Reviewerly/exHarmony huggingface dataset:
Improved Recommendation Systems based on Transition Probability Vectors

Numerous companies are now able to store and manage huge amounts of information about their customers. Accordingly, studies on recommender systems are actively being conducted to use the information more efficiently. Among them, studies that wish to have high predictability using additional information other than purchase information are presented in this paper with a simple method to reduce costs and increase accuracy. The corresponding module is a vector based on the probability that an item is transferred to another item. Experiments conducted on public datasets show that the performances of the proposed architecture have improved by an average of 9.7% compared to the benchmark models. It was also intended to provide direction for cold-start problem resolution at no additional cost.
LibGuides: Library Events: Web Cams

Want to know what is going on at the Library? Check out all of our past and upcoming events here! Want to see what's going on outside of not only our homes but all over the country? You can see a multiple of animals and fish on these cameras, as well as having access to the outdoors even when you can't be outdoors!
Visualization of Chatbot Survey Results

Chatbots are an increasingly popular technique that has seen recent steps in development thanks to artificial intelligence. There is research conducted about chatbots in different areas, but one a ...
Normbrytande reklam : En multimodal studie av hur språkliga och visuella resurser används i normbrytande reklam

This study examines two norm-breaking advertising campaigns. Through a sociosemiotic multimodal analysis I have examined a selection of advertising images from two norm-breaking campaigns: Forsvar ...
Sickle cell disease screening from thin blood smears using a smartphone-based microscope and deep learning

We report a deep learning-based framework which can be used to screen thin blood smears for sickle-cell-disease using images captured by a smartphone-based microscope. This framework first uses a deep neural network to enhance and standardize the smartphone images to the quality of a diagnostic level benchtop microscope, and a second deep neural network performs cell segmentation. We experimentally demonstrated that this technique can achieve 98% accuracy with an area-under-the-curve (AUC) of 0.998 on a blindly tested dataset made up of thin blood smears coming from 96 patients, of which 32 had been diagnosed with sickle cell disease.
A comparative study for improved hospital-based cancer registry for early stage prediction of breast cancer with highest accuracy

Breast cancer prediction has always been a challenge and machine-learning algorithms provide great assistance in this regard. This research paper precisely reports efforts in identification and development of appropriate algorithms that can predict breast cancer with high accuracy. The research undertaken is based on rigorous analysis of data collected from Bikaner, Rajasthan, India and its quality and features in comparison to the standard SEER dataset. The results confirm the applicability of classification algorithms like support vector machine in building a machine-learning model for accurate prediction of early stage breast cancer. Finally, as a highlighting contribution, a prediction model, which resulted in prediction of cancer with 99% accuracy on the data collected from patients in Bikaner, Rajasthan, India has been presented. This model will help to improve the National Cancer Registry Program and hospital based cancer registry systems.
Multi-layer Patching Algorithm for Skeleton-based Path Planning

This study proposes an optimal path planning algorithm for autonomous mobile robot navigation in a known environment, whereas avoiding possible unknown obstacles. The algorithm incorporating an off-line and an on-line locomotion mechanisms. The off-line path planning is based on skeleton extraction to establish a medial axis graph. With the use of the skeleton graph, the shortest path can be generated. In this study, a multi-layer patching algorithm is proposed to record information required in the on-line stage when unknown obstacles block the pre-determined path. Taking advantages of the Dijkstra’s algorithm and the skeleton graph, the proposed multi-layer patching algorithm is capable of avoiding obstacle and overcoming the local minima problem. From the simulation results, the feasibility of the proposed algorithm is confirmed in several experimental results.
Professional-Ethical Competence Of Leaders Of The Modern Educational System

This scientific article provides a classification of proposals and recommendations aimed at further enhancing the professional and ethical competence of the leaders of modern education systems in the management process.
International Negotiations: Language in Crisis and Conflict Handling Negotiations, and vice versa : A conceptual study on international crisis/conflict negotiations considered in Wittgensteinian, Austinian and Derridean terms, with reflections on the cases of Oslo 1 Accords 1993 and Rambouillet Negotiations 1999

The thesis presents a conceptual study engaging the theories emerged in the philosophy of language and the theories of international relations and negotiations into a single framework. The framewor ...
Real-time hand pose estimation on a smart-phone using Deep Learning

Hand pose estimation is a computer vision challenge that consists of detecting the coordinates of a hand’s key points in an image. This research investigates several deep learning-based solutions t ...
Substance and Surface in Four Composition

