Online Framework Research Articles

COVID-19 and Digital Learning in The Dominican Republic: Implications For Marginalized Communities

With COVID-19 placing a strain on its public institutions, the Dominican Republic is attempting to ensure a streamlined digital education process by extending Digital Republic, a past initiative that provides online frameworks and platforms for its students and teachers. However, already existing issues are exacerbating access to education for vulnerable communities. While past policies included special needs services, the following initiative has yet to provide information on access for disabled students. Low-income students face financial challenges to gain the resources necessary to thrive during remote learning. Likewise, migrants from Haiti and those of Haitian descent continue to be left out of the conversation. The following case study explores the country’s education system, how its policies benefit certain groups and negatively impact others. By conducting in-depth policy analysis and interviews with local officials, the study analyzes the impact of digital learning during the pandemic among vulnerable communities in the country.

An Online Security Prediction and Control Framework for Modern Power Grids

The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.

Online Adaptive Modeling Framework for Deep Belief Network-Based Quality Prediction in Industrial Processes

Online Adaptive Modeling Framework for Deep Belief Network-Based Quality Prediction in Industrial Processes

ANALISIS BIMBINGAN KELOMPOK BERBASIS DARING DENGAN TEKNIK DISCUSSION GROUP DALAM MENINGKATKAN MINAT BELAJAR SISWA

The effect of the Covid (Coronavirus) has constrained a few neighborhood governments to settle on the execution of strategies to close understudy exercises at school and carry out internet learning frameworks or distance learning. The approach for executing internet learning has been in actuality since Walk 2020 in all schools in Indonesia, including SMAN 1 Nguter. One of the impacts of learning with this online framework is the decrease in understudy revenue in SMA N 1 Nguter. This examination is a library research, which is a kind of exploration that is utilized to gather top to bottom data and information through different written works and pertinent past research results to acquire answers and hypothetical establishments in regards to the issues to be contemplated. This examination intends to look at the job of online-based gathering direction with bunch conversation methods in expanding understudy revenue in learning. Gathering direction with bunch conversation procedures can expand learning inspiration, this is on the grounds that in bunch direction there is a gathering dynamic that can make expanded understudy learning inspiration. Through bunch direction exercises, all gathering individuals lead conversations, give and get data, foster correspondence and relational abilities, inspire each other so that bunch individuals' learning inspiration increments. The execution of online-based gathering direction is one successful methodology because of the pandemic that doesn't permit the execution of up close and personal direction and advising exercises.

Remote Teaching Advanced Rendering Topics Using the Rayground Platform.

Rayground is a novel online framework for fast prototyping and interactive demonstration of ray tracing algorithms. It aims to set the ground for the online development of ray-traced visualization algorithms in an accessible manner for everyone, stripping off the mechanics that get in the way of creativity and the understanding of the core concepts. Due to the COVID-19 pandemic, remote teaching and online coursework have taken center stage. In this work, we demonstrate how Rayground can incorporate advanced instructive rendering media during online lectures as well as offer attractive student assignments in an engaging, hands-on manner. We cover things to consider when building or porting methods to this new development platform, best practices in remote teaching and learning activities, and time-tested assessment and grading strategies suitable for fully online university courses.

ACAD-Feedback: Online Framework for Assignment, Collection, Analysis, and Distribution of Self, Peer, Instructor, and Group Feedback

Working with peers is an integral part of a student’s educational experience in most disciplines from preschool to tertiary education. For secondary and undergraduate chemistry education, it is inevitable that some laboratory sessions require students to work in groups for logistic and safety reasons. In this technology report, we demonstrate use cases of Google Forms and Google Sheets to assign (A) students into groups, collect (C), analyze (A), and distribute (D) their feedback in group work as well as in self-, peer-, and instructor-assessed individual work. Our ACAD-Feedback framework is designed to streamline the manual delivery of the four processes into one online framework. This framework can be used in other settings with minimal modifications.

