Reviewerly/exHarmony huggingface dataset:
RESEARCH OF VISION-BASED NAVIGATION AND POSITIONING TECHNOLOGY FOR MOBILE CAMPUS INTELLIGENT ROBOT

This paper mainly studies the autonomous positioning and navigation technology of intelligent robots.In the campus environment to perform security monitoring tasks, this article uses gps to achieve location tracking.Due to the loss of gps signal due to campus buildings and trees, the monocular vision is further selected as a robot-assisted positioning method, and two positioning methods are combined.Achieve effective location monitoring.On the other hand, the use of monocular visual perception of the road environment to achieve autonomous navigation, mainly to achieve the GPS positioning function, manual road sign positioning function, road area detection function.Therefore, the paper tests the artificial road sign location and road identification respectively.The test results show that the acquisition success rate is 96.5% and the average processing speed is 30.34 frames/s without occlusion interference.
Political hate speech and the use of big data

Chapter 10 addresses the theme of the use of big data and profiling in politics. The advent of big data and predictive analysis has completely changed the picture of the spread of hatred online, thanks to the possibility of carefully profiling online subjects and targeted hatred. The chapter discusses psychopolitics, that is, the possibility to understand detailed information on many aspects of voters or political opponents simply by carefully analysing the data. The final section discusses voter profiling, a topic that, after the Cambridge Analytica scandal, has become very topical.
A DEVELOPMENT OF THE COMPETENCE MATRIX ON THE BASIS OF COMPARISON OF LEVELS OF PROFESSIONALISM AND FORMATION OF PROFESSIONAL COMPETENCE

As you know, professionalism can be described through the ratio of the motivational component of a person (professional values, goals, self-esteem, etc.) and its operational component (ability to learn, mastering techniques and technologies, professional abilities, etc.). As professionalism can be shown both in high indicators of results of activity, and in emotional and physical states of the person, it is necessary to allocate two groups of criteria of efficiency: 1) external (objective) criteria that focus on assessing the effectiveness of the professional task; 2) internal, psychological criteria for evaluating activities. If we talk about skills and abilities, then first they are acquired as separate components (actions), then they are “honed”, combined and form integral structures (so-called skill systems), and then – are generalized to the level of “secondary skills” which are characterized by flexibility, variability, indirectness of knowledge. However, any skills can not only be improved, but also subjected to destructive processes under the influence of subjective and objective factors. These processes are accompanied by the destruction of the structure of activity, which is manifested in various types of occupational deformities. For example, a programmer who has not worked with a programming language for a long time eventually loses the level of work achieved in it, and to return to the proper level of work, must spend some time to remember and improve previously acquired skills and abilities. In today’s conditions, this can be critical, because quite often the result is required immediately. The article defines qualitative evaluation criteria and quantitative indicators that meet them and which should be quantified to form a clear picture of the level of professionalism of the individual in decision-making in a given situation. The paper presents the criteria and levels of professionalism, stages of professional development, which describe the professional growth from a low level of professional competence to the highest, and formulates the goal to be met by the person (candidate). A matrix of competencies is also built on the basis of comparing the levels of professionalism and the formation of professional competence.
Motility Device

Out of world's population 15% of them have any kind physical disabilities most of them have issues with lower limbs.They are struggling and cannot move without aid.Most commonly above 65 years old person struggles the most.The standup Motility device helps to overcome these problems and helps to move without external aid.The device can be remote controlled when you are on bed or away from the device.It helps to person to stand and sit anywhere.The device is also smartphone connected.The speed of the device is controllable and can acquire a maximum speed of 10km/h.Our project can help the disabled by making themselves confident and competent.
Maximum entropy-based semi-supervised learning for automatic detection and recognition of objects using deep ConvNets

Object detection and localisation is one of the major research areas in computer vision that is growing very rapidly. Currently, there is a plethora of pre-trained models for object detection including YOLO, mask RCNN, RCNN, fast RCNN, multi-box, etc. In this paper, we proposed a new framework for object detection called 'maximum entropy-based semi-supervised learning for automatic detection and recognition of objects'. The main objective of this paper is to recognise objects from a number of visual object classes in a realistic scene simultaneously. The major operations of our proposed approach are preprocessing, localisation, segmentation and object detection. In the preprocessing, three processes, noise reduction, intensity normalisation, and morphology are considered. Then localisation and object segmentation is performed using maximum entropy in which optimal threshold is detected and in the end, object detection is performed using deep ConvNet. The performance of the proposed framework is evaluated using MATLAB-R2018b and it is compared with some previous state of the art techniques in terms of localisation error, detection and segmentation accuracy along with computation time.
A binary ABC algorithm based on advanced similarity scheme for feature selection

© 2015 Elsevier B.V. All rights reserved. Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers' attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection.
Research Design: Introduction And Overview

