Reviewerly/exHarmony huggingface dataset:
Distribution-free inference for regression: discrete, continuous, and in between

In data analysis problems where we are not able to rely on distributional assumptions, what types of inference guarantees can still be obtained? Many popular methods, such as holdout methods, cross-validation methods, and conformal prediction, are able to provide distribution-free guarantees for predictive inference, but the problem of providing inference for the underlying regression function (for example, inference on the conditional mean $\mathbb{E}[Y|X]$) is more challenging. In the setting where the features $X$ are continuously distributed, recent work has established that any confidence interval for $\mathbb{E}[Y|X]$ must have non-vanishing width, even as sample size tends to infinity. At the other extreme, if $X$ takes only a small number of possible values, then inference on $\mathbb{E}[Y|X]$ is trivial to achieve. In this work, we study the problem in settings in between these two extremes. We find that there are several distinct regimes in between the finite setting and the continuous setting, where vanishing-width confidence intervals are achievable if and only if the effective support size of the distribution of $X$ is smaller than the square of the sample size.
Design of a Telehealth System

This study proposes a system for constant monitoring of ECG and respiration signals using a wearable. The proposed system uses capacitively-coupled electrodes for the measuring of the ECG-signal and a resistive strain sensor for the measuring of the respiration signal. The system applies the strain sensor to the abdomen of a patient, and integrates the electrodes with the rest of the components into clothing to maximize comfort. A battery life of at least 12 hours before changing the battery to recharge is estimated. Options for changing the system or its components to favour certain applications are discussed. A graphical user interface is developed which includes a login screen based on the SHA-256 hashing algorithm, a patient tab that visualizes stress and other important features, and a physician tab that also includes the raw data and options for contacting or adding a patient. The graphical user interface uses pre-measured data stored on a Microsoft Azure server.
Methods for the automatic learning of musical features applied to the composition of tonal music

The main goal of the present Doctoral Thesis is to design, implement and test a system that resembles a human approach to the composition of music. Instead of tackling the complete composition process as a whole, we decided to take small steps to the creation of comprehensive algorithms.A novel probabilistic model for the characterisation of music learned from music samples is designed. This model makes use of automatically extracted music parameters namely rhythm, intonation (pitch contours) and harmony, to characterise music. Then a novel autonomous music composition that generates new melodies using the model developed is implemented. The methods proposed in the current Doctoral Thesis take into consideration different aspects related to the traditional way in which music is composed by humans such as harmony evolution and structure repetitions and apply them together with the probabilistic reutilisation of rhythm patterns and pitch contours learned beforehand to compose music pieces.
Análisis Factorial sobre el Uso de las TIC en Instituciones Educativas de Paraguay

espanolEl objetivo de este trabajo es recopilar informacion sobre la utilizacion y el alcance de las TIC en el aula y ahondar conocimientos sobre la experiencia docente en la educacion media en Paraguay. Es parte de un estudio mas amplio, utilizando una metodologia exploratoria cual-cuan por medio de un cuestionario en linea con preguntas cerradas y abiertas que consistio en 30 preguntas sobre el acceso y uso de tecnologia en el aula, capacidades y capacitacion docente y nivel de apoyo institucional. Se realizo un analisis estadistico descriptivo que permitio identificar el perfil de los docentes en cuanto al nivel de competencias de las TIC y el acceso limitado a los recursos tecnologicos en las instituciones de Educacion Media. Ademas, se pudo identificar tres factores asociados a la percepcion profesional de las TIC y 7 factores asociados al grado de apropiacion de las mismas por medio del Analisis Factorial Exploratorio. EnglishThe objective of this study is to gather information on the use and scope of ICT in the classroom and to deepen knowledge about the teaching experience in secondary education in Paraguay. It is part of a larger study that follows an exploratory qual-quan approach by means of an online questionnaire with closed and open-ended questions consisting of 30 questions on access to and use of technology in the classroom, teacher skills and training, and level of institutional support. A descriptive statistical analysis was carried out to identify the profile of teachers in terms of the level of ICT skills and limited access to technological resources in secondary education institutions. In addition, three factors associated with the professional perception of ICT and 7 factors associated with the degree of appropriation of ICT were identified by means of Exploratory Factor Analysis.
LibGuides: System Wide Holds - Training Guide: Mailing Items

The purpose of this guide is to provide staff a reference resource for processing Requests and Holds on NLPL materials.
Translationese in Russian Literary Texts

