58 Result(s)
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Article
A Cellular Automaton Approach for Efficient Computing on Surface Chemical Reaction Networks
A surface chemical reaction network (sCRN, Qian and Winfree in DNA Computing and Molecular Programming: 20th International Conference, DNA 20, Kyoto, Japan, September 22–26, 2014. Proceedings 20. Springer, 201...
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Article
Universality of a surface chemical reaction network using only bi-molecular reactions
In recent years, a novel molecular computation model known as surface chemical reaction network (Qian, In: DNA computing and molecular programming: 20th international conference, DNA 20, Proceedings, 2014) has...
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Article
Correction to: Multibranch multilevel federated learning for a better feature extraction and a plug-and-play dynamic-adjusting double flow personalization approach
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Article
Multibranch multilevel federated learning for a better feature extraction and a plug-and-play dynamic-adjusting double flow personalization approach
Federated learning (FL) is an emerging technique used to preserve the privacy of users’ data in training by a distributed machine learning approach. Previously, a client-edge-cloud hierarchical federated learn...
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Chapter and Conference Paper
Revisiting TENT for Test-Time Adaption Semantic Segmentation and Classification Head Adjustment
Test-time adaption is very effective at solving the domain shift problem where the training data and testing data are sampled from different domains. However, most test-time adaption methods made their success...
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Chapter and Conference Paper
Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization
Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong performance. A common approach is to use pseudo-labels with unlab...
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Chapter and Conference Paper
Enhancing Adversarial Transferability from the Perspective of Input Loss Landscape
The transferability of adversarial examples enables the black-box attacks and poses a threat to the application of deep neural networks in real-world, which has attracted great attention in recent years. Regar...
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Chapter and Conference Paper
Eventually-Consistent Replicated Relations and Updatable Views
Distributed systems have to live with weak consistency, such as eventual consistency, if high availability is the primary goal and network partitioning is unexceptional. Local-first applications are examples o...
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Chapter and Conference Paper
Neural Watermarking for 3D Morphable Models
3D morphable models (3DMMs) have been widely used in computer graphics applications. Therefore, digital watermarking for 3DMMs can also become a valuable research direction in the near future. However, previou...
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Chapter and Conference Paper
Capturing the Lighting Inconsistency for Deepfake Detection
The rapid development and widely spread of deepfake techniques have raised severe societal concerns. Thus detecting such forgery contents has become a hot research topic. Many deepfake detection methods have b...
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Chapter and Conference Paper
Bootstrapped Masked Autoencoders for Vision BERT Pretraining
We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1) momentum encoder that provid...
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Chapter and Conference Paper
Noise Simulation-Based Deep Optical Watermarking
Digital watermarking is an important branch of information hiding, which effectively guarantees the robustness of embedded watermarks in distorted channels. To embed the watermark into the host carrier, tradit...
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Chapter and Conference Paper
PTAC: Privacy-Preserving Time and Attribute Factors Combined Cloud Data Access Control with Computation Outsourcing
Cloud storage service has significant advantages on both cost reduction and convenient data sharing. It frees data owners from technical management. However, it poses new challenges on privacy and security pro...
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Chapter and Conference Paper
Supporting Undo and Redo for Replicated Registers in Collaborative Applications
A collaborative application supporting eventual consistency may temporarily violate global invariant. Users may make mistakes. Undo and redo are a generic tool to restore global invariant and correct mistakes....
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Chapter and Conference Paper
Bone Marrow Cell Segmentation Based on Improved U-Net
Automatic segmentation of bone marrow cells plays an important role in the diagnosis of many blood diseases such as anemia and leukemia. Due to the complex morphology and wide variety of bone marrow cells, the...
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Chapter and Conference Paper
Talking Face Video Generation with Editable Expression
In rencent years, the convolutional neural network have been proved to be a great success in generating talking face. Existing methods have combined a single face image with speech to generate talking face vid...
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Chapter and Conference Paper
Augmenting SQLite for Local-First Software
Local-first software aims at both the ability to work offline on local data and the ability to collaborate across multiple devices. CRDTs (conflict-free replicated data types) are abstractions for offline and ...
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Chapter and Conference Paper
Towards More Powerful Multi-column Convolutional Network for Crowd Counting
Scale variation has always been one of the most challenging problems for crowd counting. By using multi-column convolutions with different receptive fields to deal with different scales in the scene, the multi...
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Chapter and Conference Paper
Using GitHub Open Sources and Database Methods Designed to Auto-Generate Chinese Tang Dynasty Poetry
Writing a Tang-Dynasty poetry in Chinese is very popular and useful in the tradition Chinese culture education field, In this paper we use GitHub open sources “jieba” Algorithm for text segmentation of Chinese...
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Chapter and Conference Paper
Reset Attack: An Attack Against Homomorphic Encryption-Based Privacy-Preserving Deep Learning System
In some existing privacy-preserving deep learning systems, additively homomorphic encryption enables ciphertext computation across the gradients. Therefore, many learning participants can perform neural networ...
