Reviewerly/exHarmony huggingface dataset:
Multitask Learning for Aspect-Based Sentiment Classification

Aspect-level sentiment analysis identifies the sentiment polarity of aspect terms in complex sentences, which is useful in a wide range of applications. It is a highly challenging task and attracts the attention of many researchers in the natural language processing field. In order to obtain a better aspect representation, a wide range of existing methods design complex attention mechanisms to establish the connection between entity words and their context. With the limited size of data collections in aspect-level sentiment analysis, mainly because of the high annotation workload, the risk of overfitting is greatly increased. In this paper, we propose a Shared Multitask Learning Network (SMLN), which jointly trains auxiliary tasks that are highly related to aspect-level sentiment analysis. Specifically, we use opinion term extraction due to its high correlation with the main task. Through a custom-designed Cross Interaction Unit (CIU), effective information of the opinion term extraction task is passed to the main task, with performance improvement in both directions. Experimental results on SemEval-2014 and SemEval-2015 datasets demonstrate the competitive performance of SMLN in comparison to baseline methods.
CSR Image and Customer Satisfaction: The Mediating Role of Customer Trust and Customer Loyalty

Purpose: This study aims to examine the impact of Corporate Social Responsibility (CSR) image on customer satisfaction in the context of the banking sector of Pakistan. Further, the intervening role of customer trust and customer loyalty in the relationship between CSR image and customer satisfaction is also examined.&#x0D; Design/Methodology/Approach: Present research is quantitative in nature and collects the data through structured questionnaires. By employing the Smart PLS technique to test the proposed hypothesis.&#x0D; Findings: This study reveals the following salient findings: 1) the response of CSR image to customer satisfaction is positive (p&lt;0.01); 2) customer trust and customer loyalty both significantly mediates the impact of CSR image on customer satisfaction.&#x0D; Implications/Originality/Value: Based on novel findings, the current study will help the organizations to realize the significance of CSR practices to enhance the customers’ satisfaction level.
DEVELOPMENT OF DAKON GAME INTERACTIVE E-BOOK MEDIA TO IMPROVE MATHEMATICS LEARNING OUTCOME OF ELEMENTARY SCHOOL STUDENTS

The low student learning outcomes in the concept of learning addition and subtraction in learning Mathematics, especially for grade 1 in Elementary School, is still not optimal. This is one of the contributing factors is the lack of mathematics learning materials developed by the teachers, and not many teachers involve students in the learning process so that students are less active in responding to the ongoing Mathematics learning. This study is intended to design learning media that can improve student learning outcomes in learning Mathematics through the development of interactive e-book media for the Dakon game. The formulation of this problem is how is the process of developing the Dakon Game Interactive E-Book Media to Improve Mathematics Learning Outcomes of Elementary School Students and How is the feasibility of developing Dakon Game Interactive E-Book Media to Improve Mathematics Learning Outcomes of Elementary School Students, this study uses the R&amp;D development method only at level 1 with the stages of collecting potentials and problems, literature study and information gathering, product design, design validation, and tested design. With the results of the linguist validation test, the percentage of eligibility of 85% was declared very feasible to use, then the material validation test with a feasibility percentage of 83% was also declared very feasible to use, and the last one was the empirical scale test with a feasibility percentage of 86% indicating that the e-book media Dakon student interactive game is very feasible to use.
Collaboration Blockchain Technology and Gamification in iLearning systems

Purpose: Currently, many fake certificates or diplomas are used to apply for jobs to get a better paying job. With that said, currently certificates cannot prove a person's expertise or skills. However, some students have implemented a Blockchain system in securing their certificates so that data manipulation is minimized, but only partially. Therefore, this research aims to motivate anyone, especially students in this Blockchain system and students will get incentives from all activities that are followed by racing in Blockchain gamification concept 4.0. Study design: This study uses the Pieces method to classify a problem (problem), opportunities (opportunities), and existing directions. Data collection techniques used primary data obtained by distributing questionnaires in the form of google form to respondents by involving students (n = 1129). Conclusions were analyzed with the SUS trial using a Likert scale with the cut and SUS method. Result: The results of this study are expected to Gamification in the system Blockchain can run optimally in implementation. The use of Gamification on the Alphabet Blockchain is included in the acceptable category. Value: The platform can check certificates that are not genuine.
Review on Application of Augmented Reality (AR) in The Eco-Tourism Sector

