Reviewerly/exHarmony huggingface dataset:
Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study (Preprint)

<sec> <title>BACKGROUND</title> The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of extracting and summarizing relevant information in the most meticulous manner from various search systems. An artificial intelligence (AI)–based intelligent search system that can significantly bring down the manual efforts in the existing processes of the RA department while maintaining and improving the quality of final outcomes is desirable. We proposed a “frequently asked questions” component and its utility in an AI-based intelligent search system in this paper. The scenario is further complicated by the lack of publicly available relevant data sets in the RA domain to train the machine learning models that can facilitate cognitive search systems for regulatory authorities. </sec> <sec> <title>OBJECTIVE</title> In this study, we aimed to use AI-based intelligent computational models to automatically recognize semantically similar question pairs in the RA domain and evaluate the Recognizing Question Entailment–based system. </sec> <sec> <title>METHODS</title> We used transfer learning techniques and experimented with transformer-based models pretrained on corpora collected from different resources, such as Bidirectional Encoder Representations from Transformers (BERT), Clinical BERT, BioBERT, and BlueBERT. We used a manually labeled data set that contained 150 question pairs in the pharmaceutical regulatory domain to evaluate the performance of our model. </sec> <sec> <title>RESULTS</title> The Clinical BERT model performed better than other domain-specific BERT-based models in identifying question similarity from the RA domain. The BERT model had the best ability to learn domain-specific knowledge with transfer learning, which reached the best performance when fine-tuned with sufficient clinical domain question pairs. The top-performing model achieved an accuracy of 90.66% on the test set. </sec> <sec> <title>CONCLUSIONS</title> This study demonstrates the possibility of using pretrained language models to recognize question similarity in the pharmaceutical regulatory domain. Transformer-based models that are pretrained on clinical notes perform better than models pretrained on biomedical text in recognizing the question’s semantic similarity in this domain. We also discuss the challenges of using data augmentation techniques to address the lack of relevant data in this domain. The results of our experiment indicated that increasing the number of training samples using back translation and entity replacement did not enhance the model’s performance. This lack of improvement may be attributed to the intricate and specialized nature of texts in the regulatory domain. Our work provides the foundation for further studies that apply state-of-the-art linguistic models to regulatory documents in the pharmaceutical industry. </sec>
Pengaruh Motivasi Belajar Terhadap Prestasi Belajar Siswa Dikelas III SDS Muhammadiyah 06 Tebet

The purpose of this study was to determine whether there was an influence of learning motivation on the achievement of third grade students of SDS Muhammadiyah 06 Tebet.The method used is quantitative associative correlation.Data collection techniques using questionnaires and documentation.The population of this study were all third grade students.The total number of class III is divided into 3 classes with a total of 71 students.While the sample used is 18 students.After analyzing the data, the results of the study show that:The effect of learning motivation on learning achievement for class III is very low.With a value: t_count -0.152 < 2.120 at t_table (significant level 0.05) and with a significance level of 0.881 > 0.05 which means significantly low.
Pengaruh Media Video Animasi terhadap Kemampuan Membaca Permulaan

This research aims to study was to see whether there was an effect of animated video media on the early reading ability of the 1st graders of SD Negeri 13 Toboali. The approach used is quantitative in the form of an experiment or can be called, pre-experimental. The research design used in this research is the one-group pretest – posttest. The population in this study were all students of class I/B SDN 13 Toboali, totaling 26 students. The sampling technique in this study was a saturated sample. The instrument used in this study was in the form of questions to measure initial reading ability. The results of this study with the pretest value which was initially 51.923 to 77.692 after the posttest. This result is also seen from the results of hypothesis testing, namely, count &gt; table with a value of 10,775 &gt; 1,782, so the conclusion is H0 is accepted, meaning that there is an Effect of Animated Video Media on Beginning Reading Ability of First Grade Students of SD Negeri 13 Toboali.
Decoding the linguistic functions of emoji: A discourse analysis of messenger chats

This discourse analysis aimed to investigate the different emoji and its pragmatic illocutionary forces as used in messenger chats.The study utilized the qualitative content approach in identifying the different communicative uses of the emoji as reflected and as used in the context of online discourse.This explores the different signs and symbols in the form of emoji used by interactants in their computer-mediated exchange of discourse.Data revealed that the different symbols or emojis that represent the current state of being as used by interactants in their messenger chats are: church, sick emoji, morbid, confused, sad, crying, in love, like, wave, shocked, halo, bored or sleepy , and birthday emoji.The different linguistic functions reflected in the use of emoji are: expressive, interpretative, relational, politeness, emphatic, structural and referential functions.In terms of the illocutionary force, the illocutionary acts are: expressive, representative, directive and commissive.Emojis are what the modern times has to offer a different set of languages.This particular language, however, is in its infancy stage and still needs to be studied, and understood.Based on the results gathered, emojis are flexible types of languages that can either support the existing one by making more palatable on the online community or it will become the language itself in some cases.Nevertheless, the studies regarding these modern signs and symbols will prove to be fun, challenging and beneficial as it is constantly changing overtime by the course of technology.
Transitivity Analysis Annotation Scheme and Its Application to CQPweb: A Study on Stephen King’s Carrie

