Reviewerly/exHarmony huggingface dataset:
A Closer Look at Insider Threat Research

Insider threats are a danger to organizations everywhere and no organization is immune to the effects of an insider incident. Organizations suffer from individuals whose actions expose the organization to risk or harm in some ways. This situation includes insiders who intentionally or unintentionally cause actions that bring harm or significantly increases risk to the organization. Insider security breaches have been identified by organizations as a pressing problem with no simple solution. This paper presents a systematic literature review of published, scholarly articles on insider threat research from 2010 to 2020. The focus of this literature review is to survey the topics, methodologies, and theories of current insider threat research. The goal of this literature review is to provide an overview of the trends in insider threat research. Fifty-two studies were identified, and about half the papers dealt with identifying potential insiders through machine learning techniques. The most popular trend was the use of learning-based algorithms, such as neural networks and support vector machines, that classified a user as an insider versus a non-insider. Aside from the popular modeling approach, the other publications included in our review focused on human factors related to insider threat and the common methodology for these papers were the use of surveys and questionnaires. Another trend identified in the literature was the use of behavioral patterns as an insider threat indicator. Lastly, researchers identified best practices for organizations to address insider threats. The outcome of this literature review identified trends, best practices, and knowledge that can be used to further develop insider threat frameworks and methodologies. Furthermore, this literature review presents implications for researchers including challenges, issues, and future research directions.
A Dialogue Interface for Low Code Program Evolution

Low code development platforms allow for the design, deployment, and maintenance of software applications through a graphical user interface which is targeted, primarily, at non-expert users. Despite the graphical interface being more accessible than plain text programming, the end user still needs to identify and put together the required building blocks to achieve the intended behavior, and this generally requires some basic knowledge of programming. To tackle this problem, I present an approach to low code programming by introducing a dialogue interface to such platforms. A dialogue interface allows the user to write natural language instructions stating what they want in the application. The dialogue is mapped to an ontology representation. From these facts and the current state of the application, we infer a set of operations which must be applied to fulfill the user intent and reach the desired state ensuring that, at each interaction, we only apply the necessary operations.
Simultaneous Color Restoration and Depth Estimation in Light Field Imaging

Recent studies in the light field imaging have shown the potential and advantages of different light field information processes. In most of the existing techniques, the processing pipeline of light field has been treated in a step-by-step manner, and each step is considered to be independent from the others. For example, in light field color demosaicing, inferring the scene geometry is treated as an irrelevant and negligible task, and vice versa. Such processing techniques may fail due to the inherent connection among different steps, and result in both corrupted post-processing and defective pre-processing results. In this paper, we address the interaction between color interpolation and depth estimation in light field, and propose a probabilistic approach to handle these two processing steps jointly. This probabilistic framework is based on a Markov Random Fields —Collaborative Graph Model for simultaneous Demosaicing and Depth Estimation (CGMDD)—to explore the color-depth interdependence from general light field sampling. Experimental results show that both image interpolation quality and depth estimation can benefit from their interaction, mainly for processes such as image demosaicing which are shown to be sensitive to depth information, especially for light field sampling with large baselines.
Data Mining with Python

As the amount of data in the world is exponentially on the rise, we need all the tools and knowledge we can get to analyse this data and extract valuable information. This allows important stakeholders to make data-driven decisions, thus providing added value in any organisation. The data mining process can be applied in virtually all kinds of organisations ranging from the public to the private sector. Employees use data in their professional lives and therefore need to be familiar with the knowledge discovery process. The focus of this article is Python as a tool for data mining. The authors concluded that Python is a great option for this task since it is open-source, free and comes with a huge community that develops the packages needed for analytics workloads and it also has lots of documentation. Its capabilities are demonstrated at the end of this paper, where the authors have set up a case study relating to airline passenger satisfaction. The main approach is exploratory data analysis through visualisations with the goal of finding hidden patterns in the data. A decision tree machine learning model was also developed to extract the features that contribute to a higher satisfaction level.
Improving the Academic Services of As-Syafi’iyah Islamic University, Jakarta, Indonesia, through the Evaluation of Students’ Satisfaction

The purpose of this evaluation research is to study and obtain empirical data about the level of satisfaction of students for academic services that have implications for efforts to improve academic services, which can improve the reputation of the university. The research method used survey methods with an affordable population of about 4000 students of As-Syafi’iyah Islamic University, Jakarta, Indonesia. The sampling was done randomly with 733 respondents. The results of the study showed: a) the average assessment of student satisfaction with the University’s academic services in the category of ‘satisfied’ with an average assessment score = 3.8697 and this score still needs to be increased to 5.0 to improve the academic services of the University which have implications for improving the reputation of the university; b) The quality of academic services has a strategic role in meeting student satisfaction which has an impact on the emergence of high loyalty to the University, and it is expected that these students can be a ‘mouth promotion’ for the University; c) academic services that need to be improved include the ability and care of education personnel in providing quality &amp; fast service; the ability and concern of the manager/leader and education staff of the Study Program/Faculty in providing quality academic services, and adequacy, accessibility &amp; quality of supporting facilities of academic activities of students; d) The characters empathy, sympathy, and sharing must be strived to grow and develop into an inherent and settled character among education personnel or lecturers.
Melius Cras: Game-Based Learning Methods to Reduce Anxiety for Students

Introduction: Anxiety is a mental health problem that often occurs in students and can cause serious problems.Inadequate handling of anxiety will interfere with academic performance, which impacts students' mental health deterioration.Aim of Study: This study aims to develop a gamebased learning program to reduce anxiety in students.Method: This study uses a quasi-experimental pre-and post-test design.This study involved 40 students from universities throughout Indonesia, with a balanced proportion between men and women.This program is held for six days in August 2021 online using the messenger application.This game consists of 5 parts: grounding, unhooking, making a room, being kind and acting on your value, and reflecting and resharing the journey.In this activity, the researcher will give narration to mediate participants in completing each challenge in each activity.Results: The results of the pre and post-tests show that participants feel happy to follow the game and can reduce the participant's anxiety.Conclusion Online gamification development can be alternative activities at home or anywhere to reduce stress and anxiety in students
FORMING THE STRATEGIC COMPETENCE OF FOREIGN LANGUAGE TEACHERS

The article deals with strategic competence, which is included in communicative competence and is important for foreign language teacher training.The aim of the article is to reveal the essence and meaning of the concept of strategic competence in a process of its formation in foreign language teachers.It is stated that the strategy is a tool used by a language user to activate skills and abilities in communication.In the structure of communicative competence, strategic competence plays an important role along with discursive, verbal, socio-cultural and sociolinguistic competences.The following types of verbal activities for mastering certain communicative strategies were identified: perception (receptive strategies), production (productive strategies), interraction (interactive strategies), mediation (intermediary strategies).It is stated that strategic competence in foreign language acquisition includes educational and communicative strategies.It is emphasized that a teacher focuses his/her work on enriching students' communicative potential, enhancing their language competence, directing their efforts towards developing attainment strategies.The acquisition of achievement strategy competence is a productive way to raise a level of communicative competence.It is important for preparing a foreign language teacher to know that at the present stage of development of the theory of communicative competence the notion of strategic competence develops in two directions: as communicative strategies and as educational strategies.The identification of learning strategies as a separate direction of research highlights an importance of students' conscious attitude towards the learning process and their own learning activities.The teacher's goal-oriented development of learning strategies contributes to personal and active learning, increasing students' autonomy, and active thinking.Essential for successful formation of strategic competence is an ability to combine communicative and educational strategies to solve communicative tasks.Use of some strategies requires students not only intuition and mental competence, but also analysis and reflexion.
Start with the Visible: Explore Activity

In Chap. 1 , you’ve already learned how to create the first Android project. Apparently, that is not enough and it’s time to learn something more advanced. Where should we begin? Imagine that you’ve already created a high-quality app and want to introduce it to your very first user, where should you begin? Of course, you will start with the user interface! This is because users don’t care about the algorithms, system design. They are only interested in what they see, so let’s begin with what’s visible.
Correlated Boolean Operators for Uncertainty Logic

We present a correlated and gate which may be used to propagate uncertainty and dependence through Boolean functions, since any Boolean function may be expressed as a combination of and and not operations. We argue that the and gate is a bivariate copula family, which has the interpretation of constructing bivariate Bernoulli random variables following a given Pearson correlation coefficient and marginal probabilities. We show how this copula family may be used to propagate uncertainty in the form of probabilities of events, probability intervals, and probability boxes, with only partial or no knowledge of the dependency between events, expressed as an interval for the correlation coefficient. These results generalise previous results by Fréchet on the conjunction of two events with unknown dependencies. We show an application propagating uncertainty through a fault tree for a pressure tank. This paper comes with an open-source Julia library for performing uncertainty logic.
YouTube as a Source of Information for Fluoride Treatment: a Content and Quality Analysis

Introduction: Fluoride treatment has a critical role in preventive dentistry and oral health.Determining the level of information that patients can access via YouTube is considerable for the knowledge of clinicians about the patient's approach to treatment.The aim of this study is to evaluate the quality of information on YouTube videos about fluoride treatment.Methods: In Google Trends on November 14, 2019, fluoride treatment was identified as the most popular keyword and 86 videos of the first 120 videos were included.The content was evaluated considering some sub-topics on 10 points score.Video information and quality index (VIQI) was evaluated using a 5-point score.Statistically, Mann Whitney-U, Chi-Square and Spearman correlation analyses were performed.Results: 86 videos were divided into two groups as low content (%66.3,n=57) and high content videos (%33.7,n=29) considering the total content scores of each video.A significant difference was found in the total VIQI scores (p <0.001) and video lengths (p = 0.003) between groups.The most frequently mentioned content was professionally-applied topical fluoride treatments (76.7%).51.2% of the videos were uploaded by the dentist/specialist.Discussion and Conclusion: Although most of the videos were uploaded by dentists and specialists, the contents were insufficient.
Learning Through Transcription

Transcribing speech for primarily oral, local languages is often a joint effort involving speakers and outsiders.It is commonly motivated by externally-defined scientific goals, alongside local motivations such as language acquisition and access to heritage materials.We explore the task of 'learning through transcription' through the design of a system for collaborative speech annotation.We have developed a prototype to support local and remote learner-speaker interactions in remote Aboriginal communities in northern Australia.We show that situated systems design for inclusive non-expert practice is a promising new direction for working with speakers of local languages.
Research on Reader’s Reading Promotion in the Digital Age

With the rapid development of science and technology, the increasing prosperity of spiritual and cultural life, people's thirst for knowledge and the unprecedented surge of access to information, digitization will become the development direction of the library.Readers are the soil and condition for the survival of the library and the direct driving force for the development of the library.Only through the utilization of readers can the library reflect its social value.Based on the digital age, this paper studies and discusses the readers' reading promotion.
Vote mapping based improved human tracking for intelligent surveillance systems

Human tracking is a challenging task and a significant part of the design of an intelligent surveillance system. Though the existing tracking techniques accomplished the reasonable outcomes in terms of accuracy and robustness, there is a scope for improving the tracking performance. In this paper, vote mapping of patched confidence methodology is used with the consecutively increasing number of patches. The system aims to provide robustness to occlusion and global scene changes by utilising the number of patches from the bounding box of an image. An individual patch is tracked by kernelised correlation filter and applied to the vote mapping methodology. The consecutively increasing number of patch approach and vote mapping provide robustness to the occlusion in real time tracking scenarios. The qualitative and quantitative analysis reveals the superiority of the proposed vote mapping-based tracker over the existing kernel-based trackers.
SQLite

Abstract SQLite is, without doubt, the most widely used database systemworldwide at the moment. The single file database system is used, among other things, in operating systems for cell phones, such as Android, iOS or Symbian OS. On a typical smartphone, we usually find several hundred SQLite databases used by a wide variety of apps. Due to its widespread use, the database format is of particular importance in mobile forensics. It is not uncommon for the suspect to try to cover his tracks by deleting database content. Recovering deleted records from a database presents a special challenge. In this chapter, the on-disk database format of the SQLite database system is highlighted. Therefore,we take a closer look at the database header as well as record structure on a binary level. We first examine the structure of the data. Recovery options for erased records are discussed as well. Special attention is paid to the slack areas within the database: unallocated space, Freelist as well as free blocks. In this context, we discuss basic techniques for carving and acquisition of deleted data artefacts. Despite the main database format and recovery options, temporary file types like write-ahead logs and rollback journals are analyzed as well.
A novel application for DC motor-generator cascade system by changing signal density of digital chaotic oscillator

