Reviewerly/exHarmony huggingface dataset:
Comparative Study of Autonomous Path Planning Methods for Mobile Robot

Currently, robots are used to carry out dangerous tasks such as bomb detonations, spray painting, disaster management and so on. To ensure sufficient robot navigation, various path-planning methods are available in literature. These methods enable robots to create collision-free path in their working environments. Such environments can be either static (laboratory) or dynamic (realistic). The available path planning methods differ with respect to latency, applicability, computational load, power consumption rates and reliability. The ever-increasing applications of the mobile robots require computable and efficient methods. However, most path planning methods are theoretical, not always reliable and require huge computational resources, hence are not suitable for all real time applications. This paper is aimed at studying and comparing some of the available and important path planning methods for autonomous mobile robots in terms of characteristics of path planning methods, such as graph-based methods, sample-based methods and potential field methods. The paper compares characteristics of the path planning methods on the uniform performance index, by designing a comparison criterion. The performance of these methods will be studied based on the designed framework, including reliability, computational requirements, and power consumption rates. Recommendations towards a feasible solution with respect to the robot-working environment and the available resources will be provided.
Research on Environment Education Strategy Water Resource Utilization Based on Dynamics

Wu, K.R. and Tian, M., 2020. Research on environment education strategy water resource utilization based on dynamics. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 498-501. Coconut Creek (Florida), ISSN 0749-0208.Nowadays, water resource utilization is the focus of the world, and people have higher requirements for a good water resource utilization. This paper based on the system dynamics to explore strategies to increase the quality of environmental education for college students. The index evaluation system of teachers' environmental education and teaching ability, students' learning level in class and students' practice level is constructed. The system dynamics model is established on each evaluation index system, and each index system is simulated and evaluated respectively. Finally, the stability of the system dynamics model is tested by sensitivity analysis. Conclusion shows that colleges should pay attention to the output of environmental research projects and encourage students to participate in scientific research projects, which can improve the quality of environmental education.
About the Routes of Teacher Training and Their Institutionalization

The article compares a concurrent route and a consecutive route of teachers’ training and critically evaluates their institutionalization. The analysis of the practices of institutionalization (study processes, certification of the studies, higher education institutions, study grants, and the classification of the study programmes and sciences) reveals that the contemporary political power supports the concurrent route of teacher training.
Question to Question Similarity Analysis Using Morphological, Syntactic, Semantic, and Lexical Features

In the digitally connected world that we are living in, people expect to get answers to their questions spontaneously. This expectation increased the burden on Question/Answer platforms such as Stack Overflow and many others. A promising solution to this problem is to detect if a question being asked is similar to a question in the database, then present the answer of the detected question to the user. To address this challenge, we propose a novel Natural Language Processing (NLP) approach that detects if two Arabic questions are similar or not using their extracted morphological, syntactic, semantic, lexical, overlapping, and semantic lexical features. Our approach involves several phases including Arabic text processing, novel feature extraction, and text classifications. Moreover, we conducted a comparison between seven different machine learning classifiers. The included classifiers are: Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Extreme Gradient Boosting (XGB), Random Forests (RF), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP). To conduct our experiments, we used a real-world questions dataset consisting of around 19,136 questions (9,568 pairs of questions) in which our approach achieved 82.93% accuracy using our XGB model on the best features selected by the Random Forest feature selection technique. This high accuracy of our model shows the ability of our approach to correctly detect similar Arabic questions and hence increases user satisfactions.
The Correlations on Coping with Cyberviolence, Attribution Method and Social Support among Middle School Students

The purpose of this study is to compare the level of coping with cyberviolence, attribution methods and social support for middle school students.Structured survey data on coping with cyberviolence, attribution methods and social support on middle school students in City A, South Korea was collected from September, 20th, 2019 to October 25th, 2019.The survey data were analyzed from 257 responses, excluding unreturned or incomplete surveys.Data were analyzed using the Pearson correlation coefficient to identify the significant correlations on coping with cyberviolence, and psychological variables such as attribution methods and social support for middle school students.This study found that technical defense(r=.114,p<.01), active response(r=.253,p<.001) on cyberviolence had a significant and positive correlation to social support.On the other hand, passive response(r=.-154,p<.001), disregard(r=-.115,p<.01), and evasion(r=-.126,p<.001) on cyberviolence had a significantly inverse correlation to social support.Therefore, this study provided prevention education programs for coping with cyberviolence, a meaningful direction in which further research may take note.
Assistive Model to Generate Chord Progressions Using Genetic Programming with Artificial Immune Properties

