Reviewerly/exHarmony huggingface dataset:
Development of Warehouse Data Models to Support JNE Delivery Services

In order to help improving the performance of JNE business communities in this globalization era, it is necessary to encourage them to compete with each other in acquiring technology to support all their work activities so that they can be carried out as effectively and efficiently as possible, due to the increasingly dominant role and technology. The data warehouse is one of the concepts oriented to the core components of a business, that is, data can be categorized as a strategic supporting aspect because through the formation of a data warehouse an output can be obtained in the form of a report that can be used as analysis material for the executive in the decision-making process. The objectives to be achieved in this research are to formulate a data warehouse model and application design in accordance with the results of the needs analysis, which can support JNE business actors as a delivery service, as a case study JNE Klaten is used as research object. The methodology used in this study uses analysis and design methods. The method of analysis is carried out by studying literature, conducting surveys and interviews, identifying information needed by executives in decision making, and defining data warehouse requirements which will be built based on Nine-Steps Methodology. The design method is carried out by designing a data warehouse application, as a display interface that contains supporting features from the user side. The results of the research are in the form of data warehouse models and applications that are built based on operational data, which are processed in various dimensions, so that they can form a report to meet the needs of the executive about information and using the data warehouse, the executive can analyze the reports generated from multiple viewpoints easily, and can also be used as a reporting tool.
Extreme Learning Machine Prediction Model on Airbnb Base Price

This paper describes how Extreme Learning Machine is implemented as a prediction model to determine the base price of Airbnb properties. Extreme Learning Machine offers many advantages, such as fast learning speed, good generalization performance, and high prediction accuracy. The steps in the extreme learning machine method, in general, are set hidden neuron number, randomly assign input weight & hidden layer biases, calculate the output layer, the whole learning process completed through one mathematical change without iteration. The performance of the model will be measured using mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE). The ELM model will be tested with the Airbnb dataset in the London area taken from the Insideairbnb.com site with 79671 numbers of data with 21 features chosen. MAPE, MSE, RMSE are chosen for the evaluation method. From the experiments, the model show as the number of neurons increases, the link between the input and output layers would consequently increase. This leads to a better quality of learning produced from and the model generates MAPE value of 3.06% MSE value of 0. 038, and RMSE value of 0.239.This paper describes how Extreme Learning Machine is implemented as a prediction model to determine the base price of Airbnb properties. Extreme Learning Machine offers many advantages, such as fast learning speed, good generalization performance, and high prediction accuracy. The steps in the extreme learning machine method, in general, are set hidden neuron number, randomly assign input weight & hidden layer biases, calculate the output layer, the whole learning process completed through one mathematical change without iteration. The performance of the model will be measured using mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE). The ELM model will be tested with the Airbnb dataset in the London area taken from the Insideairbnb.com site with 79671 numbers of data with 21 features chosen. MAPE, MSE, RMSE are chosen for the evaluation method. From the experiments, the model show as the number of neurons increases, the link between the input and output layers would consequently increase. This leads to a better quality of learning produced from and the model generates MAPE value of 3.06% MSE value of 0. 038, and RMSE value of 0.239.
THE ARCHITECTURAL MONUMENT TO DANTE ALIGHIERI AND TO THE “DIVINE COMEDY” BY THE ARTIST CESARE LAURENTI

For the first time in Russian science the article considers a project of the architectural monument to Dante Alighieri, the Italian medieval poet, conceived by artist Cesare Laurenti in the beginning of the 20th century, but never built. One of the main features of that monument was that it was inspired by the mausoleum project attributed to Leonardo da Vinci. The author of the article studies the cultural context in which Cesare Laurenti worked and which influenced his choice of the idea and its embodiment. Particular attention is given to ideological transformation of the project through time.
Policy Research Trends of the local government through Keyword Network Analysis

The local government produces many policy researches for decision making in various fields of the city. This research aims to analyze policy researches of the local government for classify policy trend and feature of the local government. The analysis of policy research trends is based on keyword network analysis with phased process such as subject classification, network analysis, trend analysis, and implementation analysis. The case of analysis, Goyang city, has managed many research projects, which shows steadily enlarging budgets and number of researches. There are total of 243 researches has been finalized from 2011 to 2017. This paper is based on a practical research which helps to understand overall tendency of policy researches and managements on the local government. It would be value to propose a general process to analyze whole policy researches under the local government that helps to understand a trend of the local government policy and show an improvement direction of policy management and application.
Joseph R. Skeen Library : Interlibrary Loan (ILL): Book Chapter Request Tutorial

