Reviewerly/exHarmony huggingface dataset:
An image vision and automatic calibration system for universal robots

The rapid development of technology is causing the replacement of many traditional manufacture industries by automation. Robotic arms are now commonly used in many sectors. The requirements for robotic arms in different sectors are quite different; but, in general, robotic arms not only save cost and man power, they also improve safety. The aim of this study was an investigation of the integration of image identification with robotic arms. To do this, the Denavit–Hartenberg transformation matrix was used to analyze the mechanical kinematics of the joints of each robotic arm axis. This allowed the spatial relationships between the Cartesian 3 D coordinate system and the joint of each axis to be determined and communication between the robotic arm and image identification to be established. A robotic arm prototype platform with automatic image identification and calibration was constructed using a quick and robust method. Several of the variables that exist in real robotic arm applications have been solved in this study: primarily the accuracy error that occurs when repeated gripping of workpieces is done and a movement and placement track is set up. Deviations occur frequently and if image identification can be applied to offset repeated inaccuracy, the quality of finished products will be more consistent. The Universal Robots 5 six-axis robot and cameras, which use a filter, Sobel computation, and Hough transformation computer vision image processing technology, together comprise a system that can automatically identify the workpiece and carry out loading and unloading accurately.
Classification of Concentration Levels in Adult-Early Phase using Brainwave Signals by Applying K-Nearest Neighbor

The brain controls the center of human life. Through the brain, all activities of living can be done. One of them is cognitive activity. Brain performance is influenced by mental conditions, lifestyle, and age. Cognitive activity is an observation of mental action, so it includes psychological symptoms that involve memory in the brain's memory, information processing, and future planning. In this study, the concentration level was measured at the age of the adult-early phase (18-30 years) because in this phase, the brain thinks more abstractly and mental conditions influence it. The purpose of this study was to see the level of concentration in the adult-early phase with a stimulus in the form of cognitive activity using IQ tests with the type of Standard Progressive Matrices (SPM) tests. To find out the IQ test results require a long time, so in this study, a recording was done to get brain waves so that the results of the concentration level can be obtained quickly.EEG data was taken using an Electroencephalogram (EEG) by applying the SPM test as a stimulus. The acquisition takes three times for each respondent, with a total of 10 respondents. The method implemented in this study is a classification with the k-Nearest Neighbor (kNN) algorithm. Before using this method, preprocessing is done first by reducing the signal and filtering the beta signal (13-30 Hz).The results of the data taken will be extracted first to get the right features, feature extraction in this study using first-order statistical characteristics that aim to find out the typical information from the signals obtained. The results of this study are the classification of concentration levels in the categories of high, medium, and low. Finally, the results of this study show an accuracy rate of 70%.
Intelligent Inspection of Marine Disasters Based on UAV Intelligent Vision

Yue, G. and Pan, Y., 2019. Intelligent inspection of marine disasters based on UAV intelligent vision. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 410–416. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the intelligent inspection ability of marine disasters, an intelligent inspection method of marine disasters based on intelligent visual feature analysis of UAV is proposed, the remote sensing image acquisition model of UAV for intelligent inspection of marine disasters is constructed, the remote sensing feature detection of UAV for intelligent inspection of marine disasters is carried out by using the method of edge profile feature decomposition of marine disasters, and the pixel spatial reconstruction model of remote sensing images of UAV for intelligent inspection of marine disasters is constructed. Edge pixel information fusion and image region reconstruction are used to extract the high spatial features of UAV remote sensing images for intelligent inspection of marine disasters. Combined with UAV remote sensing data fusion method, the intelligent inspection of marine disasters is realized. The simulation results show that the method has good accuracy in intelligent inspection of marine disasters, and the accuracy of remote sensing feature extraction of UAV in intelligent inspection of marine disasters is high, and the intelligent inspection and dynamic real-time monitoring of marine disasters are realized.
The advent of digital pathology: A depth review

