Reviewerly/exHarmony huggingface dataset:
Cloud Segmentation and Classification from All-Sky Images Using Deep Learning

For transforming the energy sector towards renewable energies, solar power is regarded as one of the major resources. However, it is not uniformly available all the time, leading to fluctuations in power generation. Clouds have the highest impact on short-term temporal and spatial variability. Thus, forecasting solar irradiance strongly depends on current cloudiness conditions. As the share of solar energy in the electrical grid is increasing, so-called nowcasts (intra-minute to intra-hour forecasts) are beneficial for grid control and for reducing required storage capacities. Furthermore, the operation of concentrating solar power (CSP) plants canbe optimized with high resolution spatial solar irradiance data.A common nowcast approach is to analyze ground-based sky images from All-Sky Imagers. Clouds within these images are detected and tracked to estimate current and immediatefuture irradiance, whereas the accuracy of these forecasts depends primarily on the quality of pixel-level cloud recognition. State-of-the-art methods are commonly restricted to binary segmentation, distinguishing between cloudy and cloudless pixels. Thereby the optical properties of different cloud types are ignored. Also, most techniques rely on threshold-based detection showing difficulties under certain atmospheric conditions. In this thesis, two deep learning approaches are presented to automatically determinecloud conditions. To identify cloudiness characteristics like a free sun disk, a multi-label classifier was implemented assigning respective labels to images. In addition, a segmentation model was developed, classifying images pixel-wise into three cloud types and cloud-free sky. For supervised training, a new dataset of 770 images was created containing ground truth labels and segmentation masks. Moreover, to take advantage of large amounts of raw data, self-supervised pretraining was applied. By defining suitable pretext tasks, representations of image data can be learned facilitating the distinction of cloud types. Two successful techniques were chosen for self-supervised learning: Inpainting- uperresolution and DeepCluster. Afterwards, the pretrained models were fine-tuned on the annotated dataset. To assess the effectiveness of self-supervision, a comparison with random initialization and pretrained ImageNet weights was conducted. Evaluation shows that segmentation in particular benefits from self-supervised learning, improving accuracy and IoU about 3% points compared to ImageNet pretraining. The best segmentation model was also evaluated on binary segmentation. Achieving an overall accuracy of 95.15%, a state-of-the art Clear-Sky-Library (CSL) is outperformed significantly by over 7% points.
MOTIVASI, PEMAHAMAN AKUNTANSI DAN MINAT MAHASISWA UNTUK MENGIKUTI SERTIFIKASI AKUNTAN PROFESIONAL

Professional accountant certification is one way that accounting students can later be recognized as professional accountants. Unfortunately, not all accounting students are interested in joining professional accountant certification. This study aims to determine whether economic motivation, degree motivation, quality motivation and understanding of accounting affect the interest of accounting students to take professional accountant certification. In this study uses primary datahad no, which were obtained through questionnaires distributed directly and also through student e-mails to the accounting students at Satya Wacana Christian University (SWCU). This research uses purposive sampling. The data analysis technique used is binary logistic regression analysis. The results showed that degree motivation effect the interest of accounting students to take professional accountant certification. Meanwhile, economic motivation, quality motivation and understanding of accounting do not effect on interest of accounting students to take professional accountant certification.Keyword(s): Professional accountant certification, Economic motivation, Degree motivation, Quality motivation, Understanding of accounting
AAAI's New Award for the Societal Benefits of AI — An Interview with Richard Tong

In this interview, conducted in early 2020 by Ashok Goel, Richard Tong, the chief architect and general manager of Squirrel AI Learning’s US operations, discusses adaptive learning, challenges facing AI in education, and the Squirrel AI Award for Artificial Intelligence to Benefit Humanity.
Data Augmentation Using Many-To-Many RNNs for Session-Aware Recommender Systems.

The ACM WSDM WebTour 2021 Challenge organized by this http URL focuses on applying Session-Aware recommender systems in the travel domain. Given a sequence of travel bookings in a user trip, we look to recommend the user's next destination. To handle the large dimensionality of the output's space, we propose a many-to-many RNN model, predicting the next destination chosen by the user at every sequence step as opposed to only the final one. We show how this is a computationally efficient alternative to doing data augmentation in a many-to-one RNN, where we consider every subsequence of a session starting from the first element. Our solution achieved 4th place in the final leaderboard, with an accuracy@4 of 0.5566.
أثر محددات الاستثمار في الاقتصاد العراقي للمدة (2004-2018) ، دراسة قياسية

