Reviewerly/exHarmony huggingface dataset:
Encryption algorithm based on knight’s tour and n-neighbourhood addition

This paper presents a new algorithm for image encryption by extending the Knight's Tour Problem (KTP). The idea behind the proposed algorithm is to generate a Knight Tour (KT) matrix (m,n) and then divide the image according to the size of knight tour matrix into several sub matrices. Finally, apply n-neighborhood addition modulo encryption algorithm according to the solution of KT matrix over each m × n partition of the image. The proposed algorithm provides image encryption without using the cover images. Results obtained from experiments have shown that the proposed algorithm is efficient, simple and does not disclose any information from encrypted image.
Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization

A method for the development of Time-to-Collision (TTC) mathematical model for outdoor Unmanned Aerial Vehicle (UAV) using Particles Swarm Optimization (PSO), are presented. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying any braking control system when the throttle is fully released. This model provides predictions of time before UAV will collide with the obstacle in the same path based on their parameter, for instance, current speed and payload. However, this paper focus on the methodology of the implementation of PSO to develop the TTC model for 5 different set of payloads. This work utilizes a quadcopter as our testbed system, that equipped with a Global Positioning System (GPS) receiver unit, a flight controller with data recording capability and ground control station for real-time monitoring. The recorded onboard flight mission data for 5 different set of payloads has been analyzed to develop a mathematical model of TTC through the PSO approach. The horizontal ground speed, throttle magnitudes and flight time stamp are extracted from the on-board quadcopter flight mission. PSO algorithm is used to find the optimal linear TTC model function, while the mean square error is used to evaluate the best fitness of the solution. The results of the TTC mathematical model for each payload are described.
Discrete Chi-square Method for Detecting Many Signals

Unambiguous detection of signals superimposed on unknown trends is difficult for unevenly spaced data.Here, we formulate the Discrete Chi-square Method (DCM) that can determine the best model for many signals superimposed on arbitrary polynomial trends.DCM minimizes the Chi-square for the data in the multi-dimensional tested frequency space.The required number of tested frequency combinations remains manageable, because the method test statistic is symmetric in this tested frequency space.With our known tested constant frequency grid values, the non-linear DCM model becomes linear, and all results become unambiguous.We test DCM with simulated data containing different mixtures of signals and trends.DCM gives unambiguous results, if the signal frequencies are not too close to each other, and none of the signals is too weak.It relies on brute computational force, because all possible free parameter combinations for all reasonable linear models are tested.DCM works like winning a lottery by buying all lottery tickets.Anyone can reproduce all our results with the DCM computer code.a We also present one preliminary real use case, where DCM is applied to the observed (O) minus the computed (C) eclipse epochs of a binary star, XZ And.This DCM analysis reveals evidence for the possible presence of a third and a fourth body in this system.One recent study of a very large sample of binary stars indicated that the probability for detecting a fourth body from the O-C data of eclipsing binaries is only about 0.00005.
Using FLOSS for Storing, Processing and Linking Corpus Data

Corpus data is widely used to solve different linguistic, educational and applied problems. The Tatar corpus management system (http://tugantel.tatar) is specifically developed for Turkic languages. The functionality of our corpus management system includes a search of lexical units, morphological and lexical search, a search of syntactic units, a search of N-grams and others. The search is performed using open source tools (database management system MariaDB, Redis data store). This article describes the process of choosing FLOSS for the main components of our system and also processing a search query and building a linked open dataset based on corpus data.
The influential factors of business development among SMEs in the Food Industry of the GCC Region

