Reviewerly/exHarmony huggingface dataset:
The comparison of traditional machine learning and deep learning methods for malicious website detection

Today, the Internet has become more and more popular in people’s daily life. People use the Internet to get what they need. However, there are many malicious websites on the Internet. A malicious website has various unhealthy content, such as defraud content, gambling content and false information. Therefore, it is very necessary to identify malicious websites in time to prevent them from cases with various potential harm for users. Previously, the strenuous and time-consuming manual selection was the most prevalent method for malicious website detection. However, with the rise of machine learning, people could build machine learning models to train based on hand-crafted features for this task. Although being much better than manual selection, machine learning models still require a number of hand-craft features and plenty of laboring work. Nevertheless, deep learning models developed in recent ten years save a lot of work by automatically extracting features from malicious websites and produce excellent results, gaining more and more attention from researchers. This paper uses machine learning for malicious website detection, namely Random Forest [1]. And we also use a deep learning method based on LSTM (i.e., Long Short-Term Memory) to train a malicious websites detection model. Then, in order to verify the accuracy of the two models, we used malicious URLs crawled on the Internet to verify our two models. In the end, we got the results. Through comparisons in various aspects, we find that when we use the random forest method, the accuracy of our model will achieve a better result, and the model can maintain high performance than the deep learning-based method.
Handling temporality in human activity reasoning

Human-aware Artificial Intelligent systems are goal directed autonomous systems that are capable of interacting, collaborating, and teaming with humans. Activity reasoning is a formal reasoning approach that aims to provide common sense reasoning capabilities to these interactive and intelligent systems. This reasoning can be done by considering evidences –which may be conflicting–related to activities a human performs. In this context, it is important to consider the temporality of such evidence in order to distinguish activities and to analyse the relations between activities. Our approach is based on formal argumentation reasoning, specifically, Timed Argumentation Frameworks (TAF), which is an appropriate technique for dealing with inconsistencies in knowledge bases. Our approach involves two steps: local selection and global selection. In the local selection, a model of the world and of the human’s mind is constructed in form of hypothetical fragments of activities (pieces of evidences) by considering a set of observations. These hypothetical fragments have two kinds of relations: a conflict relation and a temporal relation. Based on these relations, the argumentation attack notion is defined. We define two forms of attacks namely the strong and the weak attack. The former has the same characteristics of attacks in TAF whereas for the latter the TAF approach has to be extended. For determining consistent sets of hypothetical fragments, that are part of an activity or are part of a set of non-conflicting activities, extension-based argumentation semantics are applied. In the global selection, the degrees of fulfillment of activities is determined. We study some properties of our approach and apply it to a scenario where a human performs activities with different temporal relations.
PLSUM: Generating PT-BR Wikipedia by Summarizing Multiple Websites

Wikipedia is an important free source of intelligible knowledge. Despite that, Brazilian Portuguese Wikipedia still lacks descriptions for many subjects. In an effort to expand the Brazilian Wikipedia, we contribute PLSum, a framework for generating wiki-like abstractive summaries from multiple descriptive websites. The framework has an extractive stage followed by an abstractive one. In particular, for the abstractive stage, we fine-tune and compare two recent variations of the Transformer neural network, PTT5, and Longformer. To fine-tune and evaluate the model, we created a dataset with thousands of examples, linking reference websites to Wikipedia. Our results show that it is possible to generate meaningful abstractive summaries from Brazilian Portuguese web content.
Use of Augmented Reality to Create an iOS App with Watson Studio