In Western tonal music, tension in chord progressions plays an important role in defining the path that a musical composition should follow. The creation of chord progressions that reflects such tension profiles can be challenging for novice composers, as it depends on many subjective factors, and also is regulated by multiple theoretical principles. This work presents ChordAIS-Gen, a tool to assist the users to generate chord progressions that comply with a concrete tension profile. We propose an objective measure capable of capturing the tension profile of a chord progression according to different tonal music parameters, namely, consonance, hierarchical tension, voice leading and perceptual distance. This measure is optimized into a Genetic Program algorithm mixed with an Artificial Immune System called Opt-aiNet. Opt-aiNet is capable of finding multiple optima in parallel, resulting in multiple candidate solutions for the next chord in a sequence. To validate the objective function, we performed a listening test to evaluate the perceptual quality of the candidate solutions proposed by our system. Most listeners rated the chord progressions proposed by ChordAIS-Gen as better candidates than the progressions discarded. Thus, we propose to use the objective values as a proxy for the perceptual evaluation of chord progressions and compare the performance of ChordAIS-Gen with chord progressions generators.
IN THE CONTEXT OF TIME-INDEPENDENT PARAMETERS IN TWO QUANTUM SYSTEMS: QUANTUM ENTANGLEMENT AND CORRELATIONS WITH NEGATIVITY MEASUREMENT

In this paper, we consider two phonons and three-level trapped ion in configuration forming Hilbert 12-space. The negativity and quantum correlations are revealed in trapped ion two phonon states system. Three values of LDP, = 0.006, =0.06 and =0.08 are given. The effects of the time-independent coupling in terms of the system, degree of quantum entanglement are investigated. Therefore, we have found the main optimal times for obtaining the high amount of entanglement with negativity.In this paper, we consider two phonons and three-level trapped ion in configuration forming Hilbert 12-space. The negativity and quantum correlations are revealed in trapped ion two phonon states system. Three values of LDP, = 0.006, =0.06 and =0.08 are given. The effects of the time-independent coupling in terms of the system, degree of quantum entanglement are investigated. Therefore, we have found the main optimal times for obtaining the high amount of entanglement with negativity.
Application of Kinect Technology and Artificial Neural Networks in the Control of Rehabilitation Therapies in People with Knee Injuries

In the field of physiotherapy, the recognition of the poses of the human body is obtaining more research so that the patient has an accelerated recovery rate in his rehabilitation.Nowadays, it is not so challenging to have devices like Microsoft Kinect that allow us to interact with the user for the recognition of poses and body gestures.The objective of this work to capture the data of the joints of a person's body through a set of angles using the Kinect device, then artificial neural networks with the Back-Propagation algorithm were used for machine learning, and their precision was determined.The results found on the performance of the neural network show that 99.70% accuracy was achieved in the classification of the patients' postures, which can be used as an alternative in the rehabilitation therapies of patients with knee injuries.
Path Planning with Objectives Minimum Length and Maximum Clearance

In this paper, we study the problem of bi-objective path planning with the objectives minimizing the length and maximizing the clearance of the path, that is, maximizing the minimum distance between the path and the obstacles. The goal is to find Pareto optimal paths. We consider the case that the first objective is measured using the Manhattan metric and the second one using the Euclidean metric, and propose an $$O(n^3 \log n)$$ time algorithm, where n is the total number of vertices of the obstacles. Also, we state that the algorithm results in a ( $$\sqrt{2}, 1$$ )-approximation solution when both the objectives are measured using the Euclidean metric.
Research on Quality Control of Marine Monitoring Data Based on Extreme Learning Machine

The collection and research of marine monitoring data are the main work of marine science. How to obtain the observation data of un-known sea area is of great significance to marine scientific research. Using the data provided by the Argo project, in this work we aim to effectively predict the marine monitoring data with the extreme learning machine which is simple in structure with short training time, in order to achieve the accuracy of marine information. According to the size of the sample for adjusting the corresponding model parameters, the pre-diction of marine data can be achieved. By comparing the forecast data of the unknown sea area and the data originally provided by the Argo plan, the accuracy of the ocean data is higher and the trend of the reflected data is more detailed.
EFEKTIFITAS PENGGUNAAN METODE DISCOVERY LEARNING TERHADAP HASIL BELAJAR MENULIS KALIMAT BAHASA JEPANG

