Reviewerly/exHarmony huggingface dataset:
Improvised Sentiment Analysis using One Hot Encoder and N-grams

In the field of text mining, sentiment analysis of free-text documents can be a common task. Text documents are assigned predefined sentiment labels such as and negative in sentiment analysis. Sentiment analysis, which identifies people's sentiments underlying a text, is an important area of research. Data mining is a popular field that studies sentiment analysis. It is well-studied to analyze sentiments in tweets. The proposed method seeks to improve the effectiveness of sentiment analysis of tweets by reviewing the existing research. The ever-growing popularity of social media and online marketing sites makes it an invaluable resource for analysis and decision-making. The majority of these reviews are not structured by nature, so they need to be processed like clustering or classification to provide useful information for future use. This paper aims to improve the classification of tweets with sentiment information. One-hot encoder and N–grams scheme are used. They use the sentiment lexicons to extract tweets with n-grams of high-performance gain. These include neutral, positive or negative. For the classification of human sentiments, four machine learning algorithms were considered: Logistic regression (SVM), Support Vector Machine (SVM), Random Forest (Raw Forest), and Decision Tree. To assess the accuracy of each method, we examine their performance using parameters like precision, recall, accuracy, and f-measure.
The Medical and Psychological Effect of Hate Crimes and Hate Speech on Vulnerable Social Groups

The present study would be analyzing and interpreting the intricacies of hate speech in two-fold. Firstly, theauthors would be meticulously analyzing the legal parlance of the term ‘hate speech’ in the present aeon andits repercussions, i.e., outline the constitutive and consequential harm faced by the people that goes beyondlegal texts and judgments and beyond the legal notion of hate speech.In furtherance, this research article would address the aftereffects of hate speech medically and how it takesa psychological toll on minority bigotry and pervasive gender or ethnic differences by using a plethora ofwell-known logical research approaches that are unique to analytic philosophy.Hate speech cases have resulted in bans on campus, legal rulings, and national controversy. The term ‘FreeSpeech’ and ‘Hate Speech’ has been misconstrued and used misappropriately. The prime reason is theabsence of precise clarification and the unbelievably wide usage of the term. The targeted communitiesexperiencing hate speech are of paramount importance but are often overlooked due to the legitimacy oflaws pertaining to hate speech.
Multiliteracies education for Korean language learners in the post-human era

This study addresses the development of a new educational direction for Korean language education in the post-human era, namely, the development of multiliteracies education. As the world is now enveloped in COVID-19, all fields are seeking new breakthroughs, and education in particular is the first to adapt to the post-epidemic era. This prepares new types of human resources for the post-human era, and actively responds to new concepts such as environmental mutations and the AI era at a later stage. Based on the above, this study takes Korean language education as an example of a humanities subject, and discusses how it can find its own new point of development and adapt to its surroundings in a way that provides a new concept of education. The study of multiliteracies in Korean language education has evolved in recent years, largely due to the development of science, technology and multimedia. This external environment has necessitated changes in traditional language education and has urged educators to reconsider educational goals such as the overall literacy of students. The New London Group, the first to propose multiliteracies, redefined the concept in 2013 and added elements of social activism and ethics in the virtual society to it. These two concepts are, of course, at the forefront of the future development of multiliteracies education in Korean language education, which is to prepare students to think about harmonious relationships with non-humans and nature based on a human-centred approach, and to articulate new ethical and moral concepts within the social framework of the virtual space. In line with this educational objective, researchers should take into account the diversity of today’s Korean language learners, the diverse requirements of the learners’ learning purposes, and the specialised and complex dimensions of teaching content. Furthermore, in terms of changing ethical and moral concepts within the virtual space, researchers should focus on developing learners’ ability to identify social issues through the media and how to deal with these social issues in concrete behavioural terms. (Seoul National University)
Engineering Practices and their Role in Post Adoption of Scrum at an Enterprise

Once Scrum process model is adopted by an enterprise, changes in the organization are common. New practices and processes are needed by the enterprise to start removing dysfunctions and problems that are identified with Scrum adoption. These variations are isolated when Scrum is applied on a single project. Some new practices, processes and techniques help to adopt Scrum at an enterprise level and are not new. They are objectively different in the way they work in current situation. In this research work engineering practices that helps in developing the projects of enterprise are illustrated. The study also focuses on how the new practices are different while using Scrum is also discussed.
A Study on the Cultural Elements of ‘누워서떡 먹기(Eating a Rice Cake While Lying Down)’ for Korean Culture Education

