Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of LMS Users Using Support Vector Algorithm

This Higher education institutions are now being transformed by digital technologies in terms of administrative operations and delivery of instruction using learning management systems (LMS).This study aims to determine and understand the faculty and students' perceptions, experiences, expectations and concerns regarding educational uses of learning management systems (LMS).Qualitative research method was used to better understand the faculty and students' perceptions, expectations and concerns about LMS, responses from two (2) open-ended online survey questions was used as a primary source of data.For the statistical method which is also embedded in the processing technique, Support Vector Machine was used to validate the correctly classified instances.The study reveals that faculty and students exhibited positive perceptions in practicing and using learning management systems while the biggest expectations of faculty and students are assessment feature, discussion, accessibility, interface and hardware and software.The result of this study reveals that the perception achieved an average accuracy of 91.8182 %.Based on the findings of expectations and concerns, LMS must consider assessment feature, discussion, accessibility, interface and hardware and software to be an effective e-learning tools.
Long Short-Term Memory Based Movie Recommendation

Recommender systems (RS) have become a fundamental tool for helping users make decisions around millions of different choices nowadays – the era of Big Data. It brings a huge benefit for many business models around the world due to their effectiveness on the target customers. A lot of recommendation models and techniques have been proposed and many accomplished incredible outcomes. Collaborative filtering and content-based filtering methods are common, but these both have some disadvantages. A critical one is that they only focus on a user's long-term static preference while ignoring his or her short-term transactional patterns, which results in missing the user's preference shift through the time. In this case, the user's intent at a certain time point may be easily submerged by his or her historical decision behaviors, which leads to unreliable recommendations. To deal with this issue, a session of user interactions with the items can be considered as a solution. In this study, Long Short-Term Memory (LSTM) networks will be analyzed to be applied to user sessions in a recommender system. The MovieLens dataset is considered as a case study of movie recommender systems. This dataset is preprocessed to extract user-movie sessions for user behavior discovery and making movie recommendations to users. Several experiments have been carried out to evaluate the LSTM-based movie recommender system. In the experiments, the LSTM networks are compared with a similar deep learning method, which is Recurrent Neural Networks (RNN), and a baseline machine learning method, which is the collaborative filtering using item-based nearest neighbors (item-KNN). It has been found that the LSTM networks are able to be improved by optimizing their hyperparameters and outperform the other methods when predicting the next movies interested by users.
Losing As and Fs: What works for schools implementing standards-based grading?

Learning goals such as the Common Core State Standards are helping educational leaders to better understand the critical role assessment and grading plays in the teaching and learning process. In response to the growing body of empirical research on the topic of accurate grading practices, a number of schools are moving away from letter grades and adopting standards-based grading which separately report learning goals from work habits. The purpose of this essay is to document what works when K-12 schools implement standards-based grading through a deep dive into related literature and to suggest areas for future consideration. With this improvement to educational systems on the horizon, K-12 teachers and administrators in the early stages of redesigning their grading practices will benefit from understanding the successes and struggles of early adopters in order to successfully reform grading in their own context.
An UAV with Twin Propellers Driven by Single Motor

As the technology is looping and far applications of UAV is improving so there is need to develop an UAV with optimum propulsive efficiency and also producing high thrust. So our mission is to build an UAV with twin propellers propelled by single motor. A fixed wing UAV which has a unique type of propulsion system. In this fixed wing UAV, two propellers are driven by single motor to enhance the propulsive efficiency. The bevel gear mechanism is the most important. This is the propulsion system for the aircraft model which runs the two propellers with a single motor. Bevel gears are used to transfer the power from the motor to the propeller. The bevel gear train is 80cm long through which a long shaft is passed that holds the bevel gears. Totally six bevel gears are housed inside the box which connects the motor shaft to the propeller shaft. The bevel gears are arranged in such a way that the when motor shaft rotates the propeller connected rotates both in opposite direction.
Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering

Conversational Question Answering (Con-vQA) is a Conversational Search task in a simplified setting, where an answer must be extracted from a given passage.Neural language models, such as BERT, fine-tuned on large-scale ConvQA datasets such as CoQA and QuAC have been used to address this task.Recently, Multi-Task Learning (MTL) has emerged as a particularly interesting approach for developing ConvQA models, where the objective is to enhance the performance of a primary task by sharing the learned structure across several related auxiliary tasks.However, existing ConvQA models that leverage MTL have not investigated the dynamic adjustment of the relative importance of the different tasks during learning, nor the resulting impact on the performance of the learned models.In this paper, we first study the effectiveness and efficiency of dynamic MTL methods including Evolving Weighting, Uncertainty Weighting, and Loss-Balanced Task Weighting, compared to static MTL methods such as the uniform weighting of tasks.Furthermore, we propose a novel hybrid dynamic method combining Abridged Linear for the main task with a Loss-Balanced Task Weighting (LBTW) for the auxiliary tasks, so as to automatically fine-tune task weighting during learning, ensuring that each of the tasks' weights is adjusted by the relative importance of the different tasks.We conduct experiments using QuAC, a large-scale ConvQA dataset.Our results demonstrate the effectiveness of our proposed method, which significantly outperforms both the single-task learning and static task weighting methods with improvements ranging from +2.72% to +3.20% in F1 scores.Finally, our findings show that the performance of using MTL in developing ConvQA model is sensitive to the correct selection of the auxiliary tasks as well as to an adequate balancing of the loss rates of these tasks during training by using LBTW.
A fast video haze removal algorithm via mixed transmissivity optimisation

Image restoration is an important approach to image and video defogging. One of the most popular algorithms for image restoration is dark channel prior. However, when the algorithm is applied to outdoor digital webcams with limited computing resources, its real-time performance probably cannot be guaranteed. To address the above issue, this paper presents a fast video haze removal algorithm based on mixed optimised transmissivity to improve the time performance of the dark channel prior algorithm. The proposed algorithm combines guided filter and median filter and replaces the soft matting procedure in the classical dark channel prior algorithm. A set of experiments are performed to evaluate the real-time performance and effectiveness of the algorithm. The results show that our proposed improved algorithm can significantly improve the speed of video defogging, without sacrificing much effectiveness in identification of target objects.
Diabetes Diagnostic Model Based on Truth-value Restrictions Method Using Inference of Intuitionistic Conditional and Qualified Fuzzy Propositions

Diabetes is a challenging problem nowadays. Not only in India, but it also spreads over worldwide, In the present research paper a novel scheme based on intuitionistic fuzzy propositions to explore the knowledge base rule system with uncertainty has been developed and for the extension of fuzzy propositions to the domain of factors causing diabetes. In this paper, we have constructed the conditional and qualified intuitionistic fuzzy proposition mathematically for the diabetes diagnostic model. We have also developed an algorithm for Truth-value restriction method using the conditional and qualified intuitionistic fuzzy proposition;with the help of developed algorithm for truth-value restriction method we will give a scheme to check this severity of the diabetes. Numerical computations have also been carried out to demonstrate our approach.
Secure Auditing And Storage Systems In Cloud Service

Cloud is the one of the booming technology this era; cloud technology provides various storage services for the user, which is helpful on large amount of data maintenance and management.dd in this paper, we identify the issues of integrity auditing and storage security systems on cloud. Specially, seeking to achieve both auditing and storage in cloud in secure way, we are proposing a secure system, namely secClodud+ and using the dynamic data operation with portioning method the data are stored in the server. The performance of data storage and retrieval is securely done in proposed model. Implementation of the model is based on Improved Adaptive Huffman Technique that enables the access to the user to process the secure and efficient way of data. So that this improved Adaptive Huffman technique implements to store the data with the minimized time consumption, space and computational cost.
Hybrid Jaya Algorithm for Solving Multi-Objective 0-1 Integer Programming Problem

The aim of this paper is to find the optimal solution of complex multi-objective 0-1 integer programming problem(IPP) where as other evolutionary approaches are fails to achieve optimal solution or it may take huge efforts for computation. This paper presents the Hybrid Jaya algorithm for solving Multi-objective 0-1 IPP with the use of exponential membership function. In this work, we have improved the Jaya algorithm by bring in the conception of binary and exponential membership function. To established the effectualness of the suggested algorithm, one mathematical illustration is given with a data set from the practical and sensible state. At the end, the response of the improved algorithm is compared with other reported algorithms and we found that the suggested algorithm is evenly good or better for obtaining the solution of multi-objective 0-1 IPP.
Textual Similarity Measurement Approaches: A Survey (1)

Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.
A New Pattern Mining Algorithm for Analytics of Real-Time Internet of Things Data

The rise of IoT Real time data has led to new demands for mining systems to learn complex models with millions to billions of parameters, which promise adequate capacity to digest massive datasets and offer powerful predictive analytics. To support Big Data mining, high-performance powerful computing platforms are required, which impose regular designs to unleash the full power of the Big Data. Pattern mining poses a lot of interesting research problems and there are many areas that are still not well understood. The specifically very elementary challenges are to understand the meaningful data from the junk data that anticipated into the internet, refer as “Smart Data”. Eighty-five percent of the entire data are noisy or meaningless. It is a very tough often assigned to verify and separate to refine the data from the noisy junk. Researchers’ are proposing an algorithm of distributed pattern mining to give some sort of solution of the heterogeneity, scaling and hidden Big Data problems. The algorithm has evaluated in parameters like cost, speed, space and overhead. Researchers’ used IoT as the source of Big Data that generates heterogeneous Big Data. In this paper, we are representing the results of all tests proved that; the new method gives accurate results and valid outputs based on verifying them with the results of the other valid methods. Also, the results show that, the new method can handle the big datasets and decides the frequent pattern and produces the associate rule sets faster than that of the conventional methods and less amount of memory storage for processing. Overall the new method has a challenging performance as regard the memory storage and the speed of processing as compared to the conventional methods of frequent pattern mining like Apriori and FP-Growth techniques.
DESKRIPSI PEMAHAMAN CYBERBULLYING DI MEDIA SOSIAL PADA MAHASISWA

The development of internet technology and social media is accelerating the process of disseminating information.This certainly makes it easier for human life.But, besides that, the rapid pace of information technology can have a negative impact if it is not put to good use.At the moment there is a rampant cyberbullying case, which is the bullying that occurs through cyber media, in this case, social media.Cyberbullying has a bad impact on victims such as causing depression, so that the worst is causing death due to suicide.Cyberbullying is often a big problem both in the national and global order.There have been many cases that have occurred, the most numerous being teenagers who committed suicide due to experiencing cyberbully.Because of this, researchers are interested in researching more about cyberbullying.In this study, researchers wanted to find out how much the level of understanding of adolescents towards cyberbullying behavior.This study uses a positivistic paradigm with data collection methods, namely questionnaire survey.The sample of this study were students from one of the universities in South Tangerang.
Improving the Students’ Listening Comprehension through Aural-Oral Language Approach

This research aimed to know the effectiveness of using Aural-oral approach to improve the students’ listening comprehension, and to know the students’ activeness toward teaching learning process through Aural-oral approach at the eleventh grade students of SMAN 1 Makassar. Two cycles had been conducted, where each cycle consisted of four meetings. It implemented Aural-oral approach as an approach to improve the students’ listening comprehension and employed listening test as instrument. A number of subjects of the research were 16 students. It is consist of 9 women and 7 men. The researcher took real data from the school to know the students’ listening comprehension. The students’ mean score of listening achievement at D-Test was 50.69, at the cycle I was 65.28 and at the cycle II was 75.93. The result of the student's listening test in cycle II had improved in significant score, where the means score of students in distinguish phonemes was 87.5, in understanding Statement was 50, and in understanding dialogue was 90.28. It means that the students’ mean score at the cycle II was 75.93 reached the target score 75 and the students’ activeness toward teaching learning process by using Aural-oral approach was improved. It can be seen from the observation result of the students’ activeness in the classroom from the first meeting at the first cycle until at the last meeting in cycle II always grow up. It could be stated that the research was successful.&#x0D; Keyword: Listening Comprehension, Aural-oral Approach, Student’ Listening, Student’s Activeness
Impact of Stemming on Telugu Text Classification

