Developing Adaptive and Personalized Mobile Applications: A Framework and Design Issues

The rapid growth of mobile technology has expedited ubiquitous information access via handheld devices. However, the fundamental natures of mobile information systems are different from those of desktop applications in terms of purpose of use, device features, communication networks, and working environments. This poses various challenges to mobile information systems on how to deliver and present multimedia content in an effective and adaptive manner. One of the major challenges is to deliver personalized information to the right person in a preferred format based on the changing environment. This paper proposes an innovative framework for developing mobile applications that deliver personalized, context-aware, and adaptive content to mobile users. The framework consists of four major components: information selection, content analysis, media transcoding, and customized presentation. It can be applied to a variety of mobile applications such as mobile web, news alert services, and mobile commerce

Internet of things: why we are not there yet

Twenty-one years past since Weiser’s vision of ubiquitous computing (UbiComp) has been written, and it is yet to be fully fulfilled despite of almost all the needed technologies already available. Still, the widespread interest in UbiComp and the results in some of its fields pose a question: why we are not there yet? It seems we miss the ‘octopus’ head. In this paper, we will try to depict the reasons why we are not there yet, from three different points of view: interaction media, device integration and applications

Making distributed computing infrastructures interoperable and accessible for e-scientists at the level of computational workflows

As distributed computing infrastructures evolve, and as their take up by user communities is growing, the importance of making different types of infrastructures based on a heterogeneous set of middleware interoperable is becoming crucial. This PhD submission, based on twenty scientific publications, presents a unique solution to the challenge of the seamless interoperation of distributed computing infrastructures at the level of workflows. The submission investigates workflow level interoperation inside a particular workflow system (intra-workflow interoperation), and also between different workflow solutions (inter-workflow interoperation). In both cases the interoperation of workflow component execution and the feeding of data into these components workflow components are considered. The invented and developed framework enables the execution of legacy applications and grid jobs and services on multiple grid systems, the feeding of data from heterogeneous file and data storage solutions to these workflow components, and the embedding of non-native workflows to a hosting meta-workflow. Moreover, the solution provides a high level user interface that enables e-scientist end-users to conveniently access the interoperable grid solutions without requiring them to study or understand the technical details of the underlying infrastructure. The candidate has also developed an application porting methodology that enables the systematic porting of applications to interoperable and interconnected grid infrastructures, and facilitates the exploitation of the above technical framework

Classification algorithms for Big Data with applications in the urban security domain

A classification algorithm is a versatile tool, that can serve as a predictor for the future or as an analytical tool to understand the past. Several obstacles prevent classification from scaling to a large Volume, Velocity, Variety or Value. The aim of this thesis is to scale distributed classification algorithms beyond current limits, assess the state-of-practice of Big Data machine learning frameworks and validate the effectiveness of a data science process in improving urban safety. We found in massive datasets with a number of large-domain categorical features a difficult challenge for existing classification algorithms. We propose associative classification as a possible answer, and develop several novel techniques to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. The experiments, run on a real large-scale dataset with more than 4 billion records, confirmed the quality of the approach. To assess the state-of-practice of Big Data machine learning frameworks and streamline the process of integration and fine-tuning of the building blocks, we developed a generic, self-tuning tool to extract knowledge from network traffic measurements. The result is a system that offers human-readable models of the data with minimal user intervention, validated by experiments on large collections of real-world passive network measurements. A good portion of this dissertation is dedicated to the study of a data science process to improve urban safety. First, we shed some light on the feasibility of a system to monitor social messages from a city for emergency relief. We then propose a methodology to mine temporal patterns in social issues, like crimes. Finally, we propose a system to integrate the findings of Data Science on the citizenry’s perception of safety and communicate its results to decision makers in a timely manner. We applied and tested the system in a real Smart City scenario, set in Turin, Italy

Survey of storage systems for high-performance computing

In current supercomputers, storage is typically provided by parallel distributed file systems for hot data and tape archives for cold data. These file systems are often compatible with local file systems due to their use of the POSIX interface and semantics, which eases development and debugging because applications can easily run both on workstations and supercomputers. There is a wide variety of file systems to choose from, each tuned for different use cases and implementing different optimizations. However, the overall application performance is often held back by I/O bottlenecks due to insufficient performance of file systems or I/O libraries for highly parallel workloads. Performance problems are dealt with using novel storage hardware technologies as well as alternative I/O semantics and interfaces. These approaches have to be integrated into the storage stack seamlessly to make them convenient to use. Upcoming storage systems abandon the traditional POSIX interface and semantics in favor of alternative concepts such as object and key-value storage; moreover, they heavily rely on technologies such as NVM and burst buffers to improve performance. Additional tiers of storage hardware will increase the importance of hierarchical storage management. Many of these changes will be disruptive and require application developers to rethink their approaches to data management and I/O. A thorough understanding of today's storage infrastructures, including their strengths and weaknesses, is crucially important for designing and implementing scalable storage systems suitable for demands of exascale computing

Historical Records And Current Status Of Fucales (Cystoseira and Sargassum spp.) In The Gulf of Naples

Fucales are brown macroalgae that play a relevant structural and functional role in marine ecosystems but are experiencing a huge decline in many areas of the Mediterranean Sea. Despite the long tradition of phycological studies in the Gulf of Naples (Italy), a gap of knowledge on their dynamics and current status has been highlighted for the last 60 years in this area. This thesis aimed to provide an overview on long-term changes in the distribution ad abundance of Cystoseira spp. and Sargassum spp. along different sectors of the Gulf of Naples in the bathymetric range between 0 and 50 m depth. Research was focused into three main objectives: 1) to assess the historical decline of Fucales the Gulf of Naples and their putative causes; 2) to map their current distribution; 3) to investigate the role of consumers in their loss. Results highlighted a decline of Cystoseira and Sargassum spp. mainly in the most urbanized areas of the gulf. Seven of the eighteen species recorded in the first half of the 20th century were no longer recorded; for other species the local decline could end up with their extinction if no action will be pursued for their conservation. A detailed georeferenced map (1:2,500) of Fucales has been elaborated for the shallow species; SCUBA surveys were performed for deep algae. Some human actions, such as coastal artificialization, can affect species richness and distribution in shallow sublittoral stands. Another mechanic pressure that can act on deep species is the intense fishing activities performed with fishing gears. Probably the interaction between the physic damage and the increase of water turbidity, related to a high eutrophication, can affect the vitality of deep species and their successful spread. The occurrence of patches of Cystoseira, among very dense and deep stands of Posidonia oceanica meadows in the oligotrophic waters around the island of Capri, seems to confirm this hypothesis. Another interesting result was obtained by a feeding choice experiment. The sea urchin Paracentrotus lividus fed indiscriminately on different macrophytes according to the equal choice behaviour between macrophytes offered alone or together, regardless of differences in total phenolic compounds content and C:N ratio. In conclusion, results infer that the improvement and maintenance of the environmental quality of the Gulf of Naples is a major issue not only for the welfare of the coastal ecosystems, but also for its social and economic spin off