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Unlike typical video action recognition, Dynamic Facial Expression Recognition (DFER) does not involve distinct moving targets but relies on localized changes in facial muscles. Addressing this distinctive attribute, we propose a Multi-Scale Spatio-temporal CNN-Transformer network (MSSTNet). Our approach takes spatial features of different scales extracted by CNN and feeds them into a Multi-scale Embedding Layer (MELayer). The MELayer extracts multi-scale spatial information and encodes these features before sending them into a Temporal Transformer (T-Former). The T-Former simultaneously extracts temporal information while continually integrating multi-scale spatial information. This process culminates in the generation of multi-scale spatio-temporal features that are utilized for the final classification. Our method achieves state-of-the-art results on two in-the-wild datasets. Furthermore, a series of ablation experiments and visualizations provide further validation of our approach's proficiency in leveraging spatio-temporal information within DFER., Comment: Accepted to 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)

Cooperative spectrum sensing (CSS) is a promising approach to improve the detection of primary users (PUs) using multiple sensors. However, there are several challenges for existing combination methods, i.e., performance degradation and ceiling effect for hard-decision fusion (HDF), as well as significant uploading latency and non-robustness to noise in the reporting channel for soft-data fusion (SDF). To address these issues, an integrated communication and computation (ICC) framework is proposed in this paper. Specifically, distributed semantic communication (DSC) jointly optimizes multiple sensors and the fusion center to minimize the transmitted data without degrading detection performance. Moreover, over-the-air computation (AirComp) is utilized to further reduce spectrum occupation in reporting channel, taking advantage of characteristics of wireless channel to enable data aggregation. Under the ICC framework, a particular system, namely ICC-CSS, is designed and implemented, which is theoretically proved to be equivalent to the optimal estimator-correlator (E-C) detector with equal gain SDF when the PU signal samples are independent and identically distributed. Extensive simulations verify the superiority of ICC-CSS compared with various conventional CSS schemes in terms of detection performance, robustness to SNR variations in both sensing and reporting channels, as well as scalability with respect to the number of samples and sensors., Comment: 15 pages, Journal, accepted by IEEE TCOM

Deep learning-empowered semantic communication is regarded as a promising candidate for future 6G networks. Although existing semantic communication systems have achieved superior performance compared to traditional methods, the end-to-end architecture adopted by most semantic communication systems is regarded as a black box, leading to the lack of explainability. To tackle this issue, in this paper, a novel semantic communication system with a shared knowledge base is proposed for text transmissions. Specifically, a textual knowledge base constructed by inherently readable sentences is introduced into our system. With the aid of the shared knowledge base, the proposed system integrates the message and corresponding knowledge from the shared knowledge base to obtain the residual information, which enables the system to transmit fewer symbols without semantic performance degradation. In order to make the proposed system more reliable, the semantic self-information and the source entropy are mathematically defined based on the knowledge base. Furthermore, the knowledge base construction algorithm is developed based on a similarity-comparison method, in which a pre-configured threshold can be leveraged to control the size of the knowledge base. Moreover, the simulation results have demonstrated that the proposed approach outperforms existing baseline methods in terms of transmitted data size and sentence similarity., Comment: 14 pages, Journal, accepted by IEEE TWC

Unconventional topological quasiparticles have recently garnered significant attention in the realm of condensed matter physics. Here, based on first-principles calculations and symmetry analysis, we reveal the coexistence of multiple types of interesting unconventional topological quasiparticles in the phonon spectrum of the chiral crystal CsBe$_2$F$_5$. Specifically, we identified eight entangled phonon bands in CsBe$_2$F$_5$, which give rise to various unconventional topological quasiparticles, including the spin-1 Weyl point, the charge-2 Dirac point, the nodal surface, and the hourglass nodal loop. We demonstrate that these unconventional topological quasiparticles are protected by crystal symmetry. We show that there are two large Fermi arcs connecting projections of the bulk spin-1 Weyl point and charge-2 Dirac point on the (001) surface and across the entire surface Brillouin zone. Our work not only elucidates the intriguing topological properties of chiral crystals but also provides an excellent material platform for exploring the fascinating physics associated with multiple types of unconventional topological quasiparticles., Comment: 5 pages, 4 figures

Semantic segmentation in complex scenes relies not only on object appearance but also on object location and the surrounding environment. Nonetheless, it is difficult to model long-range context in the format of pairwise point correlations due to the huge computational cost for large-scale point clouds. In this paper, we propose using regions as the intermediate representation of point clouds instead of fine-grained points or voxels to reduce the computational burden. We introduce a novel Region-Enhanced Feature Learning Network (REFL-Net) that leverages region correlations to enhance point feature learning. We design a region-based feature enhancement (RFE) module, which consists of a Semantic-Spatial Region Extraction stage and a Region Dependency Modeling stage. In the first stage, the input points are grouped into a set of regions based on their semantic and spatial proximity. In the second stage, we explore inter-region semantic and spatial relationships by employing a self-attention block on region features and then fuse point features with the region features to obtain more discriminative representations. Our proposed RFE module is plug-and-play and can be integrated with common semantic segmentation backbones. We conduct extensive experiments on ScanNetV2 and S3DIS datasets and evaluate our RFE module with different segmentation backbones. Our REFL-Net achieves 1.8% mIoU gain on ScanNetV2 and 1.7% mIoU gain on S3DIS with negligible computational cost compared with backbone models. Both quantitative and qualitative results show the powerful long-range context modeling ability and strong generalization ability of our REFL-Net., Comment: Accepted by IEEE Transactions on Multimedia 2023