Author(s): Moondy, Bryndan Shea | Advisor(s): Kim, Hi Kyung | Abstract: The following scores and the accompanying essay stand in representation of my creative work during the completion of my Master’s degree at The University of California, Santa Cruz. The compositions included herein are Entwined to Fray (2018) for string quartet, Fractured Pond (2019) for ten players, Contrasts (2019) for solo piano, and We, too, can divide ourselves…(2020) for solo flute. The essay component of this thesis has been separated into two main sections. The first portion focuses on a discussion of certain overarching concerns which have become central influences on my compositional thinking during the past two years, namely the derivation of musical forms through the exploration of abstracted dichotomous relationships found within extra-musical concepts/metaphors. Additionally, the first section also includes brief descriptions of various approaches to the conception and handling of musical materials employed within the two solo works included within this thesis. The second portion focuses on elucidating my creative process through in-depth analyses of the works Entwined to Fray and Fractured Pond.
A-183 A Preliminary Study of Comfort with Computers and Analog Tasks in Relation to Older Adults’ Performance in Virtual Reality

Abstract Objective Neuropsychologists are using virtual reality to simulate everyday activities in order to increase ecological validity in neuropsychological assessments (Kane &amp; Parsons, 2017). However, relatively little is known about the extent to which comfort with computers and analog tasks influences older adults’ performance on virtual reality-based tasks. Methods Healthy older adults (N = 42) rated how comfortable they are with computers and cooking meals in daily life and completed the Virtual Kitchen Protocol, a measure of procedural learning and memory for meal preparation tasks. Results Both higher comfort with cooking meals in a real kitchen and higher comfort with computers were associated with better learning, immediate recall, and delayed recall of the procedural task in virtual reality. However, comfort with computers did not explain a significant amount of variance in performance beyond comfort cooking in a real kitchen. Conclusion While both comfort with computers and analog versions of tasks may relate to older adults’ learning and memory in virtual reality, performance may be primarily related to analog abilities.
Instruments and Sounds as Objects of Improvisation in Collective Computer Music Practice

Collective forms of improvisation are at the heart of numerous creative processes today, in a vast range of cultures, practises and artistic disciplines, each one bearing its own definitions, traditions and customs. In this contribution, we raise the question of collective sound improvisation involving digital technologies on two levels: first, by discussing the possible nature of improvisation in relation to digital artistic creation as a transversal notion that permeates through multiple fields of scientific research and artistic practise, raising fundamentally different questions than those of traditional musical improvisation. Then, by presenting a practise-based study on an emergent collective computer music improvisation project involving the authors. Subjective experiences, interrogations and remarks from this shared practise are confronted on the one hand with traditional literature regarding musical improvisation, and, on the other hand, placed within a broader scope of improvisation involving digital technologies. In particular, we will elaborate on using the computer instrument as a means to improvise both tools and sounds in one continuous flow.
Time Series Data Augmentation and Dropout Roles in Deep Learning Applied to Fall Detection

Fall Detection is one of the most interesting and challenging research topics in the world today because of its implications in society and also because the complexity of processing Time Series (TS). Plenty of research has been published in the literature, several of them introducing Deep Learning (DL) Neural Network (NN) as the modelling element. In this study we analyse one of these contributions and address several enhancement using TS data augmentation and dropout. Moreover, the possibility of reducing the NN to make it lighter has been studied. The NN has been implemented using Keras in Python and the experimentation includes an staged fall publicly available data set. Results show the TS data augmentation together with dropout helped in learning a more robust and precise model. Future work includes introducing different types of cross-validation as well as introducing other types of DL models more suitable for TS.
Online distance education in agriculture-A case of PJTSAU

Online distance education is a popular method of instruction in Agriculture since independence. Especially for the illiterate farmers or who find it difficult to invest time and save money in travel to enhance knowledge and skills. The advanced communication technologies in India with improved connectivity to the internet and technologies generated at labs are reaching to the stakeholders due to online distance education. Mobile technologies are accelerating the promotion of online education to provide quality education to the scattered group of communities. Due to advanced network development in India, the information and communication technologies are available in a cheaper rate. Hence, it is easy to reach the farmers by using online distance education media which will be helpful for farmers to increase their knowledge and skills for the growth of agriculture. Agricultural education can be provided in the form of formal, non-formal and informal way so that the farmers can learn easily by acquiring specialized skills for adoption of latest technologies. Presently Professor Jayashankar Telangana State Agricultural University (PJTSAU) is providing online distance mode of formal education in the form of establishing virtual rooms in all its campuses for providing invigorating learning environment to provide quality education in Agriculture. Online distance non-formal education to the farmers is provided from various E-Blogs, PJTSAU YOUTUBE Channel, and public and private broadcast media such as Television News Channels, Doordharshan and All India Radio. One such information and communication technology strategy is Chenukaburlu student run radio programme, which caters the need of rural community. A study was undertaken in Telangana State to assess the status of online distance education for the students and farmers. Based on this fact the present study was planned to examines the role of online distance education in agricultural extension
Structure Learning of DAGs from Observational Data with Multivariate Spatial Processes and with Non-invertible Functional Relationships

