Reviewerly/exHarmony huggingface dataset:
THE DYNAMICS OF MOTIVATION FOR EDUCATIONAL ACTIVITIES OF BACHELOR STUDENTS IN THE DIRECTION OF «SOCIAL WORK»

The article discusses the motivation of the educational activities of bachelor students in the direction of «Social Work». The factors and stages of the motivation process, the dynamic characteristics of the motivation system are revealed. The role of the motivational block in the structure of educational activities of students is revealed. A comparative analysis of the motivational profi le of students is carried out on the basis of studies of learning motivation conducted by the author in 2015 and 2019. Diagnostic tools have become the methodology «Studying the motivation of learning at a university» T.I. Il’ina and «Studying the motives of students’ teaching activities» by Artur Rean and Valeriy Yakunin. An additional research method was the analysis of essays in which students argue for the presence or absence of a desire to continue their education in a chosen direction and to continue to work in the social sphere. The results obtained made it possible to single out the most signifi cant learning motives and their dynamics among students of the Social Work direction. An analysis of the essay showed that in general, about 80% of students spoke more positively about the prospect of working in their acquired specialty. In conclusion, a conclusion is drawn about the growth of motivation indicators on the scale of «mastering a profession», maintaining the signifi cance of the «motives acquiring knowledge» and «obtaining a diploma», as well as the inclusion of the motive «receive intellectual satisfaction» as the priority motive instead of «gaining the approval of parents and others».
CLASSIFICATION TECHNIQUES FOR SPEECHRECOGNITION USING NEURAL NETWORK ALGORITHM

This paper showcases a research on speech recognition classification and identification performance. Lots of researchworks are going on in the field of speech recognition. This paper presents a glance of speech recognition using neural networkalgorithm. Speech signals can be recognized using artificial neural network. Voices of individuals can be identified in crowd and tryto create a noise free environment by good quality of headphone. In the proposed work, the standard neural network with backpropagation algorithm is developed. Speech signals are converted in wave format. The features of the input recorded signals arebeing extracted by using the method of training of speech signals. Training is definitely required for recorded dataset because we donot have complete information about the input/output signals and then speech signals are passed through hidden layers duringtraining of speech signals. Classifiers are used to classify speech signals and identify the speaker.
A Performance Class-Based Particle Swarm Optimizer

One of the main concerns with Particle Swarm Optimization (PSO) is to increase or maintain diversity during search in order to avoid premature convergence. In this study, a Performance Class-Based learning PSO (PCB-PSO) algorithm is proposed, that not only increases and maintains swarm diversity but also improves exploration and exploitation while speeding up convergence simultaneously. In the PCB-PSO algorithm, each particle belongs to a class based on its fitness value and particles might change classes at evolutionary stages or search step based on their updated position. The particles are divided into an upper, middle and lower. In the upper class are particles with top fitness values, the middle are those with average while particles in the bottom class are the worst performing in the swarm. The number of particles in each group is predetermined. Each class has a unique learning strategy designed specifically for a given task. The upper class is designed to converge towards the best solution found, Middle class particles exploit the search space while lower class particles explore. The algorithm’s strength is its flexibility and robustness as the population of each class allows us to prioritize a desired swarm behavior. The Algorithm is tested on a set of 8 benchmark functions which have generally proven to be difficult to optimize. The algorithm is able to be on par with some cutting edge PSO variants and outperforms other swarm and evolutionary algorithms on a number of functions. On complex multimodal functions, it is able to outperform other PSO variants showing its ability to escape local optima solutions.
Local Features Supported by the Complement Feature for Image Segmentation

AnEigen formulation is proposed for image thresholding/segmentation. A vector composed of local features, normalized intensity of each pixel and that of the neighboring pixels, is used to represent each pixel.A "complement" component is appended to this vector to produce a "unit" vector.The auto-correlation matrix is computed for each pixel in the image using this unit vector.The first component (corresponding to the intensity of the current pixel) from all Eigen vectors, obtained from the autocorrelation matrix, are used as multi-level thresholds.Similar procedure can be adopted using powers of the current pixel intensity value.In general, more than one threshold can be obtained.Results on a wide range of images are demonstrated to show the effectiveness of the proposed schemes.
Logo Detection Based on FCM Clustering Algorithm and Texture Features

