Reviewerly/exHarmony huggingface dataset:
PsyCuraDat: Designing a User-Oriented Curation Standard for Behavioral Psychological Research Data

Starting from the observation that data sharing in general and sharing of reusable behavioral data in particular is still scarce in psychology, we set out to develop a curation standard for behavioral psychological research data rendering data reuse more effective and efficient. Specifically, we propose a standard that is oriented toward the requirements of the psychological research process, thus considering the needs of researchers in their role as data providers and data users. To this end, we suggest that researchers should describe their data on three documentation levels reflecting researchers’ central decisions during the research process. In particular, these levels describe researchers’ decisions on the concrete research design that is most suitable to address the corresponding research question, its operationalization as well as a precise description of the subsequent data collection and analysis process. Accordingly, the first documentation level represents, for instance, researchers’ decision on the concrete hypotheses, inclusion/exclusion criteria and the number of measurement points as well as a conceptual presentation of all substantial variables included in the design. On the second level these substantial variables are presented within an extended codebook allowing for the linkage between the conceptual research design and the actually operationalized variables as presented within the data. Finally, the third level includes all materials, data preparation and analyses scripts as well as a detailed procedure graphic that allows the data user to link the information from all three documentation levels at a single glance. After a comprehensive presentation of the standard, we will offer some arguments for its integration into the psychological research process.
Semi-supervised Word Sense Disambiguation Using Example Similarity Graph

Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing.In recent years, there has been increasing interest in applying neural networks and machine learning techniques to solve WSD problems.However, these previous supervised approaches often suffer from the lack of manually sense-tagged examples.In this paper, to solve these problems, we propose a semi-supervised WSD method using graph embeddings based learning method in order to make effective use of labeled and unlabeled examples.The results of the experiments show that the proposed method performs better than the previous semi-supervised WSD method.Moreover, the graph structure between examples is effective for WSD and it is effective to utilize a graph structure obtained by fine-tuning BERT in the proposed method.
Development and usage growth of the polyglot search translator

The Polyglot Search Translator (Polyglot) was developed and released in
ChiSquareX at TextGraphs 2020 Shared Task: Leveraging Pretrained Language Models for Explanation Regeneration

In this work, we describe the system developed by a group of undergraduates from the Indian Institutes of Technology, for the Shared Task at TextGraphs-14 on Multi-Hop Inference Explanation Regeneration (Jansen and Ustalov, 2020).The shared task required participants to develop methods to reconstruct gold explanations for elementary science questions from the WorldTree Corpus (Xie et al., 2020).Although our research was not funded by any organization and all the models were trained on freely available tools like Google Colab which restricted our computational capabilities, we have managed to achieve noteworthy results placing ourselves in the 4th place with a MAP score of 0.4902 1 in the evaluation leaderboard and 0.5062 MAP score on the post-evaluation-phase leaderboard using RoBERTa.We incorporated some of the methods proposed in the previous edition of Textgraphs-13 (Chia et al., 2019), which proved to be very effective, improved upon them, and built a model on top of it using powerful state-of-the-art pre-trained language models like RoBERTa (Liu et al., 2019), BART (Lewis et al., 2020), SciB-ERT (Beltagy et al., 2019) among others.Further optimization of our work can be done with the availability of better computational resources.
DIGITAL SKILLS AND COMPETENCES IN BUSINESS STUDENTS IN A COVID-19 LOCKDOWN SCENARIO

After the global crisis derived from COVID-19, all professional and personal areas have been forced to evolve, in order to adapt to this new context.Education has been one of the areas that has had to know how to adapt to this paradigm the fastest, integrating technology as the basis and main tool in the educational process.From this fact, the main objective of our study was born, with the aim of knowing, after a survey carried out among students of three degrees and three different masters in a prestigious Spanish business school, how this situation has affected the development of their skills and digital skills, as well as the main differentiating elements that can influence a greater or lesser adaptation.A methodological proposal is presented for the treatment of the data and the future validation of the scale, as well as future expected conclusions that derive from this study, which are: a greater development of these skills by the students of the master's degrees due to greater use of computer applications in the academic world, greater capacity for adaptation and autonomous learning, and a higher average grade in comparison with the degrees.
A Survey on Image Super-Resolution with Generative Adversarial Networks

