30,578 research outputs found
Conversational Sensing
Recent developments in sensing technologies, mobile devices and context-aware
user interfaces have made it possible to represent information fusion and
situational awareness as a conversational process among actors - human and
machine agents - at or near the tactical edges of a network. Motivated by use
cases in the domain of security, policing and emergency response, this paper
presents an approach to information collection, fusion and sense-making based
on the use of natural language (NL) and controlled natural language (CNL) to
support richer forms of human-machine interaction. The approach uses a
conversational protocol to facilitate a flow of collaborative messages from NL
to CNL and back again in support of interactions such as: turning eyewitness
reports from human observers into actionable information (from both trained and
untrained sources); fusing information from humans and physical sensors (with
associated quality metadata); and assisting human analysts to make the best use
of available sensing assets in an area of interest (governed by management and
security policies). CNL is used as a common formal knowledge representation for
both machine and human agents to support reasoning, semantic information fusion
and generation of rationale for inferences, in ways that remain transparent to
human users. Examples are provided of various alternative styles for user
feedback, including NL, CNL and graphical feedback. A pilot experiment with
human subjects shows that a prototype conversational agent is able to gather
usable CNL information from untrained human subjects
Federated AI for building AI Solutions across Multiple Agencies
The different sets of regulations existing for differ-ent agencies within the
government make the task of creating AI enabled solutions in government
dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different
agencies, which could be a significant impediment to training AI models. We
discuss the challenges that exist in environments where data cannot be freely
shared and assess tech-nologies which can be used to work around these
challenges. We present results on building AI models using the concept of
federated AI, which al-lows creation of models without moving the training data
around.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and
Public Sector, Arlington, Virginia, US
Editorial Special Issue on Enhancement Algorithms, Methodologies and Technology for Spectral Sensing
The paper is an editorial issue on enhancement algorithms, methodologies and technology for spectral sensing and serves as a valuable and useful reference for researchers and technologists interested in the evolving state-of-the-art and/or the emerging science and technology base associated with spectral-based sensing and monitoring problem. This issue is particularly relevant to those seeking new and improved solutions for detecting chemical, biological, radiological and explosive threats on the land, sea, and in the air
Trust and obfuscation principles for quality of information in emerging pervasive environments
Non peer reviewedPostprin
Ontology mapping: the state of the art
Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping
Cooperative H-infinity Estimation for Large-Scale Interconnected Linear Systems
In this paper, a synthesis method for distributed estimation is presented,
which is suitable for dealing with large-scale interconnected linear systems
with disturbance. The main feature of the proposed method is that local
estimators only estimate a reduced set of state variables and their complexity
does not increase with the size of the system. Nevertheless, the local
estimators are able to deal with lack of local detectability. Moreover, the
estimators guarantee H-infinity-performance of the estimates with respect to
model and measurement disturbances.Comment: Short version published in Proc. American Control Conference (ACC),
pp.2119-2124. Chicago, IL, 201
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