Reviewerly/exHarmony huggingface dataset:
Overcoming Language Disparity in Online Content Classification with Multimodal Learning

Advances in Natural Language Processing (NLP) have revolutionized the way researchers and practitioners address crucial societal problems. Large language models are now the standard to develop state-of-the-art solutions for text detection and classification tasks. However, the development of advanced computational techniques and resources is disproportionately focused on the English language, sidelining a majority of the languages spoken globally. While existing research has developed better multilingual and monolingual language models to bridge this language disparity between English and non-English languages, we explore the promise of incorporating the information contained in images via multimodal machine learning. Our comparative analyses on three detection tasks focusing on crisis information, fake news, and emotion recognition, as well as five high-resource non-English languages, demonstrate that: (a) detection frameworks based on pre-trained large language models like BERT and multilingual-BERT systematically perform better on the English language compared against non-English languages, and (b) including images via multimodal learning bridges this performance gap. We situate our findings with respect to existing work on the pitfalls of large language models, and discuss their theoretical and practical implications.
A Multi-Headed Transformer Approach for Predicting the Patient’s Clinical Time-Series Variables From Charted Vital Signs

Deep learning has progressively been the spotlight of innovations that aim to leverage the clinical time-series data that are longitudinally recorded in the Electronic Health Records(EHR) to forecast the patient’s survival and vital signs deterioration. However, their recording velocity, as well as their noisiness, hinder the proper adoption of the recently proposed benchmarks. The Recurrent Neural Networks (RNN) especially the Long-short Term Memory (LSTMs) have achieved better results in recent studies but they are hard to train and interpret and fail to properly capture the long-term dependencies. Moreover, the RNNs suffer greatly with clinical time series due to their sequential processing which cripples the prospect of parallel processing. Recently the Transformer approach was proposed for Natural Language Processing (NLP) tasks and achieved state-of-the-art results. Hence to tackle the drawbacks that are suffered by the RNNs we propose a clinical time series Multi-head Transformer (MHT), which is a transformer-based model that forecasts the patient’s future time series variables using the vitals signs. To prove the generalization of the model we use the same model for other critical tasks that describe the Intensive Care Unit (ICU) patient’s progression and the associated risks like the remaining Length Of Stay(LoS), the In-hospital Mortality as well as the 24 hours mortality. Our model achieves an Area Under The Curve-Receiver Operating Characteristics( AUC-ROC) of 0.98 and an Area Under the Curve, Precision-Recall (AUC-PR) of 0.424 for vital time series prediction, and an AUC-ROC of 0.875 in the mortality prediction. The model performs well for the frequently recorded variables like the Heart Rate (HR) and performs barely like the LSTM counterparts for the intermittently captured records such as the White Blood Count (WBC).
Chatting with AI Chatbots Applications to Improve English Communication Skill

Artificial intelligence allows a person to converse with machines, computers or robots. Chatbots are an application that you can talk to like you would talk to a human. Chatbots can be a tool to practice the language learned anywhere and anytime. In order to improve students' communication skills in English, researchers conducted research on the use of artificial intelligence Chatbots applications in learning English courses in the PKh Department for the 2020/2021 academic year. This study uses a descriptive method to describe the process of learning English with chatbots and to determine student perceptions of the use of these chatbots in improving their communication skills in English. After having conversations with chatbots: ELIZA, ALICE, MITSUKU/KUKI, and ANDY with conversation topics: introductions, feelings/confessions, daily activities, hobbies, lectures, COVID-19 pandemic, etc. The results obtained are the most preferred Chatbots: ANDY, there are POSITIVE meaningful expressions (86%) and no NEGATIVE meanings towards ANDY chatbot. Least favorite chatbots: ELIZA, there are a lot of NEGATIVE (43%) meaningful expressions towards ELIZA chatbot which are bigger than POSITIVE expressions (39%).
Decentralized Payment Aggregator: Hyperledger Fabric

Blockchain has become a great trend and very popular in the present era.There are two types of Blockchain technology, centralized and decentralized.In this research, the main concern is about the decentralized payment gateway, which is a trustworthy architecture and does not depend on third parties.For recording the transaction, decentralized payment systems use distributed ledger.Previously, Bitcoin and Ethereum payment systems were used to verify the consistency of the ledger of blockchain and also the transaction data along with the sender-receiver address and transaction value, but as all the payment system is public, so the transaction mode is also public.However, here the main concern is privacy and security.Because anyone can easily access the network, the attacker can also attack the network and the identity and transaction records and the address of the user identity, which is a privacy challenge.This research incorporates the Hyperledger Fabric, which is private, to overcome this challenge.Moreover, no one can access it from outside of the network.The transaction cost is low and the timing is fast during transactions.Considering the above scenario, this research proposes a decentralized payment system architecture using Hyperledger Fabric.
Students’ Educational Experience: The Conceptualization and Development of a Tool for the Assessment of Education Quality