With the rapid development of applications for mobile platforms, developers from around the world already understand the need to impress with new technologies and the creation of such applications, with which the consumer will plunge into the world of virtual or augmented reality. Some of the world’s most popular mobile operating systems, Android and iOS, already have some well-known tools to make it easier to work with the machine learning industry and augmented reality technology. However, it cannot be said that their use has already reached its peak, as these technologies are at the stage of active study and development. Every year the demand for mobile application developers increases, and therefore more questions arise as to how and from which side it is better to approach immersion in augmented reality and machine learning. From a tourist point of view, there are already many applications that, with the help of these technologies, will provide more information simply by pointing the camera at a specific object.Augmented Reality (AR) is a technology that allows you to see the real environment right in front of us with a digital complement superimposed on it. Thanks to Ivan Sutherland’s first display, created in 1968 under the name «Sword of Damocles», paved the way for the development of AR, which is still used today.Augmented reality can be divided into two forms: based on location and based on vision. Location-based reality provides a digital picture to the user when moving through a physical area thanks to a GPS-enabled device. With a story or information, you can learn more details about a particular location. If you use AR based on vision, certain user actions will only be performed when the camera is aimed at the target object.Thanks to advances in technology that are happening every day, easy access to smart devices can be seen as the main engine of AR technology. As the smartphone market continues to grow, consumers have the opportunity to use their devices to interact with all types of digital information. The experience of using a smartphone to combine the real and digital world is becoming more common. The success of AR applications in the last decade has been due to the proliferation and use of smartphones that have the capabilities needed to work with the application itself. If companies want to remain competitive in their field, it is advisable to consider work that will be related to AR.However, analyzing the market, one can see that there are no such applications for future entrants to higher education institutions. This means that anyone can bring a camera to the university building and learn important information. The UniApp application based on the existing Swift and Watson Studio technologies was developed to simplify obtaining information on higher education institutions.
Handwritten Documents Validation Using Pattern Recognition and Transfer Learning

Handwritten documents in an Enterprise Resource Planning (ERP) system can come from different sources and usually have different designs, sizes, and subjects (i.e. bills, checks, invoices, etc.). Given these documents were filled manually, they have to be inspected to detect various kinds of issues (missing signature or stamp, missing name, etc.) before being saved in the ERP system or processed by an OCR engine. In this paper, the authors present a transfer learning approach to detect issues in scanned handwritten documents, using an award-winning deep convolutional neural network (InceptionV3) and different machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Naive Bayes (NB). The experiment shows that the combination of InceptionV3 and LR got an accuracy of 91.8% for missing stamp detection. This can allow using this approach in an ERP system as an automatic verification procedure in a document processing flow.
Automatic Height and Weight Measurement Integrated Database System

Measurement of height and weight, it is needed especially for school age 5-15 years old. From the results of monitoring height and weight measurements, we can monitor whether the child is underweight or overweight and obese. We can also monitor the growth of elementary school age children. The problem faced is that monitoring the growth of children in schools cannot be carried out effectively. This is because the process of measuring children's height and weight is done manually and of course it takes time for the process, besides that, data on student height and weight are also recorded still manually, so that data processing and utilization is not optimal. The purpose of making this Automatic Height and Weight Measurement Integrated Database System to process of measuring height and weight can be done effectively and efficiently, so it can produce integrated information in Database. The existence of an integrated database will make it easier for related parties to recap and archive children's data and store history of children's growth as material for evaluating and monitoring child growth. The results of this evaluation can be used as a reference for follow-up to be conducted. The resulting output is information in the form of tables and graphs of children's growth. In this research using the prototyping method which aims to get an overview of the tool to be designed and built, then it will be evaluated by the user. The evaluated prototype will be used as a reference to make a tool as the final product as the output of this research.
Self-Perfecting Model for Managing Individualized Investment Portfolios

Individualization is a process that adapts a ser vice or product to the needs of specific individuals. This process is a key element in recommender systems and is applied to establish innovative ser vices for technological solutions in the financial industry. For this purpose, a self-perfecting model for managing individualized investment portfolios is built, through which segmentation and evaluation of users are performed and for each of them a set of investment portfolios is recommended. This paper presents the process of recommending a portfolio of investment instruments, individualized according to the individual preferences of clients, methodology for its construction, and results of the approbation. Examples are shown of individualized portfolios, as well as a comparison of the main measures of the recommended portfolios in the respective clusters.
Counterfactual Explanations for XAI Models

This chapter explains the use of the What-If Tool (WIT) to explain counterfactual definitions in AI models, such as machine learning-based regression models, classification models, and multi-class classification models. As a data scientist, you don’t just develop a machine learning model; you make sure that your model is not biased and that it is fair about the decisions it makes for new observations that it predicts for the future. It is very important to probe the decisions and verify the algorithmic fairness. Google developed the What-If Tool to address the model fairness issue in machine learning models. You will look at the implementation of the WIT in three ML models: ML for regression-based tasks, ML for binomial classification models, and ML for multi-nominal models.
Text Generation: Next Word Prediction

This chapter explores ways to generate text or predict the next word, given the sequence of previous words. Use cases or applications include word/sentence suggestions while typing an e-mail in Gmail or text messages in LinkedIn, and machines writing poems, articles, blogs, chapters of novels, or journal papers.
Fissile Discharge to Unfavorable Geometries: NCS Controls for a Calciner Off-Gas System

Fissile Discharges to Unfavorable Geometries are a Common NCS Program Problem. The Y-12 Calciner Project Demonstrates Different NCS Philosophies Dealing with Fissile Discharge to Unfavorable Geometries. The Calciner is Being Constructed as Part of an Exit Strategy for a 75+ Year Old Facility.
LSBR Speech Steganalysis Based on Percent of Equal Adjacent Samples

Wide utilization of audio files has attracted the attention of cyber-criminals to employ this media as a cover for their concealed communications. As a countermeasure and to protect cyberspace, several techniques have been introduced for steganalysis of various audio formats, such as MP3, VoIP, etc. The combination of machine learning and signal processing techniques has helped steganalyzers to obtain higher accuracies. However, as the statistical characteristics of a normal audio file differ from the speech ones, the current methods cannot discriminate clean and stego speech instances efficiently. Another problem is the high numbers of extracted features and analysis dimensions that drastically increase the implementation cost. To tackle these, this paper proposes the Percent of Equal Adjacent Samples (PEAS) feature for single-dimension least-significant-bit replacement (LSBR) speech steganalysis. The model first classifies the samples into speech and silence groups according to a threshold which has been determined through extensive experiments. It then uses an MLP classifier to detect stego instances and determine the embedding ratio. PEAS steganalysis detects 99.8% of stego instances in the lowest analyzed embedding ratio — 12.5% — and its sensitivity increases to 100% for the ratios of 37.5% and above.
ACMEORIENTED DEVELOPMENT OF TEACHERS IN THE INTEGRATED EDUCATIONAL SPACE

In this article, the problems of the teacher's professional development, as well as the pedagogical conditions of the acmeoriented mentoring within educational institutions are being examined. The characteristics of mentoring, the features of the integrated educational space of pedagogical education in the training of teachers also had been presented here. Nowadays, the need for mentoring is quite urgent to an extent that a modern teacher must be able to instantly 'react' to the ongoing changes within the educational system. A proper 'response' to such metamorphoses in the institutional environment is required such that it's encompassing alterations of the educational program's content, the introduction of brand-new teaching methods and technologies. Mentoring is organically combining acmeorinted professional development, its personalization, and guarantees a holistic approach to each teacher. It's believed by us that mentoring allows us to expand applied professional skills and competencies. The following paper represents a general experience of the educational mentoring process, in particular, considering the use of mentoring in the advancement of acting teachers' qualifications. An important role plays an innovative project of advanced training courses, "Pro-teaching", established collectively with Karaganda Buketov University and aimed at developing new training programs.
Application of Virtual Reality in Chemical Education Using Intermolecular Force Display System HaptiChem

Virtual reality technology has been recently more intensively applied in chemistry. HaptiChem, which was developed in 2006, is one of the systems appeared in the early stage of this field. It is an intermolecular force display system, which makes it possible to touch and move molecules as feeling intermolecular force in a three-dimensional virtual space by using a haptic device. The functions and graphic display were designed as simple as possible for educational use, so that learner can easily grasp the meaning of the concept of molecular forces. We introduced HaptiChem in chemical education. We held a high school chemistry class with 43 students entitled “Several Forces between Molecules” as being open to the media. The students learned about intermolecular force from a lecture together with experiences of the force using HaptiChem. They asked more questions about intermolecular force during the class than usual. Their answers to the questionnaires after the class indicated that the haptic system promoted curiosity and enhanced learning. The students could learn more effectively by combining with the active feeling with HaptiChem. The high-school teachers evaluated that such a system can more efficiently encourage students to learn and remember things by stimulating their sense of touch. The observations also suggested that a haptic device made it easier to establish three-dimensional perception, which is difficult only with 2D-display. This pilot experiment was performed on 15th March 2007. It was the first attempt at using it in the field of chemical education. The observation was done fourteen years ago. However, since the effectiveness of haptic device in chemical education has not been changed and the interests of the applications have been increased, we decided to report the data we observed, which should be still worth disclosing. We wish to dedicate the results to new developments now and in the future.
Establishing an Operational Model of Rating Scale Construction for English Writing Assessment