Logo detection methods usually depend on logo shapes and need for training data or a-priori information on the processed images. This limits their effectiveness to real-world applications. In this paper, we tackle these challenges by exploring the textural information. Specifically we propose a novel approach for administrative logo detection based on a fuzzy classification with a multi-fractal texture feature, capable of automatically characterizing texture measures describing logo and non-logo regions. Experimental results, using two real datasets, confirm the feasibility of the proposed method for degraded administrative documents. Extensive comparative evaluations demonstrate the superiority of this approach over the state-of-the-art methods.
Orthogonally Decoupled Variational Fourier Features

Sparse inducing points have long been a standard method to fit Gaussian processes to big data. In the last few years, spectral methods that exploit approximations of the covariance kernel have shown to be competitive. In this work we exploit a recently introduced orthogonally decoupled variational basis to combine spectral methods and sparse inducing points methods. We show that the method is competitive with the state-of-the-art on synthetic and on real-world data.
Models for Human Navigation and Optimal Path Planning Using Level Set Methods and Hamilton-Jacobi Equations

Author(s): Parkinson, Christian Andrew | Advisor(s): Bertozzi, Andrea L; Osher, Stanley J | Abstract: We present several models for different physical scenarios which are centered around human movement or optimal path planning, and use partial differential equations and concepts from control theory. The first model is a game-theoretic model for environmental crime which tracks criminals' movement using the level set method, and improves upon previous continuous models by removing overly restrictive assumptions of symmetry. Next, we design a method for determining optimal hiking paths in mountainous regions using an anisotropic level set equation. After this, we present a model for optimal human navigation with uncertainty which is rooted in dynamic programming and stochastic optimal control theory. Lastly, we consider optimal path planning for simple, self-driving cars in the Hamilton-Jacobi formulation. We improve upon previous models which simplify the car to a point mass, and present a reasonably general upwind, sweeping scheme to solve the relevant Hamilton-Jacobi equation.
Analyzing the College Freshmen Aptitude Results using K-means Algorithm

Universities today are operating in a very complex and highly competitive environment.Huge data available in the educational field bring out the hidden knowledge which is helpful in decision making.Cagayan State University used Rapidminer as an educational data mining (EDM) tool to extract new knowledge from huge standardized aptitude result of 3,393 data stored in an Excel database.Clustering algorithms such as K-Means where k=3 and K-means where k=4 number were used for evaluating students' records run over the Rapidminer application to enable clustering prototype.The prototype was assessed and used internal validation using the silhouette index to classify which clustering algorithms work best.Finally, the study determined the degree of the students' aptitude that can be used for advising students on the most appropriate course that they can take.The K-means algorithms using parameter k=4 was determined as the best parameter in the clustering.
Embodied Interaction for the Exploration of Image Collections in Mixed Reality (MR) for Museums and Other Exhibition Spaces

Although museums and archives dispose of extensive cultural heritage data, suitable interfaces for exploration of these collections are lacking. We describe an applied research based on an archive of historic photographs. Our main objective is to engage the general audience in the exploration of this data and thus facilitate discovery and insight. Embodied interaction [1] promises much potential for this endeavour and is highly suitable for museums and exhibition spaces when implemented with vision based technology as no contact with hardware is required. In addition, MR output is attractive for surrounding audiences. To test this assertion, an immersive interactive MR exploration space was developed applying embodied concepts, where the user’s position and movement in the interaction space combined with in-air gestures are used for application control of the super-positioned collection data space. A user study was conducted to measure differences in user engagement, exploration behaviour and discovery between this embodied interaction concept and traditional WIMP interaction. With embodied interaction, a significantly higher degree of exploration of the collection space was measured and users discovered slightly more novelties and connections. Additionally, a higher sense of immersion, fresh perspective on the contents and increased interest in the collection were reported. Hedonic qualities (e.g. stimulation, aesthetic appeal, reward factor) scored significantly higher while pragmatic qualities such as perceived usability were lower.
Virtual Blood and Sperm Bank for Dogs

Virtual blood bank is the online based management system. allows online ordering ,request delivery of blood for transfusion. sperm bank, semen bank is a facility to enhance the interface of purchasing and selling DOG semen. This project presents a bridge the gap between the blood donors and the request dog for blood. The requirement of the blood has to be requested and we supply the information of the donor through a simple online based management system.
CROSS-DISCIPLINARY INTEGRATION – AN EFFECTIVE MEANS OF IMPROVING QUALITY OF PREPARATION FOR PRIMARY EDUCATION PROFESSIONALS

