Reviewerly/exHarmony huggingface dataset:
EFL Students’ Perspective on the Use of Quizizz as Online Learning Media During COVID-19 Pandemic

The purpose of this research is to investigate and describe the students' perspectives on the use of Quizizz in their online language learning during the COVID-19 Pandemic. This research was done using a descriptive qualitative research design conducted at a public university in Central Java. Furthermore, there were two participants in this research, male and female. In addition, the data were collected from the interview as the main source for this research. Additionally, thematic analysis was used by the researcher to analyze the data findings. This research revealed that students had a positive view of the use of Quizizz as learning media. It was found that Quizizz became one of the online learning media that helped students during online learning. Moreover, the findings showed Quizizz keeps students motivated, makes them pay attention during online learning, and also affects students' concentration during their online learning.
Monolithic Ontological Methodology (MOM): An Effective Software Project Management Approach

Due to rapid changes in software applications, especially incorporating the demands of self-regulating technologies becomes a major challenge in software projects. This research focuses on technological, managerial, and procedural challenges, which are believed to the most significant factors contributing to projects failure. To address these issues, this study proposes Monolithic Ontological Methodology (MOM) which addresses the weakness in the existing benchmark methodologies including PRINCE2, Extreme Programming, and Scrum in terms of project management, quality control, and stakeholder involvement. The MOM consists of seven phases and each phase has the required number of iterations until it is approved by management. The updated information is recorded and shared with the respective teams. The standard documentation with control language is structured by Descriptive Logic (DL) that reduces ambiguity and technical debate. Furthermore, the illustration of the MOM includes figures, logical expressions, and descriptions. To demonstrate the effectiveness of the proposed methodology, an Analytic Hierarchy Process (AHP) was performed. The findings indicate the validity of MOM concerning considered performance metrics. Although the applicability of the proposed methodology involves relatively more documentation and formalities. The adaptive nature of MOM makes it suitable for the standard organization and brings sustainability to the organization by implementing distributed project management.
Toward a Better Understanding of Academic Programs Educational Objectives: A Data Analytics-Based Approach

In outcome-based academic programs, Program Education Objects (PEOs) are the key pillars on which program components are built. They are articulated linguistically as broad statements of graduates’ professional and career accomplishments within a few years of graduation. Moreover, PEOs are mapped into a set of skills and attributes known as Program Learning Outcomes (PLOs). It goes without saying that a profound understanding of the PEOs is a key factor in the success of an academic program. For this sake, this paper proposes a data analytics-based approach to examine the correlations among PEOs. More specifically, it applies a data similarity-based approach to analyze the correlations among the PEOs of engineering programs. To this end, a dataset of PEOs–PLOs mapping of a set of engineering programs has been extracted from their self-study reports. The collected dataset has undergone preprocessing steps to transform it into a suitable representation. This involves data cleaning, data annotation using a developed set of PEOs labels, and removal of data instances with multiple PEO labels. Each PEO is then represented as a vector space model whose dimensions are the PLOs, and their values are the relative frequencies of PLOs computed from all data instances of that PEO. After that, three data similarity measures, namely Euclidean distance, cosine measure, and Manhattan distance, are applied to measure the similarity between PEOs vector space models. The resultant similarity matrices are then analyzed at the level of a specific measure, an agreement between measures, and average similarity across all measures. The analysis results contribute to a better understanding of the PEOs correlations and provide very useful actionable insights for empowering decision making toward systemization and optimization of academic programs processes.
Arachnophobia Exposure Therapy Using Experience-Driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)

