4CaaSt: Comprehensive management of Cloud services through a PaaS

The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its management and lifecycle, a one stop shop for Cloud services and the management of resources in the PaaS level (including elasticity). 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud- aware immigrant technologies.

Network assisted content protection architectures for a connected world

Content protection is a key component for the success of a multimedia services platform, as proven by the plethora of solutions currently on the market. In this paper we analyze a new network scenario where permanent bidirectional connectivity and video-aware encryption technologies allow a trustful operation of ubiquitous end devices. We propose new scalable models for a content protection architecture that may achieve dramatic improvement in robustness, reliability, and scalability. Selective ciphering and countermeasures are included in those models, together with several examples of their application.

Resilient Wireless Sensor Networks Using Topology Control: A Review

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs.

Estudio del radón en viviendas mediante modelos de minería de datos basados en cubos OLAP

En esta tesis doctoral se estudian las variaciones de radón en el interior de dos viviendas similares de construcción nueva en Madrid, una de ellas ocupada y la otra no, que forman parte del mismo edificio residencial. La concentración de radón y los parámetros ambientales (presión, temperatura y humedad) se midieron durante ocho meses. La monitorización del gas radón se realizó mediante detectores de estado sólido. Simultáneamente, se adquirieron algunas variables atmosféricas de un modelo atmosférico. En el análisis de los datos, se utilizó principalmente el método de la Transformada Wavelet. Los resultados muestran que el nivel de radón es ligeramente más alto en la vivienda ocupada que en la otra. A partir del análisis desarrollado en este estudio, se encontró que había un patrón específico estacional en la concentración de radón interior. Además, se analizó también la influencia antropogénica. Se pudieron observar patrones periódicos muy similares en intervalos concretos sin importar si la vivienda está ocupada o no. Por otra parte, los datos se almacenaron en cubos OLAP. El análisis se realizó usando unos algoritmos de agrupamiento (clustering) y de asociación. El objetivo es descubrir las relaciones entre el radón y las condiciones externas como la presión, estabilidad, etc. Además, la metodología aplicada puede ser útil para estudios ambientales en donde se mida radón en espacios interiores. ABSTRACT The present thesis studies the indoor radon variations in two similar new dwellings, one of them occupied and the other unoccupied, from the same residential building in Madrid. Radon concentration and ambient parameters were measured during eight months. Solid state detectors were used for the radon monitoring. Simultaneously, several atmospheric variables were acquired from an atmospheric model. In the data analysis, the Wavelet Transform Method was mainly used. The results show that radon level is slightly higher in the unoccupied dwelling than in the other one. From the analysis developed in this study, it is found that a specific seasonal pattern exists in the indoor radon concentration. Besides, the anthropogenic influence is also analysed. Nearly periodical patterns could be observed in specific periods whether dwelling is occupied or not. Otherwise, data were stored in cubes OLAP. Analysis was carried out using clustering and association algorithms. The aim is to find out the relationships among radon and external conditions like pressure, stability, etc. Besides, the methodology could be useful to assess environmental studies, where indoor radon is measured.

Estudio empírico de la influencia de la experiencia y del conocimiento del dominio del analista en la efectividad de la educción de requisitos

