Reviewerly/exHarmony huggingface dataset:
Integrated Revision and Variation Control for Evolving Model-Driven Software Product Lines.

Software engineering projects are faced with abstraction, which is achieved by software models, historical evolution, which is addressed by revision control, and variability, which is managed with the help of software product line engineering. Addressing these phenomena by separate tools ignores obvious overlaps and therefore fails at exploiting synergies between revision and variation control for models. In this article, we present a conceptual framework for integrated revision and variation control of model-driven software projects. The framework reuses the abstractions of revision graphs and feature models and follows an iterative, revision-control-like approach to software product line engineering called product-based product line development. A single version (i.e., a variant of a selected revision) is made available in a workspace, where the user may apply arbitrary modifications. Based on a user-provided specification of the affected variants, the changes are automatically written back to a transparent repository that relies on an internal multi-version storage. The uniform handling of revisions and variants of models is achieved by transparently mapping version concepts to a semantic base layer, which is defined upon propositional logic. At the heart of the conceptual framework is a dynamic filtered editing model, which allows that the versioned artifacts and the feature model co-evolve. We contribute algorithms for checkout and commit, which satisfy a set of consistency constraints referring to variant specifications in an evolving feature model. This article furthermore addresses the orchestration of collaborative development by distributed replication and the well formedness of text and model artifacts to be checked out into the workspace. The Eclipse-based tool SuperMod demonstrates the feasibility of the conceptual framework. It allows the user to reuse arbitrary editing tools for text-based programming and/or Ecore-based modeling languages. An evaluation based on three case studies investigates the properties of SuperMod with a specific focus on filtered editing. The evaluation demonstrates that the dynamic filtered editing model reduces the cognitive complexity and the amount of user interaction necessary for variation control when compared to unfiltered model-driven approaches to software product line engineering.
Deep active learning for instance segmentation

One of the main constraints of machine learning is the common lack of annotated data. This constraint becomes even more significant when it comes to deep learning, as its performance is usually proportional to the amount of annotated data available. Active Learning emerged as a way of overcoming this drawback. Its goal is to let the model actively select which samples does it wants to learn from in order to achieve a competent performance with as few annotated data as possible. It has proven to significantly reduce the number of annotated data needed in some fields such as text or image classification. However, its potential has not been explored in a complex task such as Instance Segmentation. The goal of this Master thesis was the application of Active Learning to an Instance Segmentation task through the design and development of different Active Learning frameworks. The experiments were done using two different datasets: Common Objects into Context and the one proposed by the Data Science Bowl 2018. The sampling strategies implemented were divided into classification-based, mask-based, mask+class based and semi-supervised. The classificationbased framework measures the uncertainty of an image using the probability the model gives to each instance when associating a class to them. The mask-based framework measures the uncertainty as the spatial uncertainty a model has with the predicted masks. In the third framework, mask+class-based, the previous uncertainties were combined into one sampling strategy. Finally, the semi-supervised framework includes and additional approach that labels automatically the most certain samples using the masks and classes predicted by the model. This thesis can be considered as a starting point in the study of the application of Active Learning techniques to Instance Segmentation tasks. However, the results obtained were really promising. The different methods managed to achieve a reduction in the number of necessary segmented images. The inclusion of a semi-supervised framework to the Active Learning cycle could reduce the annotation effort even more. Another insight obtained was the importance of the balance of the different classes in the batch and the representativeness of this images with respect to the underlying sampling space.
CoRSAL Collection @ UNT Digital Library

Presentation given at the third Computational Resource for South Asian Languages symposium in 2019 at the University of North Texas, in Denton, Texas. This presentation describes the activities of the UNT Libraries to provide infrastructure and support for language documentation archiving and preservation via the CoRSAL Collection.
interpretable dynamics models for data-efficient reinforcement learning.

In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learning scheme based on variational inference. This scheme is applied to a heteroskedastic and bimodal benchmark problem on which we compare our results to NFQ and show how our approach yields human-interpretable insight about the underlying dynamics while also increasing data-efficiency.
Library Guides: NSU Libraries Online Resources: Remote Access for Research

This guide highlights the research resources that are available remotely from the NSU Libraries.
Iterative and Geometric Methods for State Estimation in Non-linear Models

