54,400 research outputs found
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
Resolving Multi-party Privacy Conflicts in Social Media
Items shared through Social Media may affect more than one user's privacy ---
e.g., photos that depict multiple users, comments that mention multiple users,
events in which multiple users are invited, etc. The lack of multi-party
privacy management support in current mainstream Social Media infrastructures
makes users unable to appropriately control to whom these items are actually
shared or not. Computational mechanisms that are able to merge the privacy
preferences of multiple users into a single policy for an item can help solve
this problem. However, merging multiple users' privacy preferences is not an
easy task, because privacy preferences may conflict, so methods to resolve
conflicts are needed. Moreover, these methods need to consider how users' would
actually reach an agreement about a solution to the conflict in order to
propose solutions that can be acceptable by all of the users affected by the
item to be shared. Current approaches are either too demanding or only consider
fixed ways of aggregating privacy preferences. In this paper, we propose the
first computational mechanism to resolve conflicts for multi-party privacy
management in Social Media that is able to adapt to different situations by
modelling the concessions that users make to reach a solution to the conflicts.
We also present results of a user study in which our proposed mechanism
outperformed other existing approaches in terms of how many times each approach
matched users' behaviour.Comment: Authors' version of the paper accepted for publication at IEEE
Transactions on Knowledge and Data Engineering, IEEE Transactions on
Knowledge and Data Engineering, 201
Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)
This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio
The commodity chain of the household: from survey design to policy and practice
Data collection and analysis and policy formulation all require a social unit to be defined, generally called the household. Multidisciplinary evidence shows that households as defined by survey practitioners often bear little resemblance to lived socio-economic units. This study examines how a shared language, the 'household', can generate misunderstandings because different groups with distinctive understandings of the term 'household' are often unaware that others may be using ‘household’ differently. Results from 4 interlinked and iterative methods are presented: review of household survey documentation (1950s-present); ethnographic ground-truthing fieldwork; in-depth key informant interviews; and modelling. Results show that whereas data collectors have a clear idea of what a `household` is, data users are often unaware of the nuances of the constraints imposed by data collection. This has implications for policy planning and practice. What interviewees consider when they think of their household can differ systematically from data collectors' definitions
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
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