Author(s): Wang, Bingling | Advisor(s): Banerjee, Sudipto; Zhou, Qing | Abstract: Directed acyclic graph (DAG) are widely used for modeling all kinds of relations and processes. Learning DAG structure from observational data is a challenging problem. In this dissertation, we develop novel methods for learning causal DAGs. First we work on learning the DAG structure with predefined outcomes by a Bayesian hierarchical model, where the outcomes are ``mixed'' in the sense that some may be continuous, some binary and others may be counts. We concern with multivariate point-referenced data, where each location yields measurements on multiple variables and build a joint model by hierarchically building conditional distributions with different spatial processes embedded in each conditional distribution. Comparisons in simulation study and data analysis across different spatial models show that the performance is greatly improved by incorporating spatial processes. The second problem we try to solve is the causal relation determination in the bivariate case, i.e. to determine the direction between X and Y. We restrict our attention to non-invertible causal relations between variables and develop a novel testing method for deciding the causal direction. We propose a bivariate nonlinear SEM, where the function f is non-invertible and identify the causal direction by testing for the non-invertibility of the function f. Piecewise linear function with two pieces can capture the change in functional relationships and is used for approximating the non-invertible function f. Causal DAGs are usually not identifiable from observational data and most existing methods only estimate a DAG up to its equivalence class. For the situation with more than two variables, instead of applying the bivariate non-invertible causal model to all of the given variables directly, we avoid the exhaustive search over all possible causal structures by combining current linear structure learning algorithms with our non-invertible causal learning algorithm. In our implementation, an initial graph is first constructed using a structure learning algorithm, then updated by our non-invertible nonlinear causal learning (NNCL) algorithm. Extensive numerical comparisons show that our algorithms outperform existing DAG learning methods in identifying causal graphical structures. We illustrate the practical application of our methods using two real-world data sets: flow cytometry data set and ChIP-Seq gene expression data set.
Seeing the wood for the trees: Insights into the complexity of developing pre-service teachers’ digital competencies for future teaching

&#x0D; &#x0D; &#x0D; Developing digital competencies is a critical component in pre-service teacher training for future practice. However, this is a complex process which includes a range of strategies, and little is known about how they should be implemented. Methods that can address this complexity are needed to improve understanding of strategies to develop digital competency in teacher training. In this paper, data mining approaches are used to explore this issue, through analysis of preservice teachers’ experiences with six key digital competency strategies. Specifically, association rules analysis was conducted on a questionnaire dataset of 931 pre- service teachers’ experiences in their training, with 24 different practices representing the sixstrategies. Results showed four distinct clusters of associated strategies, which illustrates how the approach was able to reveal some complexity among digital competency strategies. The most important strategies were Authentic Experiences, Instructional Design and Role Models, showing multiple relationships and effects across all four clusters. Implications for practice suggest certain combinations of strategies are necessary to support pre-service teachers’ developing digital competencies. Future directions for research are discussed.&#x0D; &#x0D; &#x0D;
Psychosocial self-efficacy in young people with diabetes mellitus and its influence on self-care

