This paper investigates the modeling of the personal driving style of various vehicle drivers based on several driving parameters. The purpose of such an endeavor is to classify the drivers according to their risk-proneness within the larger context of increasing traffic safety, which is a major concern worldwide. This information is valuable especially for those involved in fleet management and it can be used to improve and to make safer the driving style of various individuals who serve within that fleet. Equally important, such information could help any driver to see the danger within his or her driving style. Cluster and principal component analyses from exploratory statistics have been used to identify and explain drivers grouping according to their driving behavior. The driving parameters (behavioral indices) are collected from urban traffic by an in-house developed GPS-based device and sent to a data server for analysis.

No abstract

No abstract

No abstract

Abstract. More than a decade has passed since the start of the MIT OCW initiative, which, along with other similar projects, has been expected to change dramatically the educational paradigms worldwide. However, better findability is still expected for open educational resources and open courseware, so online guidance and services that support users to locate the appropriate such resources are most welcome. Recommender systems have a very valuable role in this direction. We propose here a hybrid architecture that combines enhanced casebased recommending (driven by a quality model tenet) with (collaborative) feedback from users to recommend open courseware and educational resources. IntroductionMore than a decade has passed since the start of the MIT OCW initiative, which, along with other similar projects, has been expected to change dramatically the educational paradigms worldwide. However, despite the huge opportunities offered by open education, traditional textbooks and readings, and intranet educational resources are still here, dominating the majority of teaching and learning venues of Higher Education institutions even though all students are effectively online. Greater adoption of OERs both within formal and informal education seems to be impeded by four issues: discoverability, quality assurance, bridging the last mile, and acquisition [1]. Modern search engines generally do an ill job when searching for educational content because they are not tailored with this purpose, focusing mainly on content and metadata, and, moreover, they lack what it takes to locate the proper educational resource that is suited for a specific user's goal, that builds up on her prerequisites (for example, learner's previous knowledge), and that provide for making the next step towards her goal (e. g. mastering of a certain concept). For the time being, there is no quality assurance mechanism that could provide support for (1) learners and instructors in their quest for reaching the most appropriate educational resources for their specific educational needs in any particular context, neither for (2) faculty or institutions that are or want to become involved in this movement, and they may be concerned about the challenges or interested in the gains of this process, nor for (3) developers who need guidelines for designing and building such educational resources, nor for (4) educational resources' evaluators [2,3,4]. In many OCW/OER repositories educational content exists only immersed in context and without a significant effort this content cannot be both sorted out from its initial environment (becoming truly reusable and remixable) and entangled within a new educational context, bridging the last mile. Acquisition is also difficult, taking into account all the fears of OCW/OERs providers (faculty, teachers, educational resources designers etc.): lack of credit, of copyright control over derivative works, and so on. Therefore, better findability is expected for open courseware and OERs, so online guidance and services that su...