Reviewerly/exHarmony huggingface dataset:
The Playful Experience Within Virtual Reality

For this thesis, an immersive 2D painting experience was developed that uses an intuitive interface with trackable painting equipment in Virtual Reality. For the immersion, the standard HTC Vive Controllers needed to be replaced with painting equipment. Through the use of desk research, interviewing an expert on electronics, the most optimal hardware was determined for the replacement. For the intuitive interface, digital artists were consulted to create a focus group to determine which essential tools and functionalities were required. Through prototyping, testing and observation evaluations were made, and multiple iterations were developed to realize the experience. This was all done for the goal of developing a demonstrator for Saxion’s XR Lab.
Development of classification model between clean water and polluted water based on capacitance properties using Levenberg Marquardt (LM) algorithm of artificial neural network / Mohamad Firdaus Mohamad Salehuddin

This project is about using the Levenberg Marquardt algorithm in Artificial Neural Network (ANN) to classified between clean and polluted water. By designing the ANN model system and then trained it, the ANN able to find the best-optimized model that can distinguish between clean and polluted water. 200 samples of clean and polluted water were collected in the process. The sample will then be divided into two cases, which are 100 clean water samples, and another 100 from polluted water samples. All samples were collected from Universiti Teknologi MARA (UiTM) Cawangan Pulau Pinang, and Sungai Derhaka, Pulau Pinang. The capacitance value calculation for both cases was carried out using LCR meter with a frequency range of 1kHz. To measure the normal distribution of the data, IBM SPSS Statistical Software were implemented. For both cases, the statistical analysis data show that the p-value is more than 0.05, which indicates that the data are normally distributed. These measurement inputs were then going through the process of classification in ANN to generate the optimized models by using LM algorithm. The model is being trained, tested, and validated to differentiate between clean water and polluted water. There were 1 optimized model selected from the classification process. The accuracy from the selected most optimized models were 100%. The selected most optimized models were then can be used to classify between clean water and polluted water based on capacitance input.
SPECIFICITY OF PROFESSIONAL SELF-DETERMINATION OF FUTURE SPECIALISTS IN SOCIONOMIC PROFESSIONS

The article is dedicated to the scientificexploration of the specifics ofprofessional self-determination of futuresocionomic specialists. In modernsocial, economic and politicaltransformations, the government paysgreat attention to professional trainingof socionomic professions. Reformingthe higher education system leads to afull review of both the content ofeducational programs and theeducational environment as a whole.The analysis of the scientific literaturegave reasons to assert that professionalself-determination is activated not onlyat the stage of optation, but also at laterstages of professionalization of theindividual, including the learningprocess. The specifics of professionalself-determination of future specialistsin socionomic professions areinvestigated, which allowed revealingnew reserves of efficiency of theirtraining. The basic characteristic of thepersonality of the future specialist insocionomic professions is established -prosocial activity of the personality,which originates in everyday life, but issuccessfully transformed into aprofessional activity through a set ofmotives. To study the individualpsychological characteristics of futurespecialists in socionomic professions,the Multifactorial personality method byR. Kettell (16PF - questionnaire) and themethod of M. Rokych "ValueOrientations" were used.
Subject Guides: 3D Printing & Laser-cutting Space: Laser Cutter / Etcher

The 3D Printing & Laser-cutting Space of the Innovation & Research Commons is where patrons can realize their 3D, 2D & electronic designs.
Identifying the Sources of Uncertainty in Object Classification

In image-based object classification, the visual appearance of objects determines which class they are assigned to. External variables that are independent of the object, such as the perspective or the lighting conditions, can modify the object's appearance resulting in ambiguous images that lead to misclassifications. Previous work has proposed methods for estimating the uncertainty of predictions and measure their confidence. However, such methods do not indicate which variables are the potential sources that cause uncertainty. In this paper, we propose a method for image-based object classification that uses disentangled representations to indicate which are the external variables that contribute the most to the uncertainty of the predictions. This information can be used to identify the external variables that should be modified to decrease the uncertainty and improve the classification.
Commercial E-books of Japanese language : an approach to E-book collection development

Virtual presentation was originally planned at Next Generation Japanese Studies Librarian Workshop at the North American Coordinating Council of Japanese Library Resources (NCC) to be held at the Edwin O. Reischauer Institute of Japanese Studies at Harvard University, Cambridge, Massachusetts on March 16, 2020. Due to the Covid-19, the workshop was cancelled and the presentation was distributed online by the NCC.
Investigating the Factors Influencing User Churning Behavior in Spam Filtering Apps: A Comparison between Churners and Users and Big Data Analysis of App Logs

