Reviewerly/exHarmony huggingface dataset:
Vision-based leader-follower approach for uncertain quadrotor dynamics using feedback linearisation sliding mode control

In this paper, a leader-follower quadrotor based on a visual system is presented. It is assumed that the follower quadrotor is equipped with a single onboard camera for determining the position of the leader. In the following quadrotor, feedback linearisation based on sliding mode control FLSMC is designed. The latter reduces complex nonlinear control solutions and highly coupled dynamic behaviour of the quadrotor. Uncertain dynamics and unexpected disturbances such as the change of payload and wind variation are overcome by designing time delay estimation, which helps in reducing chattering. The proposed controller uses a second order sliding mode exact differentiator to estimate the leader velocity and acceleration. The effectiveness of the proposed system is analysed by Lyapunov function and studied by MATLAB simulation.
Variables and Basic Data Structures

Currently, technology can acquire information from the physical world at an enormous rate. For example, commonly used sensors can now take tens of thousands of pressure, temperature, and acceleration readings per second. To make sense of all this data and process it in a way that will help solve science and engineering problems requires storing information in data structures that you and Python can easily work with. Variables are used in Python to store and work with data, which can take various forms. For example, data can be numbers, words, or have a more complicated structure. It is only natural that Python would have different kinds of variables to hold different kinds of data. In this chapter, you will learn how to create and manipulate Python's most common variable types.
Insecurity and Community Policing: Impact on Adolescent Educational Attainment in Nigeria

Nigerian has seen a rise in ethnic based killings, militancy, herdsmen-farmers clashes, kidnapping, ritual killing, and insurgency in the North-east to mention but a few.This worrying levels of insecurity has called for the adoption of a more community based approach to policing.This community based approach otherwise called "community policing" creates an avenue for collaboration between the community and the police.Scholars highlight that community policing has immensely impacted the crime levels of societies positively.However, questions continue to arise on the effectiveness of community policing in governance challenged societies such as Nigeria, particularly with the implications of insecurity to the education of adolescents who are exposed to rape, kidnapping and trafficking in the country.This paper therefore explored the use of community policing in tackling security challenges posed in the education of adolescent in secondary schools in Nigeria.It identified the insecurity problems in the country as well as the challenges faced by the Nigerian police in curbing these problems, while highlighting and analyzing the benefits of community policing on educational attainment and society in general.In addition, the paper investigated the challenges of community policing in Nigeria.For the purpose of this, a structured questionnaire titled "The Impact of Community Policing on Adolescent Educational Attainment in Kogi State" was used for data collation and the data obtained analyzed employing simple descriptive statistics.The research revealed that the main cause of insecurity in the country can be attributed to lack of education and poverty while corruption ridden was revealed as the disturbing perception of the Nigerian police force.Furthermore, the study established that the main consequence of insecurity in the attainment of education by adolescent is increased rate of school dropout as well sexual abuse and rape while in effective community policing influences school enrollment by adolescents and academic performance.The paper offered a number of recommendations which include that members of the community policing groups should cut across ethnicity, religious and political inclinations, sensitization campaigns should be carried out to educate and enlighten the public on the importance of education, tolerance should be advocated by the Government and nongovernmental bodies, greater efforts should be put into researching and discovering ways in which the Nigerian police can build its image and trust to the Nigerian citizenry and awareness campaigns should be carried out to enlighten the public on the need to collaborate with the police to mention but a few.
Modified Shuffled Frog Leaping Algorithm by adaptive Step Size: Applications to Constraint Engineering Design Problems

Shuffled frog leaping algorithm (SFLA) is an ongoing expansion to the group of evolutionary algorithm that imitates the societal and natural conduct of species. Upsides of particle swarm optimization (PSO) and shuffled complex evolution (SCE) is consolidates in SFLA i.e. local searching and information shuffling respectively. In this paper SFLA is improved to solve equality and inequality based constraint engineering design problems using penalty function. In proposed approach linear decreasing function that is adaptive in nature will be utilized to improve worst frog position for better exploration and convergence speed. The simulation results designate the superiority of present study over SFLA in term of global optimum solution and fast convergence rate
Configure Bash

In this chapter, we’ll look at various configurations that can be made to Bash. Often this is done by using configuration scripts that run when Bash starts as well as other configuration files that control installed utilities or lower-level libraries like .inputrc.
Parametric Modeling of Assessing the Development Level of Economical Thinking of Students

