The emergence of large-scale social media systems, such as Wikipedia, Facebook, and Twitter, has given rise to a new multidisciplinary effort based around the concept of social machines. For the most part, this research effort has limited its attention to the study of Web-based systems. It has also, perhaps unsurprisingly, tended to highlight the social scientific relevance of such systems. The present paper seeks to expand the scope of the social machine research effort to encompass the Internet of Things. One advantage of this expansion is that it helps to reveal some of the links between the science of social machines and the sciences of the mind. A second advantage is that it furthers our conceptual understanding of social machines and supports the quest to derive a philosophically-robust definition of the term "social machine." The results of the present analysis suggest that social machines are best conceived as systems in which a combination of social and technological elements play a role in the mechanistic realization of system-level phenomena. The analysis also highlights the relevance of cognitive science and the philosophy of mind to our general understanding of systems that transcend the cyber, physical, and social domains.
Vast amount of medical information is increasingly available on the Web. As a result, seeking medical information through queries is gaining importance in the medical domain. The existing keyword-based search engines such as Google, Yahoo fail to suffice the needs of the health-care workers (who are well-versed with the domain knowledge required for querying) using these they often face results which are irrelevant and not useful for their tasks.In this paper, we present the need and the challenges for a user-level, domain-specific query language for the specialized document repositories of the medical domain. This topic has not been sufficiently addressed by the existing approaches including SQL-like query languages or generalpurpose keyword-based search engines and document-level indexing based search. We aim to bridge the gap between information needs of the skilled/semi-skilled domain users and the query capability provided by the query language. Overcoming such a challenge can facilitate effective use of large volume of information on the Web (and in the electronic health records (EHRs)repositories).
The concept of 'social machines' is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.
In this position paper we wish to propose and discuss several open research questions associated with the IoT. In particular, we wish to consider how crowdsourcing can be used as a scalable, reliable, and sustainable approach to support various computationally difficult and ambiguous tasks recognised in IoT research. We illustrate our work by examining a number of use cases related to healthcare and smart cities, and finally consider the future development of the IoT ecosystem with respect to the socio-technical philosophy and implementation of the Web Observatory.
With large number of datasets now available through the Web, data-sharing ecosystems such as the Web Observatory have emerged. The Web Observatory provides an active decentralised ecosystem for datasets and applications based on a number Web Observatory sites, each of which can run in a different administrative domain. On a Web Observatory site users can publish and securely access datasets across domains via a harmonised API and reverse proxies for access control. However, that API provides a different interface to that of the databases on which datasets are stored and, consequently, existing applications that consume data from specific databases require major modification to be added to the Web Observatory ecosystem. In this paper we propose a lightweight architecture called Porter Proxy to address this concern. Porter Proxy exposes the same interfaces as databases as requested by the users while enforcing access control. Characteristics of the proposed Porter Proxy architecture are evaluated based on adversarial scenario-handling in Web Observatory eco-system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.