Reviewerly/exHarmony huggingface dataset:
A TWO-CHANNEL MODEL FOR REPRESENTATION LEARNING IN VIETNAMESE SENTIMENT CLASSIFICATION PROBLEM

Sentiment classification (SC) aims to determine whether a document conveys a positive or negative opinion. Due to the rapid development of the digital world, SC has become an important research topic that affects many aspects of our life. In SC based on machine learning, the representation of the document strongly influences on its accuracy. Word Embedding (WE)-based techniques, i.e., Word2vec techniques, are proved to be beneficial techniques to the SC problem. However, Word2vec is often not enough to represent the semantic of documents with complex sentences of Vietnamese. In this paper, we propose a new representation learning model called a \textbf{two-channel vector} to learn a higher-level feature of a document in SC. Our model uses two neural networks to learn the semantic feature, i.e., Word2vec and the syntactic feature, i.e., Part of Speech tag (POS). Two features are then combined and input to a \textit{Softmax} function to make the final classification. We carry out intensive experiments on $4$ recent Vietnamese sentiment datasets to evaluate the performance of the proposed architecture. The experimental results demonstrate that the proposed model can significantly enhance the accuracy of SC problems compared to two single models and a state-of-the-art ensemble method.
An Empirical Study of Generating Texts for Search Engine Advertising

Although there are many studies on neural language generation (NLG), few trials are put into practice, particularly in the advertising domain.Generating ads with NLG models can help copywriters in their creation.However, few studies have adequately evaluated the effect of generated ads with actual serving because this requires a large amount of training data and a particular environment.In this study, we demonstrate a practical use case of generating ad-text with an NLG model.Particularly, we show how to improve the ads' impact, deploy models to a product, and evaluate the generated ads.* Equal contribution. InputLP Content: Introducing popular insurance services in a ranking format!You can compare insurance services in the largest domestic comparison site of insurance A operated by B in various forms, such as by the order of request for information and the order of application by type of insurance.
Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures

The #MeToo movement on social media platforms initiated discussions over several facets of sexual harassment in our society.Prior work by the NLP community for automated identification of the narratives related to sexual abuse disclosures barely explored this social phenomenon as an independent task.However, emotional attributes associated with textual conversations related to the #MeToo social movement are complexly intertwined with such narratives.We formulate the task of identifying narratives related to the sexual abuse disclosures in online posts as a joint modeling task that leverages their emotional attributes through multitask learning.Our results demonstrate that positive knowledge transfer via context-specific shared representations of a flexible cross-stitched parameter sharing model helps establish the inherent benefit of jointly modeling tasks related to sexual abuse disclosures with emotion classification from the text in homogeneous and heterogeneous settings.We show how for more domain-specific tasks related to sexual abuse disclosures such as sarcasm identification and dialogue act (refutation, justification, allegation) classification, homogeneous multitask learning is helpful, whereas for more general tasks such as stance and hate speech detection, heterogeneous multitask learning with emotion classification works better. 1
Experiments on a Guarani Corpus of News and Social Media

While Guarani is widely spoken in South America, obtaining a large amount of Guarani text from the web is hard.We present the building process of a Guarani corpus composed of a parallel Guarani-Spanish set of news articles, and a monolingual set of tweets.We perform some word embeddings experiments aiming at evaluating the quality of the Guarani split of the corpus, finding encouraging results but noticing that more diversity in text domains might be needed for further improvements.
Contemporary tendencies in higher education – application of artificial intelligence

In the 21st century, artificial intelligence has undergone unprecedented development as a result of advances in computing power, the ability to accumulate, store and access vast amounts of data, as well as from a deeper theoretical understanding. This brings huge benefits to education, business and industry, where fundamental changes in attitudes, patterns of learning and the use of artificial intelligence to anticipate, monitor and manage risk in the dynamics of globalization and automation are observed. This article aims to explore the possibility of current trends in the development of digital technologies and globalization. The subject of the study is whether universities work in good coordination in order to be adequately prepared for the inevitable changes in society and the economy. Research methods that are used are general and specific, namely: normative, systematic, comparative analysis, induction and deduction The main task is to encourage universities to continue with their adaptation and development of their online distance learning systems, greater individualization of students’ academic development, rapid exchange and application of good practices in the processes of education, management and research.
Modified Filter Equation with Improved Fuzzy Logic System Based Directional Median Filter for Mixed Noise

