Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA

Attack-Surface Metrics, OSSTMM and Common Criteria Based Approach to “Composable Security” in Complex Systems

In recent studies on Complex Systems and Systems-of-Systems theory, a huge effort has been put to cope with behavioral problems, i.e. the possibility of controlling a desired overall or end-to-end behavior by acting on the individual elements that constitute the system itself. This problem is particularly important in the “SMART” environments, where the huge number of devices, their significant computational capabilities as well as their tight interconnection produce a complex architecture for which it is difficult to predict (and control) a desired behavior; furthermore, if the scenario is allowed to dynamically evolve through the modification of both topology and subsystems composition, then the control problem becomes a real challenge. In this perspective, the purpose of this paper is to cope with a specific class of control problems in complex systems, the “composability of security functionalities”, recently introduced by the European Funded research through the pSHIELD and nSHIELD projects (ARTEMIS-JU programme). In a nutshell, the objective of this research is to define a control framework that, given a target security level for a specific application scenario, is able to i) discover the system elements, ii) quantify the security level of each element as well as its contribution to the security of the overall system, and iii) compute the control action to be applied on such elements to reach the security target. The main innovations proposed by the authors are: i) the definition of a comprehensive methodology to quantify the security of a generic system independently from the technology and the environment and ii) the integration of the derived metrics into a closed-loop scheme that allows real-time control of the system. The solution described in this work moves from the proof-of-concepts performed in the early phase of the pSHIELD research and enrich es it through an innovative metric with a sound foundation, able to potentially cope with any kind of pplication scenarios (railways, automotive, manufacturing, ...)

Towards quality-of-service driven consistency for Big Data management

International audienceWith the advent of Cloud Computing, Big Data management has become a fundamental challenge during the deployment and operation of distributed highly available and fault-tolerant storage systems such as the HBase extensible record-store. These systems can provide support for geo-replication, which comes with the issue of data consistency among distributed sites. In order to offer a best-in-class service to applications, one wants to maximise performance while minimising latency. In terms of data replication, that means incurring in as low latency as possible when moving data between distant data centres. Traditional consistency models introduce a significant problem for systems architects, which is specially important to note in cases where large amounts of data need to be replicated across wide-area networks. In such scenarios it might be suitable to use eventual consistency, and even though not always convenient, latency can be partly reduced and traded for consistency guarantees so that data-transfers do not impact performance. In contrast, this work proposes a broader range of data semantics for consistency while prioritising data at the cost of putting a minimum latency overhead on the rest of non-critical updates. Finally, we show how these semantics can help in finding an optimal data replication strategy for achieving just the required level of data consistency under low latency and a more efficient network bandwidth utilisation

Refinement and extension of the cloud decision support framework for application migration to the cloud

The maturity and dissemination of Cloud Computing across diverse business domains is leading to an increasing amount of migration projects with the goal to leverage the associated benefits for important legacy applications. However, the migration of applications to the cloud is a complex problem that entails various technical and organizational aspects. The existing Cloud Decision Support Framework has been a first step to provide decision makers with the means to find a suitable migration strategy. This master's thesis has refined the framework's underlying knowledge base by reviewing its decision points, decisions and their relations as well as outcomes. Based on this refinement, the framework has been extended by elaborating the relations between outcomes resulting in greater potential for decision support. In order to allow decision makers to derive migration strategies based on the framework in an interactive manner, a web application has been implemented. In a final step, an evaluation has been carried out comprising a validation of the knowledge base and, by means of a use case, a demonstration of the efficacy of the extended decision support framework