Reviewerly/exHarmony huggingface dataset:
The Transformational Leadership of Water-Intensive Enterprises to Social Loafing: The Mediating Role of Person-Organization Fit

Chen, C.; Yu, C.T., and Zhang, Y.Y., 2020. The transformational leadership of water-intensive enterprises to social loafing: The mediating role of person-organization fit. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 940–944. Coconut Creek (Florida), ISSN 0749-0208.This article examines the relationships between transformational leadership, person-organization (P-O) fit, perceived organizational support (POS) and social loafing, specifically the mediating effect of person-organization fit in the correlation between transformational leadership and social loafing and the moderating effect of POS on the correlation between P-O fit and social loafing. It uses social exchange theory to look investigate person-organization fit. The author collected a sample of 382 employees from water-intensive enterprises in mainland China. SPSS 23.0 software was used to test hypotheses in the literature. Conclusions suggest that person-organization fit is avital mediator and POS is an important moderator, while transformational leadership significantly negatively influences social loafing. The implication of these results is discussed.
Joint State-Action Embedding for Efficient Reinforcement Learning

While reinforcement learning has achieved considerable successes in recentyears, state-of-the-art models are often still limited by the size of state andaction spaces. Model-free reinforcement learning approaches use some form ofstate representations and the latest work has explored embedding techniques foractions, both with the aim of achieving better generalization andapplicability. However, these approaches consider only states or actions,ignoring the interaction between them when generating embedded representations.In this work, we propose a new approach for jointly embedding states andactions that combines aspects of model-free and model-based reinforcementlearning, which can be applied in both discrete and continuous domains.Specifically, we use a model of the environment to obtain embeddings for statesand actions and present a generic architecture that uses these to learn apolicy. In this way, the embedded representations obtained via our approachenable better generalization over both states and actions by capturingsimilarities in the embedding spaces. Evaluations of our approach on severalgaming and recommender system environments show it significantly outperformsstate-of-the-art models in discrete domains with large state/action space, thusconfirming the efficacy of joint embedding and its overall superiorperformance.
A Smart Interface for Provisioning of Food and Health Advices

The purpose of this work is to create an interactive chatbot that can help specialists to get information about food and health-related questions. This project uses main techniques of information processing as grammar tagging. The program gets information from a digital encyclopedia, named medlineplus.gov . This is one of the most complete encyclopedias on the web. Firstly we had to import necessary libraries as nltk and numpy that are useful for information processing, re to use regular expression, string for string type, bs4 to parse html lines and want to know the right extension and urllib.request to use websites and get information from these ones. We also have imported pos_tag from nltk to do grammar parsing and stop-words from nltk.corpus to find meaningless words.
Investigation of temporally varying fringe pattern defects using machine learning for optical metrology

Defect identification for quality control and industrial inspection necessitates the need for novel techniques in the field of non-destructive testing and experimental mechanics. Optical interferometric techniques are quite popular for non-destructive testing, and hence they are extensively used to locate and identify defects from fringe patterns. A rapid variation of the fringe density in the vicinity of the defect’s region serves as a means for the detection algorithms to identify them. With the advent of machine learning over recent years, it has paved way for algorithms with automatic detection, thereby, eliminating the problem of manual thresholding. In this paper, we propose an elegant technique which relies on computing windowed Fourier spectrum of the fringe pattern at a given spatial frequency and subsequently utilizing this spectrum with a GPU accelerated Support Vector Machine (SVM) algorithm for classification of a defect and a non-defect region. The windowed Fourier spectrum of the fringe pattern serves as a feature vector for the GPU accelerated SVM algorithm which internally performs a pixel classification, thereby producing a binary output of the defect. The performance of the proposed technique is tested on computer-generated fringe patterns at severe noise levels. A machine learning model is trained using the windowed Fourier spectrum of a 1024 x 1024 fringe pattern which is corrupted with an additive Gaussian noise at a signal to noise ratio of 5dB. The best set of hyper-parameters are deduced from the validation data and the proposed method is tested on the fringe patterns of size 1024 x 1024 which contain temporally varying defects. The results indicate that the proposed method is computationally efficient, robust against noise, and also capable of automating the defect identification problem.
Sentiment urgency emotion conversion over time for business intelligence