To assess and manage the quality of education it is important to understand what happens to students at university, what experience they gain, and whether this experience contributes to their success and development. In foreign science student experience is considered as a student-centered idea of improving the quality of higher education and a prerequisite for many initiatives in this area. However, the approaches to its study require major revision. This article presents the study which allows the students’ educational experience to be conceptualized. To diagnose and analyze the students’ educational experience a valid and reliable tool has been created. Educational experience is defined as students’ representations which are significant from the standpoint of academic success, students’ readiness for self-education and self-development, and subjective well-being. These students’ representations are the representations of the following: their educational and professional activities, themselves as the subjects of their educational and professional activities, learning and social context. The students’ educational experience is presented in the aggregate of five components: satisfaction, intention to expand experience, self-efficacy and support, self-regulated learning experience, and engagement. The analysis of the holistic experience and its components allows not only to assess the student’s subjective perception of learning, but also to predict the effectiveness of the educational process based on understanding of the key internal factors of academic performance, subjective well-being and development. The article presents a questionnaire of students' educational experience, verified on a sample of students (N=479). It meets the psychometric requirements of internal consistency, retest reliability, content, construct and criterion validity. This questionnaire can be used to control the quality of education, to do scientific research and evidence-based pedagogical experimentation in the context of the restructuring higher education.
Fungsi dan Aplikasi Motivasi dalam Pembelajaran

The purpose of this research is to identify the factors that drive motivation, as well as the role of motivation in teaching and learning. This is a qualitative study conducted using library research methods. Work motivation is a driving force and source of encouragement that can elicit a sense of zeal, as well as modify human or individual behavior to lead to improved outcomes. Motivation is defined as the overarching driving force within the instructor that initiates and directs instructional actions in order to attain the objectives set forth by the teaching subject. So that every teacher is diligent in completing assignments, tenacious in overcoming obstacles (not easily discouraged), shows interest in a variety of problems, prefers to work independently, is not easily bored with routine tasks, and is able to defend his or her point of view, and enjoys finding and solving problems.
Aesthetic QR: Approaches for Beautified, Fast Decoding, and Secured QR Codes

A QR code is a two-dimensional code that encodes data but it is unattractive and not ideal.QR codes have been applied in item identifications, publicity campaigns, advertisements, product promotions, etc. so they need to be visually good in appearance.Visually good and decorated QR codes degrade the decoding rate as compared to the standard QR code decoding rate.As they are used for mobile payments and logins some security must be there.For this many researchers have contributed using various approaches to beautify QR codes with high decoding accuracy and to make them secure.This paper aims towards the study of works carried out in the direction of beautification of QR codes using blended type techniques and artificial intelligence based techniques by different authors.The present state of prior strategies, methods, and major features used are described in this survey.
Human as an Informational Device

Introduction and aim: Unprecedented development of the informational devices at large scale allows nowadays the intervention of the infodevices in the human body for diagnosis and treatment, or the info-assisted analysis and prediction in the healthy field. The aim of this paper is to approach the human functionality from the perspective of an informational device.
A Review on Big Data Sentiment Analysis Techniques

The areas of Natural Language Processing, Text Analysis, Text Preprocessing, Stemming, etc. are the most important study fields at the moment. Sentiment analysis is employed for all of these areas. Study of sentiment is performed by using a variety of methods and tools to the task of analyzing unstructured data in such a way that it is possible to get objective findings from the analysis of said data. These methods, in their most fundamental form, make it possible for a computer to comprehend what a human person is saying. A variety of methods are utilized in the process of sentiment analysis in order to assess the attitude conveyed by a given text or sentence. A machine learning approach and a lexicon-based approach are the two primary categories into which it may be divided, depending on which method was used to develop it. For the purpose of gaining insights into the market and improving performance, businesses utilize sentiment analysis. The use of sentiment analysis in the process of developing a smart society is enormous, and there is a pressing need to define the trend in a comprehensive manner. The fundamental objective of this study is to present an in-depth investigation of the several platforms that are now accessible for the execution of Big Data Sentiment Analysis Methods. This study examines the various hardware platforms that are currently available for big data analytics and evaluates the benefits and drawbacks of each of these platforms based on a number of different metrics, including scalability, data I/O rate, fault tolerance, real-time processing, data size supported, and iterative task support.
A Novel Selective Ensemble Learning Method for Smartphone Sensor-Based Human Activity Recognition Based on Hybrid Diversity Enhancement and Improved Binary Glowworm Swarm Optimization