DWM (Directional weighted median) filter is very popular in filtering digital image and remove mixed noise. Fuzzy logic is implemented with median filters to improve its performance. In the previous work, fuzzy logic system is implemented with switching median filter and gives better performance than directional median filter as well as switching median filter. Experimenting directional median filter with same fuzzy logic system didn’t yield to better results therefore fuzzy logic parameters has been changes as per strong points of directional weighted median filter and a constant has been included in the filtering equation to improve the results. So in this proposed work, we have successfully implemented directional weighted median filter with fuzzy logic system which is proving better results than DWM and FSMF (Fuzzy Switching Median Filter). PSNR (Peak Signal to Noise Ratio)is used for qualitative analysis of results.
Effectiveness of Human Detection from Aerial Images Taken from Different Heights

Recently, drones have been regularly used to aid in search and rescue in places where it is normally to carry out some of the early forensic victim localization. There are many suitable human detectors for drone use, such as Histogram Oriented Gradient (HOG), You Only Looks Once (YOLO), and Aggregate Channel Features (ACF). In this paper, the height of the aerial images is analyzed for its effect on the accuracy of the detection. This works compares ACF, YOLO MobileNet, and YOLO ResNet50 using a different set of aerial images varying at 10m, 20m, and 30m heights. The results show that in a single-model test, with our proposed bounding-box standardization, YOLO MobileNet achieves significant increase in test precision (AP), with 0.7 AP recorded. For single-model test, YOLO MobileNet yield best AP using 20m training data where it obtained AP of 0.88 (10m test height), 0.82 (20m test height), and 0.91 (30m test height).
P-Norm Attention Deep CORAL: Extending Correlation Alignment Using Attention and the P-Norm Loss Function

CORrelation ALignment (CORAL) is an unsupervised domain adaptation method that uses a linear transformation to align the covariances of source and target domains. Deep CORAL extends CORAL with a nonlinear transformation using a deep neural network and adds CORAL loss as a part of the total loss to align the covariances of source and target domains. However, there are still two problems to be solved in Deep CORAL: features extracted from AlexNet are not always a good representation of the original data, as well as joint training combined with both the classification and CORAL loss may not be efficient enough to align the distribution of the source and target domain. In this paper, we proposed two strategies: attention to improve the quality of feature maps and the p-norm loss function to align the distribution of the source and target features, further reducing the offset caused by the classification loss function. Experiments on the Office-31 dataset indicate that our proposed methodologies improved Deep CORAL in terms of performance.
tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets

The stance detection task aims at detecting the stance of a tweet or a text for a target.These targets can be named entities or free-form sentences (claims).Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence.Specifically, a simple tweet classification model achieved human-level performance on the WT-WT dataset and more than two-third accuracy on various other datasets.We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category.Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems.Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets.
Qualitative Assessment of Digital Forensic Tools

Forensic science is a study of science to criminals and civil laws. Digital forensics is the part of forensic science relating to proof found in computers and advanced storage media. Forensic examiners gather, protect and break down logical confirmations over the span of examination. Digital information contains data as content, pictures, sound, video and so on. These days numerous cybercrime cases, for example, hacking, banking cheats, phishing, email spamming, etc., have developed which are connected with a computerized information. Since the digital investigation is turning into an expanding concern, numerous digital forensic tools have been created to manage the difficulties of exploring computerized wrongdoings. The motivation behind digital forensics strategies is to look, protect and extract data on advanced frameworks to discover potential confirmations to exhibit them in the courtroom. In this paper, we have discussed five kinds of forensics namely Network Forensics, Live Forensics, Cyber Forensics, Database Forensics, and Mobile Forensics. The paper depicts a list of digital forensic tools in detail and looks at them based on the characterized parameters to figure out which tool suits better for any investigation. The paper looks at network, database and mobile forensic tools and examines the silent features and uniqueness of each of the tools along with their functionalities.
Reference and coreference in situated dialogue

