Reviewerly/exHarmony huggingface dataset:
Teacher-Facilitators’ Job-Crafting: Making Meaning and Relevance in Authentic Learning Environments

Purpose: Higher education institutions are being challenged to sustain their relevance in a disruptive world by moving from ego-systems to ecosystems.Teachers, as key actors, are required to redesign their work, shifting from traditional teacher-led processes towards the facilitation of student-driven learning in authentic environments.In ecosystems, with students, colleagues, and work-life partners, teachers need to modify their work by crafting their tasks, relationships, and mindsets.This study aims to make the change in teachers' work transparent on a practical level.Study design/methodology/approach: A qualitative and practice-based approach was used to explore teacher facilitators experiences of their job crafting. Findings:The findings reveal the new meaning and relevance of the teacher facilitators work and can raise the awareness of higher education institutions to cultivate the future competencies of teachers. Originality/value:The study introduces the new nature and purpose of teachers' work as a part of ecosystems.We suggest higher education institutions nurture an experimental learning culture and support teachers to go out of their comfort zones.Developing collaborative job crafting can be a sustainable way to do that and avoids the disconnection between what we are doing and what we should be doing.
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints

Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs.To improve effectiveness of the available BT data, we introduce HintedBT-a family of techniques which provides hints (through tags) to the encoder and decoder.First, we propose a novel method of using both high and low quality BT data by providing hints (as source tags on the encoder) to the model about the quality of each source-target pair.We don't filter out low quality data but instead show that these hints enable the model to learn effectively from noisy data.Second, we address the problem of predicting whether a source token needs to be translated or transliterated to the target language, which is common in crossscript translation tasks (i.e., where source and target do not share the written script).For such cases, we propose training the model with additional hints (as target tags on the decoder) that provide information about the operation required on the source (translation or both translation and transliteration).We conduct experiments and detailed analyses on standard WMT benchmarks for three crossscript low/medium-resource language pairs: {Hindi,Gujarati,Tamil}→English.Our methods compare favorably with five strong and well established baselines.We show that using these hints, both separately and together, significantly improves translation quality and leads to state-of-the-art performance in all three language pairs in corresponding bilingual settings.
<scp>VIRDOCD</scp>: A <scp>VIRtual DOCtor</scp> to predict dengue fatality

Abstract Clinicians make routine diagnosis by scrutinizing patients' medical signs and symptoms, a skill popularly referred to as ‘Clinical Eye’. This skill evolves through trial‐and‐error and improves with time. The success of the therapeutic regime relies largely on the accuracy of interpretation of such sign‐symptoms, analysing which a clinician assesses the severity of the illness. The present study is an attempt to propose a complementary medical front by mathematically modelling the ‘Clinical Eye’ of a VIR tual DOC tor, using statistical and machine intelligence tools (SMI), to analyse D engue epidemic infected patients ( 100 case studies with 11 weighted sign‐symptoms ). The SMI in VIRDOCD reads medical data and translates these into a vector comprising multiple linear regression (MLR) coefficients to predict infection severity grades of dengue patients that clone the clinician's experience‐based assessment. Risk managed through ANOVA, the dengue severity grade prediction accuracy from VIRDOCD is found higher (ca 75%) than conventional clinical practice (ca 71.4%, mean accuracy profile assessed by a team of 10 senior consultants). Free of human errors and capable of deciphering even minute differences from almost identical symptoms (to the Clinical eye), VIRDOCD is uniquely individualized in its decision‐making ability. The algorithm has been validated against Random Forest classification (RF, ca 63%), another regression‐based classifier similar to MLR that can be trained through supervised learning. We find that MLR‐based VIRDOCD is superior to RF in predicting the grade of Dengue morbidity. VIRDOCD can be further extended to analyse other epidemic infections, such as COVID‐19.
Cross-lingual transfer of abstractive summarizer to less-resource language

Automatic text summarization extracts important information from texts and presents the information in the form of a summary. Abstractive summarization approaches progressed significantly by switching to deep neural networks, but results are not yet satisfactory, especially for languages where large training sets do not exist. In several natural language processing tasks, a cross-lingual model transfer is successfully applied in less-resource languages. For summarization, the cross-lingual model transfer was not attempted due to a non-reusable decoder side of neural models that cannot correct target language generation. In our work, we use a pre-trained English summarization model based on deep neural networks and sequence-to-sequence architecture to summarize Slovene news articles. We address the problem of inadequate decoder by using an additional language model for the evaluation of the generated text in target language. We test several cross-lingual summarization models with different amounts of target data for fine-tuning. We assess the models with automatic evaluation measures and conduct a small-scale human evaluation. Automatic evaluation shows that the summaries of our best cross-lingual model are useful and of quality similar to the model trained only in the target language. Human evaluation shows that our best model generates summaries with high accuracy and acceptable readability. However, similar to other abstractive models, our models are not perfect and may occasionally produce misleading or absurd content.
All-optical cat-code quantum error correction