Purpose Social media has become a vital part of any institute’s marketing plan. Social networks benefit businesses by allowing them to interact with their clients, grow brand exposure through offers and promotions and find new leads. It also offers vital information concerning the general emotions and sentiments directly connected to the welfare and security of the online community involved with the brand. Big organizations can make use of their social media data to generate planned and operational decisions. This paper aims to look into the conversion of sentiments and emotions over time. Design/methodology/approach In this work, a model called sentiment urgency emotion detection (SUED) from previous work will be applied on tweets from two different periods of time, one before the start of the COVID-19 pandemic and the other after it started to monitor the conversion of sentiments and emotions over time. The model has been trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is high. This model will be tuned to improve results (Soussan and Trovati, 2020a; Soussan and Trovati, 2020b) and will be applied on a general business Twitter account of one of the largest chains of supermarkets in the UK to be able to see what sentiments and emotions can be detected and how urgent they are. Findings This will show the effect of COVID-19 pandemic on the conversions of the sentiments, emotions and urgencies of the tweets. Originality/value Sentiments will be compared between the two periods to evaluate how sentiments and emotions vary over time taking into consideration the COVID-19 as an affective factor. In addition, SUED will be tuned to enhance results and the knowledge that is mined when turning data into decisions is crucial because it will aid stakeholders handling the institute to evaluate the topics and issues that were mostly emphasized.
The Multilingual Amazon Reviews Corpus.

We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID, and the coarse-grained product category (e.g., 'books', 'appliances', etc.) The corpus is balanced across the 5 possible star ratings, so each rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000, and 5,000 reviews in the training, development, and test sets, respectively. We report baseline results for supervised text classification and zero-shot cross-lingual transfer learning by fine-tuning a multilingual BERT model on reviews data. We propose the use of mean absolute error (MAE) instead of classification accuracy for this task, since MAE accounts for the ordinal nature of the ratings.
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective

Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies, however, show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We aim to address this problem from an information-theoretic perspective, and propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models. InfoBERT contains two mutual-information-based regularizers for model training: (i) an Information Bottleneck regularizer, which suppresses noisy mutual information between the input and the feature representation; and (ii) a Robust Feature regularizer, which increases the mutual information between local robust features and global features. We provide a principled way to theoretically analyze and improve the robustness of representation learning for language models in both standard and adversarial training. Extensive experiments demonstrate that InfoBERT achieves state-of-the-art robust accuracy over several adversarial datasets on Natural Language Inference (NLI) and Question Answering (QA) tasks. Our code is available at this https URL.
Modular and distributed IDE

Integrated Development Environments (IDEs) are indispensable companions to programming languages. They are increasingly turning towards Web-based infrastructure. The rise of a protocol such as the Language Server Protocol (LSP) that standardizes the separation between a language-agnostic IDE, and a language server that provides all language services (e.g., auto completion, compiler...) has allowed the emergence of high quality generic Web components to build the IDE part that runs in the browser. However, all language services require different computing capacities and response times to guarantee a user-friendly experience within the IDE. The monolithic distribution of all language services prevents to leverage on the available execution platforms (e.g., local platform, application server, cloud). In contrast with the current approaches that provide IDEs in the form of a monolithic client-server architecture, we explore in this paper the modularization of all language services to support their individual deployment and dynamic adaptation within an IDE. We evaluate the performance impact of the distribution of the language services across the available execution platforms on four EMF-based languages, and demonstrate the benefit of a custom distribution.
Self-Attentive Multi-Layer Aggregation with Feature Recalibration and Deep Length Normalization for Text-Independent Speaker Verification System

One of the most important parts of a text-independent speaker verification system is speaker embedding generation. Previous studies demonstrated that shortcut connections-based multi-layer aggregation improves the representational power of a speaker embedding system. However, model parameters are relatively large in number, and unspecified variations increase in the multi-layer aggregation. Therefore, in this study, we propose a self-attentive multi-layer aggregation with feature recalibration and deep length normalization for a text-independent speaker verification system. To reduce the number of model parameters, we set the ResNet with the scaled channel width and layer depth as a baseline. To control the variability in the training, we apply a self-attention mechanism to perform multi-layer aggregation with dropout regularizations and batch normalizations. Subsequently, we apply a feature recalibration layer to the aggregated feature using fully-connected layers and nonlinear activation functions. Further, deep length normalization is used on a recalibrated feature in the training process. Experimental results using the VoxCeleb1 evaluation dataset showed that the performance of the proposed methods was comparable to that of state-of-the-art models (equal error rate of 4.95% and 2.86%, using the VoxCeleb1 and VoxCeleb2 training datasets, respectively).
Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.
Reformulating Unsupervised Style Transfer as Paraphrase Generation

Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs. However, many existing systems purportedly designed for style transfer inherently warp the input's meaning through attribute transfer, which changes semantic properties such as sentiment. In this paper, we reformulate unsupervised style transfer as a paraphrase generation problem, and present a simple methodology based on fine-tuning pretrained language models on automatically generated paraphrase data. Despite its simplicity, our method significantly outperforms state-of-the-art style transfer systems on both human and automatic evaluations. We also survey 23 style transfer papers and discover that existing automatic metrics can be easily gamed and propose fixed variants. Finally, we pivot to a more real-world style transfer setting by collecting a large dataset of 15M sentences in 11 diverse styles, which we use for an in-depth analysis of our system.
Social media and english language learning during Covid-19: KILAW students’ use, attitude, and prospective

Institutions, universities, and schools suspended teaching due to the novel coronavirus called (COVID-19) Covid19 induced educational authorities in Kuwait to find such alternatives to keep the process of teaching and learning going on They resorted to the use of E-learning by utilizing social media to finalize academic year Nowadays, E-learning is heavily used in the education field due to the Covid19 pandemic Kuwait International Law School (KILAW) implemented E-learning to sustain the process of teaching and learning This research was an attempt to scrutinize the utilization of social media by KILAW students The study was quantitative in nature The researcher conducted an online questionnaire for 116 students enrolled in the English department atrial The responses of the questionnaire were analyzed by using SPSS The finding showed that KILAW students’ use of SM for learning the English language is increased dramatically due to Covid19 The participants showed a positive attitude towards the use of SM The future of using SM in the field of education, in general, and in English language learning in particular, is promising in Kuwait This study recommended universities, schools and institutes to employ SM tools to augment the process of teaching and learning © 2020, English Language Education Journals All rights reserved
Analysis and Enlightenment of User Agreements Based on the Three Smart Teaching Tools of Rain Classroom, DingTalk Live Broadcast and Tencent Classroom in the Post-epidemic Era

A sudden epidemic disrupted the established teaching plan. With the gradual in-depth development and normalization of online teaching, the transformation of online teaching in the post-epidemic era is the first to be affected. This article selects three types of smart teaching tools: Rain Classroom, DingTalk Live Broadcast and Tencent Classroom, analyzes and summarizes the technical characteristics of the three, and then uses the user agreements of the three as samples to find the similarities and differences and existing problems, and finally puts forward optimization suggestions.
Effects of the elderly care education program for caregivers in geriatric hospital: Cased-based Small Group Learning

본 연구는 노인병원 요양보호사를 위한 노인 돌봄 교육프로그램을 개발하고, 프로그램이 노인에 대한 지식, 태도 및 직업의식에 미치는 효과를 파악하고자 시도하였다. 연구대상자는 S시에 소재한 4개 의 노인 요양병원에 근무하는 요양보호사로 실험군 32명, 대조군 30명으로 총 62명이었다. 연구 진행절차는 노인 돌봄 교육프로그램을 개 발및 5주 동안 적용한 후 교육프로그램의 효과를 확인하였다. 수집된 자료는 SPSS WIN 21.0 통계프로그램을 이용하여 x² -test, t-test로 동질성 검증을 하였고, Independent t-test로 가설검증을 하였다. 연구결과 노인 돌봄 교육프로그램이 노인에 대한 지식과 직업의식에 효과가 있는 것으로 나타났으나, 노인에 대한 태도에서는 차이가 없는 것으로 나타났다. 즉, 임상 사례를 기반으로 한 소그룹 학습방법이 요양보호사의 노인에 대한 지식과 직업의식을 높이는 데 효과적인 교육방법임을 확인하였다. 따라서 요양보호사의 서비스역량을 높이고, 직업의식을 함양하기 위해 요양보호사에게 적합한 맞춤형 교육프로그램 개발과 다양한 학습방법을 적용하여 효과를 검증할 수 있는 연구의 필요성을 제안한다.The purpose of this research was to develop an elderly care education program for caregivers in the geriatric hospital and to identify the effects of the program on knowledge, attitudes of the elderly and occupational consciousness. The subjects of the study were caregivers working at four geriatric hospital located in S city, with a total of 62 participants, 32 in the experimental group and 30 in the control group. As for the research procedure, after developing the elderly care education program and applying to for 5 weeks, the effectiveness of the education program was confirmed. The collected data was verified by x²-test and t-test using the SPSS WIN 21.0 statistical program, and the hypothesis test by independent t-test. As a result of the study, the elderly care education program was founded to be effective in knowledge and occupational consciousness of the elderly, but there was no difference in attitude of the elderly. The study confirmed that the small-group learning method based on clinical cases is an effective education method for caregivers to increase knowledge of the elderly and occupational consciousness. Therefore, proposes the necessity of research that can verify the effectiveness by applying various learning methods and developing customized education programs in order to increase the service competency and occupational consciousness of the caregivers.
HiEve ACM MM Grand Challenge 2020: Pose Tracking in Crowded Scenes

