Reviewerly/exHarmony huggingface dataset:
Data-driven machine learning and optimization pipelines for real-world applications

Machine Learning is becoming a more and more substantial technology for industry. It allows to learn from previous experiences in an automated way to make decisions based on the learned behavior. Machine learning enables the development of completely new products like autonomous driving or services which are purely driven by data.The development of such new data-driven products is often a long procedure. Even the application of machine learning algorithms to specific problems is mostly not straightforward. To illustrate this, a data-driven service, called Automated Damage Assessment, from the automotive industry is introduced in this work. Based on the gained experience from such data-driven service developments, this dissertation proposes a methodology to develop data-driven services in an accurate and fast manner. The Automated Damage Assessment service is based on sensor data, i.e. data recorded from vehicle on-board sensors over time. Using such time series from more than one sensor results in a multivariate time series. The existent methods to solve multivariate time series classification-problems are often complex and developed for specific problems without being scalable. To overcome this, suitable approaches with different complexities are proposed in this work. These approaches are applied on multiple publicly available data sets and on real-world data sets from medical and industrial domain with the result that especially two AutoML (Automated Machine Learning) approaches, namely GAMA and ATM, as well as one of the proposed approaches (PHCP) are most suitable to solve these particular multivariate time series problems.
The Impact of Educational Expenditure on Ethiopian Economic Growth: Application of Vector Auto Regressive Model (VAR).

This study has been attempted to investigate the fundamental impact of educational expenditure on the economic growth of Ethiopia.The nature of data was dynamic in nature and it covers the time ranges of 1975/76-2011/12, implies that the study adopts time series data analysis and the data was collected from secondary sources.To analyze the data, both descriptive and inferential approaches was utilized.The study employed the Solow growth model as a bench mark to specify the econometric model.The model was specified by considering real gross domestic product as a dependent variable subject to gross capital formation, labor force, educational expenditure, export and inflation as an independent variable.Both Vector Auto Regressive (VAR) and Vector Error Correction model was undertaken as an analytical procedure.The Augmented Dicky-Fuller (ADF) test was used to test the stationary nature of variables, so that all variables are stationary at level and first difference except inflation at its level form.The variables were co-integrated and confirmed the existence of long run interdependency.The result of the study was evaluated from both short run and long run aspects.It revealed that all variables were statistically significant except levels of export in the long run.Therefore, educational expenditure (our target variable), inflation and gross capital formation significantly and positively affects growth in long run aspects of the VAR system.However, labor force negatively affects growth.In the short run, only educational expenditure and inflation affects growth positively and significantly.Thus, the responsible bodies should be able to improve the level of investment in the educational sector and focus on skill and ingenuity to increase productivity at the margin.
Regularized Generative Adversarial Network

We propose a framework for generating samples from a probability distribution that differs from the probability distribution of the training set. We use an adversarial process that simultaneously trains three networks, a generator and two discriminators. We refer to this new model as regularized generative adversarial network (RegGAN). We evaluate RegGAN on a synthetic dataset composed of gray scale images and we further show that it can be used to learn some pre-specified notions in topology (basic topology properties). The work is motivated by practical problems encountered while using generative methods in the art world.
LibGuides: Searching Strategies from Basic to Advanced: Searching Only for eBooks

This guide provides an approach to strategic searching, in which you gradually target your search by learning from each result and then adjusting the search terms for a more focused set of results.
UPAYA MENINGKATKAN HASIL BELAJAR EKONOMI MELALUI MODEL PEMBELAJARAN ARTIKULASI PADA PESERTA DIDIK KELAS X

The problems in this study are 1) economic learning outcomes that are still low, 2) lack of variation in the use of learning models in the teaching and learning process in the classroom and 3) the learning process does not give rise to interaction between fellow students. In this study the authors used the articulation learning model with the aim of being expected to improve the activities and economic learning outcomes of class X students. IPS 2 on economic subjects in Bandar Lampung SMA 5 Academic Year 2018/2019. This research is a Classroom Action Research (CAR) conducted in two stages of the cycle in which the application uses an articulation learning model in the learning process. The subjects in this study were class X students. IPS 2 Bandar Lampung High School 5 numbered 33 students. The results showed that, the percentage of student activity increased from 55.56% in the first cycle to 73.33% in the census 2. The percentage of student learning outcomes also increased. In the pre cycle the completeness percentage was 27.27%, cycle 1 was 60.61% and cycle 2 was 84.85%. From the results of the analysis, it can be concluded that learning activities by applying articulation learning models can improve the activity and economic learning outcomes of class X students. IPS 2 in Bandar Lampung SMA Negeri 5 2018/2019
Systematic approach to improving the methodical competence of university teachers

