Visual reasoning about social networks using centrality sensitivity

In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in the network. We have derived an analytical solution that extracts the sensitivity as the derivative of centrality with respect to degree for two centrality metrics based on feedback and random walks. We show that these sensitivities are good indicators of the distribution of centrality in the network, and how changes are expected to be propagated if we introduce changes to the network. These metrics also help us simplify a complex network in a way that retains the main structural properties and that results in trustworthy, readable diagrams. Sensitivity is also a key concept for uncertainty analysis of social networks, and we show how our approach may help analysts gain insight on the robustness of key network metrics. Through a number of examples, we illustrate the need for measuring sensitivity, and the impact it has on the visualization of and interaction with social and other scale-free networks.     

Model-based testing of software and systems: recent advances and challenges

Model-based testing is focused on testing techniques which rely on the use of models. The diversity of systems and software to be tested implies the need for research on a variety of models and methods for test automation. We briefly review this research area and introduce several papers selected from the 22nd International Conference on Testing Software and Systems (ICTSS).     

Word sense disambiguation for spam filtering

Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms, and phishing. More than 86% of received e-mails are spam. Historical approaches to combating these messages, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Many current solutions feature machine-learning algorithms trained using statistical representations of the terms that most commonly appear in such e-mails. However, these methods are merely syntactic and are unable to account for the underlying semantics of terms within messages. In this paper, we explore the use of semantics in spam filtering by introducing a pre-processing step of Word Sense Disambiguation (WSD). Based upon this disambiguated representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages.     

Note on the use of the term subject in pre-1886 discussions of thought-transference experiments.

In response to K. Danzinger's (see record 1986-00068-001) suggestion that the first use of the term subject in the English-language psychological literature occurred in 1886 in the context of experiments involving the hypnotic state, the present author points out that there are examples of the use of the term in discussions of experiments on thought transference published by the Society for Psychical Research in the 1880's. (8 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

I3WSN: Industrial Intelligent Wireless Sensor Networks for indoor environments

Sensor Web Enablement (SWE) technologies have been successfully applied to a great variety of outdoor scenarios but, in practical terms, little effort has been applied for indoor environments, and even less in the field of industrial applications. This article presents an intelligent SWE application for an indoor and industrial scenario, with the aim of improving and increasing the levels of human safety. The base low-level architecture is built on top of wireless sensor networks (WSN) connected to a Sensor Observation Service (SOS). Higher layers in the architecture include services that make real time decisions based on the collected data. Both simulation and experimental results are presented. The paper shows the viability of our approach in terms of performance, scalability, modularity and safety.     

Sustainability in Web server systems

The exponential growth of Internet during the last decade leads us to make more efforts in the researching and developing of sustainable Web servers in order to decrease the global energy demand. In this paper, we cover, as a first step, a proper review of the literature related to the energy efficiency research in Web server systems to depict the state of the art to plan further contributions as more research in sustainable Web systems. We also propose and implement an energy metric that permits to establish a relation between the Quality of Service (QoS) obtained by the system and the power it consumes.     

A web-based platform for biosignal visualization and annotation

With the advent of wearable sensing and mobile technologies, biosignals have seen an increasingly growing number of application areas, leading to the collection of large volumes of data. One of the difficulties in dealing with these data sets, and in the development of automated machine learning systems which use them as input, is the lack of reliable ground truth information. In this paper we present a new web-based platform for visualization, retrieval and annotation of biosignals by non-technical users, aimed at improving the process of ground truth collection for biomedical applications. Moreover, a novel extendable and scalable data representation model and persistency framework is presented. The results of the experimental evaluation with possible users has further confirmed the potential of the presented framework.     

Beast methodology: An agile testing methodology for multi-agent systems based on behaviour driven development

This paper presents a testing methodology to apply Behaviour Driven Development (BDD) techniques while developing Multi-Agent Systems (MASs), termed BEhavioural Agent Simple Testing (BEAST) Methodology. This methodology is supported by the open source framework (BEAST Tool) which automatically generates test cases skeletons from BDD scenarios specifications. The developed framework allows the testing of MASs based on JADE or JADEX platforms. In addition, this framework offers a set of configurable Mock Agents with the aim of being able to execute tests while the MAS is under development. The BEAST Methodology presents transparent traceability from user requirements to test cases. Thus, the stakeholders can be aware of the project status. The methodology and the associated tool have been validated in the development of a MAS for fault diagnosis in FTTH (Fiber To The Home) networks. The results have been measured in quantifiable way obtaining a reduction of the tests implementation time.     

A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing

The job-shop scheduling problem with operators is a very interesting problem that generalizes the classic job-shop problem in such a way that an operation must be algorithm to solve this problem considering makespan minimization. The genetic algorithm uses permutations with repetition to encode chromosomes and a schedule generation scheme, termed OG&T, as decoding algorithm. This combination guaranties that at least one of the chromosomes represents and optimal schedule and, at the samhat machines and operators are idle while an operation is available to be processed. To improve the quality of the schedules for large instances, we use Lamarckian evolution and modify the OG&T algorithm to further reduce the idle time of the machines and operators, in this case at the risk of leaving all optimal schedules out of the search space. We conducted a large experimental study showing that these improvements allow the genetic algorithm to reach high quality solutions in very short time, and so it is quite competitive with the current state-of-the-art methods.     

Laser-induced fluorescence visualization of ion transport in a pseudo-porous capacitive deionization microstructure

In this paper, a microfluidic experimental set-up is introduced to study the ionic transport in an artificial capacitive deionization (CDI) cell. CDI is a promising desalination technique, which relies on the application of an external electric field and high surface area porous electrodes for ion separation and storage. Photolithography and deep reactive ion etching were used to fabricate a micro-CDI channel with pseudo-porous electrodes on a silicon-on-insulator substrate. Laser-induced fluorescence was performed using cationic Sulforhodamine B (SRB) fluorescent dye to measure ion concentration within the bulk solution and more importantly, within the porous electrodes during the desalination process, with an average normalized root mean square deviation of 8.2 %. Using this set-up, electromigration of ions within the electrode was visualized and the effect of applied electric potential on bulk solution concentration distribution is quantified. In addition, SRB and Fluorescein were used together to visualize anion and cation concentrations simultaneously. The method presented in this study can be used for solution concentrations up to approximately 0.7 mM. The ionic concentration profiles obtained by this approach can be used to test and validate the existing electrosorption models, and pseudo-porous electrodes can be modified to observe the effects of pore size, shape and distribution on electrosorption performance. Furthermore, with proper modifications, the microfabricated structure and experimental set-up can be used for CDI-on-a-chip applications and bio-separation devices.