Human activity recognition (HAR) is gaining interest with many important applications including ubiquitous computing, health-care services and detection of diseases. Smartphone sensors have high acceptance and adherence in daily life and they provide an alternative and economic way for activity recognition. To improve the performance of smartphone sensor-based HAR, a novel smartphone sensor-based HAR method (hybrid diversity enhancement with selective ensemble learning, HDESEN) that utilizes selective ensemble learning with differentiated extreme learning machines (ELMs) is proposed, where hybrid diversity enhancement is proposed to boost the diversity of base models and an improved binary glowworm swarm optimization (IBGSO) is employed to effectively enhance the learning process by choosing a superior subset for ensemble instead of all. Firstly, statistical features in the time domain and frequency domain are extracted and integrated from smartphone sensors and then three filter-based feature selection methods are utilized for desirable base models. Secondly, to enhance the diversity of the base models, three types of diversities are introduced to construct different base models, respectively. Among them, Bootstrap is introduced to design distinctive training data subsets for differential base models, random subspace and optimized subspace are proposed to obtain different feature spaces for constructing base models. Thirdly, a pruning method based on glowworm swarm optimization (GSO) is proposed to find the optimal sub-ensemble from the pool of models from all diverse types to implement selective ensemble learning. The experimental results on tow publicly available datasets (UCI-HAR and WISDM) demonstrate the proposed HDESEN can reliably improve the performance of HAR and outperforms the relevant state-of-the-art approaches.
Comparison Study of Iterative Algorithms for Center-of-Sets Type-Reduction of Takagi Sugeno Kang Type General Type-2 Fuzzy Logic Systems

Studying the block of center-of-sets (COS) type-reduction (TR) for Takagi Sugeno Kang (TSK) inference-based general type-2 fuzzy logic systems (GT2 FLSs) is meaningful for applying the systems.Blocks of fuzzy reasoning, COS type-reduction and defuzzification for TSK type GT2 FLSs are first given.According to three kinds of iterative algorithms for computing the centroids of interval type-2 fuzzy sets (IT2 FSs), the paper extends these types of algorithms for studying the COS type-reduction of TSK type GT2 FLSs.Six computer simulation experiments show the computational costs of proposed three kinds of iterative algorithms by computing the outputs of GT2 FLSs, which affords the potential guidance value designers and users of T2 FLSs.
Mobile Applications for Cybercrime Prevention: A Comprehensive Systematic Review

Now-a-days, cybercrime, cyberattacks, cyber security, phishing and malware are taking a more notorious role in people's daily lives, not only at the international level.The great technological leaps brought with them new modalities of cybercrime, the number of victims of cybercriminals has increased considerably.The objective of this study is to determine the state of the art about Mobile Applications and their impact on Computer Crime Prevention.Therefore, it has become necessary to know what preventive measures are being taken, such as techniques for detecting computer crimes, their modalities and their classification.To close this knowledge gap, a systematic literature review (SLR), a methodology proposed by Kitchenham & Charters, was proposed to obtain the detection techniques and classification of computer crimes based on the review of 68 papers published between the years 2017 and 2022.Likewise, different tables and graphs of the selected studies are provided, which offer additional information such as the most used keywords per paper, biometric networks, among others.
Teachers' and students’ perceptions on introducing the SAMR model into their classroom

This article focuses on understanding the perceptions of 31 Ecuadorian teachers and 34 Ecuadorian university students about the Puentedura Substitution, Augmentation, Modification, and Redefinition (SAMR) model in the English class. It has helped teachers integrate technology into their classes. The tool used to evaluate the model consisted of 20 items with 5 questions for each SAMR dimension. The reliability of the model was measured with McDonald's ω (0.94) in a general context and each dimension (S: (ω=0.88), A: (ω=0.82), M: (ω=0.85) R: ω=0.87)). The results on the perceptions of teachers and students were very enlightening to understand how they implement technology in English classrooms before and after the pandemic. They consider the use of technological tools to be beneficial for learning English as a foreign language.
The importance of employee involvement in work activities to overall productivity

In order for businesses to function efficiently, they should perform routine maintenance on their equipment and assets. This article aimed to evaluate the role and importance of employee involvement in work activities and their influence on overall productivity. The author selected several employee involvement dimensions (leadership, team work, training and career development, compensation, and organizational policies) to measure the impact on overall productivity. The current study used quantitative research methods to analyze and find the impact of employee involvement on overall productivity through the use of an academic questionnaire adopted from academic resources. The study targeted private sectors in the Kurdistan region of Iraq. The study was able to gather 97 questionnaires filled out properly by participants working in private sectors in the Kurdistan region of Iraq. According to the findings of the research, involvement and commitment appear to go hand in hand. Also, it is very likely that productivity will be affected by both the main effects of each factor and the effects of how these factors interact with each other.
Investigating the Incorporation of Digital Literacy and 21st-Century Skills into Postgraduate Students’ Learning Activities

