Reviewerly/exHarmony huggingface dataset:
Use case and UML Scenarios Engineering for Building B2B E-commerce

Recently, the enterprise information system is a central element of the functioning of an organization, its development uses Unified Modeling Language (UML).In this article we present two main contributions: The first relates to the application of the use case and UML scenarios approach to B2B systems.Based on the engineering of use cases and UML scenarios, we present our vision to develop Business-to-Business (B2B) systems as an activity process derived from formal specifications from the engineering of use cases and UML scenarios.We use a set of algorithms and tools developed to make all activities of the proposed process automatic.In the second contribution we describe how to use the use case diagrams and UML scenarios to analyze static and dynamic requirements by supporting B2B interactions with the intention of developing a B2B e-commerce system.
R-Tree based Density Clustering for Multispectral Images

Density based clustering is one of the most popular clustering techniques that groups data in dense regions.These dense regions are identified with the help of a few parameters which are sensitive to estimate.Clustering accuracy depends on effective selection of these parameters.The proposed method uses density-based clustering and R-Tree.R-Tree provides faster evaluation of nearest neighbours and also is capable to store and handle multi-dimensional data efficiently.The proposed algorithm has two steps: i)R-tree is constructed from multispectral image data.Data is partioned into dense clusters/regions using nearest neighbours on the R-tree data, ii) A threshold parameter `t' which is similar to `epsilon' in DBSCAN algorithm is used to regulate the dense regions formed in the initial step by merging neighbour dense regions which are within `t'-radius.This procedure ignored the sensitive parameters like `minpts' in DBSCAN by evaluating the nearest neighbours using R-Tree.Hence the number of parameters also reduced.Results show that generation of nearest neighbours is faster and better in the proposed method when compared to traditional density-based clustering methods DBSCAN, OPTICS and K-Means.
Email Spam and Malware Filtering Using Machine Learning and Its Applications

Machine Learning has been applied in various fields to train and test the for prediction. Machine Learning is an Artificial Intelligence technology which, without specific programming, provides systems with the ability to learn and improve from vast exposure. It focuses on computer programs that can access and use knowledge to learn by themselves. It has different types of examples, as mining for automation development and typical apps include web-click information for improved medical records for better healthcare automation, biological data, email spam, and much more, including malware filtering. Machine Learning is the science that deals with statistical models and algorithms used by computer systems to perform a specific job efficiently without clear guidance, relying on patterns and inferences instead. Machine Learning algorithms construct training data to generate predictions or choices without directly programming the job as a Mathematical Model. Machine-automated learning algorithms are used in every type of application, including email sorting and computer vision, whereby specific instructions or algorithms are used to perform an impossible task. Computational statistics that concentrate on computer-based predictions are closely associated with machine learning. Mathematical optimisation research provides the field of machine learning with methods, theory, and application domains. This study presents implementations in the most effective content-based spam filtering methods, such as Phishing Social Engineering, etc. It focuses on spam filters and their variants based on Machine Learning, i.e., a comprehensive survey for suitable thoughts, attempts. The original background exposure examines the basics of filtering spam email and changing spam character with ESPs. We conclude with applications and the effect of filters based on Machine Learning and explore the promising offshoots of recent innovations. However, there are still some exceptional email spam filtering issues, as mentioned above. The anti-spam study will stay in an active study area until further improvements in spam filtering occur.
Una nueva metodología de clase invertida aplicada como un programa piloto a estudiantes aspirantes a ingresar a una universidad ecuatoriana

This article describes a new methodology that we will call Active Learning used to treamline the teaching-learning process of Mathematics, Physics and Chemistry subjects within the classroom of a pre-university course of the Polytechnic University, Escuela Superior politécnica del Litoral, ESPOL. The implementation of this methodology as a pilot program is also shown to a group of students aspiring for the intensive leveling course of the year 2019, evidencing a higher rate of approval and improvement in performance than the group of students who studied under the classical methodology.
THE PREDICTABILITY OF ENGLISH READING SKILL FROM INDONESIAN READING SKILL AND ENGLISH KNOWLEDGE AT STUDENTS OF SMAN 1 PADEMAWU-PAMEKASAN

