Reviewerly/exHarmony huggingface dataset:
Signal Automatic Modulation Classification and Recognition in View of Deep Learning

With the advancement of 5G technology, wireless communication resources such as channels and spectrum become scarce. This necessitates ensuring the efficiency and security of signal modulation and demodulation, which imposes higher requirements for wireless communication systems. However, signal modulation has the problems of large amount of data, low recognition accuracy and various types. In this study, a classification network of automatic modulation classification recognition algorithm for signal-to-noise ratio is proposed to solve the problem that traditional noise reduction algorithms will damage signals with high signal-to-noise ratio, consequently reducing the accuracy of signal recognition. In order to solve the problem of high complexity of network model algorithm, in particular, a signal automatic modulation classification and recognition algorithm based on neural network autoencoder is proposed. Experimental results show that the accuracy of signal automatic modulation classification recognition in the algorithm increases as the increase of modulation signals and tends to be stable. When the modulation signal is 0dB, the recognition accuracy gradually converges to the highest, and reaches 81.6% when the modulation signal is 18dB. In contrast, the DenseNet algorithm has the lowest recognition accuracy, with only 77.5% recognition accuracy when the signal modulation classification is 18dB, a difference of 4.1%. This indicates that the algorithm performs exceptionally well in automatic signal modulation classification, and its complexity is lower than other comparative network models, providing certain advantages.
Efficient Detection of Noise Reviews Over a Large Number of Places

User reviews have been widely used to extract opinions, complaints, and requirements about a given place or product from the users’ point of view. In the process of collecting user reviews, a lot of reviews irrelevant to a given search keyword are included in the collection result. Such irrelevant reviews can easily be detected using supervised learning algorithms. However, situation changes when the number of places or products that need to be analyzed increases because manual labeling is required for each collected review. This paper presents a method to detect irrelevant reviews efficiently when a large number of places and reviews need to be analyzed. The basic idea of the proposed method is to expand the target of the learning from an individual place to a group consisting of multiple places. The method can be applied properly to any place whose number of reviews is not enough to perform the training because a classifier obtained by training sufficient reviews included in the places of a group can be used for the places with insufficient reviews in the group. As an initial study of this approach, we tried to check through experiments whether the proposed method can provide higher accuracy than the conventional method where the training is performed for individual places. Our experimental results showed that the average f1-score of the group learning was about 0.931 over real data collected online. For the places with less than 100 reviews, the f1-score of the conventional and proposed methods was 0.651 and 0.865, respectively, showing that 21.4% performance improvement.
Dynamic Object Classification of Low-Resolution Point Clouds: An LSTM-Based Ensemble Learning Approach

In unmanned vehicle perception, dynamic object classification is applied to classify objects accurately and timely, providing decision-making for obstacle avoidance and planning. Low-resolution LiDAR is one of the most important sensors for this task. Unfortunately, the existing approaches perform unsatisfactorily due to the huge domain gap between low-resolution and high-resolution point cloud classification. Some schemes try to reduce the gap by fusing multi-scan information through SLAM or completing single-scan point clouds. However, these methods rely on high positioning accuracy or the wholeness of object data. To this end, differently, we propose a dynamic object classification method of low-resolution data from the perspective of time-series fusion. By modeling time series of sparse data, we indicate change rules of separate classification models for object representation. Subsequently, based on ensemble learning, our method performs feature-level fusion on multiple networks to exploit their different expression capabilities. Finally, we utilize long short-term memory to gradually classify dynamic objects. Besides, we also propose a dataset of the low-resolution point clouds and manually annotate the ground truth, which contains abundant samples of cars, pedestrians, and motorcycles. Through testing actual low-resolution data, the accuracy of our method is verified to improve a lot than the state-of-the-art approaches.
Fence: Fee-Based Online Balance-Aware Routing in Payment Channel Networks

Scalability is a critical challenge for blockchain-based cryptocurrencies. Payment channel networks (PCNs) have emerged as a promising solution for this challenge. However, channel balance depletion can significantly limit the capacity and usability of a PCN. Specifically, frequent transactions that result in unbalanced payment flows from two ends of a channel can quickly deplete the balance on one end, thus blocking future payments from that direction. In this paper, we propose Fence, an online balance-aware fee setting algorithm to prevent channel depletion and improve PCN sustainability and long-term throughput. In our algorithm, PCN routers set transaction fees based on the current balance and level of congestion on each channel, in order to incentivize payment senders to utilize paths with more balance and less congestion. Our algorithm is guided by online competitive algorithm design, and achieves an asymptotically tight competitive ratio with constant violation in a unidirectional PCN. We further prove that no online algorithm can achieve a finite competitive ratio in a general PCN. Extensive simulations under a real-world PCN topology show that Fence achieves high throughput and keeps network channels balanced, compared to state-of-the-art PCN routing algorithms.
Toward Fine-Grained Talking Face Generation

