the matching speed. Finally, the RANSAC algorithm is used to purify the feature points for accurate matching. Experiments show that compared with the traditional image matching method, the proposed algorithm improves the efficiency and accuracy of the UAV image registration technology.
Development of Leap Motion Capture Based - Hand Gesture Controlled Interactive Quadrotor Drone Game

This paper presents an interactive drone game which is controlled by bare hand gestures. A typical handheld or gamepad remote controller bids inconvenience due to unnatural hand movements of the pilot. This study removes the use of handheld controllers and instead employ a contact less hand gesture device for drone control. Several applications can be explored like task designation in a multi-drone system for construction, inspection and relief operations. The drone is controlled using a PID controller which maintains a given trajectory which is determined by the hand gesture of the controller. The platform chosen by this research is an interactive game of two drones which fire IR lasers. The objective of the game is to hit the opponent with the laser emission. This platform showcases the accurateness and reliability of the hand gesture control system by controlling a real time drone application. The accuracy and precision of the movements based on the gesture of the pilot will be tested through simulations and actual implementations.
Theoretical Bases of Identifying Determinants of Protection Intentions towards Bring - Your-Own-Device (BYOD) Protection Behaviors

Bring-Your-Own-Device (BYOD) has been acknowledged as a very good practice in the workplace. With its increasing popularity where personal as well as organizational data are accessed using BYOD, there have been raising concerns on privacy and security. Most BYOD risks are related to unauthorized access to policy changes and information, leaking sensitive information to people, breach of organization data and privacy, access control, misuse, and even stolen devices. Privacy violations are of widely security breaches in business organizations when users are using their own personal devices. In this paper, it presents the appropriate theories and models to derive a set of validated determinants of protection intentions towards the protection behaviors of BYOD. The introduction of BYOD calls for a thorough investigation of whether or not such a practice poses vulnerabilities and threats to organizations.
Google Applications in Student’s Self-learning

Problem and rationale. Despite the spread of information technology in various areas of the modern society, the possibilities of cloud technology are not used much in the practice of university education. The article substantiates the effectiveness of the use of Google applications in the process of organizing student’s self-learning. Methodology. While researching the problem, we have used the analysis of theoretical and methodological literature, study of best practices in teaching mathematics and computer science using information technology, analysis of the advantages and disadvantages of using cloud technology in training, conduct of computer science courses using Google applications, as well as the management of independent learning of first-year students of Osh State University using cloud technology. Results. The capabilities of the specialized services of Google.docs, Google.tables, Google.presentation and Google.forms for creating a document, presentation, spreadsheet, online questionnaires and online tests have been defined. The use of Google services in the practice of training leads to the formation of student’s training in the skills of working with cloud technologies, meanwhile students demonstrate three levels of knowledge of cloud technologies: high, medium and low. Conclusions. Working with cloud technologies, it is possible for students to build ICT skills, skills of self-organization. Using Google applications, it is potential to monitor the implementation of self-learning, student projects, both in the process of completing assignments and achieving final results, the outcome of which is the improvement of teacher’s work process and saving time.
Wayang beber animation media as an effort for preserving wayang tradition based on information and technology

Abstract The aim of this study is to develop wayang beber animation media that used as a means to preserve the tradition of wayang beber Pacitan . This study was developmental research that consists of 5 phases, including problem identification, planning, design and development, stabilization, and dissemination. The data were stories and pictures of wayang beber , obtained through filed observation and interviewing informants, namely dalang (puppeteers) and expert in animation. Data validity was tested through data triangulation techniques, including source of data and theory. The result of this study produces wayang beber animation media that is disseminated to several government institutions, namely office for culture and tourism, elementary schools, junior high schools and senior high schools in Pacitan District. The existence of wayang beber animation media will supposedly be utilized as a media for introducing wayang beber to community and students in Pacitan District.
Potential Use of ASTER Derived Emissivity, Thermal Inertia and Albedo Image for Discriminating Different Rock Types of Aravalli Group of Rocks, Rajasthan

In the present study, broad band emissivity, apparent thermal inertia and albedo image products have been derived based on processing of Advanced Spaceborne Thermal Emission and reflection (ASTER) data and analyzed their potential for delineating different metasedimentary units of Aravalli Group in Rajasthan. The major focus of the work is to analyze the potential of emissivity bands with respect to conjugated image product of emissivity, albedo and inertia for discriminating different rock types. In this regard, it was observed that combined use of emissivity, thermal inertia and albedo was effective for delineating different rock types as the combined variation of thermo-physical and mineralogical properties were recorded in these parameters. On the other hand, broad band emissivity spectra of rocks of the study area had subtle contrast, which was not optimum for the discrimination of different rock types. This was evident in the ASTER derived emissivity composite in which spatial delineation of rocks of the study area was not possible. Therefore, it is concluded that the combined use of albedo, thermal inertia and emissivity was suitable to delineate different metasedimentary rocks. This was also evident from the three dimensional data plot of selected emissivity band, inertia and albedo which separated different rocks from one another.
Towards solving 2-TBSG efficiently

Two-player turn-based stochastic game (2-TBSG) is a two-player game model which aims to find Nash equilibriums and is widely utilized in reinforcement learning and AI. Inspired by the fact that the simplex method for solving the deterministic discounted Markov decision processes is strongly polynomial independent of the discount factor, we are trying to answer an open problem whether there is a similar algorithm for 2-TBSG. We develop a simplex strategy iteration where one player updates its strategy with a simplex step while the other player finds an optimal counterstrategy in turn, and a modified simplex strategy iteration. Both of them belong to a class of geometrically converging algorithms. We establish the strongly polynomial property of these algorithms by considering a strategy combined from the current strategy and the equilibrium strategy. Moreover, we present a method to transform general 2-TBSGs into special 2-TBSGs where each state has exactly two actions.