7,810 research outputs found
An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks
Despite the great success of Convolutional Neural Networks (CNNs) in Computer
Vision and Natural Language Processing, the working mechanism behind CNNs is
still under extensive discussions and research. Driven by a strong demand for
the theoretical explanation of neural networks, some researchers utilize
information theory to provide insight into the black box model. However, to the
best of our knowledge, employing information theory to quantitatively analyze
and qualitatively visualize neural networks has not been extensively studied in
the visualization community. In this paper, we combine information entropies
and visualization techniques to shed light on how CNN works. Specifically, we
first introduce a data model to organize the data that can be extracted from
CNN models. Then we propose two ways to calculate entropy under different
circumstances. To provide a fundamental understanding of the basic building
blocks of CNNs (e.g., convolutional layers, pooling layers, normalization
layers) from an information-theoretic perspective, we develop a visual analysis
system, CNNSlicer. CNNSlicer allows users to interactively explore the amount
of information changes inside the model. With case studies on the widely used
benchmark datasets (MNIST and CIFAR-10), we demonstrate the effectiveness of
our system in opening the blackbox of CNNs
VMap: An Interactive Rectangular Space-filling Visualization for Map-like Vertex-centric Graph Exploration
We present VMap, a map-like rectangular space-filling visualization, to
perform vertex-centric graph exploration. Existing visualizations have limited
support for quality optimization among rectangular aspect ratios, vertex-edge
intersection, and data encoding accuracy. To tackle this problem, VMap
integrates three novel components: (1) a desired-aspect-ratio (DAR) rectangular
partitioning algorithm, (2) a two-stage rectangle adjustment algorithm, and (3)
a simulated annealing based heuristic optimizer. First, to generate a
rectangular space-filling layout of an input graph, we subdivide the 2D
embedding of the graph into rectangles with optimization of rectangles' aspect
ratios toward a desired aspect ratio. Second, to route graph edges between
rectangles without vertex-edge occlusion, we devise a two-stage algorithm to
adjust a rectangular layout to insert border space between rectangles. Third,
to produce and arrange rectangles by considering multiple visual criteria, we
design a simulated annealing based heuristic optimization to adjust vertices'
2D embedding to support trade-offs among aspect ratio quality and the encoding
accuracy of vertices' weights and adjacency. We evaluated the effectiveness of
VMap on both synthetic and application datasets. The resulting rectangular
layout has better aspect ratio quality on synthetic data compared with the
existing method for the rectangular partitioning of 2D points. On three
real-world datasets, VMap achieved better encoding accuracy and attained faster
generation speed compared with existing methods on graphs' rectangular layout
generation. We further illustrate the usefulness of VMap for vertex-centric
graph exploration through three case studies on visualizing social networks,
representing academic communities, and displaying geographic information.Comment: Submitted to IEEE Visualization Conference (IEEE VIS) 2019 and 202
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
Recently, Graph Neural Networks (GNNs) have significantly advanced the
performance of machine learning tasks on graphs. However, this technological
breakthrough makes people wonder: how does a GNN make such decisions, and can
we trust its prediction with high confidence? When it comes to some critical
fields, such as biomedicine, where making wrong decisions can have severe
consequences, it is crucial to interpret the inner working mechanisms of GNNs
before applying them. In this paper, we propose a model-agnostic model-level
explanation method for different GNNs that follow the message passing scheme,
GNNInterpreter, to explain the high-level decision-making process of the GNN
model. More specifically, GNNInterpreter learns a probabilistic generative
graph distribution that produces the most discriminative graph pattern the GNN
tries to detect when making a certain prediction by optimizing a novel
objective function specifically designed for the model-level explanation for
GNNs. Compared with the existing work, GNNInterpreter is more computationally
efficient and more flexible in generating explanation graphs with different
types of node features and edge features, without introducing another blackbox
to explain the GNN and without requiring manually specified domain-specific
knowledge. Additionally, the experimental studies conducted on four different
datasets demonstrate that the explanation graph generated by GNNInterpreter can
match the desired graph pattern when the model is ideal and reveal potential
model pitfalls if there exist any
Secure Pick Up: Implicit Authentication When You Start Using the Smartphone
We propose Secure Pick Up (SPU), a convenient, lightweight, in-device,
non-intrusive and automatic-learning system for smartphone user authentication.
Operating in the background, our system implicitly observes users' phone
pick-up movements, the way they bend their arms when they pick up a smartphone
to interact with the device, to authenticate the users.
Our SPU outperforms the state-of-the-art implicit authentication mechanisms
in three main aspects: 1) SPU automatically learns the user's behavioral
pattern without requiring a large amount of training data (especially those of
other users) as previous methods did, making it more deployable. Towards this
end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW)
algorithm to effectively quantify similarities between users' pick-up
movements; 2) SPU does not rely on a remote server for providing further
computational power, making SPU efficient and usable even without network
access; and 3) our system can adaptively update a user's authentication model
to accommodate user's behavioral drift over time with negligible overhead.
Through extensive experiments on real world datasets, we demonstrate that SPU
can achieve authentication accuracy up to 96.3% with a very low latency of 2.4
milliseconds. It reduces the number of times a user has to do explicit
authentication by 32.9%, while effectively defending against various attacks.Comment: Published on ACM Symposium on Access Control Models and Technologies
(SACMAT) 201
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Complex computational models are often designed to simulate real-world
physical phenomena in many scientific disciplines. However, these simulation
models tend to be computationally very expensive and involve a large number of
simulation input parameters which need to be analyzed and properly calibrated
before the models can be applied for real scientific studies. We propose a
visual analysis system to facilitate interactive exploratory analysis of
high-dimensional input parameter space for a complex yeast cell polarization
simulation. The proposed system can assist the computational biologists, who
designed the simulation model, to visually calibrate the input parameters by
modifying the parameter values and immediately visualizing the predicted
simulation outcome without having the need to run the original expensive
simulation for every instance. Our proposed visual analysis system is driven by
a trained neural network-based surrogate model as the backend analysis
framework. Surrogate models are widely used in the field of simulation sciences
to efficiently analyze computationally expensive simulation models. In this
work, we demonstrate the advantage of using neural networks as surrogate models
for visual analysis by incorporating some of the recent advances in the field
of uncertainty quantification, interpretability and explainability of neural
network-based models. We utilize the trained network to perform interactive
parameter sensitivity analysis of the original simulation at multiple
levels-of-detail as well as recommend optimal parameter configurations using
the activation maximization framework of neural networks. We also facilitate
detail analysis of the trained network to extract useful insights about the
simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
Sub-wavelength Coherent Imaging of a Pure-Phase Object with Thermal Light
We report, for the first time, the observation of sub-wavelength coherent
image of a pure phase object with thermal light,which represents an accurate
Fourier transform. We demonstrate that ghost-imaging scheme (GI) retrieves
amplitude transmittance knowledge of objects rather than the transmitted
intensities as the HBT-type imaging scheme does.Comment: 5 pages, 4 figures; Any comments pls. contact: [email protected]
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