Reviewerly/exHarmony huggingface dataset:
Adapting Across Domains by Aligning Representations and Images

Author(s): Tzeng, Eric | Advisor(s): Darrell, Trevor | Abstract: The advent of deep convolutional networks has powered a new wave of progress in visual recognition. These learned representations vastly outperform hand-engineered features, achieving much higher performance on visual tasks while generalizing better across datasets. However, as general as these models may seem, they still suffer when there is a mismatch between the data they were trained on and the data they are being asked to operate on. Domain adaptation offers a potential solution, allowing us to adapt networks from the source domain that they were trained on to new target domains, where labeled data is sparse or entirely absent. However, before the rise of end-to-end learnable representations, visual domain adaptation techniques were largely limited to adapting classifiers trained on top of fixed, hand-designed visual features. In this thesis, we show how visual domain adaptation can be integrated with deep learning to directly learn representations that are resilient to domain shift, thereby enabling models to generalize beyond the source domain.In Chapter 2 we demonstrate how we can design losses that attempt to measure how different two domains are. We show that by optimizing representations to minimize these losses, we can learn representations that generalize better from source to target. In Chapters 3 and 4, rather than hand-designing these domain losses, we show that we can train models that attempt to measure domain discrepancy. Since these models are themselves end-to-end learnable, we can backpropagate through them to learn representations that minimize the learned discrepancy. This is similar in concept to Generative Adversarial Networks, and we additionally explore the relationship between the two and how we can use techniques developed for GANs in an adversarial setting as well. Finally, in Chapters 5 and 6, we show that adaptation need not be limited to intermediate features of a deep network. Adversarial adaptation techniques can also be used to train models that directly alter the pixels of images, transforming them into cross-domain analogues. These transformed images can then be used as a labeled pseudo-target dataset to learn supervised models better suited for the target domain. We show that this technique is complementary to feature-based adaptation, yielding even better performance when the two are combined.
(5) Academic Writing Skills in English - Techniques to generate ideas : clustering

Meritxell Simon-Martin video series explaining how to become a successful essay writer in English.
Classify radio signals from space

The data we are going to use consists of 2D spectrograms of deep space radio signals collected by the Allen Telescope Array at the SETI Institute. We will treat the spectrograms as images to train an image classification model to classify the signals into one of four classes. By the end of the project, you will have built and trained a convolutional neural network from scratch using Keras to classify signals from space.
The Effect of Learning Cell Toward Students in Reading Comprehension ON Narrative text (A Quasi ExperimentalResearch at the Second Grade Students of SMP Riyadul MubtadiinPandeglang)

Atiah Oktavia, 2019. The Effect of Learning Cell Toward Students inReading Comprehension ON Narrative text (A Quasi ExperimentalResearch at the Second Grade Students of SMP Riyadul MubtadiinPandeglang), (Undergraduate Research Paper. Department ofEnglish Education, Faculty of Education and Teacher Training, TheState of Islamic University, “Sultan Maulana Hasanudin” Banten).Advisers: Prof. Dr. H. Ilzamudin Ma’mur, M.A. and Tri IlmaSeptiana, M.Pd.ABSTRACTThis research wants to investigates the effect of learning celltoward students in reading comprehension on narrative text. Thisresearch is aimed to investigate the effect of learning cell increasestudents reading comprehension on narrative text of the secondgrade of SMP Riyadul Mubtadiin who apply learning cell and tofind out the students’ reading comprehension on narrative text ofthe second grade of SMP Riyadul Mubtadiin who do not applylearning cell. The method used in this research is quasiexperimental with two classes, called experimental and controlclass using pre-test and post-test technique. The researcherconducted at SMP Riyadul Mubtadiin Pandeglang from Okto er to Oktober 2019. The researcher collected data from 64students’ spreading in two classes, one class is as experimentalgroup and another one is as control group by using quasyexperimental method. The population was students of the secondgrade of SMP Riyadul Mubtadiin Pandeglang and the researchertook sample from class A as experimental class and class B ascontrol class. Based on the result of the research that there issignificant effect of teaching reading through learning cell towardsstudents’ reading comprehension. According to the data, the valueof tobservation is bigger than ttable. tobservation = 5,60 > ttable = 1,66 (5%) ortobservation = 5,60 > 2,38 (1%). It means that Ha (Alternativehypothesis) of research is accepted and Ho (null Hypothesis) isrejected. It is also concluded there is significant difference ofteaching reading through learning cell in teaching readingcomprehension
LibGuides: Online Learning in SISD: iPad Support

Discover how-to guides, videos, and resources for our online learners, families, and teachers. Additional resources for parents of students in PK-K who received a district iPad or are using a personal iPad for virtual learning.
Unsupervised pretraining for semi-supervised OCR

