Reviewerly/exHarmony huggingface dataset:
Bitcoins in the Malaysian Economy

Bitcoin is digital money, and it is public and distributed in its ecosystem.A bitcoin first released by Satoshi Nakamoto in 2008.It is a peer-to-peer (direct connections without an intermediary), open-source encryption system, where transactions occur through a public directory called a blockchain.However, bitcoin has no proper review in the Malaysian context.Also, there is no specific and descriptive analysis of bitcoin in Malaysia.Though Bitcoins have already gained widespread popularity worldwide, it would be better to control this industry through proper management.The faster it is, the more accessible.However, blockchain technology is redeeming in a positive light.This study focused on individual facts and details, such as articles, reviews, forums, Malaysians brochure, business lines, and research reports.The study relies on secondary data.This study's primary aim to review previously studied that focuses on bitcoin around the globe and Malaysia.A method of meta-analysis is used for theory-building.This study provides the Malaysian bitcoin market's key insights and understing to consumer and operator.Therefore conclude with a summarization of bitcoin's current situation in Malaysia.
machine and deep learning approaches for human activity recognition

Human Activity Recognition (HAR) is a domain that has shown great interest in the past years and tills now. The main cause for this is that it can be used in various applications. There exist several devices and sensors that can capture and record activities. In this paper, a survey about the machine learning and deep learning methodologies in HAR is provided with information about the data, filtering methods, feature extraction methods, classification, and different performance measurements. The main aim is to target the old and the recent papers published in HAR and to determine whether the machine learning or deep learning methods is better in performance. In addition to this, the survey will cover the types of actions or activities that are predicted. Then, a discussion about the main points obtained from the survey. Finally, the conclusions, limitations, and challenges in HAR are presented clearly. Human activity recognition (HAR) can be known with various types of definitions. HAR is preserved to be a field of studying and identifying the movements of the individuals or the action of the human based on sensor data . These movements can be different activities such as walking, talking, standing, and sitting. They are also called indoor activities.
A Study of the Effectiveness of PBL and MAKER Classes Based on Flipped Learning

Purpose: The purpose of this study was to find whether a team assignment in a Flipped Learning class using Project Based Learning PBL or MAKER has a greater effect on improving interest and class satisfaction than in the existing class.Methods: As an alternative to increasing the level of interest in the existing Flipped Learning class, the team project activity was operated as a PBL or MAKER project. The activityrest andtiveness was investigated through the results of pre- and post-interest surveys and a satisfaction survey.Results: The study participants in the Flipped-based PBL and MAKER classes, showed much greater interest and satisfaction than those in the existing Flipped Learning classes.Conclusion: The study results indicated that the selection of a class application offering a wider variety of teaching methods suitable to the subject and the combined application of several teaching methods complemented the shortcomings of existing teaching methods and maximized their strengths.
Eye Tracking and Its Applications

We can measure the eye movement activity using eye tracking technology.We can get information about where we look by using eye tracking.What is overlooked is how the pupil reacts to various stimuli.Although the premise of eye tracking is simple, the technique and interpretation can be quite varied and complex.ET calculates the distance between our eye's gaze points and the top of our head.Eye trackers come in both remote and mobile versions.It keeps track of where we look and how we move our sight.With the use of software, one may analyze, visualize, and interpret this data.We've already known how fingerprint analysis and applications are commonly used, but eye tracking could also be a useful biometric technique for a variety of applications.Eye tracking technology and its varied uses are discussed in this paper.ET is now used in nearly every sector, including psychology, human-computer interface, marketers, designers, academics, medical research, and many more.
Design of Optimal Deep Learning Based Human Activity Recognition on Sensor Enabled Internet of Things Environment

