523 Result(s)
within
Computer Science
Artificial Intelligence
Zhuo** **e
Conference Paper
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Chapter and Conference Paper
Semantic Preferences of Biran and Yiding: A Distinctive Collexeme Analysis of Chinese Near-Synonymous Constructions of “Mod + Verb”
The Chinese modals biran ‘must be, definitely’ and yiding ‘must be, definitely’ can both express the sense of epistemic necessity. “Modal + verb” is a representative construction that expresses modal judgment. Th...
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Chapter and Conference Paper
HiFiHR: Enhancing 3D Hand Reconstruction from a Single Image via High-Fidelity Texture
We present HiFiHR, a high-fidelity hand reconstruction approach that utilizes render-and-compare in the learning-based framework from a single image, capable of generating visually plausible and accurate 3D ha...
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Chapter and Conference Paper
Research on Comprehensive Blockchain Regulation and Anti-fraud System
The blockchain technology has attracted attention due to its characteristics of anonymity, openness, decentralization, traceability, and tamper-resistance. However, with the development of the blockchain indus...
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Chapter and Conference Paper
Rearranging Inv Message in the Bitcoin to Construct Covert Channels
Covert channels aim to conceal the communication behaviors and are widely applied to transmit sensitive data. Blockchains are well-suited for building state-of-the-art covert channels due to their decentraliza...
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Chapter and Conference Paper
PtbStolen: Pre-trained Encoder Stealing Through Perturbed Samples
Recent years have witnessed the huge success of adopting the self-supervised learning paradigm into pre-train effective encoders [1].
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Chapter and Conference Paper
Efficient 3D View Synthesis from Single-Image Utilizing Diffusion Priors
In this paper, we introduce a novel framework for synthesizing novel views of objects from a single image. Leveraging the capabilities of fine-tuned diffusion models, our method combines latent 3D knowledge as...
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Chapter and Conference Paper
A Framework Combining Separate and Joint Training for Neural Vocoder-Based Monaural Speech Enhancement
Conventional single-channel speech enhancement methodologies have predominantly emphasized the enhancement of the amplitude spectrum while preserving the original phase spectrum. Nonetheless, this may introduc...
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Chapter and Conference Paper
Few Shot Specific Emitter Identification Based on Triplet Loss
Deep learning-based RF fingerprinting has emerged as a crucial approach for device authentication. However, this technology often requires a large number of labelled samples practically. To address this issue,...
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Chapter and Conference Paper
Iterative Noisy-Target Approach: Speech Enhancement Without Clean Speech
Traditional Deep Neural Network based speech enhancement usually requires clean speech as the target of training. However, limited access to ideal clean speech hinders its practical use. Meanwhile, existing se...
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Chapter and Conference Paper
CTAN: Collaborative Tag-Aware Attentive Network for Recommendation
Graph-based methods are one of the effective means to solve the data sparsity and cold start problems. They can not only ensure the accuracy of the recommendation results but also have a certain degree of inte...
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Chapter and Conference Paper
Some Combinatorial Algorithms on the Dominating Number of Anti-rank k Hypergraphs
Given a hypergraph H(V, E), a set of vertices \(S\subseteq V\) S ⊆
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Chapter and Conference Paper
3D Multi-scene Stylization Based on Conditional Neural Radiance Fields
Neural Radiation Field (NeRF) is a scene model capable of achieving high-quality view synthesis, optimized for each specific scene. In this paper, we propose a conditional neural radiation field based on multi...
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Chapter and Conference Paper
Smart Contract Code Clone Detection Based on Pre-training Techniques
Smart contract has been utilized to realize cryptocurrencies and crowdfunding initiatives. Due to its characteristics of immutable once deployed, the security issues have been widely studied and paid much atte...
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Chapter and Conference Paper
Robust Subspace Learning with Double Graph Embedding
Low-rank-based methods are frequently employed for dimensionality reduction and feature extraction in machine learning. To capture local structures, these methods often incorporate graph embedding, which requi...
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Chapter and Conference Paper
End-to-End Streaming Customizable Keyword Spotting Based on Text-Adaptive Neural Search
Streaming keyword spotting (KWS) is an important technique for voice assistant wake-up. While KWS with a preset fixed keyword has been well studied, test-time customizable keyword spotting in streaming mode re...
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Chapter and Conference Paper
Babelt: A Pregnancy Belly Support Belt Connected with an App Designed for Pregnant Women with GDM
Gestational diabetes mellitus, a type of diabetes that develops during pregnancy in women who don’t already have diabetes, has a greater impact on the health of pregnant women than is often thought. Research d...
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Chapter and Conference Paper
Visible and NIR Image Fusion Algorithm Based on Information Complementarity
Visible and near-infrared (NIR) band sensors provide images that capture complementary spectral radiations from a scene. And the fusion of the visible and NIR image aims at utilizing their spectrum properties ...
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Chapter and Conference Paper
An Obstacle Detection Method for Visually Impaired People Based on Semantic Segmentation
Using low-cost visual sensors to assist indoor and outdoor navigation is an important method to solve the problem of visually impaired people living and going out. To this end, we proposed an obstacle-detectio...
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Chapter and Conference Paper
A Multiple Correspondence Analysis on the Adverbial Uses of the Chinese Color Term Bai ‘White’
This paper conducts a corpus-based semantic analysis on the adverbial uses of the Chinese color term bai ‘white’ regarding its two metaphorical meanings – “In vain/For no reason” and “Free of charge”. Based on di...
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Chapter and Conference Paper
Object-Aware Transfer-Based Black-Box Adversarial Attack on Object Detector
Deep neural networks have been demonstrated to be vulnerable to adversarial noise from attacks. Compared with white-box attacks, black-box attacks fool deep neural networks to yield erroneous predictions witho...
