Reviewerly/exHarmony huggingface dataset:
Impact of Accounting Information System on Internal Audit Quality: Mediating Role of Organizational Culture

This study advances the Accounting Information System (AIS) study by exploring the relationship between the AIS internal audit quality. It develops a model based on literature and considers one significant variable of mediation that is organizational culture. The AIS is expected to influence internal audit quality in different contexts, particularly in developing countries. The research model validated using AMOS and SPSS version 25. Based on a sample of 183 internal auditors in Jordanian industrial SMEs, empirical findings support the relationship between AIS and internal audit quality; however, the impact is direct while the organizational culture partially mediates this relationship. The results indicate that the AIS help enhance internal auditors' quality and the organizational culture, ultimately improving internal audit quality. The implications are useful for academics, administrators, and policymakers interested in evaluating the impact of intervening variables on the AIS and the internal audit quality relationship.
Sensorless Brushless DC Motor Control Using Type-2 Fuzzy Logic

Brushless DC motors are used in different fields today due to many advantages such as noiseless operation, maintenance cost, efficiency.In recent years, sensorless control techniques related to these engines have been developed and widely used.In addition, conventional controllers do not give the desired results as these motors are non-linear in nature.In this study, the brushless motor was used without a sensor and due to its non-linearity, the Type-2 fuzzy logic controller, which is one of the artificial intelligence techniques, was used to better resolve the uncertainties in the system.In simulation performed in Matlab -Simulink, PID, Type-1 and Type-2 Fuzzy Logic controller results were compared and as a result, it was observed that Type-2 Fuzzy Logic controller gave the most desired response in the system response.
FII-UAIC at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using CNN

The "Sentiment Analysis for Code-Mixed Social Media Text" task at the SemEval 2020 competition focuses on sentiment analysis in code-mixed social media text 1 , specifically, on the combination of English with Spanish (Spanglish) and Hindi (Hinglish).In this paper, we present a system able to classify tweets, from Spanish and English languages, into positive, negative and neutral.Firstly, we built a classifier able to provide corresponding sentiment labels.Besides the sentiment labels, we provide the language labels at the word level.Secondly, we generate a word-level representation, using Convolutional Neural Network (CNN) architecture.Our solution indicates promising results for the Sentimix Spanglish-English task (0.744), the team, Lavinia_Ap, occupied the 9th place.However, for the Sentimix Hindi-English task (0.324) the results have to be improved.
Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism

Non-autoregressive models generate target words in a parallel way, which achieve a faster decoding speed but at the sacrifice of translation accuracy.To remedy a flawed translation by non-autoregressive models, a promising approach is to train a conditional masked translation model (CMTM), and refine the generated results within several iterations.Unfortunately, such approach hardly considers the sequential dependency among target words, which inevitably results in a translation degradation.Hence, instead of solely training a Transformer-based CMTM, we propose a Self-Review Mechanism to infuse sequential information into it.Concretely, we insert a left-to-right mask to the same decoder of CMTM, and then induce it to autoregressively review whether each generated word from CMTM is supposed to be replaced or kept.The experimental results (WMT14 En↔De and WMT16 En↔Ro) demonstrate that our model uses dramatically less training computations than the typical CMTM, as well as outperforms several state-of-the-art non-autoregressive models by over 1 BLEU.Through knowledge distillation, our model even surpasses a typical left-to-right Transformer model, while significantly speeding up decoding.
BERT at SemEval-2020 Task 8: Using BERT to Analyse Meme Emotions