The cat code is a promising encoding scheme for bosonic quantum error correction as it allows for correction against losses---the dominant error mechanism in most bosonic systems. However, it has remained unclear how the required syndrome measurement and recovery can be implemented in the optical regime. Here, we introduce a teleportation-based error-correction scheme for the cat code, using elements suitable for an optical setting. The scheme detects and corrects single-photon losses while restoring the amplitude of the cat states, thereby greatly suppressing the accumulation of errors in lossy channels.
Multiclass Classification of Four Types of UML Diagrams from Images Using Deep Learning

UML diagrams are a recognized standard modelling language for representing design of software systems.For academic research, large cases containing UML diagrams are needed.One of the challenges in collecting such datasets is automatically determining whether an image is a UML diagram or not and what type of UML diagram an image contains.In this study, we collected UML diagrams from open datasets and manually labeled them into four types of UML diagrams (i.e., class diagrams, activity diagrams, sequence diagrams, and use case diagrams) and non-UML images.We evaluated the performance of five popular neural network architectures using transfer learning on the dataset of 3231 images that contains 700 class diagrams, 454 activity diagrams, 651 use case diagrams, 706 sequence diagrams, and 720 non-UML images, respectively.We also proposed our neural network architecture for multiclass classification of UML diagrams.The experiment results show that our proposed neural network architecture achieved the best performance amongst the algorithms we evaluated with an accuracy of 98.65%, a precision of 96.76%, a recall of 96.48%, and an F1-score of 96.62%.Moreover, among the neural network architectures that we have evaluated, our proposed architecture has the least parameters (around 2.4 millions) and spends the least time per image (0.0135 seconds per image using GPU) for classifying UML diagrams.
Deterministic three-photon down-conversion by a passive ultrastrong cavity-QED system

In ultra- and deep-strong cavity quantum electrodynamics (QED) systems, many intriguing phenomena that do not conserve the excitation number are expected to occur. In this paper, we theoretically analyze the optical response of an ultrastrong cavity-QED system in which an atom is coupled to the first and third harmonic modes of a cavity, and report the possibility of deterministic three-photon down-conversion of itinerant photons upon reflection at the cavity. In the conventional parametric down-conversion, a strong input field is needed because of the smallness of the transition matrix elements of the higher-order processes. However, if we use an atom-cavity system in an unprecedentedly strong-coupling region, even a weak field in the linear-response regime is sufficient to cause this rare event involving the fourth-order transitions.
Satisfiability and containment of recursive SHACL

The Shapes Constraint Language (SHACL) is the recent W3C recommendation language for validating RDF data, by verifying certain shapes on graphs. Previous work has largely focused on the validation problem, while the standard decision problems of satisfiability and containment, crucial for design and optimisation purposes, have only been investigated for simplified versions of SHACL. Moreover, the SHACL specification does not define the semantics of recursively-defined constraints, which led to several alternative recursive semantics being proposed in the literature. The interaction between these different semantics and important decision problems has not been investigated yet. In this article we provide a comprehensive study of the different features of SHACL, by providing a translation to a new first-order language, called SCL, that precisely captures the semantics of SHACL. We also present MSCL, a second-order extension of SCL, which allows us to define, in a single formal logic framework, the main recursive semantics of SHACL. Within this language we also provide an effective treatment of filter constraints which are often neglected in the related literature. Using this logic we provide a detailed map of (un)decidability and complexity results for the satisfiability and containment decision problems for different SHACL fragments. Notably, we prove that both problems are undecidable for the full language, but we present decidable combinations of interesting features, even in the face of recursion.
Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Summary Objectives: To analyze the content of publications within the medical NLP domain in 2020. Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included. Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks
Examining student satisfaction with online learning

The purpose of the article is to determine the factors affecting the satisfaction of Duy Tan University students for online learning during the acute respiratory infection caused by the new strain of Coronavirus (Covid-19). Based on survey, analysis, processing and evaluation of the obtained results, the article aims to focus the analysis within specific limits with the goal of honestly reflecting and thereby offering solutions for the effective use of online learning in the learning process of students. The results show that there are five factors affecting student satisfaction, in order of influence, as follows: (1) Tuition fees; (2) Teaching staff; (3) Interoperability; (4) Facilities and (5) Training programs. Based on these results, the Board of Directors of Duy Tan University will change several factors in order to improve student satisfaction in online learning in the near future.
Sniffing attacks on computer networks

