Reviewerly/exHarmony huggingface dataset:
The search for national identity in the discourse analysis of YouTube comments

YouTube is a popular social media site that provides the users a space to view, upload video clips or log in and write comments. Even though many of the YouTube users remain anonymous when commenting, their language use in the comments can reflect the identity of a particular community. Therefore, a variety of computer-mediated discourse can be obtained from YouTube in deciphering a specific community or a country’s identity. This qualitative study is keen to investigate the representation of the Malaysian identity in the YouTube comments. Analysis were performed on the YouTube comments of Malaysian-related YouTube videos uploaded by famous Malaysian YouTubers. Textual analysis and coding of the YouTube comments were carried out using the NVivo software based on Herring’s (2004) Computer-Mediated Discourse Analysis (CMDA) and Braun &amp; Clarke’s (2006) Thematic Analysis framework. Findings showed that there were various themes that were repeated in the YouTube comments discourse which is a representation of the Malaysian identities, namely feelings of love towards the country, Malaysian cuisine, language mix, being multiracial, Malaysian behaviours or stereotypes, and the birth country Malaysia. These results provide insights into the discourse that are associated with Malaysian identities and contributed knowledge to fill in the gap in literature regarding national identity in social media, particularly Malaysia.
The Role of Continuous Improvement Strategy (Kaizen) in organizational innovation: An analytical research in the General Company of Electrical and Electronic Industries /The Ministry of Industry and Minerals

This paper aims at the fact that most organizations today suffer from a waste of time, effort, and cost, and they have difficulty in achieving the best performance situations and compete strongly. The researcher distributed 108 questionnaires as a statistical analyzable sample society where the sample intentionally consists of general managers, department head, and division head. The questionnaire was formulated according to the Likert scale. The use of personal interviews and observations are additional tools for data collection and a number of statistical methods is used for data analysis such as simple regression and correlation coefficient (Pearson). One of the most prominent conclusions is that the company has adequate and creative capabilities on the ground and for the production of Iraqi does not have competitive and creative capabilities and must work on the development of new methods in the administrative work within the company. The results also show the lack of conviction difficulties and turn them into opportunities that promote creative work, expand growth opportunities and create cadres that start creative work and experience the element of risk. Engaging employees in training courses can acquire or increase their skills
Blockchain &amp;amp; EdTech: Emerging Opportunities for Enhancing the Scholarly Value Chain

Blockchain has proven to be a disruptive technology for many market verticals and education is one such large and growing market with significant social, economic, and technical challenges. Through 2021, blockchain technologies have emerged as a potential growth area in educational technologies (EdTech). This preliminary market analysis addresses some current challenges in higher education, surveys and analyses the edtech market, and makes recommendations for enhancing the scholarly value chain through blockchain technologies. We conclude that while blockchain offers many interesting opportunities for higher education it has not yet been widely deployed and faces significant challenges to adoption.
Sentiment Analysis With Sarcasm Detection On Politician’s Instagram

Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method on sentiment analysis process. On Salles, dkk (2018) research, in some cases Random Forest outperform the performance by Support Vector Machine that known as a superior method. In this research, we did sentiment analysis on comment section on Instagram account of Indonesian politician. This research compare the accuracy of sentiment analysis with sarcasm detection and analysis sentiment without sarcasm detection, sentiment analysis with Naïve Bayes and Random Forest method then Random Forest for sarcasm detection. This research resulted in accuracy value in sentiment analysis without sarcasm detection with Naïve Bayes 61%, with Random Forest method 72%. Accuracy on sentiment analysis with sarcasm detection using Naïve Bayes – Random Forest method is 60% and using Random Forest – Random Forest method is 71%.
Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have allowed medical researchers and clinicians to examine the structural and functional features of the human body, thereby assisting the clinical diagnosis. However, due to the highly controlled imaging environment, the imaging process often creates noise, which seriously affects the analysis of the medical images. In this study, a medical imaging enhancement algorithm is presented for ankle joint talar osteochondral injury. The gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is employed to replace the gradient to determine the diffusion coefficient of the gradient field. The differential operator is used to discretize the image, and a partial differential enhancement model is constructed to achieve image detail enhancement. Three objective evaluation indexes, namely, signal-to-noise ratio (SNR), information entropy (IE), and edge protection index (EPI), were employed to evaluate the image enhancement capability of the proposed algorithm. Experimental results show that the algorithm can better suppress noise while enhancing image details. Compared with the original image, the histogram of the transformed image is more uniform and flat and the gray level is clearer.
Development of a hyperparameter optimization method for recommendatory models based on matrix factorization