This paper tackles the challenging problem of multi-person articulated tracking in crowded scenes. We propose a simple yet effective top-down crowd pose tracking algorithm. The proposed method applies Cascade-RCNN for human detection and HRNet for pose estimation. Then IOU tracking and pose distance tracking are applied successively for pose tracking. We conduct extensive ablation studies on the recently released HiEve crowd pose tracking benchmark. Our final model achieves 56.98 Multi-Object Tracking Accuracy (MOTA) without model ensembling on the HiEve test set. Our team SimpleTrack won the 3rd place in the ACM MM'2020 HiEve Challenge.
Study on weighted-based noniterative algorithms for centroid type-reduction of interval type-2 fuzzy logic systems

Interval type-2 fuzzy logic systems (IT2 FLSs) have been widely used in many areas. Among which, type-reduction (TR) is an important block for theoretical study. Noniterative algorithms do not involve the complicated iteration process and obtain the system output directly. By discovering the inner relations between discrete and continuous noniterative algorithms, this paper proposes three types of weighted-based noniterative according to the Newton-Cotes quadrature formulas in numerical integration techniques. Moreover, the continuous noniterative algorithms are considered as the benchmarks for computing. Four simulation experiments are provided to illustrate the performances of weighted-based noniterative algorithms for computing the defuzzified values of IT2 FLSs. Compared with the original noniterative algorithms, the proposed weighted-based algorithms can obtain smaller absolute errors and faster convergence speeds under the same sampling rate, which afford the potential values for designing T2 FLSs.
Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes.

Outcomes with a natural order commonly occur in prediction tasks and oftentimes the available input data are a mixture of complex data, like images, and tabular predictors. Deep Learning (DL) methods are state-of-the-art for image classification tasks but frequently treat ordinal outcomes as unordered and lack interpretability. In contrast, classical ordinal regression models consider the outcome's order and yield interpretable predictor effects but are limited to tabular data. We present ordinal neural network transformation models (ONTRAMs), which unite DL with classical ordinal regression methods. ONTRAMs are a special case of transformation models and trade off flexibility and interpretability by additively decomposing the transformation function into terms for image and tabular data using jointly trained neural networks. We discuss how to interpret model components for both tabular and image data. The proposed ONTRAMs achieve on-par performance with common DL models while being directly interpretable and more efficient in training.
The Influence of Different Stylometric Features on the Classification of Prose by Centuries

In this paper the authors compare by classification quality different types of stylometric features: low-level features that include character-based and word-based ones, and high-level rhythm features. The authors classified texts into centuries with each feature type separately and their combinations applying four classifiers: Random Forest and AdaBoost meta-algorithms, a LSTM neural network, and a GRU neural network. The experiments with three text corpora in English, Russian, and French languages showed that combining rhythm features and low-level features significantly improved quality of classification by centuries. Besides, classification results allowed to compare the styles of writing in different languages from a point of view of structure of sentences.
Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings

We describe an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text. We use multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. We validate our technique by extracting parallel sentence pairs on the BUCC 2017 bitext mining task and observe up to a 24.5 point increase (absolute) in F1 scores over previous unsupervised methods. We then improve an XLM-based unsupervised neural MT system pre-trained on Wikipedia by supplementing it with pseudo-parallel text mined from the same corpus, boosting unsupervised translation performance by up to 3.5 BLEU on the WMT'14 French-English and WMT'16 German-English tasks and outperforming the previous state-of-the-art. Finally, we enrich the IWSLT'15 English-Vietnamese corpus with pseudo-parallel Wikipedia sentence pairs, yielding a 1.2 BLEU improvement on the low-resource MT task. We demonstrate that unsupervised bitext mining is an effective way of augmenting MT datasets and complements existing techniques like initializing with pre-trained contextual embeddings.