With the development of Information and Communication Technologies, trust has been applied more and more in various scenarios. At the same time, different organizations have published a series of trust frameworks to support the implementation of trust. There are also academic paper discussing about these trust standards, however, most of them only focus on a specific application. Unlike existing works, this paper provides an overview of all current available trust standards related to communication networks and future digital world from several main organizations. To be specific, this paper summarizes and organizes all these trust standards into three layers: trust foundation, trust elements, and trust applications. We then analysis these trust standards and discuss their contribution in a systematic way. We discuss the motivations behind each current in forced standards, analyzes their frameworks and solutions, and presents their role and impact on communication works and future digital world. Finally, we give our suggestions on the trust work that needs to be standardized in future., Comment: 7 pages, 3 figures, Magazine paper under review

The hourglass fermions in solid-state materials have been attracting significant interest recently. However, realistic two-dimensional (2D) materials with hourglass-shaped band structures are still very scarce. Here, through the first-principles calculations, we identify the monolayer Pb(ClO$_{2}$)$_{2}$ and Sr(ClO$_{2}$)$_{2}$ materials as the new realistic materials platform to realize 2D hourglass Weyl nodal loop. We show that these monolayer materials possess an hourglass Weyl nodal loop circling around the $\Gamma$ point and Weyl nodal line on the Brillouin zone (BZ) boundary in the absence of spin-orbit coupling (SOC). Through the symmetry analysis, we demonstrate that the hourglass Weyl nodal loop and Weyl nodal line are protected by the nonsymmorphic symmetries, and are robust under the biaxial strains. When we include the SOC, a tiny gap will be opened in the hourglass nodal loop and nodal line, and the nodal line can be transformed into the spin-orbit Dirac points. Our results provide a new realistic material platform for studying the intriguing physics associated with the 2D hourglass Weyl nodal loop and spin-orbit Dirac points., Comment: 10 pages, 7 figures, Accepted for publication in New Journal of Physics

Recent years have witnessed a digital explosion with the deployment of 5G and proliferation of 5G-enabled innovations. Compared with 5G, 6G is envisioned to achieve much higher performance in terms of latency, data rate, connectivity, energy efficiency, coverage and mobility. To fulfil these expectations, 6G will experience a number of paradigm shifts, such as exploiting new spectrum, applying ubiquitous ML/AI technologies and building a space-air-ground-sea integrated network. However, these paradigm shifts may lead to numerous new security and privacy issues, which traditional security measures may not be able to deal with. To tackle these issues and build a trustworthy 6G network, we introduce a novel trust framework named as SIX-Trust, which composes of 3 layers: sustainable trust (S-Trust), infrastructure trust (I-Trust) and xenogenesis trust (X-Trust). Each layer plays a different role, and the importance of each layer varies for different application scenarios of 6G. For each layer, we briefly introduce its related enabling technologies, and demonstrate how these technologies can be applied to enhance trust and security of the 6G network. In general, SIX-Trust provides a holistic framework for defining and modeling trust of 6G, which can facilitate establishing a trustworthy 6G network., Comment: 7 pages, 3 figures, under review

With the rapid development of blockchain technology in recent years, all kinds of blockchain-based applications have emerged. Among them, the decentralized finance (DeFi) is one of the most successful applications, which is regarded as the future of finance. The great success of DeFi relies on the real-world data which is not directly available on the blockchain. Besides, due to the deterministic nature of blockchain,the blockchain cannot directly obtain in-deterministic data from the outside world (off-chain). Thus, oracles have appeared as a viable solution to feed off-chain data to blockchain applications. In this paper, we carryout a comprehensive study on oracles, especially on DeFi oracles. We first briefly introduce the application scenarios of DeFi oracles, and then we talk about the past of DeFi oracles by categorizing them into several types based on their design features. After that, we introduce five popular DeFi oracles currently in use(such as Chainlink and Band Protocol), with the focus on their system architecture, data validation process,and their incentive mechanisms. We compare these present DeFi oracles from their data trustworthiness,data source trustworthiness and their overall trust models. Finally, we propose a set of metrics for designing trustworthiness DeFi oracles, and propose a potential trust architecture and a few promising techniques for building trustworthiness oracles., Comment: Under review

This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network, in which we propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV and also leverage the MUAV as a recharging power source. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network. Depending on whether there is charging between the two UAVs, two optimization problems are formulated. To solve them, two multi-agent deep reinforcement learning (DRL) approaches are proposed, which are centralized training multi-agent deep deterministic policy gradient (CT-MADDPG) and multi-agent deep deterministic policy option critic (MADDPOC). It is shown that the CT-MADDPG can greatly reduce the requirement on the computing capability of the UAV hardware, and the proposed MADDPOC is able to support low-level multi-agent cooperative learning in the continuous action domains, which has great advantages over the existing option-based hierarchical DRL that only support single-agent learning and discrete actions.

In this paper, we propose a trust-centric privacy-preserving blockchain for dynamic spectrum access in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors' real-time geolocatioin information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable sensing nodes is low, and the cooperative sensing performance is improved with the help of trust value evaluation mechanism.In addition, incentivization and security are also analyzed, which show that our design not only can encourage nodes' participation, but also resist to many kinds of attacks which are frequently encountered in trust-based blockchain systems., Comment: 15pages, 15 figures