The paper reports the results of a translationese study of literary texts based on translated and non-translated Russian. We aim to find out if translations deviate from non-translated literary texts, and if the established differences can be attributed to typological relations between source and target languages. We expect that literary translations from typologically distant languages should exhibit more translationese, and the fingerprints of individual source languages (and their families) are traceable in translations. We explore linguistic properties that distinguish non-translated Russian literature from translations into Russian. Our results show that non-translated fiction is different from translations to the degree that these two language varieties can be automatically classified. As expected, language typology is reflected in translations of literary texts. We identified features that point to linguistic specificity of Russian non-translated literature and to shining-through effects. Some of translationese features cut across all language pairs, while others are characteristic of literary translations from languages belonging to specific language families.
Deep Learning Transformer Architecture for Named Entity Recognition on Low Resourced Languages: State of the art results

Abstract4This paper reports on the evaluation of Deep Learning (DL) transformer architecture models for Named-Entity Recognition (NER) on ten low-resourced South African (SA) languages.In addition, these DL transformer models were compared to other Neural Network and Machine Learning (ML) NER models.The findings show that transformer models substantially improve performance when applying discrete finetuning parameters per language.Furthermore, fine-tuned transformer models outperform other neural network and machine learning models on NER with the low-resourced SA languages.For example, the transformer models obtained the highest F-scores for six of the ten SA languages and the highest average F-score surpassing the Conditional Random Fields ML model.Practical implications include developing high-performance NER capability with less effort and resource costs, potentially improving downstream NLP tasks such as Machine Translation (MT).Therefore, the application of DL transformer architecture models for NLP NER sequence tagging tasks on low-resourced SA languages is viable.Additional research could evaluate the more recent transformer architecture models on other Natural Language Processing tasks and applications, such as Phrase chunking, MT, and Part-of-Speech tagging.
Pengembangan bahan ajar berupa e-book pada mata pelajaran akuntansi keuangan kompetensi dasar akuntansi piutang kelas XI berbasis pendekatan saintifik di SMK Yapalis Krian

This research aims to develop e-book teaching materials, analyze the feasibility level of e-books and analyze student responses to e-books. The subjects in this study are financial accounting on the basic competence of accounts receivable accounting. Teaching materials in the form of e-books were developed according to the implementation of the 2013 curriculum based on a scientific approach. The research method used is Research and Development (R&amp;D) with a 4-D model. The validators in the study were 3 lecturers according to the intended field and one teacher who was an expert in financial accounting subjects. The assessment data distributed to the validator comes from the review sheet and then the validation sheet. A total of 20 students of class XI Financial and Institutional Accounting at SMK Yapalis Krian were also one of the respondents. This is to determine the feasibility of teaching materials so that they are suitable for use in the learning process. The product developed produces a very feasible interpretation with an average feasibility recapitulation of 89.49%. From these results, there are several components listed with very feasible interpretations, namely the feasibility of the material 89.89%, the feasibility of language 86.67% and the feasibility of graphics 91.93%. For the average results, students stated that the interpretation was very well understood with a percentage of 88.12%. So this teaching material is suitable for use in the learning process
Period Classification in Chinese Historical Texts

In this study, we study language change in Chinese Biji by using a classification task: classifying Ancient Chinese texts by time periods. Specifically, we focus on a unique genre in classical Chinese literature: Biji (literally “notebook” or “brush notes”), i.e., collections of anecdotes, quotations, etc., anything authors consider noteworthy, Biji span hundreds of years across many dynasties and conserve informal language in written form. For these reasons, they are regarded as a good resource for investigating language change in Chinese (Fang, 2010). In this paper, we create a new dataset of 108 Biji across four dynasties. Based on the dataset, we first introduce a time period classification task for Chinese. Then we investigate different feature representation methods for classification. The results show that models using contextualized embeddings perform best. An analysis of the top features chosen by the word n-gram model (after bleaching proper nouns) confirms that these features are informative and correspond to observations and assumptions made by historical linguists.
Pogled na dijete: kroz metodiku vaspitno-obrazovnog rada

Trying to find a balance between theory and educational practice, the author tries to answer the challenges of modern methodology of educational work. Throughout the paper, there is an effort to answer four key questions: Who are the actors (factors) of educational work? Where does the educational work with children take place from birth to school? What to do with children? How to work with children? These are simple questions, which are not easy to answer, because these answers are difficult to grasp in a time of rapid change, but also in a time full of paradoxes. Through thirteen chapters, a multitude of possibilities for action in numerous educational situations have been proposed. Although the work is primarily aimed at shaping future educators (professionals), it can be used very successfully by teachers in their educational work. Also, it is no less interesting for parents or future parents.
Finetuning Pretrained Transformers into Variational Autoencoders