Contexto: La presente tesis doctoral se enmarca en la actividad de educción de los requisitos. La educción de requisitos es generalmente aceptada como una de las actividades más importantes dentro del proceso de Ingeniería de Requisitos, y tiene un impacto directo en la calidad del software. Es una actividad donde la comunicación entre los involucrados (analistas, clientes, usuarios) es primordial. La efectividad y eficacia del analista en la compresión de las necesidades de clientes y usuarios es un factor crítico para el éxito del desarrollo de software. La literatura se ha centrado principalmente en estudiar y comprender un conjunto específico de capacidades o habilidades personales que debe poseer el analista para realizar de forma efectiva la actividad de educción. Sin embargo, existen muy pocos trabajos que han estudiado dichas capacidades o habilidades empíricamente. Objetivo: La presente investigación tiene por objetivo estudiar el efecto de la experiencia, el conocimiento acerca del dominio y la titulación académica que poseen los analistas en la efectividad del proceso de educción de los requisitos, durante los primeros contactos del analista con el cliente. Método de Investigación: Hemos ejecutado 8 estudios empíricos entre cuasi-experimentos (4) y experimentos controlados (4). Un total de 110 sujetos experimentales han participado en los estudios, entre estudiantes de post-grado de la Escuela Técnica Superior de Ingenieros Informáticos de la Universidad Politécnica de Madrid y profesionales. La tarea experimental consistió en realizar sesiones de educción de requisitos sobre uno o más dominios de problemas (de carácter conocido y desconocido para los sujetos). Las sesiones de educción se realizaron empleando la entrevista abierta. Finalizada la entrevista, los sujetos reportaron por escrito toda la información adquirida. Resultados: Para dominios desconocidos, la experiencia (entrevistas, requisitos, desarrollo y profesional) del analista no influye en su efectividad. En dominios conocidos, la experiencia en entrevistas (r = 0.34, p-valor = 0.080) y la experiencia en requisitos (r = 0.22, p-valor = 0.279), ejercen un efecto positivo. Esto es, los analistas con más años de experiencia en entrevistas y/o requisitos tienden a alcanzar mejores efectividades. Por el contrario, la experiencia en desarrollo (r = -0.06, p-valor = 0.765) y la experiencia profesional (r = -0.35, p-valor = 0.077), tienden a ejercer un efecto nulo y negativo, respectivamente. En lo que respecta al conocimiento acerca del dominio del problema que poseen los analistas, ejerce un moderado efecto positivo (r=0.31), estadísticamente significativo (p-valor = 0.029) en la efectividad de la actividad de educción. Esto es, los analistas con conocimiento tienden a ser más efectivos en los dominios de problema conocidos. En lo que respecta a la titulación académica, por falta de diversidad en las titulaciones académicas de los sujetos experimentales no es posible alcanzar una conclusión. Hemos podido explorar el efecto de la titulación académica en sólo dos cuasi-experimentos, sin embargo, nuestros resultados arrojan efectos contradictorios (r = 0.694, p-valor = 0.51 y r = -0.266, p-valor = 0.383). Además de las variables estudiadas indicadas anteriormente, hemos confirmado la existencia de variables moderadoras que afectan a la actividad de educción, tales como el entrevistado o la formación. Nuestros datos experimentales confirman que el entrevistado es un factor clave en la actividad de educción. Estadísticamente ejerce una influencia significativa en la efectividad de los analistas (p-valor= 0.000). La diferencia entre entrevistar a uno u otro entrevistado, en unidades naturales, varía entre un 18% - 23% en efectividad. Por otro lado, la formación en requisitos aumenta considerablemente la efectividad de los analistas. Los sujetos que realizaron la educción de requisitos después de recibir una formación específica en requisitos tienden a ser entre un 12% y 20% más efectivos que aquellos que no la recibieron. El efecto es significativo (p-valor = 0.000). Finalmente, hemos observado tres hechos que podrían influir en los resultados de esta investigación. En primer lugar, la efectividad de los analistas es diferencial dependiendo del tipo de elemento del dominio. En dominios conocidos, los analistas con experiencia tienden a adquirir más conceptos que los analistas noveles. En los dominios desconocidos, son los procesos los que se adquieren de forma prominente. En segundo lugar, los analistas llegan a una especie de “techo de cristal” que no les permite adquirir más información. Es decir, el analista sólo reconoce (parte de) los elementos del dominio del problema mencionado. Este hecho se observa tanto en el dominio de problema desconocido como en el conocido, y parece estar relacionado con el modo en que los analistas exploran el dominio del problema. En tercer lugar, aunque los años de experiencia no parecen predecir cuán efectivo será un analista, sí parecen asegurar que un analista con cierta experiencia, en general, tendrá una efectividad mínima que será superior a la efectividad mínima de los analistas con menos experiencia. Conclusiones: Los resultados obtenidos muestran que en dominios desconocidos, la experiencia por sí misma no determina la efectividad de los analistas de requisitos. En dominios conocidos, la efectividad de los analistas se ve influenciada por su experiencia en entrevistas y requisitos, aunque sólo parcialmente. Otras variables influyen en la efectividad de los analistas, como podrían ser las habilidades débiles. El conocimiento del dominio del problema por parte del analista ejerce un efecto positivo en la efectividad de los analistas, e interacciona positivamente con la experiencia incrementando aún más la efectividad de los analistas. Si bien no fue