In this study, I aim to create a Transitivity Analysis Annotation Scheme (TAAS) within which, process types and clause patterns are taken into account. Once created, TAAS is applied to a corpus data, obtained from a novel entitled Carrie [8]. The scheme is manually implemented using XML format. The XML formatted corpus is converted to a format accepted in CQPweb [6], a corpus query tool. The corpus is tokenised using the Perl tokenizer, in TreeTagger [10]. Once tokenised, the corpus is tagged using English TreeTagger [10], which supplies two grammatical annotations (Part of Speech and Lemma). The annotated corpus is then indexed to CQPweb. Once indexed, users are able to search the annotated corpus using transitivity analytic labels, POS tags and lemma, in CQPweb. Keywords: transitivity analysis; annotation; corpus linguistics; CQPweb
Pan, Zung, Imundo, et al. (2022) User-generated versus pre-made digital flashcards

Digital flashcard users typically must choose between creating their own flashcard content or using freely-available flashcard sets. The latter is more convenient and saves time, but is it more effective for learning? We conducted six experiments, each involving the use of user-generated or pre-made flashcards to learn material drawn from educational text passages, followed by a 48-hr delayed criterial test. Different approaches to generating content and variations in the quality of pre-made content were also examined. Across experiments, user-generated flashcards improved memory relative to pre-made flashcards (an estimated advantage of d = 0.45, 95% C.I. [0.25, 0.66]), and in most cases, enhanced performance on application questions (an estimated advantage of d = 0.29, 95% C.I. [0.12, 0.45]). These results suggest that generating one’s own flashcards enables productive learning processes that enhance memory and comprehension. Accordingly, digital flashcard users may benefit from eschewing pre-made versions in favor of making their own.
Multi-objective data enhancement for deep learning-based ultrasound analysis

Recently, Deep Learning based automatic generation of treatment recommendation has been attracting much attention. However, medical datasets are usually small, which may lead to over-fitting and inferior performances of deep learning models. In this paper, we propose multi-objective data enhancement method to indirectly scale up the medical data to avoid over-fitting and generate high quantity treatment recommendations. Specifically, we define a main and several auxiliary tasks on the same dataset and train a specific model for each of these tasks to learn different aspects of knowledge in limited data scale. Meanwhile, a Soft Parameter Sharing method is exploited to share learned knowledge among models. By sharing the knowledge learned by auxiliary tasks to the main task, the proposed method can take different semantic distributions into account during the training process of the main task. We collected an ultrasound dataset of thyroid nodules that contains Findings, Impressions and Treatment Recommendations labeled by professional doctors. We conducted various experiments on the dataset to validate the proposed method and justified its better performance than existing methods.
Chinese Medical Named Entity Recognition Method Based on Word-Word Relationship

To solve the problem that the current deep learning method is difficult to deal with the recognition of nested entities in Chinese medical text, a deep learning model based on word-word relationship is introduced, and the relationship between words is built by multi-granularity 2D graphs to improve the recognition of nested entities. First, we use BERT (Bidirectional Encoder Representation from Transformers) for pre-training, then we use BiLSTM (directional Long Short-Term Memory) to extract the context information. Then, we merge the token representation information, the word distance information and the word regional information, through use a multi-granularity hole convolution to obtain the role information of different words. Finally, we use decoding layer to predict entity relationships and decode the result. This model is tested on the CMeEE Chinese medical dataset. Compared with the popularity models such as BiLSTM-CRF (Conditional Random Field) and BERT-BiLSTM-CRF, the F1 value is improved by 2.52%. Experimental results show that for Chinese medical named entity recognition with nested entities, this model can better recognize the medical entities in Chinese medical text.
PENGARUH PENGGUNAAN SMARTPHONE SEBAGAI PENUNJANG PEMBELAJARAN IPS DI MASA PANDEMI COVID-19 TERHADAP HASIL BELAJAR