Learn how to use the Skeen Library Interlibrary Loan system! How to place a request for a book chapter
Causality in Machine Learning

The goal of machine learning is to find structure and correlations in data. However, correlations are not considered as causal relationships. Found relationships can for instance also be based on noise, too selective data or confounders. Based on the notion of causality defined by Jonas Peters, Dominik Janzing and Bernhard Scholkopf, we asked the question in the Machine Intelligence Seminar 2020 which influence and significance causality has in machine learning. The result is this collection of trend reports, which explores the meaning and importance of causality in several data science applications.Each report aims to gain an understanding of the current research on the influence of causality in a specific research area. The investigated research areas range from ethical and medical applications, human decision processes and human psychology, over law and politics, up to artificial general intelligence. For each area, trends influenced or driven by causality and their key drivers are identified. The trends are then projected into the future, revealing opportunities and threats as well as their impact on technology development and on our society.
Pemanfaatan Media Sosial Instagram dalam Pembelajaran Bahasa Indonesia di Tingkat Perguruan Tinggi : Sebuah Gagasan Konseptual

Technology in the digital era has a big impact on life. Technology presents a vehicle for social media for the public. It is undeniable that social media is a medium that gets great appreciation with many users who join. Conditions like this must be used as a creative medium in learning. Indonesian is the unifying language of the Indonesian people. So that the presence of Indonesian language learning becomes important for all levels of Indonesian society. In the digital era, technology provides a door for Indonesian language learning to remain active without being limited by place, time, and teacher, but also not limited to age. The language agency in its Instagram account @badanBahasakemendikbud is one of the language activists who seek to utilize Instagram social media for learning Indonesian. In the #Tuesdaylanguage and   #kamiskamus programs, the community provides knowledge of the Indonesian language through social media technology. In addition to the Language Agency, lecturers and students can also apply it in Indonesian language lectures through Instagram. In this study, researchers used data analysis methods to find out how much influence Instagram social media had for learning Indonesian. The purpose of the research is to recognize and deepen Indonesian language learning through social media Instagram. The results of the study stated that students gain knowledge of Indonesian every day when browsing Instagram.
LibGuides: Library Services Platform (LSP): FAQ

Website for the systemwide Library Services Platform Frequently asked questions and answers for LSP operations
Analysis of Minimal Clearance and Algorithm Selection Effect on Path Planning for Autonomous Systems

There are many path planning algorithms in the literature, with different classifications, domains of use, efficiency to find the shortest path or to make a complete coverage of the area to be studied. In the literature, we can also find evaluationsof all these algorithms in terms of their performance in the search for the shortest path, execution time and comparisons between them. In this work, twelve algorithms from the literature were studied to analyze their sensibility to the number of obstacles and the clearance value between them. Data analytics methods were used to make a qualitative study of the sensibility of these algorithms to the constraints studied. For investigation of the problem, two metrics were used, the length of the generated path and the number of iterations used to find the solution. The number of iterations here refers to the number of nodes evaluated by the algorithm when searching for the target node. The results are synthesized in two tables that show the sensibility of the algorithms to the change in the constraints studied and the immunity of others, and the correlation among the algorithms, the constraints and the metrics.
Erstellung eines Machine Learning Frameworks zur Sentimentanalyse von Twitterposts

The present bachelor theses discusses the creation process of a framework for the sys-tematic analysis of twitter posts regarding their sentiment. The result is an application, which links and uses the covered theoretical approaches for text classification.
Research Guides: Special Collections and University Archives: Resources Beyond TU

Learn what special collections and university archives are, what we hold, and how to access the collections
Tacoma Community College Library: ENGL 95: Sandin, J. (Fall 2021): MLA style

This library guide is to provide students in Dr. Sandin's English 95 class with starting points for their research projects, including the annotated bibliography.
A data corruption detection scheme based on ciphertexts in cloud environment