Many problems in science and engineering involve estimating a dynamic signal from indirect measurements subject to noise, where points can either evolve in continuous time or in discrete time. These problems are often formalised as inference in probabilistic state-space models, which are also frequently assumed to be Markovian. For inferring the value of the signal at a particular point in time, methods of inference can be divided into different classes, namely prediction, filtering (tracking), and smoothing. In prediction, only past measurements of the signal are used to infer its present value, whereas in filtering both past and present measurements are used, and in smoothing past, present, and future measurements are used. Prediction is useful in situations where decisions need to be made contingent on a future value of the signal before future measurements are made. On the other hand, filtering is useful when the signal needs to be inferred as the measurements arrive, that is on-line. Lastly, smoothing is the preferred choice when none of the aforementioned constraints are present as it allows the use of the entire sequence of measurements to infer the signal. In this thesis, the filtering and smoothing problems and their applications are examined. In particular, iterative Gaussian filters and smoothers are developed for both inferring continuous and discrete time signals. Furthermore, it is shown that methods for inference in state-space models can be applied to the field of probabilistic numerics. More specifically, estimating the solutions to ordinary differential equations can be formulated as inference in a probabilistic state-space model, hence the solutions can be inferred using either Gaussian filtering methods or sequential Monte Carlo. Another theme of this thesis is the exploitation of geometry - in a broad sense. Firstly, the geometry of probability densities, namely information geometry, is exploited to approximately infer the signal in filtering. Geometry is also exploited in terms of the geometry of the state-space, the space where the signal takes its values. That is, for tracking a time-varying unit vector, a continuous-time dynamic model is posed that respects the geometry of the unit sphere. Subsequently, a filtering algorithm is developed based on the von Mises-Fisher distribution for inference in this model. The method is demonstrated to have applications in tracking the local gravity and magnetic field vectors using a smartphone. Lastly, the geometry of L_2 spaces is used to approximate a stochastic differential equation with an ordinary differential equation with random coefficients. On this basis filtering and smoothing algorithms are developed.
Extracting "Documents" from Web Archives

Presentation was given at the 2019 Texas Conference on Digital Libraries in Austin, Texas. This presentation discusses an IMLS funded research grant to use machine learning techniques to help identify high-value publications from web archives.
Cognizance of road safety among young adults based on unmediated and mediated responses

Background: Road traffic accident (RTA) has been increasing to significantly higher percentages leading to disabilities and fatalities among the inhabitants, thereby causing social, emotional, and economical losses. In this study, an attempt was made to observe the prompt and the deferred effects of an “audiovisual program on road safety,” on the attitude and the knowledge of adolescents and young adults regarding the catastrophes on roadways and on how safety can be ensured. Materials and Methods: About 1760 students between 16 and 24 years were selected at random from various educational institutes in the city of Nagpur. With the aid of structured questionnaires, the participants were tested for their knowledge and attitude toward the traffic rules. At the end of this assessment, there was an interactive educational audiovisual PowerPoint presentation that highlighted the likely complications and long-term adverse effects of RTA. After the presentation phase, two questionnaire rounds were scheduled: one immediately following the presentation and another after 3 months to observe the change in their knowledge and attitude. Results: The resultant positive attitudinal change immediately after the intervention was significant. However, the positive attitudinal change dropped significantly after 3 months to a percentage almost comparable to the preexposure attitudinal level. Conclusion: It can be concluded that adolescents and young adults need to be repeatedly administered by indulging them in such enlightening pedagogical programs, in order to reduce the incidence of road accidents.
Library Guides: eBooks and audiobooks: Junior School Nonfiction

Stuck at home in lockdown? Here are Kerferd Library eBooks and audio books you can use from home
Improving The Students’ Ability In Writing Recount Text By Using Mr. Bean Video At Second Grade Of MTs PAB 2 Sampali

This research was aimed to find out the implementation of mr.bean video technique to improve the students’ writing ability in writing recount text. The subject of this study were 28 students at VIII-3 grade of MTs PAB 2 SAMPALI. This research was conducted by using Classroom Action Research. The qualitative data were obtained from test which carried out at the end of every cycles. The tests were given to the students in pre-test, post-test I, and post-test II.The result of data analysis showed that the students’ score increased from the first test, post-test of cycle I to the post-test of cycle II. It could be seen from the mean of pre-test was 43.25. In the post test of cycle I, the mean was 56.39. There were 3.57% (1 students) who passed MPG. In the post test of cycle II, the mean was 73.71. There were 60.71% (17 students) who passed MPG it indicated that was improvement of the students’ ability in writing recount text by using mr.bean video.
An Attention-Based Recommendation Algorithm