An on-line deep learning framework for low-thrust trajectory optimisation

In the preliminary interplanetary mission design stage, a fast low-thrust (LT) transfer cost approximator will improve the mission design efficiency and enable us to design more complex missions. In this study, we propose a Deep Neural Network (DNN) based on-line framework for training approximators for fast low-thrust trajectory optimisation. The online characteristic refers to the ability to continuously adjust the trained DNNs using new LT transfer data from newly found asteroids, which avoids repeated and costly re-training and is particularly useful for mission scenarios where new data are obtained regularly. The framework contains a DNN-classifier and an online-DNN regressor. The Bayesian optimisation (BO) technique is adapted to determine the network structure as well as the feature selection and processing methods. The proposed DNN-classifier significantly improves the LT optimisation convergence rate from 7% to over 98%. The proposed on-line DNN regressor proves to have better generalization ability and scalability comparing to series network structure, giving a mean relative error (MRE) of approximately 0.6% in the test Near-Earth Asteroid (NEA) rendezvous mission scenario. The proposed on-line DNN framework can also be extended to solve other trajectory optimisation problems in other mission scenarios, such as Main-Belt Asteroid (MBA) missions, active debris removal mission (ADR), etc.

BuM 2.0: Software for online generation of matrices for Brooks Parsimony Analysis.

Brooks Parsimony Analysis is a cladistic biogeographic method that aims to extract biogeographic information from phylogenetic trees, depicting from a group of cladograms a general pattern of relationships among the areas the taxa inhabit. We present here BuM 2.0, an online framework to automatically create matrices for Brooks Parsimony Analysis (BPA) using Baum Ragans algorithm for Matrix Representation with Parsimony.

Shaping Teachers' Perceptions of their role in the Digital age Through Participation in an Online PBL-based Course

Our research traced significant learning experiences of teachers enrolled in a Master's degree program in teacher education, in an attempt to understand how participation in an online course that employs the project-based learning (PBL) approach influenced their perceptions of the teachers' role in the digital age. Data was collected from 2014 to 2016 using: (a) a questionnaire gathering learners' personal and demographic details (n = 55) and (b) reflective reports on the learners' learning experiences in the course (n = 105). Content analysis of the data revealed that participants considered personal, pedagogic, and social aspects important in terms of the learning experience and this also informed their role perception as teachers in the digital age. Similarly, exposure to the PBL approach via an online framework directly influenced participants' learning experiences and role perception. The findings indicate that teachers should be given access to a learning experience combining online learning and teaching practice to allow them to form their role perception as digital-age teachers. Practical implications of the research relate to teachers' socialization in the digital age.

A novel information sharing framework for people living with type-2 diabetes in the context of a group education program

PurposeOnline health communities (OHCs) offer opportunities for people with type-2 diabetes to share information and experiences for self improvement. This study is to introduce a novel framework for identifying the potential of online diabetes communities’ practices and outcomes in empowering people living with diabetes.Method: Using a qualitative tradition, we conducted semi-structured interviews with 15 members of these communities, attempting to answer research question: how an online framework for diabetes community can be outlined for empowering patients in their self-management in the context of DGEP. We also analysed 2312 threads shared in the three popular type-2 diabetes Reddit communities (r/diabetes, r/diabetes_t2, and r/type2diabetes) comprising more than 60,000 members.?ResultsUsing thematic analysis, the study identifies three salient themes: (a) Leverage digital health technologies to ubiquitous access, share and integrate resources, (b) Community encouragement in self-care and self-monitoring, and (c) Improve mental well-being by experiencing commonality. Statistically, on average 87% of the participants contended that they would benefit the OHC framework for keeping them interconnected to the program. Furthermore, expert interviews with the healthcare team have been carried out for evaluating the usability and efficiency of the framework.ConclusionsThe proposed information-sharing framework promotes patients’ self-management of diabetes, revealing that patient participation in diabetes OHC leads to empowering self-management of their diabetes and in turns shedding some light on how healthcare organizations can improve patients’ information behaviour through OHC provisions. This qualitative study suggested that the proposed novel framework is perceived as a useful platform to empower diabetic patients in their self-management, extending value of physical group activities.