With the advent of the data era, people pay much more attention to data corruption.Aiming at the problem that the majority of existing schemes do not support corruption detection of ciphertext data stored in cloud environment, this paper proposes a data corruption detection scheme based on ciphertexts in cloud environment (DCDC).The scheme is based on the anomaly detection method of Gaussian model.Combined with related statistics knowledge and cryptography knowledge, the encrypted detection index for data corruption and corruption detection threshold for each type of data are constructed in the scheme according to the data labels; moreover, the detection token for data corruption is generated for the data to be detected according to the data labels, and the corruption detection of ciphertext data in cloud storage is realized through corresponding tokens.Security analysis shows that the algorithms in the scheme are semantically secure.Efficiency analysis and simulation results reveal that the scheme shows low computational cost and good application prospect.
Code Summarization: Generating Summary of Code Snippets

Source Code Summarization refers to the task of creating understandable natural language summaries from a given code snippet. Good-quality and precise source code summaries are needed by numerous companies for a platitude of reasons - training for newly joined employees, understanding what a newly imported project does, in brief, maintaining precise summaries on the evolution of source code (using git history), categorizing the code, retrieving the code, automatically generating documents, etc. There is a considerable distinction between source code and natural language since source code is organized, has loops, conditions, structures, classes, and so on. Most of the models follow an encoder-decoder structure, we propose an alternative approach that uses UAST(Universal Abstract Syntax Tree) of the source code to generate tokens and then use the Transformer model for a self-attention mechanism which unlike the RNN method is helpful for capturing long-range dependencies. We have considered Java code snippets for generating code summaries.
IOT Based Face Mask Identification and Temperature Detection

Face mask identification has increased quickly after corona emphatic last years for various law enforcement security schemes and other business uses.  In the present situation due to Covid-19, the requirement for face mask identification applications, identification of temperature and hand sanitizing is directly in high need for Railway Entrance, in the office Entrance, Airport Entrance, Museums, and Amusement Parks, Also in the other Public Places and companies to guarantee safety. The face appears to be growing for others to culminate in a new approach to discovering a new line and suggests defining a face mask. The recommended method for classifying face mask identification using real-time COVID-19 care for human actions. The proposed method is divided into strategies. The first approach uses a temperature sensor for cycling above the current tire temperature and mechanically spraying disinfectant. In the second technique, the work is intended to allow a safety device for people to stay away from COVID-19 by detecting a face mask. Finally, we proposed continuous monitoring of human attitudes and store people's records on the server using the in-depth knowledge of the idea.
Classification of Documents Extracted from Images with Optical Character Recognition Methods

Over the past decade, machine learning methods have given us driverless cars, voice recognition, effective web search, and a much better understanding of the human genome. Machine learning is so common today that it is used dozens of times a day, possibly unknowingly. Trying to teach a machine some processes or some situations can make them predict some results that are difficult to predict by the human brain. These methods also help us do some operations that are often impossible or difficult to do with human activities in a short time. For these reasons, machine learning is so important today. In this study, two different machine learning methods were combined. In order to solve a real-world problem, the manuscript documents were first transferred to the computer and then classified. We used three basic methods to realize the whole process. Handwriting or printed documents have been digitalized by a scanner or digital camera. These documents have been processed with two different Optical Character Recognition (OCR) operation. After that generated texts are classified by using Naive Bayes algorithm. All project was programmed in Microsoft Visual Studio 12 platform on Windows operating system. C# programming language was used for all parts of the study. Also, some prepared codes and DLLs were used.
Teacher-Student Adversarial Depth Hallucination To Improve Face Recognition

We present the Teacher-Student Generative Adversarial Network (TS-GAN) to generate depth images from single RGB images in order to boost the performance of face recognition systems. For our method to generalize well across unseen datasets, we design two components in the architecture, a teacher and a student. The teacher, which itself consists of a generator and a discriminator, learns a latent mapping between input RGB and paired depth images in a supervised fashion. The student, which consists of two generators (one shared with the teacher) and a discriminator, learns from new RGB data with no available paired depth information, for improved generalization. The fully trained shared generator can then be used in runtime to hallucinate depth from RGB for downstream applications such as face recognition. We perform rigorous experiments to show the superiority of TS-GAN over other methods in generating synthetic depth images. Moreover, face recognition experiments demonstrate that our hallucinated depth along with the input RGB images boosts performance across various architectures when compared to a single RGB modality by average values of +1.2%, +2.6%, and +2.6% for IIIT-D, EURECOM, and LFW datasets respectively. We make our implementation public at: this https URL.
Educational Activities Of Teachers In Distance Learning During A Pandemic

