Search Results for author: Zhiqiu Huang

Found 7 papers, 4 papers with code

Towards an Accurate and Secure Detector against Adversarial Perturbations

1 code implementation18 May 2023 Chao Wang, Shuren Qi, Zhiqiu Huang, Yushu Zhang, Rushi Lan, Xiaochun Cao

It expands the above works on two aspects: 1) the introduced Krawtchouk basis provides better spatial-frequency discriminability and thereby is more suitable for capturing adversarial patterns than the common trigonometric or wavelet basis; 2) the extensive parameters for decomposition are generated by a pseudo-random function with secret keys, hence blocking the defense-aware adversarial attack.

Adversarial Attack Blocking

Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection

1 code implementation19 Jul 2022 Chao Wang, Zhiqiu Huang, Shuren Qi, Yaoshen Yu, Guohua Shen, Yushu Zhang

In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation.

SOM-based DDoS Defense Mechanism using SDN for the Internet of Things

no code implementations15 Mar 2020 Yunfei Meng, Zhiqiu Huang, Senzhang Wang, Guohua Shen, Changbo Ke

To effectively tackle the security threats towards the Internet of things, we propose a SOM-based DDoS defense mechanism using software-defined networking (SDN) in this paper.

Boosting API Recommendation with Implicit Feedback

no code implementations4 Feb 2020 Yu Zhou, Xinying Yang, Taolue Chen, Zhiqiu Huang, Xiaoxing Ma, Harald Gall

In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance.

Active Learning Learning-To-Rank

DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage

no code implementations15 Oct 2019 Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang

The source code suggestions provided by current IDEs are mostly dependent on static type learning.

Code Completion

Deep Transfer Learning for Source Code Modeling

1 code implementation12 Oct 2019 Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang

A challenging issue of these approaches is that they require training from starch for a different related problem.

Transfer Learning

CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling

1 code implementation3 Mar 2019 Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang

We evaluate CodeGRU with real-world data set and it shows that CodeGRU outperforms the state-of-the-art language models and help reduce the vocabulary size up to 24. 93\%.

Code Completion Code Generation +1

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