The sniffing attack or sniffer attack, in the context of network security, corresponds to data theft or interception by capturing network traffic using a sniffer (an application that aims to capture network packets). When data is transmitted over networks, if data packets are not encrypted, data in the network packet can be read using a sniffer. Using a sniffer application, an attacker can analyze the network and obtain information so that it can eventually crash or corrupt the network or read the communications that occur in the network. Sniffing attacks can be compared to touching wires and getting to know the conversation, and for this reason it is also called "wiretapping" applied to computer networks. In this paper, a sniffing attack is shared which can significantly damage the computer networks as well as methods of combating such attacks. Sniffing is usually performed to analyze network usage, troubleshoot network problems, monitor session for development and testing purposes.
Topic Modeling of Online Media News Titles during COVID-19 Emergency Response in Indonesia Using the Latent Dirichlet Allocation (LDA) Algorithm

Online media news portals have the advantage of speed in conveying information on any events that occur in society. One way to know what a story is about is from the title. The headline is a headline that introduces the reader's knowledge about the news content to be described. From these headlines, you can search for the main topics or trends that are being discussed. It takes a fast and efficient method to find out what topics are trending in the news. One method that can be used to overcome this problem is topic modeling. Topic modeling is necessary to help users quickly understand recent issues. One of the algorithms in topic modeling is Latent Dirichlet Allocation (LDA). The stages of this research began with data collection, preprocessing, forming n-grams, dictionary representation, weighting, validating the topic model, forming the topic model, and the results of topic modeling. The results of modeling LDA topics in news headlines taken from www.detik.com for 8 months (March-October 2020) during the COVID-19 pandemic showed that the best number of topics produced each month were 3 topics dominated by news topics about corona cases, positive corona, positive COVID, COVID-19 with an accuracy of 0.824 (82.4%). The resulting precision and recall values indicate that the two values are identical, so this is ideal for an information retrieval system.
A New Hybrid Under-sampling Approach to Imbalanced Classification Problems

Among many machine learning applications, classification is one of the important tasks. Most classification algorithms have been designed under the assumption that the number of samples for each class is approximately balanced. However, if the conventional classification approaches are applied to a class imbalanced dataset, it is likely to cause misclassification and, as a result, may distort classification performance results. Thus, in this study, we consider imbalanced classification problems and adopt an efficient preprocessing technique to improve the classification performances. In particular, we focus on borderline noise and outlier samples that belong to the majority class since they may influence classification performance. For this, we propose a hybrid resampling method, called BOD-based under-sampling, which is based on density-based spatial clustering of applications with noise (DBSCAN) approach as well as noise and outlier detection methods, that is, borderline noise factor (BNF) and outlierness based on neighborhood (OBN) to divide majority class samples into four distinctive categories, i.e., safe, borderline noise, rare, and outlier. Specifically, we first determine the borderline noise samples in the overlapped region using the BNF method. Secondly, we use the OBN method to detect outlier samples and apply the DBSCAN approach to cluster the samples. Based on the results obtained from the sample identification analysis, we then segregate the safe category samples which are not abnormal samples while keeping the rest of the samples as rare samples. Finally, we remove some of safe samples by using the random under-sampling (RUS) method and verify the effectiveness of the proposed algorithm through the comprehensive experimental analysis with considering several class imbalance datasets.
TedPar: Temporally Dependent PARAFAC2 Factorization for Phenotype-based Disease Progression Modeling

PARAFAC2 factorization provides a practical solution to map the temporally irregular electronic health records (EHR) to clinically relevant and interpretable phenotypes.Existing methods ignore the effect of interdependency of diseases over clinical history.Consequently, the crucial temporal information contained in the EHR data cannot be fully utilized and the learned phenotypes can be sub-optimal to characterize patients with progressive conditions.To address this issue, we propose a novel temporally dependent PARAFAC2 (TedPar) factorization in which the temporal dependency among the phenotypes is explicitly modeled.TedPar learns a set of target phenotypes to capture the clinical features relevant to the diseases of interest and a set of background phenotypes to capture irrelevant but frequently co-occurring clinical features.By effectively modeling the temporal dependency and separating relevant and irrelevant features, the discovered target phenotypes can be used to model the progression of the diseases of interest.Empirical evaluations show that TedPar obtains up to 32.4% relative improvement in reconstruction accuracy over the test set, suggesting significantly better generalizability than the baselines for both noise-free and heavily noisy input data.Qualitative analysis also shows that TedPar is capable of discovering clinically meaningful phenotypes and capturing the temporal dependency between them.
Isomorphism Testing Parameterized by Genus and Beyond