The article discusses the systematic approach to improving the methodical competence of a university teacher. The essence of the basic concepts of the research is revealed. The aspects of integrity of the pedagogical system are substantiated. Particular attention in the article is paid to the methodical activity of a university teacher, modern organizational forms of methodical work, and methods of teaching adults which contribute to the effective improvement and self-improvement of the teacher's professional and pedagogical activity.
ANALISIS SIKLUS PENGELUARAN PADA CV. PURWA DAYA SEJAHTERA PALEMBANG

ABSTRACK The expenditure cycle is also used by companies to maintain good relations with suppliers, because low prices and good quality alone do not guarantee the smooth running of the company, but also require good relations with suppliers. With good relationships, companies can buy goods with more confidence in their quality and with more confidence in the adequacy of the goods the company needs. Without good relations, the company may get low prices and relatively good goods, but it may not be guaranteed when the goods will be in stock. The accounting information system has three basic functions in the expenditure cycle, namely obtaining and processing data on various business activities, storing and managing data to support decision making, and providing control functions to ensure data reliability and safeguarding Company resources. Keywords : accounting information system, expenditure cycle
Nilai Sosial Dalam Novel Yorick Karya Kirana Kejora: Tinjauan Sosiologi Sastra Dan Relevansinya Sebagai Bahan Ajar

This study aims to describe the social values ​​contained in Kirana Kejora's Yorick novel and its relevance as teaching material in schools. This research is a descriptive qualitative research. The data in this study are words in Yorick's novel that contain social values. The data source is obtained from Yorick's novel by Kirana Kejora. The technique used in this study is literature, refer and note technique. The analysis technique in this study uses a semiotic technique with heuristic and hermeneutic readings. To test the validity of the data used data triangulation techniques. The results of this study indicate that the discovery of 13 data relating to social values. Based on these data, it is quite relevant to be associated with RPP of Indonesian subjects basic competencies 3.11 and 4.11 so that they can be applied in literary learning in class.
실시간 온라인 화상 수업 효과 연구 -한국어 학업 성취도와 학습자 만족도를 기준으로-

The purpose of this study is to verify the effectiveness of real-time online video lessons by analyzing the satisfaction of learners and their academic achievement of real-time online Korean class students, in relation to the non-face-to-face, online classes that began in March 2020. This research analyzed the academic performance of Korean language students in the spring and summer semesters of 2019 and 2020 from the Korean Language Institute at J University located in Jeonju-si. The main findings of this study are as follows. First, there was no significant difference in the academic achievement on the intermediate test and post-test between the groups. Second, surveys regarding the satisfaction of leaners have shown that they are in a ‘normal level’ of satisfaction in all areas. Also, in particular, ‘overall satisfaction’ is in near ‘satisfaction’, showing the highest rate of satisfaction, while ‘satisfaction’ with communication’ is in a ‘normal’ level with the lowest rate of satisfaction. This study is significant in the sense that it has verified the effect of real-time video classes and analyzed the learner satisfaction at the same time. (Jeonbuk National University)
LibGuides: Stop watching Netflix and check out free eBooks and stuff: Printing On Campus

Resources to help instructors and students during college closure There is a printing station on campus. Send the print job remotely or in-person and print on campus.
Novelty Detection with Rotated Contrastive Predictive Coding

The current dominant paradigm for novelty detection relies on a learned model’s capability to recover the regularities. To this end, reconstruction-based learning is often used in which the normality of an observation is expressed in how well it can be reconstructed. However, this can be limiting as anomalous data can be reconstructed well if enough common features are shared between normal and anomalous data. In this paper, we pursue an alternative approach wherein the normality is measured by a contrastive learning objective. Specifically, we propose Rotated Contrastive Predictive Coding (Rotated CPC) where the model operates on rotated images and simultaneously learns to predict the future in latent space. Normality score is thus measured as how predictive the representations are and the score’s robustness is further improved by ensembling predictions on multiple rotations of the input signal. We demonstrate the efficacy of this formulation across a variety of benchmark datasets where our method outperforms state-of-the-art methods.
Explain discrete choice methods by animation videos

