Graça Bressan

The impact of video-quality-level switching on user quality of experience in dynamic adaptive streaming over HTTP

Dynamic adaptive streaming over HTTP (DASH) has become a promising solution for video delivery services over the Internet in the last few years. Currently, several video content providers use the DASH solution to improve the users’ quality of experience (QoE) by automatically switching video quality levels (VQLs) according to the network status. However, the frequency of switching events between different VQLs during a video streaming session may disturb the user’s visual attention and therefore affect the user’s QoE. As one of the first attempts to characterize the impact of VQL switching on the user’s QoE, we carried out a series of subjective tests, which show that there is a correlation between the user QoE and the frequency, type, and temporal location of the switching events. We propose a novel parameter named switching degradation factor (SDF) to capture such correlation. A DASH algorithm with SDF parameter is compared with the same algorithm without SDF. The results demonstrate that the SDF parameter significantly improves the user’s QoE, especially when network conditions vary frequently.

Classification of learning profile based on categories of student preferences

In an environment applied in engineering teaching, as in many knowledge areas, is very important to know and understand learner differences in a way to be able to adapt systempsilas actions to studentpsilas best learning conditions and aptitudes. Working thus makes it possible to identify learning profiles within a group of students, allowing the system to supply learners with contents and tools more suited for them. The goal of this work is to present the architecture of a system that realizes an evaluation of learning profiles based on categories of student preferences. The categories are defined from Felder-Silverman Learning Style Model. The architecture enables the teacher to specify the observable characteristics he considers most suitable within the teaching scope in question, whose characteristics are related with categories of student preferences. Through the categories create a relationship between what is observed and the learning objects used to build automatically the learning scenarios according to the student learning profile.

A Continuous Authentication System Based on User Behavior Analysis

This paper presents a continuous authentication system based on user behavior analysis that makes use of environmental context information, users’ behavior analysis and Neuro-Fuzzy Logic. This system must be able to acquire information in context, making them into a computational environment. This information is the basis of user behavior. The System, based on the evidences of the behavior, establishes if it can trust the user or not. According to the user behavior, levels of trust are released, to access the application software. Weights are attributed in the fuzzyfication process, according to the rules that were previously established for the parameters which help to establish the evidences of behavioral trust, in its different degrees. The neuro-fuzzy logic allows that the user behavioral database be continuously updated, interacting with the fuzzyfication mechanism, so as to keep trust levels updated according to the user behavior, in a more accurate and faithful way.

Personal TVware: An Infrastructure to Support the Context-Aware Recommendation for Personalized Digital TV

 Abstract—The coming of the Digital TV will bring a significant increase in the number TV programs offered by TV operators. Consequently, the user are facing it difficulty to find out the most interesting TV programs among the various options available. In this new scenario, the recommender systems stand out as a possible solution to the information overload problem. However, the current approaches to recommend content for Digital TV rarely considers the context during the recommendation process. Thus, this paper presents a software infrastructure - entitled PersonalTVware - to support context-aware recommendation of TV programs. To validate the PersonalTVware, a context-aware recommender system was implemented as a concept proof. In order to evaluate the quality of the recommendation, a number of experiments have been conducted. The results indicate that consider both users profile and context can provide better recommendations.

Video Quality Assessments on Digital TV and Video Streaming services using Objective Metrics

There is a wide range of telecommunications services that transmit voice, video and data through complex transmission networks and in some cases, the service has not an acceptable quality level for the end user. In this sense the study of methods for assessing video quality and voice have a very important role. This paper presents a classification scheme, based on different criteria, of the methods and metrics that are being studied in recent years. This paper presents how the video quality is affected by degradation in the transmission channel in two kinds of services: Digital TV (ISDB-TB) due the fading in the air interface and video streaming service on an IP network due packet loss. For Digital TV tests was set up a scenario where the digital TV transmitter is connected to an RF channel emulator, where are inserted different fading models and at the end, the videos are saved in a mobile device. The tests of streaming video were performed in an isolated scenario of IP network, which are scheduled several network conditions, resulting in different qualities of video reception. The video quality assessment is performed using objective assessment methods: PSNR, SSIM and VQM. The results show how the losses in the transmission channel affects the quality of end-user experience on both services studied.

