Approaches for Future Internet architecture design and Quality of Experience (QoE) Control

Researching a Future Internet capable of overcoming the current Internet limitations is a strategic investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow applications to transparently, efficiently and flexibly exploit the available network resources with the aim to match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision problem

Visualizing recommendations to support exploration, transparency and controllability

Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities - i.e. items bookmarked by users, recommendations and tags. Copyright © 2013 ACM

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

NewsMe: A case study for adaptive news systems with open user model

Adaptive news systems have become important in recent years. A lot of work has been put into developing these adaptation processes. We describe here an adaptive news system application, which uses an open user model and allow users to manipulate their interest profiles. We also present a study of the system. Our results showed that user profile manipulation should be used with caution. © 2007 IEEE

HandyBroker - An intelligent product-brokering agent for M-commerce applications with user preference tracking

One of the potential applications for agent-based systems is m-commerce. A lot of research has been done on making such systems intelligent to personalize their services for users. In most systems, user-supplied keywords are generally used to help generate profiles for users. In this paper, an evolutionary ontology-based product-brokering agent has been designed for m-commerce applications. It uses an evaluation function to represent a user’s preference instead of the usual keyword-based profile. By using genetic algorithms, the agent tracks the user’s preferences for a particular product by tuning some parameters inside its evaluation function. A prototype called “Handy Broker” has been implemented in Java and the results obtained from our experiments looks promising for m-commerce use

Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising

Reference and Gestures in Dialogue Generation: Three Studies with Embodied Conversational Agents

This paper reports on three studies into social presence cues which were carried out in the context of the NECA (Net-environment for Embodied Emotional Conversational Agents) project and the EPOCH network. The first study concerns the generation of referring expressions. We adopted an existing algorithm for generating referring expressions such that it could run according to an egocentric and a neutral strategy. In an evaluation study, we found that the two strategies were correlated with the perceived friendliness of the speaker. In the second and the third study, we evaluated the gestures that were generated by the NECA system. In this paper, we briefly summarize the most salient results of these two studies. They concern the effect of gestures on perceived quality of speech and information retention

Multi-agent quality of experience control

In the framework of the Future Internet, the aim of the Quality of Experience (QoE) Control functionalities is to track the personalized desired QoE level of the applications. The paper proposes to perform such a task by dynamically selecting the most appropriate Classes of Service (among the ones supported by the network), this selection being driven by a novel heuristic Multi-Agent Reinforcement Learning (MARL) algorithm. The paper shows that such an approach offers the opportunity to cope with some practical implementation problems: in particular, it allows to face the so-called “curse of dimensionality” of MARL algorithms, thus achieving satisfactory performance results even in the presence of several hundreds of Agents