Sentiment analysis, being one of the most sought after research problems within Natural Language Processing (NLP) researchers.The range of problems being addressed by sentiment analysis is ever increasing.Till now, most of the research focuses on predicting sentiment, or sentiment categories like sarcasm, humor, offense and motivation on text data.But, there is very limited research that is focusing on predicting or analyzing the sentiment of internet memes.We try to address this problem as part of "Task 8 of SemEval 2020: Memotion Analysis" (Sharma et al., 2020).We have participated in all the three tasks of Memotion Analysis.Our system built using state-of-the-art pre-trained Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) performed better compared to baseline models for the two tasks A and C and performed close to the baseline model for task B. In this paper, we present the data used for training, data cleaning and preparation steps, the fine-tuning process of BERT based model and finally predict the sentiment or sentiment categories.We found that the sequence models like Long Short Term Memory(LSTM) (Hochreiter and Schmidhuber, 1997) and its variants performed below par in predicting the sentiments.We also performed a comparative analysis with other Transformer based models like DistilBERT (Sanh et al., 2019) and XLNet (Yang et al., 2019).
Palomino-Ochoa at SemEval-2020 Task 9: Robust System Based on Transformer for Code-Mixed Sentiment Classification

We present a transfer learning system to perform a mixed Spanish-English sentiment classification task.Our proposal uses the state-of-the-art language model BERT and embed it within a ULMFiT transfer learning pipeline.This combination allows us to predict the polarity detection of codemixed (English-Spanish) tweets.Thus, among 29 submitted systems, our approach (referred to as dplominop) is ranked 4th on the Sentimix Spanglish test set of SemEval 2020 Task 9.In fact, our system yields the weighted-F1 score value of 0.755 which can be easily
Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM

Social media has become a new method of today’s communication in a new digitalize era. Children and adults have used social media a lot in interacting with others. Therefore social media has shifted conventional communication into digital one. This digital development on social media is a serious problem that must be faced because it has been found that there are more and more acts of cyberbullying. This act of cyberbullying can attack the psychic, causing depression up to suicide. The dangers of cyberbullying are troubling and cause concern to the community. Therefore, this study will analyze the sentiment on the comments contained on social media to find out the value of sentiment from comments on social media platforms. The comment data will be processed at the preprocessing stage, Term Frequency-Inverse Document Frequency (TF-IDF), and the Support Vector Machine (SVM) classification method. Comment data to be classified as 1500 data taken using crawling data through libraries in python programming and divided into 80% data training and 20% data testing. Based on the results of the test, the accuracy value is 93%, the precision value is 95%, and the recall value is 97%. In this research, a system model design is also carried out where the system can be integrated with the browser to open a user page on the classification of comments that have been input into the system.
How Accurate Are Satellite-Derived Surface Solar Radiation Products over Tropical Oceans?

Abstract Surface solar radiation (SSR) over the ocean is essential for studies of ocean–atmosphere interactions and marine ecology, and satellite remote sensing is a major way to obtain the SSR over ocean. A new high-resolution (10 km; 3 h) SSR product has recently been developed, mainly based on the newly released cloud product of the International Satellite Cloud Climatology Project H series (ISCCP-HXG), and is available for the period from July 1983 to December 2018. In this study, we compared this SSR product with in situ observations from 70 buoy sites in the Global Tropical Moored Buoy Array (GTMBA) and also compared it with another well-known satellite-derived SSR product from the Clouds and the Earth’s Radiant Energy System (CERES; edition 4.1), which has a spatial resolution of approximately 100 km. The results show that the ISCCP-HXG SSR product is generally more accurate than the CERES SSR product for both ocean and land surfaces. We also found that the accuracy of both satellite-derived SSR products (ISCCP-HXG and CRERS) was higher over ocean than over land and that the accuracy of ISCCP-HXG SSR improves greatly when the spatial resolution of the product is coarsened to ≥ 30 km.
Hourly Surface Observations Suggest Stronger Solar Dimming and Brightening at Sunrise and Sunset Over China