This study examined the process of the formation and change of the proverb ‘누워서 떡 먹기(Eating a rice cake while lying down)’ by analyzing the figurative structure before then extracting the cultural elements contained within it. This study suggests measures for educating on aspects of Korean culture. The proverb ‘누워서 떡 먹기(Eating a rice cake while lying down)’ was originally used in the form of ‘If you eat a rice cake while lying down, the powdered soybean would fall down’. The ‘rice cake’ and the action of ‘eating while lying down’, which is metaphorically expressed in the proverb, implies various cultural elements, such as food culture, ritual culture, and sedentary life culture of Korean people. Examining the social reality wherein the proverb is used in relation to the metonymic meanings of proverb, the original proverb was closely related to an agriculture-centered, labor-based, hierarchical society. After the form was changed to ‘Eating a rice cake while lying down’, the meaning also changed, reflecting the social context of routinized rice cake eating after the status system disappeared. Lastly, this study suggested the measures for proceeding a culture class according to Moran’s culture learning steps, by using various cultural elements extracted from the proverb. (Sangji University)
Improving the activities of learning and writing text description of students through contextual teaching and learning approach

This study aims to find out and describe the effects of school culture and professional competence on teacher performance in SD in Gugus 1 Dewi Sartika, East Baturaja District. This study was participated by 105 elementary school teachers in Gugus 1 Dewi Sartika, East Baturaja District who were taken through a saturated sampling technique where the entire population became the research sample as a whole. This type of research is quantitative descriptive with research survey methods and using questionnaires as data instruments. The results of this study found that (1) the sig.value of the school culture variable is 0.000 (&amp;lt;0.05) and the tobtained&amp;gt; ttable value (4.195&amp;gt; 1.659), then hypothesis 1 is accepted; (2) sig. The professional competency variable is 0.000 (&amp;lt;0.05) tobtained&amp;gt; ttable value (6.378&amp;gt; 1.659), then hypothesis 2 is accepted; (3). the sig value is 0.000 (&amp;lt;0.05) and the F value is 25.344 where the Ftable value is 2.67 (Fobtained&amp;gt; Ftable), so hypothesis 3 is accepted. Based on these results, it can be concluded that school culture and professional competence have an effect either partially or collectively on teacher performance.
Fractal Dimention EEG Dinamics in Solving Task Recognition of Bar Plots

Classic method of the diffusive Markov parabolic equation for the mean field in a stochastic medium is extended to the case of finite values of the longitudinal correlation radius of fluctuations. In the limiting case of δ-correlated fluctuations the solution constructed reproduces a well-known solution to the case of the diffusive Markov approximation for the mean field.
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification.

Annotated images are required for both supervised model training and evaluation in image classification. Manually annotating images is arduous and expensive, especially for multi-labeled images. A recent trend for conducting such laboursome annotation tasks is through crowdsourcing, where images are annotated by volunteers or paid workers online (e.g., workers of Amazon Mechanical Turk) from scratch. However, the quality of crowdsourcing image annotations cannot be guaranteed, and incompleteness and incorrectness are two major concerns for crowdsourcing annotations. To address such concerns, we have a rethinking of crowdsourcing annotations: Our simple hypothesis is that if the annotators only partially annotate multi-label images with salient labels they are confident in, there will be fewer annotation errors and annotators will spend less time on uncertain labels. As a pleasant surprise, with the same annotation budget, we show a multi-label image classifier supervised by images with salient annotations can outperform models supervised by fully annotated images. Our method contributions are 2-fold: An active learning way is proposed to acquire salient labels for multi-label images; and a novel Adaptive Temperature Associated Model (ATAM) specifically using partial annotations is proposed for multi-label image classification. We conduct experiments on practical crowdsourcing data, the Open Street Map (OSM) dataset and benchmark dataset COCO 2014. When compared with state-of-the-art classification methods trained on fully annotated images, the proposed ATAM can achieve higher accuracy. The proposed idea is promising for crowdsourcing data annotation. Our code will be publicly available.
Statistical factors: What and how academic libraries count

Library statistics is an important and effective method of analyzing library activities and identifying problems, planning and forecasting. Reliability, accuracy, completeness and comparability of the data are the most significant provisions. In her earlier publication, the author identified several inaccuracies in annual statistical reports submitted by university libraries. To identify discrepancies in 2019 statistics, the comparative analysis was accomplished for Orel State academic libraries. The study demonstrated that sometimes the libraries didn’t provide accurate and precise statistics with lacking logical connection between the indicators presented in different report sections. Individual factors differ significantly despite similarity of other factors that influence the former ones. The author concludes on the necessity of measures to be taken by RLA university library section, library methodological divisions, and by the academic libraries themselves. For this purpose, the author suggests to develop methodological recommendations on calculating statistics, statistical surveying of academic libraries and implementing the methods into practice.
Large Message Homomorphic Secret Sharing from DCR and Applications.