Many advanced recommendatory models are implemented using matrix factorization algorithms. Experiments show that the quality of their performance depends significantly on the selected hyperparameters. Analysis of the effectiveness of using various methods for solving this problem of optimizing hyperparameters was made. It has shown that the use of classical Bayesian optimization which treats the model as a «black box» remains the standard solution. However, the models based on matrix factorization have a number of characteristic features. Their use makes it possible to introduce changes in the optimization process leading to a decrease in the time required to find the sought points without losing quality. Modification of the Gaussian process core which is used as a surrogate model for the loss function when performing the Bayesian optimization was proposed. The described modification at first iterations increases the variance of the values predicted by the Gaussian process over a given region of the hyperparameter space. In some cases, this makes it possible to obtain more information about the real form of the investigated loss function in less time. Experiments were carried out using well-known data sets for recommendatory systems. Total optimization time when applying the modification was reduced by 16 % (or 263 seconds) at best and remained the same at worst (less than 1-second difference). In this case, the expected error of the recommendatory model did not change (the absolute difference in values is two orders of magnitude lower than the value of error reduction in the optimization process). Thus, the use of the proposed modification contributes to finding a better set of hyperparameters in less time without loss of quality
GooAQ: Open Question Answering with Diverse Answer Types

While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ questions are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ answers are mined from Google’s responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. We benchmark T5 models on GooAQ and observe that: (a) in line with recent work, LM’s strong performance on GooAQ’s short-answer questions heavily benefit from annotated data; however, (b) their quality in generating coherent and accurate responses for questions requiring long responses (such as ‘how’ and ‘why’ questions) is less reliant on observing annotated data and mainly supported by their pre-training. We release GooAQ to facilitate further research on improving QA with diverse response types.
Tycho: Facilitation Support for Groupware User Tests

Running user tests for groupware requires tracking the progress of multiple users performing co-dependent tasks, while facilitating and observing their coordinated actions. This requirement negatively impacts the main objective of user testing, which is detecting usability flaws. User testing of groupware becomes more challenging when run remotely. Even if there are tools for remote user testing that can get the job done for a single user, they are not prepared for collaborative scenarios. In this paper we argue that tool support for the facilitation of user tests of groupware is missing. Consequently, we propose a method for user tests with tool assistance that makes it possible to automate task synchronization, especially for different workflows that must be run concurrently. We evaluated our proposal by comparing it to a manually facilitated approach during on-site coordinated user tests. The results indicate that, while the task of designing and running user tests with tool support takes more time, it allows the testers to better focus on detecting usability problems.
Evaluating Extractive Automatic Text Summarization Techniques in Spanish

Due to the large amount of data published on the Internet, the tasks related to the automatic generation of summaries from unstructured sources have gained enormous popularity in recent years. For instance, its applications are media monitoring, newsletter generation, legal document analysis, virtual assistants that can summarize email overload, e-learning, or patent research among others. One popular approach for generating the summaries is extractive summarization, that extracts the most meaningful keywords in a document and presents them to the reader comprehensively. To the best of our knowledge, there is a lack of studies that have evaluated extractive text summarization techniques in Spanish, specially novel techniques based on state-of-the-art transformers. Consequently, we perform a benchmark of traditional and recent approaches for conducting text summarization with the Corpus-TER dataset, that consists in 240 Mexican-Spanish news articles. Our preliminary results suggest that word embeddings from Word2Vec achieves the best results based on ROUGE-1, BLEU and edit distance metrics.
On Generating Fact-Infused Question Variations

To fully model human-like ability to ask questions, automatic question generation (QG) models must be able to produce multiple expressions of the same question with different levels of detail.Unfortunately, existing datasets available for learning QG do not include paraphrases or question variations affecting a model's ability to learn this capability.We present FIRS, a dataset containing humangenerated fact-infused rewrites of questions from the widely-used SQuAD dataset to address this limitation.Questions in FIRS were obtained by combining a given question with facts of entities referenced in the question.We study a double encoder-decoder model, Fact-Infused Question Generator (FIQG ), for learning to generate fact-infused questions from a given question.Experimental results show that FIQG effectively incorporates information from facts to add more detail to a given question.To the best of our knowledge, ours is the first study to present fact-infusion as a novel form of question paraphrasing.
Rationales for Sequential Predictions

