Group-based authentication allows users within a single domain and group to access networks without repeating an individual authentication instance, greatly reducing the energy consumption of low-resource mobile devices in IoT and M2M communications. Nevertheless, in the case of the upcoming 6G massive communications with an exponentially larger number of connections, the computation and communication overhead of existing approaches on mobile devices are still significant. To this end, we propose GROSS, a novel GRoup-based authentication and key agreement (AKA) protocol that uses a One-time Secure Secret-sharing mechanism for more efficient authentication over massive wireless communications. Specifically, we employ a lightweight cryptographic operation for the above one-time secret sharing. The proposed GROSS significantly reduces both computation and communication overhead by consistently maintaining the validity of credentials for group-based authentication, thus enabling efficient verification of device legitimacy within a group. We also implement a simulation platform on JAVA for energy consumption evaluations for massive wireless communications. Our platform facilitates the flexible configuration of various energy components for authentication and supports up to million-level wireless connections. We conduct extensive experiments to show the effectiveness of our proposed GROSS.

Reconfigurable intelligent surface (RIS) facilitates the extraction of unpredictable channel features for physical layer key generation (PKG), securing communications among legitimate users with symmetric keys. Previous works have demonstrated that channel reciprocity plays a crucial role in generating symmetric keys in PKG systems, whereas, in reality, reciprocity is greatly affected by hardware interference and RIS-based jamming attacks. This motivates us to propose LoCKey, a novel approach that aims to improve channel reciprocity by mitigating interferences and attacks with a loop-back compensation scheme, thus maximizing the secrecy performance of the PKG system. Specifically, our proposed LoCKey is capable of effectively compensating for the CSI non-reciprocity by the combination of transmit-back signal value and error minimization module. Firstly, we introduce the entire flowchart of LoCKey and provide an in-depth discussion of each step. Following that, we delve into a theoretical analysis of the performance optimizations when our LoCKey is applied for CSI reciprocity enhancement. Finally, we conduct experiments to verify the effectiveness of the proposed LoCKey in improving channel reciprocity under various interferences for RIS-assisted wireless communications. The results demonstrate a significant improvement in both the rate of key generation assisted by the RIS and the consistency of the generated keys, showing great potential for the practical deployment of our LoCKey in future wireless systems.

Course recommender systems can assist students in identifying suitable or appealing courses by leveraging user interaction data. However, a prevalent issue with existing course recommender systems is their tendency to prioritize accuracy over explainability. To address this limitation, we propose a novel Knowledge Entity-Aware Model for course recommendation called KEAM, which supports explicit user profile generation based on detailed information from a knowledge graph to enhance comprehension of the students. Specifically, we exploit the information within knowledge graphs using neural networks. Then, KEAM captures students' preferences and creates profiles for explainable recommendations. Comprehensive experiments are conducted on two datasets to verify the effectiveness and explainability of KEAM.

Group Recommendation (GR), which aims to recommend items to groups of users, has become a promising and practical direction for recommendation systems. This paper points out two issues of the state-of-the-art GR models. (1) The pre-defined and fixed number of user groups is inadequate for real-time industrial recommendation systems, where the group distribution can shift dynamically. (2) The training schema of existing GR methods is supervised, necessitating expensive user-group and group-item labels, leading to significant annotation costs. To this end, we present a novel unsupervised group recommendation framework named $\underline{\text{I}}$dentify $\underline{\text{T}}$hen $\underline{\text{R}}$ecommend ($\underline{\text{ITR}}$), where it first identifies the user groups in an unsupervised manner even without the pre-defined number of groups, and then two pre-text tasks are designed to conduct self-supervised group recommendation. Concretely, at the group identification stage, we first estimate the adaptive density of each user point, where areas with higher densities are more likely to be recognized as group centers. Then, a heuristic merge-and-split strategy is designed to discover the user groups and decision boundaries. Subsequently, at the self-supervised learning stage, the pull-and-repulsion pre-text task is proposed to optimize the user-group distribution. Besides, the pseudo group recommendation pre-text task is designed to assist the recommendations. Extensive experiments demonstrate the superiority and effectiveness of ITR on both user recommendation (e.g., 22.22\% NDCG@5 $\uparrow$) and group recommendation (e.g., 22.95\% NDCG@5 $\uparrow$). Furthermore, we deploy ITR on the industrial recommender and achieve promising results.