The research aims to measure and analyze the determinants of investment in the Iraqi economy and study the theoretical foundations of investment and analyze the viewpoint of the most important schools of economic thought regarding investment and investment determinants and their effects on economic activity , and by using possible standard models as the results of standard analysis using the joint integration tests of time series . cointegration tests, they have proven the existence of a long-term equilibrium relationship according to the methodology of the results of estimating the short and long-term parameters and the error correction parameter(ECM) , it is moving from a set of explanatory variables towards The dependent variable, while the value of the error correction vector coefficient was negative and significant , as it reached (-0.59%) , which means the fulfillment of the two basic conditions in this parameter , namely : its negative value and the statistical significance . This means that (0.59) of the short-term errors are automatically corrected during the unit of time (year) to reach the equilibrium in the long term, meaning that the investment requires about less then a year (1.6) , that is , approximately a year and 6 days to reach its equilibrium value in the long term , In other words , the previous period deviates from the long-term equilibrium and is corrected in the current by (59%) . This indicates that the adjustment in the model was relatively fast .
LibGuides: Curriculum and Instructional Studies: Electronic Resources

This guide will help students to access information resources on curriculum and instructional studies
UML Diagram E-Learning Application: A Pilot Study

Unified Modelling Language diagrams is a topic in Universiti Tun Hussein Onn Malaysia’s Diploma in Information Technology’s Object-Oriented Programming course, used to graphically present system entities and or flow, for better understanding of a system during its design. Students were found to have trouble differentiating the purpose of and how to draw the diagrams. This paper proposed a learning application for self-regulated learning of the topic. The application designed using the ADDIE model, developed using Adobe Flash and tested by 10 Diploma in Information Technology students in Universiti Tun Hussein Onn Malaysia. Results of usability survey are generally positive, with rooms for improvement in terms of design and navigation.
LibGuides: Interlibrary Loan Policy Page for Other Libraries: ILL Policy Page

This guide provides policy information for libraries borrowing material from Okanagan College Library.
LibGuides: Maker Kits: Home

Maker Kits are part of the loanable equipment available at the OSU Valley Library. These kits can be used to invent, create, and develop new skils.
7 Musical Terminology

The book presents the fundamentals of music science, followed by a discussion on the historical evolution of music. An introduction to the analysis of signals in time and frequency is presented, which includes sound and noise. Features and mathematical aspects of the sound are discussed, including vibration and timbre. The book presents a review of existing voice models and discusses the voice production, sound perception, music characteristics and acoustics, tempo, rhythm and harmony. Musical theory is presented, including staff, notes, alterations, keys and intervals, tones and associated frequencies and wavelengths. The creation of major and minor scales is emphasized, along with a study on consonance and dissonance, measure, metric, tempo markings, dynamics, modulation. The book also explains the chord formation, and discusses melody and composition. The book has four appendices, including an appendix on the basic differentiation and integration theorems, another with useful Fourier tables, and an appendix featuring the notes, their frequencies and wavelengths. The book also has a glossary of music terms.
Explainability via Responsibility.

Procedural Content Generation via Machine Learning (PCGML) refers to a group of methods for creating game content (e.g. platformer levels, game maps, etc.) using machine learning models. PCGML approaches rely on black box models, which can be difficult to understand and debug by human designers who do not have expert knowledge about machine learning. This can be even more tricky in co-creative systems where human designers must interact with AI agents to generate game content. In this paper we present an approach to explainable artificial intelligence in which certain training instances are offered to human users as an explanation for the AI agent's actions during a co-creation process. We evaluate this approach by approximating its ability to provide human users with the explanations of AI agent's actions and helping them to more efficiently cooperate with the AI agent.
Vers une littéracie de la traduction automatique : l’aspect syntaxique

Bibliographie:Bowker, L. & Buitrago Ciro, J. (2019). Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Emerald Publishing Limited.Croft, W. (Hrsg.) (2001) Radical Construction Grammar: Syntactic Theory in Typological Perspective. Oxford: Oxford University Press.Lemaire, C. (2017) Traductologie et traduction outillee : du traducteur specialise professionnel a l’expert metier en entreprise. Linguistique. Universite Grenoble Alpes.Martindale, M. J. & Carpuat, M. (2018). Fluency over accuracy: A pilot study in measuring user trust in imperfect MT. Proceedings of AMTA 2018 1: 13-25.
A STUDY ON CONTENT BASED IMAGE RETRIEVAL USING DATAMINING ALGORITHM

