Reviewerly/exHarmony huggingface dataset:
Τεχνολογία AR/VR στις βιβλιοθήκες

Technology emergence has created the corresponding need for innovation. This innovation has to be integrated to society’s needs. Augmented reality technologies are increasingly gaining ground in various aspects of everyday life and reasonably in education and culture. Present work aims to present the possibilities of the above technologies, as well as their experimental application in a library.In the first part of the dissertation, we will refer to historical data and the evolution of technology and describe the available technologies. In the second part we will focus specifically on virtual and augmented reality and we will present examples of usage in the field of academic libraries. In the third part, using modern tools such as the Unity development environment and the Vuforia AR library, we will develop an AR application, exploring the possibilities and describing how it can be extended to serve a library environment. Current’s works results are extremely interesting, as they present a dimension of implementation that can become extremely useful for library’s users.
LibGuides: Pickup Lockers: Pickup Items

Frequently Asked Questions and Help Instructions for how to retrieve your item(s) from the hold locker.
PERSONAL TRANSFORMATIONS UNDER THE CONDITIONS OF THE COMPLETION OF CRISIS OF PERSONALITY

In the article, the basis of the described research as the goal of verifying the hypothesis that the effective overcoming of the signs of criticality of the situation or stage of the individual’s life is due to his own aspirations of the individual to improve and self-actualization, and actualizes multi-directional, typical for a particular person typological models of initiation of personal transformations that requires the application appropriate methods. The results of the research show that the transformation of the personal sphere of a person in the conditions of overcoming the crisis states of the person has its structure, stages, mechanisms, types and levels. Personal transformation is the process and result of changing the personal sphere of a person due to internal or external influence. The initial result of personal transformation in harmonious overcoming of a crisis state is the change of certain characteristics of the person with improvement and increase of the level of development of the individual. There are: changes in the elements of value-semantic sphere of personality; accumulation of experience takes place, its structuring; activization of own opportunities, creative abilities of the person; expansion of the circle of forms andmeans of existence; and, finally, the awareness of the existence of personal changes and personal productivity. In this case, the main thing in the very transformation is its initiation or initiation directly by the subject of development. Initiation appears as a special process of initiating personal actions in generating new phenomena and phenomena in human life. Initiation is the triggering mechanism that prompts people to change and, as a result, to overcome the signs of crisis. It is through the initiation of personality transformations that a person starts the process of self-development and self-improvement, is updated to move to a new level of development. The conclusions suggest that the psychological peculiarities of initiating personaltransformations, which may differ in nature and origin, vary in different conditions and circumstances, lead to polar, antinomic effects.
Robust Multi-view Representation Learning

Multi-view data has become ubiquitous, especially with multi-sensor systems like self-driving cars or medical patient-side monitors. We look at modeling multi-view data through robust representation learning, with the goal of leveraging relationships between views and building resilience to missing information. We propose a new flavor of multi-view AutoEncoders, the Robust Multi-view AutoEncoder, which explicitly encourages robustness to missing views. The principle we use is straightforward: we apply the idea of drop-out to the level of views. During training, we leave out views as input to our model while forcing it to reconstruct all of them. We also consider a flow-based generative modeling extension of our approach in the case where all the views are available. We conduct experiments for different scenarios: directly using the learned representations for reconstruction, as well as a two-step process where the learned representation is subsequently used as features for the data for a down-stream application. Our synthetic and real-world experiments show promising results for the application of these models to robust representation learning.
TWITTER SENTIMENT ANALYSIS USING VADER ON PYTHON

The growing popularity of E-commerce, social medias, forums, blogs etc. created a new platform where anyone can discuss and exchange his/her views, ideas , suggestions and experience about any product or services. This trend accumulated a huge amount of user generated data on the web. If this content can be extracted and analyzed properly then it can act as a key factor in decision making. Twitter is one such a platform widely used by people to express their opinions and display sentiments on different occasions. But manual extraction and analysis of this content is an impossible task, as the content is unstructured in nature and it is written in natural language. This situation opened a new area of research called Opinion Mining and Sentiment Analysis. Opinion Mining and Sentiment Analysis is an extension of Data Mining that extracts and analyzes the unstructured data automatically. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user. In the past decades, the research in this field has consistently grown. The reason behind this is the challenging format of the tweets which makes the processing difficult. The tweet format is very small which generates a whole new dimension of problems like use of slang, abbreviations etc. The main motive of this project is to classify the polarity of the tweet where it is either positive or negative efficiently compared to already existing algorithms and provide the user with live graph of the topic, which gets updated every second in accordance with the tweets posted every second which makes decision making a very easy process by providing data in pictorial way. This project also presents a theoretical comparative analysis of various techniques to the hybrid technique used in this project.
DISTANCE LEARNING DURING THE QUARANTINE PERIOD: АDVANTAGES AND DISADVANTAGES