The article examines the issues of organizing the educational activities of university teachers in the context of an emergency transition to the format of distance learning during a pandemic. The emergency transition created unique conditions for scaling classical forms of distance education, the experience gained requires description and analysis. Purpose of the study: to analyze the organization of educational activities of university teachers, to identify problems, advantages, positive and problematic educational practices. The study is based on theoretical approaches that describe changes in social practices under the influence of digital technologies, the impact of digital devices on the personality and the development of social relations. To identify positive and problematic practices of educational activity in the context of an emergency transition to the format of distance learning, a questionnaire survey method was used. The possibilities and limitations of digital educational technologies in the context of emergency scaling of distance learning, problem areas in the technical equipment of teachers, socio-psychological, methodological readiness of teachers to work in a digital environment were identified. It is shown that the development and preservation of the existing teaching corps require organized meaningful professional retraining associated with the formation of digital didactics skills. The scaling of the distance learning experience has launched the processes of qualitative changes in the education system. The groups of teachers and students were identified who consider the use of distance learning technologies as an opportunity to achieve well-being in education, implement an individualized and humanistic approach and equalize educational opportunities.
Improved Exploration with Stochastic Policies in Deep Reinforcement Learning

Deep reinforcement learning has recently shown promising results in robot control, but even current state-of-the-art algorithms fail in solving seemingly simple realistictasks. For example, OpenAI et al. 2019 demonstrate the learning of dexterous in-hand manipulation of objects lying on the palm of an upside oriented robot hand. However, manipulating an object from above (i.e., the hand is oriented upside-down) turns out to be fundamentally more difficult to learn for current algorithms because the object has to be robustly grasped at all times to avoid immediate failure. In this thesis, we identify the commonly used naive exploration strategies as the main issue. Therefore, we propose to utilize more expressive stochastic policy distributions to enable reinforcement learning agents to learn to explore in a targeted manner. In particular, we extend the Soft Actor-Critic algorithm with policy distributions of varying expressiveness. We analyze how these variants explore in simplified environments with adjustable difficulties that we designed specifically to mimic the core problem of dexterous in-hand manipulation. We find that stochastic policies with expressive distributions can learn fundamentally more complex tasks. Moreover, beyond the exploration behavior, we show that in not perfectly observable environments, agents that represent their final (learned) policy with expressive distributions can solve tasks where commonly used simpler distributions fail.
Estrategias organizacionales que deben desarrollar las instituciones educativas para afrontar la educación virtual con calidad

Virtual education in Colombia must seek quality in each process, that is, from the national level approach, from the Ministry of Education, where there are clear guidelines so that the institutions that provide this learning methodology are competitive against face-to-face programs , through those who exercise the leadership role of the processes, so that when investing in technologies that support this process they do not see it as unnecessary expenses, but on the contrary, they understand the importance of technology to achieve results proposed by universities, regarding the delivery of competent and well-prepared professionals, to face the labor dynamics of a specific society. Ending with the teachers and students, in whom the strategic planning of the top management will fall, either teaching or learning. Education, whatever the methodology, has the obligation to meet the needs of society.
Sound Design in the Context of Sonification

This chapter investigates techniques for designing sound, with an emphasis on parameter mapping sonification. Methods emerging from user-centered design create new tools for sound design in the context of sonification of data. Design approaches using ecological sound design are discussed and analyzed. Some case studies are provided to explore manipulation and control of sound in the context of data sonification. Furthermore, the effect of understanding auditory display and the exposure to sound on its aesthetic evaluation and acceptance by scientists is examined.
A Quantum Multi-agent Blind Signature Scheme

Abstract With the development of information society, people are increasingly concerned about the security problems caused by information leakage, and digital signatures play an irreplaceable and important role in the protection of information security, which replace traditional manual signatures and seals in the network environment and have important roles such as anti-forgery, anti-tampering, anti-replay, identifiable identity and confidentiality [1]. However, with the in-depth study of quantum mechanics, it has been found that the confidentiality mechanism of classical digital signatures is under serious threat. Since quantum computing is a parallel operation with exponentially increasing operational capability, a series of complex mathematically difficult problems can be rapidly overcome. Based on the above, this paper proposes a multiple agent blind signature scheme, which adopts the key technologies such as the Unitary transformation and Hadamard gate operation, and adds multiple agent signature to quantum signature to improve the efficiency of the protocol, and adds blind signature to improve the security of the protocol. At the same time, the scheme uses quantum key distribution to ensure its unconditional security, which is applicable in systems such as electronic voting and electronic payment, and has certain anti-attack capability [2].