posible obtener conclusiones sólidas respecto al efecto de la titulación académica, si parece claro que la formación específica en requisitos ejerce una importante influencia positiva en la efectividad de los analistas. Finalmente, el analista no es el único factor relevante en la actividad de educción. Los clientes/usuarios (entrevistados) también juegan un rol importante en el proceso de generación de información. ABSTRACT Context: This PhD dissertation addresses requirements elicitation activity. Requirements elicitation is generally acknowledged as one of the most important activities of the requirements process, having a direct impact in the software quality. It is an activity where the communication among stakeholders (analysts, customers, users) is paramount. The analyst’s ability to effectively understand customers/users’ needs represents a critical factor for the success of software development. The literature has focused on studying and comprehending a specific set of personal skills that the analyst must have to perform requirements elicitation effectively. However, few studies have explored those skills from an empirical viewpoint. Goal: This research aims to study the effects of experience, domain knowledge and academic qualifications on the analysts’ effectiveness when performing requirements elicitation, during the first stages of analyst-customer interaction. Research method: We have conducted eight empirical studies, quasi-experiments (four) and controlled experiments (four). 110 experimental subjects participated, including: graduate students with the Escuela Técnica Superior de Ingenieros Informáticos of the Universidad Politécnica de Madrid, as well as researchers and professionals. The experimental tasks consisted in elicitation sessions about one or several problem domains (ignorant and/or aware for the subjects). Elicitation sessions were conducted using unstructured interviews. After each interview, the subjects reported in written all collected information. Results: In ignorant domains, the analyst’s experience (interviews, requirements, development and professional) does not influence her effectiveness. In aware domains, interviewing experience (r = 0.34, p-value = 0.080) and requirements experience (r = 0.22, p-value = 0.279), make a positive effect, i.e.: the analysts with more years of interviewing/requirements experience tend to achieve higher effectiveness. On the other hand, development experience (r = -0.06, p-value = 0.765) and professional experience (r = -0.35, p-value = 0.077) tend to make a null and negative effect, respectively. On what regards the analyst’s problem domain knowledge, it makes a modest positive effect (r=0.31), statistically significant (p-value = 0.029) on the effectiveness of the elicitation activity, i.e.: the analysts with tend to be more effective in problem domains they are aware of. On what regards academic qualification, due to the lack of diversity in the subjects’ academic degrees, we cannot come to a conclusion. We have been able to explore the effect of academic qualifications in two experiments; however, our results show opposed effects (r = 0.694, p-value = 0.51 y r = -0.266, p-value = 0.383). Besides the variables mentioned above, we have confirmed the existence of moderator variables influencing the elicitation activity, such as the interviewee and the training. Our data confirm that the interviewee is a key factor in the elicitation activity; it makes statistically significant effect on analysts’ effectiveness (p-value = 0.000). Interviewing one or another interviewee represents a difference in effectiveness of 18% - 23%, in natural units. On the other hand, requirements training increases to a large extent the analysts’ effectiveness. Those subjects who performed requirements elicitation after specific training tend to be 12% - 20% more effective than those who did not receive training. The effect is statistically significant (p-value = 0.000). Finally, we have observed three phenomena that could have an influence on the results of this research. First, the analysts’ effectiveness differs depending on domain element types. In aware domains, experienced analysts tend to capture more concepts than novices. In ignorant domains, processes are identified more frequently. Second, analysts get to a “glass ceiling” that prevents them to acquire more information, i.e.: analysts only identify (part of) the elements of the problem domain. This fact can be observed in both the ignorant and aware domains. Third, experience years do not look like a good predictor of how effective an analyst will be; however, they seem to guarantee that an analyst with some experience years will have a higher minimum effectiveness than the minimum effectiveness of analysts with fewer experience years. Conclusions: Our results point out that experience alone does not explain analysts’ effectiveness in ignorant domains. In aware domains, analysts’ effectiveness is influenced the experience in interviews and requirements, albeit partially. Other variables influence analysts’ effectiveness, e.g.: soft skills. The analysts’ problem domain knowledge makes a positive effect in analysts’ effectiveness; it positively interacts with the experience, increasing even further analysts’ effectiveness. Although we could not obtain solid conclusions on the effect of the academic qualifications, it is plain clear that specific requirements training makes a rather positive effect on analysts’ effectiveness. Finally, the analyst is not the only relevant factor in the elicitation activity. The customers/users (interviewees) play also an important role in the information generation process.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Identification of thermophilic species by the amino acid compositions deduced from their genomes

