11,095 research outputs found
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
We provide a comprehensive evaluation of salient object detection (SOD)
models. Our analysis identifies a serious design bias of existing SOD datasets
which assumes that each image contains at least one clearly outstanding salient
object in low clutter. The design bias has led to a saturated high performance
for state-of-the-art SOD models when evaluated on existing datasets. The
models, however, still perform far from being satisfactory when applied to
real-world daily scenes. Based on our analyses, we first identify 7 crucial
aspects that a comprehensive and balanced dataset should fulfill. Then, we
propose a new high quality dataset and update the previous saliency benchmark.
Specifically, our SOC (Salient Objects in Clutter) dataset, includes images
with salient and non-salient objects from daily object categories. Beyond
object category annotations, each salient image is accompanied by attributes
that reflect common challenges in real-world scenes. Finally, we report
attribute-based performance assessment on our dataset.Comment: ECCV 201
Effect of user tastes on personalized recommendation
In this paper, based on a weighted projection of the user-object bipartite
network, we study the effects of user tastes on the mass-diffusion-based
personalized recommendation algorithm, where a user's tastes or interests are
defined by the average degree of the objects he has collected. We argue that
the initial recommendation power located on the objects should be determined by
both of their degree and the users' tastes. By introducing a tunable parameter,
the user taste effects on the configuration of initial recommendation power
distribution are investigated. The numerical results indicate that the
presented algorithm could improve the accuracy, measured by the average ranking
score, more importantly, we find that when the data is sparse, the algorithm
should give more recommendation power to the objects whose degrees are close to
the users' tastes, while when the data becomes dense, it should assign more
power on the objects whose degrees are significantly different from user's
tastes.Comment: 8 pages, 4 figure
A general coupling matrix synthesis method for all-resonator diplexers and multiplexers
Coupling matrix (CM) synthesis methods for all-resonator diplexers and multiplexers are far from mature. For complex coupling topologies, the existing methods are often not able to find the appropriate CMs that satisfy the S-parameter specifications. To address this challenge, a new synthesis method, which hybridizes analytical and optimization techniques, called general all-resonator diplexer/multiplexer CM synthesis (GACMS) method, is proposed in this article. The two main innovations of GACMS are: 1) an optimization framework incorporating filter design knowledge, which effectively reduces the search space for CM synthesis and 2) a new memetic algorithm-based optimizer, which tackles the challenges from the complex landscape (function characteristics) of CM synthesis problems. GACMS is tested by six complex practical problems and CMs are successfully obtained for all of them. Comparisons with the existing methods demonstrate the advantages of GACMS in terms of solution quality and robustness
Image based magnetic field background correction for aortic and pulmonary artery flow measurement using phase contrast
Static detection of control-flow-related vulnerabilities using graph embedding
© 2019 IEEE. Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges
A 3-D printed bandpass filter using tm-mode slotted spherical resonators with enhanced spurious suppression
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