Abstract Exploration of the diurnal pattern of global dimming and brightening has been limited by the paucity of high‐temporal resolution observations. Based on 22 years’ continuous observations of hourly surface solar radiation (SSR) over 96 stations across China for 1993–2014, this study evidences higher relative changes in diurnal SSR in terms of both frequency and magnitude at sunrise and sunset, with a longer pathway for the sun passing through the atmosphere, than the hours in between. This, in general, leads to a further shortening of the days between sunrise and sunset in the cold seasons and the polluted region of north China with low radiation levels but a further extension in the other seasons and regions with sufficient sunlight. Pollution reduction by 39.4%–69.7% during the 2014 Asia‐Pacific Economic Cooperation summit at Beijing are accompanied with a 10.4–80.0% rebound of diurnal SSR in the forenoon and late afternoon.
Quantifying the Predictability of Visual Scanpaths Using Active Information Storage

Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of a visual scanpath as the entropy of transitions between fixations and has been shown to correlate with changes in task demand or changes in observer state. Measuring scanpath predictability is thus a promising approach to identifying viewers’ cognitive states in behavioral experiments or gaze-based applications. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate the actual predictability of the current fixation given past gaze behavior. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes’ multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human–machine interaction.
Learning Management System (LMS) pada E-Learning Menggunakan Metode Agile dan Waterfall berbasis Website

The education information delivery system at the MA Alwutsqo school is still being carried out as usual even though the pandemic outbreak in Indonesia is still not over. Because the school does not have a system to support distance learning. By implementing health protocols and teaching and learning time events, students often do not get an understanding of the material presented by the teacher. Learning Management System (LMS) is one of the systems needed by the school. Therefore, the authors designed a website-based E-learning application system with the aim of helping the learning and teaching process at MA Alwutsqo Depok City. The design of this information system uses waterfalls and system development is carried out using the Agile method. The result is that students can download the material that has been delivered and can do the exercises given by the teacher without recommendations by time and space.Keywords:E-learning, Webite, Metode Waterfall, Metode Agile, Learning Management System (LMS).
The Influence of National Music on the Reform of College Music Piano Teaching

The unique value connotation and emotions exhibited by ethnic music itself can play a positive role in improving people's artistic cultivation and aesthetic level. Piano classroom is the main content of music education in colleges and universities. In order to ensure the orderly conduct of classroom teaching activities, teachers can combine the actual teaching and actively introduce folk music into it, so that students can learn through the content of classroom knowledge, which guarantees the interest of learning Enhancement can also establish the correct sense of pride of the clan, and then grow into the music talents that the country needs.
iCompass at SemEval-2020 Task 12: From a Syntax-ignorant N-gram Embeddings Model to a Deep Bidirectional Language Model

We describe our submitted system to the SemEval 2020.We tackled Task 12 entitled "Multilingual Offensive Language Identification in Social Media", specifically subtask 4A-Arabic.We propose three Arabic offensive language identification models: Tw-StAR, BERT and BERT+BiLSTM.Two Arabic abusive/hate datasets were added to the training dataset: L-HSAB and T-HSAB.The final submission was chosen based on the best performances which was achieved by the BERT+BiLSTM model.
THE VARIETY GENERATED BY AN AI-SEMIRING OF ORDER THREE

Up to isomorphism, there are 61 ai-semirings of order three. The finite basis problem for these semirings is investigated. This problem for 45 semirings of them is answered by some results in the literature. The remaining semirings are studied using equational logic. It is shown that with the possible exception of the semiring \(S_7\), all ai-semirings of order three are finitely based.
Efficient qubit phase estimation using adaptive measurements

Estimating correctly the quantum phase of a physical system is a central problem in quantum parameter estimation theory due to its wide range of applications from quantum metrology to cryptography. Ideally, the optimal quantum estimator is given by the so-called quantum Cram\'er-Rao bound, so any measurement strategy aims to obtain estimations as close as possible to it. However, more often than not, the current state-of-the-art methods to estimate quantum phases fail to reach this bound as they rely on maximum likelihood estimators of non-identifiable likelihood functions. In this work we thoroughly review various schemes for estimating the phase of a qubit, identifying the underlying problem which prohibits these methods to reach the quantum Cram\'er-Rao bound, and propose a new adaptive scheme based on covariant measurements to circumvent this problem. Our findings are carefully checked by Monte Carlo simulations, showing that the method we propose is both mathematically and experimentally more realistic and more efficient than the methods currently available.
SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models