The global amino acid compositions as deduced from the complete genomic sequences of six thermophilic archaea, two thermophilic bacteria, 17 mesophilic bacteria and two eukaryotic species were analysed by hierarchical clustering and principal components analysis. Both methods showed an influence of several factors on amino acid composition. Although GC content has a dominant effect, thermophilic species can be identified by their global amino acid compositions alone. This study presents a careful statistical analysis of factors that affect amino acid composition and also yielded specific features of the average amino acid composition of thermophilic species. Moreover, we introduce the first example of a ‘compositional tree’ of species that takes into account not only homologous proteins, but also proteins unique to particular species. We expect this simple yet novel approach to be a useful additional tool for the study of phylogeny at the genome level.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Spatial Distribution of Marine Epibenthic Communities in the Hola Trough (Vesterålen, Northern Norway)

The Lofoten-Vesterålen marine shelf is one of the most geologically diverse coast and offshore margin areas in Norway. This leads to huge heterogeneity in marine environments, and often high biodiversity. However, little is known yet about the benthic communities in this region. Within the ARCTOS LoVe MarineEco project the epibenthic communities of the Hola trough (Vesterålen) are analysed to give a first description of their spatial distribution. In this trough both a complex hydrodynamic system and varied topographic submarine elements occur. Trawling samples were collected for two different approaches: one in a meso-scale and another in a small-scale. For the broad scale a transect consisting in three stations was developed, while for the fine scale a small area on a sand wave field, consisting in five stations called HolaBox, was sampled. All organisms were intended to be identified to species level and colonial fauna was discarded for the analysis. Different diversity indexes were assessed (Shannon index (H’) and Pielou’s eveness (J’)). Clustering and nMDS analyses identified four statistically significant groups in terms of abundance (ind./100m2). A total amount of 211 different taxa were found within all stations. The more outer part of the transect (close to the shelf edge) presented a huge abundance of organisms and was dominated by the hemi sessile tube-builder polychaetes Nothria conchylega and Eunice dubitata and the sea urchin Gacilechinus acutus, while the more inner parts presented less abundance of individuals. Probably some upwelling produced by the Norwegian Atlantic Current (NWAC) is influencing the shelf edge increasing the primary production and, therefore, enriching the seafloor in this region. The sand wave field presented two different groups with few amount of individuals. Small-scale variability could be produced by the high heterogeneity within the different types of sand waves, while the scarce abundance of animals can be produced by the permanent changing environment that movable sand waves produce. Here more active and mobile fauna was found such as brittle stars and hermit crabs (among others). Finally, a fourth group was found in the most inner station of the transect, laying on a ridge in the central part of the trough. This station, with coarse substrate, was mainly dominated again by brittle stars and sea urchins. We can conclude that this is a really heterogeneous trough in environments and therefore in communities (even in a local scale). More detailed studies that focus in the local environmental drivers have to be carried out to get an integrated understanding of the structure of benthic communities in this system.

Recent developments in the synthesis of novel xanthone-1,2,3-triazole dyads

The development of multi-target drugs for treating complex multifactorial diseases constitutes an active research ield. This kind of drugs has gained much importance as alternative strategy to combination therapy (“cocktail drugs”).1 A common way to design them brings together two different pharmacophores in one single molecule (so-called dyads). Following this idea and being aware that xanthones2 and 1,2,3-triazoles3 possess important pharmacological properties, we combined these two heterocycles in one molecule to create new dyads with improved therapeutic potential. In this work, new xanthone-1,2,3-triazole dyads were prepared from novel (E)-2-(4-arylbut-1-en-3-yn-1-yl)chromones by two different approaches to evaluate their eficiency and sustainability. Both methodologies involved Diels-Alder reactions to build the xanthone core, which were optimized using microwave irradiation as alternative heating method, and 1,3-dipolar cycloadditions to insert the 1,2,3-triazole moiety (Figure 1).4 All final and intermediate compounds were fully characterized by 1D and 2D NMR techniques.

