Reviewerly/exHarmony huggingface dataset:
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience

Pretrained transformer-based models such as BERT have demonstrated state-of-the-art predictive performance when adapted into a range of natural language processing tasks. An open problem is how to improve the faithfulness of explanations (rationales) for the predictions of these models. In this paper, we hypothesize that salient information extracted a priori from the training data can complement the task-specific information learned by the model during fine-tuning on a downstream task. In this way, we aim to help BERT not to forget assigning importance to informative input tokens when making predictions by proposing SaLoss; an auxiliary loss function for guiding the multi-head attention mechanism during training to be close to salient information extracted a priori using TextRank. Experiments for explanation faithfulness across five datasets, show that models trained with SaLoss consistently provide more faithful explanations across four different feature attribution methods compared to vanilla BERT. Using the rationales extracted from vanilla BERT and SaLoss models to train inherently faithful classifiers, we further show that the latter result in higher predictive performance in downstream tasks.
Transcendental personality in the chronotype of being: psychological aspect

One of various philosophical and psychological problems has been studied - the problem of the transcendental component of personality in its individual life and environment of social realization. The peculiarities of the transcendent in the perspective of psychology are analysed, and namely: psychoanalytic, transpersonal, and existential ones. Based on the performed analysis, it has been concluded that transcendence is the reality that goes beyond the person and the world given to the man in his emotional experience and purposefulness. Transcendence be a perspective of human development. Transcendent is the very essence of man, his desire for self-realization in the process of philosophical understanding of the transcendence of human existence and the psychological content of personality development.
Проектування та реалізація системи прогнозування погоди

This study displays the main considerations in building weather forecasting systems, showing the main forecasting methods used in building systems of this kind. In total, this study presents the main statistical methods, in particular regression analysis methods, based on which weather forecasting systems are built. The methods presented are linear regression, multiple linear regression, and time series method. The main purpose of the study is to create a weather forecasting application using these regression-forecasting methods.
PARENTS’ AND TEACHERS’ PERSPECTIVES ON ONLINE CLASS DURING THE COVID-19 PANDEMIC: THEMATIC ANALYSIS

How do students’ parents and teachers react to online learning at primary schools during COVID-19 pandemic in Medan, Indonesia? The research aimed at discussing the perspectives of students’ parents and teachers concerning such online learning and used qualitative method to uncover their oral expressions. Online learning is considered the most appropriate method today to carry out teaching and learning process. Students’ parents and teachers faced new paradigm of online learning in which there are changes they should adapt to. Parents’ and teachers’ interview transcripts become the data in this research and were analyzed by thematic analysis as proposed by Braun and Clarke (2013). Students’ parents and primary school teachers become the research subjects. The results show that the subjects found obstacles specifically in technical and skill matters when they would take part in online class and the solution is to provide training for them so they can be accustomed with this learning method and to advance their knowledge of digital platform. Furthermore, building good and warm relationship among parents and teachers is necessary to increase their understanding on current condition of the COVID-19 pandemic in which online learning becomes the only method in which teachers are challenged to prepare their online materials well.
Path Estimation and Path Improvements in Local Guidance of Automated Mobile Robots Using a Bio-mementic Method

Robotic is now the world's most popular technology, with numerous applicability in various sectors.  Autonomous robots operate without the supervision of humans. The key procedures for automated robot movement are localization, path planning, and motor control. As per the poor closure speed in mobile services robot route planning, a global path scheduling approach based on upgraded ant colony enhancement is suggested. The crucial obstacle interaction factor determines the first pheromone dispersal. A new fragrance updating mechanism is provided that uses fuzzy controls to alter the evaporating rate in phases by changing the values of the fragrance heuristics factor and expectancy heuristic factor. The approach ensures local navigation lookup capabilities while achieving quick convergence. Moreover, simulation outcomes indicate that the enhanced approach not only reduces the duration it takes to design a local navigational path, and also increases the likelihood of finding a global optimal resolution. The algorithm's closure speed is faster than the classic ant colony approach.
A New Data Mining and Visualization Tool for Brazilian Educational Indicators