The formation of professional competence of future specialists is determined by the acuteness of social, cultural and economic problems that are forming in the world. Modern higher education is aimed at finding acceptable ways to train professionally competent specialists, with a new way of thinking, with comprehensive knowledge, ready to independently solve professional problems. This article discusses one of the tools for assessing the development of economic thinking among students of higher educational institutions, which serves as a criterion for the selection and application of the most effective teaching technologies. Existing valuation models are designed primarily to determine the level of mastery of economic knowledge. In this study, the authors propose using not only the cognitive component, but also such parameters as the psycho-emotional component and the level of stereotyping of thinking. The relevance of this study is to identify the indisputable relationship of the forms, methods and content of education with the prospects for the development of their economic thinking, allowing them to become full members of society. Mastering the fundamentals of economic literacy at the present stage of development of society to achieve this goal is insufficient, the authors propose using the parametric assessment model they developed, accumulating achievements in such fields as pedagogy, economics, economic sociology and economic psychology.
Stochastic Hill Climbing- Computing Perspective for Load Balancing in Cloud Computing

A new concept for cloud computing is to keep resources in a virtual pool.an Internet environment in which virtual resources those are dynamically scalable are served over internet as service has become a main problem. Cloud computing serves as platform and as an application too. As needed, a cloud computing platform offers, instantiates, reshapes and deprivations data centers dynamically. Cloud servers can be hardware machines or network-wide VM..To implement complex tasks which needs the large processing facility it uses computing resources over the network .Choosing nodes to perform menial tasks in cloud computing must be termed and, in order to capture the resource efficiency, they must be selected properly according to the task's properties. In this article, a load balancing approach has been suggested. Randomize Hill climbing uses a local computation approach to resource allocation to the servers or VM. Using CloudSim, the performance of the algorithm is examined objectively. Cloud Analyst is a visual modeler based on CloudSim to examine environments and applications in cloud computing.
Character Segmentation for Handwritten Bangla Words using Image Processing

The significance of behavior-based robots and the indoor navigation system has increased significantly over the past few years in its monitoring and wayfinding, taking into account the existence of robot behavior in various fields of social interest.The basic strategy of the overall behavior tasks in a behavior-based system are divided into smaller independent behaviors that focus on the performance of specific tasks such as the behavior of a robot for indoor environments.One behavior will concentrate on the wall, while the other focuses on avoiding obstacles, etc. Indoor navigation is the main concern since almost a lot of individuals spend more time in the indoor climate.In different fields such as hospitals, transportation, marketing, and military purposes, it becomes an essential thought.The main goal of this paper to give a survey of distance estimation in IndoorGML, behaviors system, and the questions of their robotic implementation possibilities.In this essay, we define robot activities and two basic approaches to distance estimation, such as combinatorial planning and sampling-based planning.These two planning strategies are the principles of motion planning.Combinatorial planning is used for finding the path over the continuous configuration space without restoring it to approximations.The most used concept in planning is sampling-based planning, it provides a successful solution in wayfinding path planning, and because of this, it is performed in different robotics fields.Therefore, for distance estimation in IndoorGML maps, we apply combinatorial and sampling-based approaches.
Research on Data Analysis, Processing and Model Construction of "Purple Collar" Talent Development Based on Big Data

Aiming at the new situation of the development of vocational education under the background of the intelligent era, this article combs the data of professional talents in the intelligent manufacturing industry under the background of big data, adopts big data analysis methods, studies the data distribution and statistical algorithms of "purple collar" professional talents, and builds professional talents. The data processing model of the talent ability of the position proposes a digital solution for the training mode and path of "purple collar" talents.
THE JOURNAL OF ANALYTICAL PSYCHOLOGY

Journal of Analytical PsychologyVolume 65, Issue 5 p. 958-962 Index for Volume THE JOURNAL OF ANALYTICAL PSYCHOLOGY INDEX FOR VOLUME 65, 2020 First published: 17 November 2020 https://doi.org/10.1111/1468-5922.12644Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume65, Issue5November 2020Pages 958-962 RelatedInformation
High-performance computing memetic algorithm (HPCMA) model to process image fingerprint dataset

