Reviewerly/exHarmony huggingface dataset:
Computer-assisted language comparison with RefLex

The RefLex database (www.reflex.cnrs.fr/database) is devoted to lexical data from African languages. So far (as of January, 2019), it contains 1,168,209 lexical entries from 1,312 sources covering 799 languages. Besides its importance as a (freely available) online Reference Lexicon (nearly all entries are linked with an image of the original page where it appears), RefLex proposes many tools designed to help the linguist perform various taslks pertaining to language comparison, language typology or historical linguistics. The way by which the tools may help are best summed up as follows: Counting; Organising; Retrieving.After a quick overview, my talk will present some of these tools:1. Counting: the Statistical tool. It allows to count all kind of combinations in a given field, and to display the result as a contingency table where the values that are well above or well under the expected ones are highlighted. The statistical tool is also able to count combinations of values in two different fields (as, for instance, Part of Speech and Tone Pattern). As a side effect, this tool is also very useful as an error finder, be it errors in the original source or in the coding of the data. Examples will show how, for instance, specific counts can be helpful in suggesting regular sound changes.2. Organising: the Reconstruction tool. It consists of a set of user-friendly interfaces designed to select sources, then to select words, then to align words phonetically and finally to manage the correspondence sets thus created. There is nothing automatic here: the choice of cognates as well as the details of phonetic alignments are left to the linguist alone, but the interface makes it very easy to handle. In addition, it is also possible for several registered users to work collaboratively on the same dataset.3. Retrieving: the Loanword tool. Currently under development, this very specific tool will take advantage of the fact that whenever the information is available in the original source, the borrowing status of words is hard-coded in the database. In addition, it is possible for any user to add borrowing information to any record. So far, more than 20,000 entries are identified as loanwords. The Loanword tool will make it possible to know what are the prominent donor languages, which notions are borrowed most and where, and other similar facts. It will of course make use of the mapping features already present in RefLex. These three features of RefLex illustrate how an online lexical database can contribute to a better knowledge of language history, by going far beyond the mere display of lexical material.
Teaching Culture Through ICT in a Postmodern World – (Un)Used Possibilities of New Technologies

Teaching culture of foreign countries was until recently done by traditional means i.e. movies, letters sent by post to schools in other countries or by means of a textbook. Now, when schools are getting more computerized, certain elements of teaching have been revolutionized. They include, for example, culture teaching, which received a new meaning through the use of eTwinning. Because of it, teachers and students can communicate much easier through the platform, Facebook or by e-mail with other schools from the EU that are in this program. They can take part in projects with other schools and learn the cultural differences between their countries together. In this article I try to point out the challenges which multicultural ICT use can bring to education.
Person Search with Joint Detection, Segmentation and Re-identification

Person search is a new and challenging task proposed in recent years. It aims to jointly handle person detection and person re-identification in an end-to-end deep learning neural network. In this paper, we propose a new multi-task framework, which jointly learn person detection, person instance segmentation and person re-identification. In this framework, a segmentation branch is added into the person search pipeline to generate a high-quality segmentation mask for each person instance. Then, the segmentation feature maps are concatenated with corresponding convolution feature maps in the re-identification branch, which results as a self-attention mechanism, provides more discriminative feature for person re-identification. The experimental results on the public dataset PRW demonstrate the effectiveness of the framework.
Algorithms, applications and systems towards interpretable pattern mining from multi-aspect data