Author(s): Venkatswammy Reddy, Kishore Pacharamakalahalli | Advisor(s): Berg-Kirkpatrick, Taylor; Vasconcelos, Nuno | Abstract: Recent works like BERT, GPT, ELMO, ULMFiT have successfully demonstrated the effectiveness of pretraining for a variety of downstream NLP tasks. We propose to use a similar approach for a different learning scenario - semi-supervised OCR/text recognition.We hypothesize that for the supervised character image classifier of an OCR system, it is more effective to classify pre-learned OCR representations of character images rather than learn to do OCR from scratch in the traditional sense. For this purpose, components of the classifier are pretrained in an unsupervised fashion to consume a sequence of character images obtained by segmenting an image of a line of text, and reconstruct the same at the output. The pretraining is optimized for the masked language modeling objective, without access to the OCR labels for the character images. In our results, we show that a classifier trained on top of the pretrained representations achieves almost 100\% higher accuracy than a classifier trained from scratch, when supervised with just 5 line images. Our pretraining procedure can leverage the large amount of unsupervised data to learn useful OCR representations and enhances the performance of supervised OCR systems, especially when supervised data is scarce like in historical documents.
with KNN-Based Composite Memory for Dialog

Various machine learning tasks can benefit from access to external information of different modalities, such as text and images. Recent work has focused on learning architectures with large memories capable of storing this knowledge. We propose augmenting generative Transformer neural networks with KNN-based Information Fetching (KIF) modules. Each KIF module learns a read operation to access fixed external knowledge. We apply these modules to generative dialog modeling, a challenging task where information must be flexibly retrieved and incorporated to maintain the topic and flow of conversation. We demonstrate the effectiveness of our approach by identifying relevant knowledge required for knowledgeable but engaging dialog from Wikipedia, images, and human-written dialog utterances, and show that leveraging this retrieved information improves model performance, measured by automatic and human evaluation.
The Effect Of The Use Of Audio Visual Media (VCD) And Audio Media On Achievements Learning Arabic Reviewed From Student Achievement Motivation

The purpose of this study was to determine: (1) The difference in the influence of the use of VCD learning media with audio media on learning achievement in Arabic. This study used a quantitative method with an experimental approach with a 2 x 2 factorial design and descriptive analysis of data presentation. The data analysis technique used was the Two-Way Analysis of Variance (ANOVA) Technique. Prior to the analysis, the validity test was carried out with Product Moment correlation and reliability using Spearman Brown. The results of the research trial showed that all research variables were valid and reliable before testing the hypothesis.Hypothesis test results and research conclusions show: (1) there is a significant difference in learning achievement in Arabic between those who learn with VCD media and AUDIO media. VCD media resulted in higher Arabic learning achievement compared to AUDIO media. price P = 0.000 <0.05. This means that the first statistical hypothesis (Ho) is rejected. And H1 is accepted. The average difference between VCD media and AUDIO media, the results of the analysis show that students with VCD learning media have an average score of Arabic learning achievement of 80.70, as for the group of students with AUDIO learning media the average score of Arabic learning achievement is 71.20. It can be concluded that the results of the Arabic learning achievement test of students taught using VCD media are higher than the AUDIO media; (2) There is a significant difference in the effect of learning achievement in Arabic between those who have high and low achievement motivation. Students with high achievement motivation have better achievement in learning Arabic than students with low achievement motivation. This is evidenced by the price P = 0.000 <0.05. This means that the first statistical hypothesis (Ho) is rejected. This means that the first statistical hypothesis (Ho) is rejected. And H1 is accepted. Difference in mean between high and low achievement motivation. The results of the analysis showed that the group of students with high achievement motivation had an average score of Arabic learning achievement of 78.00 while the average score of learning achievement in Arabic with low achievement motivation was 73.90. It can be concluded that the Arabic learning achievement scores of students who have high achievement motivation are better than students who have low achievement motivation ;; (3) There is an interaction between learning media and achievement motivation on learning achievement in Arabic. Price P = 0.000 <0.05. This means that the first statistical hypothesis (Ho) is rejected, then the H1 hypothesis is accepted. This means that there is an interaction between the influence of the use of learning media and achievement motivation on the achievement of learning achievement in Arabic. VCD learning is equally effective for students who have high achievement motivation and students who have low achievement motivation.
From Engagement to User Experience: A Theoretical Perspective Towards Immersive Learning