Recently, various spam calls and messages targeting mobile phone users have been continuously increasing. As the types of spam are becoming diversified with increasing frequency, it not only causes considerable inconvenience to mobile users, but also becomes a risk factor in information security and financial accidents. Telecommunication companies have developed and distributed spam filtering apps, and mobile users are increasingly interested in spam blocking and spam information sharing services. Nevertheless, there have been behaviors that do not use or stop using spam filtering apps. Therefore, it is important for companies and organizations, which need to increase the usage rate of spam filtering apps, to diagnose the use status of spam filtering apps and to analyze how customer churn occurs. This study compares the usage patterns of spam filtering apps between users and churners, and identifies the characteristic behaviors of churners. In particular, by analyzing the big data of app usage log, we derive the factors that affect the increase in churn rate. With the results of this study, it contributes to establishing a safer mobile phone environment by preventing customers from churning and activating the use of spam filtering functions.
Ontology of Saudi Arabian Vision 2030

Ontology has become one of the most important pillars of the new generation of the Internet, Semantic Web. It is also a strong tool to support knowledge management, especially for knowledge organization, with the potential to build maps of concepts and terms for any knowledge domain. The objective of this study is to build an Ontology for the Saudi Arabia Vision 2030, develop a knowledge base, and make it available through a web site based on semantic web. The Saudi Vision2030, and its resources of knowledge, and the lessons learned, represents a comprehensive reference in strategic planning, which contains many terms and concepts in this domain, and there is a need to organize, preserve and manage these resources, by Ontology. The methodology used in this study was Content Analysis method, to collect the terms, concepts, and relations between them. The objectives of the study were achieved by building an integrated knowledge map of the Vision of the Kingdom of Saudi Arabia 2030, establishing a pilot site for search, retrieval, and presentation of the elements of Vision, and the relations between concepts, which constitute the Ontology. The most important recommendation of this study was to restructure and reorganize the official web site of Vision2030, using Taxonomy, Metadata and Thesaurus concepts .
P‐8.2: Anti‐occlusion depth estimation method based on multi‐clue fusion for light field image

SID Symposium Digest of Technical PapersVolume 52, Issue S1 p. 560-560 Poster Session: P-8: Display systemFree to Read P-8.2: Anti-occlusion depth estimation method based on multi-clue fusion for light field image Liu Xiaomin, Liu Xiaomin School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorDu Mengzhu, Du Mengzhu School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorZang Huaping, Zang Huaping School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorMa Zhibang, Ma Zhibang School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorZhu Yunfei, Zhu Yunfei School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorChen Pengbo, Chen Pengbo School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this author Liu Xiaomin, Liu Xiaomin School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorDu Mengzhu, Du Mengzhu School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorZang Huaping, Zang Huaping School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorMa Zhibang, Ma Zhibang School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorZhu Yunfei, Zhu Yunfei School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this authorChen Pengbo, Chen Pengbo School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan, ChinaSearch for more papers by this author First published: 24 February 2021 https://doi.org/10.1002/sdtp.14554AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume52, IssueS1International Conference on Display Technology (ICDT 2020)February 2021Pages 560-560 RelatedInformation
Σύστημα ηλεκτρονικής ψηφοφορίας για φοιτητική συνέλευση : μελέτη περίπτωσης με βάση το Blockchain

The object of this study is the research of online electronic voting systems in combination with blockchain technology. The student assembly voting system is selected as a case study and a blockchain protocol is suggested that aims to not only fulfill the system requirements but improve upon them, covering some of its shortcomings.The goal was the creation of a fully decentralized voting system able to operate using only the network of blockchain nodes, without third party, centrally controlled, oracle systems. For the creation of the protocol multiple cryptographic primitives are used such as digital signatures for voter authentication, homomorphic encryption for achieving ballot secrecy combined with the ability to tally them, secure multiparty computation for the generation of distributed decryption keys and non-interactive proofs of knowledge.Additionally, within the scope of this work, a proof of concept system was created which implements the designed crypto-protocol whilst leveraging smart contract technology provided by the Ethereum blockchain for the voting system infrastructure of a decentralized voting system.
LibGuides: ENGL 3050: Writing Pedagogy: ENGL 3050 Activity

The purpose of this course guide is to provide research and library resources for ENGL 3050.
Variation in Time and Space