There is a tendency at this moment to see augmented reality (AR) application to be used in eco-tourism sector.In this day and age of the Fourth Industrial Revolution, the way we communicate with the aid of technology has already been revolutionized, making things easier and quicker than ever before.The advancements in artificial intelligence (AI) have allowed us to broaden the scope of their functional implementations to include advanced, personalized, and more 'human' areas.This research paper shares findings form a study of augmented reality characteristics in the eco-tourism sector.Quantitative data from existing augmented reality, virtual reality and mixed reality characteristics and previous research in the market provide a significant perspective on the topic of characteristics of AR for ecotourism sector.Four major practices in eco-tourism sector were examined to understand more on AR characteristics: Local community involvement, nature literacy, incomegenerating and natural environment.Findings from this research paper show that much more needs to be achieved not only in the creation and implementation of AR in eco-tourism sector but also, more precisely, in order to ensure that the process of familiarizing and normalizing the use of AR with eco-tourism is more aggressive.
A Survey of Digital Image Watermarking Techniques Based on Discrete Cosine Transform

In this study, a literature review was presented to examine digital image watermarking techniques based on Discrete Cosine Transform (DCT). General features of the digital watermarking method, frequency domain & spatial domain techniques, and some important issues about DCT were also presented. The studies were classified according to the application methods of the DCT, the features of the watermarking technique, the watermark extraction method, robustness, and usage area. Finally, the results have shown that DCT has increased the robustness of watermarking by providing ease of use in many methods from simple to complex, machine learning and optimization techniques would be frequently used methods for improving the studies.
Hinglish to English Machine Translation using Multilingual Transformers

Code-Mixed language plays a very important role in communication in multilingual societies and with the recent increase in internet users especially in multilingual societies, the usage of such mixed language has also increased.However, the cross translation between the Hinglish Code-Mixed and English and vice-versa has not been explored very extensively.With the recent success of large pretrained language models, we explore the possibility of using multilingual pretrained transformers like mBART and mT5 for exploring one such task of code-mixed Hinglish to English machine translation.Further, we compare our approach with the only baseline over the PHINC dataset and report a significant jump from 15.3 to 29.5 in BLEU scores, a 92.8% improvement over the same dataset.
On the Instruction Design of the Course Business English Writing Based on Blended Learning Model

Blended learning based on information technology, emphasizes students’ central position and teachers’ leading role in teaching and learning. In view of this, the author attempts to apply the model to the instruction of the course Business English Writing. In the instructional design, the author navigates students to enhance the input of sample texts on the basis of webbased autonomous learning and organizes cooperative learning so as to increase students’ writing practice, thus improving their research ability and communicative skills. Consequently, the two major problems—deficiency in language input because of limited class hours and lack of interaction owing to the large size of classes can be effectively solved.
Learning to Translate Kannada and English Queries for Mixed Script Information Retrieval

Due to increase in the availability of numerous languages in the Web, cross language information retrieval is one of the happening issues in the field of natural language processing and information retrieval. Nowadays, people are habituated to combine two or more language words during oral or written discourse. Speakers have also employed intermixing of different languages and scripts in digital media while querying, blogging and on social media platforms. The way of representing two different language words of an utterance in their native scripts is known as mixed scripting. In the present work, we attempted to translate mixed script queries of Kannada and English languages into monolingual queries. We proposed three approaches for translation by constructing bilingual dictionary, word embeddings and Google translate. The proposed method outperforms the conventional dictionary based approach, when word embeddings were combined with the translations learnt from Google Translate and Dictionary.
Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach

This paper aims to meaningfully analyse the Horizon 2020 data existing in the CORDIS repository of EU, and accordingly offer evidence and insights to aid organizations in the formulation of consortia that will prepare and submit winning research proposals to forthcoming calls. The analysis is performed on aggregated data concerning 32,090 funded projects, 34,295 organizations participated in them, and 87,067 public deliverables produced. The modelling of data is performed through a knowledge graph-based approach, aiming to semantically capture existing relationships and reveal hidden information. The main contribution of this work lies in the proper utilization and orchestration of keyphrase extraction and named entity recognition models, together with meaningful graph analytics on top of an efficient graph database. The proposed approach enables users to ask complex questions about the interconnection of various entities related to previously funded research projects. A set of representative queries demonstrating our data representation and analysis approach are given at the end of the paper.
Gradient Temporal Difference with Momentum: Stability and Convergence

Gradient temporal difference (Gradient TD) algorithms are a popular class of stochastic approximation (SA) algorithms used for policy evaluation in reinforcement learning. Here, we consider Gradient TD algorithms with an additional heavy ball momentum term and provide choice of step size and momentum parameter that ensures almost sure convergence of these algorithms asymptotically. In doing so, we decompose the heavy ball Gradient TD iterates into three separate iterates with different step sizes. We first analyze these iterates under one-timescale SA setting using results from current literature. However, the one-timescale case is restrictive and a more general analysis can be provided by looking at a three-timescale decomposition of the iterates. In the process we provide the first conditions for stability and convergence of general three-timescale SA. We then prove that the heavy ball Gradient TD algorithm is convergent using our three-timescale SA analysis. Finally, we evaluate these algorithms on standard RL problems and report improvement in performance over the vanilla algorithms.
Numerical integration of quantum time evolution in a curved manifold