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
Image Enhancement using Recursive Standard Intensity Deviation Based Clipped Sub Image Histogram Equalization

The low exposure image enhancement has become indispensable inimage processing for better visibility. The most challenging in image enhancement is especially to curtail over-enhancement problems. This paper presents a method, performs the separation of the histogram based on respective standard intensity deviation value and then recursively equalizes all sub histograms independently. The over-enhancement problem is minimized by this method. It applies more in an underwater image, because of its low light conditions. The experiment results are analyzed in terms of entropy and output image inspection. The proposed method results show significant improvement over earlier recursive based histogram equalization algorithms.
Dynamic interplay in the information security risk management process

In this paper, the formal processes so often assumed in information security risk management and its activities are investigated. For instance, information classification, risk analysis, and security controls are often presented in a predominantly instrumental progression. This approach, however, has received scholarly criticism, as it omits social and organisational aspects, creating a gap between formal and actual processes. This study argues that there is an incomplete understanding of how the activities within these processes actually interplay in practice. For this study, senior information security managers from four major Swedish government agencies were interviewed. As a result, 12 characteristics are presented that reflect an interplay between activities and that have implications for research, as well as for developers of standards and guidelines. The study's conclusions suggest that the information security risk management process should be seen more as an emerging process, where each activity interplays dynamically in response to new requirements and organisational and social challenges.
The Back End: Programming Languages

You have spent the last couple of weeks brainstorming and refining the vision for MyAppoly. What’s next? Many would-be products never make it past this question due to the technological barrier of creating an application. Software applications are written or coded in a programming language. The code defines how the application will run and respond, encompassing everything from what you see to what happens in the background. One of the first technical decisions to make is to choose a programming language for the product. You may have heard of languages like Python, Ruby, and C, but what are they, and what are the significant differences between them? What factors should you consider when selecting a language?
Sensor based Human Activity Recognition

Human activity recognition (HAR) is to recognize another person’s activities and it is one of the active research areas in the computer field. The goal of this System is to understand people's actions and interactions. We proposed a method of Human Activity is by predicting the person's activity, their personality, and their psychological state like Human activity recognition (HAR). We propose a recurrent neural network of deep learning architecture. The critical factor of RNN includes bidirectional connection that is simply called from the input node, the information only flows in forwarding direction after that it passthrough so many hidden layers to reach the output.. This system is to design the six different activities of a human. The final model should use as a good source of information about human's daily activities. The dataset has taken from UCI Machine Learning Repository. Our system accuracy is higher than the previous results.
Minimum noiseless description length (MNDL) thresholding

In this thesis, the problem of data denoising is considered and a new data denoising method is developed. This approach is an adaptive, data-driven thresholding method that is based on Minimum Noiseless Description Length (MNDL). MNDL is an approach to subspace selection which estimates bounds on the desired Mean Square Error (MSE). The subspace minimizing these bounds is chosen as the optimum one. In this research, we explore application of MNDL Subspace Selection (MNDL-SS) as a thresholding method. Although the basic idea and desired criterion of MNDL thresholding and MNDL-SS are the same, the challenges in calculation of the desired criterion in MNDL thresholding are very different. In MNDL-SS, the additive noise effects are in the form of samples of a Chi-Square random variable. However, this assumption does not hold for MNDL thresholding anymore. In this research, we developed a new method for calculation of the desired criterion based on characteristics of noise in thresholding. Our simulation results show that MNDL thresholding outperforms the compared methods. In this thesis, we also explore the area of image denoising. In image denoising approaches, some properties of the image are considered. One of the well known image denoising methods, that outperforms other methods, is BayesShrink. We compare our method with BayesShrink. We show that the results of MNDS thresholding are comparable with BayesShrink in our simulations.
Statistical Validation of e-Learning Assessment

This paper focuses on how to fulfill the objectivity and reliability goals, as well as the efficiency of the e-learning evaluation tools, and their integration in a blended evaluation system.In order to contribute to these goals, a new branch of statistics, i.e. "Statistical Learning", has been chosen to support this study.The proposed techniques can be very simply implemented with little knowledge of arithmetic and with the help of a standard spreadsheet.These techniques can allow us to get the whole picture of the evaluation procedure output, in order to systematically sort the main categories of the different students, and to easily identify the outliers for further assessment.
Finding the Right Fit: What Contributes to the Successful? Use of Speech Generating Devices?

