Journal of Information and Intelligence最新文献

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).

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.

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.

Integrated sensing and communication (ISAC) technology enhances the spectrum utilization of the system by interchanging the spectrum between communication and sensing, which has gained popularity in scenarios such as vehicle-to-everything (V2X). With the aim of providing more dependable services for vehicles in high-speed mobile scenarios, we propose a scheme based on sense-assisted polarisation coding. Specifically, the base station acquires the vehicle's positional information and channel strength parameters through the forward time slot echo information. This information informs the creation of the coding architecture for the following time slot. This approach not only optimizes resource consumption but also enhances system dependability. Our simulation results confirm that the introduced scheme displays a notable improvement in the bit error rate (BER) when compared to traditional communication frameworks, maintaining this advantage across both unimpeded and compromised channel conditions.

The integration of communications, sensing and computing (I-CSC) has significant applications in vehicular ad hoc networks (VANETs). A roadside unit (RSU) plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication. Due to the constraints of limited resources, RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method. However, direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue. In this paper, we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks. The renormalization method is compared with two deployment schemes: genetic algorithm (GA) and memetic framework-based optimal RSU deployment (MFRD), to verify the improvement of communication performance. Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.

The combination of integrated sensing and communication (ISAC) with mobile edge computing (MEC) enhances the overall safety and efficiency for vehicle to everything (V2X) system. However, existing works have not considered the potential impacts on base station (BS) sensing performance when users offload their computational tasks via uplink. This could leave insufficient resources allocated to the sensing tasks, resulting in low sensing performance. To address this issue, we propose a cooperative power, bandwidth and computation resource allocation (RA) scheme in this paper, maximizing the overall utility of Cramér-Rao bound (CRB) for sensing accuracy, computation latency for processing sensing information, and communication and computation latency for computational tasks. To solve the RA problem, a twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to explore and obtain the effective solution of the RA problem. Furthermore, we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks, as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations. Simulation demonstrates that compared to other benchmark methods, TD3 achieves an average utility improvement of 97.11% and 27.90% in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.

In modern Wi-Fi systems, channel state information (CSI) serves as a foundational support for various sensing applications. Currently, existing CSI-based techniques exhibit limitations in terms of environmental adaptability. As such, optimizing the utilization of subcarrier CSI stands as a critical avenue for enhancing sensing performance. Within the OFDM communication framework, this work derives sensing outcomes for both detection and estimation by harnessing the CSI from every individual measured subcarrier, subsequently consolidating these outcomes. When contrasted against results derived from CSI based on specific extraction protocols or those obtained through weighted summation, the methodology introduced in this study offers substantial improvements in CSI-based detection and estimation performance. This approach not only underscores the significance but also serves as a robust exemplar for the comprehensive application of CSI.