Super-resolution is a process to increase image dimensions with a specific upscaling factor while trying to preserve details that matche with the original high-resolution form. Super-resolution can be done with many techniques. But the most effective technique is the one that takes advantage of several neural network designs. Some network designs are more appropriate than others on the specific subject. This study focuses on super resolution studies using Generative Adversarial Network. Many studies use this neural network type to look at various topics such as artificial data production and making the data more meaningful. The key point of this neural network type is having two different sub-networks that try to defeat each other in order to make more realistic results. Performance metrics that measure the quality of a generated image, loss functions used in a neural network and research papers on super-resolution with Generative Adversarial Network are the main domains of this study.&nbsp;
Big data platform for health monitoring systems of multiple bridges

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.
TEICORPO: A Conversion Tool for Spoken Language Transcription with a Pivot File in TEI

CORLI is a consortium of Huma-Num, the French national infrastructure dedicated to the technical support and promotion of digital humanities. The goal of CORLI is to promote and provide tools and information for good and efficient research practices in corpus linguistics, especially on spoken language corpora. Because of the time required to collect and transcribe spoken language resources, their number is limited and thus corpora need to be interoperable and reusable in order to improve research on themes such as phonology, prosody, interaction, syntax, and textometry. To help researchers reach this goal, CORLI has designed a pair of tools: TEICORPO to assist in the conversion and use of spoken language corpora, and TEIMETA for metadata purposes. TEICORPO is based on the principle of an underlying common format, namely TEI XML as described in its specification for spoken language use (ISO 2016). This tool enables the conversion of transcriptions created with alignment software such as CLAN, Transcriber, Praat, or ELAN as well as common file formats (CSV, XLSX, TXT, or DOCX) and the TEI format, which plays the role of a lossless pivot format. Backward conversion is possible in many cases, with limitations inherent in the destination target format. TEICORPO can run the Treetagger part-of-speech tagger and the Stanford CoreNLP tools on TEI files and can export the resulting files to textometric tools such as TXM, Le Trameur, or Iramuteq, making it suitable for spoken language corpora editing as well as for various research purposes.
ATTITUDES OF RUSSIAN UNIVERSITY ACADEMIC STAFF TO FORCED DISTANT

It is time to reflect on the experience of implementing educational programs by Russian universities in forced distant to identify the best practices, to outline the ways to transform tertiary education system, to optimize the work of academic staff, and to reduce the negative impact on physical health of participants of the educational process. In academic discourse on this topic, the voices of managerial personnel in the field of higher education are most often heard, but it is necessary to give the floor the participants of the educational process, namely, academic staff. The article describes the “force majeure” experience of the university of the 5–100 program in the implementation of educational programs in conditions of forced distant. The purpose of the article is to identify the attitudes of university teachers to the educational activities in conditions of forced transition to training using distant technologies, to analyze the response of teachers to the new format of the educational process. The questionnaire of 63 teachers of the Department of Foreign Languages of South Ural State University revealed that that the key issues are the time spent in front of the computer screen, increased bureaucracy that deteriorates physical health, low ICT competence of academic staff, while the main positive results are the fast and furious professional development in the field of ICT use, increased confidence in the use of online educational services, the possibility of individualizing the educational process, independence from the technical equipment of the university audience. Based on the analysis of the experience of the academic staff of the Russian university and the data of analytical reports of the Ministry of Science and Higher Education of the Russian Federation, the author offers a number of recommendations that will help optimize the academic staff work in emergency situations.
Forecasting System Marginal Price Using Multilayer Perceptron and Nonlinear Autoregressive exogenous model