2020 was the year in which distance learning (also known as Virtual Education) gained importance, providing an analysis framework on existing tools in order to enhance their results. Our goal was to provide a tool that would assist students in understanding the Discrete Choice Methods, and for this we have started from an analysis of the existing types of education: Traditional Education, Virtual Education and Hybrid Education, summarizing the importance of the latter, and the positive results what can be obtained in relation to the other two. Educational videos, their elements and classifications were also analyzed, making a distinct emphasis on how to achieve Student Engagement through Working Memory, which can be stimulated through audiovisual channels. Then the types of Animated videos were discussed, of which one called Whiteboard Video was selected for our project. After that, a theoretical framework of the Discrete Choice Models was developed, its elements and the Utility Function that was used as a basis for the analysis, getting to explain the Multinomial Logit Model and the Blue Bus/Red Bus paradox. Finally, our experience preparing the video was shared, exposing both advantages and disadvantages of each alternative analized about wich animation program should be used and stating the decision made. The creative process was also shared along with the difficulties faced to achieve our outcome. The result was an Animated Whiteboard Video, which meets cognitive requirements according to what was analyzed. How this video influences the students, and if it can improve understanding remains to be tested in the future.
Understanding Inclusion from the Educators’ Viewpoint

Inclusion has been the preferred method of educating students with disabilities for several decades.However, the understanding of the concept of inclusion seems vast and diverse.The purpose of this study was to examine the perceptions of general and special educators from two geographic locations-United States and St. Lucia-on their understanding of inclusion.Findings indicate that the majority of educators' understanding of inclusion was based on the placement definition where instruction for students with disabilities occurs in the general education classroom.The main difference found was that most general educators used the placement definition of inclusion while special educators used the individual instruction and general instruction definitions as discussed by Göransson and Nilholm (2014).There was no major difference in responses between general and special educators based on context and state policy.Recommendations and implications for future practice are discussed.
Caption Generation from Road Images for Traffic Scene Construction.

In this paper, an image captioning network is proposed for traffic scene modeling, which incorporates element attention into the encoder-decoder mechanism to generate more reasonable scene captions. Firstly, the traffic scene elements are detected and segmented according to their clustered locations. Then, the image captioning network is applied to generate the corresponding caption of each subregion. The static and dynamic traffic elements are appropriately organized to construct a 3D corridor scene model. The semantic relationships between the traffic elements are specified according to the captions. The constructed 3D scene model can be utilized for the offline test of unmanned vehicles. The evaluations and comparisons based on the TSD-max and COCO datasets prove the effectiveness of the proposed framework.
Presenting explanation text

This teaching resource slide was presented in English language class or bahasa Inggris 2 class. the topic was about understanding, identifying, and comprehending the topic, main idea, subject and supporting details of reading comprehension. The competence taught in this class was of course reading comprehension related to genre-based task (explanation text)
Pfeiffer Library: Accessing Pfeiffer Library: Welcome

Tutorials on how to log into your online accounts at Tiffin University and Pfeiffer Library
Engaging and Active Security Education (EASE) Workshop

Security is an increasingly important topic within higher education, but it is often challenging to identify effective learning and teaching practices that actively involve all students.The Engaging and Active Security Education (EASE) workshop at the UK ACM SIGCSE Computing Education Practice conference aims to support cyber security educators in exchanging practice as well as devising new practices. The objectives of the EASE workshop are:to expand the community of cyber security educatorsto provide an avenue for cyber security educators to exchange learning and teaching practiceto afford opportunities for cyber security educators to collaborate and form novel learning and teaching practices.The EASE workshop is seeking practices from the wider community that can be presented at the workshop and can be subsequently accessed on the workshop website.
Echo-CGC: A Communication-Efficient Byzantine-Tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network

