127 research outputs found
Fan Localisation in the Chinese Overwatch Game Community: Conflicts about the Information Transmission
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
The IoT (Internet of Things) technology has been widely adopted in recent
years and has profoundly changed the people's daily lives. However, in the
meantime, such a fast-growing technology has also introduced new privacy
issues, which need to be better understood and measured. In this work, we look
into how private information can be leaked from network traffic generated in
the smart home network. Although researchers have proposed techniques to infer
IoT device types or user behaviors under clean experiment setup, the
effectiveness of such approaches become questionable in the complex but
realistic network environment, where common techniques like Network Address and
Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic
analysis using traditional methods (e.g., through classical machine-learning
models) is much less effective under those settings, as the features picked
manually are not distinctive any more. In this work, we propose a traffic
analysis framework based on sequence-learning techniques like LSTM and
leveraged the temporal relations between packets for the attack of device
identification. We evaluated it under different environment settings (e.g.,
pure-IoT and noisy environment with multiple non-IoT devices). The results
showed our framework was able to differentiate device types with a high
accuracy. This result suggests IoT network communications pose prominent
challenges to users' privacy, even when they are protected by encryption and
morphed by the network gateway. As such, new privacy protection methods on IoT
traffic need to be developed towards mitigating this new issue
Quantum Gaussian process regression
In this paper, a quantum algorithm based on gaussian process regression model
is proposed. The proposed quantum algorithm consists of three sub-algorithms.
One is the first quantum subalgorithm to efficiently generate mean predictor.
The improved HHL algorithm is proposed to obtain the sign of outcomes.
Therefore, the terrible situation that results is ambiguous in terms of
original HHL algorithm is avoided, which makes whole algorithm more clear and
exact. The other is to product covariance predictor with same method. Thirdly,
the squared exponential covariance matrices are prepared that annihilation
operator and generation operator are simulated by the unitary linear
decomposition Hamiltonian simulation and kernel function vectors is generated
with blocking coding techniques on covariance matrices. In addition, it is
shown that the proposed quantum gaussian process regression algorithm can
achieve quadratic faster over the classical counterpart
Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation
Generating videos for visual storytelling can be a tedious and complex
process that typically requires either live-action filming or graphics
animation rendering. To bypass these challenges, our key idea is to utilize the
abundance of existing video clips and synthesize a coherent storytelling video
by customizing their appearances. We achieve this by developing a framework
comprised of two functional modules: (i) Motion Structure Retrieval, which
provides video candidates with desired scene or motion context described by
query texts, and (ii) Structure-Guided Text-to-Video Synthesis, which generates
plot-aligned videos under the guidance of motion structure and text prompts.
For the first module, we leverage an off-the-shelf video retrieval system and
extract video depths as motion structure. For the second module, we propose a
controllable video generation model that offers flexible controls over
structure and characters. The videos are synthesized by following the
structural guidance and appearance instruction. To ensure visual consistency
across clips, we propose an effective concept personalization approach, which
allows the specification of the desired character identities through text
prompts. Extensive experiments demonstrate that our approach exhibits
significant advantages over various existing baselines.Comment: Github: https://github.com/VideoCrafter/Animate-A-Story Project page:
https://videocrafter.github.io/Animate-A-Stor
DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors
Animating a still image offers an engaging visual experience. Traditional
image animation techniques mainly focus on animating natural scenes with
stochastic dynamics (e.g. clouds and fluid) or domain-specific motions (e.g.
human hair or body motions), and thus limits their applicability to more
general visual content. To overcome this limitation, we explore the synthesis
of dynamic content for open-domain images, converting them into animated
videos. The key idea is to utilize the motion prior of text-to-video diffusion
models by incorporating the image into the generative process as guidance.
Given an image, we first project it into a text-aligned rich context
representation space using a query transformer, which facilitates the video
model to digest the image content in a compatible fashion. However, some visual
details still struggle to be preserved in the resultant videos. To supplement
with more precise image information, we further feed the full image to the
diffusion model by concatenating it with the initial noises. Experimental
results show that our proposed method can produce visually convincing and more
logical & natural motions, as well as higher conformity to the input image.
Comparative evaluation demonstrates the notable superiority of our approach
over existing competitors.Comment: Project page: https://doubiiu.github.io/projects/DynamiCrafte
EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography
This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a
novel self-supervised method for recognizing standard views in pediatric
echocardiography. EDMAE introduces a new proxy task based on the
encoder-decoder structure. The EDMAE encoder is composed of a teacher and a
student encoder. The teacher encoder extracts the potential representation of
the masked image blocks, while the student encoder extracts the potential
representation of the visible image blocks. The loss is calculated between the
feature maps output by the two encoders to ensure consistency in the latent
representations they extract. EDMAE uses pure convolution operations instead of
the ViT structure in the MAE encoder. This improves training efficiency and
convergence speed. EDMAE is pre-trained on a large-scale private dataset of
pediatric echocardiography using self-supervised learning, and then fine-tuned
for standard view recognition. The proposed method achieves high classification
accuracy in 27 standard views of pediatric echocardiography. To further verify
the effectiveness of the proposed method, the authors perform another
downstream task of cardiac ultrasound segmentation on the public dataset CAMUS.
The experimental results demonstrate that the proposed method outperforms some
popular supervised and recent self-supervised methods, and is more competitive
on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal
Processing and Contro
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