While academic prose has been extensively investigated, there is a dearth of analyses across different countries. The present study addresses this research gap through an exploration of academic writing in ten countries (i.e. Great Britain, Hong Kong, India, Ireland, Jamaica, Kenya, New Zealand, the Philippines, Singapore, and Tanzania). To this end, we draw on the Humanities and Technology printed academic writing in International Corpus of English (ICE). The equivalence of texts in any category across ICE corpora has not been challenged to date, and this is the second gap – a methodological one – that is explored in the present chapter. Our study examines ten ICE corpora contrastively with a view to (1) identifying the lexical-semantic characteristics of the Humanities and Technology printed academic writing in each ICE corpus; and (2) checking the potential homogeneity of these two disciplinary domains across ICE corpora. Our research employs the key key word method: each text in one disciplinary domain in one ICE corpus was compared with all the other printed academic writing texts in the other three disciplinary domains in that same ICE corpus. The aim was to foreground the lexical indices which are specific of the focal disciplinary domain. Following their automatic identification, key key words were analysed qualitatively through concordance lines, and they were clustered into semantic groups. These groups were first considered on a country-by-country basis and then contrasted across the ten countries. The findings reveal not only what is characteristic of Humanities and Technology printed academic writing in each of the ten national ICE corpora but also whether these features are idiosyncratically found in one ICE corpus or dispersed across several of them. While, from the perspective of corpus design, there is an assumption of comparability among the texts which make up each of the four disciplinary domains in ICE corpora, our investigation shows that this does not seem to be the case. We hold that the availability of corpus documentation is therefore a must to allow for comparative studies across ICE corpora.
Provable Robust Learning for Deep Neural Networks under Agnostic Corrupted Supervision

Training deep neural models in the presence of corrupted supervisions is challenging as the corrupted data points may significantly impact the generalization performance. To alleviate this problem, we present an efficient robust algorithm that achieves strong guarantees without any assumption on the type of corruption and provides a unified framework for both classification and regression problems. Different from many existing approaches that quantify the quality of individual data points (e.g., loss values) and filter out data points accordingly, the proposed algorithm focuses on controlling the collective impact of data points on the averaged gradient. Even when a corrupted data point failed to be excluded by the proposed algorithm, the data point will have very limited impacts on the overall loss, as compared with state-of-the-art filtering data points based on loss values. Extensive empirical results on multiple benchmark datasets have demonstrated the robustness of the proposed method under different types of corruption.
Novel reconstruction algorithms that facilitate real-time 4D tomosynthesis

Tomosynthesis has become a vital interventional tool for breast biopsy procedures. It is used to orient, advance and confirm the biopsy needle’s movement. However, at the end of a procedure, success is determined only after the biopsy sample shows the presence of the targeted lesion. Contrarily, failures, such as a target miss, are realized only after healthy tissue has been incorrectly excised. If real-time 4D tomosynthesis is made possible, it could not only guide and confirm the needle advancement but also anticipate any inadvertent target displacement and prevent healthy tissue damage. This study explores three classes of novel reconstruction algorithms that facilitate real-time 4D tomosynthesis guided biopsy procedures namely, Image-Processed algorithm, Segmented algorithm and Difference-Exploiting algorithm. A conventional tomosynthesis reconstruction algorithm applied to an incrementally moving needle shows a blurred needle tip - a consequence of superimposing and averaging the back-projections where the tip exists at different positions. The Image-Processed algorithm contrast-enhances all the back-projections before reconstruction thereby curbing the blurring and producing a more discernible needle tip. Pixel-based Segmented and Difference-Exploiting algorithms reconstruct individual pixels differently. The Segmented algorithm uses only the latest back-projection to reconstruct the pixels of the needle thereby capturing its most recent position. The Difference-Exploiting algorithm utilizes the superimposed differences of back-projections that helps in selectively identifying those elements, like the moving needle, that show a variation. Reconstructing these elements differently compared to other static elements of the breast allows capturing them in real-time. This work details the formulation of the three algorithms.
Research Guides: Electronic Resources at The Evergreen Library: Accessibility

This guide aims to bring together all the electronic resources (e-books, e-journals, databases, streaming media) and digital content available through The Evergreen State College Library.
Category Disentangled Context: Turning Category-irrelevant Features Into Treasures

