211,979 research outputs found
Domain-Aware Session Types
We develop a generalization of existing Curry-Howard interpretations of (binary) session types by relying on an extension of linear logic with features from hybrid logic, in particular modal worlds that indicate domains. These worlds govern domain migration, subject to a parametric accessibility relation familiar from the Kripke semantics of modal logic. The result is an expressive new typed process framework for domain-aware, message-passing concurrency. Its logical foundations ensure that well-typed processes enjoy session fidelity, global progress, and termination. Typing also ensures that processes only communicate with accessible domains and so respect the accessibility relation.
Remarkably, our domain-aware framework can specify scenarios in which domain information is available only at runtime; flexible accessibility relations can be cleanly defined and statically enforced. As a specific application, we introduce domain-aware multiparty session types, in which global protocols can express arbitrarily nested sub-protocols via domain migration. We develop a precise analysis of these multiparty protocols by reduction to our binary domain-aware framework: complex domain-aware protocols can be reasoned about at the right level of abstraction, ensuring also the principled transfer of key correctness properties from the binary to the multiparty setting
Toward a Robust Diversity-Based Model to Detect Changes of Context
Being able to automatically and quickly understand the user context during a
session is a main issue for recommender systems. As a first step toward
achieving that goal, we propose a model that observes in real time the
diversity brought by each item relatively to a short sequence of consultations,
corresponding to the recent user history. Our model has a complexity in
constant time, and is generic since it can apply to any type of items within an
online service (e.g. profiles, products, music tracks) and any application
domain (e-commerce, social network, music streaming), as long as we have
partial item descriptions. The observation of the diversity level over time
allows us to detect implicit changes. In the long term, we plan to characterize
the context, i.e. to find common features among a contiguous sub-sequence of
items between two changes of context determined by our model. This will allow
us to make context-aware and privacy-preserving recommendations, to explain
them to users. As this is an ongoing research, the first step consists here in
studying the robustness of our model while detecting changes of context. In
order to do so, we use a music corpus of 100 users and more than 210,000
consultations (number of songs played in the global history). We validate the
relevancy of our detections by finding connections between changes of context
and events, such as ends of session. Of course, these events are a subset of
the possible changes of context, since there might be several contexts within a
session. We altered the quality of our corpus in several manners, so as to test
the performances of our model when confronted with sparsity and different types
of items. The results show that our model is robust and constitutes a promising
approach.Comment: 27th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI 2015), Nov 2015, Vietri sul Mare, Ital
Mobility Management in beyond 3G-Environments
Beyond 3G-environments are typically defined as environments that integrate different wireless and fixed access network technologies. In this paper, we address IP based Mobility Management (MM) in beyond 3G-environments with a focus on wireless access networks, motivated by the current trend of WiFi, GPRS, and UMTS networks. The GPRS and UMTS networks provide countrywide network access, while the WiFi networks provide network access in local areas such as city centres and airports. As a result, mobile end-users can be always on-line and connected to their preferred network(s), these network preferences are typically stored in a user profile. For example, an end-user who wishes to be connected with highest bandwidth could be connected to a WiFi network when available and fall back to GPRS when moving outside the hotspot area.\ud
In this paper, we consider a combination of MM for legacy services (like web browsing, telnet, etc.) using Mobile IP and multimedia services using SIP. We assume that the end-user makes use of multi-interface terminals with the capability of selecting one or more types of access networks\ud
based on preferences. For multimedia sessions, like VoIP or streaming video, we distinguish between changes in network access when the end-user is in a session or not in a session. If the end-user is not in a session, he or she needs to be able to start new sessions and receive invitations for new sessions. If the end-user is in a session, the session needs to be handed over to the new access network as seamless as possible from the perspective of the end-user. We propose an integrated but flexible solution to these problems that facilitates MM with a customizable transparency to applications and end-users
RMD-QOSM: The NSIS Quality-of-Service Model for Resource Management in Diffserv
This document describes a Next Steps in Signaling (NSIS) Quality-of- Service (QoS) Model for networks that use the Resource Management in Diffserv (RMD) concept. RMD is a technique for adding admission control and preemption function to Differentiated Services (Diffserv) networks. The RMD QoS Model allows devices external to the RMD network to signal reservation requests to Edge nodes in the RMD network. The RMD Ingress Edge nodes classify the incoming flows into traffic classes and signals resource requests for the corresponding traffic class along the data path to the Egress Edge nodes for each flow. Egress nodes reconstitute the original requests and continue forwarding them along the data path towards the final destination. In addition, RMD defines notification functions to indicate overload situations within the domain to the Edge nodes
A personal networking solution
This paper presents an overview of research being conducted on Personal Networking Solutions within the Mobile VCE Personal Distributed Environment Work Area. In particular it attempts to highlight areas of commonality with the MAGNET initiative. These areas include trust of foreign devices and service providers, dynamic real-time service negotiation to permit context-aware service delivery, an automated controller algorithm for wireless ad hoc networks, and routing protocols for ad hoc networking environments. Where possible references are provided to Mobile VCE publications to enable further reading
Wireless internet architecture and testbed for wineglass
One of the most challenging issues in the area of mobile communication is the deployment of IPbased
wireless multimedia networks in public and business environments. The public branch may involve public
mobile networks, like UMTS as 3G system, while the business branch introduces local radio access networks by
means of W-LANs. Conventional mobile networks realise mobile specific functionality, e.g. mobility management
or authentication and accounting, by implementing appropriate mechanisms in specific switching nodes (e.g.
SGSN in GPRS). In order to exploit the full potential of IP networking solutions a replacement of these
mechanisms by IP-based solutions might be appropriate. In addition current and innovative future services in
mobile environments require at least soft-guaranteed, differentiated QoS. Therefore the WINE GLASS project
investigates and implements enhanced IP-based techniques supporting mobility and QoS in a wireless Internet
architecture. As a means to verify the applicability of the implemented solutions, location-aware services
deploying both IP-mobility and QoS mechanisms will be implemented and demonstratedPeer ReviewedPostprint (published version
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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