https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Data Mining Practical Machine Learning Tools and Techniques

羟氨苄青霉素引起大疱性类天疱疮样疹

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

The Essential Guide to Doing Research

The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence environment. In a five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user behavior and adapt to user needs. The embedded agent discreetly controls the iDorm according to user preferences. Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambient-intelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.

https://www4.di.uminho.pt/~pjn/2006-07/MIEBSI/Aulas/AmI/01363729.pdf

Creating an ambient-intelligence environment using embedded agents

We describe a novel life-long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realize the vision of ambient intelligence in intelligent inhabited environments (IIE) by providing ubiquitous computing intelligence in the environment supporting the activities of the user. An unsupervised, data-driven, fuzzy technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularized behaviors in the environment. The user's learned behaviors can then be adapted online in a life-long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learned and adapted to the user's behavior, during a stay of five consecutive days in the intelligent dormitory (iDorm), which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other approaches, while operating online in a life-long mode to realize the ambient intelligence vision.

A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments

We explain basic features of an emerging area called Intelligent Environments. We give a short overview on how it has developed, what is the current state of the art and what are the challenges laying ahead. The aim of the article is to make aware the Computer Science community of this new development, the differences with previous dominant paradigms and the opportunities that this area offers to the scientific community and society.

/pdf/intelligent-environments-a-manifesto-2nzm7u0yud.pdf

"Intelligent Environments: a manifesto"

This paper presents an analysis, implementation and evaluation of the physical mobile interaction techniques touching, pointing and scanning. Based on this we have formulated guidelines that show in which context which interaction technique is preferred by the user. Our main goal was to identify typical situations and scenarios in which the different techniques might be useful or not. In support of these aims we have developed and evaluated, within a user study, a low-fidelity and a high-fidelity prototype to assess scanning, pointing and touching interaction techniques within different contexts. Other work has shown that mobile devices can act as universal remote controls for interaction with smart objects but, to date, there has been no research which has analyzed when a given mobile interaction technique should be used. In this research we analyze the appropriateness of three interaction techniques as selection techniques in smart environments.

/pdf/an-experimental-comparison-of-physical-mobile-interaction-23xrvwstge.pdf

An experimental comparison of physical mobile interaction techniques: touching, pointing and scanning

We describe a new approach to intelligent building systems, that utilises an intelligent agent approach to autonomously governing the building environment. We discuss the role of learning in building control systems, and contrast this approach with existing IB solutions. We explain the importance of acquiring information from sensors, rather than relying on pre‐programmed models, to determine user needs. We describe how our architecture, consisting of distributed embedded agents, utilises sensory information to learn to perform tasks related to user comfort, energy conservation, safety and monitoring functions. We show how these agents, employing a behaviour‐based approach derived from robotics research, are able to continuously learn and adapt to individuals within a building, while always providing a fast, safe response to any situation. Finally, we show how such a system could be used to provide support for older people, or people with disabilities, allowing them greater independence and quality of life.

Vic Callaghan

Papers

Creating an ambient-intelligence environment using embedded agents

A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments

"Intelligent Environments: a manifesto"

An experimental comparison of physical mobile interaction techniques: touching, pointing and scanning

A multi‐agent architecture for intelligent building sensing and control