Topological side-chain classification of beta-turns: Ideal motifs for peptidomimetic development

beta-turns are important topological motifs for biological recognition of proteins and peptides. Organic molecules that sample the side chain positions of beta-turns have shown broad binding capacity to multiple different receptors, for example benzodiazepines. beta-turns have traditionally been classified into various types based on the backbone dihedral angles (phi 2, psi 2, phi 3 and psi 3). Indeed, 57-68% of beta-turns are currently classified into 8 different backbone families (Type I, Type II, Type I', Type II', Type VIII, Type VIa1, Type VIa2 and Type VIb and Type IV which represents unclassified beta-turns). Although this classification of beta-turns has been useful, the resulting beta-turn types are not ideal for the design of beta-turn mimetics as they do not reflect topological features of the recognition elements, the side chains. To overcome this, we have extracted beta-turns from a data set of non-homologous and high-resolution protein crystal structures. The side chain positions, as defined by C-alpha-C-beta vectors, of these turns have been clustered using the kth nearest neighbor clustering and filtered nearest centroid sorting algorithms. Nine clusters were obtained that cluster 90% of the data, and the average intra-cluster RMSD of the four C-alpha-C-beta vectors is 0.36. The nine clusters therefore represent the topology of the side chain scaffold architecture of the vast majority of beta-turns. The mean structures of the nine clusters are useful for the development of beta-turn mimetics and as biological descriptors for focusing combinatorial chemistry towards biologically relevant topological space.

Context obfuscation for privacy via ontological descriptions

Context information is used by pervasive networking and context-aware programs to adapt intelligently to different environments and user tasks. As the context information is potentially sensitive, it is often necessary to provide privacy protection mechanisms for users. These mechanisms are intended to prevent breaches of user privacy through unauthorised context disclosure. To be effective, such mechanisms should not only support user specified context disclosure rules, but also the disclosure of context at different granularities. In this paper we describe a new obfuscation mechanism that can adjust the granularity of different types of context information to meet disclosure requirements stated by the owner of the context information. These requirements are specified using a preference model we developed previously and have since extended to provide granularity control. The obfuscation process is supported by our novel use of ontological descriptions that capture the granularity relationship between instances of an object type.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Finding natural groups in data:an application to strategic group research

The best way of finding “natural groups” in management research remains subject to debate and within the literature there is no accepted consensus. The principle motivation behind this study is to explore the effect of choices of method upon strategic group research, an area that has suffered enduring criticism, as we believe that these method choices are still not fully exploited. Our study is novel in the use of a variety of more robust clustering and validation techniques, rarely used in management research, some borrowed from the natural sciences, which may provide a useful and more robust base for this type of research. Our results confirm that methods do exist to address the concerns over strategic group research and adoption of our chosen methods will improve the quality of management research.

Capacity improvements for a direct-sequence code division multiple access network

The rapidly increasing demand for cellular telephony is placing greater demand on the limited bandwidth resources available. This research is concerned with techniques which enhance the capacity of a Direct-Sequence Code-Division-Multiple-Access (DS-CDMA) mobile telephone network. The capacity of both Private Mobile Radio (PMR) and cellular networks are derived and the many techniques which are currently available are reviewed. Areas which may be further investigated are identified. One technique which is developed is the sectorisation of a cell into toroidal rings. This is shown to provide an increased system capacity when the cell is split into these concentric rings and this is compared with cell clustering and other sectorisation schemes. Another technique for increasing the capacity is achieved by adding to the amount of inherent randomness within the transmitted signal so that the system is better able to extract the wanted signal. A system model has been produced for a cellular DS-CDMA network and the results are presented for two possible strategies. One of these strategies is the variation of the chip duration over a signal bit period. Several different variation functions are tried and a sinusoidal function is shown to provide the greatest increase in the maximum number of system users for any given signal-to-noise ratio. The other strategy considered is the use of additive amplitude modulation together with data/chip phase-shift-keying. The amplitude variations are determined by a sparse code so that the average system power is held near its nominal level. This strategy is shown to provide no further capacity since the system is sensitive to amplitude variations. When both strategies are employed, however, the sensitivity to amplitude variations is shown to reduce, thus indicating that the first strategy both increases the capacity and the ability to handle fluctuations in the received signal power.