The National Institute of Educational Studies and Research Anísio Teixeira provides open data that help to understand Brazilian Education through Educational Indicators. Despite the easy access to the data, it is hard to manipulate and analyze it to understand the educational scenario in different contexts. This work presents a new visualization tool, inspired by the data mining process, which aims to allow the extraction of knowledge through these indicators using selections provided by users in a very simple manner. To show the tool's potential, graphics of several works were recreated and new graphics are presented.
Flipped Clasroom y su efectividad en Moodle

The objective of this work is to determine the effectiveness of the Flipped Cassroom or Inverted Classroom (FC or AI) methodology in Moodle with fourth grade students in the Natural Sciences subject, for which there is a teacher who teaches two classes. courses with a total of 72 students, a group is taken for the experimentation of FC and with the approval and authorization of the parents, it is applied, who made the commitment to supervise the treatment of FC with their represented. The results show that the students improve their performance unlike the group that did not experience the FC methodology. The author's recommendation is to continuously apply FC, since, during 2020 and 2021, research shows that learning has greater performance when implementing FC methodologies through online platforms and digital resources. Students make use of technology more frequently on social media and open web pages for distraction, making it easier to use Virtual Learning Environments for teaching.
Training Neural Networks with Fixed Sparse Masks

During typical gradient-based training of deep neural networks, all of the model's parameters are updated at each iteration. Recent work has shown that it is possible to update only a small subset of the model's parameters during training, which can alleviate storage and communication requirements. In this paper, we show that it is possible to induce a fixed sparse mask on the model's parameters that selects a subset to update over many iterations. Our method constructs the mask out of the $k$ parameters with the largest Fisher information as a simple approximation as to which parameters are most important for the task at hand. In experiments on parameter-efficient transfer learning and distributed training, we show that our approach matches or exceeds the performance of other methods for training with sparse updates while being more efficient in terms of memory usage and communication costs. We release our code publicly to promote further applications of our approach.
BART for Post-Correction of OCR Newspaper Text

Optical character recognition (OCR) from newspaper page images is susceptible to noise due to degradation of old documents and variation in typesetting. In this report, we present a novel approach to OCR post-correction. We cast error correction as a translation task, and fine-tune BART, a transformer-based sequence-to-sequence language model pretrained to denoise corrupted text. We are the first to use sentence-level transformer models for OCR post-correction, and our best model achieves a 29.4% improvement in character accuracy over the original noisy OCR text. Our results demonstrate the utility of pretrained language models for dealing with noisy text.
THE ROLE OF AUTOMATION IN LIBRARY ROUTINE OPERATIONS

The paper examined the role of automation in library day-to-day activities, major libraryactivities includes acquisition, processing, organization (cataloguing and classification),circulation, serials management, reference and administrative unit etc. The process ofconversion through associated technologies and uses of computer hardware and software forthe tasks that otherwise required a lot of paper work and staff time. The study will be used toreduce cost and increase performance in the library routine operations.
To be Closer: Learning to Link up Aspects with Opinions

Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA) (CITATION). However, the trees obtained from off-the-shelf dependency parsers are static, and could be sub-optimal in ABSA. This is because the syntactic trees are not designed for capturing the interactions between opinion words and aspect words. In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure. The aspect and opinion words are expected to be closer along such tree structure compared to the standard dependency parse tree. The learning process allows the tree structure to adaptively correlate the aspect and opinion words, enabling us to better identify the polarity in the ABSA task. We conduct experiments on five aspect-based sentiment datasets, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis demonstrates the average distance between aspect and opinion words are shortened by at least 19% on the standard SemEval Restaurant14 (CITATION) dataset.
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations

We present polynomial time and sample efficient algorithms for learning an unknown depth-2 feedforward neural network with general ReLU activations, under mild non-degeneracy assumptions. In particular, we consider learning an unknown network of the form $f(x) = {a}^{\mathsf{T}}\sigma({W}^\mathsf{T}x+b)$, where $x$ is drawn from the Gaussian distribution, and $\sigma(t) := \max(t,0)$ is the ReLU activation. Prior works for learning networks with ReLU activations assume that the bias $b$ is zero. In order to deal with the presence of the bias terms, our proposed algorithm consists of robustly decomposing multiple higher order tensors arising from the Hermite expansion of the function $f(x)$. Using these ideas we also establish identifiability of the network parameters under minimal assumptions.
Evaluation Paradigms in Question Answering

Question answering (QA) primarily descends from two branches of research: (1) Alan Turing’s investigation of machine intelligence at Manchester University and (2) Cyril Cleverdon’s comparison of library card catalog indices at Cranfield University. This position paper names and distinguishes these paradigms. Despite substantial overlap, subtle but significant distinctions exert an outsize influence on research. While one evaluation paradigm values creating more intelligent QA systems, the other paradigm values building QA systems that appeal to users. By better understanding the epistemic heritage of QA, researchers, academia, and industry can more effectively accelerate QA research.
The Utilizing Of Whatsapp Application For Teaching Speaking

The pandemic has accompanied the education sector for about one year especially in Indonesia. Every student used to use mobile phone as the schools’ tool to have a home class learning. While, the signal of internet has not spread all over the villages in north Sumatera, they have difficulty to get the signal for joining the learning process. Among all the learning applications now, WhatsApp is the chosen application for the students in learning speaking class.The purpose of this research describes the students’ real situation and condition in the field. The method uses in this research is descriptive method The data obtained from the true and recent condition of the third semester students of civics program at education and teaching faculty of Graha Nusantara University Padangsidimpuan. The result of this research tells the students’ real condition in learning speaking by using WhatsApp application for this pandemic situation. WhatsApp was chosen because there were many students lived in the remote area which has no internet signal. A researcher focuses on teaching the English basic for daily communication. The advantages of teaching speaking for non-English students give them supplies to communicate using English in this recent developing era.
To Map or Not to Map: Rethinking Crosswalk Agendas

In the two decades since their publication, the Functional Requirements of Bibliographic Records and succeeding standards such as the Library Reference Model have had a marked impact on discourse concerning descriptive theory and practice. The BIBFRAME model, which began as an effort to replace MARC as a linked data-capable modeling format, offers an alternate view of the bibliographic universe with three principal entities rather than four. Differences between BIBFRAME and LRM are based in competing intuitions on the nature of creative works, and at first the two approaches appear to compete for the same intellectual space. BIBFRAME offers us a less constrained model of bibliographic descriptions than the FRBR models, and if interoperability between BIBFRAME and WEMI-aligned standards like Resource Description and Access requires translation of RDA records both to and from BIBFRAME descriptions, then the latter’s flexibility poses problems for mapping between the models. Proposed solutions to those problems reveal as much about different modeling philosophies as they do about different views of creative works and their relationships to texts and copies. Linked data protocols are intended to support resources and scenarios that are far too diverse for either a single account of creative works or for a subsumption-based taxonomy of models. But a need for descriptions flexible enough to include them all does not require us to retreat from modeling commitments to either reductionism or operationalism. BIBFRAME can be seen as reaching for or pointing toward a descriptive domain that supports a complementary role to the IFLA standards.
Standish Library: CS390--Web Application Development: Home

Guide meant to assist Dr. Lim's CS390 students with the web development software assignment.
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning

We demonstrate a library for the integration of domain knowledge in deep learning architectures. Using this library, the structure of the data is expressed symbolically via graph declarations and the logical constraints over outputs or latent variables can be seamlessly added to the deep models. The domain knowledge can be defined explicitly, which improves the explainability of the models in addition to their performance and generalizability in the low-data regime. Several approaches for such integration of symbolic and sub-symbolic models have been introduced; however, there is no library to facilitate the programming for such integration in a generic way while various underlying algorithms can be used. Our library aims to simplify programming for such integration in both training and inference phases while separating the knowledge representation from learning algorithms. We showcase various NLP benchmark tasks and beyond. The framework is publicly available at Github(https://github.com/HLR/DomiKnowS).
Sequence-to-Lattice Models for Fast Translation