The numerical integration of the Schr\"odinger equation by discretization of time is explored for the curved manifolds arising from finite representations based on evolving basis states. In particular, the unitarity of the evolution is assessed, in the sense of the conservation of mutual scalar products in a set of evolving states, and with them the conservation of orthonormality and particle number. Although the adequately represented equation is known to give rise to unitary evolution in spite of curvature, discretized integrators easily break that conservation, thereby deteriorating their stability. The Crank-Nicolson algorithm, which offers unitary evolution in Euclidian spaces independent of time-step size $dt$, can be generalized to curved manifolds in different ways. Here we compare a previously proposed algorithm that is unitary by construction, albeit integrating the wrong equation, with a faithful generalization of the algorithm, which is, however, not strictly unitary for finite $dt$.
Superiority of Siriraj Stroke Score Over Guy’s Hospital Score in Diagnosing Acute Hemorrhagic Stroke at Bedside

Background: The clinical diagnosis of stroke in a patient admitted in the intensive care unit (ICU) is undeniably challenging. Several point-based risk scores have been developed to predict clinical outcomes after ischemic stroke. Objective: To assess the Siriraj stroke score and Guy’s Hospital stroke score in the clinical diagnosis of acute stroke. Materials and Methods: All patients were subjected to Computed tomography (CT) scan head within 72 hours of admission. The sensitivity, specificity, positive predictive value was calculated for both the scores. Comparability between the scores and CT scan head finding was determined with the help of Kappa statistic program. Results: Sensitivity of Guy’s Hospital stroke score for ischemic stroke is 100%, specificity is 96.4%, accuracy 97.1%, positive predictive value of 87.5% and negative predictive value 100%. The sensitivity of Guy’s Hospital stroke score for hemorrhage stroke is 96.4%, specificity is 100%, accuracy 97.1%, positive predictive value of 100% and negative predictive value 87.5% Conclusion: Siriraj stroke score as a simple method of screening patients for intracerebral hemorrhage, as it is easier to use at bedside and has a greater accuracy in diagnosing hemorrhage than Guy’s Hospital score. KYAMC Journal. 2021;12(3): 142-146
An Approach to Enhance Business Intelligence and Operations by Sentimental Analysis

Sentimental analysis is rapidly getting inducted into businesses as a direct result of the technology growth in every sector owing to globalization and industry 4.0.Sentimental analysis which is also known as opinion mining is used in identifying and analyzing text based on the tone that was conveyed by the person which can be categorized broadly into positive, negative and neutral.Businesses can utilize sentimental analysis to tap insight important insights regarding companies, organizations, people, trends and services.With the vast amount of Big Data increasing every day, especially from social media such as Twitter, Facebook etc. businesses can utilize sentimental analysis.This paper thus focuses on implementing machine learning models in Python to perform sentimental analysis from twitter tweets as a viable approach to enhance business intelligence, improve decision marking and target effective operations.The data used in this analysis is obtained from Kaggle collections of COVID-19 twitter dataset.This paper also discusses the various types of applications for sentimental analysis in business and their benefits.The findings from this paper will help improve understanding sentimental analysis for businesses and their practicality in real world scenarios as Big Data advances whilst business intelligence of companies rigorously demands outshining competitive advantage.
Hybrid technique for improving underwater image

As light attenuate when disseminating in water, descriptions confined beneath water is generally corrupted towards varying degrees.An acquired image underneath water gets degraded due to optical properties of light in water.First, we describe optical behaviour of light in ocean due to which acquired images gets degraded.However, due to degradation of observed picture, conventional forms are not correct enough for faithful reconstruction.So, to improve the perception, we intend a Non-Locally Centralized Method (NLCM) for deblurring underwater descriptions.Later by considering characteristic of light propagation, we propose Gradient Guided Filter (GGF) method for improving the visibility of picture details.Finally, the image is well enhanced by Hybrid technique called Nonlocally centralized gradient guided filter (NLCM-GGF).Tentative outcome demonstrates that proposed technique produces improved results than several conventional techniques together in quality metrics and visual evaluation.
Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric

Methods of prevention and equipment to reduce the risk of falls based on accelerometer and gyroscope sensor have developed rapidly because its operations are cheaper than video cameras. Improved accuracy of detection and fall prediction based on accelerometer and gyroscope sensor is carried out by utilizing Artificial Intelligence (AI) to predict falling patterns. However, the existing fall prediction system is less responsive and also has a low level of accuracy, sensitivity and specificity. The current system does not have a notification system to care givers or doctors in the hospital. To overcome the above problems, this study proposes the development of smart fall prediction system based on accelerometer and gyroscope for the prevention of fractures in geriatric populations (JaPiGi) which are accurate and have high sensitivity and specificity. This study uses Fuzzy Mamdani to recognize movements falling forward, falling sideways, sitting, sleeping, squatting and praying. The total data tested was 100 data from 10 participants. The introduction of this movement is based on 6 input variables from data of accelerometer and gyroscope sensor. To calculate the accuracy, precision, sensitivity and specificity in this study using the equation Receiver Operating Characteristic (ROC). Motion recognition is carried out 3 times with an average accuracy of 90%.
THE APPROACH FOR QOS BASED WEB SERVICE SELECTION WITH USER’S PREFERENCES

This paper is devoted to applying the LSP method for Web service selection based on quality characteristics and the user’s preferences. The purpose of this work is to develop an approach for the Web service selection, which, based on a reasonable choice of Web service quality attributes and the use of the LSP method, allows for a deeper consideration of consumer preferences. The efficiency the LSP method for QoS-based Web service selection is conformed. The novelty of this work is that the LSP method was fully used for QoS-based Web service selection in comparison to the publications authors are aware of. Keywords: Web service, quality attributes, LSP method, aggregation framework, multicriteria decision-making method.
Implementation of EM Algorithm in Opinion Mining Movies Review Case Studies

Movies are very familiar to everyone, from children, adolescents to adults, whether just because they want to watch, a hobby, or fill their spare time. Movies that used to be watched only on television and had to wait months after release or directly to the cinema, with the development of technology, of course, it is increasingly easier for everyone to enjoy movies, now they can be watched through paid television services to smartphones. One of the websites that viewers often use to review movies they have watched is IMDb. The data review can be used to get an opinion or opinion mining from the audience, whether the title of the movie being reviewed is good or not. One of the algorithms that are often used is Naïve Bayes, apart from being easy to implement, Naïve Bayes is also known to be very fast and easy to use to predict classes on a test dataset. The purpose of this study is to see how much influence the Expectation-Maximization to increase accuracy on implementation of Expectation-Maximization algorithm in opinion mining movies review case studies. From the results of this study using the Expectation-Maximization method, it was found that the accuracy increased by 4% compared to using only Naïve Bayes.
Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network

Rice (Oryza sativa) is a grain that comes in third place among all grains after corn and wheat. 80 percent of Indonesians eat rice as a staple diet, especially in Southeast Asian countries, but the International Rice Research Institute (IRRI) reports that farmers lose 37 percent of their rice crops each year owing to pests and illnesses. Based on this study, it is critical to investigate the detection of rice pests and illnesses. Using the Convolution Neural Network (CNN) technique, an automatic classification system to identify and predict plant illnesses has been developed. A study titled Classification of Rice Leaf Diseases was undertaken by the author. The CNN Algorithm is being used to help farmers learn how to combat rice leaf diseases. Bacterial leaf blight, Rice blast, and Rice tungro virus were among the rice leaf types classified in this study. There are 6000 datasets in all, with 80% of them being training data, 10% being validation data, and 10% being testing data. The accuracy of the results obtained for epochs 25, 50, 75, and 100 varies. The best training accuracy results come from epoch 100, which has a 98% accuracy rate, and testing using a confusion matrix has a 98% accuracy rate. In diagnosing rice leaf diseases, the Convolutional Neural Network (CNN) algorithm delivers great accuracy.
A Constant-time AVX2 Implementation of a Variant of ROLLO

This paper introduces a key encapsulation mechanism ROLLO+ and presents a constant-time AVX2 implementation of it. ROLLO+ is a variant of ROLLO-I targeting IND-CPA security. The main difference between ROLLO+ and ROLLO-I is that the decoding algorithm of ROLLO+ is adapted from the decoding algorithm of ROLLO-I. Our implementation of ROLLO+-I-128, one of the level-1 parameter sets of ROLLO+, takes 851823 Skylake cycles for key generation, 30361 Skylake cycles for encapsulation, and 673666 Skylake cycles for decapsulation. Compared to the state-of-the-art implementation of ROLLO-I-128 by Aguilar-Melchor et al., which is claimed to be constant-time but actually is not, our implementation achieves a 12.9x speedup for key generation, a 10.6x speedup for encapsulation, and a 14.5x speedup for decapsulation. Compared to the state-of-the-art implementation of the level-1 parameter set of BIKE by Chen, Chou, and Krausz, our key generation time is 1.4x as slow, but our encapsulation time is 3.8x as fast, and our decapsulation time is 2.4x as fast.