Reviewerly/exHarmony huggingface dataset:
Analisis Sentimen Pada Review Produk Kosmetik Bahasa Indonesia Dengan Metode Naive Bayes

Sentiment analysis is a computational study of the opinions, behaviors and emotions of people toward the entity which in this research is a product review. By reading the review of products based on the experiences of other consumers, it will be recognized the quality of a product. As cosmetic products on the market are very diverse, both in terms of type and brand. However, not all cosmetics have good quality and it is to be noticed by the consumer. So, reexamination of the cosmetic product review by classifying these reviews into positive and negative class is an excellent way to determine the response of other consumers about the product quickly and accurately. The classification technique that mostly used is Naive Bayes. Naive Bayes is a popular classification technique for text because very simple, efficient, and has good performance in many subject. But, Naive Bayes has weaknesses that is too sensitive when there are too many features which cause wrong classfication. Because of that, in this research tf-idf is used as feature selection to improve Naive Bayes performance. This research aim is to produce a system that can classify reviews to their respective positive or negative class. The accuracy measurement of Naive Bayes is done by using confusion matrix and indicate the results of average accuracy from 69% to 82%.
New Methods and Abstractions for RSA-Based Forward Secure Signatures.

We put forward a new abstraction for achieving forward-secure signatures that are (1) short, (2) have fast update and signing and (3) have small private key size. Prior work that achieved these parameters was pioneered by the pebbling techniques of Itkis and Reyzin (CRYPTO 2001) which showed a process for generating a sequence of roots \(h^{1/e_1}, h^{1/e_2}, \ldots , h^{1/e_T}\) for a group element h in \(\mathbb {Z}_N^*\). However, the current state of the art has limitations.
Evaluating Automatically Generated Phoneme Captions for Images

Image2Speech is the relatively new task of generating a spoken description of an image. This paper presents an investigation into the evaluation of this task. For this, first an Image2Speech system was implemented which generates image captions consisting of phoneme sequences. This system outperformed the original Image2Speech system on the Flickr8k corpus. Subsequently, these phoneme captions were converted into sentences of words. The captions were rated by human evaluators for their goodness of describing the image. Finally, several objective metric scores of the results were correlated with these human ratings. Although BLEU4 does not perfectly correlate with human ratings, it obtained the highest correlation among the investigated metrics, and is the best currently existing metric for the Image2Speech task. Current metrics are limited by the fact that they assume their input to be words. A more appropriate metric for the Image2Speech task should assume its input to be parts of words, i.e. phonemes, instead.
Pulse Repetition Interval Modulation Classification using Machine Learning

Radar signals are used for estimating location, speed and direction of an object. Some radars emit pulses, while others emit a continuous wave. Both types of radars emit signals according to some p ...
A Nobel Approach Towards Designing A Personalizing bot to Reduce Human Efforts

A bot is “a computer program that completes automatic repetitive tasks.” To expand on this definition, bots also act as the primary tool for autosmating interactions and meeting with website content on a large scale.When thinking about bots, however, it’s important to maintain viewpoint. Bots are a software that are becoming easier to implement. They can serve a variety of purposes and what they ultimately accomplish is dependent on the humans that control them. With BOT, one gets to understand with the ability to connect with them on a level where they feel understood, listened to and accommodated.
Cryptographic Divergences: New Techniques and New Applications

In the recent years, some security proofs in cryptography have known significant improvements by replacing the statistical distance with alternative divergences. We continue this line of research, both at a theoretical and practical level. On the theory side, we propose a new cryptographic divergence with quirky properties. On the practical side, we propose new applications of alternative divergences: circuit-private FHE and prime number generators. More precisely, we provide the first formal security proof of the prime number generator PRIMEINC [8], and improve by an order of magnitude the efficiency of a prime number generator by Fouque and Tibouchi [16, 17] and the washing machine technique by Ducas and Stehlé [15] for circuit-private FHE.
Implementing Asynchronous Linear Solvers Using Non-Uniform Distributions

Asynchronous iterative methods present a mechanism to improve the performance of algorithms for highly parallel computational platforms by removing the overhead associated with synchronization among computing elements. This paper considers a class of asynchronous iterative linear system solvers that employ randomization to determine the component update orders, specifically focusing on the effects of drawing the order from non-uniform distributions. Results from shared-memory experiments with a two-dimensional finite-difference discrete Laplacian problem show that using distributions favoring the selection of components with a larger contribution to the residual may lead to faster convergence than selecting uniformly. Multiple implementations of the randomized asynchronous linear system solvers are considered and tested with various distributions and parameters. In the best case of parameter choices, average times for the normal and exponential distributions were, respectively, 13.3% and 17.3% faster than the performance with a uniform distribution, and were able to converge in approximately 10% fewer iterations than traditional stationary solvers.
DESIGN AND ACCEPTANCE OF CHATBOTS FOR INFORMATION AUTOMATION IN UNIVERSITY CLASSROOMS

