1,721,045 research outputs found
Graph matching - Exact and error-tolerant methods and the automatic learning of edit costs
No full textA survey on ambient intelligence in health care
No full textAmbient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive, and anticipatory communications. Such innovative interaction paradigms make AmI technology a suitable candidate for developing various real life solutions, including in the healthcare domain. This survey will discuss the emergence of AmI techniques in the healthcare domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of AmI, such as smart environments and wearable medical devices. We will summarize the state-of-the-art artificial intelligence (AI) methodologies used for developing AmI system in the healthcare domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions), and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Enhancing smart home resident activity prediction and anomaly detection using temporal relations
Full text linkTechnological enhancements aid development and research in smart homes and intelligent environments. The temporal nature of data collected in a smart environment provides us with a better understanding of patterns that occur over time. Predicting events and detecting anomalies in such datasets is a complex and challenging task. To solve this problem, we suggest a solution using temporal relations. Our temporal pattern discovery algorithm, based on Allen's temporal relations, has helped discover interesting patterns and relations on smart home datasets. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and when these are incorporated with temporal information, the results can be used to enhance prediction and to detect anomalies. We describe a method of discovering temporal relations in smart home datasets and applying them to perform anomaly detection on the frequently-occurring events and enhance sequential prediction by incorporating temporal relation information shared by the activity. We validate our hypothesis using empirical studies based on the data collected from real resident and virtual resident (or synthetic) data
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Smart home adaptation based on explicit and implicit user feedback
Full text linkIn current work we introduce CASAS, an adaptive smart home system that utilizes machine learning techniques in order to dynamically adapt to user advice or changes in daily routine activities. The main components of CASAS include a frequent and periodic activity miner (FPAM), a hierarchal activity model (HAM), a dynamic adapter and CASAS's user interface and visualizer, CASA-U. The FPAM algorithm discovers arbitrary length periodic and frequent patterns from the resident's daily activities efficiently by utilizing the minimum description length principle. HAM, as a hybrid model of a decision tree combined with a Markov decision process, provides a hierarchal abstraction of patterns while utilizing temporal information such as temporal relations, temporal granules, start time and duration distribution. HAM is used to identify potential automations. The dynamic adapter component allows HAM to dynamically adapt to user's explicit feedback (advice) or implicit feedback (changes in daily routine activities) based on four techniques of explicit manipulation, explicit rating, explicit request and smart detection. It exploits guidance-based learning and observation-based learning along with the Activity Adaptation Miner (AAM) to adapt to these types of feedback. Finally, to allow users have a greater control over their personal environment and to provide a framework for explicit manipulation and rating of suggested automation policies, a user interface is provided that enables residents to navigate through a map of the home, view a history of events, modify the events and provide guidance to the smart home's automation policies. Integrating all these components together, the architecture of CASAS is provided that shows how resident interactions in a smart home can be automated and continually adapted to explicit or implicit changes in the resident's patterns. We also show the results of our successful experiments with CASAS on both synthetic and real world data, besides a usability test of CASA-U
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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Activity recognition in complex smart environment settings
Full text linkSmart environments rely on artificial intelligence techniques to make sense of the sensor data and to use the information for recognizing and tracking activities. However, many of the techniques that have been developed are designed for simplified situations. In this thesis we investigate more complex situations like recognizing activities when they are interweaved in realistic scenarios and when the space is inhabited by multiple resident performing tasks concurrently. This technology is beneficial for monitoring the health of smart environment residents and for correlating activities with parameters such as energy usage. We describe our approach to sequential, interleaved and concurrent (multi-resident) activity recognition and evaluate various probabilistic techniques for activity recognition. In addition to demonstrating that these activities can be recognized by sensors in physical environments using Markov and Hidden Markov models, we also show variants of these models that help in improving the recognition accuracy. We validate our algorithm on real sensor data collected in the CASAS smart apartment testbed
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Learning relationships between detected activities, sleep patterns, and physiological data
Full text linkThe U.S Census Bureau has estimated that by 2050, the number of people with the age of 65 and above will be over 80 million. The present development of the demography of elderly people in the U.S as well as other parts of the world will generate a shortage of caretakers for elderly people in the near future. Further, older adults prefer staying in their own houses rather than staying at an elder care facility. Technological advancements are currently moving towards building a smart home system than can monitor every activity performed by on older adult using motion sensors, wearable sensors, object sensors etc. We focus on three topics that are of great importance to understand the well-being of the elderly population. The first topic is activity prediction using data collected from a wearable Actigraph sensor. We use machine learning algorithms to identify activities from this data. The second part of this thesis concentrates on predicting the sleep quality of older adults. Unfortunately many people have a poor understanding of the factors that influence their daily sleep quality. Many psychology studies have concentrated on identifying sleep quality using a user-annotated sleep diary. However, the presence of cognitive disabilities may influence their impression of sleep quality. In this thesis, we focus on identifying the sleep quality of people with cognitive disabilities. The third part of this thesis focuses on finding correlations between blood glucose levels and activities of people with diabetes. We find that there exists high correlation between glucose levels during sleep and the activities they perform during the da
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