As we see immersive technology becoming ever more prominent in education, from our mobile devices to highly immersive virtual reality headsets, there is a growing need to look beyond existing, pragmatic conceptualizations of the technology. We need to explore immersive technology from a theoretical perspective focused on the user and learner experience for it to make meaningful contributions to education. The way we experience technology often frames how we engage with it and determines whether we continue to use it. Immersive technology is often conceptualized based on the affordances provided to the user without any associations made back to theoretical underpinnings drawn from user experience and learning experience research. The authors take known conceptualizations of user experience and immersive technology and link them to the learning experience to form a working theoretical framework that can help develop an understanding of learner engagement with this emerging technology. We begin with concepts related to immersive technology, then build a theoretical framework and conclude with implications for learning approaches through examples from the spatial disciplines.
누리과정에 기초한 유치원 다문화 프로그램 분석 - 서울시 남부교육청 관내 유치원을 중심으로 -

Objective: This study aims to find ways to improve the quality and improvement of multicultural education for young children in kindergarten by analyzing the kindergarten multicultural program based on the Nuri course. It also helps kindergarten teachers to plan and deploy a ''multicultural'' curriculum in a balanced and effective manner, and provides multicultural program materials that the state is implementing in kindergartens to develop and disseminate ''multicultural'' related materials and programs. Methods: The study target was to select 17 kindergartens with multicultural education programs among the year-long education courses at kindergartens in the Nambu Office of Education, where multicultural children are concentrated, and compare and analyze the content of education related to ''multiculturalism'', activity types and activity areas, and multicultural topics by frequency. Results: As a result of the study, first, it was found that the educational content of multicultural activities was presented with a focus on ''cultural understanding'' and ''diversity'' at all ages of 3, 4, and 5. Second, the types of activities related to ''multiculturalism'' were the most important for all age groups of 3, 4, and 5 years of age. Third, the theme of activities related to ''multiculturalism'' by living topics was concentrated on the two living themes of the ''world''s countries'' and ''our country'' at all ages of 3, 4 and 5. Conclusion: For successful multicultural education in kindergarten, an educational approach is required in which the content elements of multicultural education should be evenly reflected in educational content and life topics, and teacher awareness and teacher education are needed to promote real multicultural classes. In addition, multicultural programs should be continuously developed and researched nationwide, and multicultural programs should be distributed to early childhood educational institutions.
Misspelling Detection from Noisy Product Images

Misspellings are introduced on products either due to negligence or as an attempt to deliberately deceive stakeholders. This leads to a revenue loss for online sellers and fosters customer mistrust. Existing spelling research has primarily focused on advancement in misspelling correction and the approach for misspelling detection has remained the use of a large dictionary. The dictionary lookup results in the incorrect detection of several non-dictionary words as misspellings. In this paper, we propose a method to automatically detect misspellings from product images in an attempt to reduce false positive detections. We curate a large scale corpus, define a rich set of features and propose a novel model that leverages importance weighting to account for within class distributional variance. Finally, we experimentally validate this approach on both the curated corpus and an out-of-domain public dataset and show that it leads to a relative improvement of up to 20% in F1 score. The approach thus creates a more robust, generalized deployable solution and reduces reliance on large scale custom dictionaries used today.
VUT Libraries: Faculty of Applied and Computer Sciences: Home

This guide contains genral library information for the Faculty of Applied and Computer Sciences.
LibGuides: Criminal Justice Resources: What is Criminal Justice?

This guide provides resources for information pertaining criminal justice or government. Information pertaining to criminal justice or government.
Catch the fraudster : The development of a machine learning based fraud filter

E-commerce has seen a rapid growth the last two decades, making it easy for customers to shop wherever they are. The growth has also led to new kinds of fraudulent activities affecting the customer ...
PENGARUH LINGKUNGAN KELUARGA DAN MOTIVASI INTRINSIK TERHADAP MINAT MENJADI GURU PADA MAHASISWA PENDIDIKAN AKUNTANSI 2015