In recent years, a large number of corpora have been developed for vision and language tasks.We argue that there is still significant room for corpora that increase the complexity of both visual and linguistic domains and which capture different varieties of perceptual and conversational contexts.Working with two corpora approaching this goal, we present a linguistic perspective on some of the challenges in creating and extending resources combining language and vision while preserving continuity with the existing best practices in the area of coreference annotation.
Anlirika: An LSTM–CNN Flow Twister for Spoken Language Identification

The paper presents Anlirika's submission to SIGTYP 2021 Shared Task on Robust Spoken Language Identification.The task aims at building a robust system that generalizes well across different domains and speakers.The training data is limited to a single domain only with predominantly single speaker per language while the validation and test data samples are derived from diverse dataset and multiple speakers.We experiment with a neural system comprising a combination of dense, convolutional, and recurrent layers that are designed to perform better generalization and obtain speaker-invariant representations.We demonstrate that the task in its constrained form (without making use of external data or augmentation the train set with samples from the validation set) is still challenging.Our best system trained on the data augmented with validation samples achieves 29.9% accuracy on the test set.
Use of Twitter by a Public Library During the Pandemic

Social media such as Twitter and Facebook are used by organizations including public libraries to disseminate a variety of information to community members. This research aims to identify key areas in which Twitter was used to share information with users during the initial pandemic phase by a large public library operating in the Western region of Canada. The findings resulting from the analysis of 150 tweets revealed that the library shared information primarily in the following areas: Programs and Services, Library Operations, Health and Hygiene, Resources, and Other.
Use of artificial intelligence technologies for building individual educational trajectories of students

Problem and goal. Developed and tested solutions for building individual educational trajectories of students, focused on improving the educational process by forming a personalized set of recommendations from the optional disciplines. Methodology. Data mining and machine learning methods were used to process both numeric and textual data. The approaches based on collaborative and content filtering to generate recommendations for students were also used. Results. Testing of the developed system was carried out in the context of several periods of elective courses selection, in which 4,769 first- and second-year students took part. A set of recommendations was automatically generated for each student, and then the quality of the recommendations was evaluated based on the percentage of students who used these recommendations. According to the results of testing, the recommendations were used by 1,976 students, which was 41.43% of the total number of participants. Conclusion. In the study, a recommendation system was developed that performs automatic ranking of subjects of choice and forms a personalized set of recommendations for each student based on their interests for building individual educational trajectories.
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests

Like many predictive models, random forests provide point predictions for new observations. Besides the point prediction, it is important to quantify the uncertainty in the prediction. Prediction intervals provide information about the reliability of the point predictions. We have developed a comprehensive R package, [RFpredInterval](https://CRAN.R-project.org/package=RFpredInterval), that integrates 16 methods to build prediction intervals with random forests and boosted forests. The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by [@roy_prediction_2020]. We perform an extensive simulation study and apply real data analyses to compare the performance of the proposed method to ten existing methods for building prediction intervals with random forests. The results show that the proposed method is very competitive and, globally, outperforms competing methods.
Experimental Comparison of Classification Methods under Class Imbalance

The class imbalance problem is prevalent in many domains including medical, natural language processing, image recognition, economic and geographic areas etc.We perform a systematic experimental comparison of different imbalance classification algorithms -ranging from sampling, distance metric learning, costsensitive learning to ensemble learning approaches -on several datasets from UCI, KEEL and OpenML.The algorithms included DDAE, MWMOTE, SMOTE, RUSBoost, AdaBoost, cost-sensitive decision tree (csDCT), self-paced Ensemble Classifier, MetaCost, CAdaMEC and Iterative Metric Learning (IML).As the substantial bias potentially caused by imbalance classification can be harmful for underrepresented classes which are of critical social and economic values and even lives, the main objective of our study is thus to understand the impact of imbalance ratio and the size of the utilized datasets on the performance of the above-mentioned algorithms.Our experiments show that 1) Sampling methods perform the worst and cannot be used directly for imbalanced classification, since they lack of consideration of neighborhoods based on distance.However, some classifiers can be improved after the balance of class distribution.2) Cost-sensitive learning models should be utilized when the dataset is less imbalanced, because it is difficult to set an appropriate cost matrix for a specific dataset, which can cause performance fluctuations.3) IML consistently shows good performance (in terms of F1 and AUCPRC), is resilient to different imbalance ratios but sensitive to the data distribution of the dataset.4) Ensemble learning techniques generally perform better over other approaches due to their combined intelligence of multiple basic classifiers.5) In terms of system performance, self-paced Ensemble Classifier performs fairly well with regards to learning time, while IML and DDAE yield the longest learning time; AdaBoost and self-paced Ensemble Classifier are two algorithms require lowest memory usage.We also provide our empirical recommendation for algorithm selection under different requirements and usage scenarios based on our analysis.
Improving Students' Listening Skills and Vocabulary Mastery through Contextual Teaching and Learning by Using Online Learning

