81,351 research outputs found
Robust Spoken Language Understanding for House Service Robots
Service robotics has been growing significantly in thelast years, leading to several research results and to a numberof consumer products. One of the essential features of theserobotic platforms is represented by the ability of interactingwith users through natural language. Spoken commands canbe processed by a Spoken Language Understanding chain, inorder to obtain the desired behavior of the robot. The entrypoint of such a process is represented by an Automatic SpeechRecognition (ASR) module, that provides a list of transcriptionsfor a given spoken utterance. Although several well-performingASR engines are available off-the-shelf, they operate in a generalpurpose setting. Hence, they may be not well suited in therecognition of utterances given to robots in specific domains. Inthis work, we propose a practical yet robust strategy to re-ranklists of transcriptions. This approach improves the quality of ASRsystems in situated scenarios, i.e., the transcription of roboticcommands. The proposed method relies upon evidences derivedby a semantic grammar with semantic actions, designed tomodel typical commands expressed in scenarios that are specificto human service robotics. The outcomes obtained throughan experimental evaluation show that the approach is able toeffectively outperform the ASR baseline, obtained by selectingthe first transcription suggested by the AS
Immune cognition, social justice and asthma: structured stress and the developing immune system
We explore the implications of IR Cohen's work on immune
cognition for understanding rising rates of asthma morbidity
and mortality in the US. Immune cognition is conjoined with
central nervous system cognition, and with the cognitive
function of the embedding sociocultural networks by which
individuals are acculturated and through which they work with others to meet challenges of threat and opportunity.
Using a mathematical model, we find that externally-
imposed patterns of 'structured stress' can, through their
effect on a child's socioculture, become synergistic with
the development of immune cognition, triggering the persistence of an atopic Th2 phenotype, a necessary precursor to asthma and other immune disease. Reversal of the rising tide of asthma and related chronic diseases in the US thus seems unlikely without a 21st Century version of the earlier Great Urban Reforms which ended the scourge of infectious diseases
Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
Action detection and temporal segmentation of actions in videos are topics of
increasing interest. While fully supervised systems have gained much attention
lately, full annotation of each action within the video is costly and
impractical for large amounts of video data. Thus, weakly supervised action
detection and temporal segmentation methods are of great importance. While most
works in this area assume an ordered sequence of occurring actions to be given,
our approach only uses a set of actions. Such action sets provide much less
supervision since neither action ordering nor the number of action occurrences
are known. In exchange, they can be easily obtained, for instance, from
meta-tags, while ordered sequences still require human annotation. We introduce
a system that automatically learns to temporally segment and label actions in a
video, where the only supervision that is used are action sets. An evaluation
on three datasets shows that our method still achieves good results although
the amount of supervision is significantly smaller than for other related
methods.Comment: CVPR 201
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
We develop a natural language interface for human robot interaction that
implements reasoning about deep semantics in natural language. To realize the
required deep analysis, we employ methods from cognitive linguistics, namely
the modular and compositional framework of Embodied Construction Grammar (ECG)
[Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference
resolution problems and other issues related to deep semantics and
compositionality of natural language. This also includes verbal interaction
with humans to clarify commands and queries that are too ambiguous to be
executed safely. We implement our NLU framework as a ROS package and present
proof-of-concept scenarios with different robots, as well as a survey on the
state of the art
A Processing Model for Free Word Order Languages
Like many verb-final languages, Germn displays considerable word-order
freedom: there is no syntactic constraint on the ordering of the nominal
arguments of a verb, as long as the verb remains in final position. This effect
is referred to as ``scrambling'', and is interpreted in transformational
frameworks as leftward movement of the arguments. Furthermore, arguments from
an embedded clause may move out of their clause; this effect is referred to as
``long-distance scrambling''. While scrambling has recently received
considerable attention in the syntactic literature, the status of long-distance
scrambling has only rarely been addressed. The reason for this is the
problematic status of the data: not only is long-distance scrambling highly
dependent on pragmatic context, it also is strongly subject to degradation due
to processing constraints. As in the case of center-embedding, it is not
immediately clear whether to assume that observed unacceptability of highly
complex sentences is due to grammatical restrictions, or whether we should
assume that the competence grammar does not place any restrictions on
scrambling (and that, therefore, all such sentences are in fact grammatical),
and the unacceptability of some (or most) of the grammatically possible word
orders is due to processing limitations. In this paper, we will argue for the
second view by presenting a processing model for German.Comment: 23 pages, uuencoded compressed ps file. In {\em Perspectives on
Sentence Processing}, C. Clifton, Jr., L. Frazier and K. Rayner, editors.
Lawrence Erlbaum Associates, 199
Selection pressure and organizational cognition: implications for the social determinants of health
We model the effects of Schumperterian 'selecton pressures' -- in particular Apartheid and the neoliberal 'market economy' -- on organizational cognition in minority communities, given the special role of culture in human biology. Our focus is on the dual-function social networks by which culture is imposed and maintained on individuals and by which immediate patterns of opportunity and threat are recognized and given response. A mathematical model based on recent advances in complexity theory displays a joint cross-scale linkage of social, individual central nervous system, and immune cognition with external selection pressure through mixed and synergistic punctuated 'learning plateaus.' This provides a natural mechanism for addressing the social determinants of health at the individual level. The implications of the model, particularly the predictions of synergistic punctuation, appear to be empirically testable
Genomics and proteomics: a signal processor's tour
The theory and methods of signal processing are becoming increasingly important in molecular biology. Digital filtering techniques, transform domain methods, and Markov models have played important roles in gene identification, biological sequence analysis, and alignment. This paper contains a brief review of molecular biology, followed by a review of the applications of signal processing theory. This includes the problem of gene finding using digital filtering, and the use of transform domain methods in the study of protein binding spots. The relatively new topic of noncoding genes, and the associated problem of identifying ncRNA buried in DNA sequences are also described. This includes a discussion of hidden Markov models and context free grammars. Several new directions in genomic signal processing are briefly outlined in the end
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