Articles

Empathic Robot for Group Learning: A Field Study

Alves Oliveira P., Sequeira P., Melo F.S., Castellano G., Paiva A.

ACM Transactions on Human-Robot Interaction

2019

Abstract:

This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot sustain positive educational outcomes in long-term collaborative learning interactions with groups of students? To answer these questions, we developed an autonomous robot with empathic competencies that is able to interact with a group of students in a learning activity about sustainable development. Two studies were conducted. The first study compares learning outcomes in children across 3 conditions: learning with an empathic robot; learning with a robot without empathic capabilities; and learning without a robot. The results show that the autonomous robot with empathy fosters meaningful discussions about sustainability, which is a learning outcome in sustainability education. The second study features groups of students who interact with the robot in a school classroom for two months. The long-term educational interaction did not seem to provide significant learning gains, although there was a change in game-actions to achieve more sustainability during game-play. This result reflects the need to perform more long-term research in the field of educational robots for group learning.

Empirical centroid fictitious play: An approach for distributed learning in multi-agent games

Swenson B., Kar S., Xavier J.

IEEE Transactions on Signal Processing

2015

Abstract:

The paper is concerned with distributed learning in large-scale games. The well-known fictitious play (FP) algorithm is addressed, which, despite theoretical convergence results, might be impractical to implement in large-scale settings due to intense computation and communication requirements. An adaptation of the FP algorithm, designated as the empirical centroid fictitious play (ECFP), is presented. In ECFP players respond to the centroid of all players’ actions rather than track and respond to the individual actions of every player. Convergence of the ECFP algorithm in terms of average empirical frequency (a notion made precise in the paper) to a subset of the Nash equilibria is proven under the assumption that the game is a potential game with permutation invariant potential function. A more general formulation of ECFP is then given (which subsumes FP as a special case) and convergence results are given for the class of potential games. Furthermore, a distributed formulation of the ECFP algorithm is presented, in which, players endowed with a (possibly sparse) preassigned communication graph, engage in local, non-strategic information exchange to eventually agree on a common equilibrium. Convergence results are proven for the distributed ECFP algorithm.

Empirical Study of Restarted and Flaky Builds on Travis CI

Durieux T., Le Goues C., Hilton M., Abreu R.

17th International Conference on Mining Software Repositories (MSR '20)

2020

Abstract:

Continuous Integration (CI) is a development practice where developers frequently integrate code into a common codebase. After the code is integrated, the CI server runs a test suite and other tools to produce a set of reports (e.g., the output of linters and tests). If the result of a CI test run is unexpected, developers have the option to manually restart the build, re-running the same test suite on the same code; this can reveal build flakiness, if the restarted build outcome differs from the original build. In this study, we analyze restarted builds, flaky builds, and their impact on the development workflow. We observe that developers restart at least 1.72% of builds, amounting to 56,522 restarted builds in our Travis CI dataset. We observe that more mature and more complex projects are more likely to include restarted builds. The restarted builds are mostly builds that are initially failing due to a test, network problem, or a Travis CI limitations such as execution timeout. Finally, we observe that restarted builds have an impact on development workflow. Indeed, in 54.42% of the restarted builds, the developers analyze and restart a build within an hour of the initial build execution. This suggests that developers wait for CI results, interrupting their workflow to address the issue. Restarted builds also slow down the merging of pull requests by a factor of three, bringing median merging time from 16h to 48h.

Encodings for Enumeration-Based Program Synthesis

Orvalho P., Terra-Neves M., Ventura M., Martins R., Manquinho V.

International Conference on Principles and Practice of Constraint Programming, CP 2019: Principles and Practice of Constraint Programming

2019

Abstract:

Program synthesis is the problem of finding a program that satisfies a given specification. Most program synthesizers are based on enumerating program candidates that satisfy the specification. Recently, several new tools for program synthesis have been proposed where Satisfiability Modulo Theories (SMT) solvers are used to prune the search space by discarding programs that do not satisfy the specification. The size of current tree-based SMT encodings for program synthesis grows exponentially with the size of the program. In this paper, a new compact line-based encoding is proposed that allows a faster enumeration of the program space. Experimental results on a large set of query synthesis problem instances show that using the new encoding results in a more effective tool that is able to synthesize larger programs. Keywords Program synthesis Satisfiability Modulo Theories Enumerative search SQL 

Energy efficient indoor tracking on smartphones

Yao D., Yu C., Dey A.K., Koehler C., Min G., Yang L.T., Jin H.

Future Generation Computer Systems

2014

Abstract:

Continuously identifying a user’s location context provides new opportunities to understand daily life and human behavior. Indoor location systems have been mainly based on WiFi infrastructures which consume a great deal of energy mostly due to keeping the user’s WiFi device connected to the infrastructure and network communication, limiting the overall time when a user can be tracked. Particularly such tracking systems on battery-limited mobile devices must be energy-efficient to limit the impact on the experience of using a phone. Recently, there have been a lot of studies of energy-efficient positioning systems, but these have focused on outdoor positioning technologies. In this paper, we propose a novel indoor tracking framework that intelligently determines the location sampling rate and the frequency of network communication, to optimize the accuracy of the location data while being energy-efficient at the same time. This framework leverages an accelerometer, widely available on everyday smartphones, to reduce the duty cycle and the network communication frequency when a tracked user is moving slowly or not at all. Our framework can work for 14 h without charging, supporting applications that require this location information without affecting user experience.

Energy harvesting from electromagnetic energy radiating from AC power lines

Gupta V., Kandhalu A., Rajkumar R.

Proceedings of the 6th Workshop on Hot Topics in Embedded Networked Sensors, HotEmNets 2010

2010

Abstract:

There has been considerable interest in energy harvesting for wireless sensor networks. Energy harvesting from thermal sources such as body heat and mechanical sources such as human motion have been proposed. There are also sensor network systems that harvest energy from the visible part of the electromagnetic spectrum. However, ambient light levels in indoor environments are typically significantly lower than those found outdoors and highly dependent on the nature of the indoor environment considered. Recently, low-power clock synchronization using electromagnetic energy radiating from AC power lines was proposed. In this paper, we go a step ahead and try to answer the question: Can energy be harvested from the electromagnetic energy radiating from AC power lines and use it to operate a wireless sensor network with a low duty-cycle? We find that such energy harvesting appears promising.

Enhancement of Retinal Fundus Images via Pixel Color Amplification

Gaudio, A. ; Smailagic, A. ; Campilho, A.

ICIAR 2020

2020

Abstract:

We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distortion model underlying dehazing theory shows how three existing priors commonly used by the dehazing community and a novel fourth prior are related. We utilize the theory to develop a family of enhancement methods for retinal images, including novel methods for whole image brightening and darkening. We show a novel derivation of the Unsharp Masking algorithm. We evaluate the enhancement methods as a pre-processing step to a challenging multi-task segmentation problem and show large increases in performance on all tasks, with Dice score increases over a no-enhancement baseline by as much as 0.491. We provide evidence that our enhancement preprocessing is useful for unbalanced and difficult data. We show that the enhancements can perform class balancing by composing them together.

Enhancing human understanding of a mobile robot’s state and actions using expressive lights

Baraka K., Rosenthal S., Veloso M.

25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016

2016

Abstract:

In order to be successfully integrated into human-populated environments, mobile robots need to express relevant information about their state to the outside world. In particular, animated lights are a promising way to express hidden robot state information such that it is visible at a distance. In this work, we present an online study to evaluate the effect of robot communication through expressive lights on people’s understanding of the robot’s state and actions. In our study, we use the CoBot mobile service robot with our light interface, designed to express relevant robot information to humans. We evaluate three designed light animations on three corresponding scenarios for each, for a total of nine scenarios. Our results suggest that expressive lights can play a significant role in helping people accurately hypothesize about a mobile robot’s state and actions from afar when minimal contextual clues are present. We conclude that lights could be generally used as an effective non-verbal communication modality for mobile robots in the absence of, or as a complement to, other modalities.

Entropy-based pruning for phrase-based machine translation

Ling W., Graca J., Trancoso I., Black A.

EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

2012

Abstract:

Phrase-based machine translation models have shown to yield better translations than Word-based models, since phrase pairs encode the contextual information that is needed for a more accurate translation. However, many phrase pairs do not encode any relevant context, which means that the translation event encoded in that phrase pair is led by smaller translation events that are independent from each other, and can be found on smaller phrase pairs, with little or no loss in translation accuracy. In this work, we propose a relative entropy model for translation models, that measures how likely a phrase pair encodes a translation event that is derivable using smaller translation events with similar probabilities. This model is then applied to phrase table pruning. Tests show that considerable amounts of phrase pairs can be excluded, without much impact on the translation quality. In fact, we show that better translations can be obtained using our pruned models, due to the compression of the search space during decoding.