Articles

Assessing the reliability of fog computing for smart mobility applications in VANETs

Pereira J., Ricardo L., Luís M., Senna C., Sargento S.

Future Generation Computer Systems

2019

Abstract:

As a recent computing paradigm, Fog Computing is opening new sets of opportunities by bringing the computational resources, applications and services closer to their consumers, making it a good foundation for the Internet of Things (IoT). As part of the IoT umbrella, Vehicular ad-hoc Networks (VANETs) offer new communication and computing opportunities that traditional paradigms, such as Cloud Computing, are unable to solve, e.g. location-awareness, latency and capacity of detecting and acting in real time to unforeseen events. As a natural computing extension, Fog Computing can be the next requirement of VANETs, enabling the deployment of mobility-related applications and services. In this work we propose a generic architecture for the deployment of Fog Computing applications and services in a VANET environment. Moreover, we provide a proof-of-concept system to perform data analytics in a hybrid VANET/Fog environment. Our system is used by two Fog applications, one for city traffic anomaly detection, and another to estimate the bus time of arrival to feed traveller information. The reliability of such applications are validated through real mobility information from a large vehicular testbed currently deployed. The results show that using Fog Computing with a small set of recent regional data is very suitable for this type of applications, since the estimations of traffic anomalies and bus arrival times are very similar to those provided by the Cloud. Additionally, network performance results show that Fog applications can provide reliable information in a considerable shorter period of time, while at the same time they reduce the amount of traffic over the VANET backhaul infrastructure.

Assessment of Motor Compensation Patterns in Stroke Rehabilitation Exercises

Cóias A., Bernardino A.

RECPAD 2020

2022

Abstract:

The increasing demand concerning stroke rehabilitation and in-home exercise promotion requires objective methods to assess patients’ quality of movement, allowing progress tracking and promoting consensus among treatment regimens. In this work, we propose a method to detect diverse compensation patterns during exercise performance with 2D pose data to automate rehabilitation programs monitorization in any device with a 2D camera, such as tablets, smartphones, or robotic assistants.

Asymptotic performance of distributed detection over random networks

Bajovic D., Jakovetic D., Xavier J., Sinopoli B., Moura J.M.F.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

2011

Abstract:

We show that distributed detection over random networks, or using a random protocol, e.g., of the gossip type, is asymptotically optimal, if the rate of information flow across the random network is large enough. Asymptotic optimality is in the sense of Chernoff information; in other words, we determine when the exponential rate of decay of the error probability for distributed detection is the best possible and equal to the rate of decay of the best centralized detector. The rate of information flow is defined by |log r|, where r is the second largest eigenvalue of the second moment of the random, consensus weight matrix. We quantify interesting tradeoffs in distributed detection, between the rate of information flow and the achievable detection performance.

Augmenting Search-based Techniques with Static Synthesis-based Input Generation

Santos P., Campos J., Timperley C., Fonseca A.

ICSE2021

2021

Abstract:

Automatic Abstraction in SMT-Based Unbounded Software Model Checking

Anvesh KomuravelliArie GurfinkelSagar ChakiEdmund M. Clarke

CAV 2013: Computer Aided Verification

2013

Abstract:

Software model checkers based on under-approximations and SMT solvers are very successful at verifying safety (i.e., reachability) properties. They combine two key ideas – (a) concreteness: a counterexample in an under-approximation is a counterexample in the original program as well, and (b) generalization: a proof of safety of an under-approximation, produced by an SMT solver, are generalizable to proofs of safety of the original program. In this paper, we present a combination of automatic abstraction with the under-approximation-driven framework. We explore two iterative approaches for obtaining and refining abstractions – proof based and counterexample based – and show how they can be combined into a unified algorithm. To the best of our knowledge, this is the first application of Proof-Based Abstraction, primarily used to verify hardware, to Software Verification. We have implemented a prototype of the framework using Z3, and evaluate it on many benchmarks from the Software Verification Competition. We show experimentally that our combination is quite effective on hard instances.

Automatic generation of Cloze question stems

Correia R., Baptista J., Eskenazi M., Mamede N.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2012

Abstract:

Fill-in-the-blank questions are one of the main assessment devices in REAP.PT tutoring system. The problem of automatically generating the stems, i.e. the sentences that serve as basis to this type of question, has been studied mostly for English, and it remains a challenge for a language as morphologically rich as European Portuguese (EP), for which additional data scarcity problems arise. To address this problem, a supervised classification technique is used to model a classifier that decides whether a given sentence is suitable to be used as a stem in a cloze question. The major focus is put in the feature engineering task, describing both the development of new criteria, and the adaptation to EP of features already explored in the literature. The resulting classifier filters out inadequate stems, allowing experts to build and personalize their instruction focusing on a set of potentially good sentences.

Automatic generation of listening comprehension learning material in European Portuguese

Pellegrini T., Correia R., Trancoso I., Baptista J., Mamede N.

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

2011

Abstract:

The goal of this work is the automatic selection of materials for a listening comprehension game. We would like to select automatically transcribed sentences from recent broadcast news corpora, in order to gather material for the games with little human effort. The recognized words are used as the ground solution of the exercises, thus sentences with misrecognitions need to be filtered out. Our experiments confirmed the feasibility of the filter chain that automatically selects sentences, although harder confidence thresholds may be needed. Together with the correct words, wrong candidates, namely distractors, are also needed to build the exercises. Two techniques of distractor generation are presented, either based on the confusion networks produced by the recognizer, or on phonetic distances. The experiments confirmed the complementarity of both approaches.

Automatic keyword extraction on twitter

Marujo L., Ling W., Trancoso I., Dyer C., Black A.W., Gershman A., De Matos D.M., Neto J.P., Carbonell J.

ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference

2015

Abstract:

In this paper, we build a corpus of tweets from Twitter annotated with keywords using crowdsourcing methods. We identify key differences between this domain and the work performed on other domains, such as news, which makes existing approaches for automatic keyword extraction not generalize well on Twitter datasets. These datasets include the small amount of content in each tweet, the frequent usage of lexical variants and the high variance of the cardinality of keywords present in each tweet. We propose methods for addressing these issues, which leads to solid improvements on this dataset for this task.

Basis pursuit in sensor networks

Mota J.F.C., Xavier J.M.F., Aguiar P.M.Q., Puschel M.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

2011

Abstract:

Basis Pursuit (BP) finds a minimum ℓ 1 -norm vector z that satisfies the underdetermined linear system Mz = b, where the matrix M and vector b are given. Lately, BP has attracted attention because of its application in compressed sensing, where it is used to reconstruct signals by finding the sparsest solutions of linear systems. In this paper, we propose a distributed algorithm to solve BP. This means no central node is used for the processing and no node has access to all the data: the rows of M and the vector b are distributed over a set of interconnected compute nodes. A typical scenario is a sensor network. The novelty of our method is in using an optimal first-order method to solve an augmented Lagrangian-based reformulation of BP. We implemented our algorithm in a computer cluster, and show that it can solve problems that are too large to be stored in and processed by a single node.