The article focuses on the tools and methods of cross-disciplinary integration as a part of the professional education of primary schools teachers. According to the Federal Standards of High Education, primary school teachers must be able to ensure students’ progress in nine or ten subjects, which requires expertise in several non-closely connected subject areas. Currently each of these areas is studied separately, while reasonable integration would allow for more systematic knowledge. University lecturers and professors normally prefer to merge the subject areas with the methods of teaching these subjects, but rarely use the existing tools for exploring connections between science and humanities. Cross-disciplinary tasks are the most effective tool of such exploration. Hence, in this article the authors provide the defi nition of the cross-disciplinary tasks, its goals and purposes, methodological requirements for such tasks, and analyse their own experience of implementation such tasks in a university setting. The article provides examples of cross-disciplinary tasks the authors used in their Russian language and Mathematics courses, taught in 2018-2019 academic year in Ushinsky Yaroslavl State Pedagogic University. The tasks are applicable while studying the following topics – Theory of Sets, Theory of Combinations, Mathematical Logics, Algebra, as well as Phonetics, Phonology and Calligraphy. The authors found that the implementation of the tasks increased students’ motivation and helped establish connection between the two disciplines, while also enhancing the understanding of both subject areas. Based in these results, authors propose using such tasks as a form of the fi nal examination for the academic module «Subject Areas and Methods of Teaching» at higher education institutions offering degrees in Primary Education degrees.
Navigation of a Quadratic Potential with Ellipsoidal Obstacles

Given a convex quadratic potential of which its minimum is the agent's goal and a Euclidean space populated with ellipsoidal obstacles, one can construct a Rimon-Koditschek (RK) artificial potential to navigate. Its negative gradient attracts the agent toward the goal and repels the agent away from the boundary of the obstacles. This is a popular approach to navigation problems since it can be implemented with local spatial information that is acquired during operation time. However, navigation is only successful in situations where the obstacles are not too eccentric (flat). This paper proposes a modification to gradient dynamics that allows successful navigation of an environment with a quadratic cost and ellipsoidal obstacles regardless of their eccentricity. This is accomplished by altering gradient dynamics with a Hessian correction that is intended to imitate worlds with spherical obstacles in which RK potentials are known to work. The resulting dynamics simplify by the quadratic form of the obstacles. Convergence to the goal and obstacle avoidance is established from almost every initial position (up to a set of measure one) in the free space, with mild conditions on the location of the target. Results are corroborated empirically with numerical simulations.
GESTÃO DO CONHECIMENTO E COMUNIDADE DE PRÁTICA NA CIÊNCIA DA INFORMAÇÃO: UMA ANÁLISE NA PRODUÇÃO CIENTÍFICA INDEXADA NA BRAPCI

Knowledge management is understood as a macroprocess that requires, mainly, the establishment of collaborative conditions for the creation, sharing and use of knowledge.The community of practice presents itself as a knowledge management strategy in the field of information science.From this perspective, it aims to analyze the scientific production about community of practice as a strategy adopted in knowledge management.
Bayesian bi-clustering methods with applications in computational biology

Bi-clustering is a useful approach in analyzing large biological data sets when the observations come from heterogeneous groups and have a large number of features. We outline a general Bayesian approach in tackling bi-clustering problems in moderate to high dimensions and propose three Bayesian bi-clustering models on categorical data which increase in complexities in their modeling of the distributions of features across bi-clusters. Our proposed methods apply to a wide range of scenarios: from situations where data are cluster-distinguishable only among a small subset of features but masked by a large amount of noise to situations where different groups of data are identified by different sets of features or data exhibit hierarchical structures. Through simulation studies we show that our methods outperform existing (bi-)clustering methods in both identifying clusters and recovering feature distributional patterns across bi-clusters. We further apply the developed approaches to a human genetic dataset, a human single-cell genomic dataset, and a collection of 1774 mouse genomic datasets with a focus on 58 genes from two pathways.
Investigating Agile Requirements Engineering Practices in the South African Software Development Market

The aim of this research study was to assess Agile RE practices in the South African software development industry and investigate secure Agile RE initiatives towards developing secure products. This qualitative research study was contextualized in seventeen South African software development companies. The researchers used structured interviews and document reviews as the primary data collection instruments. Qualitative data was analyzed inductively using content analysis. Emanating from the research were recommendations to guide a regular software developer on good Agile RE practices. The study concluded that although Agile Software Development is practiced in the South African software industry, there needs to be stricter adherences to the Agile Manifesto and Agile Security Manifesto in requirements engineering.
Energy Optimization using Cloud Computing