Impact of virtual vs. face-to-face learning on 21st-century skills among pre-service bilingual teachers

The so-called ‘twenty-first century skills’ (Dudeney, Hockly and Pegrum, 2014) have been identified as essential literacies for the young generation, who must use creativity and innovation, collaboration and teamwork, critical thinking, problem-solving, autonomy, flexibility and lifelong learning to function effectively. Bilingual pre-service teachers are entitled to develop these skills for both their own education and that of their future students (Savage and Barnett, 2015). This research will analyse the opinions of 45 bilingual pre-service teachers on the development of their twenty-first century skills through either online or face-to-face teaching. Quantitative and qualitative data will be scrutinized under a mixed-methods research, which will throw light on how participants think such skills are better developed. Findings will show that teacher students think that some of these skills are better developed under a face-to-face modality of learning (e.g. teamwork and collaboration), whereas others (e.g. autonomy and innovation) evolve better under an online framework of learning. Twenty-first century literacies push the educational boundaries of bilingual teachers as they are entitled to have a repertoire of communication skills that make us, teacher educators, pursue academic rigour regarding our own teaching and updated training.

Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning

In modern volatile and complex manufacturing environment, dynamic events such as new job insertions and machine breakdowns may randomly occur at any time and different objectives in conflict with each other should be optimized simultaneously, leading to an urgent requirement of real-time multi-objective rescheduling methods that can achieve both time efficiency and solution quality. In this regard, this paper proposes an on-line rescheduling framework named as two-hierarchy deep Q network (THDQN) for the dynamic multi-objective flexible job shop scheduling problem (DMOFJSP) with new job insertions. Two practical objectives including total weighted tardiness and average machine utilization rate are optimized. The THDQN model contains two deep Q network (DQN) based agents. The higher-level DQN is a controller determining the temporary optimization goal for the lower DQN. At each rescheduling point, it takes the current state features as input and chooses a feasible goal to guide the behaviour of the lower DQN. Four different goals corresponding to four different forms of reward functions are suggested, each of which optimizes an indicator of tardiness or machine utilization rate. The lower-level DQN acts as an actuator. It takes the current state features together with the higher optimization goal as input and chooses a proper dispatching rule to achieve the given goal. Six composite dispatching rules are developed to select an available operation and assign it on a feasible machine, which serve as the candidate action set for the lower DQN. A novel training framework based on double DQN (DDQN) is designed. The trained THDQN is compared with each proposed composite dispatching rule, existing well-known dispatching rules as well as other reinforcement learning based scheduling methods on a wide range of test instances. Results of numerical experiments have confirmed both the effectiveness and generality of the proposed THDQN.

Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization

Accurate prediction of traffic status in real time is critical for advanced traffic management and travel navigation guidance. There are many attempts to predict short-term traffic flows using various deep learning algorithms. Most existing prediction models are only tested on spatiotemporal data assuming no missing data entries. However, this ideal situation rarely exists in real world due to sensor or network transmission failure. Missing data is a nonnegligible problem. Previous studies either remove time series with missing entries or impute missing data before building prediction models. The former may cause insufficient data for model training, while the latter adds extra computational burden and the imputation accuracy has direct impacts on the prediction performance. In this study, we propose an online framework that can make spatiotemporal predictions based on raw incomplete data and impute possible missing values at the same time. We design a novel spatial and temporal regularized matrix factorization model, namely LSTM-GL-ReMF, as the key component of the framework. The Long Short-term Memory (LSTM) model is chosen as the temporal regularizer to capture temporal dependency in time series data and the Graph Laplacian (GL) serves as the spatial regularizer to utilize spatial correlations among network sensors to enhance prediction and imputation performance. The proposed framework integrating with the LSTM-GL-ReMF model are tested and compared with other state-of-the-art matrix factorization models and deep learning models on three uni-variate and multi-variate spatiotemporal traffic datasets. The experimental results show our approach has a robust and accurate performance in terms of prediction and imputation accuracy under various data missing scenarios.