Nearly four million Americans are unable to communicate using natural speech.One intervention utilized by therapists to overcome the problem is through the use of speech generating alternative forms of communication.Though the majority of users benefit from these tools, it is reported that more than one third eventually abandon them.Existing literature suggests a variety of factors that lead to abandonment, yet the phenomenon continues.In an attempt to discover what constitutes successful device use, this qualitative study examined the experiences of self-reported speech generating device users to identify commonalities among them to offer guidance for practitioners prescribing such devices.The data from the study resulted in three themes: user and device attribute match, support from communication partners and engagement in occupations with device use to fulfil roles and routines that resulted in meaningful experiences that provided motivation for continued device use.
Automatically evolving difficult benchmark feature selection datasets with genetic programming

© 2018 Copyright held by the owner/author(s). There has been a wealth of feature selection algorithms proposed in recent years, each of which claims superior performance in turn. A wide range of datasets have been used to compare these algorithms, each with different characteristics and quantities of redundant and noisy features. Hence, it is very difficult to comprehensively and fairly compare these feature selection methods in order to find which are most robust and effective. In this work, we examine using Genetic Programming to automatically synthesise redundant features for augmenting existing datasets in order to more scientifically test feature selection performance. We develop a method for producing complex multi-variate redundancies, and present a novel and intuitive approach to ensuring a range of redundancy relationships are automatically created. The application of these augmented datasets to well-established feature selection algorithms shows a number of interesting and useful results and suggests promising directions for future research in this area.
Towards optimal thread pool configuration for run-time systems of integration platforms

Companies seek technological alternatives to increase competitiveness, an example, are the integration platforms, that develop integration processes in order to connect functionalities and data from applications that compose software ecosystems. Threads are computational resources of the platforms, responsible for integration processes execution. Thus, the configuration of threads has a direct influence on the performance of platforms. However, this is a challenge faced by software engineers, who do this configuration empirically. Our scientific and technical literature review did not identify a systematic approach to find the ideal configuration, which depends on factors such as workload, hardware and integration process. Thus, it is appropriate to seek alternatives for configuration that provide a positive impact on the performance of the run-time system, increase productivity, and reduce costs. Inspired by the Particle Swarm Optimisation meta-heuristic, this article proposes an algorithm that finds the ideal configuration for local thread pool, minimising the total average processing time to improve the execution of integration platforms. The algorithm was implemented and tested using a real-life integration process and its performance measures show the feasibility and efficiency of our proposal, supported by a rigorous statistical analysis of results.
RECOGNITION OF BODY PART USING NEURAL NETWORKS.

This work belongs to the training of neural networks for solving problems related with computer vision.The first chapter discusses the general concepts of neural networks and their structure.The second chapter reviews the models of convolutional neural networks and the ways of learning them.The third chapter describes the project architecture, implementation and testing.
Ethical Leadership and Organisational Discipline: A Case Study on a Telecommunication Organisation in Oman

This study aims to explore the impact of ethical leadership dimensions (ethical role modelling, reward and punishment, and communicating about ethics and values) on organisational discipline in Omantel, a telecommunication organisation in Oman. Using random sampling, data was collected from employees working at Omantel, Dhofar Governorate. Additionally, a multiple regression analysis was used to investigate the impact of ethical leadership on organisational discipline. The study’s results showed that only two dimensions of ethical leadership, namely ethical role modelling and communicating about ethics and values, had an effect on organisational discipline, whereas reward and punishment had no significant effect on organisational discipline.
Arrays

Arrays are, alongside structs, the most basic data structure and, at the same time, the default choice for implementing algorithms. A one-dimensional array that is also called a “vector” is a contiguous structure consisting of the elements of the same type. One of the ways to create such arrays, in Lisp, is this:
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.
Gamification for Mobile-Based Al-Quran Writing Learning Applications

Al-Quran is the holy book of Islam. Although Indonesia has the largest Muslim population in the world, children's interest in learning to write Al-Quran is still small. Whereas in the activity of writing Al-Quran there are several virtues such as being able to train accuracy, train patience and speed up the ability to read Al-Quran. This research developed an application to help people learn to write Hijayah letters (the letters used in the Al-Quran). This study uses a gamification approach in the form of score collection, leaderboard, and high score. Gamification is used to increase children's experience in learning to write Al-Quran. The results of the validation test showed that there were seven requirements resulting in a value of 100% valid. The results of the analysis from usability testing were 70.5 percent. The results of the comparison between the score from the application and the teacher (Ustadz) assessment obtained a score of 80%
Explanation Generation Mechanism for Black Box Recommendation Model

The recommender system is everywhere, and even streaming platform they have been looking for a maze of user available information handling products and services. Unfortunately, these black box systems do not have sufficient transparency, as they provide littlie description about the their prediction. In contrast, the white box system by its nature can produce a brief description. However, their predictions are less accurate than complex black box models. Recent research has shown that explanations are an important component in bringing powerful big data predictions and machine learning techniques to a mass audience without compromising trust.This paper proposes a new approach using semantic web technology to generate an explanation for the output of a black box recommender system. The developed model is trained to make predictions accompanied by explanations that are automatically extracted from the semantic network.