Rating scales for writing assessment are critical in that they determine directly the quality and fairness of such performance tests. However, in many EFL contexts, rating scales are made, to certain extent, based on the intuition of teachers who strongly need a feasible and scientific route to guide their construction of rating scales. This study aims to design an operational model of rating scale construction with English summary writing as an example. Altogether 325 university English teachers, 4 experts in language assessment and 60 English majors in China participated in the study. 20 textual attributes were extracted, through text analysis, from China&amp;rsquo;s Standards of English Language Ability (CSE), theoretical construct of summary writing, comments on sample summary writing essays from 8 English teachers and their personal judgement. The textual attributes were then investigated through a large-scale questionnaire survey. Exploratory factor analysis and expert judgement were employed to determine rating scale dimensions. Regression analysis and expert judgement were conducted to determine the weighting distribution across all dimensions. Based on such endeavors, a tentative operational model of rating scale construction was established, which can also be applied and adapted to develop rating scales in other writing assessment.&amp;nbsp;
Agile Visualization

Visualization is central to many domains, including data analysis and artificial intelligence. Nowadays, it has never been so easy to write a visualization. Most programming languages come with several libraries dedicated to visualizing data. As such, the technical aspects necessary to implement a visualization engine and to build a visualization are now largely understood. Despite all the experience gained by the software engineering community in building efficient visualization engines, there are still some challenges that have been poorly addressed globally. In particular, one of the challenges that needs to be properly addressed when designing a library to visualize data is being able to integrate visualizations in an existing production environment. Connecting and integrating one or more visualizations in a given environment is an important and non-trivial challenge. Agile visualization, as promoted by the Roassal visualization engine for the Pharo programming language, provides a solution to that problem, which is the topic of this book. This chapter develops the idea of Agile visualization.
The Applicability of Internet Voting in Africa

The covid-19 pandemic has brought about new ways of conducting business through the use of Information Communication Technologies and elections have not been spared either. Internet voting is another form of strengthening democracy through the use of Information Communication Technologies. Africa lags in the implementation of electronic voting, especially Internet voting. This chapter applied a critical socio-technical analysis that analyses factors that influence the applicability of Internet voting within the African context. The researcher applied desktop research which included 30 journals to gather data from the Internet and other documentation sources. The findings reveal that decision-makers can partially implement Internet voting in some of the countries in Africa like Kenya, Libya, Nigeria, Morocco, Mauritius, Tunisia, and Seychelles. To successfully implement Internet voting, the decision-makers in African nations have to fully invest in the Information Communication Technology infrastructure, provide the necessary security, legislation and carry out intensive voter education to build trust among voters.
University Library Projects for the Formation of China's Digital Library and Information Space

The peculiarities of project actions on formation of digital library and information space of the People&#x0027;s Republic of China are analyzed. The leading role of the state in generation, development, financing, and realization of projects of technical and technological, organizational development of university libraries is defined. The directions of increasing the efficiency of library project management are outlined. An emphasis is placed on the system of project management standards, the innovative nature of national and corporate projects aimed at creating a digital environment, systemic nature and sequence of program and project construction of the library and information sphere of China.
Triple-VAE: A Triple Variational Autoencoder to Represent Events in One-Class Event Detection

Events are phenomena that occur at a specific time and place. Its detection can bring benefits to society since it is possible to extract knowledge from these events. Event detection is a multimodal task since these events have textual, geographical, and temporal components. Most multimodal research in the literature uses the concatenation of the components to represent the events. These approaches use multi-class or binary learning to detect events of interest which intensifies the user's labeling effort, in which the user should label event classes even if there is no interest in detecting them. In this paper, we present the Triple-VAE approach that learns a unified representation from textual, spatial, and density modalities through a variational autoencoder, one of the state-ofthe-art in representation learning. Our proposed Triple-VAE obtains suitable event representations for one-class classification, where users provide labels only for events of interest, thereby reducing the labeling effort. We carried out an experimental evaluation with ten real-world event datasets, four multimodal representation methods, and five evaluation metrics. Triple-VAE outperforms and presents a statistically significant difference considering the other three representation methods in all datasets. Therefore, Triple-VAE proved to be promising to represent the events in the one-class event detection scenario.