Students in higher education institutions require digital literacy and 21st-century skills for their learning activities. Despite the reality of students born into digital technologies that have become increasingly integrated into learning activities, no studies have looked into the situation, particularly study in the context of postgraduate students. This study aims to determine the extent of Master of English Language Education (MELE) students’ incorporation of digital literacy and 21st-century skills within their learning activities. This study was carried out using a survey research method. The sample consisted of 34 MELE students from Jakarta State University enrolled in various gap years (2019-2021) in the Master English Language Education department. The data were collected using 24 questionnaire items. The instruments were employed based on the International Society for Technology in Education (ISTE) Student Standard and disseminated using Google Form. The findings revealed that most MELE students had a high level of digital literacy and 21st-century skills. However, some students marked themselves as having low levels on it. Thus, the researchers recommended that the students enhance their engagement with technology, incorporate 21st-century skills, and develop digital literacy according to today’s requirements for completing their learning goals.
The Parla-CLARIN Recommendations for Encoding Corpora of Parliamentary Proceedings

Parliamentary proceedings are a rich source of data that can be used by scholars in various humanities and social sciences disciplines. Unlike the sources of most other language corpora, parliamentary proceedings are not subject to copyright or personal privacy protections, and are typically available online, thus making them ideal for compilation into corpora and for open distribution. For these reasons many countries have already produced corpora of parliamentary proceedings, but each typically in their own encoding, limiting their comparability and utilization in a multilingual setting. In this paper we propose an encoding schema which could serve as an interchange format for parliamentary corpora compiled for the purposes of scholarly investigations. The schema, called Parla-CLARIN, was developed within the CLARIN research infrastructure, and is written as a TEI ODD which includes a TEI customization and prose guidelines with examples of use. We discuss the coverage and choices made in designing the recommendations, and give an overview of the guidelines. We also discuss two other standard schemas for encoding parliamentary data, Akoma Ntoso and RDF, and their relation to Parla-CLARIN. We conclude by presenting corpora already encoded in Parla-CLARIN and discussing further work, especially the provision of a set of example documents and of transformation scripts that would make the proposed encoding more usable.
Object Tracking in Satellite Videos Based on Siamese Network With Multidimensional Information-Aware and Temporal Motion Compensation

The availability of many commercial satellites has created favorable conditions for tracking typical objects in remote sensing sequences, making them widely useful in numerous applications. However, small objects, multiple similar disruptors, background clutter, and occlusion are significant challenges to this field. This study proposes the novel tracker-temporal motion compensation Siamese network (Siam-TMC) for remote sensing tracking. Our method relies on a multidimensional information-aware module and a temporal motion compensation mechanism. Notably, we propose a dual branch-based Dim-Aware module that brings together foreground and high frequency information to distinguish between critical small objects and interferers. In addition, a TMComp mechanism using temporal motion information was designed to mitigate object trajectory drift through the supervision of occlusion detection. Detailed experimental comparisons on a benchmark dataset show that our method outperforms the state-of-the-art tracking models, particularly in occlusion scenarios.
HEAVY METALS DISTRIBUTION AND FRACTIONATION IN MANGROVE SEDIMENTS LINKED TO ORGANIC DEPOSITS VIS-À-VIS ACCUMULATION IN RHIZOPHORA SPP. AT TANJUNG PIAI, JOHOR, MALAYSIA

Mangrove ecosystems are crucial for ecological processes.However, mangrove forests are currently under peril due to urbanization, aquaculture expansion, and growing pollutant burdens.Marine debris including organic deposits (ODs) is intentionally or unintentionally deposited into the marine environment, which could alter the natural ecology of the ecosystem.ODs from Tanjung Piai, Johor, Malaysia have high concentration of heavy metals (HMs), specially Cd and PB, with respective concentration of 2.45 mg kg -and 82.41 mg kg -1 , which surpassed the compost guidelines of the European and United Kingdom.Four sampling sites were established in Tanjung Piai based on ODs levels: T1: without ODs, T2: new ODs, T3 and T4: decomposed ODs 90 m and 150 m from shoreline, respectively.T3 and T4 had the highest Cu and Pb.Modified sequential extraction European Community Bureau of Reference (BCR) reveals that Fe and Mn are highly mobile for mangrove species uptake, with concentrations of these HMs in Rhizophora spp.leaves higher than Cd, Pb, and Zn, implying that these species did not meet the criteria for hyperaccumulators due to low metals accumulation, necessitating further research to identify species suited for phytoextraction of HMs.