This study examines the effect of smartphones as learning support during the COVID-19 pandemic on junior high school students learning outcomes in social science subjects. The research method used is a quantitative approach in an ex-post facto form. The data collection techniques used were an indirect form of communication using a closed-ended questionnaire, and a documentary study using the final even semester assessment of social science subjects for class VII and class VIII. Based on the data analysis, (1) the use of smartphones to support the learning process during the COVID-19 pandemic has a 58.9% ratio and is categorized as adequate. (2) A total of 56.3% of students have social studies learning outcomes with a predicate of C (adequate). (3) Based on a simple linear regression test, obtained a significance value of 0.919 &gt; 0.05, showing that Ha is rejected, while H0 is accepted. In conclusion, the use of smartphones as a support for the learning process during the COVID-19 pandemic does not affect the learning outcomes of junior high school students in social science subjects.
PERAN STRATEGI PEMBELAJARAN TERHADAP MOTIVASI BELAJAR SISWA

Learning activities will be successful if students have motivation in learning. Therefore, the article that we created aims to better recognize and examine the effect of learning strategies applied by teachers in increasing students' learning motivation. In achieving the goal of increasing student motivation, there needs to be a better effort in choosing and implementing learning strategies that are in accordance with the competencies to be achieved. The research we conducted used a descriptive analysis approach through data collection methods. The data collection is done by studying literature through articles, journals and books. To increase students' learning motivation, it is necessary to choose the right learning strategy by considering the characteristics of the objectives and content of the field of study. The results of the analysis show that there is a big influence given by the learning strategy on increasing students' learning motivation. This influence has an impact on student learning outcomes for the better. Each learning strategy has a different effect on students' learning motivation. The application of correct learning strategies also increases students' understanding of the learning content.
KONSEKUENSI DARI KEBIJAKAN PEMERINTAH TENTANG VAKSINASI COVID-19 BAGI ANAK USIA 6-11 TAHUN TERHADAP PELAKSANAAN PEMBELAJARAN Di SD NEGERI 04 KAMPUNG OLO

Government policies based on a circular from the Padang City Education and Culture Office regarding the implementation of vaccinations for children aged 6-11 years for the prevention of Covid-19 are known to have consequences for children, teachers and parents. at the age of 6-11 years towards learning at SD Negeri 04 Kampung Olo. The theory used in this research is the theory of structural functionalism of money proposed by Robert K. Merton. The research approach used is a qualitative approach with descriptive research type. Withdrawal of informants using purposive sampling technique with the number of informants 14 people. The types of data used in this research are primary data and secondary data. Data collection methods used are non-participant observation, in-depth interviews and document studies. Data analysis techniques used by the group with data analysis Miles and Huberman are data collection, data reduction, data presentation, and drawing conclusions. The results of this study show that the consequences of government policies regarding Covid-19 vaccination for children aged 6-11 years on the implementation of learning at SD Negeri 04 Kampung Olo have consequences, namely: 1) the consequences of policies on the implementation of learning, 2) Learning is divided into 2 shifts, 3) Differences in lesson hours, 4) grouping students online and offline.
Residue Number System-Based Approach to Minimise Energy Consumption in Wireless Sensor Networks

This study harnesses the useful number properties of the residue number system (RNS) to minimise energy consumption in a wireless sensor network. In a traditional cluster-based wireless sensor network, large bit representations of aggregated packets are transmitted to the base station. However, large bit patterns of packets are slower compared to smaller bits. The proposed approach splits aggregated data into a pre-specified number of transmission channels using a moduli set. Cheap energy cost routes from the cluster heads are computed to deliver the chunked aggregated data to the base station. Forward and reverse converters are proposed to encode data into RNS and decode the RNS data that reaches the base station. MATLAB simulation is used to implement the proposed data splitting method and to evaluate network performance. The experimental results suggest that the proposed method is more effective at minimising transmission energy when compared with traditional approaches in which complete packets are transmitted.
LEARNING OF ALGORITHMS IN THE LICENSE COURSE IN COMPUTING OF THE FEDERAL INSTITUTE OF EDUCATION, SCIENCE AND TOCANTINS TECHNOLOGY – IFTO - DIANÓPOLIS CAMPUS : CHALLENGES AND POSSIBILITIES

Over the centuries, the human being has been discovering more and more about the processes of learning and cognitive development.According to the evolution of society, this knowledge has also been evolving in order to better serve professionals, especially in the area of education, so that they know how to deal effectively in the teaching-learning process, aiming at a greater performance in the development of their students.In this sense, a man or woman stands out in relation to the other, mainly due to their intellectual capacity, that is, the ability to solve more complex problems is what distances the intellectual man from the common man.The work is based on LEFRANÇOIS (2013) and LOPES & GARCIA (2002), using bibliographic analysis as a research tool.The method used is a quantitative approach, with observation and recording of information in the field of research and completion of questionnaires.Through the data obtained, it can be concluded that, in general, the knowledge acquired before entering higher education largely contributes to the success of the Introduction to Logic and Algorithms course.
Digital Forensic Signal Instant Messages Services in Case of Cyberbullying using Digital Forensic Research Workshop Method