Sequence models are a critical component of modern NLP systems, but their predictions are difficult to explain. We consider model explanations though rationales, subsets of context that can explain individual model predictions. We find sequential rationales by solving a combinatorial optimization: the best rationale is the smallest subset of input tokens that would predict the same output as the full sequence. Enumerating all subsets is intractable, so we propose an efficient greedy algorithm to approximate this objective. The algorithm, which is called greedy rationalization, applies to any model. For this approach to be effective, the model should form compatible conditional distributions when making predictions on incomplete subsets of the context. This condition can be enforced with a short fine-tuning step. We study greedy rationalization on language modeling and machine translation. Compared to existing baselines, greedy rationalization is best at optimizing the sequential objective and provides the most faithful rationales. On a new dataset of annotated sequential rationales, greedy rationales are most similar to human rationales.
Research on Kinematics and Trajectory Planning of SCARA Robot

Aiming at the problems of track unevenness and acceleration discontinuity in trajectory planning of SCARA robot, the kinematics modeling and trajectory planning of SCARA robot were studied. The forward and reverse kinematics models of the robot were set up by D-H parameter method and analytical method. The characteristics of cubic polynomial interpolation algorithm and quintic polynomial interpolation algorithm were analyzed. The two interpolation algorithms were compared in trajectory planning of joint angle change, velocity change and acceleration change. The problem of point-to-point(PTP) trajectory planning was studied by quintic polynomial interpolation algorithm. The validity of the modeling method was verified by the simulation of Robotics Toolbox.The conclusion that the robot could run smoothly and safely was verified by the generated curve.
Design of a BIST implemented AES crypto-processor ASIC

This paper presents the design of a Built-in-self-Test (BIST) implemented Advanced Encryption Standard (AES) cryptoprocessor Application Specific Integrated Circuit (ASIC). AES has been proved as the strongest symmetric encryption algorithm declared by USA Govt. and it outperforms all other existing cryptographic algorithms. Its hardware implementation offers much higher speed and physical security than that of its software implementation. Due to this reason, a number of AES cryptoprocessor ASIC have been presented in the literature, but the problem of testability in the complex AES chip is not addressed yet. This research introduces a solution to the problem for the AES cryptoprocessor ASIC implementing mixed-mode BIST technique, a hybrid of pseudo-random and deterministic techniques. The BIST implemented ASIC is designed using IEEE industry standard Hardware Description Language(HDL). It has been simulated using Electronic Design Automation (EDA)tools for verification and validation using the input-output data from the National Institute of Standard and Technology (NIST) of the USA Govt. The simulation results show that the design is working as per desired functionalities in different modes of operation of the ASIC. The current research is compared with those of other researchers, and it shows that it is unique in terms of BIST implementation into the ASIC chip.
Proportional representation to increase data utility in k-anonymous tables

The increasing number of published data has allowed the development of data mining, resting on the use of the data to extract knowledge. At the same time, to tackle privacy concerns, anonymization models such as k-anonymity have emerged. Because k-anonymity transforms original data, there is an impact on the utility of altered data for data mining. In this paper, we propose a new writing of the anonymous tables using an anonymization post-treatment. The proposed representation allows to keep more information on the distribution of the original values in the anonymous equivalence classes while being usable directly as input for neural networks for data mining purposes. We test our experimental protocol on two data sets from anonymization research field: Adult data set and an extract from the register of voters of Florida (USA). With these experiments, we show the superiority in data utility of our approach against classical approaches.
Ontology Based Text Classifier for Information Extraction from Coronavirus Literature