Conventional recommendation algorithms adopt collaborative filtering approach to recommend items to one user through rating history from other users who share similar preferences. However, this method lacks of consideration of the interest levels, as the rating behavior of users varies with different evaluation standards. In this paper, we introduce one Attention-based Recommendation Algorithm (ABRA), which makes full use of users' comment information to make more reasonable recommendations. ABRA facilitates emotion recognition by analyzing the emotions from users' comments and identifying the polarity of emotions by designing an emotional attention-based model. ABRA integrates the emotional attention model into the Recurrent Neural Network (RNN) to train the comments in order to extract the emotion vector, which is further used to classify the comments. Meanwhile, the context attention mechanism is used in the RNN to identify the emotional polarity of each word. This enables model to identify not only the emotion polarity, but also the tendency value of the emotion words for constructing one more accurate and efficient emotion vector. In addition, we adopt the score correcting gate to decide whether the users' rating operation needs to be updated or not. A modified user-item matrix is applied to recommendation and collaborative filtering recommendation is carried out for users according to emotional score. We train the ABRA using movie review data from Cornell University, and test the model using the Amazon Movies&TV and Yelp datasets. The experimental results indicate that, compared with conventional recommendation algorithms, ABRA can more reasonable perceive the users' emotions and make more accurate recommendation.
Real-Time Ear Landmark Detection Using Ensemble of Regression Trees

Human face landmark detection algorithms have numerous applications. Current face landmark detection algorithms limit themselves to features around eyes, nose, cheeks and lips. Face landmark detection combined with augmented reality technology has given rise to commercially popular virtual try-on applications. To realize use cases of virtual jewelry try-on like earrings on smartphones, landmark points of human ear is required, but this field is not much explored in the literature. Existing methods are not accurate enough in different face poses and lighting conditions. Proposed method offers solution for ear landmark detection considering the computational requirements of mobility devices, and comprises ear localization followed by ear landmark detection. It adopts Haar cascade based model for ear localization and an Ensemble of Regression Trees for ear landmark detection. The experimental results and comparison with state-of-the-art methods show that the proposed method accurately localizes the ear, provides correct landmark points and is fast enough to run on mobility devices with low memory footprints. Comparison with popular methods shows the novelty points in the proposed approach.
Smart voting system through face recognition

Facial recognition is a category of biometric software which works by matching the facial features. We will be studying the implementation of various algorithms in the field of secure voting methodology. There are three levels of verification which were used for the voters in our proposed system. The first is UID verification, second is for the voter card number, and the third level of verification includes the use of various algorithms for facial recognition. In this paper, we will provide a comparative study between these algorithms, namely: Eigenface, FisherFace & SURF.
LibGuides: Online Library Resources and Services for Faculty: Online library materials

Resources created by the Canada Librarians for faculty to provide library instruction online.
Selkirk College Library: Business: Company & Industry Research

A subject guide to help locate print and electronic resources for Business Resources to help you conduct research on businesses.
CONVERSATIONAL USER INTERFACES AND SECOND LANGUAGE SKILLS

Companies such as Amazon, Google, and Apple are heavily investing in research and development of voice-based conversational language interfaces. Platforms based on Conversational User Interfaces (CUIs) such as Amazon’s Alexa, Google Assistant, and Apple’s Siri have seen rapid advances in the scope of functionality and skills they are capable of across a variety of languages. As users are able to complete more meaningful tasks with these tools in multiple languages, they have the ability to move away from traditional interactions (e.g., using voice commands to carry out task-based instructions such as turning on lights, ordering items online, and playing music) and increasingly consider interacting with their devices with the purpose of practicing a second language. Understanding the benefits of the multilingual abilities of these platforms and devices, as well as the current limitations, is essential in order to develop meaningful language learning tasks with CUIs. This paper provides a comparative overview of the multilingual capabilities of Amazon’s Alexa, Google’s Assistant, and Apple’s Siri and provides a discussion of current and potential educational applications for second language learning and acquisition.
A 3 : Activation Anomaly Analysis.

Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.
INNOVATIVE LEARNING METHODOLOGIES FOR THE STUDIES ON FORENSIC SCIENCES

M. Ramos, A. Jimenez, Y. Flores, C.J. Pelegrín, I. Solaberrieta, A.C. Mellinas, M.C. GarrigósUniversity of Alicante (SPAIN)
Teori Belajar Elaborasi (Suatu Strategi Pembelajaran)

This brief paper will elaborate on the theory of learning elaboration and elaboration learning proposed by Charles M Reigeluth and Stein (1983) using seven components of strategy, namely the elaborative sequence, the prerequisite sequence of learning (within each subject), summarizer (summary), synthesizer (synthesis),Analogy, Cognitive Strategy Activator (activating cognitive strategies), and learning control.
Variation among Copies of Titles Catalogued as Identical Should Inform Retention Decisions

