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1. Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway

   https://doi.org/10.1002/cpe.6080  

   Vekaria, K. ; Calyam, P. ; Sivarathri, S. ; Wang, S. ; Zhang, Y. ; Pandey, A. ; Chen, C. ; Xu, D. ; Joshi, T. ; Nair, S.S. ( January 2020 , Wiley Concurrency and Computation: Practice and Experience)

   Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity,we present a novel “OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons). 

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2. Fuzzy-Based Conversational Recommender for Data-intensive Science Gateway Applications

   Chandrashekara, A. ; Talluri, R. ; Sivarathri, S. ; Mitra, R. ; Calyam, P. ; Kee, K. ; Nair, S.S. ( January 2018 , IEEE International Workshop on Conversational Agents and Chatbots with Machine Learning (ChatbotML), in conjunction with IEEE Big Data)

   Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways have democratized relevant high performance/throughput resources, users require expert knowledge about programming and infras-tructure configuration that is beyond the repertoire of most neuroscience programs. These factors become deterrents for the successful adoption and the ultimate diffusion (i.e., systemic spread) of science gateways in the neuroscience community. In this paper, we present a novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows. The users interact with a context-aware chatbot that is embedded within custom web-portals to obtain simulation tools/resources to accomplish their goals. In order to ensure user goals are met, the chatbot profiles a user’s cyberinfrastructure and neuroscience domain proficiency level using a ‘usability quadrant’ approach. Simulation of user queries for an exemplary neuroscience use case demonstrates that our chatbot can provide step-by-step navigational support and generate distinct responses based on user proficiency. 

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3. Fuzzy-Based Conversational Recommender for Data-intensive Science Gateway Applications

   https://doi.org/10.1109/BigData.2018.8622046  

   Chandrashekara, A ; Talluri, R ; Sivarathri, S ; Mitra, R ; Calyam, P ; Kee, K ; Nair, S. S ( January 2018 , IEEE International Workshop on Conversational Agents and Chatbots with Machine Learning (ChatbotML), in conjunction with IEEE Big Data)

   Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways have democratized relevant high performance/throughput resources, users require expert knowledge about programming and infrastructure configuration that is beyond the repertoire of most neuroscience programs. These factors become deterrents for the successful adoption and the ultimate diffusion (i.e., systemic spread) of science gateways in the neuroscience community. In this paper, we present a novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows. The users interact with a context-aware chatbot that is embedded within custom web-portals to obtain simulation tools/resources to accomplish their goals. In order to ensure user goals are met, the chatbot profiles a user’s cyberinfrastructure and neuroscience domain proficiency level using a ‘usability quadrant’ approach. Simulation of user queries for an exemplary neuroscience use case demonstrates that our chatbot can provide step-by-step navigational support and generate distinct responses based on user proficiency. 

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4. Science Gateway Development to aid Cyber and Software Automation for Neuroscience Researchers and Educators

   Calyam, P. ; Nair, S.S ( January 2018 , 13th Gateway Computing Environments Conference (Gateways))

   Neuroscientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. This requires expert knowledge of programming and cyberinfrastructure configuration, which is beyond the repertoire of most neuroscience programs. This paper presents early experiences from a one-credit graduate research training course titled ECE 8001 “Software and Cyber Automation in Neuroscience” at the University of Missouri for engendering multi-disciplinary collaborations between computational neuroscience and cyberinfrastructure students and faculty. Specifically, we discuss the course organization and exemplar outcomes involving a next-generation science gateway for training novice users on exemplar neuroscience use cases that involve using tools such as NEURON and MATLAB on local as well as Neuroscience Gateway resources. We also discuss our vision towards a course sequence curriculum for graduate/undergraduate students from biological/psychological sciences and computer science/engineering to jointly build “self- service” training modules using Jupyter Notebook platforms. Thus, our efforts show how we can create scalable and sustainable cyber and software automation for fulfilling a broad set of neuroscience research and education use cases. 

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5. Extending Tapis Workflow Management Framework with Elastic Google Cloud Distributed System using CloudyCluster by Omnibond

   Eric Lam ; Sean Cleveland ; Cole Mcknight ; Boyd Wilson ; Richard Cardone ; Maytal Dahan ; Joe Stubbs ; Gwen A. Jacobs ( October 2022 , Science Gateways 2022)

   The goal of a robust cyberinfrastructure (CI) ecosystem is to catalyse discovery and innovation. Tapis does this through offering a sustainable production-quality set of API services to support modern science and engineering research, which increasingly span geographically distributed data centers, instruments, experimental facilities, and a network of national and regional CI. Leveraging frameworks, such as Tapis, enables researchers to accomplish computational and data-intensive research in a secure, scalable, and reproducible way and allows them to focus on their research instead of the technology needed to accomplish it. This project aims to enable the integration of the Google Cloud Platform (GCP) and CloudyCluster resources into Tapis- supported science gateways to provide on-demand scaling needed by computational workflows. The new functionality uses Tapis event-driven Abaco Actors and CloudyCluster to create an elastic distributed cloud computing system on demand. This integration allows researchers and science gateways to augment cloud resources on top of existing local and national computing resources. 

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