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 2452-2462ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0757Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
RELATIONSHIP BETWEEN CHARACTERISTICS SOCIO-PSYCHOLOGICAL PERSONAL ATTITUDES AND THE PERSONAL ORIENTATIONS OF VIETNAMESE STUDENTS STUDYING IN THE RUSSIAN FEDERATION

RELATIONSHIP BETWEEN CHARACTERISTICS SOCIO-PSYCHOLOGICAL PERSONAL ATTITUDES AND THE PERSONAL ORIENTATIONS OF VIETNAMESE STUDENTS STUDYING IN THE RUSSIAN FEDERATION
Formalization of Forcing in Isabelle/ZF.

We formalize the theory of forcing in the set theory framework of Isabelle/ZF. Under the assumption of the existence of a countable transitive model of \( ZFC \), we construct a proper generic extension and show that the latter also satisfies \( ZFC \). In doing so, we remodularized Paulson’s ZF-Constructibility library.
Long Term Memory in Conversational Robots

This study discusses an implementation of a long term memory in the robot Furhat. The idea was to find a way to prevent identical and very similar questions from being asked several times and to st ...
Optimized CSIDH Implementation Using a 2-torsion Point.

The implementation of isogeny-based cryptography mainly use Montgomery curves, as they offer fast elliptic curve arithmetic and isogeny computation. However, although Montgomery curves have efficient 3- and 4-isogeny formula, it becomes inefficient when recovering the coefficient of the image curve for large degree isogenies. Because the Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) requires odd-degree isogenies up to at least 587, this inefficiency is the main bottleneck of using a Montgomery curve for CSIDH. In this paper, we present a new optimization method for faster CSIDH protocols entirely on Montgomery curves. To this end, we present a new parameter for CSIDH, in which the three rational two-torsion points exist. By using the proposed parameters, the CSIDH moves around the surface. The curve coefficient of the image curve can be recovered by a two-torsion point. We also proved that the CSIDH while using the proposed parameter guarantees a free and transitive group action. Additionally, we present the implementation result using our method. We demonstrated that our method is 6.4% faster than the original CSIDH. Our works show that quite higher performance of CSIDH is achieved while only using Montgomery curves.
Surveying global verifiability.

Abstract We explore global verifiability; discovering that voting systems vulnerable to attack can be proven to satisfy that security notion, whereas many secure systems cannot. We conclude that current definitions are unsuitable for the analysis of voting systems, fuelling the exploration for a suitable definition.
When Classical Chinese Meets Machine Learning: Explaining the Relative Performances of Word and Sentence Segmentation Tasks

We consider three major text sources about the Tang Dynasty of China in our experiments that aim to segment text written in classical Chinese. These corpora include a collection of Tang Tomb Biographies, the New Tang Book, and the Old Tang Book. We show that it is possible to achieve satisfactory segmentation results with the deep learning approach. More interestingly, we found that some of the relative superiority that we observed among different designs of experiments may be explainable. The relative relevance among the training corpora provides hints/explanation for the observed differences in segmentation results that were achieved when we employed different combinations of corpora to train the classifiers.
Movie Recommendation Using Content-Based and Collaborative Filtering

In the current time, we want to spend our money wisely and judiciously. In order to achieve this, we either turn to reviews of things that we want to try or better check the recommendation based on our previous experiences. This is where the need for a recommendation system comes in. As the online entertainment industry and the e-commerce markets grow rapidly, so is the need for efficient recommendation engines and efficient algorithms, for the business of the companies so that a large amount of revenue can be generated. The paper proposes a hybrid collaborative and content-based filtering algorithm so that the online entertainment market can be benefited, especially the online movie market, which gives the plus points of both, semantics and frequency-based filtering along with a collaborative-based approach which predicts the ratings of every movie. In the end, this paper shows the results of the proposed hybrid algorithm, along with the other known filtering techniques and algorithms used to recommend movies. In addition to this, we also tried to compare our method with the other methods already implemented in this field and tried to overcome the disadvantages offered by them to the best of our knowledge. Our method gives promising results for the top 5–10 movie recommendations as per user query string.
Concept Drift Detection on Stream Data for Revising DBSCAN

Data stream mining of IoT data can support operator to immediately isolate causes of equipment alarms. The challenge, however, is to keep their classifiers high purity (the data ratio with same proper class in a cluster) with concept drifting ascribed to differences between alarm models and entities. We propose to continuously update data class according to their distribution changes. Through evaluation, no purity deterioration was verified for oscillation condition data with a drifting rate of 1%. The result suggested that the method improves operator decision making.
한국어 비통사적 합성어의 긴밀성 연구