EnglishObjective: to identify psychosocial self-efficacy in young people with Type 1 diabetes mellitus and its association with sociodemographic variables, health profile, and search for health care. Methods: cross-sectional study conducted with 35 young people through audio-recorded interviews. Two instruments were used: a) script for characterization of sociodemographic data, health profile, and search for health care; b) Diabetes Management Self-Efficacy Scale - Short Version. In the analysis, we used descriptive and inferential statistics - Chi-square test and Fisher’s Exact test. Results: all young people had satisfactory (moderate and high) self-efficacy levels. Although not statistically significant, there was a slightly higher proportion of high self-efficacy in young males, people over 18 years old, white skinned, unemployed, with overweight, with health insurance, with more than eight years of diagnosis, and not hospitalized. Conclusion: young people present satisfactory self-efficacy, without significant association with sociodemographic and clinical variables. portuguesObjetivo: identificar a autoeficacia psicossocial em jovens com Diabetes Mellitus tipo 1 e associacao com variaveis sociodemograficas, perfil de saude e busca por atendimento de saude. Metodos: estudo transversal realizado com 35 jovens, mediante entrevista audiogravada. Usou-se dois instrumentos: a) roteiro de caracterizacao sociodemografica, perfil de saude e busca por atendimento em saude; b) Escala de Autoeficacia em Diabetes-Versao Curta. Na analise, utilizou-se estatistica descritiva e inferencial - teste Qui-quadrado e Exato de Fisher. Resultados: a totalidade dos jovens apresentou indices satisfatorios de autoeficacia (moderada e alta). Embora sem significância estatistica, observou-se a proporcao ligeiramente maior de autoeficacia alta em jovens do sexo masculino, maiores de 18 anos, da cor branca, que nao trabalham, com plano de saude, acima do peso, com mais de oito anos de diagnostico e que nao sofreram hospitalizacao. Conclusao: os jovens apresentam autoeficacia satisfatoria, nao sendo observada associacao significativa com variaveis sociodemograficas e clinicas.
A Refined Speed Limit for the Imaginary-Time Schrödinger Equation

Recently, Kieu proposed a new class of time-energy uncertainty relations for time-dependent Hamiltonians, which is not only formal but also useful for actually evaluating the speed limit of quantum dynamics. Inspired by this work, Okuyama and Ohzeki obtained a similar speed limit for the imaginary-time Schrödinger equation. In this paper, we refine the latter one to make it be further like that of Kieu formally. As in the work of Kieu, only the initial states and the Hamiltonians, but neither the instantaneous eigenstates nor the full time-dependent wave like functions, which would demand a full solution for a time-dependent system, are required for our optimized speed limit. It turns out to be more helpful for estimating the speed limit of an actual quantum annealing driven by the imaginary-time Schrödinger equation. For certain case, the refined speed limit given here becomes the only useful tool to do this estimation, because the one given by Okuyama and Ohzeki cannot do the same job.
Learning nonlinear differentiable models for signals and systems: with applications

Building empirical models from data is of fundamental importance in engineering, and pushing the boundaries of current engineering technology requires us to model and understand nonlinear systems. In this thesis, nonlinear differentiable models and its applications are studied. This class of models has gained traction in machine learning tasks with the introduction of deep learning. Indeed, deep models of stacked differentiable components have recently achieved super-human performance on several tasks, including computer games, image classification, and medical diagnosis. The application of nonlinear differentiable models is studied for modeling signals and systems both for engineering and machine learning applications. One central question is the role of recurrence and the pros and cons of recurrent and feedforward models. The question is approached from more than one angle: 1) by studying the effect of recurrence in neural networks in terms of robustness to noise, computational cost, and convergence; 2) by analyzing the smoothness of the cost function in nonlinear system identification problems and its relation to the model internal dynamics – and proposing the use of a technique called multiple shooting for improving the cost-function smoothness; and, 3) by investigating the interplay between the internal dynamics, the attractors and the expressiveness of the model in deep recurrent neural networks. The more applied part of the thesis consists of the use of deep neural networks to solve complex tasks and to model nonlinear behavior from real data. Data from the Telehealth Center of Minas Gerais is used to train a deep neural network capable of identifying abnormalities in the electrocardiogram exam with performance superior to the medical residents in the studied scenario. Also, a deep neural network is used for modeling an electronic oscillator and an F-16 aircraft using data from ground vibration experiments, obtaining competitive results in both cases.
DEVELOPING VOCABULARY MASTERY THROUGH WORD ASSOCIATION BY THE SECOND YEAR STUDENTS OF SMP MUHAMMADIYAH AL AMIN SORONG

This research was conducted with the aim of proving that teaching English through word association can develop the vocabulary of the second year students of SMP Muhammadiyah Al Amin Sorong. The sample of the research was 31 students in VIII A class at SMP Muhammadiyah Al Amin Sorong. In collecting the data he used tests (Pre-test and Post-test). The research applied pre-test and post-test one group design. The instrument of data collection was a test. The data analyzed statistically. The result of the data analysis has shown that there was a significant difference between the results of pre-test and post-test. It was proved through the testing hypothesis, i.e. applying 0.05 level significance with 30 (31-1) the degree of freedom (df), the researcher found that t-test (7.3) was greater than the value of the t-table (1.697). In conclusion, the word association was effective to be used in teaching English Vocabulary. Keywords: Developing, Developing Vocabulary, Word Association