Personalized therapy, in which a therapeutic practice is adapted to an individual patient, leads to better health outcomes. Typically, this is accomplished by relying on a therapist's training and intuition along with feedback from a patient. While there exist approaches to automatically adapt therapeutic content to a patient, they rely on hand-authored, pre-defined rules, which may not generalize to all individuals. In this paper, we propose an approach to automatically adapt therapeutic content to patients based on physiological measures. We implement our approach in the context of arachnophobia exposure therapy, and rely on experience-driven procedural content generation via reinforcement learning (EDPCGRL) to generate virtual spiders to match an individual patient. In this initial implementation, and due to the ongoing pandemic, we make use of virtual or artificial humans implemented based on prior arachnophobia psychology research. Our EDPCGRL method is able to more quickly adapt to these virtual humans with high accuracy in comparison to existing, search-based EDPCG approaches.
MDA Approach for Designing and Developing Data Warehouses: A Systematic Review &amp; Proposal

A data warehouse (DW) is a vast repository of data that facilitates decision-making for businesses and companies. This concept dates back to the 1980s and it has been widely accepted. One of the key points for the success of the process of data warehousing lies in the definition of the warehouse model depending on data sources and analysis needs. Once the data warehouse is designed, the content and structure of the data sources, as well as the requirements analysis are required to evolve, therefore, an evolution of the model must take place (diagram and data). In this context, several approaches have been developed to design and implement data warehouses. Nevertheless, there is no standard process that deals with designing all of the data warehouse layers, also, there is no software that encompasses this type of problem. In general, the majority of these approaches focus on a particular aspect of data warehouse such as data storage, ETL process, OLAP, reporting, etc, and does not cover its entire lifecycle. A Model-Driven Architecture (MDA) is a standard approach, its aims to support all phases of software manufacturing by promoting the use of models and the transformations between them. Moreover, this approach aims to automate the process of software engineering, thereby decreasing the cost of software development and enhancing its productivity. In this study, we present a systematic review of various works on the data warehouse design methods. We compare and discuss these works according to the criteria that seem relevant for this issue. We present a new design approach for multidimensional schemas construction from relational models using MDA techniques, we also develop the resulting research perspectives.
Potential Roles for Science Librarians in Research Data Management: A Gap Analysis

As many sciences move to be more data-intensive, some science librarians are offering more research data services and perform research data management roles. Job analyses provide insight and context to the tasks employees actually do versus what their job descriptions depict or employers assume. Two separate job analyses studies investigated the roles and responsibilities of data services librarians and research integrity officers among the top 10 private and top 10 public higher education institutions. The focus of these interviews was research data management support roles. Comparing these two groups’ responses indicates that the role-based responsibilities for research data services are not always clear within institutions and are predominantly placed on individual researchers or research teams, but science librarians may provide some solutions to address this gap. This paper presents a model for the potential roles of science librarians in research data management.
The Development of Interactive Learning Media on Android Platform Assisted By Google Sites

This study aims to (1) produce interactive learning media on the android platform assisted by Google Sites that is feasible for junior high school Science learning of respiratory system, (2) produce media that is practical for junior high school Science learning of respiratory system. This development research employed 4D model (Define, Design, Development, and Dissemination). The subject of this research was the second semester of the eighth-grade students in Junior High School 1 Wonosari. Research instruments used in this research included validation sheets, practicality assessment sheets, and student response questionnaires. The results of this study indicate that (1) media is feasible with a percentage value in terms of material is 80% (feasible) and in terms of media is 87% (very feasible). (2) media are practically used with values of V in the aspect of material clarity of 0.86 with a very practical category, the aspect of clarity of display 0.80 with a practical category, the benefit aspect of 0.81 with a very practical category, the aspect of ease of use 0.79 with a practical category. The results from the students’ responses of media's practicality showed a P value of 79 %, included in the practical category.
A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.
Guiding Visual Question Generation

In traditional Visual Question Generation (VQG), most images have multiple concepts (e.g.objects and categories) for which a question could be generated, but models are trained to mimic an arbitrary choice of concept as given in their training data.This makes training difficult and also poses issues for evaluationmultiple valid questions exist for most images but only one or a few are captured by the human references.We present Guiding Visual Question Generation -a variant of VQG which conditions the question generator on categorical information based on expectations on the type of question and the objects it should explore.We propose two variant families: (i) an explicitly guided model that enables an actor (human or automated) to select which objects and categories to generate a question for; and (ii) 2 types of implicitly guided models that learn which objects and categories to condition on, based on discrete variables.The proposed models are evaluated on an answer-category augmented VQA dataset and our quantitative results show a substantial improvement over the current state of the art (over 9 BLEU-4 increase).Human evaluation validates that guidance helps the generation of questions that are grammatically coherent and relevant to the given image and objects.
Unsupervised Novelty Characterization in Physical Environments Using Qualitative Spatial Relations