The basic problem wants to discuss is: Do Indonesian reading skill and English knowledge correlate with English reading skill at students of SMAN 1 Pademawu ? The objective of the research is to know the correlation between Indonesian reading skill and English knowledge on English reading skill at students of SMAN 1 Pademawu&#x0D; The research gave some useful information about students’ Indonesian reading skill and English knowledge on English reading skill. This research also presented the description and the correlation between students’ Indonesian reading skill and English knowledge on English reading skill. The technique of collecting data was by using a test. After the data of the students’ Indonesian reading tests and English knowledge tests and English reading tests collected, the data was statistically computed (SPSS 17.00) to find out the correlation between the two variables.&#x0D; From the tests, it was found out that there was not a significant correlation between students’ Indonesian reading skill and English knowledge on English reading skill. It suggested that to have a good capability in English reading skill, students should have a good ability in Indonesian reading skill and knowledge.&#x0D; &#x0D;
Elliptic Curves in Modern Cryptographic Systems

Modern cryptographic algorithms used in cryptocurrencies applications, EDS systems, and blockchain technologies use a mathematical apparatus based on elliptic curves (EC).It is known that not every EC, described by elliptic functions can be used in practice.This is due to the requirements for cryptographic stability.For EC-based cryptosystem algorithms, it is necessary to use subgroups of high-order points.It is theoretically clear that the parameters a, b, k of random ECs can be chosen by the attacker arbitrarily.These curves, used to generate public and private keys, exist in the public domain.These ECs are described in public libraries.It is important to know whether it is possible to predict in practice the values of the secret parameter k , which depend on a particular EC, for all signatures or random number generators used in asymmetric cryptographic algorithms and systems.
BMBI: A Development of a Special Corpus on Homonyms for Multi-Lingual Sentiment Analysis

Research in the area of sentiment analysis is growing rapidly.Along with this the need for a corpus that can help in increasing the validity of the sentiment results is very much needed.But there are special cases against languages where data sources are very rare.One of them is a homonym word which means the word which has the same vocabulary but has a different meaning.In this study, an annotation scheme model named BMBI annotation scheme model will be designed to meet the needs of the corpus.This model has several elements namely <KATA>, <KALIMAT>, <HOLDER>, <APPRAISAL GROUP>, <TARGET>, and <MODIFIER>.The annotation process of the scheme model was done by six (6) annotators with certain criterion.An agreement evaluation of the annotation process was performed using the Fleiss.The calculation of the agreement among annotators is focused on 7 tasks namely language identifiers, BI polarity on the <KATA> element, BM polarity on the <KATA> element, BI polarity on the <KALIMAT> element, BM polarity on the <KALIMAT> element, BI tagset, and BM tagset; produce a Moderate value of the agreement which indicates that the agreement results are feasible to be used as a basis for further research.
PROJECT TECHNOLOGIES IN THE PROCESS OF PREPARING FUTURE SOCIAL TEACHERS FOR WORK WITH GIFTED CHILDREN

The article emphasizes the importance of activities engaging gifted children, revealing their proneness and skills, creating favorable conditions to advance their gifts, and facilitating self-actuation and successful socialization in modern pedagogy. It necessitates conducting social and pedagogical activities with gifted children and training the relevant professionals in higher educational establishments for fulfilling this task. In training future social educators, one shall consider the peculiarities of gifted children and specific nature of social and pedagogical activities engaging them. We have defined the terms “giftedness” and “gifted children”, and discovered the peculiarities of gifted children to be considered by educational professionals working with them. We have also outlined the special nature of social and pedagogical activities with children with signs of giftedness. A poll conducted for future social educators revealed that the latter are not completely ready to work with gifted children. It emphasizes the necessity of their professional training in this domain in higher education establishments. The author's definition of “professional activities of social teacher in work with gifted children” and the definition of “professional training of future social educators for work with gifted children” were examined. We have explained the reasonability and benefits of using project technologies in professional training of these students to work with gifted children. The following definitions were made: “project”, “project method”, and “project technologies”. Projects classification was considered. We have revealed the demands and peculiarities of project technologies use. We have also outlined the specific nature of project technologies applied to course entitled “Social-pedagogical work with gifted children”. A case study of a collective student project was examined. The project was aimed at defining a professional identity for gifted children. It considered the requirements for project technologies and the special nature of social and pedagogical activities conducted while working with these children. The implementation of the consummated project proved its efficiency. As the project was implemented, students applied the knowledge and skills acquired during course studying, and developed the necessary competences making them ready to work with gifted children professionally.
A Comparative Analysis of Filters towards Sign Language Recognition