SMEs are the core engine of most economies around the world.Empowering SMEs with the right tools, especially those tools utilized to formulate business models would help the ventures to thrive more successfully.This study aims to provide such business development support tools to the food sectors SMEs of the GCC.This paper presents the results of an on-going research, part of which is investigating the factors influencing business development decision making, among SMEs operating in the food industry of the Gulf Cooperation Council (GCC).To that end, the paper conducts an extensive literature review on the topic, which is followed by an industrial field study, and analysis of the data obtained.The field study involved 36 companies within the food industry of the GCC region.The findings from this study show that the SMEs in the food industry in the GCC region will need to have an effective business model in order to successfully run or grow their businesses.The initial result of this research shows that the business model canvas is the most suitable for SMEs in the GCC food sector.Furthermore, from the field study, a total of eleven influential factors have been identified, and these factors influence the approach to the business development process and the eventual success of such business development activities.Finally, from the eleven factors, three themes were established.These themes showed the relationship between the influential factors and the BMC; the themes were the value proposition design, customer segmentation and business planning.In conclusion, it was identified that the major challenge the food SMEs face is that of establishing a product-market fit in the development of their products and services.Product market fit as a challenge in the GCC is associated with the rapid geopolitical and social changes being experienced in the region.Hence, leading to changes in the demand and delivery of food products.Product market fit challenge is also linked to the fundamental weakness of the BMC identified in the study.Therefore, further research is required to see how the BMC can be enhanced, to make it more effective to the specific needs and structure of the GCC market environment.To do this will require to capture and incorporate the knowledge from this study and those from successful food SMEs into a single enhanced BMC.
A MULTIPLE FACE RECOGNITION SYSTEM WITH DLIB’S RESNET NETWORK USING DEEP METRIC LEARNING

Despite many approaches in facial recognition models, we often come across single facial recognition systems from an image.But the detection and recognition of a single face from an image is not very practical in an ever-changing world.For this, we need systems capable of detecting and recognizing multiple faces from a single image with which we can solve many real-world problems with a fewer number of images.So, we proposed an enhanced and efficient model, called HPMR (High Performance Multiple-face Recognition), to detect and recognize multiple faces from a single image.In this paper, we used Dlibs ResNet network with 29 convolution layers to recognize faces. The network supports both Predictor 5 and Predictor 68 model to estimate facial landmarks. This model is trained on a data set of three million faces from well-known data sets like face scrub and VGG using deep metric learning. It uses HOG feature descriptor for multiple face detection. We used this model because it gives us better results compared to other existing models. Among many possible applications for multiple facial recognition, we have implemented an automated attendance system to demonstrate our approach.
Analyzing teacher talk using topics inferred by unsupervised modeling from textbooks

We propose a method that automatically describes teacher talk. The method allows us to describe and compare classroom lessons, as well as visualizing changes in teacher discourse throughout the course of a lesson. The proposed method uses a machine learning model to infer topics from school textbooks. Certain topics are related to different contents (e.g. kinematics, solar system, electricity), while others are related to different teaching functions (e.g. explanations, questions, numerical exercises). To describe teacher talk, the machine learning method measures the appearance of the inferred topics throughout each lesson. We apply the proposed method to a collection of transcripts from physics lessons, as well as discussing the potentialities of integrating the proposed method with other kinds of automatic and manual classroom lesson descriptions.
Temporal Association Rule Mining and Updating and Their Application to Blast Furnace in the Steel Industry

Blast furnace (BF) is the main method of modern iron-making. Ensuring the stability of the BF conditions can effectively improve the quality and output of iron and steel. However, operations of BF depend on mainly human experience, which causes two problems: (1) human experience is not objective and is difficult to inherit and learn and (2) it is difficult to acquire knowledge that contains time information among multiple variables in BF. To address these problems, a data-driven method is proposed. In this article, we propose a novel and efficient algorithm for discovering underlying knowledge in the form of temporal association rules (TARs) in BF iron-making data. First, a new TAR mining framework is proposed for mining temporal frequent patterns. Then, a novel TAR mining algorithm is proposed for mining underlying, up-to-date, and effective knowledge in the form of TARs. Finally, considering the updating of the BF database, a rule updating method is proposed that is based on the algorithm that is proposed in this article. Our extensive experiments demonstrate the satisfactory performance of the proposed algorithm in discovering TARs in comparison with the state-of-the-art algorithms. Experiments on BF iron-making data have demonstrated the superior performance and practicability of the proposed method.
Usability as Speculum Mundi: A Core Concept in Socio-technical Systems Development

Usability covers the breadth and depth of the rich interaction of users and technology in the socio-technical context. Though the concept of usability is well established, the integration of usability thinking in system development is challenging, partly due to the difficulty in understanding the importance of usability and justifying the costs incurred by usability work. This article aims to bring forth three fundamental attributes of usability that originate in classical architecture design, namely, utilitas , firmitas , and venustas . We provide a model of conceptualizing usability as speculum mundi , a lens through which the impacts of interaction at all levels of the organization and society can be identified by drawing parallels between the Vitruvian design principles and the paradigms of usability conceptualization. We restate the importance of the concept of usability in the context of socio-technical systems.
Development and Optimization of NoSQL Database in Food Insecurity Early Warning System Based on Local Community Participation

