277,703 research outputs found
Smart Signs: Showing the way in Smart Surroundings
This paper presents a context-aware guidance and messaging
system for large buildings and surrounding venues. Smart Signs are
a new type of electronic door- and way-sign based on wireless sensor networks.
Smart Signs present in-situ personalized guidance and messages,
are ubiquitous, and easy to understand. They combine the easiness of
use of traditional static signs with the flexibility and reactiveness of navigation
systems. The Smart Signs system uses context information such
as user’s mobility limitations, the weather, and possible emergency situations
to improve guidance and messaging.
Minimal infrastructure requirements and a simple deployment tool make
it feasible to easily deploy a Smart Signs system on demand.
An important design issue of the Smart Signs system is privacy: the
system secures communication links, does not track users, allow almost
complete anonymous use, and prevent the system to be used as a tool
for spying on users
Unified Pragmatic Models for Generating and Following Instructions
We show that explicit pragmatic inference aids in correctly generating and
following natural language instructions for complex, sequential tasks. Our
pragmatics-enabled models reason about why speakers produce certain
instructions, and about how listeners will react upon hearing them. Like
previous pragmatic models, we use learned base listener and speaker models to
build a pragmatic speaker that uses the base listener to simulate the
interpretation of candidate descriptions, and a pragmatic listener that reasons
counterfactually about alternative descriptions. We extend these models to
tasks with sequential structure. Evaluation of language generation and
interpretation shows that pragmatic inference improves state-of-the-art
listener models (at correctly interpreting human instructions) and speaker
models (at producing instructions correctly interpreted by humans) in diverse
settings.Comment: NAACL 2018, camera-ready versio
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
We propose a neural sequence-to-sequence model for direction following, a
task that is essential to realizing effective autonomous agents. Our
alignment-based encoder-decoder model with long short-term memory recurrent
neural networks (LSTM-RNN) translates natural language instructions to action
sequences based upon a representation of the observable world state. We
introduce a multi-level aligner that empowers our model to focus on sentence
"regions" salient to the current world state by using multiple abstractions of
the input sentence. In contrast to existing methods, our model uses no
specialized linguistic resources (e.g., parsers) or task-specific annotations
(e.g., seed lexicons). It is therefore generalizable, yet still achieves the
best results reported to-date on a benchmark single-sentence dataset and
competitive results for the limited-training multi-sentence setting. We analyze
our model through a series of ablations that elucidate the contributions of the
primary components of our model.Comment: To appear at AAAI 2016 (and an extended version of a NIPS 2015
Multimodal Machine Learning workshop paper
Recommended from our members
The Role of Landscapes and Landmarks in Bee Navigation: A Review.
The ability of animals to explore landmarks in their environment is essential to their fitness. Landmarks are widely recognized to play a key role in navigation by providing information in multiple sensory modalities. However, what is a landmark? We propose that animals use a hierarchy of information based upon its utility and salience when an animal is in a given motivational state. Focusing on honeybees, we suggest that foragers choose landmarks based upon their relative uniqueness, conspicuousness, stability, and context. We also propose that it is useful to distinguish between landmarks that provide sensory input that changes ("near") or does not change ("far") as the receiver uses these landmarks to navigate. However, we recognize that this distinction occurs on a continuum and is not a clear-cut dichotomy. We review the rich literature on landmarks, focusing on recent studies that have illuminated our understanding of the kinds of information that bees use, how they use it, potential mechanisms, and future research directions
The eye contact effect: mechanisms and development
The ‘eye contact effect’ is the phenomenon that perceived eye contact with another human face modulates certain aspects of the concurrent and/or immediately following cognitive processing. In addition, functional imaging studies in adults have revealed that eye contact can modulate activity in structures in the social brain network, and developmental studies show evidence for preferential orienting towards, and processing of, faces with direct gaze from early in life. We review different theories of the eye contact effect and advance a ‘fast-track modulator’ model. Specifically, we hypothesize that perceived eye contact is initially detected by a subcortical route, which then modulates the activation of the social brain as it processes the accompanying detailed sensory information
Multi-Paradigm Reasoning for Access to Heterogeneous GIS
Accessing and querying geographical data in a uniform way has become easier in recent years. Emerging standards like WFS turn
the web into a geospatial web services enabled place. Mediation
architectures like VirGIS overcome syntactical and semantical heterogeneity
between several distributed sources. On mobile devices,
however, this kind of solution is not suitable, due to limitations,
mostly regarding bandwidth, computation power, and available storage
space. The aim of this paper is to present a solution for providing
powerful reasoning mechanisms accessible from mobile applications
and involving data from several heterogeneous sources.
By adapting contents to time and location, mobile web information
systems can not only increase the value and suitability of the
service itself, but can substantially reduce the amount of data delivered
to users. Because many problems pertain to infrastructures
and transportation in general and to way finding in particular, one
cornerstone of the architecture is higher level reasoning on graph
networks with the Multi-Paradigm Location Language MPLL. A
mediation architecture is used as a “graph provider” in order to
transfer the load of computation to the best suited component –
graph construction and transformation for example being heavy on
resources. Reasoning in general can be conducted either near the
“source” or near the end user, depending on the specific use case.
The concepts underlying the proposal described in this paper are
illustrated by a typical and concrete scenario for web applications
- …