How do humans move around in the urban space and how do they differ when the city undergoes terrorist attacks? How do users behave in Massive Open Online courses~(MOOCs) and how do they differ if some of them achieve certificates while some of them not? What areas in the court elite players, such as Stephen Curry, LeBron James, like to make their shots in the course of the game? How can we uncover the hidden habits that govern our online purchases? Are there unspoken agendas in how different states pass legislation of certain kinds? At the heart of these seemingly unconnected puzzles is this same mystery of multi-aspect mining, i.g., how can we mine and interpret the hidden pattern from a dataset that simultaneously reveals the associations, or changes of the associations, among various aspects of the data (e.g., a shot could be described with three aspects, player, time of the game, and area in the court)? Solving this problem could open gates to a deep understanding of underlying mechanisms for many real-world phenomena. While much of the research in multi-aspect mining contribute broad scope of innovations in the mining part, interpretation of patterns from the perspective of users (or domain experts) is often overlooked. Questions like what do they require for patterns, how good are the patterns, or how to read them, have barely been addressed. Without efficient and effective ways of involving users in the process of multi-aspect mining, the results are likely to lead to something difficult for them to comprehend. This dissertation proposes the M^3 framework, which consists of multiplex pattern discovery, multifaceted pattern evaluation, and multipurpose pattern presentation, to tackle the challenges of multi-aspect pattern discovery. Based on this framework, we develop algorithms, applications, and analytic systems to enable interpretable pattern discovery from multi-aspect data. Following the concept of meaningful multiplex pattern discovery, we propose PairFac to close the gap between human information needs and naive mining optimization. We demonstrate its effectiveness in the context of impact discovery in the aftermath of urban disasters. We develop iDisc to target the crossing of multiplex pattern discovery with multifaceted pattern evaluation. iDisc meets the specific information need in understanding multi-level, contrastive behavior patterns. As an example, we use iDisc to predict student performance outcomes in Massive Open Online Courses given users' latent behaviors. FacIt is an interactive visual analytic system that sits at the intersection of all three components and enables for interpretable, fine-tunable, and scrutinizable pattern discovery from multi-aspect data. We demonstrate each work's significance and implications in its respective problem context. As a whole, this series of studies is an effort to instantiate the M^3 framework and push the field of multi-aspect mining towards a more human-centric process in real-world applications.
The former San Niccolò psychiatric hospital in Siena: a polymorphic reality between architecture, archive and library

Following the closure of the asylums approved by the Basaglia Law, the care of the sick was entrusted to the extra-hospital services set up in public or private hospitals. The heritage of the asylum institutions, which conists not only of the archive, of real estate, of libraries, of museum collections of medical tools and of asylum furnishings, was entrusted to the competent ASL (Local Health Bodies). The sanitary archive of the San Niccolo psychiatric hospital was kept in the premises of the former Chiarugi department, located within the former mental hospital complex in via Roma. It consists of a total of 1916 pieces, which cover a continuous chronological period from 1819 to 2003. In 2003, however, the ASL of Siena decided to donate the Library of San Niccolo to the Faculty of Medicine of the city to avoid dispersing the huge library heritage. The archive as well as the library of the San Niccolo of Siena psychiatric hospital allow us, through the knowledge of the institution’s history, to recover the real experience of the doctors, nurses and patients whopopulated the asylum.
Compressed Data Structures for Astronomical Content-Aware Resource Search

We introduce an efficient approach that aims at supporting content-based queries on the Chilean Virtual Observatory. In particular we are interested in retrieving relevant information from virtual-observatory tables. This introduces several challenges that make the information-retrieval process harder. We define an algorithm that uses a compressed data structure to obtain the count of the number of occurrences of a string query within each column of a table. This kind of query has been used in the literature for faceted and semantic search as well as for retrieving information from web tables. This is in order to improve search effectiveness. We show that using only 15%–25% the space of a table our approach contains the table data (and hence the table can be deleted) and is able to answer queries efficiently in a few milliseconds.
Library Guides: How to Archive Tweets using TAGS: What is a Tweet?

A guide exploring the use of TAGS via a Google Sheet to capture recent Twitter data, and explore options for text analysis and visualizations.
LibGuides: Virtual Reality: Oculus Go

A guide to assist with the use of VR headsets and in finding excellent resources This page gives information and assistance with using the Oculus Go headsets.
The Influence of Clipping on the Performance of a Low Bit Rate Parametric Speech Coder

This paper presents a study of the influence of clipping on low bit rate parametric speech coder. By selecting three parameters of MELP vocoder: the fundamental frequency, voicing and LPC, the influence of different clipping degree on the MELP vocoder is studied. In the experimental stage, the performance of the MELP vocoder at different clipping degree are evaluated by using pitch estimation accuracy rate, voicing decision error rate and average Log-spectral distance. And then, the reasons which lead to a decrease in the performance of parameter extraction are analyzed. The experimental results show that clipping has a certain influence on the accuracy of pitch estimation and voicing decision. However, the accuracy of pitch estimation and voicing decision can keep high accuracy even when the clipping degree is large, which indicates that the pitch and voicing are strongly resistant to clipping. For LPC, the increase of the clipping degree increases the average Log-spectral distance, resulting in an inaccurate estimation of LPC.
Analysis Of Images Obtained By Unmanned Aerial Vehicle By Deep Learning Methods

In artificial intelligence applications, many sub-methods such as machine learning, artificial neural networks, classification, clustering algorithms are used. One of these methods is deep learning. Deep learning is an advanced machine learning class. Using the Deep Learning method, video analysis, image classification, speech recognition and natural language processing are very successful. The data and experiences to be provided by the projects covering Deep Learning and Unmanned Aerial Vehicles will increase the number of qualified studies on these issues and contribute to the development of high value-added products in these technologies. In this study, a control software that evaluates image data from unmanned aerial vehicles and makes various inferences (classification, positioning, marking) is created. By using the method of retraining the last layers of the pre-trained artificial neural network models with our data set, it has been tried to reduce the training time and increase the success. In these studies, 2 pre-educated models were used and as a result of training of these models, as a result of 190 thousand steps of training, 25.39 and 27.87 mAP values were reached.
LibGuides: Library Research Tips: But Can I Trust It?

This page provides guidelines to help you determine which sources may be more trustworthy than others.
Integral image computation algorithm for GPU-enabled automotive platforms

Nowadays, automotive industry is shapeshifting towards utilization of solutions in which software and algorithms are the dominant concern. Autonomous driving functions require processing-intensive decision algorithms which involve a lot of image manipulation software techniques. CPUs cannot meet such a demand; therefore, hardware acceleration again becomes mandatory in any design. GPUs have become great candidates for image processing in automotive algorithms, due to large number of cores. However, efficient porting of traditional image processing algorithms to GPUs is still a challenge. In this paper, we present an improvement of integral image computation on the GPU in an automotive context. The main benefit of the proposed improvement is reduced computational complexity and consequently speedup of execution time. Therefore, frame rate in an image processing algorithm is increased, which is necessary in modern automotive industry. Furthermore, we address specific challenges such as data dependency, resolution and optimization of arithmetic. Finally, we evaluate the approach utilizing NVIDIA CUDA for a Xavier-based automotive electronic control unit.
The Effects of Perceived Organizational Culture of Early Childhood Education Institutions and Ego-resilience on Happiness in Early Childhood Teachers

The purpose of this study was to determine the effects of perceived organizational culture of early childhood education institutions and ego-resilience on happiness in early childhood teachers.The survey was conducted in 300 teachers at early childhood education institutions in Incheon city.The collected data were statistically processed using an SPSS 20.0 program.Descriptive statistics was used to determine teachers' socio-demographic characteristics; Cronbach's coefficient was estimated for reliability of α the instruments.Pearson's correlation analysis was carried out to determine the correlation among research variables.Lastly, multiple regression analysis was performed to determine the relative impact of perceived organizational culture of early childhood education institutions and ego-resilience on happiness in early childhood teachers.The results of this study can be summarized as follows.First, significant positive correlation was found among all of perceived organizational culture of early childhood education institutions, ego-resilience, and happiness in early childhood teachers.Second, the relative impact of perceived organizational culture of early childhood education institutions and ego-resilience on happiness in early childhood teachers had meaningful explanatory power.These results suggest that perceived organizational culture of early childhood education institutions and ego-resilience can be important factors for happiness in early childhood teachers.
Frequency-Domain Analysis Based Exploitation Of Color Channels For Color Image Demosaicking

Color-difference interpolation (CDI) has been a widely used technique for various color demosaicking methods. CDI-based methods perform interpolation in the color-difference domain assuming that the color-difference signal is a low-pass signal. Recently, a residual interpolation (RI) algorithm, which conducts interpolation in the residual domain, has been developed, and it assumes that the residual domain is flatter or smoother than the channel-difference domain. In this paper, we comprehensively show a frequency domain analysis of these assumptions and observe that it is image dependent and creates artifacts in the interpolated image. With this view, we propose an algorithm that uses the inter-color correlation as well as the residual smoothness among the different channel much better than the existing algorithms. Experimental results emphasize that the proposed algorithm atribute better performances the existing algorithms in terms of both visual and objective quality.
Noise-augmented offline training of ANN unresolved-scale models

An effective way to solve the forced Burgers' equation in a variational multiscale framework is to close the system of equations using unresolved-scale interaction terms predicted by an artificial neural network. The goal of this thesis is to investigate the accuracy and stability of the said system by training a neural network offline with data enriched by two different noise -augmentation techniques - White and Gaussian.
PENINGKATAN PROFESIONALISME GURU-GURU BAHASA INGGRIS SMA KABUPATEN JAYAPURA

Professionalism in taching has become the target of teachers in Indonesia to be achieved. For this reason, this community service program has been conducted. The purpose of this activity is to improve professionalism of Senior High School English teachers in the regency of Jayapura, especially in mastering text types. The method used was interviews and group discussion. This activity has resulted in the improvement of the teachers’ more understanding on text types so they can have enough knowledge to teach their students confidently. This program has also revealed some problems the schools have. Keywords : English teachers, Professionalism, Text Types
Scoring Mechanism of Defect Report Based on Text Similarity

