258 research outputs found
Attention-driven auditory stream segregation using a SOM coupled with an excitatory-inhibitory ANN
Auditory attention is an essential property of human hearing. It is responsible for the selection of information to be sent to working memory and as such to be perceived consciously, from the abundance of auditory information that is continuously entering the ears. Thus, auditory attention heavily influences human auditory perception and systems simulating human auditory scene analysis would benefit from an attention model. In this paper, a human-mimicking model of auditory attention is presented, aimed to be used in environmental sound monitoring. It relies on a Self-Organizing Map (SOM) for learning and classifying sounds. Coupled to this SOM, an excitatory-inhibitory artificial neural network (ANN), simulating the auditory cortex, is defined. The activation of these neurons is calculated based on an interplay of various excitatory and inhibitory inputs. The latter simulate auditory attention mechanisms in a human-inspired but simplified way, in order to keep the computational cost within bounds. The behavior of the model incorporating all of these mechanisms is investigated, and plausible results are obtained
Semantic labeling of places using information extracted from laser and vision sensor data
Indoor environments can typically be divided into places with different functionalities like corridors, kitchens,
offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly,
we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor
environments
Implicit Sensor-based Authentication of Smartphone Users with Smartwatch
Smartphones are now frequently used by end-users as the portals to
cloud-based services, and smartphones are easily stolen or co-opted by an
attacker. Beyond the initial log-in mechanism, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data, whether in the cloud or in the smartphone. But attackers who
have gained access to a logged-in smartphone have no incentive to
re-authenticate, so this must be done in an automatic, non-bypassable way.
Hence, this paper proposes a novel authentication system, iAuth, for implicit,
continuous authentication of the end-user based on his or her behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We design a system that gives accurate authentication using
machine learning and sensor data from multiple mobile devices. Our system can
achieve 92.1% authentication accuracy with negligible system overhead and less
than 2% battery consumption.Comment: Published in Hardware and Architectural Support for Security and
Privacy (HASP), 201
Efficient Learning with Partially Observed Attributes
We describe and analyze efficient algorithms for learning a linear predictor
from examples when the learner can only view a few attributes of each training
example. This is the case, for instance, in medical research, where each
patient participating in the experiment is only willing to go through a small
number of tests. Our analysis bounds the number of additional examples
sufficient to compensate for the lack of full information on each training
example. We demonstrate the efficiency of our algorithms by showing that when
running on digit recognition data, they obtain a high prediction accuracy even
when the learner gets to see only four pixels of each image.Comment: This is a full version of the paper appearing in The 27th
International Conference on Machine Learning (ICML 2010
Human action recognition using local spatiotemporal discriminant embedding
Human action video sequences can be considered as nonlinear dynamic shape manifolds in the space of image frames. In this paper, we address learning and classifying human actions on embedded low-dimensional manifolds. We propose a novel manifold embedding method, called Local Spatio-Temporal Discriminant Embedding (LSTDE). The discriminating capabilities of the proposed method are two-fold: (1) for local spatial discrimination, LSTDE projects data points (silhouette-based image frames of human action sequences) in a local neighborhood into the embedding space where data points of the same action class are close while those of different classes are far apart; (2) in such a local neighborhood, each data point has an associated short video segment, which forms a local temporal subspace on the embedded manifold. LSTDE finds an optimal embedding which maximizes the principal angles between those temporal subspaces associated with data points of different classes. Benefiting from the joint spatio-temporal discriminant embedding, our method is potentially more powerful for classifying human actions with similar space-time shapes, and is able to perform recognition on a frame-byframe or short video segment basis. Experimental results demonstrate that our method can accurately recognize human actions, and can improve the recognition performance over some representative manifold embedding methods, especially on highly confusing human action types. 1
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