Presented is a new method for the realization of a chaotic oscillator in a digital environment.First, a twostroke sampling mathematical regulation is developed for discrete-time oscillator equations to change signal densities of chaotic signals.This proposed mathematical regulation is applied to Lorenz's chaotic oscillator, which presents a complex dynamical behavior.An application is shown with simulation through a Matlab-Simulink environment with time-dependent density changes of and graphics and phase space graphics that are dependent on different density changes.Further to this, in an experimental study, Lorenz's chaotic oscillator's signals with variable density is applied to a DC motor as armature voltage via an 8-bit microcontroller based hardware environment.Chaotic supply voltage is applied to the motor rotor to generate a chaotic angular velocity.Time-dependent density change results of and graphics are obtained and shown on an oscilloscope by converting chaotic rotor angular velocity to electrical signals, through a tacho-generator.The observed results revealed that chaotic signal production with variable density is achieved both in the simulation environment and the experimental environment.Also, it is shown that the proposed program and mathematical equations are feasible in terms of hardware and software implementations.
IMPLEMENTATION OF E-BOOK-BASED TEACHING MATERIALS FOR MAKEUP LEARNING: INDONESIA EXPERIENCE

This article aims to uncover the implementation of e-book-based teaching materials for makeup learning. The method used in this article is the literature review. Based on the study of literature it can be concluded that among the teaching materials e-book is important to pay attention to the constituent components, namely. (a) Display of e-book teaching materials, including book cover design, shape, size, and image. (b) The content of the e-book teaching materials, including the composition of the text, the standard rules of book preparation (aspects of material completeness, language, and usefulness), and dictionaries or glossaries. The learning of the history of Makeup is very important to be mastered by students because the learning of the history of makeup trains students in practicing their knowledge and for the provision of students after graduating from college so that students majoring in skin beauty can open a business in the field of bridal makeup. Related to this, students must be able to master the history of makeup well. On the other hand, students still have a lot of difficulty in mastering the concept of learning the history of makeup and still lack references obtained by students to increase their knowledge. The use of learning media is considered as one of the most appropriate solutions in presenting bridal makeup materials in learning. In addition, the study of the history of makeup is one of the learnings that must students in understanding the concepts contained in it, such as the wedding ceremony, wearing clothes and accessories, and how to apply makeup of the bride.
An Efficient Image Dehazing Algorithm Using Multiple Priors Constrains

In this paper, a robust and efficient image dehazing technique based on atmospheric scattering model is proposed, which effectively overcomes the limitation of single prior condition. It is mainly composed of two modules, transmission estimation module and atmospheric light estimation module. The transmission estimation module integrates multiple dehazing prior strategy and optimizes transmission estimation and application range effectively.Â The atmospheric light estimation module uses the fuzzy C-means clustering algorithm (FCM) to estimate the atmospheric light of different scenes in the image. Unlike the previous work, the atmospheric light in this module is a non-global value, and a pixel-level atmospheric light value matrix is obtained. A large number of experiments show that the proposed dehazing algorithm is superior to the state-of-the-art methods.
Investigating the Generative Approach for Question Answering in E-Commerce

Many e-commerce websites provide Productrelated Question Answering (PQA) platform where potential customers can ask questions related to a product, and other consumers can post an answer to that question based on their experience.Recently, there has been a growing interest in providing automated responses to product questions.In this paper, we investigate the suitability of the generative approach for PQA.We use state-of-the-art generative models proposed by Deng et al. ( 2020) and Lu et al. ( 2020) for this purpose.On closer examination, we find several drawbacks in this approach: (1) input reviews are not always utilized significantly for answer generation, (2) the performance of the models is abysmal while answering the numerical questions, (3) many of the generated answers contain phrases like "I do not know" which are taken from the reference answer in training data, and these answers do not convey any information to the customer.Although these approaches achieve a high ROUGE score, it does not reflect upon these shortcomings of the generated answers.We hope that our analysis will lead to more rigorous PQA approaches, and future research will focus on addressing these shortcomings in PQA.
Using Machine Learning Techniques to Predict Bugs in Classes: An Empirical Study

Software bug prediction is an important step in the software development life cycle that aims to identify bug-prone software modules.Identification of such modules can reduce the overall cost and effort of the software testing phase.Many approaches have been introduced in the literature that have investigated the performance of machine learning techniques when used in software bug prediction activities.However, in most of these approaches, the empirical investigations were conducted using bug datasets that are small or have erroneous data leading to results with limited generality.Therefore, this study empirically investigates the performance of 8 commonly used machine learning techniques based on the Unified Bug Dataset which is a large and clean bug dataset that was published recently.A set of experiments are conducted to construct bug prediction models using the considered machine learning techniques.Each constructed model is evaluated using three performance metrics: accuracy, area under the curve, and F-measure.The results of the experiments show that logistic regression has better performance for bug prediction compared to other considered techniques.