Reviewerly/exHarmony huggingface dataset:
Angular spectrum influence and entanglement characterization of Gaussian-path encoded photonic qudits

Entangled quantum states play an important role in quantum information science and also in quantum mechanics fundamental investigations. Implementation and characterization of techniques allowing for easy preparation of entangled states are important steps in such fields. Here we generated entangled quantum states encoded in photons’ transverse paths, obtained by pumping a nonlinear crystal with multiple transversal Gaussian beams. This approach allows us to generate entangled states of two qubits and two qutrits encoded in a Gaussian transversal path of twin photons. We make a theoretical analysis of this source, considering the influence of the pump angular spectrum on the generated states, further characterizing those by their purity and entanglement degree. Our experimental results reveal that the generated states present both high purity and entanglement, and the theoretical analysis elucidates how the pump beams profile can be used to manipulate such photonic states.
The Degree of Realization of Fundamental Existential Motivations as a Predictor of Procrastination in University Students

The problem is related to the prevalence of procrastination, including among students, on the one hand, and the lack of development of approaches to explaining the psychological mechanisms of procrastination, on the other. The article substantiates the possibility of explaining the phenomenon of procrastination in the context of resilience by referring to the categories of meaningfulness of life and existential fulfillment. The goal is to identify the features of the implementation of fundamental existential motivations by students with different levels of procrastination. Method of research. We used the Lay “General procrastination Scale” in the adaptation of Windecker, Ostanina, the” test of existential motivations “ by Shumsky, Ukolova, Osin, and Lupandina; methods of mathematical statistics (Mann-Whitney and Spearman criteria). Sample: 45 procrastinator students, 51 non-procrastinators, and 117 students with an average level of procrastination. Results. Significant differences between all groups were revealed, and there were significant correlations between the level of procrastination and the severity of existential motivations. Conclusion. It was revealed that the degree of existential fulfillment of procrastinator students differs from the other two groups. They have less money: 1) the desire to “be-in-the-world”; 2) the value attitude to life; 3) the desire to be oneself; 4) the desire for meaning. Students with an average level of procrastination, like non-procrastinators, have more realized the ability to freely be in the world and realize meaning, but less than non-procrastinators, the existential motivations “Value of life” and “self-Worth” are realized. It is proved that the theory of fundamental existential motivations can be used to explain procrastination.
White Library: Library Handouts: Home

Need a printout that you received in class but have misplaced it? Just looking to see if we have written instructions on something? Browse our handouts to locate what you need!
Social Media Usage Among English Language Learners in Primary School

Social media has becoming a game changer among primary learners, adhering to the transformation and integration of ICT in education. Social media as a part of pupils' daily life routine is being used widely in and out of English classrooms. Apart from education purpose, they actively use social media for interaction, fun and entertainment. Therefore, this survey explores the use of social media by 20 primary learners at the ages of 10 and 11 in English classrooms in the district of Cameron Highlands, Pahang, Malaysia. Respondents responded to survey questionnaires and open-ended questions. Data collected revealed that the primary 4 and 5 pupils highly used YouTube as their mainstream social media platform for language learning, communication, skills building and gain knowledge. They also infused social media to enhance their English language skills. Hence, social media is being one of the main choices of language learning tools among primary learners to keep themselves updated with latest knowledge and skills in performing the ever-challenging education world.
EFEKTIVITAS TEKNIK BRAINWRITING BERBANTUAN APLIKASI CHARACTER PLANNER DALAM PEMBELAJARAN MENULIS CERPEN : Penelitian Eksperimen Kuasi pada Siswa Kelas XI SMAN 6 Bandung Tahun Ajaran 2019/2020

Penelitian ini dilatarbelakangi oleh kesulitan siswa dalam memunculkan pada pembelajaran menulis cerpen. Tujuan dari penelitian ini untuk mengetahui kemampuan menulis cerpen siswa kelas XI sebelum diberikan teknik brainwriting berbantuan aplikasi character planner, mengetahui kemampuan menulis cerpen siswa kelas XI sesudah diberikan teknik brainwriting berbantuan aplikasi character planner, mendeskripsikan ada tidaknya perbedaan pada pembelajaran menulis cerpen sebelum dan sesudah penerapan teknik brainwriting berbatuan aplikasi character planner. Metode yang digunakan adalah eksperimen semu dengan subjek penelitian siswa kelas XI SMA. Nilai rata-rata yang diperoleh di kelas eksperimen pada saat pretes adalah 65 dan postes adalah 74. Sedangkan nilai rata-rata yang diperoleh di kelas kontrol pada saat pretes adalah 59 sedangkan postes adalah 69. Pada pembelajaran menulis cerpen, sebagian siswa tidak begitu antusias. Sehingga muncul hasil data perhitungan statistik menggunakan aplikasi SPSS versi 25, di dapat p-value sebesar 0,007 < 0,05 yang terdapat perbedaan signifikan antara siswa kelas eksperimen dan kelas kontrol pada pembelajaran menulis cerpen.;---------This research is motivated by the difficulty of students in eliciting short story writing learning. The purpose of this study was to determine the ability to write short stories for class XI students before being given a brainwriting technique assisted by character planner application, to know the ability to write short stories of class XI students after being given a character planner assisted brainwriting technique, to describe whether there were differences in learning to write short stories before and after the application of the technique rocky brainwriting character planner application. The method used is a quasi-experimental research subject of class XI high school students. The average value obtained in the experimental class at the time of the pretest was 65 and the posttest was 74. While the average value obtained in the control class at the time of the pretest was 59 while the posttest was 69. In the short story writing learning, some students were not so enthusiastic. So that the statistical calculation data results appear using the SPSS application version 25, it can get a p-value of 0.007 <0.05 which is a significant difference between the experimental class students and the control class in learning to write short stories.
Music to our ears: Erik Steigen’s amazing journey from Norway to LA

As a young boy, Erik Steigen was in love with music. He played in bands in his native Norway as a teenager and was determined to follow his passion as an ...
The Impact of the Mini-batch Size on the Dynamics of SGD: Variance and Beyond

We study mini-batch stochastic gradient descent (SGD) dynamics under linear regression and deep linear networks by focusing on the variance of the gradients only given the initial weights and mini-batch size, which is the first study of this nature. In the linear regression case, we show that in each iteration the norm of the gradient is a decreasing function of the mini-batch size b and thus the variance of the stochastic gradient estimator is a decreasing function of b. For deep neural networks with L2 loss we show that the variance of the gradient is a polynomial in 1/b. The results theoretically back the important intuition that smaller batch sizes yield larger variance of the stochastic gradients and lower loss function values which is a common believe among the researchers. The proof techniques exhibit a relationship between stochastic gradient estimators and initial weights, which is useful for further research on the dynamics of SGD. We empirically provide insights to our results on various datasets and commonly used deep network structures. We further discuss possible extensions of the approaches we build in studying the generalization ability of the deep learning models.
FURTHER TRAINING AND ADDITIONAL PROFESSIONAL EDUCATION OF SPECIALISTS AS PART OF A SYSTEM OF CONTINUOUS TRAINING FOR THE NEEDS OF THE ECONOMY

The article describes the problems in the field of advanced training and additional professional education as part of a system of continuous training and retraining of specialists, taking into account the requirements of the current economic situation.Using the example of a higher educational institution: Voronezh State Technical University, we consider a system for organizing continuing professional education using digital technologies in teaching.The basis of the system of advanced training and retraining of personnel was reasonably chosen by a specialized faculty: the faculty of additional professional education.The problems that arise in the system of continuing professional education and retraining of personnel associated with a different level of training of specialists in the training flows are revealed, which are proposed to be solved with the help of educational interdisciplinary blocks and blocks of professional disciplines.Thanks to modern teaching methods based on the online format, it is possible to build a flexible system of continuing education for students of different qualifications and different basic levels of training.
Machine Translation for Less-Resourced Languages - Data Sparsity and Divergence for English-Maithili

Machine Translation (MT) is a resource intensive task requiring huge sets of parallel corpora. Maithili– one of the 22 scheduled Indian languages – is a low resource language facing data sparsity related challengesin technology development. The authors in this paper discuss the efforts to create (a) English-Maithili parallelcorpora by partially bootstrapping English-Hindi and Hindi-Maithili automatic translations, (b) training aStatistical Machine Translation (SMT) model on the Moses platform to develop an English to Maithili MachineTranslation (EMMT), and (c) evaluate errors caused by divergence between the English-Maithili pair. Thepaper also presents data, training and evaluation statistics.
Gesture Controlled Wheelchair

Wheelchairs are widely used by people who suffer from a disability, spinal cord injury, accident, or have difficulty in walking. Wheelchairs are of great use now-a-days to aged persons at home, orphanage and hospitals. This paper aims at presenting a wheelchair which can be used without external assistance. The wheelchair can be controlled by using the hand gestures of the user themselves. The hand gestures are being sensed with the help of the 3-axis accelerometer sensor. The output of this MEMS sensor is being given to the ATMEGA-328 microcontroller chip.
Component Sharing in English and Chinese Clause Complex

NT Clause Complex Framework defines a clause complex as a combination of NT clauses through component sharing and logic-semantic relationship. This paper clarifies the existence of component sharing mechanism in both English and Chinese clause complexes, illustrates the differences in component sharing between the two languages, and introduces a formal annotation scheme to represent clause-complex level structural transformations. Under the guidance of the annotation scheme, the English-Chinese Clause Alignment Corpus is built. It is believed that this corpus will aid comparative linguistic studies, translation studies and machine translation studies by providing abundant formal and computable samples for English-Chinese structural transformations on the clause complex level.
Generalized Support Vector Quantile Regression

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the є-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within є. In most studies, the є-insensitive loss function is used symmetrically, and it is of interest to determine the value of є. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of є and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of є. In this study, a new e-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new e-insensitive loss function can control the asymmetry of the width of є and the slope of the penalty using the parameters p1 and p2 , respectively. Moreover, the figures show that the asymmetry of the width of є and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.
Designing and Evaluating Young Children’s Interaction During an Alexa Trivia Game

Author(s): Du, Yao | Advisor(s): Salen Tekinbas, Katie | Abstract: With the increased adoption and use of smart home speakers in households, many young children have learned to use listening and speaking to interact with the voice assistants (VAs) for a variety of daily activities (e.g., asking questions, listening to music). Despite the proliferation of VAs, due to the technical limitations such as automatic speech recognition (ASR) and natural language understanding (NLU), children often experience communication breakdowns with VAs, limiting educational opportunities through these voice-based interfaces. Informed by prior literature on child-computer interaction, child language development, and audio and trivia game design, I first designed a trivia game Animal Actions using Amazon Alexa with interdisciplinary research teams. I then conducted an in-home user evaluation study with 18 children between 3 and 6 years old using a mobile phone. Through video analysis of children's gameplay, I examined children's interaction with Alexa from four areas: children's verbal behaviors, children's response errors, Alexa communication breakdowns, and children's communication repair strategies. I found that single-word responses were more accurately recognized and processed by Alexa than were multi-word phrase-level responses. When comparing children's response accuracy and errors, children's responses included a wide range of speech articulation, semantic, and syntactic differences leading to incidents of communication breakdowns, which were not accounted for during game design and game item development. Parent questionnaires also revealed similar usability issues with automatic speech recognition and natural language understanding when children use VAs at home for daily routines, and parents reported using VAs independently and collaboratively with their children to mitigate communication breakdowns. Based on these findings, I discuss sociotechnical, design, and ethical considerations to inform future research on designing accessible user interfaces in voice games to support children's communication skills, including those with communication disorders.
Библиотеки стран СНГ в период карантина

The article is devoted to the activities of libraries of the CIS countries during the quarantine declared in almost all post-Soviet countries in March 2020 in connection with the spread of the coronavirus infection that causes COVID-19. The article focuses on the library working hours, professional and sociocultural activities, interlibrary cooperation, as well as security measures applied to employees and readers. The information is provided as of the beginning of July 2020.
LibGuides: Scholarly and Creative Work from DePauw University: Using the Institutional Repository: Impact Tools

Learn more about DePauw's institutional repository, such as how to add your research, what types of materials you can share, and the impact on open access scholarship
Methods for probabilistic modeling of knowledge elicitation for improving machine learning predictions