Detecting, characterizing and adapting to novelty, whether in the form of previously unseen objects or phenomena, or unexpected changes in the behavior of known elements, is essential for Artificial Intelligence agents to operate reliably in unconstrained real-world environments. We propose an automatic, unsupervised approach to novelty characterization for dynamic domains, based on describing the behaviors and interactions of objects in terms of their possible actions. To abstract from the variety of realizations of an action that can occur in physical domains, we model states in terms of qualitative spatial relations (QSRs) between their entities. By first learning a model of actions in the non-novel environment from the state transitions observed as the agent interacts with the world, we can detect novelty by the persistent deviations from this model that it causes, and characterize the novelty by new or modified actions. We also present a new method of learning action models from observation, based on conceptual similarity and hierarchical clustering.
A Comprehensive Survey on Privacy-Security and Scalability Solutions for Block Chain Technology

Blockchain has various merits such as decentralization, greater transparency and improved traceability. Nowadays blockchain is used at various numbers of applications such as financial related services, business &amp; industry, integrity verification, governance, health and education sectors. Even though the blockchain technology has promising approaches for the building the future of internet systems and also extensive research is going on about the technical challenges. In this paper we presented the comprehensive survey on blockchain and the techniques that can be used to address the scalability issues with respect to storage and also different privacy preserving techniques that can be incorporated during the implementation of blockchain application.
Representing and Reasoning with Event Models for Epistemic Planning

The standard representation formalism for multi-agent epistemic planning has one central disadvantage: When you use event models in dynamic epistemic logic (DEL) to describe the action of one agent, the model must specify not only the actual change and the change of that agent's knowledge. Also required is the epistemic change of any agents that may be observing the first agent performing the action, plus the epistemic change for any further agents that failed to observe that anything had taken place. To overcome the gap between this complex DEL notion of events and a more commonsense notion of actions, we propose a simple high-level action description language for multi-agent epistemic planning domains with just one type of effect laws: a causes x if y. Effect x can either be a physical effect, or an observation from an independent set that is specific to individual agents. We formally prove that any DEL event model can be described in this way. We show how this language provides a framework for expressing a variety of executability and action models; such as describing actions that are both ontic and epistemic, partially observable, or nondeterministic. We further combine our representation of event models with a description language for finitary initial epistemic theories, and we show how this allows us to reason about the effects of a sequence of actions in a multi-agent epistemic domain by updating a single multi-pointed epistemic model.
Bone Cancer Detection Using Feature Extraction with Classification Using K-Nearest Neighbor and Decision Tree Algorithm

The malignant cells that cannot be controlled from spreading throughout the body is Cancer. Among which the cancer occurs in bone is their type. It is malignant disease occur in bone of human body where their growth cant be controlled from growing. This bone cancer is very critical of all the cancer types since the malignant cells are not identified at their earlier stage and it is the major challenge. Bone cancer is highly common for children and teenagers. For earlier detection of this cancer the correlation of medical imaging has been adapted with image processing and machine learning techniques where maximum accuracy can be obtained similarly even for bone cancer. This paper proposes the detection of bone cancer from the dataset taken from clinical dataset. Here the proposed design comprises of 2 phases in predicting the disorder with higher accuracy. The first stage is extracting the feature of segmented bone image using Gray-Level Co-occurrence Matrix (GLCM) method is applied to extract the features in terms of statistical texture-based and the second phase is classification of extracted feature using K-NN with decision tree algorithm. The simulation results show the enhanced classification results and extracted output with higher accuracy.
Optimized Stacking Ensemble (OSE) for Credit Card Fraud Detection using Synthetic Minority Oversampling Model

