Journal of Information and Intelligence最新文献

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.

With the prevalence of Internet of Things (IoT) devices, data collection has the potential to improve people's lives and create a significant value. However, it also exposes sensitive information, which leads to privacy risks. An approach called N-source anonymity has been used for privacy preservation in raw data collection, but most of the existing schemes do not have a balanced efficiency and robustness. In this work, a lightweight and efficient raw data collection scheme is proposed. The proposed scheme can not only collect data from the original users but also protect their privacy. Besides, the proposed scheme can resist user poisoning attacks, and the use of the reward method can motivate users to actively provide data. Analysis and simulation indicate that the proposed scheme is safe against poison attacks. Additionally, the proposed scheme has better performance in terms of computation and communication overhead compared to existing methods. High efficiency and appropriate incentive mechanisms indicate that the scheme is practical for IoT systems.

In the field of hyperspectral image (HSI) classification in remote sensing, the combination of spectral and spatial features has gained considerable attention. In addition, the multiscale feature extraction approach is very effective at improving the classification accuracy for HSIs, capable of capturing a large amount of intrinsic information. However, some existing methods for extracting spectral and spatial features can only generate low-level features and consider limited scales, leading to low classification results, and dense-connection based methods enhance the feature propagation at the cost of high model complexity. This paper presents a two-branch multiscale spectral-spatial feature extraction network (TBMSSN) for HSI classification. We design the multiscale spectral feature extraction (MSEFE) and multiscale spatial feature extraction (MSAFE) modules to improve the feature representation, and a spatial attention mechanism is applied in the MSAFE module to reduce redundant information and enhance the representation of spatial features at multiscale. Then we densely connect series of MSEFE or MSAFE modules respectively in a two-branch framework to balance efficiency and effectiveness, alleviate the vanishing-gradient problem and strengthen the feature propagation. To evaluate the effectiveness of the proposed method, the experimental results were carried out on bench mark HSI datasets, demonstrating that TBMSSN obtained higher classification accuracy compared with several state-of-the-art methods.

Partially blind signatures are introduced on the basis of blind signatures, which not only retain the advantages of blind signatures, but also solve the contradiction between anonymity and controllability in blind signatures. With the development of quantum computing technology, it becomes more urgent to construct secure partially blind signature schemes in quantum environments. In this paper, we present a new partially blind signature scheme and prove the security under the Ring-SIS assumption in the random oracle model. To avoid the restart problem of signature schemes caused by rejection sampling, a large number of random numbers are sampled in advance, so that they only need to be re-selected at the current stage without terminating the whole signature process when the conditions are not met. In addition, the hash tree technology is used to reduce communication costs and improve interactive performance. In order to avoid the errors in the security proof of the previous scheme, our proof builds upon and extends the modular framework for blind signatures of Hauck et al. and the correctness, partial blindness, and one-more unforgeability of the scheme are proved in detail according to the properties of the linear hash function.

The pace of society development is faster than ever before, and the smart city paradigm has also emerged, which aims to enable citizens to live in more sustainable cities that guarantee well-being and a comfortable living environment. This has been done by a network of new technologies hosted in real time to track the activities and provide smart solutions for the incoming requests or problems of the citizens. One of the most often used methodologies for creating a smart city is the Internet of Things (IoT). Therefore, the IoT-enabled smart city research topic, which consists of many different domains such as transportation, healthcare, and agriculture, has recently attracted increasing attention in the research community. Further, advances in artificial intelligence (AI) significantly contribute to the growth of IoT. In this paper, we first present the smart city concept, the background of smart city development and the components of the IoT-based smart city. This is followed up by a literature review of the research literature on the most recent IoT-enabled smart cities developments and breakthroughs empowered by AI techniques to highlight the current stage, major trends and unsolved challenges of adopting AI-driven IoT technologies for the establishment of desirable smart cities. Finally, we summarize the paper with a discussion of future research to provide recommendations for research direction in the smart city domain.

S-boxes play a central role in the design of symmetric cipher schemes. For stream cipher applications, an S-box should satisfy several criteria such as high nonlinearity, balanceness, correlation immunity, and so on. In this paper, by using disjoint linear codes, a class of S-boxes possessing high nonlinearity and 1st-order correlation immunity is given. It is shown that the constructed correlation immune S-boxes can possess currently best known nonlinearity, which is confirmed by the example 1st-order correlation immune (12, 3) S-box with nonlinearity 2000. In addition, two other frameworks concerning the criteria of balanced and resiliency are obtained respectively.

Nowadays, flying ad hoc network (FANET) has captured great attention for its huge potential in military and civilian applications. However, the high-speed movement of unmanned aerial vehicles (UAVs) in three-dimensional (3D) space leads to fast topology change in FANET and brings new challenges to traditional routing mechanisms. To improve the performance of packet transmission in the 3D high dynamic FANETs, we propose a 3D greedy perimeter stateless routing (GPSR) algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna (KOGPSR). Especially, in data forwarding part of the KOGPSR, we propose a new link metric for greedy forwarding based on a torus-shaped radiation pattern of the omnidirectional antenna of UAVs, and a restricted flooding strategy is introduced to solve the 3D void node problem in geographic routing. In addition, in order to enhance the accuracy of the location information of high dynamic UAVs, we design an adaptive Kalman algorithm to track and predict the motion of UAVs. Finally, a FANET simulation platform based on OPNET is built to depict the performance of the KOGPSR algorithm. The simulation results show that the proposed KOGPSR algorithm is more suitable for the actual 3D high dynamic FANET.

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.

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).