Text variational autoencoders (VAEs) are notorious for posterior collapse, a phenomenon where the model’s decoder learns to ignore signals from the encoder. Because posterior collapse is known to be exacerbated by expressive decoders, Transformers have seen limited adoption as components of text VAEs. Existing studies that incorporate Transformers into text VAEs (Li et al., 2020; Fang et al., 2021) mitigate posterior collapse using massive pretraining, a technique unavailable to most of the research community without extensive computing resources. We present a simple two-phase training scheme to convert a sequence-to-sequence Transformer into a VAE with just finetuning. The resulting language model is competitive with massively pretrained Transformer-based VAEs in some internal metrics while falling short on others. To facilitate training we comprehensively explore the impact of common posterior collapse alleviation techniques in the literature. We release our code for reproducability.
The uniqueness of eco-friendly Pagerjurang pottery products

Abstract This study aimed to investigate and to describe the characteristics of Pagerjurang pottery products which are environmentally friendly and safe to be used as tableware. The pottery art village in Pagerjurang has existed for more than 400 years. The following characteristics of Pagerjurang pottery products make them deserve to be called a tradition, i.e.: 1). Its dark land (earthenware land). 2). Its making process, i.e. by using slant-turning rotary technique. 3). The existence of human resources. 4) Abundantly available material resources. These five characteristics are the advantages which can be developed and considered as the superior qualities of Pagerjurang pottery. This research is based on the following assumptions: What materials are used to make Pagerjurang pottery? What standards indicate that this pottery product is environmentally friendly (eco-friendly)? This study used qualitative research methods to obtain general, flexible, and dynamic results. In addition, the data were obtained from interviews conducted with resource persons and observation to the products from the artisans. In general, Pagerjurang pottery is environmentally friendly and is supported by the traditions of the people who feel bound to their natural surroundings, but the artisan are not familiar with environmental issues at a wider level.
The Digital Services Act and Copyright Enforcement : The case of Article 17 of the DSM Directive

In late 2020, the European Commission unveiled its Proposal for a Digital Services Act (DSA). Once adopted, the DSA may confirm the core principles of the ‘safe harbour’ regime for certain informat ...
Does Commonsense help in detecting Sarcasm

Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums. It is a challenging task requiring a deep understanding of language, context, and world knowledge. In this paper, we investigate whether incorporating commonsense knowledge helps in sarcasm detection. For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input. Our experiments with three sarcasm detection datasets indicate that the approach does not outperform the baseline model. We perform an exhaustive set of experiments to analyze where commonsense support adds value and where it hurts classification. Our implementation is publicly available at: https://github.com/brcsomnath/commonsense-sarcasm.
Speech Act Analysis of Hillary Clinton’s Speech: Be Resilent At Yale University 2018 Senior Class Day Ceremony

Language plays an important role in social life, namely as a tool of communication. Without language, it can be ensured that all kinds of interacting activities in society will be paralyzed. Given the importance of language in carrying out all daily activities, of course every member of society is always involved in communication, either acting as a communicator (speaker) or as a communicant (listener). In Pragmatics theory, communication events that take place can be used as a place or media to express ideas, ideas, thoughts, intentions, reality, and so on. Thus, language is used as a communication tool to convey the message or the intention of the speaker to the listener. In every communication process, several things occur, such as speech events and speech acts in one speech situation. Speech events and speech acts that occur will differ in each speech situation, depending on who the speaker is, the interlocutor, the topic, the time and place the speech takes place. Likewise, what happens when someone makes a speech at a certain event, in this event, the speech acts used can be seen. The classification of Speech act proposed by Yule according to the general function namely declarations, representatives, expressive, directives and commissive. Based on this theory, the writer is interested in examining more deeply about speech acts in speech.
Automatic Resolution of Domain Name Disputes

We introduce the new task of domain name dispute resolution (DNDR), that predicts the outcome of a process for resolving disputes about legal entitlement to a domain name. The ICANN UDRP establishes a mandatory arbitration process for a dispute between a trademark owner and a domain name registrant pertaining to a generic Top-Level Domain (gTLD) name (one ending in .COM, .ORG, .NET, etc). The nature of the problem leads to a very skewed data set, which stems from being able to register a domain name with extreme ease, very little expense, and no need to prove an entitlement to it. In this paper, we describe the task and associated data set. We also present benchmarking results based on a range of models, which show that simple baselines are in general difficult to beat due to the skewed data distribution, but in the specific case of the respondent having submitted a response, a fine-tuned BERT model offers considerable improvements over a majority-class model.
LibGuides: Open and Affordable Learning and Research: OA at Sims Library