Digital pathology is an image based information environment which is enabled by computer technology that allows for the management of information generated from a digital slide. Digital pathology is enabled in part by virtual microscopy, which is the practice of converting glass slides into digital slides that can be viewed, managed, shared and analyzed on a computer monitor. Growing demand for accurate and reliable diagnosis along with issues of patient safety is pushing traditional diagnosis towards an update. Over the last two-three decades the field of optics has made great advancements in the form of ever-improving optics and digital cameras. Persistent gains in computer processing power, data transfer speeds, advances in software and cloud storage solutions have enabled the use of digital images for a wide variety of purposes in pathology. High-resolution images are generated from whole glass slides which can be analyzed and managed using software. Digital Pathology has become a useful and valuable tool in clinical and research pathology. A fully digital workflow would mean that image analysis could be performed on any pathology image without the need for specific image preparation. Image analysis software is already widely available, and has FDA regulatory approval. The digital decade will likely redefine how pathology is practiced and the role of the pathologist.
THE EFFECT OF THE THEORY OF PLANNED BEHAVIOR AND THE THEORY OF ETHICS IN DIGITAL PIRACY

This study aims to analyze and describe the factors that influence Turkish computer users’ behaviors related to digital piracy in the context of the theory of planned behavior and ethical models. Relational research model was used to reveal the relationship between variables. Structured questionnaires were used for obtaining data and research units were easily sampled. Both single and multi-variabe statistical analysis methods were used while analysing research data and after all structural equilibrium model and path analyses were carried out for testing the hypotheses. While habit the additional factor of the model and the perceived behavioral control factor does not affect the behavior of the individual; attitude and subjective norm factors have influence on intention. Also, intention factor affects the behaviors of the individual for digital piracy as expected. In the context of general ethical theory, perceived benefit factor forming the teleological ethics does not affect the intention of the individual but it affects the attitude with the perceived risk factor. It is also accepted that the moral obligation and justice factors forming the deontological ethics affect the subjective norm and the moral obligation affects the intention of the individual on the digital piracy.
Data pixelization for predicting completion time of events

Nowadays, a company uses many sensors to record its entire activity process; the recorded data are called event-log. However, event-log prevalently contains discrete data that many powerful machine-learning algorithms are unable to deal with. One-hot encoding is an outstanding method for transforming discrete data into a binary vector. Nonetheless, if there are many distinct values, the problem of dimensionality will be incurred. To tackle this issue, we propose a new approach, called the Pixelization method, which transforms event data into images. We experimentally performed causal inference for prediction of pixels (representing the processing time of each event) by using a generative model with our novel convolution technique. We compared our approach with a baseline method, one-hot encoding, and an entity-embedded approach combined with a neural network model. The results showed that our approach outperforms the state-of-the-art methods in terms of accuracy.
The Impact of Green Human Resource Management Practices (GHRMP) on the competitive advantage of the organization

The present study aims to investigate the impact of the green human resources management practices (GHRM) on the Jordanian educational organization competitive advantage. Furthermore, the study attempts to investigate the relationship between the GHRM and the competitive advantage of the organization; in terms of the Green selection and recruitment (S&R), training and development (T&D) and rewards system (RS) in the Jordanian educational organization. To achieve the study goals, I designed a questionnaire for data collection. However, the study community is the Jordanian organizations, and the study sample is the educational organizations in Jordan. The questionnaire is distributed to (9) educational organizations in Jordan within the period August to October 2018. The study found that the implementation of the green human resources management practices have a positive relation to increase the competitive advantage of the educational organizations in Jordan.
Socialization of Information Technology Utilization and Knowledge of Information System Effectiveness at Hospital Nurses in Medan, North Sumatra