With the rapid development of the software industry in recent years, the demand for software testing talents in the society is also growing. Therefore, more and more colleges and universities set up "software testing technology" courses in related majors. Finding defects is a basic skill of software testing technology training. Some defects are difficult to find and some defects are easy to find. How to give reasonable evaluation according to the difficulty and quantity of defects in the defect report submitted by students often requires much of teacher's time and effort. This paper proposes a defect repetitive detection method based on the combination of TF-IDF and cosine similarity and proposes a defect report scoring mechanism based on the difficulty level and number of defects found by students. Comparing the manually scores of the defect reports by the teachers, the scoring mechanism is reasonable and effective, and greatly improves the work efficiency of the teachers.
LibGuides: Allen 6th Grade Ebooks: Home

You will find links to all the Ebooks available in our library. Please see your librarian for passwords.
A Conversational User Interface (CUI) ChatBot Using Microsoft Bot Framework

Over the past few years, messaging applications have become more popular than social networking sites. Instead of using a specific application or website to access some service, Catboats are created on messaging platforms to allow users to interact with the company’s products and also give assistance as needed. Text communication nowadays takes a huge part in people’s lives. Almost all age groups for personal, family and social communication as well as for business purposes use text messaging. Many companies use text messaging for communication between employees.In this paper, we used the Bot Framework which is an open-source and free platform from Microsoft [1] that provides tools for building catboats. We have used this framework to develop a Chabot application that will interact with the user using text.
The Development of Student Worksheet Based on Contextual Approach to Improve Critical Thinking Skills in the X Grade of High School Economics Lessons

The research aims to develop student  worksheet based on a contextual approach to improve critical thinking skills in the economic subjects of the high school class, which uses to analyze and describe 1) the feasibility of resulting student activity sheets, 2) student responses to participants' activity sheets the students produced, and the effectiveness of using the student activity sheet on improving critical thinking skills. This study uses the 4D development models of Thiagarajan, Semmel, and Sammel (1947) which includes defining, design, develop, and disseminate. The types of data in this study are qualitative and quantitative data obtained from the study and validation of experts as well as filling in the questionnaire of educators and students. The trial design used in this pre-experimental study with the form of one grub pretest-posttest results were analyzed using n-gain. The results of this study are 1) based on the assessment of the material produced by student training sheets, a percentage of 98%, 91% of linguists, and 85% of graphic experts. From the assessment of the three experts, the average percentage was 89.6% so it could be categorized as feasible, 2) the response from students showed that it was very feasible because of the percentage of assessment reached 83.9%, 3) limited test activities are effective in improving students' critical thinking skills, this is evident in limited n-gain trials showing an average of 0.5 with a percentage of effectiveness of 95% and when the n-gain trial field shows an average of 0.7 with a percentage of effectiveness of 97%.
A Convolutional Neural Network for Real-Time Vehicle Detection Under the Unmanned Aerial Vehicle Platform

The detection of vehicles under the unmanned aerial vehicle platform has several technical challenges. Compared with surveillance videos, aerial videos have more complex background and broader range which lead to larger space for searching. It is also difficult to build a effective background model due to the movement of the unmanned aerial vehicle. The low performance of development board on the aerial vehicle also make it difficult to perform real-time detection. We proposes a convolutional neural network to carry out real-time detection of vehicles under the unmanned aerial vehicle platform. Multi-scale anchor design is carried out to improve the adaptability to different vehicles and the algorithm is time optimized based on the binary weight network. As a consequence, our algorithm runs at 28.8 FPS with average precision of 91.2% under the unmanned aerial vehicle platform.
EFFECTIVENESS OF PUBLIC SPEAKING SKILL TRAINING IN REDUCING PUBLIC SPEAKING ANXIETY IN VOCATIONAL HIGH SCHOOL X

The number of Vocational students, especially in office administration majors who feel anxious when speaking in public, especially when making presentations. This is because the lack of student's ability to speak in public makes students feel difficult while they are required to be able to speak in public when graduating because it will be placed in an office directly dealing with new people to speak in front of many people. So researchers are interested in using psychoeducation in improving student skills when speaking in public, especially in presentations. This study uses an experimental design with one group pre and post-test, with a total of 60 research subjects, majoring in office administration, in class XI who experience public speaking anxiety. The results of this study indicate that the provision of effective public speaking skills interventions to students of class Vocational High School X