Talking face generation is the process of synthesizing a lip-synchronized video when given a reference portrait and an audio clip. However, generating a fine-grained talking video is nontrivial due to several challenges: 1) capturing vivid facial expressions, such as muscle movements; 2) ensuring smooth transitions between consecutive frames; and 3) preserving the details of the reference portrait. Existing efforts have only focused on modeling rigid lip movements, resulting in low-fidelity videos with jerky facial muscle deformations. To address these challenges, we propose a novel Fine-gRained mOtioN moDel (FROND), consisting of three components. In the first component, we adopt a two-stream encoder to capture local facial movement keypoints and embed their overall motion context as the global code. In the second component, we design a motion estimation module to predict audio-driven movements. This enables the learning of local key point motion in the continuous trajectory space to achieve smooth temporal facial movements. Additionally, the local and global motions are fused to estimate a continuous dense motion field, resulting in spatially smooth movements. In the third component, we devise a novel implicit image decoder based on an implicit neural network. This decoder recovers high-frequency information from the input image, resulting in a high-fidelity talking face. In summary, the FROND refines the motion trajectories of facial keypoints into a continuous dense motion field, which is followed by a decoder that fully exploits the inherent smoothness of the motion. We conduct quantitative and qualitative model evaluations on benchmark datasets. The experimental results show that our proposed FROND significantly outperforms several state-of-the-art baselines.
FedFM: Anchor-Based Feature Matching for Data Heterogeneity in Federated Learning

One of the key challenges in federated learning (FL) is local data distribution heterogeneity across clients, which may cause inconsistent feature spaces across clients. To address this issue, we propose Federated Feature Matching (FedFM), which guides each client’s features to match shared category-wise anchors (landmarks in feature space). This method attempts to mitigate the negative effects of data heterogeneity in FL by aligning each client’s feature space. We tackle the challenge of varying objective functions in theoretical analysis and provide convergence guarantee for FedFM. In FedFM, to mitigate the phenomenon of overlapping feature spaces across categories and enhance the effectiveness of feature matching, we propose a feature matching loss called contrastive-guiding (CG), which guides each local feature to match with the corresponding anchor while keeping away from non-corresponding anchors. Additionally, to achieve higher efficiency and flexibility, we propose a FedFM variant, called FedFM-Lite, which enables flexible trade-off between algorithm utility and communication bandwidth cost. Through extensive experiments, we demonstrate that FedFM with CG outperforms seven classical and representative works by quantitative and qualitative comparisons. FedFM-Lite can achieve better performance than state-of-the-art methods with five to ten times less communication costs.
Image-Guided Trajectory Tracking Control of the Nanopositioning Stages Using Regional Dense Photometic Information

For compliant nanopositioning stages, the closed-loop control can effectively reduce the trajectory tracking error caused by modeling uncertainties and unknown disturbances. However, pose sensing solutions with multiple degrees of freedom, high precision, and high sampling rate required for closed loops are difficult to find. In this article, rendering the closed-loop trajectory tracking as an image-guided problem, a visual servo scheme for trajectory tracking control of the CNPSs is presented. Specifically, by updating the planning trajectory parameters in the feedback image area, the closed-loop trajectory tracking problem is transformed into the problem of minimizing the errors of the regional dense photometric information. Subsequently, an image-guided trajectory tracking controller with submillisecond delay is designed based on iterative optimization technique, and its stability is analyzed using Lyapunov’s first stability law. Simulation results show that when the size of the template is appropriate, the theoretical accuracy of translation-/rotation components can easily reach 0.1 pixel and 0.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> , respectively, even under the interference of image noise. The experimental results show that the proposed method can successfully track complex trajectories, with the tracking error smaller than 50 nm. Comparison experiments demonstrate that the proposed method can effectively reduce the tracking error caused by model uncertainties.
Accelerating Convolutional Neural Networks by Exploiting the Sparsity of Output Activation