Advances in information technology have a negative impact, including the public can freely act in cybercrimes.One of the popular instant messaging services is Signal Instant Messenger, which can trigger cyber crimes such as cyberbullying, pornography, gambling, fraud, and phishing.Cyberbullying crimes can occur as long as the application used provides features to send text messages, pictures, or videos.This study aims to analyze digital evidence so that it can be identified as a case of cyberbullying on the Signal Instant Messenger application.The investigation process uses one of the methods of the Digital Forensic Research Workshop (DFRWS) framework, with stages including identification, preservation, collection, examination, analysis, and presentation in the digital evidence search process.The digital forensic tools used to collect evidence are MOBILedit Forensic Express Pro, Belkasoft Evidence Center, and SQLite DB Browser.The process of identifying digital evidence uses a repetition of words that contain ridicule or insult to the victim so that the perpetrator is identified as committing cyberbullying.The results of this study are in the form of digital evidence in the form of reports to be analyzed and used to support cyberbullying investigations in court.This study obtained the results that the actions or conversations that led to cyberbullying were as many as 4 people, 2 as victims, and 1 as witness in accordance with the expected research objectives, even though they had not been able to read deleted or accidentally deleted chats.
Application of Artificial Neural Network for Soil Moisture Prediction Incorporating the Effects of Surface Roughness and Vegetation

During this critical period of pandemic, agriculture is the main source of income for any country, to be specific developing countries like India.During the 21 st century agriculture is not the profession of illiterate villagers but the main occupation of literates too.Nowadays farmers are using modern equipment and technology in the field of agriculture to grow more crop with less effort and in uncongenial atmosphere.Farmers have to grow different crops in different areas and at different time period.To select the type of crop in a particular time period soil moisture of the given field plays a major role which directly depicts the water absorbing capacity of the soil in a given field.So, measurement of the soil moisture of a given field becomes utmost important.After a thorough literature survey it was found that soil moisture probes are inserted in a given field at a particular distance gap which gives the measure of soil moisture.This method is useful for a small field.In order to measure the soil moisture globally, satellite images are decoded using different algorithms to calculate soil moisture.In a step ahead soil moisture is predicted from the previous data using Artificial Neural Network.Since the satellite images are captured from an altitude, surface conditions to be specificvegetation cover and surface roughness will have a serious effect on the captured image.In this paper an attempt is made to develop an algorithm incorporating the effects of surface conditions to decode the satellite images in calculating the soil moisture.
QR Code Security: Mitigating the Issue of Quishing (QR Code Phishing)

To accommodate new technologies and communication methods, cybersecurity must advance.For security experts, especially those working in fields like digital forensics, new technologies provide both opportunities and challenges.New technologies like smartphones and new ways of disseminating information, like social media, might provide difficulties.Use of QR (Quick Response) codes is one of the rapidly expanding interface technologies.This paper explores privacy issues that might arise with QR codes and other information security-harming technologies.Additionally, it emphasizes the necessity for experts in the field to solve security concerns raised by the increasing use of QR codes.Each URL's words were extracted using a count vectorizer, and the URLs that were part of the QR code were used to obtain features.To distinguish between legitimate and phishing URLs, traits and words were tokenized, and naive Bayesian machine learning classification techniques were used in a recursive loop alongside logistic regression.A very accurate model was created, aiding in the reduction of quishing behaviour.
Automated Cancer Detection using Machine Learning and Image Processing

Cancer is becoming more prevalent around the globe.Even in Sri Lanka, the total cancer rate has doubled in the last 20 years, with a corresponding increase in cancer-related deaths.Cancer is the second leading cause of hospital death.Therefore, a solution to the problem should be an arrangement to reduce time waste, a correct method of directing the patient to detect symptoms, with highly accurate cancer detection, and a better monitoring system.The proposed system is an arrangement that permits and guides a patient to recognize symptoms on their own, directing them to an appropriate healthcare specialist, accurately detecting cancer in its initial stages and monitoring the patient throughout treatment.While cancer detection systems are analyzed, the existing research studies only use one machine learning methodology at a time to diagnose cancer.In the proposed work, Convolutional Neural Network (CNN), Random Forest and XGB Classifier are applied in detecting the presence of breast cancer, brain tumor, skin cancer and lung cancer which outputs results faster with a higher accuracy.This proposed system will be published as a modern cloud-based application which provides better userexperience and ease-of-use.