Search Results for author:
Found 34 papers, 5 papers with code
Finding MNEMON: Reviving Memories of Node Embeddings
Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of graph neural networks.
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation
Sign language recognition and translation first uses a recognition module to generate glosses from sign language videos and then employs a translation module to translate glosses into spoken sentences.
Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling
Unfortunately, the large amount of interactive sample labeling efforts has dramatically hindered the application of deep learning methods, especially for 3D modeling tasks, which require heterogeneous samples.
Visualizing Ensemble Predictions of Music Mood
Music mood classification has been a challenging problem in comparison with some other classification problems (e. g., genre, composer, or period).
Inference Attacks Against Graph Neural Networks
Second, given a subgraph of interest and the graph embedding, we can determine with high confidence that whether the subgraph is contained in the target graph.
Deep Fraud Detection on Non-attributed Graph
The successes of most previous methods heavily rely on rich node features and high-fidelity labels.
ML4ML: Automated Invariance Testing for Machine Learning Models
In machine learning workflows, determining invariance qualities of a model is a common testing procedure.
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4. 0 on the edge computing level.
Using Simulation to Aid the Design and Optimization of Intelligent User Interfaces for Quality Assurance Processes in Machine Learning
Because QA4ML users have to view a non-trivial amount of data and perform many input actions to correct errors made by the ML model, an optimally-designed user interface (UI) can reduce the cost of interactions significantly.
Graph Unlearning
In the context of machine learning (ML), it requires the ML model provider to remove the data subject's data from the training set used to build the ML model, a process known as \textit{machine unlearning}.
A Bounded Measure for Estimating the Benefit of Visualization: Case Studies and Empirical Evaluation
The recently-proposed information-theoretic measure for analyzing the cost-benefit ratio of visualization processes can explain such usefulness experienced in practice, and postulate that the viewers' knowledge can reduce the potential distortion (e. g., misinterpretation) due to information loss.
Human-Computer Interaction Graphics Information Theory Information Theory
A Bounded Measure for Estimating the Benefit of Visualization: Theoretical Discourse and Conceptual Evaluation
We examine a number of bounded measures that include the Jenson-Shannon divergence and a new divergence measure formulated as part of this work.
Information Theory Graphics Human-Computer Interaction Information Theory
NeRF--: Neural Radiance Fields Without Known Camera Parameters
Considering the problem of novel view synthesis (NVS) from only a set of 2D images, we simplify the training process of Neural Radiance Field (NeRF) on forward-facing scenes by removing the requirement of known or pre-computed camera parameters, including both intrinsics and 6DoF poses.
Study on multi-fold bunch splitting in a high-intensity medium-energy proton synchrotron
Bunch splitting is an RF manipulation method of changing the bunch structure, bunch numbers and bunch intensity in the high-intensity synchrotrons that serve as the injector for a particle collider.
Accelerator Physics
Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images
Finally, multi-view information from both the nadir and oblique images is used in a robust voting procedure to label changes in existing buildings.
DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks
A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve.
When Machine Unlearning Jeopardizes Privacy
More importantly, we show that our attack in multiple cases outperforms the classical membership inference attack on the original ML model, which indicates that machine unlearning can have counterproductive effects on privacy.
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models
In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models.
A Bounded Measure for Estimating the Benefit of Visualization
Information theory can be used to analyze the cost-benefit of visualization processes.
FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation
We present FlowNet3D++, a deep scene flow estimation network.
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
In this paper, we proposed two strategies to fuse information extracted from different modalities, i. e., audio and visual.
AI-Skin : Skin Disease Recognition based on Self-learning and Wide Data Collection through a Closed Loop Framework
First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed.
Hierarchical Reinforcement Learning for Multi-agent MOBA Game
Real Time Strategy (RTS) games require macro strategies as well as micro strategies to obtain satisfactory performance since it has large state space, action space, and hidden information.
Wearable Affective Robot
The learning algorithm for the life modeling embedded in Fitbot can achieve better user's experience of affective social interaction.
EEG
Human-Computer Interaction
Background Subtraction using Compressed Low-resolution Images
The essential first step involved in almost all the visual tasks is background subtraction with a static camera.
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication.
Collimated GeV attosecond electron-positron bunches from a plasma channel driven by 10 PW lasers
High-energy positrons and bright {\gamma}-ray sources are unique both for fundamental research and practical applications.
Plasma Physics
Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning
The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term.
Detecting Small Signs from Large Images
Experimental results on a real-world conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.
Brain Intelligence: Go Beyond Artificial Intelligence
Specifically, we plan to develop an intelligent learning model called Brain Intelligence (BI) that generates new ideas about events without having experienced them by using artificial life with an imagine function.
Underwater Optical Image Processing: A Comprehensive Review
This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers.
POMDP-lite for Robust Robot Planning under Uncertainty
We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks.
Service-Oriented Architectures and Web Services: Course Tutorial and Lab Notes
This document presents a number of quick-step instructions to get started on writing mini-service-oriented web services-based applications using OpenESB 2. 31, Tomcat 6, GlassFish 2. x/3. 0. 1 with BPEL support, and Java 1. 6+ primarily in Scientific Linux 6. 6 with user quota restrictions.
Distributed, Parallel, and Cluster Computing Software Engineering 68N15, 68U35 H.3.5; C.2.4; D.1.3
