Reviewerly/exHarmony huggingface dataset:
Revisit the implementation status of research data management in Chinese academia

The paper examines the current literature of RDM implementation status, then follows a three-level model to revisit the implementation status of RDM in Chinese academia by reviewing the governmental documents, funding agencies' policies, and institutional efforts. The findings suggest that the macro-environment of RDM in China is interrupted at the agency level caused by the inactions of strategies and lack of policies. The results also suggest that national universities varied in RDM services and repositories, and the macro-environment of RDM in China is problematic. The paper then identifies the problems, discusses the fundamental issues, and proposes solutions from three principles.
Augmented Reality Information for Food (ARIF): Design and Development

research-article Share on Augmented Reality Information for Food (ARIF): Design and Development Author: Nur Azlina Mohamed Azlina Mokmin Centre of Instructional Technology and Multimedia Universiti Sains, Malaysia Centre of Instructional Technology and Multimedia Universiti Sains, MalaysiaView Profile Authors Info & Claims ICEEL 2020: 2020 The 4th International Conference on Education and E-LearningNovember 2020 Pages 193–196https://doi.org/10.1145/3439147.3439162Published:25 February 2021Publication History 0citation81DownloadsMetricsTotal Citations0Total Downloads81Last 12 Months32Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Knowledge Interaction Enhanced Knowledge Tracing for Learner Performance Prediction

One of the fundamental tasks when providing personalized tutoring services to learners in online learning systems is to predict learner performance on future exercises. To achieve this goal, it is necessary to estimate and trace the knowledge proficiency (KP) of learners by modeling their learning performance. The existing models either fail to capture the long-term dependencies in the exercising sequence to model the influence of a previous exercise to the current one or find it difficult to explain the results. To solve these issues, we propose herein a novel model, called the knowledge interaction-enhanced knowledge tracing (KIKT), to estimate and trace the evolution of learners' KP. We first propose a framework by unifying the strength of the memory network to enhance the representation of the knowledge state and the interpretability of the Item Response Theory to explain learner performance. In this framework, we trace each learner's KP on each knowledge concept overtime, and further infer their proficiencies and the item characteristics using two kinds of neural networks. Moreover, we incorporate the knowledge interaction and the cognitive difficulty into our model to further exploit the long-term dependencies and the adaptive item difficulty in the exercising sequences. Extensive experiments conducted on five real-world datasets demonstrate the superiority of our model.
Improving the Output Quality of Official Statistics Based on Machine Learning Algorithms

Abstract National statistical institutes currently investigate how to improve the output quality of official statistics based on machine learning algorithms. A key issue is concept drift, that is, when the joint distribution of independent variables and a dependent (categorical) variable changes over time. Under concept drift, a statistical model requires regular updating to prevent it from becoming biased. However, updating a model asks for additional data, which are not always available. An alternative is to reduce the bias by means of bias correction methods. In the article, we focus on estimating the proportion (base rate) of a category of interest and we compare two popular bias correction methods: the misclassification estimator and the calibration estimator. For prior probability shift (a specific type of concept drift), we investigate the two methods analytically as well as numerically. Our analytical results are expressions for the bias and variance of both methods. As numerical result, we present a decision boundary for the relative performance of the two methods. Our results provide a better understanding of the effect of prior probability shift on output quality. Consequently, we may recommend a novel approach on how to use machine learning algorithms in the context of official statistics.
Prediction of Patients’ Length of Stay at Hospital During COVID-19 Pandemic

Abstract Machine learning has been extensively used in diverse healthcare settings since the 21st century. Statistical models are proven to be powerful in detecting early disease symptoms and could potentially aid decision-making in the healthcare system. To help improve medical resource allocation during COVID-19 pandemic, we aim to develop machine learning models that predict each patient’s length of stay (LOS) in hospital. Three machine learning models, namely, K-nearest Neighbors Algorithm, Logistic Regression and Random Forest are implemented and optimized on the same healthcare dataset. The final accuracy of each model is 0.3442, 0.3524 and 0.3541 respectively, which are not very high. Our subsequent correlation analysis on the healthcare dataset shows the patients’ features used do not provide sufficient information for accurate LOS prediction. Yet, machine learning approaches could potentially yield much better results if the data quality can be improved by including additional relevant patient features and breaking LOS into more appropriate intervals. More detailed healthcare data should be obtained to make the LOS prediction useful for healthcare management.
A Novel Full Reference-Image Quality Assessment (FR-IQA) for Adaptive Visual Perception Improvement

