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

Insights into the multitrophic interactions between the biocontrol agent Bacillus subtilis MBI 600, the pathogen Botrytis cinerea and their plant host

Botrytis cinerea is a plant pathogen causing the gray mold disease in a plethora of host plants. The control of the disease is based mostly on chemical pesticides, which are responsible for environmental pollution, while they also pose risks for human health. Furthermore, B. cinerea resistant isolates have been identified against many fungicide groups, making the control of this disease challenging. The application of biocontrol agents can be a possible solution, but requires deep understanding of the molecular mechanisms in order to be effective. In this study, we investigated the multitrophic interactions between the biocontrol agent Bacillus subtilis MBI 600, a new commercialized biopesticide, the pathogen B. cinerea and their plant host. Our analysis showed that this biocontrol agent reduced B. cinerea mycelial growth in vitro, and was able to suppress the disease incidence on cucumber plants. Moreover, treatment with B. subtilis led to induction of genes involved in plant immunity. RNAseq analysis of B. cinerea transcriptome upon exposure to bacterial secretome, showed that genes coding for MFS and ABC transporters were highly induced. Deletion of the Bcmfs1 MFS transporter gene, using a CRISP/Cas9 editing method, affected its virulence and the tolerance of B. cinerea to bacterial secondary metabolites. These findings suggest that specific detoxification transporters are involved in these interactions, with crucial role in different aspects of B. cinerea physiology

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

AN ATTEMPT TO DEFINE CONTEXT AWARENESS IN MOBILE E-HEALTH ENVIRONMENTS

Nurses, doctors, physiotherapists, psychologists and other professionals or specialists come together to provide care to home residing patients, making continuous assessment, diagnosis and treatment possible beyond the walls of hospitals. Such teams of professionals are focused on each individual patient, and are virtual, i.e. they make decisions without being together physically, dynamically, i.e. professionals come and go as needed, and collaborate, as they combine their knowledge to provide effective care. Our system, coined DITIS, is a web based system that enables the effective management and collaboration of virtual healthcare teams and accessing medical information in a secure manner from a variety of mobile devices from anytime and anyplace, adapting the information according to various parameters like, user role, access right, device capabilities and wireless medium. This paper introduces the DITIS system, and identifies the needs and challenges of co-ordinated teams of multidisciplinary healthcare professionals (HCPs) functioning in a context awareness environment under the wireless environment. Pilo

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

In-network data acquisition and replication in mobile sensor networks

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting and aggregating spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of values from the network in cases of failures. We also extend DRA with a spatio-temporal in-network aggregation scheme based on minimum bounding rectangles to form the Hierarchical-DRA (HDRA) algorithm, which enables the approximate retrieval of events from the network. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures across all nodes are less than 60%, our framework can recover over 80% of detected values exactly

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

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