One of the major drawbacks of our society is the barrier that is created between the deaf and the normal.Communication is the only medium by which we can share our message through speech.But, for a person who is having hearing impairment faces difficulties in communication with normal people, sign language is the main medium for deaf people to communicate.There have been few attempts in the past to recognize hand gestures, but with low rate of recognition.Furthermore, the dataset accessible in this subject is still noisy.The objective of this study is to determine filtering techniques that give good performance for hand sign language recognition.Dataset used in this study is Gesture Dataset 2012.In this study, three types of filtering techniques have been chosen which are average filter, median filter, and Laws' masks filters.A comparative analysis has been done and the performance of the filtering techniques are based on recognition performance.Meanwhile, Canny, Sobel, Prewitt, and Roberts algorithms are used for edge detection and features extracted are Histogram of Oriented Gradients (HOG).Finally, for classification, multiclass Support Vector Machine (SVM) classifier is used.At the end of this study, it has been found that using a combination of Laws' masks filters and Canny edge detection algorithm is proven to be a promising combination to increase sign language recognition performance since they give the best results of recognition performance compared to other methods used in the experiments.
Types and Genre Specificity of the Comics in Ukraine

The article is devoted to the coverage of the main trends in the comics’ development in Ukraine from the standpoint of its types and genre specificity. The increase of comic books popularity as an information product on the domestic book publishing market has been revealed. Based on the analysis of statistical data, the tendency to increase both the number of names of domestic comics and the number of publishers specializing in comics’ products has been shown. The perspectives of comics as a tool for engaging in reading in an informative society have been substantiated.An attempt to classify comics in Ukraine by format, genres and originality (translated, adaptations of classic and contemporary Ukrainian literary works, original ones) has been made. The development of the domestic comic book market towards more expensive and better products – production of graphic novels has been revealed. The peculiarities of different types of domestic comics are revealed, the influence on the popularity of historical and heroic types of comic books of social and political processes, in particular the events of the Revolution of Dignity and War in the East of Ukraine, has been emphasized, the attempt to create a national pantheon of superheroes has been reviewed. The educational functions of comics in Ukraine, modernization and integration of comics by means of modern social contexts that are important in terms of national identity have been underlined. Against the backdrop of the growing popularity and the fantasy comic book genre development and widespread presence on the book market of historical and heroic comics, the decline of the humorous comic book in Ukraine has been noted.Such forms and means of comic book promotion and dissemination of comics’ culture in Ukraine as festivals, blogs, thematic evenings, presentations, special Internet resources have been substantiated. It has been determined that cinema is a powerful means of promoting comics.
Efektivitas penerapan aplikasi m-health untuk posyandu di Puskesmas Kembang Seri Kabupaten Bengkulu Tengah

Background: The use of dynamic information system is expected to provide outputs that can be used as a reference in order to support policy making.All the activities listed in Information System which is divided into four (4) format register.The process of recording and data processing is still done manually using handwriting causing a lot of problems in the processing, search, and report generation Posyandu activities.Necessary to develop an information system posyandu using m-health application for Posyandu in order to facilitate the processing, search, and data reporting Posyandu activities. Objective: To identify needs and develop m-Health applications based on Android to Posyandu health centers Seri Kembang working area of CentralBengkulu District Health Office.Method: The study uses Action Research.This design was selected because it will explore in depth menegenai application development process and management of m-Health for the Posyandu.This application aims to build Android-based smartphone in the data collection program Posyandu.Results: The development of m-Health applications for android based Posyandu at the start of the
Optimizing the Confidentiality of Lecturer Database using Elgamal Algorithm

Each Higher Education Institution must have many databases, one of which is a database of lecturers who store the confidentiality of data and information from lecturers as instructors.Securing lecturer databases is needed to protect lecturer data and information from database theft.Creating an optimal system in securing lecturer databases to avoid individuals who have no right to access and process data and information.The lecturer database processed in this study uses the ElGamal algorithm with key formation using primes and solving the problem requires discrete logarithmic calculations.The keys used in this algorithm are public keys and private keys.The results of testing this method is the encrypted lecturer database.ElGamal's algorithm is very helpful in securing the lecturer database at the Universitas Potensi Utama.
Exploring the CLASS with Item Response Theory