To introduce a new IQA method called SC-QI, this research work adapts a structural contrast index (SCI) that characterizes the perceptions of local and also the global visual features on behalf of different image characters through different varieties of structural distortion. This research work also attempts for the development of SC-QI visual dependability involvement and expand the refitted picture quality optimization (SC-QI) called SC-DM. For local image characteristics & different forms of distortion, several appearances recycled in computational IQA can almost not describe visual quality conditions [18]. The complexity of the texture is a quality that increases as the visibility of distortions, which increases due to the contrast effect in the texture of the background image. Selecting user-friendly methods for optimization depends on FR-IQA. Here, image quality is considered as an important aspect of image processing, so these approaches can certainly contribute towards enhancing the visual quality by reducing the image structural distortion. FR-IQA (SC-QI) methods (SC-DM) have directly or indirectly affected the role of contrast/structural data in image signals that have observed the pictorial feature. Conclusion operative geographies that are used to describe such contrast/structural datacould therefore remain as a frequent problem in displaying.
Integration of Artificial Intelligence into software reuse: An overview of Software Intelligence

Software reuse not only reduces time-to-market but also improve productivity and reliability of the software. Developers can take the benefits of reusable modules of the software by simply accessing the software repository. To create these repositories Data Mining techniques are used and later to predict the reusability factor of the modules Artificial Intelligence comes into light. This integration of Data Mining with Artificial Intelligence to perform software engineering task is termed as software Intelligence. This paper provides a roadmap of conventional reuse approaches to Software Intelligence approaches. Intense survey is conducted on various Artificial Intelligence algorithms applied to measure reusability of the extracted component. From the survey it is concluded that Components Based Development fits best for modern applications as it supports object-oriented paradigm. Besides, the existing Artificial intelligence approaches lack is efficient prediction of reusable module.
Dynamic Scheduling Management Method for Multi-energy System Digital Twin Simulation Computing Tasks

Digital twin technology provides dynamic planning, operation optimization and other complex business applications for multi-energy system based on high-performance simulation computing. As it involves the scheduling and management of massive simulation computing tasks, how to assign simulation computing tasks to computing servers in a balanced manner and minimize the completion time of all tasks are the key to scheduling. Analyzed and discussed the existing problems of task scheduling algorithms, based on this, combined with the characteristics of simulation computing tasks, this paper proposes a dynamic scheduling management method for multi-energy system digital twin simulation computing tasks. Taking into account the characteristics of stable computing completion time for simulation computing tasks, a comprehensive priority index is designed to quantify the execution sequence of simulation computing tasks, then based on this comprehensive priority index, a task assignment mechanism as the goal of minimizing computing completion time of application is proposed. Illustrate the rationality and efficiency of this scheduling management method for simulation computing tasks through a specific case.
Disappearing boundaries? Reality, virtuality and the possibility of “pure” mixed reality (MR)

ABSTRACTABSTRACTThis article argues that reality and virtuality are still very much phenomenologically distinguishable, although this might not be the case forever. I argue for two main types of virtuality – one inherently involved in the dynamic horizons of perceptual experiences, while the other is all of our experiences of digital images – in order to show that a particular possible instantiation of the latter type, namely "pure" mixed reality (MR), might come to blur and collapse various experiential categories in the future, not least real and irreal, like never before. To show this, there are three main sections. First, I outline my understanding of the two basic types of virtuality, as understood from a classical phenomenological analysis. Second, I give an account of the most important family of "virtual technologies" relevant to the question at hand, namely virtual, augmented and mixed reality (VR, AR and MR respectively) technologies. After homing in on MR, I explain what "pure" MR is and how, through tactile holograms, this might change even basic experiential distinctions going forward, and not necessarily for the better.KEYWORDS: hologramsimage-consciousnessimaginationmixed reality (MR)perceptionphenomenologyrealityvirtuality
A Corpus Based Transformation-Based Learning for Hausa Text Parts of Speech Tagging

We discuss in this paper the challenge of enhancing the dendritic cell algorithm preprocessing phase.In short, to minimize data dimensionality we propose a new dendritic cell algorithm based on Non-negative Matrix Factorization.The aim of this method is to extract latent features from lower rank data transformation.The proposed method was divided in two steps.The first step is a factorization of the original data.Secondly, the new reduced space should be assigned to its respective signal category.Experimental findings show that the preprocessing step of the dendritic cell algorithm is significantly improved with respect to its execution and a higher accuracy rate.Our algorithm is also compared to other classification algorithms particularly MLP, SVM, and KNN.The comparison shows that the actual rate of current algorithms is outperformed by the proposed algorithm.
Prediction Performance of Crime Against Women Using Rule Based Decision Tree J48 Classification Algorithms in various states of India