We present the first homomorphic secret sharing (HSS) construction that simultaneously (1) has negligible correctness error, (2) supports integers from an exponentially large range, and (3) relies on an assumption not known to imply FHE—specifically, the Decisional Composite Residuosity (DCR) assumption. This resolves an open question posed by Boyle, Gilboa, and Ishai (Crypto 2016). Homomorphic secret sharing is analogous to fully-homomorphic encryption, except the ciphertexts are shared across two non-colluding evaluators. Previous constructions of HSS either had non-negligible correctness error and polynomial-size plaintext space or were based on the stronger LWE assumption. We also present two applications of our technique: a two server ORAM with constant bandwidth overhead, and a rate-1 trapdoor hash function with negligible error rate.
Symmetric Norm Estimation and Regression on Sliding Windows

The sliding window model generalizes the standard streaming model and often performs better in applications where recent data is more important or more accurate than data that arrived prior to a certain time. We study the problem of approximating symmetric norms (a norm on $\mathbb{R}^n$ that is invariant under sign-flips and coordinate-wise permutations) in the sliding window model, where only the $W$ most recent updates define the underlying frequency vector. Whereas standard norm estimation algorithms for sliding windows rely on the smooth histogram framework of Braverman and Ostrovsky (FOCS 2007), analyzing the smoothness of general symmetric norms seems to be a challenging obstacle. Instead, we observe that the symmetric norm streaming algorithm of Braverman et. al. (STOC 2017) can be reduced to identifying and approximating the frequency of heavy-hitters in a number of substreams. We introduce a heavy-hitter algorithm that gives a $(1+\epsilon)$-approximation to each of the reported frequencies in the sliding window model, thus obtaining the first algorithm for general symmetric norm estimation in the sliding window model. Our algorithm is a universal sketch that simultaneously approximates all symmetric norms in a parametrizable class and also improves upon the smooth histogram framework for estimating $L_p$ norms, for a range of large $p$. Finally, we consider the problem of overconstrained linear regression problem in the case that loss function that is an Orlicz norm, a symmetric norm that can be interpreted as a scale-invariant version of $M$-estimators. We give the first sublinear space algorithms that produce $(1+\epsilon)$-approximate solutions to the linear regression problem for loss functions that are Orlicz norms in both the streaming and sliding window models.
Comparative study on TDMRC and E-TDMRC

Among the various encryption techniques Time Dependant Multiple Random Cipher Code (TDRMC) is a novel approach in Character Coding or encryption The random number generator used in TDMRC is Linear Congruential Generator or LCG. Concept of nesting would improve the randomness produced by traditional generator LCG. The mathematical formula for Nested Linear Congruential Generator is same as in traditional formula. But the constant values used will be substituted by another random number generated by a Random Number Generator nested within. By breaking the constant nature the period becomes infinity or no subsequence ever repeats in the generated sequence. By substituting LCG with NLCG the performance of TDMRC is enhanced resulting in Enhanced TDMRC. In this paper TDMRC is compared with E-TDMRC using statistical and graphical methods like Kolmogorov Smirnov Test, Runs Test, Bar Graph Analysis and Scatter Diagram Analysis.
Learning Highly Recursive Input Grammars

This paper presents Arvada, an algorithm for learning context-free grammars from a set of positive examples and a Boolean-valued oracle. Arvada learns a context-free grammar by building parse trees from the positive examples. Starting from initially flat trees, Arvada builds structure to these trees with a key operation: it bubbles sequences of sibling nodes in the trees into a new node, adding a layer of indirection to the tree. Bubbling operations enable recursive generalization in the learned grammar. We evaluate Arvada against GLADE and find it achieves on average increases of 4.98x in recall and 3.13x in F1 score, while incurring only a 1.27x slowdown and requiring only 0.87x as many calls to the oracle. Arvada has a particularly marked improvement over GLADE on grammars with highly recursive structure, like those of programming languages.
A Survey on Machine Learning Algorithms