As a part of the food insecurity early warning system based on local participation, a robust and scalable database service is required. This necessity caused by the large area of services which include 34 provinces, 416 districts, 7,215 sub-districts and 80,534 villages in Indonesia. The abundant number of the expected daily transaction might not be handled properly using the traditional model. In this research, we design, implement, and optimize the NoSQL database to create scalable, dynamic, and flexible database service for the early warning system. The cohesion of the model is then measured, resulting in 5 entities with high cohesion, 16 with moderate cohesion, and 3 with low cohesion. After refactoring, we reduced the number of the low-cohesion entity into one and increased the average cohesion from 0.62 to 0.67. An empirical experiment was conducted to compare the response time before and after the refactoring. As the results, the average response time is decreased from 11.0 ms to 7.99 ms or equal to 1.38 in speedup. The experiment results suggest there is an impact of the logical data model improvement, by increasing their cohesion, to the performance of the NoSQL database.
A New Testing Strategy for the Diagnosis of COVID-19 and Similar Pandemics

Objective: COVID-19 forced the entire world to close all borders yet to work together to contain the pandemic.One dimension of this joint work is sharing medical supplies, promising medications, and diagnostic tools.From the start of the pandemic, one difficulty was to obtain reliable diagnostic tools for this new virus that produces the results in a reasonable time window.We bring a new angle to this strife to increase the performance of a given diagnostic test.Material and Methods: In this research, we worked on improving the performance of the RT-PCR (Real Time-Polymerase Chain Reaction) COVID-19 diagnostic test.By obtaining the number of tests conducted and number of positive COVID-19 cases reported by the Ministry of Health of Turkey, using the Bayes' Rule, we predicted the prevalence, the number of false positives and number of false negatives, and we proposed several new testing strategies to improve the COVID-19 test.Results: We first presented the single test results.Then we showed that strong negative testing strategies would control the false negative successfully while inflating the false positives.On the other hand, strong positive testing strategy controls the false positives very well while inflating the false negative significantly.Following these results, we have also shown that three-test consensus call strategy perform the best in controlling both false negative and false positive rate.Conclusion: To contain COVID-19 or similar epidemics or pandemics, we propose a three-test consensus call strategy, which finds a reasonable balance between false positives and false negatives.
An algorithm for the restoration of blurred image obtained with a rotating camera tilted to the horizon

This work is a continuation of the authors’ previous publications, in which the restoration of images obtained with a horizontally rotating camera was considered. In this paper, a mathematical model is constructed for reconstructing blurred images obtained with a camera rotating in the horizontal plane and having its optical axis tilted at an angle to the horizon. The method of image restoration involves constructing a strip of spherical panorama from the original images. Results of numerical experiments that confirm the good quality of the presented image recovery method and the high performance of the developed algorithm are presented.
Age-based facial recognition using convoluted neural network deep learning algorithm

Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the good way from the front to the jaw are the main variables. This code distinguishes the highlight of the face that is important for your facial separation and creates your facial expression. Therefore, this study gives an overview of age detection using a different combination of machine learning and image processing methods on the image dataset.
The Readiness of Future Specialists of Preschool Education to Creative Self-Realization in Professional Activity

The article is devoted to the issue of future specialists of preschool education training to creative self-realization in professional activity. The authors of the article reveal the pedagogical diagnostics of readiness to creative self-realization in professional activity of higher educational institutions’ students. The purpose of the study is to reveal the results of pedagogical diagnostics of future specialists of preschool education readiness to creative self-realization in professional activity. A diagnostic methodology and criteria of future specialists of preschool education to creative self-realization in professional activity readiness has developed. The criteria of future specialists of preschool education to creative self-realization in professional activity readiness are: motivational and valuable, creative activity, assessment and reflective. Each criterion includes a group of quality indicators. The methodological tools was developed, which was used for pedagogical diagnostics of this professional quality forming in future specialists of preschool education. The experimental study helped to identify the characteristics of qualitative measurements for primary, reproductive and creative levels of readiness of future preschool teachers’ professional training. We see the prospect of further research in the development of methodology of future specialists of preschool education preparing to creative self-realization in professional activity.
The Clinical Effect of Psychological Nursing on Patients Undergoing Coronary Angiography Based on Gold Nanoparticle Contrast Agents