The world is fighting an unprecedented coronavirus pandemic, and no country was prepared for it. Understanding the nature of this disease, when there is no available cure, is vital to encourage accurate clinical diagnosis and drug discovery prospects. When the amount of literature available is vast, it is important to represent the disease domain as completely as possible. The system should capture the morphology, semantics, syntax, and pragmatics of the given literature, in order to extract useful information. Also, building a classifier for a particular domain suffers from a zero frequency issue. To solve this effectively, latent topics are extracted and semantically represented in ontology to build a text classifier for coronavirus literature. The classifier is equipped with 2 components- ‘ontology’ and ‘machine learning data model’. Ontology helps to model the morphology and the semantic and pragmatic aspects of the text data through Latent Drichlet Allocation (LDA). It also preserves the contextual information in the document space, providing holistic feature representation facilities. To solve zero frequency and to extract actionable insights, a machine learning algorithm, Multi class Support Vector Machine (M-SVM), is incorporated with the ontology. It encodes features and achieves a classifier with highly discriminated classes. Further, to preserve contextual information space, and to enable data model formulation, the ontology is generated as a knowledge graph with their respective predefined classes. The resulting dataset can be used for clinical diagnosis and further research on the disease. Experimental results have shown that the proposed classifier outperforms the existing systems, with better domain representation. HIGHLIGHTS When the amount of literature available is vast, it is important to represent the disease domain as completely as possible. The system should capture the morphology, semantics, syntax, and pragmatics of the given literature, in order to extract useful information The classifier is equipped with 2 components- ‘ontology’ and ‘machine learning data model’. Ontology helps to model the morphology and the semantic and pragmatic aspects of the text data through Latent Drichlet Allocation (LDA). It also preserves the contextual information in the document space, providing holistic feature representation facilities To preserve contextual information space, and to enable data model formulation, the ontology is generated as a knowledge graph with their respective predefined classes. The resulting dataset can be used for clinical diagnosis and further research on the disease GRAPHICAL ABSTRACT
Semi-Supervised and Unsupervised Sense Annotation via Translations

Acquisition of multilingual training data continues to be a challenge in word sense disambiguation (WSD).To address this problem, unsupervised approaches have been proposed to automatically generate sense annotations for training supervised WSD systems.We present three new methods for creating sense-annotated corpora which leverage translations, parallel bitexts, lexical resources, as well as contextual and synset embeddings.Our semi-supervised method applies machine translation to transfer existing sense annotations to other languages.Our two unsupervised methods refine sense annotations produced by a knowledge-based WSD system via lexical translations in a parallel corpus.We obtain state-of-the-art results on standard WSD benchmarks.
Determinants of Commercial Banks Deposit Growth Evidence from Ethiopian Commercial Banks

The purpose of this research is to examine major causes of deposit growth in commercial banks in Ethiopia with explicit inference on industry specific and Macro-Economic variables.The research used secondary data from 2010-2019.Macro-economic factors selected under this study consist of age dependency ratio, unemployment rate, population growth, broad money supply, and Inflation.While bank-specific variables included are branch expansion and bank size.Since the study employed panel data in line with the nature and data of the study ordinary least square method estimation were used subsequently after the necessary diagnostic tests and Hausman test performed to determine the appropriateness of fixed effect.The result of the study indicate branch macroeconomic factors such as bank size, broad money supply, and inflation significant positive effect on deposit growth of commercial banks.Contrarily, Age dependency ratio and population growth have a statistically significant negative effect on deposit growth.The unemployment rate, on the other hand, has been found to have a positive but statistically insignificant relationship.The bank-specific factors indicate the branch expansion has a positive significant association while Bank size has a positive but insignificant relationship with deposit growth of commercial banks.Finally, the study suggests there should be a stable macroeconomic environment that reduces the effect of inflation, money supply, population growth, and from a bank-specific perspective aggressive branch expansion is essential for the growth of deposits in commercial banks in Ethiopia.
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time

From CNNs to attention mechanisms, encoding inductive biases into neural networks has been a fruitful source of improvement in machine learning. Auxiliary losses are a general way of encoding biases in order to help networks learn better representations by adding extra terms to the loss function. However, since they are minimized on the training data, they suffer from the same generalization gap as regular task losses. Moreover, by changing the loss function, the network is optimizing a different objective than the one we care about. In this work we solve both problems: first, we take inspiration from transductive learning and note that, after receiving an input but before making a prediction, we can fine-tune our models on any unsupervised objective. We call this process tailoring, because we customize the model to each input. Second, we formulate a nested optimization (similar to those in meta-learning) and train our models to perform well on the task loss after adapting to the tailoring loss. The advantages of tailoring and meta-tailoring are discussed theoretically and demonstrated empirically on several diverse examples: encoding inductive conservation laws from physics to improve predictions, improving local smoothness to increase robustness to adversarial examples, and using contrastive losses on the query image to improve generalization.