No abstract

The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions. The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers' workflows. In this paper, we propose an "evidencebased" recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients. The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a "social plane" to jointly find answers to critical clinical questions to fight the pandemic. We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness. In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts.

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a threedimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.

No abstract

Effective water resource management requires information on water availability -both in terms of quality and quantityspatially and temporally. In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions. We propose a standard recurrent neural network in the form of Long Short-Term Memory (LSTM) to fit on a sufficiently large source domain dataset and repurpose the learned weights to a significantly smaller, yet similar target domain datasets. We present a methodology to implement transfer learning approaches for spatiotemporal applications by separating the spatial and temporal components of the model and training the model to generalize based on categorical datasets representing spatial variability. The framework is developed on a rich benchmark dataset from the US and evaluated on a smaller dataset collected by The Nature Conservancy in Kenya. The LSTM model exhibits generalization performance through our TL technique. Results from this current experiment demonstrate the effective predictive skill of forecasting streamflow responses when knowledge transferring and static descriptors are used to improve hydrologic model generalization in datasparse regions.

There is a growing need for next-generation science gateways to increase the accessibility of emerging large-scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID-19-related challenges. Such science gateways that enable access to distributed computing resources for large-scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio-technical approach for developing a next-generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID-19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence-based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug-in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot-assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. K E Y W O R D Sdiffusion of innovations, intelligent agents, intelligent middleware, microservices, science gateways INTRODUCTIONManaging the emerging collections of large-scale medical datasets such as scientific publications and electronic health records (EHRs) can be a challenging task for medical data consumers (e.g., clinicians, medical researchers) who need to make timely decisions for combating COVID-19-related issues. Data consumers are constantly faced with complex tasks that are labor-intensive and require domain-specific knowledge discovery over medical information for critical decision making. When synthesizing scientific literature for knowledge discovery, data consumers often rely clinical methodologies such as levels of evidence 1,2 to improve information reliability and reduce the quantity of literature by prioritizing scientific rigor (e.g., expert opinions to systematic reviews and meta-analyses). The challenge of manually filtering high-volume of literature based on evidence-based methods presents the need from data consumers to adopt next-generation science gateways to gain access to emerging large-scale datasets and resources for developing timely pandemic-related solutions.Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

No abstract

No abstract