In this, we first do the research in the lower layer which is the image retrieval by using certain features like colors and importing relevance techniques on the basis of feedback. With the proper cooperation between the human and the computer, making of the computers limited ability in understanding and enhancing the effects of image retrieval. Here to retrieve the image we use feedback on the basis of CBIR which develops from low-grade to high-grade. So the research made by this paper extracts the result off content-based image retrieval using the feedback
LibGuides: Diversity, Equity, and Inclusion Resources: Resources at IU

This guide contains materials and resources on the topic of DEI at Robert McKinney and is meant to serve as a resource for faculty, adjuncts, students, staff, and librarians.
Use of the Virtual Reality Environment to Improve the Self-efficacy of Faculty Members in the Production of Electronic Courses: أثر استخدام بيئة الواقع الافتراضي (Blackboard) في تحسين الكفاءة الذاتية لإنتاج المقررات الإلكترونية لدى أعضاء هيئة التدريس بجامعة المجمعة

The study is amid to evaluate the impact of virtual blackboard Environment in optimizing the quality and skills of the staff of Majmaah University, in order to develop the e- curriculums, and their satisfaction of Virtual Blackboard. The researcher has applied the quasi-experimental design which based on individual group, to evaluate the impact of virtual blackboard Environment in optimizing the quality and skills of the staff. The researcher has also applied the descriptive approach to measure staff satisfaction on the virtual blackboard Environment. A purposive sample was selected which comprised (25) university staff of Majmaah. The sample of study of staff has been trained to develop e-curriculums. The study was conducted within two questionnaires; the first one was dedicated to measure the impact of in optimizing the quality and skills of developing the e-curriculums; the second one was dedicated to evaluate the satisfaction of the staff of applying Virtual Blackboard Environment. Consequently, the study tools and treatment have been conducted, and the results indicated that there were statistically significant of Virtual Blackboard Environment in optimizing the quality and skills of developing the Virtual Blackboard environment within the study sample. The results also indicated that there was significant satisfaction of applying the Virtual Blackboard environment within the study sample in developing the e-curriculums. The satisfaction average was reached to (2.55) and (78%). The researcher proposed appropriate recommendations including stimulation of the staff within the universities to develop the e-curriculums and progressing it, which tends to optimize the quality and skills of the staff in this scope.
Developing Associative and Visual Thinking in the Process of Creating Colour Graphic Compositions

The article addresses topical issues of associative and visual thinking development in students through the process of creating formal compositions. The purpose of the research is to develop and test the author’s methodology within the scope of experimental creative tasks in colour science, since it is with the help of colour that one can most vividly, succinctly and unconventionally express feelings and emotions. Scientific novelty of the research lies in proposing an algorithm for creating formal colour graphic compositions used for associative and visual thinking development, as well as in expressing obtained emotions and associations through a reflective essay upon the task completion. As a result, the presented algorithm can be considered one of the methods in associative and visual thinking development, which is very important for educational process and professional growth of students majoring in creative arts.
LibGuides: Master of Education in Early Childhood Education: LHML Resources

Early childhood education is a branch of education theory that relates to the teaching of children from birth up to the age of eight. This box references all the academic resources LHML has.
Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data. User heterogeneity has imposed significant challenges to FL, which can incur drifted global models that are slow to converge. Knowledge Distillation has recently emerged to tackle this issue, by refining the server model using aggregated knowledge from heterogeneous users, other than directly averaging their model parameters. This approach, however, depends on a proxy dataset, making it impractical unless such a prerequisite is satisfied. Moreover, the ensemble knowledge is not fully utilized to guide local model learning, which may in turn affect the quality of the aggregated model. Inspired by the prior art, we propose a data-free knowledge distillation} approach to address heterogeneous FL, where the server learns a lightweight generator to ensemble user information in a data-free manner, which is then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Empirical studies powered by theoretical implications show that, our approach facilitates FL with better generalization performance using fewer communication rounds, compared with the state-of-the-art.
Intermediate coordinate based pose non-perspective estimation from line correspondences.