In Text categorization, Information retrieval and document clustering stemming is absolutely necessary especially for morphological rich languages like Indian. The process of stemming is, reducing the inflected or resultant terms to their stem word, root or origin form. However, stemming is a tricky task - particularly for extremely inflected natural languages having a lot of words for the same normalized word form. In Text classification, stemming tries to cut off details like either suffix or prefix of a word and produce basic word. In this paper, we apply various stemming methods on Telugu text classification and ensure the performance of the classifier is effect by stemming. Telugu is suffix oriented language, so we have performed number of experiments on erratically selected Telugu text documents and finally we conceive that the performance of the classifier is improved.
Hand motions recognition based on sEMG nonlinear feature and time domain feature fusion

In recent years, the development of many rehabilitation robots, bionic prostheses and other sports rehabilitation equipment, which are used to assist the body to restore body movement function, has been paid more and more attention. The classification framework of this paper is a pattern recognition framework. The feature extraction of sEMG is to extract the physical quantity or a set of physical features that fully represent the characteristics of the action class from the electromyogram corresponding to the action of the human hand, in order to distinguish the other types of motion. It is very important step in hand movement recognition. In this paper, the newly developed sEMG nonlinear features AMR are fused with the traditional sEMG time-domain features WL. Feature fusion using SVM-DS fusion algorithm. Hand motions recognition based on feature fusion is improved in accuracy and stability. The accuracy of recognition can be stabilised over 95%.
Blockchain-based Biometric Election System

The use of biometric technology in the electoral process has undoubtedly produced positive results in terms of protecting the electoral process, speeding up the results and enhancing the feeling of objectivity among the voters. But there is still room for falsification of election results, as the number of votes received by the candidates and the used ballot papers are kept centrally. It is also important to ensure the reliability of the templates for the biometric characteristics of the voters. In order to solve these problems, this work proposes the distributed database of key data, in particular, the blockchain storage technology. The electoral process scheme and the blockchain-based biometric election system architecture and protocols are elaborated according to the proposed method.
ArMTFr: a new permutation-based image encryption scheme

In this paper, a new image encryption scheme named (ArMTFr) is proposed. An image is encrypted using a combination of keyed permutations and substitution, where a fractal is XORed with the scrambled image. Fractal images are employed in order to improve the performance of the encryption scheme from the viewpoint of randomisation and to increase the encryption key space, thus boosting its security. The employed permutations are the Arnold map and Mersenne-Twister's permutation algorithm. Before the encryption process starts, histogram equalisation is used to enhance the contrast of the image by transforming the intensity values in it, so that the histogram of the output image approximately matches a uniform histogram. First, greyscale images are considered and then the basic algorithm is extended to handle coloured images. Three representations for coloured images are considered: RGB, YCbCr and HSI colour spaces. The security of the algorithm is enhanced in this case by applying RGB colour channels multiplexing. The experimental results show that the encrypted image has low correlation coefficients among adjacent pixels and a good histogram distribution, as well as resistance to various attacks.
Facing Industry Revolution 4.0 for Millennial Accountants

The background of this research is to give an overview to millennial accountants of various challenges that will be faced by the accounting profession in the future. The background of writing this essay is because Indonesia will be affected by the Industrial Revolution 4.0 which will cause jobs to disappear, emerge and change. This research is directed to solve the solutions that occur in millennial accountants in making strategies to deal with these changes by paying attention to conditions in the 4.0 era where technology has developed rapidly and is living side by side with humans. Technology that has developed rapidly is certainly not an obstacle for millennial generation accountants to get a job. One technology that is booming is Artificial Intelligence (AI). AI Began to become a part of work activities at the start of the Industrial Revolution 4.0. With this advanced technology, all things can be handled well and is a helper for today's companies in processing large amounts of data. Therefore, many people assume that the role of accountants will be replaced by machines. But if you look further, it is the lack of skills and information regarding the operation of the technology that causes new accountants such as millennial generation to feel threatened by the presence of technology. By learning more about the accounting profession in the future, accountants can collaborate with machines to advance life as a unit of valuable assets. All of this was done so that they could remain competitive and not be left behind in the rapid development of technology.