Non-autoregressive machine translation (NAT) approaches enable fast generation by utilizing parallelizable generative processes. The remaining bottleneck in these models is their decoder layers; unfortunately unlike in autoregressive models (Kasai et al., 2020), removing decoder layers from NAT models significantly degrades accuracy. This work proposes a sequence-to-lattice model that replaces the decoder with a search lattice. Our approach first constructs a candidate lattice using efficient lookup operations, generates lattice scores from a deep encoder, and finally finds the best path using dynamic programming. Experiments on three machine translation datasets show that our method is faster than past non-autoregressive generation approaches, and more accurate than naively reducing the number of decoder layers.
News Articles Classification

Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access and rapid dissemination of information lead people to seek out and consume news from social media. For the last few years, text mining has been gaining significant importance. Since Knowledge is now available to users through variety of sources e.g. electronic media, digital media, print media, and many more. Due to becoming a very hot research area, a lot of unstructured data has been recorded by research experts and have found numerous ways in literature to convert this scattered text into defined structured volume, commonly known as text classification. Focuses on full text classification e.g. full news, huge documents, long length texts, etc. is more prominent as compared to the short length text. We have discussed text classification process, classifiers, and numerous feature extraction methodologies but all in context of texts, e.g. news classification based on their headlines. Existing classifiers and their working methodologies are being compared and results are presented effectively. We also discuss related research areas, open problems, and future research directions for news article classification.
Movie Recommendation System Mistreatment Current Trends and Sentiment Analysis from Micro Blogging Knowledge

Abstract: Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the public database have yielded promising results. Keywords: Collaborative filtering, Content based filtering, Recommendation System, Sentiment Analysis, Twitter
Serpentinization rates in low-temperature, continental, ultramafic environments: Clues from H<sub>2</sub> and CH<sub>4</sub> outgassing rates in the Samail ophiolite, Oman

Low-temperature (<50 °C), hyperalkaline (pH >11), and reduced (H 2 -and CH 4 -rich) fluids are products of subsurface reactions accompanying the serpentinization of ultramafic rocks.H 2 and CH 4 generated through serpentinization can fuel chemotrophic microbial communities and thus, have profound implications on the habitability of Earth's ultramafic aquifers as well as in Ocean Worlds where ultramafic minerals are believed to be present in contact with water.The rate at which these reduced volatiles are delivered to the surface as well as the rate of reactions that generate these volatiles at low temperature conditions are poorly known.In this work, we measured H 2 and CH 4 outgassing rates in seven hyperalkaline spring sites in the Samail ophiolite, Oman.Gas bubbling was observed in all but one site.H 2 /CH 4 ratios of outgassing gas ranges from 0.3 to 30, consistent with published gas data from Oman.Outgassing rates per bubbling point source are also variable ranging from 0.1-4.2 and 0.1-45 µmol sec -1 for CH 4 and H 2 , respectively.The highest outgassing rates are associated with warmer fluids and imply deeper sources that are less modified by shallow processes.These warmer spring sites often have 20-50 individual bubbling sources and thus, total outgassing rates for these sites would amount to 80-200 and 900-2,250 µmol sec -1 for CH 4 and H 2 , respectively.Serpentinization rates that can account for these volatile fluxes were estimated using thermodynamic simulations of active subsurface water-ultramafic rock interactions.Using results of simulations, we estimate times to complete 1% serpentinization of a given volume of subsurface rock ranging from 10 4 to 10 6 years.The former is close to low-temperature rates extrapolated from high-temperature laboratory experiments.In contrast, the latter timescale can be decreased if contributions from volatiles released from fluid inclusions, which requires ~1,000-10,000 times larger rock volumes to account for similar volatile fluxes are considered.These findings show that lowtemperature serpentinization on geologically short timescales can account for observed flux of reduced volatiles in continental hyperalkaline environments.