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Dialog systems (e.g., chatbots) have been widely studied, yet related research that leverages artificial intelligence (AI) and natural language processing (NLP) is constantly evolving. These systems have typically been developed to interact with humans in the form of speech, visual, or text conversation. As humans continue to adopt dialog systems for various objectives, there is a need to involve humans in every facet of the dialog development life cycle for synergistic augmentation of both the humans and the dialog system actors in real-world settings. We provide a holistic literature survey on the recent advancements inhuman-centered dialog systems(HCDS). Specifically, we provide background context surrounding the recent advancements in machine learning-based dialog systems and human-centered AI. We then bridge the gap between the two AI sub-fields and organize the research works on HCDS under three major categories (i.e., Human-Chatbot Collaboration, Human-Chatbot Alignment, Human-Centered Chatbot Design & Governance). In addition, we discuss the applicability and accessibility of the HCDS implementations through benchmark datasets, application scenarios, and downstream NLP tasks. 

This study provides statistical validation of three composite scales designed to calculate metrics for gateway user competence in terms of domain knowledge, technical skills, and problem-solving orientation. Based on an online survey (N = 365) fielded by an online panel company (Centiment.co) with US based participants, analyses using SPSS software demonstrated that technical competence varied between age groups (lower scores for participants aged 60 and higher) and educational levels (lower scores for participants without a bachelor’s degree) at a statistically significant level (at 95% confidence interval). These findings suggest that gateway developers may need to provide more technical support to users who are senior researchers and when gateways are being introduced into high school classrooms. Conversely, ethnicity and gender were found to be non-predictors of technical competence. These findings suggest the stereotype of white males being more tech-savvy than other ethnic and gender groups may not hold true anymore. 

The COVID-19 pandemic is an unprecedented global emergency. Clinicians and medical researchers are suddenly thrown into a situation where they need to keep up with the latest and best evidence for decision-making at work in order to save lives and develop solutions for COVID-19 treatments and preventions. However, a challenge is the overwhelming numbers of online publications with a wide range of quality. We explain a science gateway platform designed to help users to filter the overwhelming amount of literature efficiently (with speed) and effectively (with quality), to find answers to their scientific questions. It is equipped with a chatbot to assist users to overcome infodemic, low usability, and high learning curve. We argue that human-machine communication via a chatbot play a critical role in enabling the diffusion of innovations. 

There is a growing need for next-generation science gateways to increase the accessibility of data sets and cloud computing resources using latest technologies. Most science gateways today are built for specific purposes with pre-defined workflows, user interfaces, and fixed computing resources. There is a need to modernize them with middleware that can provide ‘plug in’ support to programmatically increase their extensibility and scalability to meet users’ growing needs. In this paper, we propose a novel middleware that can be integrated into science gate ways using a “bring-your-own” plug-in management approach. This approach features microservice architectures to decouple applications, and allows users (i.e., administrators, developers, researchers) to customize and incorporate domain-specific components in an existing science gateway. We detail the application programming interfaces in our middleware for creation of end-to end pipelines with diverse infrastructure, customized processes, detailed monitoring and flexible programmability for a scientific domain. We also demonstrate via a OnTimeRecommend case study on how our “bring-your-own” approach can be seamlessly integrated by a science gateway administrator/developer using a web application. 

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).