- Articles -
01
Experimental full-duplex amplify-and-forward relay scheme for OFDM with power gain control
Pu Yang, Xiang-Gen Xia, Qingyue Qu, Yi Liu
Citation
Pu Yang, Xiang-Gen Xia, Qingyue Qu, Yi Liu. Experimental full-duplex amplify-and-forward relay scheme for OFDM with power gain control[J].Journal of Information and Intelligence, 2024, 2(5): 375-387. DOI: 10.1016/j.jiixd.2024.05.001
Abstract
The fundamental challenges for full-duplex (FD) relay networks are the self-interference cancellation (SIC) and the cooperative transmission design at the relay. This paper presents a practical amplify-and-forward (AF) FD one-way relay scheme for orthogonal frequency division multiplexing (OFDM) transmission with multi-domain SIC. It is found that the residual self-interference (SI) signals at the relay can be regarded as an equivalent multipath model. The effects of the residual SI at the relay are incorporated into the equivalent end-to-end channel model, and the inter-block interference can be removed at the destination by using cyclic prefix (CP) protection. Based on the equivalent multipath model, we present a solution for optimizing the amplification factor on the performance of signal-to-interference-plus-noise ratio (SINR) when the equivalent multipath length is longer than the CP. Furthermore, a practical one way FD relay network with 3 nodes is built and measured. The simulation and measured results verify the superior performance of the proposed scheme.
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https://www.sciencedirect.com/science/article/pii/S294971592400036
02
DI-VTR: Dual inter-modal interaction model for video-text retrieval
Jie Guo, Mengying Wang, Wenwei Wang, Yan Zhou, Bin Song
Citation
Jie Guo, Mengying Wang, Wenwei Wang, Yan Zhou, Bin Song. DI-VTR: Dual inter-modal interaction model for video-text retrieval[J]. Journal of Information and Intelligence, 2024, 2(5): 388-403. DOI: 10.1016/j.jiixd.2024.03.003
Abstract
Video-text retrieval is a challenging task for multimodal information processing due to the semantic gap between different modalities. However, most existing methods do not fully mine the intra-modal interactions, as with the temporal correlation of video frames, which results in poor matching performance. Additionally, the imbalanced semantic information between videos and texts also leads to difficulty in the alignment of the two modalities. To this end, we propose a dual inter-modal interaction network for video-text retrieval, i.e., DI-VTR. To learn the intra-modal interaction of video frames, we design a contextual-related video encoder to obtain more fine-grained content-oriented video representations. We also propose a dual inter-modal interaction module to accomplish accurate multilingual alignment between the video and text modalities by introducing multilingual text to improve the representation ability of text semantic features. Extensive experimental results on commonly-used video-text retrieval datasets, including MSR-VTT, MSVD and VATEX, show that the proposed method achieves significantly improved performance compared with state-of-the-art methods.
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https://www.sciencedirect.com/science/article/pii/S2949715924000362
03
A survey on membership inference attacks and defenses in machine learning
Jun Niu, Peng Liu, Xiaoyan Zhu, Kuo Shen, Yuecong Wang, Haotian Chi, Yulong Shen, Xiaohong Jiang, Jianfeng Ma, Yuqing Zhang
Citation
Jun Niu, Peng Liu, Xiaoyan Zhu, Kuo Shen, Yuecong Wang, Haotian Chi, Yulong Shen, Xiaohong Jiang, Jianfeng Ma, Yuqing Zhang. A survey on membership inference attacks and defenses in machine learning[J]. Journal of Information and Intelligence, 2024, 2(5): 404-454. DOI: 10.1016/j.jiixd.2024.02.001
Abstract
Membership inference (MI) attacks mainly aim to infer whether a data record was used to train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a tremendous amount of attention in the research community. One existing work conducted — to our best knowledge — the first dedicated survey study in this specific area: The survey provides a comprehensive review of the literature during the period of 2017∼2021 (e.g., over 100 papers). However, due to the tremendous amount of progress (i.e., 176 papers) made in this area since 2021, the survey conducted by the one existing work has unfortunately already become very limited in the following two aspects: (1) Although the entire literature from 2017∼2021 covers 18 ways to categorize (all the proposed) MI attacks, the literature during the period of 2017∼2021, which was reviewed in the one existing work, only covered 5 ways to categorize MI attacks. With 13 ways missing, the survey conducted by the one existing work only covers 27% of the landscape (in terms of how to categorize MI attacks) if a retrospective view is taken. (2) Since the literature during the period of 2017∼2021 only covers 27% of the landscape (in terms of how to categorize), the number of new insights (i.e., why an MI attack could succeed) behind all the proposed MI attacks has been significantly increasing since year 2021. As a result, although none of the previous work has made the insights as a main focus of their studies, we found that the various insights leveraged in the literature can be broken down into 10 groups. Without making the insights as a main focus, a survey study could fail to help researchers gain adequate intellectual depth in this area of research. In this work, we conduct a systematic study to address these limitations. In particular, in order to address the first limitation, we make the 13 newly emerged ways to categorize MI attacks as a main focus on the study. In order to address the second limitation, we provide — to our best knowledge — the first review of the various insights leveraged in the entire literature. We found that the various insights leveraged in the literature can be broken down into 10 groups. Moreover, our survey also provides a comprehensive review of the existing defenses against MI attacks, the existing applications of MI attacks, the widely used datasets (e.g., 107 new datasets), and the evaluation metrics (e.g., 20 new evaluation metrics).
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https://www.sciencedirect.com/science/article/pii/S2949715924000064
