Toward a mathematical formalism of performance, task difficulty, and activation

The rudiments of a mathematical formalism for handling operational, physiological, and psychological concepts are developed for use by the man-machine system design engineer. The formalism provides a framework for developing a structured, systematic approach to the interface design problem, using existing mathematical tools, and simplifying the problem of telling a machine how to measure and use performance

FireWatch: G.i.S. -assisted wireless sensor networks for forest fires

Traditional satellite and camera-based systems, are currently the predominant methods for detecting forest fires. Our study has identified that these systems lack immediacy as detected fires must gain some momentum before they are detected. In addition, they suffer from decreased accuracy especially during the night, where visibility is diminished. In this paper, we present FireWatch, a system that aims to overcome the aforementioned limitations by combining a number of technologies including Wireless Sensor Networks, Computer-supported Cooperative Work and Geographic Information Systems in a transparent manner. Compared to satellite and camera-based approaches, FireWatch is able to detect forest fires more accurately and forecast the forest fire danger more promptly. FireWatch is currently scheduled to be deployed at the Cypriot Department of Forests

Towards a human-centered e-commerce personalization framework

This paper presents a personalization framework, namely PersonaWeb that adapts the visual and interaction design of E-Commerce Web environments based on human cognitive differences. In particular, it describes a user model formalization that incorporates a set of human cognitive factors (i.e., cognitive styles and working memory capacity) and an adaptation engine that personalizes the visual and interaction design attributes of E-Commerce product views. The proposed framework has been applied in a real-life E-Commerce Web-site and two subsequent user studies were conducted in which 135 users interacted with the personalized and the original (non-personalized) version of the same Web environment. Results indicate the added value of personalizing content and functionality of E-Commerce product views in terms of users' task completion performance

Power efficiency through tuple ranking in wireless sensor network monitoring

In this paper, we present an innovative framework for efficiently monitoring Wireless Sensor Networks (WSNs). Our framework, coined KSpot, utilizes a novel top-k query processing algorithm we developed, in conjunction with the concept of in-network views, in order to minimize the cost of query execution. For ease of exposition, consider a set of sensors acquiring data from their environment at a given time instance. The generated information can conceptually be thought as a horizontally fragmented base relation R. Furthermore, the results to a user-defined query Q, registered at some sink point, can conceptually be thought as a view V . Maintaining consistency between V and R is very expensive in terms of communication and energy. Thus, KSpot focuses on a subset V′ (⊆ V ) that unveils only the k highest-ranked answers at the sink, for some user defined parameter k. To illustrate the efficiency of our framework, we have implemented a real system in nesC, which combines the traditional advantages of declarative acquisition frameworks, like TinyDB, with the ideas presented in this work. Extensive real-world testing and experimentation with traces from University of California-Berkeley, the University of Washington and Intel Research Berkeley, show that KSpot provides an up to 66% of energy savings compared to TinyDB, minimizes both the size and number of packets transmitted over the network (up to 77%), and prolongs the longevity of a WSN deployment to new scales

Integrating Human Factors and Semantic Mark-ups in Adaptive Interactive Systems

This paper focuses on incorporating individual differences in cognitive processing and semantic mark-ups in the context of adaptive interactive systems. In particular, a semantic Web-based adaptation framework is proposed that enables Web content providers to enrich content and functionality of Web environments with semantic mark-ups. The Web content is created using a Web authoring tool and is further processed and reconstructed by an adaptation mechanism based on cognitive factors of users. Main aim of this work is to investigate the added value of personalising content and functionality of Web environments based on the unique cognitive characteristics of users. Accordingly, a user study has been conducted that entailed a psychometric-based survey for extracting the users' cognitive characteristics, combined with a real usage scenario of an existing commercial Web environment that was enriched with semantic mark-ups and personalised based on different adaptation effects. The paper provides interesting insights in the design and development of adaptive interactive systems based on cognitive factors and semantic mark-ups

Towards a Back-End Framework for Supporting Affective Avatar-Based Interaction Systems

Avatar-based systems provide an intuitive way of interacting with users in the context of Ambient Assisted Living (AAL). These systems are typically supported by a diverse set of services for, e.g, social daily activities, leisure, education and safety. This paper studies the importance of specific services for two organizations, namely MRPS in Geneva, Switzerland and ORBIS in Sittard, Netherlands. Based on this study, we present the design of a backend framework that supports Avatar interaction by means of a comprehensive set of services for safe and independent living

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain,however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P3 system deployed over our SmartLab4 testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors

Mint views: Materialized in-network top-k views in sensor networks

In this paper we introduce MINT (materialized in-network top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V(sube. V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively- defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models