The terminology of the memetic refers to “meme”. The meme is a part of natural information of the individual population and this information can be transmitted among the individual of the population. The memetic algorithm (MA) uses the evolutionary concept based on the Genetic Algorithm (GA) and is combined with a local search feature. Thus, as in GA, MA employs the basic steps such as selection, crossover, and mutation with additional components a local search to improve the solution of candidates. The major challenge of this algorithm is how to develop a good local search operator that can do a good exploration of the entire population. Several methods have been proposed and we have studied several parallel methods implemented on MA (PMA) to increase the efficiency of processing time. The parallel method is included in cluster computing, and that is a part of high-performance computing systems. In this work, we proposed the two new models of the memetic algorithm that run in a high-performance computing system (HPCMA) environment by utilizing the multi-threads feature of processor, so-called HPCMA1 and HPCMA2 model, to process fingerprints image dataset. The result shows that the HPCMA1 model runs with high inconsistency data caused by its overlaps in the macro-process and to address the problem from the HPCMA1 model we use the HPCMA2 model.
Logics and Illogic’s in Information Security Flow on Process Development Environment

The objective of the work is to propose a formal technique encompassing the logic and the illogic involved in the security of information science. The received or collected information about the existing or past security incidents in business processes is to be identified for their correctness and preciseness. The emphasis on various segments of the information is analyzed as per the intention of the informer and the involvement of the recipient. The expectation and the acceptance of business information is quantitatively modeled so as to take a correct decision with the proposed logics when multiple business processes handling variety of information with different truth factors are collaborated. The logical deduction, abduction and induction techniques are applied to minimize the vagueness and ambiguity factors. The preciseness of the present information and the emphasis of the past information related to security are formally used to predict the reality of future information security incidents in a business scenario.
Joint Hypergraph Learning using feature fusion for Image Retrieval

As the picture sharing sites like Flicker become increasingly well known, broad researchers focus on tagbased picture recovery (TBIR). It is one of the essential approaches to discover pictures contributed by social clients. In this exploration field, label data and various visual highlights have been explored. Be that as it may, most existing strategies utilize these visual includes independently or successively. In this paper, we propose a worldwide and neighborhood visual highlights combination way to deal with get familiar with the significance of pictures by hypergraph approach. A hypergraph is built first by using worldwide, neighborhood visual highlights and tag data. At that point, we propose a pseudo-significance input system to get the pseudopositive pictures. At last, with the hypergraph and pseudo importance input, we receive the hypergraph learning calculation to figure the pertinence score of each picture to the inquiry. Trial results illustrate the adequacy of the proposed methodology.
Automatic Prediction of Age group from Frontal Facial Images

Methods to automatically assess the age group of a person using his/her frontal facial image are proposed in this paper. This work is done for three major ethnicities: African, American and Asian with five different age-groups such as (1-10 years), (11-30 years), (31-50years), (51-70 years), (71-100 years). The performances of the classifiers were tested with face images of African, American and Asian population belonging to both genders. For this, first the facial parts such as the left eye, right eye, nose, mouth etc., are detected using the well-known Viola Jones Object Detection technique.450 sample images of the FERET database were considered for this study. Histogram of Gradient (HoG) and face-structure features are extracted and modeled using ANN and SVM. The efficiency of the proposed methods was tested with the facial images of various races belonging to different age-group and gender. Artificial neural network gave an accuracy of 92.10% whereas support vector machine gave an improved accuracy of 94.60%.
An automated ensemble learning framework using genetic programming for image classification

© 2019 Association for Computing Machinery. An ensemble consists of multiple learners and can achieve a better generalisation performance than a single learner. Genetic programming (GP) has been applied to construct ensembles using different strategies such as bagging and boosting. However, no GP-based ensemble methods focus on dealing with image classification, which is a challenging task in computer vision and machine learning. This paper proposes an automated ensemble learning framework using GP (EGP) for image classification. The new method integrates feature learning, classification function selection, classifier training, and combination into a single program tree. To achieve this, a novel program structure, a new function set and a new terminal set are developed in EGP. The performance of EGP is examined on nine different image classification data sets of varying difficulty and compared with a large number of commonly used methods including recently published methods. The results demonstrate that EGP achieves better performance than most competitive methods. Further analysis reveals that EGP evolves good ensembles simultaneously balancing diversity and accuracy. To the best of our knowledge, this study is the first work using GP to automatically generate ensembles for image classification.
Transformational leadership and organisational commitment with mediating effect of work motivation in higher educational institutions

As higher education in India is facing challenges in quality delivery, financing, mushrooming of low quality education institutions, it is the need of the hour to investigate whether leadership can turn around the situation. This survey after intensive literature review has proposed a model to study the relation of transformational leadership with organisational commitment and mediating effect of work motivation. The survey was conducted in Haridwar and Dehradun district of Uttarakhand, India. Descriptive research design has been used in the study. In the survey data supports the hypothesised model. The survey indicated that the institutions with transformational leaders can enhance the organisational commitment with mediating effect of work motivation amongst the employees and literature supports that employees with organisational commitment will improve the performance of organisation, which is the need of the hour.
Illusional Marketing: The Use of Storytelling, User Experience and Gamification in Business