Deep Convolutional Neural Networks (CNNs) are the most widely used family of machine learning methods that have had a transformative effect on a wide range of applications. Previous studies have made great breakthroughs in accelerating CNNs, but they only target on the input sparsity of activation and weight, thus do not eliminate the unnecessary computations due to the fact that more zeros in the output results are not directly caused by the zero-valued positions of the input data. In this paper, we take advantage of the output activation sparsity to reduce the execution time and energy consumption of CNNs. First, we propose an effective prediction method that leverages the output activation sparsity. Our method first predicts the output activation polarity of convolutional layers based on the singular value decomposition (SVD) approach. Then, it uses the predicted negative value to skip invalid computations. Second, an effective accelerator is designed to take advantage of sparsity to achieve CNN inference acceleration. Each PE is equipped with a prediction unit and a non-zero value detection unit to remove invalid computation blocks. And an instruction bypass technique is proposed which further exploits the sparsity of the weights. The efficient dataflow graph mapping approach and pipeline execution ensure high computational resource utilization. Experiments show that our approach achieves up to 1.63× speedup and 55.30% energy reduction compared with dense networks with a slight loss of accuracy. Compared with Eyeriss, our accelerator achieves on average 1.31 × performance improvement and 54% energy reduction. Our accelerator also achieves a similar performance to SnaPEA, but with a better energy efficiency.
Federated Double Deep Q-Learning-Based Computation Offloading in Mobility-Aware Vehicle Clusters

On the edge side of internet of vehicles (IoV), mobile edge computing (MEC) servers with certain computational resources are deployed to provide computational service for vehicles. However, with the generation of a series of vehicle computing intensive and delay sensitive applications such as virtual reality (VR) navigation and vehicle-mounted games, the quality of service (QoS) for vehicle applications cannot be guaranteed with the limited computational resources in MECs. In this article, adjacent vehicles in IoV are converged into clusters based on topological stability. The idle computational resources of vehicles within the cluster are integrated and scheduled uniformly to serve vehicles within the cluster with computation task requirements. This reduces the dynamic changes in idle computational resources caused by rapid vehicle movement, and effectively ensured reliable feedback of computing results after offloading computing tasks. In order to reduce the task completion time, the task is decomposed into multiple subtasks with time dependencies, which can be processed in parallel. A directed acyclic graph (DAG) is used to characterize the task model. To further minimize latency in completing computing tasks, a federated double deep Q network-based computation task offloading (FDTO) strategy is proposed for vehicle clusters. The optimal computation task offloading decision is obtained based on the collaborative training and updating of double deep Q network (DDQN). Unlike traditional machine learning algorithms that require centralized training, federated learning (FL) allows only the training model parameters to be passed between vehicles, which not only protects data privacy but also reduces communication overhead. Simulation results show that the proposed strategy can effectively reduce task completion latency compared to the benchmark strategies.
Improved Cyber Defense Modeling Framework for Modeling and Simulating the Lifecycle of Cyber Defense Activities

It is difficult to assess the business impact of a cyberattack and implement appropriate strategies or policies to enhance cyber resilience and counter future attacks. Penetration testing, which is currently gaining popularity, has been employed to assess cyber defense levels in actual operating environments. However, it is expensive and time-consuming and only reveals the current state of a problem without providing insights into potentially better alternative strategies. To overcome these limitations, cybersecurity modeling and simulation (M&S) research, which includes the crucial component of cyber-defense modeling, is being actively conducted. Most cyber defense modeling approaches only model defenses as a response to cyberattacks, neglecting to consider the complexities in the actual cyber defense activities of organizations. Consequently, the intended aim to evaluate and enhance cyber defense capabilities through analysis cannot be met. In this study, we present a cyber defense process model that models the entire lifecycle of cyber defense activities as the following five phases: prevention, monitoring and detection, initial response, attack analysis, and recovery response. This model not only accounts for defense steps that had been neglected in previous studies but also offers improvements to previously introduced defense steps. Additionally, we present a framework for applying initial and recovery response models by progressively integrating a unit response behavior model to counter cyberattacks. The applicability of the proposed model was verified by using a constructed prototype. The results of this study can be applied to developing an M&S-based experimental environment for assessing the sustainability of missions/businesses that have faced cyberattacks.
PillarNet++: Pillar-Based 3-D Object Detection With Multiattention

Light detection and ranging (LiDAR)-based 3-D object detection constitutes a fundamental component of autonomous driving technology. In this research, we propose a novel approach called PillarNet++ to tackle the challenges associated with fine-grained information loss during point cloud encoding and the inadequate interaction or incomplete fusion of feature maps across different scales in subsequent feature extraction stages, resulting in a decrease in partial occlusion and long-distance 3-D object detection accuracy, leading to false and missed detections. The PillarNet++ method primarily comprises two modules: the multiattention-pillar-encoding (MAPE) module and the pseudo-image-split-multibranch-feature-pyramid-network (PSMB-FPN) module. The MAPE module enhances the information extraction capability in nonempty pillars by integrating max pooling and average pooling, by fusion of the pointwise, channelwise, and pillarwise attention; the MAPE module can adaptively focus on the important information and suppress the secondary point clouds. In addition, the stacked MAPE modules can refine pillars and extract finer features. On the other hand, the PSMB-FPN module splits the pseudo-image along the channel dimension and subsequently performs MB-FPN feature extraction and fusion on each channel, facilitating the interaction of multiscale and multilevel feature maps and improving prediction accuracy. Experimental results on the KITTI 3-D object detection benchmark show that the PillarNet++ method has the best performance among single-stage object detection algorithms and even exceeds most two-stage methods.
Learning to Find the Optimal Correspondence Between SAR and Optical Image Patches

This study addresses the problem of finding the optimal correspondence for a given synthetic aperture radar (SAR) image patch from a large collection of optical reference patches, which is crucial for various applications, including remote sensing, place recognition, and aircraft navigation. However, achieving one-to-one SAR-Optical Patch Correspondence is challenging due to the distinct modal discrepancy and the poor discriminability of the target instances. To address these challenges, we propose a Cross-Modal Patch Correspondence (CMPC) scheme that consists of two modules: a retrieval-based coarse search module and a correspondence refinement module. Specifically, to explicitly represent the modal discrepancy, we first introduce a cross-modal adversarial learning strategy in the coarse search module and learn the modal-invariant feature embedding for retrieval. Furthermore, to improve the instance discriminability of retrieved candidates, we propose a graph representation in the refinement module to integrate the visual and spatial information, which is finally fed to an attention graph network to estimate the optimal correspondence. To evaluate the effectiveness of the proposed scheme, we also propose three new SAR-Optical patch correspondence datasets. Comprehensive experiments show that our approach significantly outperforms the competitors on all three datasets. The project is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://Collebt.github.io/CMPC</uri> .
LA-ShuffleNet: A Strong Convolutional Neural Network for Edge Computing Devices

ShuffleNetV2 is a prominent player in the field of lightweight networks and has significant implications for the development of lightweight networks and edge computing. However, it has limitations, as its accuracy falls short compared to other larger models, and it is not friendly to datasets. It loses its advantage for small-sized images with fewer channels. In this study, we analysed the design structure of ShuffleNetV2 and found room for improvement in its computational complexity and accuracy. To further improve its performance, we first upgraded the ShuffleNetV2 network structure based on the lightweight network design criteria and constructed a new network model. Second, we introduced a novel attention module named the Adaptive Pooling Attention Module (APAM) and integrated it with the new network model, constructing a high-performance model referred to as LA-ShuffleNet. Then, we proposed a convolution operation acceleration strategy called Pack. Finally, we combined the two and conducted corresponding tests on the Windows and JETSON platforms. Extensive experiments indicate that our proposed model not only exhibits substantial improvements over the baseline model but also achieves noteworthy enhancements on the ImageNet dataset, with a rise of 1.4% in Top-1 accuracy and 3.6% in Top-5 accuracy, coupled with a reduction of 0.7M parameters. Moreover, its performance surpasses that of certain prevalent lightweight networks, such as MobileNet.
W-EFLN: A Wavelet Transform-Based Extendable Frequency Learning Network for Image Super-Resolution

With the advancement of the economy and technology, visual information is exerting an unprecedented amount of influence on all facets of modern culture and life. Deep neural networks have been incredibly successful at achieving single image super-resolution. Most of these methods utilize features from the low-resolution image space exclusively, and ignoring the frequency relationship of the image, resulting in their limited ability to enhance the high-frequency details. To address this problem, we propose a wavelet transform-based extendable frequency learning network, which enables the reconstructed image to contain abundant detail information. By learning the multi-level wavelet coefficients of the target image, we can learn the high-frequency information of the images. The extendable branch of the method is formed by stacking residual distillation blocks which are composed of a high-frequency attention module and an improved deformable convolution using side convolution kernel. In order to improve reconstruction outcomes, we also train our method with loss constraints in the wavelet and spatial domains. Experimental results demonstrate that our proposed method achieves superior performance against the state-of-the-art super-resolution methods in terms of both subjective visual quality and objective quantitative metrics.