Background of this research is the globalization and development of science, especially in the field of information and communication technology and communication that has influenced and has implications for changes and renewal of people's lives, including in the field of nursing.So that the role of information and communication in this aspect of life is very important, even the futurists, for the most part, have an agreement that one of the most important strengths as the source of future power is information.Purpose: identify the use of information technology in nursing to determine the effectiveness of the use of information systems in nursing, identify nurses 'knowledge about the effectiveness of nursing information systems, identify nurses' knowledge seen from the socialization of the effectiveness of nursing information systems.Method: Quantitative Research Type with a survey approach conducted on 220 nurses.Significant validity test is <0.05,Cronbach Alpha reliability test> 0.60.The data is then tested in a classic assumption test consisting of multicollinearity tests, autocorrelation tests, heteroscedasticity tests, normality tests, multiple linear regression, t-tests, F tests, coefficient of determination tests.Results: the use of information technology affects the effectiveness of nursing information systems.Nurse knowledge does not affect the effectiveness of nursing information systems.Nurse knowledge seen from socialization does not affect the effectiveness of nursing information systems.The use of information technology and nurse knowledge influences the effectiveness of nursing information systems.The results of the coefficient of determination that affect the use of information technology, knowledge of nurses, socialization as a control variable on the effectiveness of nursing information systems.Suggestion: Hospital managers must pay attention to the quality of nursing human resources, through training, certification, recognition of competencies, supervision, selection, and guidance aimed at improving safe, comfortable and satisfying services for patients, families, communities.
Volumetric Representation of Semantically Segmented Human Body Parts Using Superquadrics

Superquadrics are one of the ideal shape representations for adapting various kinds of primitive shapes with a single equation. This paper revisits the task of representing a 3D human body with multiple superquadrics. As a single superquadric surface can only represent symmetric primitive shapes, we present a method that segments the human body into body parts to estimate their superquadric parameters. Moreover, we propose a novel initial parameter estimation method by using 3D skeleton joints. The results show that superquadric parameters are estimated, which represent human body parts volumetrically.
Understanding the behavior of stutter through the sequencing of STR alleles

This work explores the influence of several variables on stutter formation across sequenced autosomal STR loci (simple, compound, and complex motifs) and different alleles within each locus. The variables are sequence variations within the repeating motifs and flanking region [[1]Woerner A.E. et al.Flanking variation influences rates of stutter in simple repeats.Genes (Basel). 2017; 8: 1-20Crossref Scopus (17) Google Scholar,[2]Woerner A.E. et al.Compound stutter in D2S1338 and D12S391.Forensic Sci. Int. Genet. 2019; 39: 50-56Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar]; longest uninterrupted stretch (LUS) [[3]Brookes C. et al.Characterizing stutter in forensic STR multiplexes.Forensic Sci. Int. Genet. 2012; 6: 58-63Abstract Full Text Full Text PDF PubMed Scopus (105) Google Scholar]; parental allele length [[3]Brookes C. et al.Characterizing stutter in forensic STR multiplexes.Forensic Sci. Int. Genet. 2012; 6: 58-63Abstract Full Text Full Text PDF PubMed Scopus (105) Google Scholar]; and base pair content and length value of each repeating motif from which the stutter has generated [[3]Brookes C. et al.Characterizing stutter in forensic STR multiplexes.Forensic Sci. Int. Genet. 2012; 6: 58-63Abstract Full Text Full Text PDF PubMed Scopus (105) Google Scholar,[4]Vilsen S.B. et al.Stutter analysis of complex STR MPS data.Forensic Sci. Int. Genet. 2018; 35: 107-112Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar]. Over six hundred unrelated individuals from different populations were amplified with the prototype PowerSeq 46GY System and sequenced on the Illumina MiSeq platform. Raw FASTQ files were analyzed with STRait Razor v3 [[5]Woerner A.E. et al.Fast STR allele identification with STRait razor 3.0.Forensic Sci. Int. Genet. 2017; 30: 18-23Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar]. Stutter ratio was calculated for motifs that exhibited stutter using the ratio of the observed coverage of the stutter sequence at (N-1) position to the observed coverage of the allelic sequence. Understanding the behavior (abundance, reproducibility, sequence context) of non-allelic artifacts will help in establishing probabilistic models for the prediction of stutter rate and interpretation of sequence-based STR profiles.
VCOS: A Novel Synergistic Oversampling Algorithm in Binary Imbalance Classification

Learning from class-imbalanced data is a challenging problem as standard classification algorithms are designed to handle balanced class distributions. Scholars solve this problem by modifying classifiers or and generating artificial data by oversampling. The former usually design corresponding classifier to adapt them to the imbalanced data, while the latter exploits the sampling algorithm, which are the data preprocessing steps independent of the classifier. In this paper, we propose a novel synergistic oversampling algorithm to combine the oversampling and classification into one without training the classifier repeatedly, which can generate new pertinent samples according to the classification performance of the classifier without repeat training or deep understanding of the classifier, so the generated samples can guarantee the performance improvement of the classifier. Moreover, The proposed framework enclosures the oversampling method without traditional parameters in oversampling methods. Experimental results on several real-life imbalanced datasets demonstrate the effectiveness and efficiency of the proposed algorithm in binary classification problems.
Chatbot: A Virtual Medical Assistant

A Chatbot is a software that allows to initiate a conversation between the user and the system. It is an Artificial Intelligence (AI) based software that can be established as messaging applications, web applications or mobile applications. A chatbot represents a question answering system that uses Natural Language Processing (NLP). Formulating responses to questions in natural language. Chatbots are making a mark in the field of Medicine and provides an effective way to handle patients of medical organizations. Medical chatbots are able to conduct one-on-one conversations with patients and evaluate each patient's individual requests. The objective of this project is to analyze the existing e healthcare system that involves a novel human -machine interaction and proposes an alternative system: a Chat Interface that is designed and trained in order to act and interact with patients as a human being. The Proposed system is a web application with a chatbot in it. The system provides a chat interface which allows the user to enter their symptoms manually; this helps the system to predict the diseases. After predicting the disease it shows the nearby physician to consult. Also the Patient can book an appointment with the physician.The system also provides daily health tips to the users to help them keep their health in check.
Ensemble convolutional neural networks for automatic fusion recognition of multi‐platform radar emitters

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.
The Challenges and Strategies of Teachers in Teaching TOEFL and IELTS Test Preparation

High-stake English proficiency tests, such as TOEFL and IELTS, open up opportunities for the people in Indonesia to study in reputable universities, both local and abroad. Since Indonesian learners are non-native English speakers, some institutions help them to overcome the problems they have with the tests. Related to these issues, the teachers may face some challenges in teaching the students who want to reach their target scores. For that reason, this study was conducted to find out: 1) teacher challenges in teaching the TOEFL and IELTS tests, and 2) teacher strategies in motivating the students. The location of the study was in an English educational institution in Pekanbaru. The participants were 10 teachers who had taught these tests preparation for more than two years. The method used was a qualitative approach with face-to-face interviews as the data collection instrument. It was found that the challenges of the teachers in teaching the tests were 1) students’ high expectations, 2) lack of effort from students, 3) study time limitations, 4) teachers’ experience, 5) issues with high school students, and 6) the number of students in a class. It was also found that the teachers’ strategies in motivating the students were 1) students’ awareness of the opportunities of passing the tests, 2) fun learning, 3) telling stories of people who are successful through passing the tests, and 4) reminding the students why they joined the class, spending much money, time and energy on these tests. All in all, though teachers faced some challenges in teaching IELTS and TOEFL tests, they also had strategies to overcome them.
Motion Signatures for the Analysis of Seizure Evolution in Epilepsy

In epilepsy, semiology refers to the study of patient behavior and movement, and their temporal evolution during epileptic seizures. Understanding semiology provides clues to the cerebral networks underpinning the epileptic episode and is a vital resource in the pre-surgical evaluation. Recent advances in video analytics have been helpful in capturing and quantifying epileptic seizures. Nevertheless, the automated representation of the evolution of semiology, as examined by neurologists, has not been appropriately investigated. From initial seizure symptoms until seizure termination, motion patterns of isolated clinical manifestations vary over time. Furthermore, epileptic seizures frequently evolve from one clinical manifestation to another, and their understanding cannot be overlooked during a presurgery evaluation. Here, we propose a system capable of computing motion signatures from videos of face and hand semiology to provide quantitative information on the motion, and the correlation between motions. Each signature is derived from a sparse saliency representation established by the magnitude of the optical flow field. The developed computer-aided tool provides a novel approach for physicians to analyze semiology as a flow of signals without interfering in the healthcare environment. We detect and quantify semiology using detectors based on deep learning and via a novel signature scheme, which is independent of the amount of data and seizure differences. The system reinforces the benefits of computer vision for non-obstructive clinical applications to quantify epileptic seizures recorded in real-life healthcare conditions.