Completing a motion sequence based on sparse key-frames remains a challenging task. The limited grasp of the human skeleton's spatial structure and the complexity of handling sparsely distributed motion sequences pose challenges for traditional interpolation algorithms, hindering their ability to generate authentic and smooth results. Recent progress utilizes Graph Convolutional Networks (GCN) to analyze human skeleton data, yielding promising results. In this paper, we introduce an improved Attention-Based Graph Convolutional Network framework for motion completion that tackles two key challenges: modeling correlations across indirectly connected joints and modeling correlations across frames in motion sequences with diverse sparsity. The method is designed to augment the learning capability of the GCN-based model without being constrained by the inherent human skeleton structure. Furthermore, this design can be concurrently applied to scenarios involving the completion of both single-person and multi-person motions. Experimental results on public human action datasets NTU RGB-D affirm the Spatio-Temporal Attention-Based Graph Convolutional Network's ability to generate smooth and authentic motion results.

We introduce HOSNeRF, a novel 360° free-viewpoint rendering method that reconstructs neural radiance fields for dynamic human-object-scene from a single monocular in-the-wild video. Our method enables pausing the video at any frame and rendering all scene details (dynamic humans, objects, and backgrounds) from arbitrary viewpoints. The first challenge in this task is the complex object motions in human-object interactions, which we tackle by introducing the new object bones into the conventional human skeleton hierarchy to effectively estimate large object deformations in our dynamic human-object model. The second challenge is that humans interact with different objects at different times, for which we introduce two new learnable object state embeddings that can be used as conditions for learning our human-object representation and scene representation, respectively. Extensive experiments show that HOSNeRF significantly outperforms SOTA approaches on two challenging datasets by a large margin of 40%~50% in terms of LPIPS. The code, data, and compelling examples of 360° free-viewpoint renderings from single videos: https://showlab.github.io/HOSNeRF.

In recent years, there has been a growing interest in the application of Graph Convolutional Networks (GCNs) for classifying or generating human skeleton-based action sequences. Despite the progress in this field, there exists a relative dearth of research on the underlying mechanisms of how these network structures learn and represent the information features of the human skeleton. This paper proposes a novel GCN-based reconstruction network ST-ATGCN that utilizes spatial and temporal attention mechanisms for analyzing the extraction and reconstruction patterns of human action sequences. This versatile network can be effectively employed in a wide array of applications, including data compression, noise reduction, and interpolation. Experimental results on a public dataset demonstrate that ST-ATGCN network outperforms most of the currently prevailing GCN-based methods. This indicates the efficacy of the proposed network architecture in accurately extracting and reconstructing human skeleton information. Moreover, the reconstruction network exhibits proficiency in effectively restoring noisy action sequences.

Unmanned aerial vehicle (UAV) swarms have shown great potential for Internet of Things (IoT). Meantime, its malicious use may cause huge threat to the national security. UAV swarms show the characteristic of high density which poses formidable challenges to radar resolution in the defense of critical areas. In this article, we consider a radar equipped with the coprime array, and then, use the coherent long-time integration (LTI) technique and gridless sparse technique to detect and localize UAVs in a swarm. This strategy takes full account of advantages of the coprime array, coherent LTI technique, and gridless sparse technique, i.e.: 1) the coprime array can provide a larger array aperture than the uniform linear array with the same number of array elements to relieve the stress of the gridless sparse technique and 2) the combination of coherent LTI technique and gridless sparse technique can maximize their advantages and make up for their shortcomings. By mathematical analyses and extensive numerical examples, we show the superiority of the proposed strategy in terms of accurate detection and localization of UAV swarms.

Various kinds of data are generated from industrial Internet of Things, and these data can be applied for connecting production equipment, identifying and locating items, etc. These data should be forwarded to the decision center for further analyses, especially in wartime. Thus, the channel status information (CSI) for industrial big data transmission has to be acquired. In this article, we develop a system architecture for industrial big data (BD) transmission based on radar-communication integration with arbitrary geometrical array. The traditional channel estimation method, which usually utilizes the regular antenna array to estimate the CSI, cannot be applied to the arbitrary geometrical array. Here, we use the manifold separation technique to transform the complex array configuration into regular array and the downlink channel covariance matrix is estimated by exploiting the frequency calibration technique when the uplink channel covariance matrix is received. The computational complexity for the proposed method and other state-of-the-art methods are analyzed. The simulation results prove that the proposed method can achieve excellent estimation performance for its application in radar-communication integration.

Unmanned aerial vehicle (UAV) swarms-enabled mobile edge computing system can be deployed in critical industrial zones for monitoring. Meanwhile, its malicious use may bring great threat to the security, and the accurate detection, and localization are important. UAV swarms show characteristics of the high density, small radar cross section, far range, and time-varying motion, and have posed formidable challenges to the accurate detection and localization. In this article, the accurate detection and localization of UAV swarms are investigated, and an effective method is proposed based on the Dechirp-keystone transform, and frequency-selective reweighted trace minimization. It inherits high robustness of the coherent long-time integration technique and superresolution of the gridless sparse technique. Mathematical analyzes and numerical simulations validate its superiorities in accurate detection and localization of UAV swarms.