This paper describes the participation of SINAI team at Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media.In particular, the participation in Sub-task A in English which consists of identifying tweets as offensive or not offensive.We preprocess the dataset according to the language characteristics used on social media.Then, we select a small set from the training set provided by the organizers and fine-tune different Transformerbased models in order to test their effectiveness.Our team ranks 20th out of 85 participants in Subtask-A using the XLNet model.
Combating the hate speech in Thai textual memes

&lt;span&gt;Thai textual memes have been popular in social media, as a form of image information summarization. Unfortunately, many memes contain some hateful content that easily causes the controversy in Thailand. &lt;/span&gt;&lt;span&gt;For global protection, t&lt;/span&gt;&lt;span&gt;he &lt;/span&gt;&lt;em&gt;&lt;span&gt;Hateful Memes Challenge&lt;/span&gt;&lt;/em&gt;&lt;span&gt; is also provided by &lt;/span&gt;&lt;em&gt;&lt;span&gt;Facebook AI&lt;/span&gt;&lt;/em&gt;&lt;span&gt; to enable researchers to compete their algorithms for combating the hate speech on memes as one of &lt;/span&gt;&lt;em&gt;&lt;span&gt;NeurIPS’20&lt;/span&gt;&lt;/em&gt;&lt;span&gt; competitions. As well as in Thailand, this paper introduces the Thai textual meme detection as a new research problem in Thai natural language processing (Thai-NLP) that is the settlement of transmission linkage between scene text localization, Thai optical recognition (Thai-OCR) and language understanding. From the results, both regular and irregular text position can be localized by one-stage detection pipeline. More scene text can be augmented by different resolution and rotation. The accuracy of Thai-OCR using convolutional neural network (CNN) can be improved by recurrent neural network (RNN). Since misspelling Thai words are frequently used in social, this paper categorizes them as synonyms to train on multi-task pre-trained language model. &lt;/span&gt;
Prognostic Significance of Microvessel Density as Assessed by anti CD34 Monoclonal Antibody in Invasive Ductal Carcinoma of Breast

Context and Aim: Breast carcinoma is one of the leading causes of cancer related mortality in females. A number of established prognostic indicators exist for breast cancer. One potential indicator of adverse prognosis in breast cancer is tumor induced angiogenesis. Hematogenous spread of tumor cells is quantitatively related to microvessel density (MVD). This study aims to find possible association of microvessel density with other recognized prognostic factor such as patient age, tumor size, lymph node status, histologic grade, Nottingham Prognostic Index (NPI), vascular invasion, hormone receptor status and HER2/Neu expression in breast carcinoma.Methods and Materials: This is a cross sectional, laboratory based descriptive type of observational study. This study was carried out in the Department of Pathology, SMS medical college. In our study we did histological evaluation of mastectomy specimen of breast cancer including ER, PR and Her 2 status and calculated the microvessel density (MVD) in appropriate section by applying IHC. MVD correlation with other clinicopathological parameters was done. Stastical analysis used in this study include Fischer Exact test and Chi Square test. Results: A significant correlation was obtained between MVD and tumor size, lymph node metastasis, lymphovascular invasion, histologic grade and NPI (P value of 0.01). Conclusions: Angiogenesis is an important component of cancer growth, invasion and metastasis. Therefore, inhibition of angiogenesis is an attractive strategy for treatment of cancer. The correlation of MVD with prognosis suggests the utility of antiangiogenic drugs in breast cancer patients with high angiogenesis
UR_NLP @ HaSpeeDe 2 at EVALITA 2020: Towards Robust Hate Speech Detection with Contextual Embeddings

