34,114 research outputs found
You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems
Properly benchmarking Automated Program Repair (APR) systems should
contribute to the development and adoption of the research outputs by
practitioners. To that end, the research community must ensure that it reaches
significant milestones by reliably comparing state-of-the-art tools for a
better understanding of their strengths and weaknesses. In this work, we
identify and investigate a practical bias caused by the fault localization (FL)
step in a repair pipeline. We propose to highlight the different fault
localization configurations used in the literature, and their impact on APR
systems when applied to the Defects4J benchmark. Then, we explore the
performance variations that can be achieved by `tweaking' the FL step.
Eventually, we expect to create a new momentum for (1) full disclosure of APR
experimental procedures with respect to FL, (2) realistic expectations of
repairing bugs in Defects4J, as well as (3) reliable performance comparison
among the state-of-the-art APR systems, and against the baseline performance
results of our thoroughly assessed kPAR repair tool. Our main findings include:
(a) only a subset of Defects4J bugs can be currently localized by commonly-used
FL techniques; (b) current practice of comparing state-of-the-art APR systems
(i.e., counting the number of fixed bugs) is potentially misleading due to the
bias of FL configurations; and (c) APR authors do not properly qualify their
performance achievement with respect to the different tuning parameters
implemented in APR systems.Comment: Accepted by ICST 201
Practical issues for the implementation of survivability and recovery techniques in optical networks
Mixed marker-based/marker-less visual odometry system for mobile robots
When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test
Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots
Reliable and real-time 3D reconstruction and localization functionality is a
crucial prerequisite for the navigation of actively controlled capsule
endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic
technology for use in the gastrointestinal (GI) tract. In this study, we
propose a fully dense, non-rigidly deformable, strictly real-time,
intraoperative map fusion approach for actively controlled endoscopic capsule
robot applications which combines magnetic and vision-based localization, with
non-rigid deformations based frame-to-model map fusion. The performance of the
proposed method is demonstrated using four different ex-vivo porcine stomach
models. Across different trajectories of varying speed and complexity, and four
different endoscopic cameras, the root mean square surface reconstruction
errors 1.58 to 2.17 cm.Comment: submitted to IROS 201
Secret Key Generation Based on AoA Estimation for Low SNR Conditions
In the context of physical layer security, a physical layer characteristic is
used as a common source of randomness to generate the secret key. Therefore an
accurate estimation of this characteristic is the core for reliable secret key
generation. Estimation of almost all the existing physical layer characteristic
suffer dramatically at low signal to noise (SNR) levels. In this paper, we
propose a novel secret key generation algorithm that is based on the estimated
angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an
outstanding performance at very low SNR levels. Our algorithm can exploit
either the Azimuth AoA to generate the secret key or both the Azimuth and
Elevation angles to generate the secret key. Exploiting a second common source
of randomness adds an extra degree of freedom to the performance of our
algorithm. We compare the performance of our algorithm to the algorithm that
uses the most commonly used characteristics of the physical layer which are
channel amplitude and phase. We show that our algorithm has a very low bit
mismatch rate (BMR) at very low SNR when both channel amplitude and phase based
algorithm fail to achieve an acceptable BMR
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