With the introduction of competition in the power generation sector in Korea, determining how much each electricity company should develop and maintain a stable system marginal price (SMP) has become an important issue.In this study, SMP was predicted by comparing the values for each company using a multilayer perceptron (MLP) and nonlinear autoregressive exogenous (NARX) model among artificial neural networks (ANN), and setting electricity demand as an external variable.The Autoregressive Integrated Moving Average (ARIMA) was also compared.Each method predicted 30, 40, and 50 days.In the case of MLP, optimal results were obtained when the number of hidden layer units were 90, 85, and 80, whereas in the case of NARX, optimal results were obtained when the number of hidden layer units were 95, 80, and 70.Overall, the forecast errors were small in the order of ARIMA, MLP, and NARX for all predictions.
REVIEWING THE RESULT OF MACHINE TRANSLATION: A CASE FOR INDONESIAN TRANSLATION VERSION BY GOOGLE TRANSLATE AND IMTRANSLATOR

This research is intended to describe the translation version of a literary story that produced by the machine translation. This study used qualitative research. The research was conducted from a literary story of the storybook of Robin Hood: Level 2, retold by Liz Austin. The book was published by Pearson Education Limited in 2000 (the first edition). Research data and analysis were taken from the documentation of the translation results of different machine translation, that is Google Translate and IMTranslator. The result proves that both Google Translate and IMTranslator can help translators to do their works quickly. There are also some weaknesses from those MT as follows: (1) some versions are inaccurate and inappropriate according to the grammatical rules; (2) those MT can only translate on words for word and literal level, so the versions are looked inaccurate and inappropriate for some contexts; (3) those MT also cannot distinguish connotative or associative meanings; and (4) the results of MT serve only as pre-translation, therefore, they still need further discussion and adjustment by the human translators to achieve more acceptable, readable, and understandable results.
Self-Diagnosis of Web-Based Pregnancy and Childbirth Disorders Using Forward Chaining Methods

The high mortality rate for pregnant women and childbirth in Bali, Indonesia, is caused by a lack of initial diagnosis of the diseases and complaints experienced by pregnant women during pregnancy, as well as a lack of health medical personnel scattered throughout Bali, to be able to provide optimal health services. It is necessary to have an online information system that helps pregnant women to be able to independently and online diagnose diseases, complaints, and symptoms experienced during pregnancy. The system must be able to be accessed anytime and anywhere, with high reliability and availability, and provide fast diagnostic results. Focus of this research is design and implementation of an Information System for Diagnosis of Pregnancy Disorders Based on Cloud Computing based on Forward Chaining Method, using Design Science Research Methodology (DSRM) and tested using the Technology Acceptance Model (TAM) method. The application is placed on the Hybrid Cloud. The results of this research, can help pregnant women in diagnosing diseases and complaints online, to reduce the mortality rate for pregnant women and giving birth.
Benign interpolation of noise in deep learning

The understanding of generalisation in machine learning is in a state of flux, in part due to the ability of deep learning models to interpolate noisy training data and still perform appropriately on out-of-sample data, thereby contradicting long-held intuitions about the bias-variance tradeoff in learning. We expand upon relevant existing work by discussing local attributes of neural network training within the context of a relatively simple framework. We describe how various types of noise can be compensated for within the proposed framework in order to allow the deep learning model to generalise in spite of interpolating spurious function descriptors. Empirically, we support our postulates with experiments involving overparameterised multilayer perceptrons and controlled training data noise. The main insights are that deep learning models are optimised for training data modularly, with different regions in the function space dedicated to fitting distinct types of sample information. Additionally, we show that models tend to fit uncorrupted samples first. Based on this finding, we propose a conjecture to explain an observed instance of the epoch-wise double-descent phenomenon. Our findings suggest that the notion of model capacity needs to be modified to consider the distributed way training data is fitted across sub-units.
Attention-Based BiLSTM for Negation Handling in Sentimen Analysis

Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation

Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for tasks such as Question Answering (QA) and Fact Verification.Recently, pre-trained sequence to sequence (seq2seq) models have proven to be very effective in jointly making predictions, as well as generating NL explanations.However, these models have many shortcomings; they can fabricate explanations even for incorrect predictions, they are difficult to adapt to long input documents, and their training requires a large amount of labeled data.In this paper, we develop FiD-Ex 1 , which addresses these shortcomings for seq2seq models by: 1) introducing sentence markers to eliminate explanation fabrication by encouraging extractive generation, 2) using the fusion-in-decoder architecture to handle long input contexts, and 3) intermediate fine-tuning on re-structured open domain QA datasets to improve few-shot performance.FiD-Ex significantly improves over prior work in terms of explanation metrics and task accuracy on five tasks from the ERASER explainability benchmark in both fully supervised and few-shot settings.
Geochemical Risks of Diamond Mining in Siberia

Geochemical risk is caused by the release of hazardous chemicals to the earth surface. Primary diamond deposits are located in difficult mining and geological conditions. They represent natural geochemical anomalies associated with the mineral composition of rocks and groundwater, which contain a number of impurity elements with high toxic properties (Tl, Di, As, Cd, Hg), and increased concentrations of heavy metals (Cu, Zn, Pb, Ti, V and others). The paper presents the physical-geographical and mining-geological conditions of the diamondiferous region, where three large mining and processing divisions operate: Udachninsky, Aikhalsky and Nyurbinsky. pH, organic matter (humus), total nitrogen, and physical clay were identified in the study samples, by using potentiometric, photoelectric colorimetric, spectrophotometric methods, and pipette method for particle size analysis. Gross and mobile forms of trace elements were determined by atomic absorption and emission spectrometry. The groups of elements were identified that determined the natural and man-made anomalies. The accumulation of Cr, Ni, and Co determines the influence of kimberlite magmatism in general. Cu, Sr, and Li are accumulated in the soils of the Daldyn-Alakit diamond-bearing region. Increased concentrations of Mn and Cu are typical in the soils of the Sredne-Markhinsky diamond-bearing region. An assessment of the ecological and geochemical state of the study areas was carried out according to the indicator of total pollution (Zc), which is the sum of the excess of the concentration coefficients of chemical elements accumulating in anomalies. Areas of pollution and zones of the greatest risk are localized, which occupy up to 75% of the total area of industrial sites. They confined to quarry-dump complexes and to areas of impact of tailing dumps of processing plants.
VLSI Design of a Processor for Discrete Wavelet Packet and Hilbert Transforms

Very Large Scale Integration (VLSI) design is a technological advancement in electronics that has widely shortened the window from concept to a working prototype in any design.It has also made it possible to design and develop sophisticated and intelligent electronic systems which are easily adaptable to any field of human endeavor with relative ease.In this paper, the VLSI design of a processor system is presented, which implements two transforms i.e. the discrete wavelet packet, and the Hilbert transforms.The combination of these two transforms in a single processor makes it possible to have a system with enhanced subband frequency edge detection in a wideband signal and other specialized areas, which is very useful in such specialized areas of application as spectrum sensing in cognitive radio networks.The results obtained from the simulation and design verification of the processor system showed the effectiveness of the design methodology presented in this paper.As a matter of fact, the arithmetic operators designed in this paper outperformed the arithmetic operators of the Xilinx IP CORE when compared in terms of speed.From the results obtained, it was clear that the processor design performed as expected, at a great speed.
Enhancement of Human Anatomy and Nervous System Learning using Mobile Augmented Reality Application

Advancements of recent technology allows the delivery of educational materials to be highly interactive. Augmented reality (AR) is one of the most promising technology that augments reality with computer generated imagery, objects, information etc. and allows user interaction. By providing visualization and interactive 3D models, it allows students to understand any abstract concept on any time, at any place easily. In this work we have developed an AR based mobile application that can create an interactive 3D view of human skeleton and nervous system by using a target image captured by a mobile phone camera. The main motive of this application is to help the medical students and school level science students to learn human anatomy and nervous system in an interactive way so that they can enhance their learning procedure. This research narrates the concept, application development and results of the pilot test. This test is conducted by the medical students of different medical colleges of Bangladesh. We have done this pilot test to get the users’ experiences from technical viewpoint. According to this test we can concluded that students were satisfied with this application in terms of the features, usability, feasibility and understandability.