In this paper, a non-iterative solution to the non-perspective pose estimation from line correspondences was proposed. Specifically, the proposed method uses an intermediate camera frame and an intermediate world frame, which simplifies the expression of rotation matrix by reducing to the two freedoms from three in the rotation matrix R. Then formulate the pose estimation problem into an optimal problem. Our method solve the parameters of rotation matrix by building the fifteenth-order and fourth-order univariate polynomial. The proposed method can be applied into the pose estimation of the perspective camera. We utilize both the simulated data and real data to conduct the comparative experiments. The experimental results show that the proposed method is comparable or better than existing methods in the aspects of accuracy, stability and efficiency.
PENGARUH KOMPETENSI WIDYAISWARA, PENGELOLA DIKLAT, KUALITAS PESERTA DIKLAT, DAN SARANA PRASARANA TERHADAP EFEKTIFITAS PELAKSANAAN DIKLAT PADA BALAI DIKLAT KEAGAMAAN BANJARMASIN

This study aims to analyze the effect of widyaiswara competence, education and training management, quality of training participants, and infrastructure on the effectiveness of education and training implementation at Banjarmasin Religious Training Center. The population in thisstudy is widyaiswara and employees who work at Banjarmasin Religious Training Center andalumni of education and training. Given the small population of 40 respondents, the sample taking uses the census method of the entire population to be sampled. Analysis of the results in this study using qualitative and quantitative methods, by testing the validity and reliability of research instruments. Meanwhile, to test the hypothesis using the classic assumption test, F test, t test, partial correlation coefficient test and multiple regression. The analysis results are obtained: F test results show the value of Fcount> Ftable and significant value of 0,000 <0.05 at a level of confidence a = 0.05, meaning that the independent variables X1, X2, X3, and X4 simultaneously influence the dependent variable Y. Test Results t indicates the value of t for each independent variable more than the value of t table means that each independent variable has a partial effect on the dependent variable. From the partial correlation coefficient, the motivation variable has the highest correlation coefficient, which is 0.359, which means that infrastructure is the variable that has the most dominant influence on the performance variable (Y).
A large-scale image retrieval system for everyday scenes.

We present a system for large-scale image retrieval on everyday scenes with common objects. Our system leverages advances in deep learning and natural language processing (NLP) for improved understanding of images by capturing the relationships between the objects within an image. As a result, a user can retrieve highly relevant images and obtain suggestions for similar image queries to further explore the repository. Each image in the repository is processed (using deep learning) to obtain the most probable captions and objects in it. The captions are parsed into tree structures using NLP techniques, and stored and indexed in a database system. When a query image is posed, an optimized tree-pattern query is executed by the database system to obtain candidate matches, which are then ranked using tree-edit distance of the tree structures to output the top-k matches. Word embeddings and Bloom filters are used to obtain similar image queries. By clicking the suggested similar image queries, a user can intuitively explore the repository.
Library Guides: Open Access Publishing: Repositories

An overview of OA, including government and publisher policies, resources, and key issues pertaining to scholarly publishing and Tulane
Comparative analysis of the prediction and classification accuracy of artificial neural networks with respect to traditional statistical methods

ANNs are flexible tools that have shown utility as function estimators and prediction methods in Data Mining, Machine Learning, among others. The aim of this research is to carry out comparative analysis of the prediction and classification accuracy between artificial neural networks and traditional statistical methods. To carry out this comparative analysis, different cases of prediction and supervised classification were selected. The samples of the different cases were divided into samples for training and validation. Finally, a comparative analysis of the prediction and classification accuracy between artificial neural networks and traditional statistical methods was obtained, using the sum of squared errors (SSE), the coefficient of determination (R2), among others.
„The Dervish Sound Dress“ – A Garment using Sensors that emit Sound and Haptic Feedback

The Dervish Sound Dress is a wearable piece of technology, and a garment inspired by the sacred experience of the Whirling Dervishes of Turkey. The dress is a body instrument that emits musical sounds when the wearer moves in it, as well as triggering a haptic vibration response.
Robust visual tracking via scale-aware localization and peak response strength.

Existing regression-based deep trackers usually localize a target based on a response map, where the highest peak response corresponds to the predicted target location. Nevertheless, when the background distractors appear or the target scale changes frequently, the response map is prone to produce multiple sub-peak responses to interfere with model prediction. In this paper, we propose a robust online tracking method via Scale-Aware localization and Peak Response strength (SAPR), which can learn a discriminative model predictor to estimate a target state accurately. Specifically, to cope with large scale variations, we propose a Scale-Aware Localization (SAL) module to provide multi-scale response maps based on the scale pyramid scheme. Furthermore, to focus on the target response, we propose a simple yet effective Peak Response Strength (PRS) module to fuse the multi-scale response maps and the response maps generated by a correlation filter. According to the response map with the maximum classification score, the model predictor iteratively updates its filter weights for accurate target state estimation. Experimental results on three benchmark datasets, including OTB100, VOT2018 and LaSOT, demonstrate that the proposed SAPR accurately estimates the target state, achieving the favorable performance against several state-of-the-art trackers.