In the present time every one of the administrations devoured by the clients is utility-arranged.Organizations give benefits based on the client add up to request and utilization of the administrations empowers the facilitating of uses from a customer, logical, and business spaces.Because of enormous vitality utilization by server farms the operational costs rises and furthermore builds substance of carbon impressions in nature.Thusly we require a green distributed computing system so we can limit the expanded operational expenses and furthermore decrease the natural effect.We require a building structure and standards for vitality productive Cloud figuring.
Comparison Between Single-Aperture and Multi-Aperture Approaches to Computer Correction of Images

The effectiveness of the reconstruction of images formed by single- and multi-aperture systems and distorted by an atmospheric turbulence is analyzed in this work. Based on numerical simulation, we show that the use of multi-aperture observation systems for the computer correction of atmospheric distortions under anisoplanar conditions can significantly reduce the exposure time. Main distortions are well corrected during image synthesis for a short exposure time, which corresponds to a “frozen” turbulence. The time required for the correction of residual small-scale distortions is an order of magnitude shorter than in the case of synthesis of long-exposure images with the use of traditional single-aperture observation systems.
Synthetic-to-Real Domain Adaptation for Lane Detection

Accurate lane detection, a crucial enabler for autonomous driving, currently relies on obtaining a large and diverse labeled training dataset. In this work, we explore learning from abundant, randomly generated synthetic data, together with unlabeled or partially labeled target domain data, instead. Randomly generated synthetic data has the advantage of controlled variability in the lane geometry and lighting, but it is limited in terms of photo-realism. This poses the challenge of adapting models learned on the unrealistic synthetic domain to real images. To this end we develop a novel autoencoder-based approach that uses synthetic labels unaligned with particular images for adapting to target domain data. In addition, we explore existing domain adaptation approaches, such as image translation and self-supervision, and adjust them to the lane detection task. We test all approaches in the unsupervised domain adaptation setting in which no target domain labels are available and in the semi-supervised setting in which a small portion of the target images are labeled. In extensive experiments using three different datasets, we demonstrate the possibility to save costly target domain labeling efforts. For example, using our proposed autoencoder approach on the llamas and tuSimple lane datasets, we can almost recover the fully supervised accuracy with only 10% of the labeled data. In addition, our autoencoder approach outperforms all other methods in the semi-supervised domain adaptation scenario.
LibGuides: Online Summer Reading Club: Storytime Videos

Experience Summer Reading Club--even from home! We've got videos, activities and a fun Scavenger Hunt. Our children's librarians share stories and fun!
Dota Underlords game is NP-complete

In this paper, we demonstrate how the problem of the optimal team choice in the popular computer game Dota Underlords can be reduced to the problem of linear integer programming. We propose a model and solve it for the real data. We also prove that this problem belongs to the NP-complete class and show that it reduces to the maximum edge weighted clique problem.
Thought, Energy, Time and Social Confines of Knowledge

In this paper, three virtual but dimensioned entities are used to contain knowledge; while it is by itself abstract. Knowledge resides in knowledge-banks of computers and the Internet. More importantly, knowledge resides in all living species. The main emphasis is on the human species that construct their personalized knowledge structures and banks that they deploy to resolve their personal Needs. Such needs drive behavior and adaptive. Both these human characteristics are alive and get influence by human interaction. These dimensions have human (thought), scientific and physical (energy and time) orientations. It becomes feasible to build a hyperspace for knowledge and confine it in the three dimensions of thought (anchored in the personality of an individual), energy, and time (both anchored in both physiological and physical spaces). We present the personality aspects based on the human needs that drive the human being (a noun object, n) to perform actions (one or more verb functions v) in intelligent steps (convolutions (⁎) between n’s and v’s ) to gratify the needs. Needs are inherent in human personality to maintain life.
A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain

Automatic processing of 3D Point Cloud data for object detection, tracking and segmentation is the latest trending research in the field of AI and Data Science, which is specifically aimed at solving different challenges of autonomous driving cars and getting real time performance. However, the amount of data that is being produced in the form of 3D point cloud (with LiDAR) is very huge, due to which the researchers are now on the way inventing new data compression algorithms to handle huge volumes of data thus generated. However, compression on one hand has an advantage in overcoming space requirements, but on the other hand, its processing gets expensive due to the decompression, which indents additional computing resources. Therefore, it would be novel to think of developing algorithms that can operate/analyse directly with the compressed data without involving the stages of decompression and recompression (required as many times, the compressed data needs to be operated or analyzed). This research field is termed as Compressed Domain Processing. In this paper, we will quickly review few of the recent state-of-the-art developments in the area of LiDAR generated 3D point cloud data compression, and highlight the future challenges of compressed domain processing of 3D point cloud data.