Proposed Natural Science E-Instructional Systems Design (E-ISD) for the Mendiola Consortium

Proposed Natural Science E-Instructional Systems Design (E-ISD) for the Mendiola Consortium

Challenges, benefits & drawbacks of chemical engineering on-line teaching during Covid-19 pandemic

Distance learning demand has increased and turned into an obligatory educational system during the COVID-19 Pandemic. Within this period, students need exceptional support and assistance to be adjusted to the Online framework environment. The current study investigates distance learning on chemical engineering students' educational performance during this pandemic period. This study addresses many issues related to distance learning, such as the household setting suitability with the internet connections, Online lecture activities, Online classroom experience and assessment tools, and student's perception of the Online graduation projects. A comprehensive survey was organized and structured to observe and evaluate these associated issues to distance learning. The study targeted the third, fourth, and fifth-year students. The responses of the students revealed positive and negative assessments of the Online teaching during this Pandemic. Generally, the participated students did not experience significant technical obstacles to acquiring all the Online activities. Most of the contributed students expressed some difficulties concentrating during the Online sessions with limited student-instructor interactions. Graduation Project students encountered problems in performing technical discussions with their academic advisors, coordinators, and teammates compared to the face-to-face approach.

Technical Capability, Instructional Strategies and Learners’ Engagement in Online Learning Instruction: Basis for Framework Development

Schools are faced with the challenge of developing and implementing an online framework model especially in Radiology technology program. Online courses offer an excellent way for students to broaden their educational opportunities and stay competitive in the ever-demanding realm of education. Learning engagement in technology mediated learning and its effect on learning effectiveness and satisfaction, and their experimental data showed that the effects of technology-mediated learning were mediated by learning engagement, learning engagement had a positive effect on learning effectiveness whether in face-to-face learning or technology-mediated learning. Moreover, effective design of instructional materials elicits appropriate cognitive processes in the learner and mediates more successful learning outcomes ensuring an engaging and worthwhile online learning is necessary to achieve the same result of achieving quality learning, therefore it is important to address these topics on faculty technical capability, standard instructional strategies and learner’s engagement strategies to the development of dynamic and an effective online framework model for Radiology technology program. This study aimed to assess the teachers’ level of technical capability, instructional strategies utilization and level of learners’ engagement strategies that set as the basis to enhance the online framework model for radiology technology program. Descriptive correlational research design was employed. The study revealed that Radiologic Technology Professors in terms of technical capabilities, instructional strategies and learner’s engagement had a very confident concept in online learning. In essence, the results yield the delivery of education with the use of technology introduces a platform that gives a more accessible learning system.

On Bayesian active vibration control of structures subjected to moving inertial loads

This study introduces a novel Bayesian framework for online and real-time vibration control of beam type structures, which represent a comprehensive control system associated with input-state algorithms. Control design systems typically require knowledge of system states, which in structures are displacements and velocities at some degrees of freedom. Currently, full-field measurements of displacements and velocities in large structural systems are not feasible. Also, properties of the moving inertial loads as key parameters in control designs are assumed known; however, in practice, measuring their characteristics is a challenging issue. As a remedy, an observer is required to estimate quantities of interest needed in control algorithms via indirect measurement of structural response. To estimate the entire state of the system in the absence of input information, often the input needs to be estimated together with the unknown states. Three state of the art methods for estimation of input and states of a linear state-space system, namely augmented Kalman filter (AKF), dual Kalman filter (DKF), and joint input state estimation algorithm (JIS), were adopted to assess controller performance in the absence of direct displacement measurements in comparison to conventional Kalman filter (KF). The linear quadratic Gaussian (LQG) framework, in association with these three filters, presents a novel control algorithm. Various sensor networks featuring pure accelerations and a combination of strain gauges and accelerometers with different noise levels were considered. Extensive numerical examples and comparison with the passive control system proved the viability of input-state estimation as an integral part of active control algorithms in structural systems subjected to moving inertial loads with different speeds.

Toward Privacy-Preserving Task Assignment for Fully Distributed Spatial Crowdsourcing

With the proliferation of human-carried mobile devices, spatial crowdsourcing has emerged as a transformative system, where requesters outsource their spatiotemporal tasks to a set of workers who are willing to perform the tasks at the specified locations. However, in order to make efficient assignments, the existing spatial crowdsourcing system usually requires workers and/or tasks to expose their locations, which raises a significant concern of compromising location privacy. In addition, traditional spatial crowdsourcing systems employ a centralized server to manage the information of workers and tasks. Such a centralized design does not scale to a large number of workers/tasks, making the server easily a bottleneck. In this article, we present an online framework for assigning tasks to workers without compromising the location privacy in a fully distributed manner. Our system protects the location privacy of both workers and tasks through homomorphic encryption. We further propose a novel wait-and-decide mechanism and a proportional-backoff mechanism to increase the number of assigned tasks. Extensive experiments on real-world data sets illustrate that our proposed system achieves a large number of task assignments in an efficient and privacy-preserving manner.

Online discriminative graph learning from multi-class smooth signals

Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks. However, the success of GSP in downstream learning and inference tasks such as supervised classification is heavily dependent on the prior identification of the relational structures. Graphs are natural descriptors of the relationships between entities of complex environments. The underlying graph is not readily detectable in many cases and one has to infer the topology from the observed signals which admit certain regularity over the sought representation. Assuming that signals are smooth over the latent class-specific graph is the notion we build upon. Firstly, we address the problem of graph signal classification by proposing a novel framework for discriminative graph learning. To learn discriminative graphs, we invoke the assumption that signals belonging to each class are smooth with respect to the corresponding graph while maintaining non-smoothness with respect to the graphs corresponding to other classes. Discriminative features are extracted via graph Fourier transform (GFT) of the learned representations and used in downstream learning tasks. Secondly, we extend our work to tackle increasingly dynamic environments and real-time topology inference. To this end, we adopt recent advances of GSP and time-varying convex optimization. We develop a proximal gradient (PG) method which can be adapted to situations where the data are acquired on-the-fly. Beyond discrimination, this is the first work that addresses the problem of dynamic graph learning from smooth signals where the sought network alters slowly. The introduced online framework is guaranteed to track the optimal time-varying batch solution under mild technical conditions. The validation of the proposed frameworks is comprehensively investigated using both synthetic and real data. The proposed classification pipeline outperforms the-state-of-the-art methods when applied to the problem of emotion classification based on electroencephalogram (EEG) data. We also perform network-based analysis of epileptic seizures using electrocorticography (ECoG) records. Moreover, by applying our method to financial data, our approach infers the relationships between the stock-price behavior of leading US companies and the recent events including the COVID-19 pandemic.

An Online Framework for Joint Network Selection and Service Placement in Mobile Edge Computing

With the rapid development and deployment of 5G wireless technology, mobile edge computing (MEC) has emerged as a new computing paradigm to facilitate a large variety of infrastructures at the network edge to reduce user-perceived communication delay. One of the fundamental problems in this new paradigm is to preserve satisfactory quality-of-service (QoS) for mobile users in light of densely dispersed wireless communication environment and often capacity-constrained MEC nodes. Such user-perceived QoS, typically in terms of the end-to-end delay, is highly vulnerable to both access network bottleneck and communication delay. Previous works have primarily focused on optimizing the communication delay through dynamic service placement, while ignoring the critical effect of access network selection on the access delay. In this work, we study the problem of jointly optimizing the access network selection and service placement for MEC, with the objective of improving the QoS in a cost-efficient manner by judiciously balancing the access delay, communication delay, and service switching cost. Specifically, we propose an efficient online framework to decompose a long-term time-varying optimization problem into a series of one-shot subproblems. To address the NP-hardness of the one-shot problem, we design a computationally-efficient two-phase algorithm based on matching and game theory, which achieves a near-optimal solution. Both rigorous theoretical analysis on the optimality gap and extensive trace-driven simulations are conducted to validate the efficacy of our proposed solution.