In the past few years, many Byzantine-tolerant distributed machine learning (DML) algorithms have been proposed in the point-to-point communication model. In this paper, we focus on a popular DML framework - the parameter server computation paradigm and iterative learning algorithms that proceed in rounds, e.g., [Gupta and Vaidya, 2020; El-Mhamdi et al., 2020; Chen et al., 2017]. One limitation of prior algorithms in this domain is the high communication complexity. All the Byzantine-tolerant DML algorithms that we are aware of need to send n d-dimensional vectors from worker nodes to the parameter server in each round, where n is the number of workers and d is the number of dimensions of the feature space (which may be in the order of millions). In a wireless network, power consumption is proportional to the number of bits transmitted. Consequently, it is extremely difficult, if not impossible, to deploy these algorithms in power-limited wireless devices. Motivated by this observation, we aim to reduce the communication complexity of Byzantine-tolerant DML algorithms in the single-hop radio network [Alistarh et al., 2010; Bhandari and Vaidya, 2005; Koo, 2004].Inspired by the CGC filter developed by Gupta and Vaidya, PODC 2020 [Gupta and Vaidya, 2020], we propose a gradient descent-based algorithm, Echo-CGC. Our main novelty is a mechanism to utilize the broadcast properties of the radio network to avoid transmitting the raw gradients (full d-dimensional vectors). In the radio network, each worker is able to overhear previous gradients that were transmitted to the parameter server. Roughly speaking, in Echo-CGC, if a worker agrees with a combination of prior gradients, it will broadcast the instead of the its raw local gradient. The echo message contains a vector of coefficients (of size at most n) and the ratio of the magnitude between two gradients (a float). In comparison, the traditional approaches need to send n local gradients in each round, where each gradient is typically a vector in a ultra-high dimensional space (d ≫ n). The improvement on communication complexity of our algorithm depends on multiple factors, including number of nodes, number of faulty workers in an execution, and the cost function. We numerically analyze the improvement, and show that with a large number of nodes, Echo-CGC reduces 80% of the communication under standard assumptions.
Predicting Potato Diseases Using Tensorflow in Mobile Apps Android

Abstract Detecting disease is something that can help to determine how to cure it. Potato diseases, namely early blight and late blight, are the problem in agriculture. Both diseases have almost the same characteristics so it is difficult to recognize. The technique used by farmers only utilizes their eyesight. In addition, previous studies have mostly used the biological aspect approach. In this study utilizing Machine Learning to detect an object. The development of this application uses the training configuration used is SSD Mobilenet v1. Application test results obtained an average value of 90% can detect and distinguish the types of diseases in the potato plant.
Variational Mixture of Normalizing Flows

In the past few years, deep generative models, such as generative adversarial networks \autocite{GAN}, variational autoencoders \autocite{vaepaper}, and their variants, have seen wide adoption for the task of modelling complex data distributions. In spite of the outstanding sample quality achieved by those early methods, they model the target distributions \emph{implicitly}, in the sense that the probability density functions induced by them are not explicitly accessible. This fact renders those methods unfit for tasks that require, for example, scoring new instances of data with the learned distributions. Normalizing flows have overcome this limitation by leveraging the change-of-variables formula for probability density functions, and by using transformations designed to have tractable and cheaply computable Jacobians. Although flexible, this framework lacked (until recently \autocites{semisuplearning_nflows, RAD}) a way to introduce discrete structure (such as the one found in mixtures) in the models it allows to construct, in an unsupervised scenario. The present work overcomes this by using normalizing flows as components in a mixture model and devising an end-to-end training procedure for such a model. This procedure is based on variational inference, and uses a variational posterior parameterized by a neural network. As will become clear, this model naturally lends itself to (multimodal) density estimation, semi-supervised learning, and clustering. The proposed model is illustrated on two synthetic datasets, as well as on a real-world dataset. Keywords: Deep generative models, normalizing flows, variational inference, probabilistic modelling, mixture models.
Authentication Database Leakage Detection

Authentication is the first step for any important applications.Password verification is widely employed in the Internet.Some advanced mechanisms including bio features, such as fingerprints or retina.However, digital password authentication credential is commonly used in the public.In other words, traditional password verification is still an important authentication credential mechanism for today's online services.Password database may be compromised.Once if the database is stolen, it is dangerous and critical for any services.Consequently, password database leakage becomes an important issue.Detection and countermeasure are essential for such a disaster.Our research proposing a scheme based on a authentication database storage mapping method to detect possible leakage of authentication database.By multiple mapping mechanism, if the authentication database is stolen, an attacker could not know any user's correct password.This method can reduce additional space of storing passwords with ability to detect security event for stolen authentication databases.
Keystroke Dynamics Analysis to Enhance Password Security of Mobile Banking Applications

Nowadays, there are many cases where users’ personal accounts get hacked using their own password. The factors for such cases can vary depending on password strength and obvious passwords which are similar to the user’s details such as usernames and emails. For that, there are new ways of preventing such incidents to happen and to strengthen the security of the accounts. This paper studies the usage of keystroke analysis to enhance password security which includes biometrics and typing patterns. This paper will also discuss the previous researches regarding this method on many platforms including touch screen devices. After that, this paper will look deeply into the implementation process of this technique followed by a detailed experiments and analysis. using keystroke dynamics analysis to enhance password security on mobile devices proved to have a great chance of success and how it can affect the everyday users of banking applications.