Objectives: Credit fraud is a global threat to financial institutions due to specific challenges like imbalanced datasets and hidden patterns in real-life scenarios.The objective of this study is to propose a model that effectively identifies fraudulent transactions.Methods: Methods such as Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) that artificially generate synthetic data are used in this paper to approximate the distribution of data among the two classes in the original dataset.After balancing the dataset, the individual models Multi-Layer Perceptron (MLP), k-Nearest Neighbors algorithm (kNN) and Support Vector Machine (SVM) are trained on the augmented dataset to establish an initial improvement at the data level.These base-classifiers are further incorporated into the Optimized Stacked Ensemble (OSE) learning process to fit the meta-classifier which creates an effective predictive model for fraud detection.All base-classifiers and the final Optimized Stacked Ensemble (OSE) have been implemented to critically assess and evaluate their performances.Findings: Empirical results obtained in this paper show that the quality of the final dataset is considerably improved when Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) are used as oversampling algorithms.The Multi-Layer Perceptron model showed an increase of 10% in the F1 Score while kNN and SVM showed an increase of 3% each.The optimized model is built using a Stacking Classifier that combines the GAN-improved Multi-Perceptron Model with the other standard classification models such as KNN and SVM.This ensemble outperforms the existing enhanced Multi-Layer Perceptron with near-perfect accuracy (99.86%) and an increase of 16% in F1 Score, resulting in an effective fraud detection mechanism.Novelty: For the current dataset, the Optimized Stacked Ensemble model shows an increase of 16% in F1 Score as compared to the existing Multi-Perceptron model.
Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations

Despite recent stereo matching algorithms achieving significant results on public benchmarks, the problem of requiring heavy computation remains unsolved. Most works focus on designing an architecture to reduce the computational complexity, while we take aim at optimizing 3D convolution kernels on the Pyramid Stereo Matching Network (PSMNet) for solving the problem. In this paper, we design a series of comparative experiments exploring the performance of well-known convolution kernels on PSMNet. Our model saves the computational complexity from 256.66 G MAdd (Multiply-Add operations) to 69.03 G MAdd (198.47 G MAdd to 10.84 G MAdd for only considering 3D convolutional neural networks) without losing accuracy. On Scene Flow and KITTI 2015 datasets, our model achieves results comparable to the state-of-the-art with a low computational cost.
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers

We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply.Answering the questions requires compositional logical reasoning across complex context.We call this dataset ConditionalQA.In addition to conditional answers, the dataset also features:(1) long context documents with information that is related in logically complex ways; (2) multi-hop questions that require compositional logical reasoning; (3) a combination of extractive questions, yes/no questions, questions with multiple answers, and not-answerable questions; (4) questions asked without knowing the answers.We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions.We believe that this dataset will motivate further research in understanding complex documents to answer hard questions. 1
A Security Management and Control Solution of Smart Park Based on Sensor Networks

As a typical application of sensor networks, there exist many information security problems in smart parks, such as confusion of personnel access, lack of security management, disorderly data flow, insufficient collection of audit evidence, and so on. Aiming at the scenario of personnel and equipment moving in different areas of smart parks, the paper proposes a joint authorization and dynamic access control mechanism, which can provide unified identity management services, access control services, and policy management services, and effectively solve the problem of multi-authorization in user identity and authority management. The license negotiation interaction protocol is designed to prevent common network attack threats in the process of identity authentication and authority management. In order to realize the tamper-proof storage of personnel and equipment movement trajectory, the paper also designs a movement trajectory traceability protocol based on a Merkle tree, which solves the problems of internal personnel malicious attack, trusted third-party dependency bottleneck, high overheads of tracking algorithms, and so on. The experimental results show that compared with the current security control mechanisms for sensor networks, the joint authorization, and dynamic access control mechanism can support multi-party authorization and traceability, while the overhead it generates in initialization, encryption, decryption, and key generation steps are basically the same as other mechanisms do.