1,012 research outputs found
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Las ontologías en la ingeniería de software: un acercamiento de dos grandes áreas del conocimiento
Los conceptos ontológicos se suelen acercar más a la ingeniería del conocimiento, por lo que los ingenieros del software no los suelen aplicar para resolver problemas de su área. Es necesario que los ingenieros de software se apropien de las ontologías, pues éstas proporcionan un vocabulario común, que podría contribuir en la solución de problemas recurrentes en ingeniería del software, tales como la dificultad de la comunicación entre analista e interesado para definir los requisitos de un sistema, la baja reutilización de componentes y la escasa generación automática de código, entre otros. En este artículo se presenta un primer enlace entre las ontologías y la ingeniería de software mediante la recopilación y análisis de la literatura relativa a la utilización de las ontologías en las diferentes fases del ciclo de vida de un producto de software
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
Gapeau: Enhancing the Sense of Distance to Others with a Head-Mounted Sensor
Human perception lacks the capabilities to accurately assess distance. The recent Covid-19 pandemic outbreak rendered this ability particularly important. Augmenting our sense of distance can help maintain safe separation from others when required. To explore how systems can help users maintain physical distance, we designed, implemented and evaluated Gapeau - a head-mounted system for augmenting the sense of distance. Our system uses proximity sensors and thermal sensing to detect and measure the distance to other people. We conducted a validation protocol, an experiment, in which we compared different feedback modalities, and an in-the-wild study to evaluate Gapeau\u27s performance and suitability for use in social contexts. We found that our system enabled users to more accurately determine whether they were maintaining a safe distance from others. Vibration and auditory feedback were found most effective and usable. Gapeau was perceived as socially acceptable. Our work contributes insights for augmented sensing systems with social relevance
Business Intelligence for Small and Middle-Sized Entreprises
Data warehouses are the core of decision support sys- tems, which nowadays
are used by all kind of enter- prises in the entire world. Although many
studies have been conducted on the need of decision support systems (DSSs) for
small businesses, most of them adopt ex- isting solutions and approaches, which
are appropriate for large-scaled enterprises, but are inadequate for small and
middle-sized enterprises. Small enterprises require cheap, lightweight
architec- tures and tools (hardware and software) providing on- line data
analysis. In order to ensure these features, we review web-based business
intelligence approaches. For real-time analysis, the traditional OLAP
architecture is cumbersome and storage-costly; therefore, we also re- view
in-memory processing. Consequently, this paper discusses the existing approa-
ches and tools working in main memory and/or with web interfaces (including
freeware tools), relevant for small and middle-sized enterprises in decision
making
Ubiquitous Computing for Remote Cardiac Patient Monitoring: A Survey
New wireless technologies, such as wireless LAN and sensor networks, for telecardiology purposes give new possibilities for monitoring vital parameters with wearable biomedical sensors, and give patients the freedom to be mobile and still be under continuous monitoring and thereby better quality of patient care. This paper will detail the architecture and quality-of-service (QoS) characteristics in integrated wireless telecardiology platforms. It will also discuss the current promising hardware/software platforms for wireless cardiac monitoring. The design methodology and challenges are provided for realistic implementation
Managing Road Safety through the Use of Linked Data and Heat Maps
Road traffic injuries are a critical public health challenge that requires valuable efforts for effective and sustainable prevention. Worldwide, an estimated 1.2 million people are killed in road crashes each year and as many as 50 million are injured. An analysis of data provided by authoritative sources can be a valuable source for understanding which are the most critical points on the road network. The aim of this paper is to discover data about road accidents in Italy and to provide useful visualization for improving road safety. Starting from the annual report of road accidents of the Automobile Club of Italy, we transform the original data into an RDF dataset according to the Linked Open Data principles and connect it to external datasets. Then, an integration with Open Street Map allows to display the accident data on a map. Here, the final user is able to identify which road sections are most critical based on the number of deaths, injuries or accidents
A Pragmatic Approach for the Semantic Description and Matching of Pervasive Resources
The increasing popularity of personal wireless devices has raised new demands for the efficient discovery of heterogeneous devices and services in pervasive environments. With the advancement of the electronic world, the diversity of available services is increasing rapidly. %This raises new demands for the efficient discovery and location of heterogeneous services and resources in dynamically changing environments. Traditional approaches for service discovery describe services at a syntactic level and the matching mechanisms available for these approaches are limited to syntactic comparisons based on attributes or interfaces. In order to overcome these limitations, there has been an increased interest in the use of semantic description and matching techniques to support effective service discovery. In this paper, we present a semantic matching approach to facilitate the discovery of device-based services in pervasive environments. The approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request during the matching process. The solution has been systematically evaluated for its retrieval effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. Another important practical concern is the efficiency and the scalability of the semantic matching solution. Therefore, we have evaluated the scalability of the proposed solution by investigating the variation in matching time in response to increasing numbers of advertisements and increasing request sizes, and have presented the empirical results
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