This work applied exploratory factor analysis (EFA) and graded Item Response Theory (IRT) to a large sample (N = 4522) of Colorado Learning Attitudes about Science Survey (CLASS) post-test scores.EFA failed to reproduce the factor structure suggested by the authors of the CLASS and strongly supported the alternate 3-factor model suggested by Douglas et al.Graded IRT allowed an examination of the progression from nonexpert-like to expert-like beliefs.This progression was generally uniform with a linear relation between the difficulty of each step in the progression.Some items within the factors identified by Douglas et al. had difficulty and discrimination parameters substantially different from other items in the factor suggesting the subscale is not unidimensional.The expert-like latent ability trait estimated by IRT correlated more strongly with measures of physics performance than measures of general academic performance indicating that expert-like beliefs are not a general property of high performing students.
A Multi Keyword Searchable Attribute-based Encryption Technique for Data Access Control in Cloud Storage

MultiAuthority Cipher-text-policy Attribute-Based Encryption (MA-C-ABE) scheme is a modern form of basic cryptographic and data encryption, which is useful for data cloud storage for fine-grained access control.Multi-Keyword searchable encryption process allows legitimate users to quickly identify useful data stored in a cloud server without disclosing the searched keywords relevant data.Nonetheless, most of the preceding multi-authority attributebased models are just considered to be true in poor design or lack of user revocation performance.In this study, it includes a multi-keyword searchable attribute-based encryption technique with a cloud-based attribute update, which is a hybrid of the attribute-based encryption algorithm and multi-keyword searchable encryption technique.It provide a Multi -Keyword Searchable Cipher-text -Attribute-based Encryption (MK-C-ABE) with user revocation to solve the existing issues in cloud services, where access mechanisms are partially hidden so that recipients may never extract confidential data from the ciphertext.However, based on the idea of Bi-Linear (BL) and Bi-Linear Diffie -Hellman (BDH), our framework is proven to be safe toward specifically selected keyword attacks and selectively identified ciphertext attacks, and other ensuring privacy protection.
Esquemas de combinación de árboles de decisión como estrategia para la detección de anomalías

Security of the data consumed, generated and stored is crucial to the quality of life in today's society. For this reason, this paper proposes a comparative study of different combination schemes of multiple classifiers based on decision trees, due to its scalability and easy implementation. As a result, precision and recall values of about 97% and 100% were obtained, showing their high reliability, reducing false alarms and high generalization capacity. A comparison with a deep learning based algorithm showed that tree combination strategies are competitive and with statistically similar and superior results to the state-of-the-art. In the end, the results suggest that adaptive strategies such as XGBoost or highly randomized strategies such as Random Forest or Extra-Tree can be alternatives for the protection of precious data on the network.
Identification of Fake Identities on Social Media using various Machine Learning Algorithm

In the current scenario, online social media platforms are the technology's most common and fastest tools for information exchange.The majority of people from all backgrounds expand their time on social networking platforms.An enormous amount of information is developed and shared worldwide through social networks.Such motives have contributed to unauthorized participants engaged in malicious acts against members of the social platform.False account formation is seen on social media as doing more damage than in any other form of Cybercrime.This offense must be identified well before the consumer is told about both the development of the fake identity.Numerous algorithms and approaches have been suggested for the identification of false identities, most of which use the vast amounts of raw data produced by social platforms.In this research, we proposed fake identity detection of social accounts on twitter dataset.Various machine learning algorithms have been used to evaluate the proposed results using NLP techniques.SVM, Fuzzy Random Forest, and Naïve Bayes have used for classification.The experimental analysis shows the effectiveness of the system and how it produces better accuracy than other machine learning algorithms as well as existing systems.
Large Norms of CNN Layers Do Not Hurt Adversarial Robustness

Since the Lipschitz properties of convolutional neural networks (CNNs) are widely considered to be related to adversarial robustness, we theoretically characterize the L-1 norm and L-infinity norm of 2D multi-channel convolutional layers and provide efficient methods to compute the exact L-1 norm and L-infinity norm. Based on our theorem, we propose a novel regularization method termed norm decay, which can effectively reduce the norms of convolutional layers and fully-connected layers. Experiments show that norm-regularization methods, including norm decay, weight decay, and singular value clipping, can improve generalization of CNNs. However, they can slightly hurt adversarial robustness. Observing this unexpected phenomenon, we compute the norms of layers in the CNNs trained with three different adversarial training frameworks and surprisingly find that adversarially robust CNNs have comparable or even larger layer norms than their non-adversarially robust counterparts. Furthermore, we prove that under a mild assumption, adversarially robust classifiers can be achieved using neural networks, and an adversarially robust neural network can have an arbitrarily large Lipschitz constant. For this reason, enforcing small norms on CNN layers may be neither necessary nor effective in achieving adversarial robustness. The code is available at https://github.com/youweiliang/norm_robustness.