Helping faculty, scholars, researchers, and authors join the open resource movement. Find out about the latest Affordable Learning presentations, resources, and initiatives at Sims Library!
Analysis of the Japanese media’s emotions about Korea-related news using text mining techniques; by implementation the Semantic Orientations of Words analysis method using Python

In the era of big data, it is no longer possible to effectively collect, refine, and perform meaningful interpretations of necessary information only by methods such as conventional manual and intuitive insight. Therefore, in this paper, we built a large-scale text data independently and performed the text mining analysis based on the recognition that the text mining technique needs to be used in the field of Japanese studies as well, and that the verification of the practical technique itself is also necessary.<BR> As a result, it was confirmed that an analysis of semantic orientations of the text using ‘the Semantic Orientations of Words’ is effective for the analysis of the ‘emotion’ of the text. Besides, it has been confirmed that Japanese media’s ‘semantic orientations’ toward Korea has been deteriorating over the past decade. Above all, it was also confirmed that semantic orientations of Japanese media news articles about Korea were actively reflected in the issue of Korea-Japan relations.
LibGuides: Citing and Writing MLA 8: Writing

Information on style guides, specifically MLA (8th edition), example citations, and other resources for citing and writing.
Adversarially robust learning for security-constrained optimal power flow

In recent years, the ML community has seen surges of interest in both adversarially robust learning and implicit layers, but connections between these two areas have seldom been explored. In this work, we combine innovations from these areas to tackle the problem of N-k security-constrained optimal power flow (SCOPF). N-k SCOPF is a core problem for the operation of electrical grids, and aims to schedule power generation in a manner that is robust to potentially k simultaneous equipment outages. Inspired by methods in adversarially robust training, we frame N-k SCOPF as a minimax optimization problem - viewing power generation settings as adjustable parameters and equipment outages as (adversarial) attacks - and solve this problem via gradient-based techniques. The loss function of this minimax problem involves resolving implicit equations representing grid physics and operational decisions, which we differentiate through via the implicit function theorem. We demonstrate the efficacy of our framework in solving N-3 SCOPF, which has traditionally been considered as prohibitively expensive to solve given that the problem size depends combinatorially on the number of potential outages.
The number of inversions of permutations with fixed shape

The Robinson-Schensted correspondence naturally induces a map from permutations to partitions.In this work, we study the number of inversions of permutations corresponding to a fixed partition λ under this map.Hohlweg characterized permutations having shape λ with the minimum number of inversions.Here, we give the first results in this direction for higher numbers of inversions.We give explicit conjectures for both the structure and the number of permutations associated with λ where the extra number of inversions is less than the length of the smallest column of λ.We prove the result when λ has two columns.
Small-Scale Cross-Language Authorship Attribution on Social Media Comments

Cross-language authorship attribution is the challenging task of classifying documents by bilingual authors where the training documents are written in a different language than the evaluation documents. Traditional solutions rely on either translation to enable the use of single-language features, or language-independent feature extraction methods. More recently, transformer-based language models like BERT can also be pre-trained on multiple languages, making them intuitive candidates for cross-language classifiers which have not been used for this task yet. We perform extensive experiments to benchmark the performance of three different approaches to a smallscale cross-language authorship attribution experiment: (1) using language-independent features with traditional classification models, (2) using multilingual pre-trained language models, and (3) using machine translation to allow single-language classification. For the language-independent features, we utilize universal syntactic features like part-of-speech tags and dependency graphs, and multilingual BERT as a pre-trained language model. We use a small-scale social media comments dataset, closely reflecting practical scenarios. We show that applying machine translation drastically increases the performance of almost all approaches, and that the syntactic features in combination with the translation step achieve the best overall classification performance. In particular, we demonstrate that pre-trained language models are outperformed by traditional models in small scale authorship attribution problems for every language combination analyzed in this paper.
Professor AE Ringwood

Research School of Earth Sciences, Physical Sciences - Moon Rocks Analysis - Dr. B. W. Chappell, Mr. M. I. Vernon, Dr. W. Compston, Mrs. A. Brown, Prof. A. E. Ringwood, Dr. J. E. Lovering, Dr. S. R. Taylor, Dr. W. Compston, Mr. J. Pennington