43,334 research outputs found
Bringing Coq Into the World of GCM Distributed Applications
International audienceAmong all programming paradigms, component-based engineering stands as one of the most followed approaches for real world software devel- opment. Its emphasis on clean separation of concerns and reusability makes it appealing for both industrial and research purposes. The Grid Component Model (GCM) endorses this approach in the con- text of distributed systems by providing all the means to define, compose and dynamically reconfigure component-based applications. While structural re- configuration is one of the key features of GCM applications, this ability to evolve at runtime poses several challenges w.r.t reliability. In this paper we present Mefresa, a framework for reasoning on the struc- ture of GCM applications. This contribution comes in the form of a formal specification mechanized in the Coq Proof Assistant. Our aim is to demon- strate the benefits of interactive theorem proving for the reasoning on software architectures. We provide a configuration and reconfiguration language for the safe instantiation of distributed systems
Open Programming Language Interpreters
Context: This paper presents the concept of open programming language
interpreters and the implementation of a framework-level metaobject protocol
(MOP) to support them. Inquiry: We address the problem of dynamic interpreter
adaptation to tailor the interpreter's behavior on the task to be solved and to
introduce new features to fulfill unforeseen requirements. Many languages
provide a MOP that to some degree supports reflection. However, MOPs are
typically language-specific, their reflective functionality is often
restricted, and the adaptation and application logic are often mixed which
hardens the understanding and maintenance of the source code. Our system
overcomes these limitations. Approach: We designed and implemented a system to
support open programming language interpreters. The prototype implementation is
integrated in the Neverlang framework. The system exposes the structure,
behavior and the runtime state of any Neverlang-based interpreter with the
ability to modify it. Knowledge: Our system provides a complete control over
interpreter's structure, behavior and its runtime state. The approach is
applicable to every Neverlang-based interpreter. Adaptation code can
potentially be reused across different language implementations. Grounding:
Having a prototype implementation we focused on feasibility evaluation. The
paper shows that our approach well addresses problems commonly found in the
research literature. We have a demonstrative video and examples that illustrate
our approach on dynamic software adaptation, aspect-oriented programming,
debugging and context-aware interpreters. Importance: To our knowledge, our
paper presents the first reflective approach targeting a general framework for
language development. Our system provides full reflective support for free to
any Neverlang-based interpreter. We are not aware of any prior application of
open implementations to programming language interpreters in the sense defined
in this paper. Rather than substituting other approaches, we believe our system
can be used as a complementary technique in situations where other approaches
present serious limitations
On the Fly Orchestration of Unikernels: Tuning and Performance Evaluation of Virtual Infrastructure Managers
Network operators are facing significant challenges meeting the demand for
more bandwidth, agile infrastructures, innovative services, while keeping costs
low. Network Functions Virtualization (NFV) and Cloud Computing are emerging as
key trends of 5G network architectures, providing flexibility, fast
instantiation times, support of Commercial Off The Shelf hardware and
significant cost savings. NFV leverages Cloud Computing principles to move the
data-plane network functions from expensive, closed and proprietary hardware to
the so-called Virtual Network Functions (VNFs). In this paper we deal with the
management of virtual computing resources (Unikernels) for the execution of
VNFs. This functionality is performed by the Virtual Infrastructure Manager
(VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. We
discuss the instantiation process of virtual resources and propose a generic
reference model, starting from the analysis of three open source VIMs, namely
OpenStack, Nomad and OpenVIM. We improve the aforementioned VIMs introducing
the support for special-purpose Unikernels and aiming at reducing the duration
of the instantiation process. We evaluate some performance aspects of the VIMs,
considering both stock and tuned versions. The VIM extensions and performance
evaluation tools are available under a liberal open source licence
A Plausibility Semantics for Abstract Argumentation Frameworks
We propose and investigate a simple ranking-measure-based extension semantics
for abstract argumentation frameworks based on their generic instantiation by
default knowledge bases and the ranking construction semantics for default
reasoning. In this context, we consider the path from structured to logical to
shallow semantic instantiations. The resulting well-justified JZ-extension
semantics diverges from more traditional approaches.Comment: Proceedings of the 15th International Workshop on Non-Monotonic
Reasoning (NMR 2014). This is an improved and extended version of the
author's ECSQARU 2013 pape
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
QueRIE: Collaborative Database Exploration
Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach
UML-F: A Modeling Language for Object-Oriented Frameworks
The paper presents the essential features of a new member of the UML language
family that supports working with object-oriented frameworks. This UML
extension, called UML-F, allows the explicit representation of framework
variation points. The paper discusses some of the relevant aspects of UML-F,
which is based on standard UML extension mechanisms. A case study shows how it
can be used to assist framework development. A discussion of additional tools
for automating framework implementation and instantiation rounds out the paper.Comment: 22 pages, 10 figure
- …