Advantages of distance communication in the educational process are led by the possibility of any discipline teaching material according to the abilities and level of students’ preparation, through the use of various sites, video lessons, scientific and popular and documentary films and online tests on the platform of the institution of higher education or other educational links. The urgent is that distance learning extends the possibility of introducing the latesttechnologies, social and psychological, pedagogical innovative technologies and methodological developments of such interaction at a convenient time for the teacher and most students, without leaving home, the main place of work, or that place in the world, which occupy the participants in the distant process.Among disadvantages of distance learning is that the teacher fulfils a triple role i.e. tutor, organizer and administrator. This can lead to some distraction during possible poor communication, the web application overloading, and opportunities of a personal computer or gadget, limited broadcasting time and etc. The socio-psychological aspect in distance learning is the ability to control the quality of all students training, the use of self-control and self-actualization, the absence of psychological barriers between participants in communication. Experience shows that quarantine-based distance learning has far more benefits than we expected. Student’s employment at home becomes effective in educational and cognitive activities, self-education, and constructive communication with the educator. In terms of socio-psychological, educational, economic and sanitary-hygienic factors, distance learning is optimal not only for our country, but also for the whole world society in the conditions of the pandemic spread.
LibGuides: Using JSTOR Essential: JSTOR Overview

The Merced College Library now subscribes to JSTOR Essential, a version of JSTOR which provides access to content from over 680 journals in the humanities and social sciences.
Correction to: Predictive Rate-Distortion for Infinite-Order Markov Processes

We uncovered an error in 1 that claims the predictive rate-distortion function separates achievable from unachievable sensors.
GO!GAN: Grasping Objects with Generative Adversarial Networks

Image editing encompasses the process of altering images, and has been an active and interdisciplinary research topic from several decades. Images constitute the core element in different fields like marketing, television, arts, and more recently computer vision. Graphic software programs such as vector graphics editors and raster graphics have been the primary tools with which users manipulate, alter and enhance images. In the last decades, the advances in Artificial Intelligence (AI), with improved neural networks together with increased computing power, has led to strong improvements in the field of computer vision. Computer vision aims to gain a high-level understanding of digital images and video, seeking to understand and automate tasks that were exclusive to the graphic software programs. In this context, generative models are attracting the attention of the research community and the last decade has experienced an important upturn since the Generative Adversarial Networks (GANs) appeared in 2014, which researchers as Yann Lecun claim to be the most interesting machine learning idea of the decade. GANs were first used for image generation, as a way to infer an artistic component to an AI. Soon, GAN applications expanded to fields like image inpainting, future state prediction, style transfer and superresolution. The use of conditional GANs opened the door to learning specific image transformations, with strong enough capabilities to generate what are known as “Deep Fakes”, in which a person in an image or video is edited in a way that looks like real content when it is actually fake. In the realm of “Deep Fakes”, most of the existing applications transform existing components of the image into different shapes or positions. As so, mouth position can be modified so the person seems to be speaking, or the body position can be changed into different postures. Introducing new objects into the image is one of the next challenges to be faced by generative models. It is especially challenging because 2D images lack the third dimensional depth information, finding it hard to render new objects in a spatially coherent way. In this study, we focus on the rendering of new objects that will be held by a previously existing hand in the source image.
End-to-End Automatic Speech Recognition with Deep Mutual Learning

