166 research outputs found
Empirical Evaluation of Test Coverage for Functional Programs
The correlation between test coverage and test effectiveness is important to justify the use of coverage in practice. Existing results on imperative programs mostly show that test coverage predicates effectiveness. However, since functional programs are usually structurally different from imperative ones, it is unclear whether the same result may be derived and coverage can be used as a prediction of effectiveness on functional programs. In this paper we report the first empirical study on the correlation between test coverage and test effectiveness on functional programs. We consider four types of coverage: as input coverages, statement/branch coverage and expression coverage, and as oracle coverages, count of assertions and checked coverage. We also consider two types of effectiveness: raw effectiveness and normalized effectiveness. Our results are twofold. (1) In general the findings on imperative programs still hold on functional programs, warranting the use of coverage in practice. (2) On specific coverage criteria, the results may be unexpected or different from the imperative ones, calling for further studies on functional programs
Fast Untethered Soft Robotic Crawler with Elastic Instability
High-speed locomotion of animals gives them tremendous advantages in
exploring, hunting, and escaping from predators in varying environments.
Enlightened by the fast-running gait of mammals like cheetahs and wolves, we
designed and fabricated a single-servo-driving untethered soft robot that is
capable of galloping at a speed of 313 mm/s or 1.56 body length per second
(BL/s), 5.2 times and 2.6 times faster than the reported fastest predecessors
in mm/s and BL/s, respectively, in literature. An in-plane prestressed hair
clip mechanism (HCM) made up of semi-rigid materials like plastic is used as
the supporting chassis, the compliant spine, and the muscle force amplifier of
the robot at the same time, enabling the robot to be rapid and strong. The
influence of factors including actuation frequency, substrates,
tethering/untethering, and symmetric/asymmetric actuation is explored with
experiments. Based on previous work, this paper further demonstrated the
potential of HCM in addressing the speed problem of soft robots
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
The combination of global and partial features has been an essential solution
to improve discriminative performances in person re-identification (Re-ID)
tasks. Previous part-based methods mainly focus on locating regions with
specific pre-defined semantics to learn local representations, which increases
learning difficulty but not efficient or robust to scenarios with large
variances. In this paper, we propose an end-to-end feature learning strategy
integrating discriminative information with various granularities. We carefully
design the Multiple Granularity Network (MGN), a multi-branch deep network
architecture consisting of one branch for global feature representations and
two branches for local feature representations. Instead of learning on semantic
regions, we uniformly partition the images into several stripes, and vary the
number of parts in different local branches to obtain local feature
representations with multiple granularities. Comprehensive experiments
implemented on the mainstream evaluation datasets including Market-1501,
DukeMTMC-reid and CUHK03 indicate that our method has robustly achieved
state-of-the-art performances and outperformed any existing approaches by a
large margin. For example, on Market-1501 dataset in single query mode, we
achieve a state-of-the-art result of Rank-1/mAP=96.6%/94.2% after re-ranking.Comment: 9 pages, 5 figures. To appear in ACM Multimedia 201
Scene Graph Lossless Compression with Adaptive Prediction for Objects and Relations
The scene graph is a new data structure describing objects and their pairwise
relationship within image scenes. As the size of scene graph in vision
applications grows, how to losslessly and efficiently store such data on disks
or transmit over the network becomes an inevitable problem. However, the
compression of scene graph is seldom studied before because of the complicated
data structures and distributions. Existing solutions usually involve
general-purpose compressors or graph structure compression methods, which is
weak at reducing redundancy for scene graph data. This paper introduces a new
lossless compression framework with adaptive predictors for joint compression
of objects and relations in scene graph data. The proposed framework consists
of a unified prior extractor and specialized element predictors to adapt for
different data elements. Furthermore, to exploit the context information within
and between graph elements, Graph Context Convolution is proposed to support
different graph context modeling schemes for different graph elements. Finally,
a learned distribution model is devised to predict numerical data under
complicated conditional constraints. Experiments conducted on labeled or
generated scene graphs proves the effectiveness of the proposed framework in
scene graph lossless compression task
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