Crimes against women be situated collective societal difficult distressing eminence of lifecycle in addition monetary development of a civilization. Through expanding violations, rule implementations organizations remain proceeding towards request propelled frameworks and new ways to deal with improving wrongdoing investigation and better ensure their networks. Rule Based Decision tree (RBDT J48) and Naive Bayesian algorithm functional happening framework of commandment implementation then cleverness investigation embraces capacity of improving problems. Information Mining stands method in the direction of citation information available, frequently huge record sets; the widest collection of machine learning solicitations consumes prepared this one significant field of exploration. Crime type stands progression intentions towards classify law-breaking individualities. Exploration measured improvement in wrongdoing expectation model utilizing Rule Based Decision Tree (RBDT J48) calculation and Naive bayes since it has been considered as the most effective AI calculation for a forecast of wrongdoing information. And furthermore think about the two distinctive grouping calculations to be specific, Naive bayes and Rule Based Decision Tree J48 for anticipating wrongdoing type for various states in India.
Duality of path distinguishability and quantum coherence

We generalize the duality relation between path distinguishability and quantum coherence proposed by Bera et al. (2015) [12]. In contrast to earlier works, the quantum states are no longer restricted to any special forms. A theorem about the positivity of 2×2 block matrices is applied to prove a relation between the diagonal blocks and the off-diagonal blocks in any positive 2×2 block matrices, which plays the central role in our reasoning.
Online Algorithm for Differentially Private Genome-wide Association Studies

Digitization of healthcare records contributed to a large volume of functional scientific data that can help researchers to understand the behaviour of many diseases. However, the privacy implications of this data, particularly genomics data, have surfaced recently as the collection, dissemination, and analysis of human genomics data is highly sensitive. There have been multiple privacy attacks relying on the uniqueness of the human genome that reveals a participant or a certain group’s presence in a dataset. Therefore, the current data sharing policies have ruled out any public dissemination and adopted precautionary measures prior to genomics data release, which hinders timely scientific innovation. In this article, we investigate an approach that only releases the statistics from genomic data rather than the whole dataset and propose a generalized Differentially Private mechanism for Genome-wide Association Studies (GWAS). Our method provides a quantifiable privacy guarantee that adds noise to the intermediate outputs but ensures satisfactory accuracy of the private results. Furthermore, the proposed method offers multiple adjustable parameters that the data owners can set based on the optimal privacy requirements. These variables are presented as equalizers that balance between the privacy and utility of the GWAS. The method also incorporates Online Bin Packing technique [1], which further bounds the privacy loss linearly, growing according to the number of open bins and scales with the incoming queries. Finally, we implemented and benchmarked our approach using seven different GWAS studies to test the performance of the proposed methods. The experimental results demonstrate that for 1,000 arbitrary online queries, our algorithms are more than 80% accurate with reasonable privacy loss and exceed the state-of-the-art approaches on multiple studies (i.e., EigenStrat, LMM, TDT).
Fraud Detection by Machine Learning

Fraud detection is to find unusual events during screen credit card transactions, insurance claims, account applications, etc. In this paper, five supervised models were built to identify which transactions are fraud. This work gathered the real transaction data from web site. The process of this work included data description, data cleaning, variable creation, feature selection (using filter and wrapper) and modelling. In the process of variable creation, this work created about 220 variables. After feature selection, this work finally selected 40 variables. During modelling process, five machine learning models, including logistic regression, support vector machine, random forest, neural network and boosted tree, were built. The best model turns out to be Boosted Tree, with a 54.3% FDR at 3% cutoff for testing and a 54% FDR at 3% cutoff for OOT. The research significance of this work lies in how to deal with possible risks in credit card transactions in real time. The final research results show that Boosted Tree is the most suitable model for this type of unbalanced large data sets.
A New Evaluation Mechanism on Investment Effectiveness of a Production and Technical Transformation Project in a Power System

To evaluate the investment effectiveness of and provide decision-making support for a production and technical transformation project (PTTP) in a power system, the concept and methodology of perfect technical transformation (PTT) are presented in this work. The well-established analytic hierarchy process (AHP) is employed to disassemble the evaluation objectives of a PTTP while the weights and scores of indicators at each level can be determined. Then, the comprehensive evaluation result of a PTTP can be obtained by weighted calculation. A perfection percentage index is defined to judge the satisfaction degree of an implemented PTTP. Next, a deviation identification and improvement method is used to accurately locate the poorly implemented parts of the PTTP, and optimize the technical scheme of the PTTP. Finally, an actual PTTP is employed to demonstrate the presented PTT based approach for investment effectiveness evaluation of a PTTP.
Comparison of genetic algorithm and dynamic programming solving knapsack problem