Tujuan penelitian ini adalah untuk mengetahui gambaran lingkungan keluarga, motivasi intrinsik, minat menjdi guru, serta untuk mengetahui pengaruh lingkungan keluarga terhadap minat menjadi guru dan motivasi intrinsik terhadap minat menjadi guru. Metode penelitian yang digunakan adalah metode survey ekpsplanatori. Populasi penelitian ini adalah mahasiswa Pendidikan Akuntansi angkatan 2015 Fakultas Pendidikan Ekonomi dan Bisnis Universitas Pendidikan Indonesia yang berjumlah 90 orang. Metode pengumpulan data menggunakan kuesioner. Uji coba instrumen dianalisis menggunakan uji validitas dan uji reliabilitas. Penelitian ini menggunakan analisis regresi linier multipel. Uji hipotesis menggunakan Uji Keberartian Koefisien Regresi ( Uji F) dan Uji Keberartian Regresi Linier (Uji t). Hasil penelitian ini menunjukkan bahwa untuk pengaruh lingkungan keluarga terhadap minat menjadi guru menunjukkan nilai thitung (2,498) > ttabel (1,988) sehingga dapat ditarik kesimpulan bahwa lingkungan keluarga berpengaruh positif terhadap minat menjadi guru. Serta untuk pengaruh motivasi intrinsik terhadap minat menjadi guru menunjukkan nilai thitung (6,148) > ttabel (1,988) sehingga dapat disimpulkam bahwa motivasi intrinsik berpengaruh positif terhadap minat menjadi guru;The purpose of this study is to determine the description of family environment, intrinsic motivation, interest in becoming a teacher and to determine the influence of family environment and intrinsic motivation on the interest in becoming a teacher. Methodology of this study is using Explanatory Survey Method. The population of this study are 90 students of 2015 Accounting Education students of study program Faculty of Economics and Business, Indonesia University of Education. Data collection methods of this study use questionnaires. Instrument testing is analyzed using validity and reliability testing. This study uses multiple linear regression analysis. The hypothesis is uses Meaning Test of Linear Regression (t-Test) and Test of the Meaning of Regression Coefficients (F-Test). The results of this study indicates that the influence of family environment on interest in becoming a teacher shows tcount (2,498) > ttable (1,988). It can be concluded that family environment have a positive effect on interest in becoming a teacher. As well as for the influence of intrinsic motivation on interest in becoming a teacher shows tcount (6,148) > ttable (1,988). It concludes that intrinsic motivation has a positive effect on interest in becoming a teacher.
Open and reproducible science: from theory to equations, algorithms and plots

&amp;lt;p&amp;gt;Scientific theory is commonly formulated in the form of mathematical equations and new theory is often derived from a set of pre-existing equations. Most of us have experienced difficulty in following mathematical derivations in scientific publications and even more so their transfer into numerical algorithms that eventually result in quantitative tests and data plots. The Python package Environmental Science using Symbolic Math (ESSM, https://github.com/environmentalscience/essm) offers an open and transparent way to (a) verify derivations in the literature, (b) ensure dimensional consistency of the equations, (c) perform symbolic derivations, and (d) transfer mathematical equations into numerical code, perform computations and (e) generate plots.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we present an example workflow using jupyter notebooks illustrating the capabilities of the package from (a) to (e), including recently added advanced features.&amp;lt;/p&amp;gt;
The Systems Theory Of Economic Development; As Formulated By Kwabena Meneabe Ackon on 12th November 2020; Discussed In The Context Of Human Capital and, Economic, Social And Political Institutions In Africa.

Africa is not poor, yet Africa has piqued the interests of economists for the last half century and there is a good number of academic literature on why Africa is poor. This is because for the resources available to Africa, both natural and human capital potential, the continent has underperformed abysmally. The Systems Theory Of Economic Development explains the current state of Africa. It explains why despite the resources available and the human capital potential, the African continent lags the rest of the world in every measure of economic and social development. The Systems Theory Of Economic Development also provides a framework of what Africa can do to write a new story where economic development is inclusive and provides economic opportunities for the young and able-bodied, reversing the ever-rising tide of public debt while providing a self-sustaining economic, social, and political system that works for all and sundry.
IMPROVED HUMAN ACTIVITY RECOGNITION ACCURACY USING MACHINE LEARNING METHODOLOGIES

WBAN is having numerous applications in all over the world. There are many methods areavailable for predicting the human activity recognition (HAR). Conventional HAR identifiers are based onRFID, gyroscope, accelerometer and optical devices. By using these devices, the maintenance and cost of theequipment is increasing. To reduce this complexity in this paper we proposed HAR by using Machine Learning(ML) methodologies. Based on communication link between sensors in this paper we considered pathloss andgain as the main features. Then second by using ML techniques like linear regression, logistic regression,decision tree, SVM and Naive Bayes methods are compared with experimental results for run, stand and walk.From experimental results we proved that the accuracy of activity recognition is good by using machine learningmethodologies.
SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI SEKOLAH MENENGAH PERTAMA DI KOTA BANDAR LAMPUNG BERBASIS SISTEM INFORMASI GEOGRAFIS MENGGUNAKAN METODE SAW

Along with the development of information technology in the rapidly advancing era of globalization, the school education office in the city of Lampung conducted a zoning system in the acceptance of prospective elementary school students and above. With the advancement of technology, registration of prospective new students can access online registration which makes it easy to register. And now there needs to be a decision support system for prospective students in choosing schools according to the zoning mapping of the region. Then a decision support system is applied using the SAW (Simple Additive Weighting) method based on geographic information systems because the method is more appropriate in selecting criteria that are suitable for prospective students. In this case there are 3 (three) criteria contained in the system in the form of distance criteria, criteria for the difference in the number of UN scores of students with passing grade, and school quota criteria. This System will display the results in the form of a school name table with the highest to lowest percentage of the prospective students so that it will facilitate and assist prospective students in making decisions.