This paper is the first study to apply deep mutual learning (DML) to end-to-end ASR models. In DML, multiple models are trained simultaneously and collaboratively by mimicking each other throughout the training process, which helps to attain the global optimum and prevent models from making over-confident predictions. While previous studies applied DML to simple multi-class classification problems, there are no studies that have used it on more complex sequence-to-sequence mapping problems. For this reason, this paper presents a method to apply DML to state-of-the-art Transformer-based end-to-end ASR models. In particular, we propose to combine DML with recent representative training techniques. i.e., label smoothing, scheduled sampling, and SpecAugment, each of which are essential for powerful end-to-end ASR models. We expect that these training techniques work well with DML because DML has complementary characteristics. We experimented with two setups for Japanese ASR tasks: large-scale modeling and compact modeling. We demonstrate that DML improves the ASR performance of both modeling setups compared with conventional learning methods including knowledge distillation. We also show that combining DML with the existing training techniques effectively improves ASR performance.
LibGuides: Information Privacy & Data Security: Home

A collection of resources available to the LLS community on the topics of privacy and data security.
Self-Pretraining for Small Datasets by Exploiting Patch Information

Deep learning tasks with small datasets are often tackled by pretraining models with large datasets on relevent tasks. Although pretraining methods mitigate the problem of overfitting, it can be difficult to find appropriate pretrained models sometimes. In this paper, we proposed a self-pretraininng method by exploiting patch information in the dataset itself without pretraining on other datasets. Our experiments show that the self-pretraining method leads to better performance than training from scratch both in the condition of not using other data.
PHOTOGRAPHY AS A METHOD OF RESEARCH IN PSYCHOLOGY AND SOCIOLOGY

Photographs as a source of information in scientific sociological and psychological research aren’t often used. First their functions were limited to illustrating, keeping and proving some data. But nowadays photographs as a source of visual data are used much wider. Not only can they illustrate and add to the other methods but be used as an independent method and have a diagnostic, psycho-corrective and therapeutic aim. Quantitative (formal) and qualitative kinds of analyses are acceptable for photo documents. The discussion of methodological questions of "visual turn" has become very topical in psychology nowadays as the reality it studies, represents itself with the help of visual aids more and more often. To work out the new methodology for the research of visual images has proved very necessary. Hermeneutic, semiotic, structural and discourse approaches are used as the main ones to analyze photos. While the hermeneutic approach comes down to the author of a photo, the semiotic approach studies the system of sighs bearing some cultural meaning. It is continued by the structural approach which is targeted at revealing some hidden social and cultural meanings of an image and their decoding. The discourse analysis is aimed at the audience perceiving visual information. It must be admitted that some definite methods of interpretation depend on the aim, peculiarities of the researched problem and the type of visual material and are usually interconnected. The variety of ways to use photographs in scientific research is entailed with a number of technical and methodical difficulties. This is, for instance, labor-intensiveness, the problem of citing photographs in scientific text, the ability of a photograph to add aesthetics to the plot, the influence of views and stereotypes which dictate what photos must be taken and how to interpret them. Nevertheless, the use of photographs in scientific research has some advantages. It helps to establish trustful relations, interview people with limitedabilities to express themselves. In general, it remains a complicated but promising method of research.
İNGİLİZCE ÖĞRETMENLİĞİ ÖĞRENCİLERİNİN COVID19 SALGINI SIRASINDA UZAKTAN EĞİTİME BAKIŞ AÇILARININ GRICE-TARZI ANALİZİ: BİR DURUM ÇALIŞMASI

Clear communication is both a crucial need and a strong desire on the part of every human being who famous Greek philosopher Aristotle named "a social animal".As a natural part of their social nature, human beings are in constant communication with each other to serve their many different needs and wants.However, this does not hold true in some, if not most, cases when it comes to interpersonal communication, including student and lecturer interactions.That is, human interaction continues to suffer from ambiguity and/or obscurity, leading to ineffective communications, which might then result in nothing but unsatisfied needs.Linguist H. Paul Grice proposed an approach called the Cooperative Principle (CP) to help facilitate clear, effective communication.The CP, along with its four maxims, is intended to help gauge clarity and effectiveness of human interactions.With Grice's CP and its four maxims in mind, the researcher designed this study to analyze English Language Teaching (ELT) students' views regarding their own distance learning experiences during the Covid19 pandemic.Descriptive in its nature, this case study involved 33 ELT students, including 15 males and 18 females, volunteering to be interviewed.In the fall semester of 2020-2021 academic year, the researcher interviewed these interviewees one by one, asking ten specific pre-determined, open-ended Gricean Analysis Of Elt Students' Perspectives On Distance Learning During Covid19Pandemic: A Case Study
Designing Application-Based COVID-19 Patient Track Records