Deep neural networks have achieved great success in computer vision, thanks to their ability in extracting category-relevant semantic features. On the contrary, irrelevant features (e.g., background and confusing parts) are usually considered to be harmful. In this paper, we bring a new perspective on the potential benefits brought by irrelevant features: they could act as references to help identify relevant ones. Therefore, (1) we formulate a novel Category Disentangled Context (CDC) and develop an adversarial deep network to encode it; (2) we investigate utilizing the CDC to improve image classification with the attention mechanism as a bridge. Extensive comparisons on four benchmarks with various backbone networks demonstrate that the CDC could bring remarkable improvements consistently, validating the usefulness of irrelevant features.
Rancang Bangun E-Care Aplikasi Konseling Online Berbasis Website Dengan Menggunakan Metode Client Centered Pada Universitas Dinamika

Counseling Guidance section on campus has the function of helping and addressing the problems that students have both academically, socially, personally. The many problems experienced have an impact on the development of academic value, personality and social life of students. So with this, it is expected that students can re-focus on achievement and actively socialize. But many students do not do counseling even though they have problems. Various reasons include being ashamed, not trusting a counsellor, fearing the negative stigma of her peers and not knowing about counseling. So to solve the problem, an application was created that helps students to do counseling online called E-care. This application is built by Client Centered method, because in this method counselors can apply the necessary properties such as sincerity, warmth, nonposesive acceptance and accurate empathy through text/non-verbal in the chat process. Based on the results and trials during the E-care application development process obtained results in the form of a website application that can be used for online counseling. In the application there are several features such as MBTI test and anxiety level test. As well as some of the reports produced are medical records reports and monthly recap reports.
Race Classification from Face Images Using Fast Fourier Transform and Discrete Cosine Transform

Ethnicity identification and recognition is a key biometric technology with a wide range of applications related to homeland security, safety, access control, and automatic annotation. Ethnicity identification from face images is a process of gathering facial features of an individual face image compared to existing face images in the dataset to interpretation his/her ethnic class. In this paper, a propose method in multi-level fusion schema for ethnicity identification by using two global features; fast Fourier transform (FFT) and discrete cosine transform (DCT) on the pre-processed face image of size 128 * 128 in YCbCr color space. A dataset is consisting of 750 face image of three different ethnicities (Kurd 300, Oriental 300 and African 150). The query image feature is compared with a dataset image features using k – nearest neighbor classifier using City block distance for evaluating similarity measurement. The experimental result shows good accuracy and demonstrate the effectiveness of the combined features reached an accuracy rate 96.22% of classiﬁcation.
Introducing a genetic algorithm based Method for Community person's stance Detection in social media and news

News reports in social media are presented with large volumes of different kinds of documents. The presented topics in these documents focus on different communities and person stances and opinions. Knowing the relationships among persons in the documents can help the readers to obtain a basic knowledge about the subject and the purpose of various documents. In the present paper, we introduce a method for detecting communities that includes the persons with the same stances and ideas. To do this, the persons referenced in different documents are clustered into communities that have related positions and stances. In the presented method. Community-based personalities are identified based on a friendship network as a base method. Then by using a genetic algorithm, the way that these communities are identified is improved. The criterion in the tests is rand index of detection of these communities. The experiments are designed based on real databases that published in Google News on a particular topic. The results indicate the efficiency and desirability of the proposed method
THE IMPACT OF SRI AUROBINDO GHOSHS EDUCATIONAL PHILOSOPHY

Sri Aurobindo can be rightly called a perfectionist because he was never satisfied with partial remedies. Born in Kolkata , India Aurobindo was educate at Cambridge University . The presence study highlights the philosophical contribution of Aurobindo Ghosh in our education system . It explains different philosophical aspects of Aurobindo Ghosh- aims of education relationship of teacher and pupil and finally the implication of Aurobindos philosophy of education.
Best Computer For Sketchup

As a professional architect, video game designer, interior designer, or AEC processional, you will agree that Sketchup is one of the best applications ...
Understanding, Analyzing, and Optimizing the Complexity of Deep Models

This paper aims to evaluate and analyze the complexity of feature transformations encoded in DNNs. We propose metrics to measure three types of complexity of transformations based on the information theory. We further discover and prove the negative correlation between the complexity and the disentanglement of transformations. Based on the proposed metrics, we analyze two typical phenomena of the change of the transformation complexity during the training process, and explore the ceiling of a DNN’s complexity. The proposed metrics can also be used as a loss to learn a DNN with the minimum complexity, which also controls the significance of over-fitting of the DNN. Comprehensive comparative studies have provided new perspectives to understand the DNN. We will release the code when the paper is accepted.