This research aimed to examine the effectiveness of the use of discovery learning method in writing Japanese sentences. This research used the quasi-experiment method with pretest and posttest design. The population in this research was the students of class X Tourism major with a total of 24 students. The data of this research were obtained from students learning outcomes before and after being treated using the discovery learning method. After three treatments, the students' average pretest score was 17,166 and the average posttest score was 89,583 so that an average difference of 72,417 was obtained. T-test results obtained t-table with a significance level of 5% at the degree of freedom = 46 was 30.82. Therefore, t-count was greater than t-table which meant that Ha was accepted and H0 was rejected. After calculating the average normalized gain, a value of 0.89 was obtained, which was interpreted as a very effective criterion. The results of the research proved that there was an increase in the students learning outcomes after using the discovery learning method. The use of the discovery learning method in Japanese sentence writing skills proved to be very effective and had a great contribution to improve the students’ learning outcomes.
Theoretical basis of criteria for the formation of value orientations of future music teachers

The article is devoted to the problem of formation of value orientations of future music teachers in the process of professional preparation. The criteria of revealing the state of formation of value orientations, consistent with the component structure of the identified phenomenon and determining the levels of their formation are considered. Value orientations are formed on the basis of the system of values of the person and are the most important element in the structure of personality. They reflect the selective, subjective attitude of the individual towards objective phenomena or objects. The importance of the individual depends on how he assimilates and realizes the achievements of the practice of society and manifests in it his individual abilities. The need for radical reform of education today is often associated with the idea of ​​reviving the principle of the priority of spiritual values over the material and economic ones. This requires that in each of the available pedagogical students the system of values and the problem of their formation should be specially investigated. The reason that hinders the formation of correct value orientations is that the higher spiritual values of humankind began to play an increasingly important role in modern life, but to satisfy only primitive needs at the level of «pseudo-values». That is why the value-oriented, active, personal approach to the upbringing of young people is an important condition for the formation of value orientations of a person embodied in the forms of social-pedagogical practice. Any work of human activity realizes the value aspect, but it becomes valuable only when it satisfies a certain need. Interest in certain needs is a prerequisite for evaluation. Lack of need makes assessment impossible. Thus, the assessment combines the potential value of the phenomenon or object with the needs and interests of the subject. The assessment assumes the existence of the basis or criterion against which the evaluation takes place. This is justified by the need to determine the value of an object or standard, a norm that compares an object with a fixed pattern. Thus, the act of evaluation involves a comparison of two realities - spiritual and material.
Path Planning of the Manipulator Arm FANUC Based on Soft Computing Techniques

In this paper, direct and inverse geometric models for a 6 degrees of freedom manipulator robot arm are developed, and a set of homogeneous matrices are generated by Denavit-Hartenberg formalism. Moreover, a path planning method based on soft computing techniques is presented, which consists of using the neural network to model the end-effector workspace, and then determining the optimal trajectory to reach a desired position. The optimization of the trajectory depends on the minimization of the cost function, defined by the sum of two energies. The first one is the collision penalty (Ec) generated by each obstacle shape; the second one is the trajectory length penalty (El). Four steps are considered; at first, the configuration of the robot workspace where any assumption is taken into account. The robot is assimilated to a particle that moves inside a small two-dimensional space, and the obstacle is a polygon. Second, the Multilayer Perceptron Neural Network (MLP neural network) is used for the robot workspace modeling. Model block inputs are robot’s previous position values and the outputs are robot’s following position values. Then, the proposed approach is applied to optimize the cost function and to determine the end-effector trajectory in case of obstacles. The obtained result is a smooth trajectory, which represents robot motion. Finally, the presented approach is validated on a FANUC robot arm in a virtual environment, where the simulation results show this approach efficiency.
Petrochemistry of Lan Sang metamorphic suites

Western metamorphic belt in Thailand elongated in North-South direction has been grouped into the Chiang Mai-Lincang belt.As part of this metamorphic belt, the Lan Sang National Park located in the west of Tak Province is an affected area from northwest-southeast striking Mae Ping fault.Along the fault zone, rock formations have been strongly deformed, yielding steep foliation and lineation.Based on petrographic study, these rocks are mainly characterized by gneisses showing very fine-to coarse-grained textures which consist of quartz, plagioclase, K-feldspar, biotite, pyroxene, and chlorite.Calc-silicate and impure marble are partly interlayered in these gneisses; they are composed of calcite, quartz, feldspar, pyroxene, and garnet.Moreover, granitic gneiss, which cuts into the host gneisses and calc-silicate, appears to have a similar composition of gneiss.Based on geothermobarometry, these rocks should have undertaken high-grade metamorphism of lower amphibolite facies with P-T (pressure-temperature) ranges of 570-590 °C and 5.8-8.1 Kbar for gneiss.These rocks appear to have several evolution stages.Sedimentary protoliths of gneisses and calc-silicate may have situated in the area prior to regional metamorphism.Subsequently, dikes and veins crosscut into these gneissic and calc-silicate rocks before later metamorphism appears to have continued and reached the ductile-brittle stage.
Cloud Computing Adoption at Higher Education Institutions in the Republic of Sudan