In recent times, Human activity recognition (HAR) becomes a major challenging issue among computer vision applications in day to day lives. The HAR is mainly envisioned to be utilized with other technologies, namely Internet of Things (IoT) and sensor technologies. Due to the advancements of deep learning (DL) approaches, automated high level feature extraction process can be utilized to improve the HAR results. In addition, DL techniques can be employed in different domains of sensor enabled HAR. In this aspect, this study designs an optimal DL based HAR (ODL-HAR) model on sensor enabled IoT environments. The ODL-HAR technique aims to determine human activities in day to day lives using wearables and IoT devices. The proposed ODL-HAR technique involves different stages of operations namely data acquisition, data preprocessing, feature extraction, classification, and parameter optimization. The ODL-HAR technique uses a MobileNet-v2 model as a feature extractor and bidirectional long short-term memory (BiLSTM) model as a classifier. In order to optimally tune the hyperparameter involved in the BiLSTM model, chaos game optimization (CGO) algorithm is employed and thereby raises the recognition performance. The design of CGO algorithm for hyperparameter optimization of HAR shows the novelty of the work. A wide range of simulations takes place to point out the betterment of the ODL-HAR technique on two benchmark datasets. The experimental results portrayed the enhanced performance of the ODL-HAR technique over the other recent HAR approaches interms of different evaluation parameters.
Reading Habits in Digital Era during Lockdown among Adolescent

Objectives : To assess the reading habits in digital era during COVID-19 lockdown period and to find the association between reading habits with selected demographic variables.Methods: A descriptive study design was adopted among 300 undergraduate students of Chennai, India using three tools such as Reading Attitude Scale, Self-Report Habit Index Questionnaire and Self-structured questionnaire on Reading Habit in Digital Era.The data was collected by using Google Forms considering the surge in COVID-19.Findings: Most of the samples 132 (44%) were reading newspaper during the lockdown.Many of the samples 114 (38%) were using google classroom for their academic activity, 131 (44%) samples were spending 1 to 3 hours per day for academic work.Half of the samples 151 (50.33%) accepted that reading online increases access to information sources.LinkedIn is famous among UG students.The study concluded that the undergraduate students are interested in digital reading during this lockdown period.The undergraduate students spent most of their time in digital reading, and they are connected with their studies during lockdown.Novelty: Many review articles have been published in online learning and related to education.Due to Covid-19 pandemic, many are forced to be away from direct books and started online reading.Therefore, our current review article highlights reading habits among adolescent during lockdown.
A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes

This paper outlines a system for detecting printing errors and misidentifications on hard disk drive sliders, which may contribute to shipping tracking problems and incorrect product delivery to end users. A deep-learning-based technique is proposed for determining the printed identity of a slider serial number from images captured by a digital camera. Our approach starts with image preprocessing methods that deal with differences in lighting and printing positions and then progresses to deep learning character detection based on the You-Only-Look-Once (YOLO) v4 algorithm and finally character classification. For character classification, four convolutional neural networks (CNN) were compared for accuracy and effectiveness: DarkNet-19, EfficientNet-B0, ResNet-50, and DenseNet-201. Experimenting on almost 15,000 photographs yielded accuracy greater than 99% on four CNN networks, proving the feasibility of the proposed technique. The EfficientNet-B0 network outperformed highly qualified human readers with the best recovery rate (98.4%) and fastest inference time (256.91 ms).
Analysis of the dynamics of the publication activity of the university in terms of employee categories

Introduction. In recent years, the goal of the state policy in the field of science and higher education was to make the universities occupy competing positions in international assessment systems, including scientific potential. Therefore, the universities need constructive solutions able to ensure the growth of the corresponding indicators. The purpose of the research is to analyze the dynamics of the publication activity of the university staff. Materials and methods. Data on the number of publications of university employees in the world's leading scientometric databases Web of Science and Scopus for the period from 2000-2020 served as the research materials. The study used classification, descriptive statistics and comparative data analysis. Research results. The categories of the teaching staff were determined, which to a greater extent affect the publication activity of the institution as a whole. As a result of a comparative analysis, all employees were divided into four categories according to the type of work (full-time teaching staff, external part-time workers, internal part-time workers and non-teaching staff), as well as into 6 groups depending on the number of publications ("0", "1-2", "3-4","5-6","7-8", "&gt; 9"). The probabilities were calculated and matrices of statistical estimates of the transitions of employees of various categories from one group to another for each status were formed (the employees who continue to work at the university, newly hired or dismissed during the year). It was revealed that the drivers of scientific activity are the full-time teaching staff, except for those included in the group "0". Discussion and conclusion. The obtained matrices of transition probabilities will form the basis for modeling the number of employees in various groups and predicting the total number of publications of an institution. As a result, it will be possible not only to predict the total number of articles of the university while maintaining current trends, but also to consider various management scenarios in order to improve efficiency and meet the goals of the institution. This will help universities not only conduct self-analysis, but also manage the performance indicators of the institutions and departments, in terms of the growth of publication activity results.
Adversarial Scrubbing of Demographic Information for Text Classification

Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task.We aim to scrub such undesirable attributes and learn fair representations while maintaining performance on the target task.In this paper, we present an adversarial learning framework "Adversarial Scrubber" (ADS), to debias contextual representations.We perform theoretical analysis to show that our framework converges without leaking demographic information under certain conditions.We extend previous evaluation techniques by evaluating debiasing performance using Minimum Description Length (MDL) probing.Experimental evaluations on 8 datasets show that ADS generates representations with minimal information about demographic attributes while being maximally informative about the target task.
Contextual Rephrase Detection for Reducing Friction in Dialogue Systems

For voice assistants like Alexa, Google Assistant and Siri, correctly interpreting users' intentions is of utmost importance.However, users sometimes experience friction with these assistants, caused by errors from different system components or user errors such as slips of the tongue.Users tend to rephrase their query until they get a satisfactory response.Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e.g.users' implicit feedback).To this end, we propose a contextual rephrase detection model ContReph to automatically identify rephrases from multiturn dialogues.We showcase how to leverage the dialogue context and user-agent interaction signals, including user's implicit feedback and the time gap between different turns, which can help significantly outperform the pairwise rephrase detection models.
The Domain Mismatch Problem in the Broadcast Speaker Attribution Task

The demand of high-quality metadata for the available multimedia content requires the development of new techniques able to correctly identify more and more information, including the speaker information. The task known as speaker attribution aims at identifying all or part of the speakers in the audio under analysis. In this work, we carry out a study of the speaker attribution problem in the broadcast domain. Through our experiments, we illustrate the positive impact of diarization on the final performance. Additionally, we show the influence of the variability present in broadcast data, depicting the broadcast domain as a collection of subdomains with particular characteristics. Taking these two factors into account, we also propose alternative approximations robust against domain mismatch. These approximations include a semisupervised alternative as well as a totally unsupervised new hybrid solution fusing diarization and speaker assignment. Thanks to these two approximations, our performance is boosted around a relative 50%. The analysis has been carried out using the corpus for the Albayzín 2020 challenge, a diarization and speaker attribution evaluation working with broadcast data. These data, provided by Radio Televisión Española (RTVE), the Spanish public Radio and TV Corporation, include multiple shows and genres to analyze the impact of new speech technologies in real-world scenarios.
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning

Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents. As environments grow in size, effective credit assignment becomes increasingly harder and often results in infeasible learning times. Still, in many real-world settings, there exist simplified underlying dynamics that can be leveraged for more scalable solutions. In this work, we exploit such locality structures effectively whilst maintaining global cooperation. We propose a novel, value-based multi-agent algorithm called LOMAQ, which incorporates local rewards in the Centralized Training Decentralized Execution paradigm. Additionally, we provide a direct reward decomposition method for finding these local rewards when only a global signal is provided. We test our method empirically, showing it scales well compared to other methods, significantly improving performance and convergence speed.
Transaction pricing mechanism design and assessment for blockchain

The importance of transaction fees in maintaining blockchain security and sustainability has been confirmed by extensive research, although they are not mandatory in most current blockchain systems. To enhance blockchain in the long term, it is crucial to design effective transaction pricing mechanisms. Different from the existing schemes based on auctions with more consideration about the profit of miners, we resort to game theory and propose a correlated equilibrium based transaction pricing mechanism through solving a pricing game among users with transactions, which can achieve both the individual and global optimum. To avoid the computational complexity exponentially increasing with the number of transactions, we further improve the game-theoretic solution with an approximate algorithm, which can derive almost the same results as the original one but costs significantly reduced time. We also propose a truthful assessment model for pricing mechanism to collect the feedback of users regarding the price suggestion. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed mechanism.
SENTIMIX (HINDI- ENGLISH)