The aim of this research is to describe the process of improving the students’ listening skill and vocabulary mastery through contextual teaching and learning (CTL) by using online learning at the eleventh grade of SMAN 1 Sajira. This study also attempts to find out the improvement of the students’ skill in listening and vocabulary mastery through contextual teaching and learning (CTL) by using online learning. The method used in this research was qualitative research method by implementing classroom action research (CAR). This research was conducted in three cycles actions in cycle I, cycle 2 and cycle 3. The result of the research is the use of contextual teaching and learning (CTL) by using online learning can improve the students’ listening skill and vocabulary mastery. The finding of the research are (1) the process of teaching and learning using CTL through online learning is conducted successfully in terms of the feeling of happiness and enthusiasm of the students, (2) there is an improvement of the students’ listening skill from 57.85 in pre-test became 81.11 in post-test. This study proves that English teacher can improve students’ listening and vocabulary mastery by optimizing the use of materials at school, and be creative in selecting, developing, and giving materials at class.
The Implementation of Multisensory Technique for Children with Dyslexia

Children with learning disabilities are a children's physiological or biological condition in which the competence or achievement is not according to predetermined standard criteria—learning disabilities in the form of errors in reading called dyslexia. Children with specific learning difficulties dyslexia experience difficulties in academic aspects; therefore, it is necessary to carry out an academic assessment and material for tutors to improve dyslexia reading skills. The multisensory technique is alternatives that used as reference material for tutors to improve dyslexia reading skills. Multisensory Techniques that can be used include 1) Reading and Spelling Training; 2) Visual Technique; 3) Auditory Technique, and 4) Tactile Technique. The reading and spelling focus on maintaining relationships between sounds and symbols starts with a single letter and continues with consonant combinations, vowel continuation, and complex letter groupings. The Visual Technique can start by using a picture card with the word written on the bottom (flashcard). Auditory technique for children who have difficulty with sound problems, teach a pair of short words and ask the child to say which word is correct. In addition, children with dyslexia will have the best learning by touch, so it is essential to incorporate this learning style into the instruction as a tactile technique.
What Makes My Model Perplexed? A Linguistic Investigation on Neural Language Models Perplexity

This paper presents an investigation aimed at studying how the linguistic structure of a sentence affects the perplexity of two of the most popular Neural Language Models (NLMs), BERT and GPT-2.We first compare the sentence-level likelihood computed with BERT and the GPT-2's perplexity showing that the two metrics are correlated.In addition, we exploit linguistic features capturing a wide set of morpho-syntactic and syntactic phenomena showing how they contribute to predict the perplexity of the two NLMs.
Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).
Classification of Censored Tweets in Chinese Language using XLNet

In the growth of today's world and advanced technology, social media networks play a significant role in impacting human lives.Censorship is the overthrowing of speech, public transmission, or other details that play a vast role in social media.The content may be considered harmful, sensitive, or inconvenient.Authorities like institutes, governments, and other organizations conduct Censorship.This paper has implemented a model that helps classify censored and uncensored tweets as a binary classification.The paper describes submission to the Censorship shared task of the NLP4IF 2021 workshop.We used various transformerbased pre-trained models, and XLNet outputs a better accuracy among all.We fine-tuned the model for better performance and achieved a reasonable accuracy, and calculated other performance metrics.