Education today is becoming completely associated with the Information and Communication Technology (ICT) on the content delivery, communication and collaboration.Cloud computing simply involves the provision of information technology (IT) solutions as a service rather than as a product through the Internet.Sudan just like any other developing countries is yet to fully adopt cloud based systems according to trust and security challenges.The motive of this paper is to identify the extent and characteristics of Cloud Computing adoption in Sudanese universities and higher education institutions.The study assessed Cloud Computing uses in terms of actual use of cloud services and applications, ability of transition from conventional systems to cloud computing technology, and efficiency of the technical staff of IT to deal with modern technologies.The adoption of the Cloud Computing services in higher education in Sudan was relatively low.The results showed that here is a significant relationship between the adoption of Cloud Computing in Sudan and the four independent variables; (Basic knowledge of cloud computing services and applications, the actual use of cloud computing technology, ability of transition from conventional systems to Cloud Computing Technology and skills of IT human resources) at level of significance α= 0.05.
A Bayesian Small Area Model with Dirichlet Processes on the Responses

Abstract Typically survey data have responses with gaps, outliers and ties, and the distributions of the responses might be skewed. Usually, in small area estimation, predictive inference is done using a two-stage Bayesian model with normality at both levels (responses and area means). This is the Scott-Smith (S-S) model and it may not be robust against these features. Another model that can be used to provide a more robust structure is the two-stage Dirichlet process mixture (DPM) model, which has independent normal distributions on the responses and a single Dirichlet process on the area means. However, this model does not accommodate gaps, outliers and ties in the survey data directly. Because this DPM model has a normal distribution on the responses, it is unlikely to be realized in practice, and this is the problem we tackle in this paper. Therefore, we propose a two-stage non-parametric Bayesian model with several independent Dirichlet processes at the first stage that represents the data, thereby accommodating some of the difficulties with survey data and permitting a more robust predictive inference. This model has a Gaussian (normal) distribution on the area means, and so we call it the DPG model. Therefore, the DPM model and the DPG model are essentially the opposite of each other and they are both different from the S-S model. Among the three models, the DPG model gives us the best head-start to accommodate the features of the survey data. For Bayesian predictive inference, we need to integrate two data sets, one with the responses and other with area sizes. An application on body mass index, which is integrated with census data, and a simulation study are used to compare the three models (S-S, DPM, DPG); we show that the DPG model might be preferred.
Breast Cancer Classification using Python Programming in Machine Learning

Breast cancer is a disease in which cells in the breast grow out of control in a rapidly.Breast cancer occurs when a malignant (cancerous) tumor originates in the breast cells.It is the most commonly occurring cancer in women and the second most common cancer overall.Around 2 million cases were observed in 2018.The early diagnosis of breast cancer can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients affected.Further accurate classification from the data of benign tumors can prevent patients from undergoing unnecessary treatments.Thus, the correct diagnosis of breast cancer and the classification of patients into malignant or benign groups is the subject of all research done and observed.Because of its unique advantages in critical features detection from complex breast cancer datasets, machine learning (ML) is widely recognized as the methodology of choice in Breast Cancer pattern classification.This project is a relative study of the implementation of models using Logistic Regression, SVM, KNN, Random Forest, and Decision tree, which is done on the data set taken from the UCI repository.We have obtained the highest accuracy for the random forest that is 97%.We have also obtained the accuracy of 95%, 93%, 95%, 94% for logistic regression, SVM, KNN and Decision tree respectively
A Clear and Comprehensive Guide to GPS [Book/Software Reviews]

The author of Introduction to GPS: The Global Positioning System, Dr. Ahmed El-Rabbany, is currently a professor of civil engineering at Ryerson University in Toronto, Canada. He is also an honorary research associate at the University of New Brunswick and an adjunct professor at York University. He holds a Ph.D. in GPS from the University of New Brunswick, Canada, and has more than 20 years of research, instructional, and industrial experience in the discipline of geomatics engineering.
Unsupervised Anomaly Detection Using Intelligent and Heterogeneous Autonomous Systems: Highlights From the 2020 IEEE Signal Processing Cup Student Competition [SP Competitions]

Presents highlights from the Highlights from the 2020 IEEE Signal Processing Cup student competition.