An Efficient and Self-Adapting Localization in Static Wireless Sensor Networks

Localization is one of the most important subjects in Wireless Sensor Networks (WSNs). To reduce the number of beacons and adopt probabilistic methods, some particle filter-based mobile beacon-assisted localization approaches have been proposed, such as Mobile Beacon-assisted Localization (MBL), Adapting MBL (A-MBL), and the method proposed by Hang et al. Some new significant problems arise in these approaches, however. The first question is which probability distribution should be selected as the dynamic model in the prediction stage. The second is whether the unknown node adopts neighbors’ observation in the update stage. The third is how to find a self-adapting mechanism to achieve more flexibility in the adapting stage. In this paper, we give the theoretical analysis and experimental evaluations to suggest which probability distribution in the dynamic model should be adopted to improve the efficiency in the prediction stage. We also give the condition for whether the unknown node should use the observations from its neighbors to improve the accuracy. Finally, we propose a Self-Adapting Mobile Beacon-assisted Localization (SA-MBL) approach to achieve more flexibility and achieve almost the same performance with A-MBL

Self-aware SGD: reliable incremental adaptation framework for clinical AI models

Healthcare is dynamic as demographics, diseases, and therapeutics constantly evolve. This dynamic nature induces inevitable distribution shifts in populations targeted by clinical AI models, often rendering them ineffective. Incremental learning provides an effective method of adapting deployed clinical models to accommodate these contemporary distribution shifts. However, since incremental learning involves modifying a deployed or in-use model, it can be considered unreliable as any adverse modification due to maliciously compromised or incorrectly labelled data can make the model unsuitable for the targeted application. This paper introduces self-aware stochastic gradient descent (SGD) , an incremental deep learning algorithm that utilises a contextual bandit-like sanity check to only allow reliable modifications to a model. The contextual bandit analyses incremental gradient updates to isolate and filter unreliable gradients. This behaviour allows self-aware SGD to balance incremental training and integrity of a deployed model. Experimental evaluations on the Oxford University Hospital datasets highlight that self-aware SGD can provide reliable incremental updates for overcoming distribution shifts in challenging conditions induced by label noise

Fluctuations and information filtering in coupled populations of spiking neurons with adaptation

Finite-sized populations of spiking elements are fundamental to brain function, but also used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasi-renewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks, and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales

Two mechanisms for optic flow and scale change processing of looming

Published in final edited form as: J Vis. ; 11(3): . doi:10.1167/11.3.5.The detection of looming, the motion of objects in depth, underlies many behavioral tasks, including the perception of self-motion and time-to-collision. A number of studies have demonstrated that one of the most important cues for looming detection is optic flow, the pattern of motion across the retina. Schrater et al. have suggested that changes in spatial frequency over time, or scale changes, may also support looming detection in the absence of optic flow (P. R. Schrater, D. C. Knill, & E. P. Simoncelli, 2001). Here we used an adaptation paradigm to determine whether the perception of looming from optic flow and scale changes is mediated by single or separate mechanisms. We show first that when the adaptation and test stimuli were the same (both optic flow or both scale change), observer performance was significantly impaired compared to a dynamic (non-motion, non-scale change) null adaptation control. Second, we found no evidence of cross-cue adaptation, either from optic flow to scale change, or vice versa. Taken together, our data suggest that optic flow and scale changes are processed by separate mechanisms, providing multiple pathways for the detection of looming.We thank Jonathan Victor and the anonymous reviewers of the paper for feedback and suggestions regarding the stimuli used here. This work was supported by NIH grant R01NS064100 to LMV. (R01NS064100 - NIH)Accepted manuscrip

DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings