Reviewerly/exHarmony huggingface dataset:
Deep Radio Frequency Fingerprinting Based on Wavelet Scattering Network

With the deployment of 5G and large-scale Internet of Things (IoT), the equipment identification and authentication scheme based on RF fingerprint shows unique advantages in terms of lightweight and uniqueness. However, traditional RF fingerprint identification scheme based on machine learning has the disadvantages of high computational complexity and low accuracy. Meanwhile, this scheme requires large-scale labeled datasets to realize network learning, and due to the nonlinearity of the cascade, we can not well understand the properties and optimal configurations of these networks. To solve above problems, in this paper, we propose an RF fingerprint identification method based on wavelet scattering network in the small-scale dataset. Specifically, in this method, we first design a hybrid network model of wavelet scattering network combined with deep residual network (Resnet18). Then, since one of the main problems of RF fingerprinting is the diversity of signal information at different time scales, we choose to use the construction of scattering network based on wavelet basis to complete the accurate feature decomposition of the nonlinear features of RF fingerprint. These features are stable against deformations and retain high frequency information for identification. Finally, we can use the obtained detailed features to realize the accurate identification of RF radiation source equipments. The experimental results show that our scheme can better suppress the interference of noise in the signal, improve the feature representation ability, and it can obtain higher identification accuracy than other comparison schemes.
Communication Efficient Federated Learning via Channel-wise Dynamic Pruning

Federated Learning (FL) received widespread attention in 5G mobile edge networks (MENs) as it enables collaborative training deep learning models without disclosing users' private data. As the increasing number of parameters in the machine learning model poses a tremendous challenge for resource-constrained devices, there is a growing interest in applying model compression methods in federated learning. However, most existing model compression methods require a cumbersome procedure that introduces many additional hyperparameters and much more training time. In this paper, we propose a novel Channel-wise Dynamic Pruning method for communication efficient Federated Learning (FedCDP). The scheme dynamically evaluates the channel-wise parameter importance via a fast Taylor series evaluation and only communicates the important parameters in Federated Learning. Extensive experiments show the proposed method achieves both communication efficiency and model effectiveness in the benchmark datasets. The source codes are available at https://github.com/tabo0/FedCDP.
Towards Native Support for Federated Learning in 6G

6G mobile communication system is expected to be intelligence inclusive, that is, its system architecture would be designed to provide AI services to anyone, anywhere, including the network system itself. This mandates a clean-slate approach to its design. In this paper, this broad question is addressed from the perspective of providing distributed learning services, more specifically, using the Federated Learning (FL) paradigm. The relationship between two network metrics and the FL performance in a hierarchical federated learning system is explored, in order to determine the new features that the network architecture needs to support. An estimation of the dependency of bandwidth requirements on different ML models is also provided.
Enhancing Soft AC Based Reliable Offloading for IoV with Edge Computing

Reliability is one of the evaluation indices of system performance. For improving the reliability of the system, offloading scheme in vehicle edge computing (VEC) has been extensively studied. In order to improve reliability, a priority based reliable offloading scheme is proposed applying a reliability model, which is modeled considering the impact of various evaluation. Specifically, a multi-factor priority evaluation approach is first proposed, taking into account the importance and the urgency of tasks in a comprehensive manner. And then, based on the multi-factor priority evaluation approach, an optimization problem is formulated to maximize the system reliability. Thereafter, a priority based soft actor-critic (P-SAC) algorithm is developed to solve the complex optimization problem effectively. Performance evaluation results validate that the proposed scheme can improve system reliability.
Latency Minimization for Wireless Federated Learning with Heterogeneous Local Updates

In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local updates in each communication round. We formulate a total latency minimization problem, taking into account both the communication and computation latency in the whole FL procedure. We reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting sub-problems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulations show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve single-round latency reduction.
Self-supervised Multi-Modal Video Forgery Attack Detection

Video forgery attacks threaten surveillance systems by replacing the video captures with unrealistic synthesis, which can be powered by the latest augmented reality and virtual reality technologies. From the machine perception aspect, visual objects often have RF signatures that are naturally synchronized with them during recording. In contrast to video captures, the RF signatures are more difficult to attack given their concealed and ubiquitous nature. In this work, we investigate multimodal video forgery attack detection methods using both visual and wireless modalities. Since wireless signal-based human perception is environmentally sensitive, we propose a self-supervised training strategy to enable the system to work without external annotation and thus adapt to different environments. Our method achieves a perfect human detection accuracy and a high forgery attack detection accuracy of 94.38% which is comparable with supervised methods. The code is publicly available at: https://github.com/ChuiZhao/Secure-Mask.git
A Lightweight Bit-Operation Abnormal Traffic Detection Method Based On XNOR-CNN

The rapid development of the Internet and the increasingly complex structure of network space make the network security situation more and more serious. Abnormal traffic detection is an important part of network intrusion detection and plays an important role in the field of network security. Traditional machine learning methods rely too much on feature selection and extraction and have high false positive rate and delay caused by abnormal behavior recognition. Deep learning models such as CNN and RNN have many parameters and long training time, which seriously affect the early warning function of network intrusion detection. Aiming at the problem that the parameter redundancy of deep learning models limits the corresponding model deployment in some scenarios and devices, this paper proposes a lightweight bit-operation abnormal traffic detection method based on XNOR-net convolutional neural network (XNOR-CNN). The model uses convolutional neural network(CNN) and long-short term memory(LSTM) to comprehensively analyze the temporal and spatial characteristics of network traffic, and predicts and classifies the attack behaviors in the future network traffic. In particular, the model transforms the complex convolution process into bit operation between vectors by XNOR operation, which reduces the storage of weight vectors and complex redundant calculation, greatly improves the speed of network training and reduces the consumption of network memory. In this paper, the accuracy and efficiency of XNORCNN model are proved through a large number of comparative experiments on real traffic datasets.
Efficient Privacy-Preserving Federated Learning Against Inference Attacks for IoT

Based on the vulnerability of federated learning (FL) to inference attacks and the high computation overhead, lack of label protection and degraded model performance occurred in existing defense methods, we design an efficient privacy-preserving federated learning scheme based on compressed sensing (CS), where CS is used as both a compression method and an encryption method. Double aggregation is adopted together to ensure that gradients are not generally disclosed in a way that would allow attackers to infer private information. Meanwhile, gradient perturbation is implemented through CS-based decompression algorithm, and it also zeros the gradients for the fully connected layer which is the most important in label restoration. The proposed scheme can provide image protection and label protection simultaneously, while few additional computing resources are required, making it appropriate for IoT scenarios. Simulation results demonstrate our scheme’s effective and efficient defense under different settings with negligible impact on the model performance.
Task-Oriented Semantic Communication Based on Semantic Triplets

Task-oriented semantic communication has received growing interests, which can significantly reduce the amount of transmitted data without affecting task performance. In this paper, a novel semantic communication system based on semantic triplets (SCST) is proposed, in which the semantics is represented via the explainable semantic triplets. Specifically, we propose a semantic extraction method to convert the transmitted texts into semantic triplets, which can be further compressed via the designed semantic filtering method. The semantic triplets then will be encoded and transmitted via the wireless channel to complete intelligent tasks at the receiver. Moreover, we then apply the SCST to sentiment analysis task and question-answering task to verify the effectiveness, where the semantic encoder and decoder are designed respectively considering the final task. The experiment results show that the proposed SCST can obtain at least 43.5% and 52% accuracy gains, compared to the baselines using traditional communication method.
Lightweight Deep Joint Source-Channel Coding for Gauss-Markov Sources over AWGN channel

In this paper, we study the design of neural network based joint source-channel coding (JSCC) for point-to-point communication of Gauss-Markov sources over the additive white Gaussian noise (AWGN) channel with bandwidth compression. Among the existing deep learning (DL) -based JSCC methods for such sources, the long short-term memory (LSTM) based structure has good performance. However, it takes up huge time and space consumption because of its complex structure. In this work, we propose to adopt the causal convolution and dilated convolution to form our encoder and decoder due to their abilities of effectively extracting the temporal information of sources and their superiority in terms of reducing the time and space consumption. Experimental results show that the proposed model outperforms the traditional JSCC schemes and is comparable to the LSTM-based model in terms of source reconstruction quality. Besides, the proposed model shows a great robustness in the case of channel quality mismatch and correlation coefficient mismatch. Furthermore, our model takes lower time in the test phase and much lower space consumption compared to LSTM-based model.
Detecting Linear Block Codes via Deep Learning

In the channel code detection problem, given a sequence of noise-affected codewords generated by an unknown code, the aim is to identify the correct channel code from the given set of potential codes. This problem has many applications in military spectrum surveillance and in cognitive radios. In this paper, we consider the situation when the set of potential channel codes consists of two linear block codes and propose a deep learning based classification approach for the corresponding code detection problem. In this work, we propose data processing strategies suitable for linear block codes that reduce the amount of data required to train the deep neural network classifier. This however comes at the cost of having a lower probability of detection. For the proposed data processing strategies, we analytically obtained the optimal probability of correct detection that one can hope to achieve using any neural network classifier. The proposed results are validated numerically using a variety of examples.
Graph based Joint Computing and Communication Scheduling for Virtual Reality Applications

Virtual Reality (VR) applications delivered over wireless networks have attracted interest from academia and industry. The delay of VR applications is mainly composed of computing delay and communication delay. Although cloud computing centers have adequate computing power, accessing them requires long communication delay. Mobile edge computing (MEC), which offloads the computing power from the cloud computing center to the edge, is regarded as a feasible way to alleviate communication delay. However, due to the differences in the capability and location of MEC nodes, the selection of MEC nodes will affect both the computing delay and communication delay. In this paper, we focus on the joint representation of computing and communication resources and the selection of the optimal MEC node. First, we adopt graph-based joint computing and communication resources (GCC) model for VR applications routing and formulate the VR routing problem as an ILP problem. Then we design a Computing Nodes Expanded (CNE) algorithm, which allows us to use the Dijkstra algorithm to quickly obtain the optimal computing node and the path of shortest total delay. Finally, we run numerical experiments to evaluate the performance of the proposal algorithm. Simulation shows that the CNE algorithm can reduce the total delay by 42.9% and increase the delay satisfaction ratio by 23.3% compared to other benchmark algorithms.
Double-Sided Auction based Data-Energy Trading Architecture in Internet of Vehicles

In the era of big data, the unprecedented growth of data has spawned the commercial application of data trading markets in the Internet of Vehicles (IoV), while also posing challenges to their economic feasibility. In this paper, we propose a data-energy trading architecture in IoV consisting of a market operator, electric vehicles (EVs), and roadside units (RSUs), where RSUs exchange energy for data collected by EVs, and the market operator solves the data/energy allocation problem to maximize social welfare. However, due to the information asymmetry and fragmentation in the market, it is difficult to determine the optimal data and energy trading amount. To this end, we design an iterative double-sided auction (IDA) mechanism to regulate the interactive behaviors among the trading entities, where the market operator gathers local information from RSUs and EVs, and gradually adjusts the submitted bids of two sides to reach the desired payment and reward rules. The proposed IDA-based data-energy trading algorithm is convergent and satisfies the economic properties of efficiency, incentive compatibility, individual rationality, and budget balance. Numerical results demonstrate the performance of the proposed IDA-based data-energy trading architecture in IoV.
Random Railings Enhancement For RFF Imbalanced Data Augmentation

Radio Frequency Fingerprint (RFF) technology is an effective means to defend against cheating and counterfeit attacks in wireless communication. A deep learning-based RFF recognition algorithm can achieve well recognition performance, but it needs many balanced samples to train the model. However, the problem of sample imbalance is widespread in RFF identification tasks, and the number of signal samples of illegal devices is minimal. Neural network models usually can't learn these minority representations well, which seriously affects the performance of RFF recognition. Many advanced algorithms proposed to alleviate the problem of data imbalance don't perform well in the task of RFF recognition because they ignore the characteristics of RFF signals. Therefore, an algorithm based on Random Railings Enhancement (RRE) is proposed in this paper, which fills the data set with random masks according to the signal values front and rear. RRE protects the original signal's information, effectively expands the rare dataset, and has the effect of data enhancement. The experimental results show that the RRE can improve the performance of Radio Frequency (RF) identification technology tasks in the case of imbalanced data sets.
Multiscale Correlation Networks Based on Deep Learning for Automatic Modulation Classification

Automatic Modulation Classification (AMC) is a challenging yet significant technique for communication systems. Deep learning methods, though widely employed for AMC, are challenged by the poor representation in noisy scenarios. In this letter, we propose a novel Multiscale Correlation Networks (MSCNs) approach to enhance noise suppression and bolster representation power for AMC. MSCNs leverage learning correlation wavelet transform to redistribute radio signal and noise features across various scales, and incorporate multiscale correlation and attention mechanisms to characterize feature properties in terms of frequency. Experiments reveal that MSCNs achieve an overall classification rate of 91.36% at 10dB using 152k training samples on the public RadioML 2018.01A benchmark.
Structure-to-Shape Aortic 3-D Deformation Reconstruction for Endovascular Interventions

Fluoroscopy-guided endovascular interventions by using X-ray images are challenging. The catheter needs to be manipulated precisely inside the aorta, while only 2-D views from the X-ray fluoroscopy are currently used to help the surgeons. Because the catheter is operated in a 3-D space, a visualization of the deforming 3-D aorta will be useful as guidance for catheter manipulation. Existing 3-D reconstruction methods fall short in only focusing on the deformation reconstruction of the aortic 3-D centerline, or using additional prior knowledge of 3-D catheter position for estimating the aortic 3-D deformation. In this article, we propose a novel framework that reconstructs the aortic 3-D deformation by fusing a preoperative 3-D model and two intraoperative X-ray images. Different from existing methods, the proposed framework reconstructs aortic deformation using a coarse-to-fine pipeline by first reconstructing the aortic 3-D centerline and then reconstructing the 3-D shape. To obtain the accurate features for the fluoroscopic-based 3-D reconstruction, we extract semantic features from the X-ray images, and compute the distance field to efficiently calculate the 3-D–2-D nonrigid correspondence. Nonlinear least squares optimization is used to solve the deformation of both centerline and shape. The proposed framework is validated using phantom and patient datasets, whose results demonstrate improved efficiency and accuracy compared with the existing methods. This framework provides a valuable clinical tool for endovascular interventions.
JDT-NAS: Designing Efficient Multi-Object Tracking Architectures for Non-GPU Computers

Recent years have witnessed online multiple object tracking (MOT) revealing its enormous potential in numerous fields. However, existing MOT models are not suitable for deployment and application on non-GPU platforms (e.g. CPU-based computers), due to their inefficient inference. In this paper, we offer an automated approach for designing CPU-efficient multi-object tracking architectures, called JDT-NAS. Specifically, we first design several lightweight basic units as candidate operations to construct a heterogeneous search space for multi-object tracking. Then, based on the predefined space, we develop a four-stage hardware-aware search strategy to achieve the automated design of MOT models. Benefiting from our heterogeneous search space and special search strategy, our JDT-NAS not only allows flexible adjustment of the network scale (including depth, width and resolution dimensions), but also better meets the needs of efficient deployment. Finally, on publicly available standard datasets (e.g. MOT17), we apply JDT-NAS to search for multi-object trackers for fast inference on non-GPU computers. Comprehensive experiments show that these searched MOT architectures achieve superior speed and comparable accuracy compared to their state-of-the-art counterparts. This also indicates that our proposed JDT-NAS is a practical and feasible solution for efficient MOT architecture design.
Stepwise Cooperative Trajectory Planning for Multiple BUVs Based on Temporal–Spatial Bezier Curves

Biomimetic underwater vehicles (BUVs) are important instrumentation in applications such as water quality monitoring and underwater detection. In this paper, we study the cooperative trajectory planning problem for multiple BUVs and propose a stepwise trajectory planning algorithm based on temporal-spatial Bezier curves to enable cooperative formations. We introduce the time variable into the three-dimensional (3D) Bezier curve, designing a four-dimensional temporal-spatial Bezier curve. Considering the various physical constraints of the BUV, we obtain a smooth and feasible trajectory using Particle Swarm Optimization (PSO) when taking the shortest trajectory distance as the optimization goal. On this basis, we propose a method to perform cooperative trajectory planning and efficient obstacle avoidance replanning for multiple BUVs by integrating the real-time collision avoidance, real-time detection, and underwater complex terrain constraints. We verify the effectiveness of the proposed cooperative planning method through simulation and comparison.
Outlier Mining Techniques for Software Defect Prediction

Using software metrics as a method of quantification of software, various approaches were proposed for locating defect-prone source code units within software projects. Most of these approaches rely on supervised learning algorithms, which require labeled data for adjusting their parameters during the learning phase. Usually, such labeled training data is not available. Unsupervised algorithms do not require training data and can therefore help to overcome this limitation. In this work, we evaluate the effect of unsupervised learning by means of cluster-based algorithms and outlier mining algorithms for the task of defect prediction, i.e., locating defect-prone source code units. We investigate the effect of various class balancing and feature compressing techniques as preprocessing steps and show how sliding windows can be used to capture time series of source code metrics. We evaluate the Isolation Forest and Local Outlier Factor, as representants of outlier mining techniques. Our experiments on three publicly available datasets, containing a total of 11 software projects, indicate that the consideration of time series can improve static examinations by up to 3%. The results further show that supervised algorithms can outperform unsupervised approaches on all projects. Among all unsupervised approaches, the Isolation Forest achieves the best accuracy on 10 out of 11 projects.