Abstract: In this paper, mainly three machine learning algorithms are discussed in detail and how they work is shown, their pseudo codes are shown and a comparison is done among them, and their performance is analyzed. Keywords: Machine Learning, pseudo code, algorithms, data
Multi-Sample based Contrastive Loss for Top-k Recommendation

The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key in contrastive learning that has received more attention recently and we find it is well suited for top-k recommendations. However, it is a problem that CL treats the importance of the positive and negative samples as the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, positive items are so few in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. So we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a Multi-Sample based Contrastive Loss (MSCL) function which solves the two problems by balancing the importance of positive and negative samples and data augmentation. And based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. We will release our code on GitHub upon the acceptance.
CompoNet: Toward Incorporating Human Perspective in Automatic Music Generation Using Deep Learning

Abstract The art nature of music makes it difficult, if not impossible, to extract solid rules from composed pieces and express them mathematically. This has led to the lack of utilization of music expert knowledge in the AI literature for automation of music composition. In this study, we employ intervals, which are the building blocks of music, to represent musical data closer to human composers’ perspectives. Based on intervals , we developed and trained OrchNet which translates musical data into and from numerical vector representation. Another model called CompoNet is developed and trained to generate music. Using intervals and a novel monitor-and-inject mechanism, we address the two main limitations of the literature: lack of orchestration and lack of long-term memory. The music generated by CompoNet is evaluated by Turing Test: whether human judges can tell the difference between the music pieces composed by humans versus. generated by our system. The Turing test results were compared using Mann-Whitney U Test, and there was no statistically significant difference between human-composed music versus what our system has generated.
UGC Quality Prediction Model Based on Persona

[Purpose/significance] The main purpose of this paper is to solve the UGC quality problem of social networks and communication platforms.Individual personae are constructed on the basis of identifying users’ low quality abnormal behaviors.And the UGC quality prejudging/prediction model based on the persona is generated to implement the UGC quality prediction.[Method/process] The isolated forest algorithm is used to identify abnormal behaviors of users,based on which the quality attribute of UGC is also analyzed.The low quality UGC is identified from both emotional quality and content quality,and the personae are constructed for the low quality UGC.The machine learning model is trained to obtain the UGC quality prejudging/prediction model.The prediction model is then applied to the test set to verify the feasibility and effectiveness of the method.[Result/conclusion] The experimental results show that when abnormal behaviors occur,the proportion of users’ generating low-quality UGC increases significantly,so low-quality UGC can be identified by focusing on monitoring users’ abnormal behaviors.Compared with detecting all behaviors of users,the method has higher efficiency and less resource occupation.
Student Problems In Time Management At Online Learning To Support Student Achievement In Class

The purpose of this research is to know the student problem in time management, the data is get from the student who was learn by using online learning in class. The data is using questionnarre and collect by google form, this research using three tests to the data that is validity, reliability and percentage. The result of the data is valid, reliable and ten points of data is proven true by the percentage. With this research the student can knowing first what the problem that will happen in online learning when the student wat to do time management.Keyword: Problem, Online Learning, Student, Time Management
Comparative Analysis of YOLOv3, YOLOv4 and YOLOv5 for Sign Language Detection

Sign language is a visual means of communication using hand signals, gestures and body language. It's the main form of communication for deaf people. Hearing-impaired people often find it quite challenging to communicate with others because most of the people do not know sign language. This requires a translator in most cases. Deep learning-based detection methods can solve this issue. Deep learning makes use of different methodologies that can extract features from images, videos and give the conclusion about the different objects. These methods find the meaning of a particular sign from the visual representation of the sign. This paper will discuss the YOLO algorithm used for object detection. YOLO algorithm uses Convolutional Neural Network (CNN) to detect objects in real-time. The algorithm requires only a single forward propagation through a neural network to detect objects. This means that the prediction of the entire image is done in a single algorithm run. YOLO v3 uses Darknet53 as the backbone feature extractor. The YOLOv4 architecture is composed of CSPDarknet53 as a backbone, spatial pyramid pooling additional module, PANet path-aggregation neck and YOLOv3 head [1]. YOLOv5 model can be summarized as Focus structure and CSP network Backbone, SPP block and PANet Neck and YOLOv3 head using GIoU-loss. This paper discusses the performance comparison of YOLOv3, YOLOv4 and YOLOv5 for sign language detection. The comparative analysis is done by using the same data set of sign languages for all three methodologies. The recall, precision and accuracy of each of the algorithms are discussed in the paper.