Track record is a record of the success of objects from the past until now and also can be said to be a record of the past community. Information about the track record is data in both individuals and organizations. The world is being stirred up by the Corona Virus Disease pandemic (COVID-19) and in its spread through touching bodies. A powerful way to break the COVID-19 chain is that every country or region does a Lockdown. However, in some countries lockdown steps cannot be taken so it is recommended to carry out isolation independently. So we need a system that monitors a person's track record from the previous 14 days to record and record someone interacting with patients affected by COVID-19. At this time the data collection is done independently by the local government by reporting to the health sector, with the development of technology at the present time, the authors are interested in making an electronic information media about the track record of patients with Covid-19. Based on the test results on the Trace Record application, the application is designed to successfully display information about the Trace Track of COVID -19 patients so that it is expected that this application will facilitate the public and researchers to study the track record and data of COVID-19 patients can be used in the field of big data, data science and data training and testing.
Designing a crossover user experience between telephony and web

The present work was performed at Aurora Innovation. Its purpose was to develop a proof-of-concept of an application where you could book an appointment and add personal information.This applicatio ...
All Guides: Criminal Justice: Forensic Science

Overview of research options and suggestions for the Criminal Justice major at Ferrum College
Scaling the Convex Barrier with Active Sets

Tight and efficient neural network bounding is of critical importance for the scaling of neural network verification systems. A number of efficient specialised dual solvers for neural network bounds have been presented recently, but they are often too loose to verify more challenging properties. This lack of tightness is linked to the weakness of the employed relaxation, which is usually a linear program of size linear in the number of neurons. While a tighter linear relaxation for piecewise linear activations exists, it comes at the cost of exponentially many constraints and thus currently lacks an efficient customised solver. We alleviate this deficiency via a novel dual algorithm that realises the full potential of the new relaxation by operating on a small active set of dual variables. Our method recovers the strengths of the new relaxation in the dual space: tightness and a linear separation oracle. At the same time, it shares the benefits of previous dual approaches for weaker relaxations: massive parallelism, GPU implementation, low cost per iteration and valid bounds at any time. As a consequence, we obtain better bounds than off-the-shelf solvers in only a fraction of their running time and recover the speed-accuracy trade-offs of looser dual solvers if the computational budget is small. We demonstrate that this results in significant formal verification speed-ups.
Researching lexical thresholds and lexical profiles across the common European framework of reference for languages (CEFR) levels assessed in the Aptis test

This project uses automated analysis software (www.textinspector.com) to research the lexical and metadiscourse thresholds, and lexical and metadiscourse profiles, of test-takers’ writing in the British Council's Aptis Writing test, benchmarked to the Common European Framework of Reference for Languages (CEFR). Large quantities of Aptis writing responses (n=6,407), representing 65 countries, together with their score data, were analysed in terms of their use of lexis and metadiscourse. Measures and datasets used in the analysis include standard readability measures, the British National Corpus, the Corpus of Contemporary American English, English Vocabulary profile, the Academic Word List, and a bespoke corpus of metadiscourse markers. The purpose of the research is to enhance the validation argument for the Aptis test through large-scale profiling of candidates’ writing performance.The findings reveal that the Aptis writing test provides evidence that lexical complexity changes systematically as the CEFR level of learners increases. Of the 110 Text Inspector metrics used in the study, 26 metrics were significant across all CEFR boundaries, including measures of text length (sentence, token and type count), and metrics of lexical sophistication (syllable count and number of words with more than two syllables). Fourteen of the 26 metrics represent vocabulary use. One metric of text complexity (voc-d) was also significant across all thresholds.The study also explores the utility of these metrics for use in an automated scoring engine. Twenty metrics were used to build an ordinal logistic regression which was trained on a stratified subset of the data. This model was then used to predict the CEFR band of a testing subset which held nationality data constant. The data revealed that lexical use metrics from the Cambridge Learner Corpus (CLC) were the most successful at identifying CEFR level, and the model was most successful in identifying A1 and C-level responses. However, the model failed to accurately differentiate A2, B1 and B2 responses, suggesting that other, organisational variables play a significant role in human judgements, which are not accounted for in this study. The paper concludes with recommendations for rater training on the basis of the findings.