INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Using Analytical Models to Bootstrap Machine Learning Performance Predictors

Abstract: Performance modeling is a crucial technique to enable the vision of elastic computing in cloud environments. Conventional approaches to performance modeling rely on two antithetic methodologies: white box modeling, which exploits knowledge on system's internals and capture its dynamics using analytical approaches, and black box techniques, which infer relations among the input and output variables of a system based on the evidences gathered during an initial training phase. In this paper we investigate a technique, which we name Bootstrapping, which aims at reconciling these two methodologies and at compensating the cons of the one with the pros of the other. We analyze the design space of this gray box modeling technique, and identify a number of algorithmic and parametric trade-offs which we evaluate via two realistic case studies, a Key-Value Store and a Total Order Broadcast service.

Measurements As First-class Artifacts

Abstract: The emergence of programmable switches has sparked a significant amount of work on new techniques to perform more powerful measurement tasks, for instance, to obtain fine-grained traffic and performance statistics. Previous work has focused on the efficiency of these measurements alone and has neglected flexibility, resulting in solutions that are hard to reuse or repurpose and that often overlap in functionality or goals.In this paper, we propose the use of a set of reusable primitive building blocks that can be composed to express measurement tasks in a concise and simple way. We describe the rationale for the design of our primitives, that we have named MAFIA (Measurements As FIrst-class Artifacts), and using several examples we illustrate how they can be combined to realize a comprehensive range of network measurement tasks. Writing MAFIA code does not require expert knowledge of low-level switch architecture details. Using a prototype implementation of MAFIA, we demonstrate the applicability of our approach and show that the use of our primitives results in compiled code that is comparable in size and resource usage with manually written specialized P4 code, and can be run in current hardware.

A Cryptographic Coarse Grain Reconfigurable Architecture Robust Against DPA

Abstract: This work addresses the problem of information leakage of cryptographic devices, by using the reconfiguration technique allied to an RNS based arithmetic. The information leaked by circuits, like power consumption, electromagnetic emissions and time to compute may be used to find cryptographic secrets. The results issue of prototyping shows that our coarse grained reconfigurable architecture is robust against power analysis attacks.

Co-presence, collaboration, and control in environmental studies: A Second Life-based approach

Abstract: In this paper, we describe a framework for synchronous collaborative visualization and remote control in the agricultural domain. The framework builds on “Second Life” (SL), a popular networked online 3D virtual world, where users are represented as avatars (graphical self-representations). Co-presence in SL takes the form of instant (real-time) two-way interaction among two or more avatars. The aim of our work is to facilitate co-presence for sharing knowledge and exchanging wisdom about environmental practices. In order to establish a realistic simulated context for communication in SL, virtual counterparts of real devices are created in the virtual world. Specifically, we aim to represent field servers that sense and monitor fields such as rice paddies and vineyards. The Twin-World Mediator (TWM) is developed in order to replicate the behavior of real devices in virtual counterparts, and to facilitate seamless communication between real and virtual world. The TWM is an easy-to-use, extensible, and flexible communication framework. A small study demonstrated how the TWM can support collaboration and experience sharing in the agricultural domain.

Learning bayesian networks consistent with the optimal branching

Abstract: We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branching, that we call consistent k-graphs (CkG). The optimal branching is used as an heuristic for a primary causality order between network variables, which is subsequently refined, according to a certain score, into an optimal CkG Bayesian network. This approach augments the search space exponentially, in the number of nodes, relatively to trees, yet keeping a polynomial-time bound. The proposed algorithm can be applied to scores that decompose over the network structure, such as the well known LL, MDL, AIC, BIC, K2, BD, BDe, BDeu and MIT scores. We tested the proposed algorithm in a classification task. We show that the induced classifier always score better than or the same as the Naive Bayes and Tree Augmented Naive Bayes classifiers. Experiments on the UCI repository show that, in many cases, the improved scores translate into increased classification accuracy.