To study the clinical effect of psychological nursing on cardiovascular patients undergoing coronary angiography. Sixty cardiovascular patients in our hospital who needed coronary angiography were randomly divided into two groups (30 in each group); contrast agents based on gold nanoparticles were used for all patients. The experimental group received psychological nursing alongside conventional nursing care, and the control group received only conventional nursing care. The two groups were compared for ease of receiving coronary angiography, medical imaging, anxiety, depression, blood pressure, heart rate, efficacy, and patient satisfaction. A gold nanoparticle contrast agent has very low toxicity to cardiovascular endothelial cells, good biocompatibility, and a metabolic effect in vivo. Compared to the control group, the coronary angiography in the experimental group was easier; the medical images were clearer; patients’ anxiety, depression, blood pressure, and heart rate decreased; and curative effect and patient satisfaction were higher. Psychological nursing may significantly improve the psychological and physical condition of cardiovascular patients undergoing coronary angiography, effectively improve the efficacy as well as satisfaction of patients, and has obvious application value in clinical treatment. Similarly, gold nanoparticle contrast agents have important value in the clinical application of coronary angiography.
A medoid-based weighting scheme for nearest-neighbor decision rule toward effective text categorization

Abstract The k -nearest-neighbor ( k NN) decision rule is a simple and robust classifier for text categorization. The performance of k NN decision rule depends heavily upon the value of the neighborhood parameter k . The method categorize a test document even if the difference between the number of members of two competing categories is one. Hence, choice of k is crucial as different values of k can change the result of text categorization. Moreover, text categorization is a challenging task as the text data are generally sparse and high dimensional. Note that, assigning a document to a predefined category for an arbitrary value of k may not be accurate when there is no bound on the margin of majority voting. A method is thus proposed in spirit of the nearest-neighbor decision rule using a medoid-based weighting scheme to deal with these issues. The method puts more weightage on the training documents that are not only lie close to the test document but also lie close to the medoid of its corresponding category in decision making, unlike the standard nearest-neighbor algorithms that stress on the documents that are just close to the test document. The aim of the proposed classifier is to enrich the quality of decision making. The empirical results show that the proposed method performs better than different standard nearest-neighbor decision rules and support vector machine classifier using various well-known text collections in terms of macro- and micro-averaged f -measure.
Interactive Algorithms in Complex Image Processing Systems Based on Big Data

In the era of big data, images and videos are one of the main means of information dissemination. In recent years, research on the problem of image and video reorganization and integration has become a hot topic in digital image processing technology. Using a computer for image processing, complicated programming is unavoidable. Therefore, it is necessary to optimize the interactive algorithms for image processing. In this paper, the content of image processing experiment is screened and integrated, and an image processing experiment system based on Matlab GUI platform is established for different levels of image processing knowledge modules. In order to verify the effectiveness and practicability of the optimization algorithm proposed in this paper, experimental simulations were performed on complex natural images and complex human eye images. The speed of the USB camera is generally between 15 frames/second and 25 frames/second, and in a 240 × 320 picture, the interactive algorithm in this article only needs about 59 ms, which is enough to complete the automatic interaction in the video in real time, which is convenient for subsequent image extraction. The experimental results show that the interactive algorithm in the complex image processing system in this paper optimizes the image extraction rate and improves the antinoise performance of the segmentation and the segmentation effect of the deep depression region.
Prediksi Kelalaian Pinjaman Bank Menggunakan Random Forest dan Adaptive Boosting

Abstract — A loan is one of the most important products on the bank, which used for main revenue. All bank tries to find the most effective business strategy to persuade a customer to use the loan, but loan default has a negative effect after the application is approved. Loan default causes loss on the bank, therefore it is mandatory to calculate in order to decrease the risk of the loan default. This study uses random forest and adaptive boosting machine learning methods to get the prediction and decision. The random forest uses a voting method from many decision trees and adaptive boosting can support to increase accuracy, stability and handle an underfit or overfit problem. The experimental results show that Adaptive Boosted Random Forest outperformed normal random forest and Deep learning Neural Network (DNN) in recall rate evaluation metrics with small trade-offs in the accuracy.&#x0D; &#x0D; Keywords— Adaptive Boosting; Bank; Loan Default; Machine learning; Random Forest;