A Review of:&#x0D; Teper, J. H. (2019). Considering “sameness” of monographic holdings in shared print retention decisions. Library Resources &amp; Technical Services, 63(1), 29-45. https://doi.org/10.5860/lrts.63n1.29&#x0D; Abstract &#x0D; Objective – To investigate the degree to which books catalogued using the same bibliographic record differ and to consider the implications of these differences for cooperative monographic print retention programs.&#x0D; Design – Book condition survey.&#x0D; Setting – Academic library consortium in the United States of America.&#x0D; Subjects – 47 monographic titles, publication years 1851-1922, held by all consortium members and catalogued using the same respective OCLC record number. 625 out of a possible 705 circulating copies of these titles were available for item-level analysis via interlibrary loan.&#x0D; Methods – Book condition surveys were completed for all items and the resulting sets of assessment data points were analyzed to reveal trends.&#x0D; Main Results – 3.4% of items analyzed exhibited cataloguing errors (i.e., were catalogued using the wrong OCLC records), 56.8% retained their original bindings, 17.8% were marked to show previous ownership, 95.7% were complete with no missing content, 9.8% had no damage, and 18.9% had received identifiable preservation action.&#x0D; Conclusion – Books catalogued using the same OCLC record demonstrated many differences when compared at the item level. These differences are important in light of shared print retention programs and highlight a need for inquiry into the number of copies that should be retained to minimize the loss of uniqueness in print materials.
INCREASING UNIVERSITY STUDENTS ENGAGEMENT: A GUIDE ON HOW TO ACHIEVE IT IN EXTRACURRICULAR NON-MANDATORY ACADEMIC EXPERIENCES BASED ON SUCCESS CENTER KNOW HOW

Information about the paper titled "INCREASING UNIVERSITY STUDENTS ENGAGEMENT: A GUIDE ON HOW TO ACHIEVE IT IN EXTRACURRICULAR NON-MANDATORY ACADEMIC EXPERIENCES BASED ON SUCCESS CENTER KNOW HOW" at IATED Digital Library
Status and Case of Curriculum Developments of Standard Education at the Primary and Secondary Education in Republic of Korea

이 연구의 목적은 초&#8228;중&#8228;고등학교 교육에서 표준 교육의 현황과 교육과정 개발 사례를 분석하기 위한 것이었다. 이 연구는 주로 표준 교육정책에 참여한 지은이의 경험과 관련 교과서와 같은 문헌 분석을 바탕으로 이루어졌다. 이 연구에서의 결과는 다음과 같이 요약할 수 있다. 우리나라에서 초중고교학생들을 대상으로 표준교육이 본격적으로 이루어진 것은 2006년 이후이다. 초등학교의 경우에는 ‘찾아가는 표준교육’과 ‘연구학교’ 운영 등이 대표적인 표준교육 활동으로 손꼽히고, 초등학생을 위한 인정교과서인 ‘우리 생활 속 표준’의 개발이 우리나라 초등 교육에서의 표준 교육 교육과정의 가장 중요한 개발 사례라 할 수 있다. 중학교의 경우에는 ‘국제 표준올림피아드’의 창설과 ‘연구학교’ 운영, 국가 교육과정에서의 ‘기술&#8228;가정’ 과목에서 표준 단원의 신설 등이 대표적인 표준교육 활동으로 손꼽힌다. 중학교 필수과목인 ‘기술&#8228;가정’과의 표준 단원’의 신설이 표준 교육 교육과정의 가장 중요한 개발 사례이다. 고등학교의 경우에도 ‘국제 표준올림피아드’의 창설과 ‘연구학교’ 운영, 국가 교육과정에서의 ‘공학일반’ 과목에서 표준 단원의 신설 등이 대표적인 표준교육 활동으로 손꼽힌다. 일반고등학교 선택 과목인 ‘공학일반’의 표준 단원’의 신설이 표준 교육 교육과정의 가장 중요한 개발 사례이다.The purpose of this study was to analyze the status of standard education and the development of curriculum of standard education in elementary, middle and high school education. This study was mainly based on the experience of authors who participated in the standard education policy and the contents analysis of related textbooks. The results of this study are as follows. It was after 2006 that standard education was fully implemented for elementary and middle school students in Korea. In the case of elementary school, ‘standard education’ and ‘research school’ are one of the representative standard education activities. The development of ‘standards in our lives’, which is a recognized textbook for elementary school students, This is the most important development case. In the case of middle schools, the establishment of the International Standard Olympiad, the operation of the Research School, and the establishment of the Standard Unit in the ‘Technology / Family’ courses in the national curriculum are considered to be the representative standards activities. The establishment of the Standard Unit with Technology and Home, a required subject in middle school, is the most important development example of the standard education curriculum. In the case of high schools, the creation of the International Standard Olympiad, the operation of a research school, and the establishment of standard units in the general course of engineering in the national curriculum are considered as standard education activities. The establishment of the Standard Unit of General Engineering, an elective in high school, is the most important development of the standard curriculum. In the future, the role of standards and the importance of standardization will become more important in the fourth industrial society. Accordingly, the development, application and implementation of more active and planned curriculum are required in standard education for youth, the future leader.