I2TS01 - A Taxonomy for Locality Algorithms on Peer-to-Peer Networks

The continuous growth of peer-to-peer networks has made them responsible for a considerable portion of the current Internet traffic. For this reason, improvements in P2P network resources usage are of central importance. One effective approach for addressing this issue is the deployment of locality algorithms, which allow the system to optimize the peers selection policy for different network situations and, thus, maximize performance. To date, several locality algorithms have been proposed for use in P2P networks. However, they usually adopt heterogeneous criteria for measuring the proximity between peers, which hinders a coherent comparison between the different solutions. In this paper, we develop a thoroughly review of popular locality algorithms, based on three main characteristics: the adopted network architecture, distance metric, and resulting peer selection algorithm. As result of this study, we propose a novel and generic taxonomy for locality algorithms in peer-to-peer networks, aiming to enable a better and more coherent evaluation of any individual locality algorithm.

Learning Profile Identification Based on the Analysis of the User's Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve a efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analized in different dimensions allows the system to deal with the different focus of the learning.

OCP: A protocol for secure communication in federated content networks

Abstract Content Distribution Networks (CDNs) are networks that make intense use of caching and multicast overlay techniques for transmitting video and other stream media, a type of traffic that has been growing tremendously in the last years. To cope with this increasing demand, several telecommunications companies have been associated to create federated CDNs (FCDNs), that involves many different providers and, hence, distinct domains. Although beneficial in terms of increased capillarity and scalability of service delivery, the interaction between FCDN elements from different providers brings many new challenges. Among them, one that has received little attention so far refers to security, an essential service for preventing the misuse of the FCDN resources. Aiming to tackle this issue, this paper presents the Overlay Communication Protocol (OCP), a security mechanism that allows the secure signaling communication in FCDNs. OCP takes into account all elements involved in the content delivery process, addressing Route Forgery attacks and concealing the network structure from potential attackers. Together with the protocol description and security analysis, we also present experimental results on its implementation, showing that it introduces little impact on the overall network performance.

An approach to design the student interaction based on the recommendation of e-learning objects

In the last years, the adoption of recommender systems for improving user interaction has increased in e-learning applications. In the educational area, the recommendation of relevant and interesting content can attract the students attention, motivating her/him during the learning-teaching process. It is very important, thus, to know learner preferences to suggest suitable contents to the students. The goal of this work is to present an approach to design the student interaction based on the recommendation of e-learning content, determining a more suitable relationship between learning objects and learning profiles. In our proposal, the learning profile is split into categories to attend different student preferences during the teaching-learning process: perception, presentation-format and participation. Our recommendation uses these categories to filter out the most suitable learning objects organized according to the IEEE LOM standard. We present a prototype architecture named e-LORS, over which we perform demonstrative experiments.

CollaboraTVware: uma proposta de infra-estrutura ciente de contexto para suporte a participaçao colaborativa no cenário da TV digital interativa

The growing of the number and diversity of programs available to users, as well as the interactive services provided, resulting from the emergence of the Interactive Digital TV increases the difficulty of selecting relevant content. In this scenario, it is important the user participation in a collaborative. Thus, this paper presents a software infrastructure in an Interactive Digital Television environment - entitled CollaboraTVware - to guide, in a transparent way, users in the choice of programs and interactive services through the collaborative participation of other users with similar profile and context. The classification task, from the theory of data mining, is the approach adopted in the infrastructure design. To demonstrate the functionalities in a use scenario was developed an application (collaborative EPG) as a case study which uses the CollaboraTVware.