This paper aims to investigate the strictness of korean asyntactic compounds through the word recognition, phonological rules reflected in the orthography and the combination pattern of constituents. The strictness means a close relationship among the constituents of compounds and is also the essence of asyntactic compounds. An asyntactic compound''s essence is the close combination of practical concepts, that is vocabulary meaning factors with lacking the empty morphemes like connective ending or category changing ending which are necessarily required in formative a syntactic compound. We attempt to reaffirm the status of asyntactic compound formation rule in the compound formation system through the investigation.
Penerapan Web Service Untuk E-Commerce Makro Komputer Sintang

The need to produce an effective sales system requires a new innovation in building a system that can assist businesses in reaching market segments. Because the business activities require internet media to make a booking and storage of item data is typed manually by employees of the company. To meet the needs of customers needed a e-commerce-based application for sales activities and item data storage with web service. Used in this study is a case study using the method of research and development Research and Development. Data collection techniques used were observation, interviews, and studies with primary data obtained from interviews and secondary data obtained from the data, tables, diagrams and articles and journals from previous studies. Design method used is the method Incremental Development with the system modeling tool is the Unified Modeling Language (UML) and the programming language used is PHP language and MySQL as the database. Results of this study will generate a E-Commerce with Web Service that is used to meet the needs of every customer requests quickly and accurately, and is expected to assist in marketing the product.
CURRENT PROBLEMS OF INTERNATIONAL COOPERATION IN HIGHER PROFESSIONAL EDUCATION

1 Peter the Great St. Petersburg Polytechnic University (RUSSIAN FEDERATION)2 Peter the Great St.Petersburg Polytechnic University (RUSSIAN FEDERATION)
ANALYSIS AND DEVELOPMENT OF DIGITAL SKILLS AT SCHOOL: OBSERVATION AND DIDACTIC PLANNING

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 8293-8299ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.2049Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Writing Behavior as an Estimator of Self-Confidence on English Spelling Questions

Reviewing plays an important role in the learning process of humans. A user's self-confidence is as important as the correctness of the answer to distinguish what to review. Giving feedback based on self-confidence enables learners to improve the quality of the review. Since it is still a familiar way for many students to write by hand, we propose a self-confidence estimation method based on handwriting behavior. To evaluate the performance of the proposed method, we employed the data collected while completing English spelling questions. The results revealed that the proposed method was able to predict self-confidence with 76% accuracy.
INTRODUCTION OF AUTOMATIC TESTS FOR TEACHING WEB TECHNOLOGIES

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 1555-1560ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0508Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
BasicBlocker: Redesigning ISAs to Eliminate Speculative-Execution Attacks.

Recent research has revealed an ever-growing class of microarchitectural attacks that exploit speculative execution, a standard feature in modern processors. Proposed and deployed countermeasures involve a variety of compiler updates, firmware updates, and hardware updates. None of the deployed countermeasures have convincing security arguments, and many of them have already been broken. The obvious way to simplify the analysis of speculative-execution attacks is to eliminate speculative execution. This is normally dismissed as being unacceptably expensive, but the underlying cost analyses consider only software written for current instruction-set architectures, so they do not rule out the possibility of a new instruction-set architecture providing acceptable performance without speculative execution. A new ISA requires compiler updates and hardware updates, but those are happening in any case. This paper introduces BasicBlocker, a generic ISA modification that works for all common ISAs and that removes most of the performance benefit of speculative execution. To demonstrate feasibility of BasicBlocker, this paper defines a BBRISC-V variant of the RISC-V ISA, reports implementations of a BBRISC-V soft core and an associated compiler, and presents a performance comparison for a variety of benchmark programs.
A Candy-Coated Cyclist's Fantasy

Author(s): Shingo Crawford, Michael | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
Crowd Estimation of Real-Life Images with Different View-Points

This paper found out the problem andFahad, Md Shah suggested a solution for crowd estimation of real-life images. In general, the cameraDeepak, Akshay position is fixed at the public places and capture the top view of the images. Most of the recent neural networks are developed with these images. Recently, the CSRNet model was developed for the ShanghaiTech dataset. This model achieved better accuracy than state-of-the-art methods. It is difficult to capture the top view of images where the crowd is gathered at random such as strike, and riot. Therefore, we capture both the top and the front view of images to deal with such circumstances. In this work, the CSRNet model is evaluated using two different test cases consisting of either only top view images or front view images. The mean absolute error (MAE) and mean squared error (MSE) values of the front view images are higher than the top view images. The relative MAE and MSE of the CSRNet model for the front view images are 28.64 and 47.86%, respectively, higher than the top view images. It is noted that higher MAE and MSE means lower performance. This issue can be resolved using the suggested GANN network, which can project the front view images into the top view images. After that, these images can be evaluated using the CSRNet model.
THE APPLICATION OF OBJECT-ORIENTED PROGRAMMING AND GAME ENGINE TO PROMOTE CULTURAL AND HISTORICAL HERITAGE

THE APPLICATION OF OBJECT-ORIENTED PROGRAMMING AND GAME ENGINE TO PROMOTE CULTURAL AND HISTORICAL HERITAGE
EFFECTIVE COMMUNICATION OF UKRAINIAN MEDICALOPINION LEADERS IN SOCIAL NETWORK

The article deals with the analysis of effective communication of Ukrainian opinion leaders in social media. InUkraine, the use of social networking sites Facebook and Instagram has become widespread. There are also tworecognized opinion leaders in the field of health care, Yevhen Komarovskiy and Ulana Suprun. Based on a two-step flowmodel of communication, the authors investigate how opinion leaders create their Internet content on social networks andestablish communication with the audience. The study uses qualitative and quantitative research methods, which allowfor reproducing the overall scheme of building the opinion leaders' internet content. The features of Ukrainian opinionleaders' effective communication with the audience were determined by specifying content distribution technologies,specifying formats for presenting information as well as identify the subject of messages, selecting out hashtags.According to the results of the study, opinion leaders use different channels of communication with the audience, andpatients (own website, a YouTube channel, participation in media projects). Their activities in social networks Instagramand Facebook are confirmed by the regular posting and communication with other users, encouraging feedback.Opinion leaders in the medical field apply the following technologies to create a positive image and maintain credibilityamong the audience: minimizing messages about private life; creating a column about breaking down health stereotypes;spreading medical innovations; quality visual content; hyperlinks to their other channels; use and creating hashtags;simple and clear advice to their patients.
Arirang Fantasie for String Quartet

Author(s): Chung, Jiyoun | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
User perception of varying level of detail for Imposters

As the time to render frames increases with the amount of 3D models present and the level of detail of these models, finding a way to render these objects more efficiently would allow real time app ...
UCDiff: Difference Detection in Use Case Models of Healthcare System

Development of large-scale healthcare software projects essentially need the efficient management of the created software artifacts during software development process. In such projects different versions of an artifact are created at different times. Traditional software configuration management systems, such as Git, Subversion (SVN), etc., are designed for later phases of software development, which mainly handle the source code document. These systems are unable to perform difference detection and version management tasks on models such as unified modeling language diagrams. UML use case model is used for capturing functional requirements at analyses phase. Different versions of the use case model are created during analyses phase. This paper addresses the detection of differences between two versions of a use case model. In order to perform difference detection, we need to perform three main tasks, i. e., extract the contents of the model, comparison of models and difference representation. Most of the existing approaches in literature of model comparison deal with UML class diagrams. To the best of our knowledge, so far no appropriate approach addresses difference computation of use case model. Existing approaches are not applicable on use case model due to different semantics of use case model. In this research, the concept of model-based software configuration management (SCM) for use case difference detection is proposed. The use case models are created in an open source tool, starUML. The proposed difference algorithm is applied on intermediate tree structure representation of models. As a case study, different versions of a patient appointment healthcare system is used to evaluate different evaluation parameters, such as accuracy, domain independence, high conceptual level and tool independence.
Analysis and Forecast of Car Sales Based on R Language Time Series

This data comes from the data hall website, mainly using the R language as a tool for data analysis. This analysis mainly focuses on the time series analysis of a certain sedan. First, the monthly sales of the car are extracted as historical data and then analyze the forecast and draw the conclusion.
DESIGN AND EVALUATION OF TWO MOBILE COMPUTER-ASSISTED PRONUNCIATION TRAINING TOOLS TO FAVOR AUTONOMOUS PRONUNCIATION TRAINING OF ENGLISH AS A FOREIGN LANGUAGE

DESIGN AND EVALUATION OF TWO MOBILE COMPUTER-ASSISTED PRONUNCIATION TRAINING TOOLS TO FAVOR AUTONOMOUS PRONUNCIATION TRAINING OF ENGLISH AS A FOREIGN LANGUAGE
EXPERIENCES AND FEELINGS OF BEING A TEACHER IN THE INITIAL STAGE OF PROFESSIONAL ACTIVITY

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 8855-8860ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.2190Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
EDUCATIONAL TOOL BASED ON AUGMENTED REALITY TO SUPPORT THE TEACHING OF ELECTRIC FIELDS

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 8510-8517ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.2094Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference