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.

Self-tuning batching in total order broadcast protocols via analytical modelling and reinforcement learning

Abstract: Batching is a well known technique to boost the throughput of Total Order Broadcast (TOB) protocols. Unfortunately, its manual configuration is not only a time consuming process, but also a very delicate one, as incorrect settings of the batching parameter can lead to severe performance degradation. In this paper we address precisely this issue, by presenting an innovative mechanism for self-tuning the batching level in TOB protocols. Our solution combines analytical modeling and reinforcement learning techniques, taking the best of these two worlds: drastic reductions of the learning time and the ability to correct inaccurate predictions by accumulating feedback from the operation of the system.

Performance Simulation Studies of the Clear-PEM DAQ/Trigger System

Abstract: The Clear-PEM detector is a positron emission mammography scanner based on a high-granularity avalanche photodiode readout with 12 288 channels. The front-end sub-system is instrumented with low-noise 192:2 channel amplifier-multiplexer ASICs and free-running sampling ADCs. The off-detector trigger, implemented in a FPGA based architecture, computes the pulses amplitude and timing required for coincidence validation from the front-end data streams. A high-level C++ simulation tool was developed for data acquisition performance analysis and validated at bit-level against FPGA VHDL testbenches. In this work, simulation studies concerning the performance of the on-line/off-line energy and time extraction algorithms and the foreseen detector energy and time resolution are presented. Time calibration and trigger efficiency are also discussed

Cheating with robots: how at ease do they make us feel?

Abstract: People are not perfect, and if given the chance, some will be dishonest with no regrets. Some people will cheat just a little to gain some advantage, and others will not do it at all. With the prospect of more human-robot interactions in the future, it will become very important to understand which kind of roles a robot can have in the regulation of cheating behavior. We investigated whether people will cheat while in the presence of a robot and to what extent this depends on the role the robot plays. We ran a study to test cheating behavior with a die task, and allocated people to one of the following conditions: 1) participants were alone in the room while doing the task; 2) with a robot with a vigilant role or 3) with a robot that had a supporting role in the task, accompanying and giving instructions. Our results showed that participants cheated significantly more than chance when they were alone or with the robot giving instructions. In contrast, cheating could not be proven when the robot presented a vigilant role. This study has implications for human-robot interaction and for the deployment of autonomous robots in sensitive roles in which people may be prone to dishonest behavior.