In this paper we describe the system used for the participation to the ABSITA, GxG, HaSpeeDe and IronITA shared tasks of the EVALITA 2018 conference.We developed a classifier that can be configured to use Bidirectional Long Short Term Memories and linear Support Vector Machines as learning algorithms.When using Bi-LSTMs we tested a multitask learning approach which learns the optimized parameters of the network exploiting simultaneously all the annotated dataset labels and a multiclassifier voting approach based on a k-fold technique.In addition, we developed generic and specific word embedding lexicons to further improve classification performances.When evaluated on the official test sets, our system ranked 1st in almost all subtasks for each shared task, showing the effectiveness of our approach.
YNU_OXZ @ HaSpeeDe 2 and AMI : XLM-RoBERTa with Ordered Neurons LSTM for classification task at EVALITA 2020

This paper describes the system that team YNU_OXZ submitted for EVALITA 2020. We participate in the shared task on Automatic Misogyny Identification (AMI) and Hate Speech Detection (HaSpeeDe 2) at the 7th evaluation campaign EVALITA 2020. For HaSpeeDe 2, we participate in Task A - Hate Speech Detection and submitted two-run results for the news headline test and tweets headline test, respectively. Our submitted run is based on the pre-trained multi-language model XLM-RoBERTa, and input into Convolution Neural Network and K-max Pooling (CNN + K-max Pooling). Then, an Ordered Neurons LSTM (ON-LSTM) is added to the previous representation and submitted to a linear decision function. Regarding the AMI shared task for the automatic identification of misogynous content in the Italian language. We participate in subtask A about Misogyny & Aggressive Behaviour Identification. Our system is similar to the one defined for HaSpeeDe and is based on the pre-trained multi-language model XLM-RoBERTa, an Ordered Neurons LSTM (ON-LSTM), a Capsule Network, and a final classifier.
Cyber Security Solutions for Businesses in Financial Services

This paper examines the challenges that small, medium, and large businesses in the financial services industry are facing concerning data security and providing relevant tools and strategies to protect the same. A qualitative research-based approach has been used where one-on-one interviews were conducted with 10 CIOs (chief information officers) and CISOs (chief information security officers). This data was compared with secondary data sources to validate the findings. This paper presents an in-depth analysis regarding security technologies and their efficacy to protect data assets and sensitive information. It will also opine about the technologies that each business type can use economically to cover the gamut of cyber-attacks. Existing research is restricted to either addressing small and medium businesses (SMBs) or large businesses. This paper attempts a comprehensive review for all sizes of businesses.
A Comparison of Machine Learning and Deep Learning Methods with Rule Based Features for Mixed Emotion Analysis

Multi-class classification of sentiments from text data still remains a challenging task to detect the sentiments hidden behind the sentences because of the probable existence of multiple meanings for some of the texts in the dataset. To overcome this, the proposed rule based modified Convolutional neural network-Global Vectors (RCNN-GloVe) and rule-based modified Support Vector Machine - Global Vectors (RSVM-GloVe) were developed for classifying the twitter complex sentences at twelve different levels focusing on mixed emotions by targeting abstract nouns and adjective emotion words. To execute this, three proposed algorithms were developed such as the optimized abstract noun algorithm (OABNA) to identify the abstract noun emotion words, optimized complex sentences algorithm (OCSA) to extract all the complex sentences in a tweet precisely and adjective searching algorithm (ADJSA) to retrieve all the sentences with adjectives. The results of this study indicates that our proposed RCNNGloVe method used in the sentiment analysis was able to classify the mixed emotions accurately from the twitter dataset with the highest accuracy level of 92.02% in abstract nouns and 88.93% in adjectives. It is distinctly evident from the research that the proposed deep learning model (RCNN-GloVe) had an edge over the machine learning model (RSVM-GloVe).