4,274 research outputs found
A Framework for Controlling Quality of Sessions in Multimedia Systems
Collaborative multimedia systems demand overall session quality control beyond the level of quality of service (QoS) pertaining to individual connections in isolation of others. At every instant in time, the quality of the session depends on the actual QoS offered by the system to each of the application streams, as well as on the relative priorities of these streams according to the application semantics. We introduce a framework for achieving QoSess control and address the architectural issues involved in designing a QoSess control laver that realizes the proposed framework. In addition, we detail our contributions for two main components of the QoSess control layer. The first component is a scalable and robust feedback protocol, which allows for determining the worst case state among a group of receivers of a stream. This mechanism is used for controlling the transmission rates of multimedia sources in both cases of layered and single-rate multicast streams. The second component is a set of inter-stream adaptation algorithms that dynamically control the bandwidth shares of the streams belonging to a session. Additionally, in order to ensure stability and responsiveness in the inter-stream adaptation process, several measures are taken, including devising a domain rate control protocol. The performance of the proposed mechanisms is analyzed and their advantages are demonstrated by simulation and experimental results
Research on network anycast
Anycast is defined as a service in IPv6, which provides stateless best effort delivery of an anycast datagram to at least one, and preferably only one host. It is a topic of increasing interest. This paper is an attempt to gather and report on the work done on anycast. There are two main categories at present: network-layer anycast and application-layer anycast. Both involve anycast architectures, routing algorithms, metrics, applications, etc. We also present an efficient algorithm for application-layer anycast, and point out possible research directions based on our research. <br /
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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