Many applications of supervised machine learning consist of training data with a large number of features and small sample size. Constructing models with reliable predictive performance in such applications is challenging. To alleviate these challenges, either more samples are required, which could be very difficult or even impossible in some applications to obtain, or additional sources of information are required to regularize models. One of the additional sources of information is the domain expert, however, extracting knowledge from a human expert can itself be difficult; it will require some computer systems that experts could effectively and effortlessly interact with. This thesis proposes novel knowledge elicitation approaches, to improve the predictive performance of statistical models. The first contribution of this thesis is to develop methods that incorporate different types of knowledge on features extracted from domain expert, into the construction of the machine learning model. Several solutions are proposed for knowledge elicitation, including interactive visualization of the effect of feedback on features, and active learning. Experiments demonstrate that the proposed methods improve the predictive performance of an underlying model through adoption of limited interaction with the user. The second contribution of the thesis is to develop a new approach to the interpretability of Bayesian predictive models to facilitate the interaction of human users with Bayesian black-box predictive models. The proposed approach separates model specification from model interpretation, via a two-stage decision – theoretical approach: first construct a highly predictive model without compromising accuracy and then optimize the interpretability. Conducted experiments demonstrate that the proposed method constructs models which are more accurate, and yet more interpretable than the alternative practice of incorporation of interpretability constraints into the model specification via prior distribution.
Protection of copyright on the Internet

The problem of improving of legislation in the field of copyright protection is one of the urgent problems facing modern legislation. Due to the rapid development of information and communication technologies, dishonest users have the opportunity to post fragments of other people's creative works on their sites without reference to the author, source of borrowing and without disclosing information about the owners of the site.According to the main international copyright convention, the Berne Convention for the Protection of Literary and Artistic Works (1886), copyright protection covers a wide array of human creativity - all “literary and artistic works.” Due to the process of digitization all these forms of creativity can be transmitted across the Internet and in case of violation of rights to them, a mechanism for their protection should be developed in a modern society.It is concluded that information is transmitted via the Internet mainly in the form of creative works that are object to copyright. Moreover, the implementation of any formalities and the fact of publication of the creative work are not needed.The peculiarities of the legal regime of creative works placed on the Internet, the rights of which are the object of civil protection, were disclosed in the article. The author discloses problems of copyright protection for works posted on the Internet, including the problem of identification of the offender. In the article it is also identified basic ways to protect copyright, such as self-defense and judicial protection.The author examines issues of copyright protection for creative works posted on the Internet. Technical and legal means and methods of protecting the rights of authors are analyzed, which allow effective protection of creative works placed on the Internet both before and after their infringement.In the article it is argued that the protection of interests of copyright entities on the Internet depends to a large extent on the technological means that they use. The evolution of these tools and techniques will diminish the role of copyright as a regulator of civil legal relations on the Internet.
Graph Structural Aggregation for Explainable Learning

Graph neural networks have proven to be very efficient to solve several tasks in graphs such as node classification or link prediction. These algorithms that operate by propagating information from vertices to their neighbors allow one to build node embeddings that contain local information. In order to use graph neural networks for graph classification, node embeddings must be aggregated to obtain a graph representation able to discriminate among different graphs (of possibly various sizes). Moreover, in analogy to neural networks for image classification, there is a need for explainability regarding the features that are selected in the graph classification process. To this end, we introduce StructAgg, a simple yet effective aggregation process based on the identification of structural roles for nodes in graphs that we use to create an end-to-end model. Through extensive experiments we show that this architecture can compete with state-of-the-art methods. We show how this aggregation step allows us to cluster together nodes that have comparable structural roles and how these roles provide explainability to this neural network model.
Kommunal informationssäkerhet : En undersökning av informationssäkerhet inom kommunal IT-styrning

This study examined how municipalities work with information security focusing on the use of security standards. Data was collected from three different municipalities differentiating in population ...
IoT based Context-Aware Smart Home Caregivers System for Monitoring Elderly People

In pervasive computing, the area of contextawareness is considered as an important technology and also plays a important role in the area of healthcare environments. In this paper we propose a system for elderly people signs monitoring and critical health conditions which is sends to the caregivers to identify the daily activities by using internet of things (IoT) this can be done in real-time. For example the elderly people who are not well in daily activities like current health status is “normal” or “critical” and the caregivers can give the early suggestions to the people who are in nearby. We have build a fuzzy logic from the beginning of data acquisition, processing of the data namely filtering , aggregating it into contextual information and reasoning to identify the elderly people health condition. Variety of connected devices can modify the remote characteristic, real-time monitoring, measuring and transmitting numerous body health parameters for making the decision and medication. The goal of this paper is to introduce the approach Context Aware Smart Home Caregivers System (CASHCS) to identify the normal and abnormal conditions of the people health status so that doctor can cure the problems in early stage without any complication. So this can be used to build a framework for context-aware healthcare applications.