— The study of emotions is an important part of research in the excavation of literature. It involves removing ideas from articles such as reviews, news, blog posts, etc. and then classifying them as positive, negative or negative. The senses of English were studied but not much work was done for the Indian language. Studies were conducted in Hindi, Bengali, Marathi and Punjabi. Today, most communications are made on social media using the Hinglish language which is a combination of both Hindi and English. Hinglish is a native language that is very popular in India because people are more comfortable speaking their own language. This paper provides a new method for expressing the emotions of Hinglish (Hindi + English). Emotional analysis (SA) uses mixed data from the social media with many programs. Feedback from consumer satisfaction in evaluating social activity in multiple languages society. Progress in this area is hampered by a lack of relevant sequential data. We Introduces a Hi-En code-mixing set for sensitive information and satisfying performance. A comparative study of the feasibility and implementation of SA methods in social media. We also derive and describe Hinglish language. We examine this problem by using sets of lexicon, emotion, and form metadata to construct a classification that can vary between “positive”, “negative” and “neutral” feelings.
Analysis of earthquake forecasting using random forest

The subject of forecasting earthquakes is an intriguing one to investigate. As a natural calamity, earthquakes continue to be devastating, not just to the economy but also to the lives of individuals. This gave rise to the concept of creating an early warning system against seismic catastrophes to minimize deaths. Researchers have been making earthquake forecasts and seismic hazard ratings of a location for a few years now. In this work, we attempt to forecast earthquakes before they occur using p-arrival data, which includes information on disaster arrival time and amplitude height from the arrival station. Several studies on earthquake prediction have been carried out so far and have developed and used the Random Forest method and one of the Machine Learning. According to [1], the process of predicting earthquakes has been studied for a long time, but there is still uncertainty due to the diversity and complexity of the earthquake phenomenon itself. According to [2], conducting a random forest prediction model to identify the structural safety status of buildings damaged by the earthquake is probabilistic. An earthquake's latitude, longitude, magnitude, and depth may be predicted using the random forest algorithm. A random forest with multioutput technique is employed, with variables being each station's recorded value and geographic position. This study's predictions were accurate to within 63 percent.
An AMBTC Authentication Scheme With Recoverability Using Matrix Encoding and Side Match

This paper presents an authentication scheme with recoverability based on Absolute moment block truncation coding (AMBTC). Previous works do not take into account characteristics of the AMBTC compressed codes. Therefore, the qualities of marked and recovered images are not satisfactory. In this paper, we generate the recovery codes according to block similarities. The recovery codes are scrambled and stored in the bitmaps of smooth blocks using Matrix encoding. The bits in the bitmap are flipped to obtain a set of authentication codes. The code that causes the least error is embedded into the quantization values using the adaptive pixel pair matching technique (APPM). For those complex blocks, the authentication codes are generated from the bitmaps, and are embedded into the quantization values. After authenticating, the tampered blocks are recovered by untampered ones with the same cluster information. Some blocks that cannot be recovered in the previous stage are recovered by using the Side match technique. The experimental results show that our method provides better image quality and recoverability than prior works.
Application of Learner-Centered Approach in College English Instruction in China: A Case Study

With a development history of more than half a century, the learner-centered approach has become a new teaching paradigm worldwide. The approach can produce effective and significant learning focusing on learners&amp;rsquo; development, learning, and learning outcomes. This study aimed to determine how a learner-centered approach can increase students&amp;rsquo; engagement, improve their English learning strategies, and enhance their academic performance in a specific EFL setting. Bloom&amp;rsquo;s taxonomy, Zhao&amp;rsquo;s &amp;ldquo;Neo Tri-Centers,&amp;rdquo; Krashen&amp;rsquo;s L2 acquisition theory, and primarily Biggs&amp;rsquo; constructive alignment functioned as valuable guidelines when designing the instructional activities. Constructive alignment holds that intended learning outcomes, teaching and learning activities, and assessment tasks should be aligned organically to achieve effective learning. Data collected through the instructor&amp;rsquo;s observations, questionnaires, assessments of the students&amp;rsquo; performances, and colleague&amp;rsquo;s feedback show that the learner-centered approach has remarkably motivated the students, improved their learning strategies, and enhanced their academic performance. The results and implications of this study may be of reference importance for future language teaching in a foreign language or second language setting.&amp;nbsp;