We present an isomorphism test for graphs of Euler genus g running in time 2^{{O}(g⁴ log g)}n^{{O}(1)}. Our algorithm provides the first explicit upper bound on the dependence on g for an fpt isomorphism test parameterized by the Euler genus of the input graphs. The only previous fpt algorithm runs in time f(g)n for some function f (Kawarabayashi 2015). Actually, our algorithm even works when the input graphs only exclude K_{3,h} as a minor. For such graphs, no fpt isomorphism test was known before.The algorithm builds on an elegant combination of simple group-theoretic, combinatorial, and graph-theoretic approaches. In particular, we introduce (t,k)-WL-bounded graphs which provide a powerful tool to combine group-theoretic techniques with the standard Weisfeiler-Leman algorithm. This concept may be of independent interest.
Machine translation use outside the language industries: a comparison of five delivery formats for machine translation literacy instruction

The Machine Translation Literacy Project is funded (2020-2026) through an Insight Grant (#435-2020-0089) from the Social Sciences and Humanities Research Council of Canada. Seed funding was also provided by Concordia University Library through the 2019 Researcher-in-Residence program.
Librarians’ Emotional Intelligence and Conflict Management in Private University Libraries in South-West and South-South, Nigeria

This study investigates the influence of librarians„ emotional intelligence on conflict management in private university libraries in South-West and South-South, Nigeria. The study adopted a survey research design. The population comprises 200 librarians in South-West and South-South, Nigeria. Total enumeration technique was used. A questionnaire was used for data collection. The instrument was tested for reliability, yielding Cronbach‟s alpha coefficients ranging from 0.87 to 0.95. Descriptive &amp; inferential Statistics were used to analyse the data. The findings revealed that intrapersonal, interpersonal, intragroup, and intergroup conflicts as predominant in libraries. Collaborating, accommodating, sharing, avoidance and competing were prevalent techniques for managing conflicts in libraries. Librarians were found to be highly emotionally intelligent. The study concluded that librarians‟ emotional intelligence influenced conflict management. The study recommended that there is a need for private University Libraries to tackle misunderstandings among librarians while at the same time ensuring librarians are constantly emotionally intelligent.
Horizontal Nonlinear Path Following Guidance Law for a Small UAV With Parameter Optimized by NMPC

In this study, the problem of guiding a small fixed-wing unmanned aerial vehicle (UAV) toward a predefined horizontal path is studied.A stable nonlinear guidance law, which is a function of the inertial positions and velocities of the UAV and the predefined path, is designed using Lyapunov stability arguments.The concept of the nonlinear model predictive control (NMPC) technique was applied to optimize a key parameter of the guidance law to improve the performance of the controller (PFC_NMPC), where the stability of the relative nonlinear system is maintained.The proposed method was verified in the MATLAB/Simulink environment to realize following the straight-line, square and circular paths.The pathfollowing performance of the proposed method is compared with those of the guidance laws with parameter fixed (PFC) or tuned by fuzzy logic (PFC_FL).With the predictive ability, the proposed method can make the UAV fly more on the desired square and circular paths than the other two methods, PFC and PFC_FL.The error overshoot by using PFC_NMPC is much smaller than those by using the PFC and PFC_FL methods in the presence of wind at 8m/s.
A Systematic Review and Analysis of Multilingual Data Strategies in Text-to-Speech for Low-Resource Languages

We provide a systematic review of past studies that use multilingual data for text-to-speech (TTS) of low-resource languages (LRLs).We focus on the strategies used by these studies for incorporating multilingual data and how they affect output speech quality.To investigate the difference in output quality between corresponding monolingual and multilingual models, we propose a novel measure to compare this difference across the included studies and their various evaluation metrics.This measure, called the Multilingual Model Effect (MLME), is found to be affected by: acoustic model architecture, the difference ratio of target language data between corresponding multilingual and monolingual experiments, the balance ratio of target language data to total data, and the amount of target language data used.These findings can act as reference for data strategies in future experiments with multilingual TTS models for LRLs.Language family classification, despite being widely used, is not found to be an effective criterion for selecting source languages.