In this paper, the genetic algorithm and the dynamic programming algorithm are used to solve the 0-1 knapsack problem, and the principles and implementation process of the two methods are analyzed. For the two methods, the initial condition values are changed respectively, and the running time, the number of iterations and the accuracy of the running results of each algorithm under different conditions are compared and analyzed, with the reasons for the differences are studied to show the characteristics in order to find different features of these algorithms.
Combining quantitative analysis with an elliptic Fourier descriptor: A study of pottery from the Gansu-Zhanqi site based on 3D scanning and computer technology

Pottery is an important material in archaeological studies, and the accurate classification of pottery shapes largely depends on the experience and knowledge of archaeologists. In this paper, pottery collected from the Gansu-Zhanqi site was used for sampling. Three-dimensional (3D) models of the pottery were obtained using 3D scanning, and a computer-assisted pottery typology was studied through quantitative analysis and elliptic Fourier descriptor analysis. This method, which can enhance and supplement the traditional methods of classifying pottery in archaeology, enriches the parameters and breadth of pottery analysis, represents a new means of exploring and experimenting with objective classification, and provides a new tool for traditional archaeological analysis methods.
Retrospective Sensing Based on Federated Learning in the IoT

Knowledge in the IoT is typically distributed and sparse. Combining information from multiple sources remains a challenge to researchers. In addition, due to the strict capabilities of sensing devices, they may go to sleep for extended durations and miss critical data. In this paper, we propose a system that is capable of retrospective sensing based on federated machine learning models. The system is implemented on a distributed edge computing architecture and is capable of fusing learned data from several sensors to produce accurate estimations of missed data. The proposed system is evaluated using computer simulations on real and synthetic datasets and shows high accuracy in predicting missed data.
Educational Assistance Bot

Abstract An Internet bot performs automated tasks over the Internet. This software application is also known as a web robot or robot or simply bot, Typically, bots runs simple and structurally repetitive tasks, at a much higher rate than would be possible for a human alone. This effort is to help the users by providing all the basic student’s academic details like subjects, faculty names, books, notes and routine according to their need using bot. Users can also get other information like fees structure, library inventory list, calendar etc. Additional information like top educational sites, most trending educational videos etc will be regularly updated by the admin. This is an all-in-one application which can also be used to fetch and update information which is purely related to COVID-19.
Heterogeneity-Aware Gradient Coding for Tolerating and Leveraging Stragglers

Distributed gradient descent has been widely adopted in the machine learning field because considerable computing resources are available when facing the huge volume of data. Specifically, the gradient over the whole data is cooperatively computed by multiple workers. However, its performance can be severely affected by slow workers, namely stragglers. Recently, coding-based approaches have been introduced to mitigate the straggler problem, but they could hardly deal with the heterogeneity among workers. Besides, they always discard the results of stragglers causing huge resource waste. In this article, we first investigate how to tolerate stragglers by discarding their results and then seek to leverage the stragglers. For tolerating stragglers, we propose a heterogeneity-aware coding scheme that encodes gradients adaptive to the computing capability of workers. Theoretically, this scheme is optimal for stragglers tolerance. Relying on the scheme, we further propose an algorithm called DHeter-aware to exploit the gradients of stragglers which we called delayed gradients. Moreover, theoretical results characterized for DHeter-aware exhibits the same convergence rate as the gradient descent without delayed gradients. Experiments on various tasks and clusters demonstrate that our coding scheme outperforms all the state-of-the-art methods and the DHeter-aware further accelerates the coding scheme by achieving 25 percent time savings.
LSO: A Dynamic and Scalable Blockchain Structuring Framework

This paper proposes a dynamic and scalable blockchain system framework for structuring a blockchain systems (BC). Traditionally a BC maintain multiple nodes with a smart-contract engine (SC) running on nodes, possibly with one or more Oracles Machines (OMs). However, many sophisticated applications require a much flexible yet still secure and scalable system architecture. The new framework LSO (Ledgers, Smart contracts, Oracles) with an inter-unit collaboration protocol that can be used for system registration, identification, communication, privacy protocols, and scalability management. The LSO framework is a dynamic and scalable framework because BCs, SCs, and OMs can be added without affecting the overall structure and without performance degradation. This framework support this by running a collection of cooperative Collaboration Layers (CLs) that acts like a DNS (Domain Name System) in Internet but this time to interconnect various BCs, SCs, and OMs. This LSO framework is a part of ChainNet initiative where BCs are used as building blocks in information and communication systems.