Digital platforms know how to “hook” consumers and keep them glued to the screen. These products were developed based on psychologists’ research into the way the human brain works. These are new weapons in the marketing toolkit that will become even more effective when combined with nearfuture enhancements like augmented and virtual reality. As the children of Generation Z and its successor Generation Alpha meet the internet at life’s earliest stages, the likelihood they will develop addictions to such devices seems very high. These illusional marketing techniques offer new weapons for commercial brands; their efficiency has been proven over and over. They give marketing managers powers to alter behavior and to turn inclinations into habits by manipulating the unconscious mind. At this point, marketing professionals need to take significant responsibilities because illusional marketing practices that do not serve a meaningful cause may bring about dangerous outcomes. A system that is only designed for the sake of making more money will serve the interest of no party in the long run, while using the tools of illusional marketing in a positive manner could serve humanity. In our current era, exposing these techniques along with their positive and negative aspects becomes a vital and highly significant task, one best fulfilled by academia.
RETRACTED ARTICLE: Retraction

This article refers to:A smart review and two new techniques using 4-bit Boolean functions for cryptanalysis of 4-bit crypto S-boxes
Roadmaps to Support Implementation of DLT/Smart Contract Applications in Construction

The construction sector is at a point where distributed ledger technologies (DLT) such as blockchain and smart contracts (SCs) are accepted as a future technology in the sphere of digital construction [Xue and Lu, 2020]. The challenge lies in how to move from today’s poorly digitalised sector [Perera, 2020] to a highly digitalised future where DLT and SCs are commonplace throughout the construction lifecycle. In a sector that is resistant to change [McNamara and Sepasgozar, 2020] and second only to agriculture [Argawal et al., 2016] in terms of its digital capabilities, this is an area of research that requires focus. This study aims to support the adoption of DLT and SCs in construction through introduction of two roadmaps to support their implementation. The first is a macro roadmap that supports the sector in readying the construction sector ecosystem that will enable the success of such applications and their contribution to the digital transformation of the sector. The second is a meso roadmap that supports development and implementation of DLT- and SC-based applications through taking a sociotechnical approach to analysis and actions to get the applications to market.
Test Accuracy Improvement in Face Recognition Using Convolutional Neural Networks

Now-a-days face recognition plays a major role in identifying face of the specific person. There are different face recognition algorithms such as Eigenfaces algorithm, Local binary pattern histograms, Fisherfaces algorithm. All these algorithms face the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. In this study, the face recognition using neural network and convolutional neural network (CNN) techniques were utilized and implemented with the help of Python software 3.6.6. It is noticed that the test accuracy is improved against translation, rotation, and scale invariance in face recognition using CNN.
The fecund interval: visualizing acoustic space in Canadian experimental cinema.

"In this paper, I seek to develop an analysis of an aesthetic framework in which my filmmaking practice is situated, through Marshall McLuhan's ideas concerning acoustic space and their resonances within the avant-garde cinema. This may sound at first to be an inherently contradictory formula, given that a visually predominant medium such as the cinema may not be considered to impart experiences beyond this realm, with the exception of its acoustic components. (Sound cinema, similarly, is generally assumed to evoke sense impressions that exist merely in support of the fidelity of its 'realistic' image). However, I seek to demonstrate how the visualization of acoustic space forms a centrally unifying aesthetic strategy within the Canadian avant-garde cinema and many of the perceptual investigations conducted by film artists working during the time of Mcluhan's writings, resulting in a tradition of works which aim to translate visual experience beyond a fragmentary, visually-abstracted field, thus embodying aspects of kinesthetic vision and tactile/acoustic spatial experience. begin by examining the reception of Mcluhan's scholarship from within communication studies, suggesting the most fruitful reading of his often controversial figure as an artist-mystic rather than strictly as a theorist. I will then examine the varied influences from literary and cultural traditions that shaped his unique approach to media analysis and the development of his model of acoustic space-which Richard Cavell proposes as the primary feature that "connects a multiplicity of elements in Mcluhan's large and diverse oeuvre" (2003: xiii)-and consider the relationship of the cinema to Mcluhan's project of a history of the human sensorium."--Introduction.