Reviewerly/exHarmony huggingface dataset:
Robust Bluetooth RF-Fingerprint Identifier Using Wavelet Scattering Network

A novel robust Bluetooth radio-frequency (RF) fingerprint identification scheme using wavelet scattering network is introduced in this paper. In this approach, a wavelet scattering network is first established using the Gabor wavelet and then employed to extract the RF fingerprint of the received Bluetooth signal emitted by an electronic device. Finally, the acquired RF fingerprint serves as the feature and the multi-class support vector machine (SVM) with a cubic polynomial kernel is adopted to identify the Bluetooth RF fingerprints emitted from different wireless transmitters. Monte Carlo simulation results demonstrate the effectiveness and superiority of our novel Bluetooth RF-fingerprint identification method.
Resource Allocation Mechanism of Federated Learning System for Highly Dynamic and Changeable Businesses

As an emerging distributed machine learning architecture, federated learning (FL) allows multiple vehicle terminals in the Internet-of-Vehicles (IoV) to conduct local model training and achieve global aggregation of models under the condition of data privacy protection, thus providing reliable IoV service. However, in the process of FL training, due to the variability of the business requirements of vehicle terminals in the IoV, the terminal model within the coverage of each edge node changes dynamically, resulting in low accuracy of the regional model. Therefore, we propose a training node dynamic selection algorithm based on user behavior prediction, which comprehensively considers the movement trajectory, number of terminals, terminal reliability and business type which improving the training accuracy of the regional model. The edge resource allocation model is designed to improve the resource utilization of edge nodes. In the dual-layer model of FL, the edge-terminal collaborative aggregation layer (ETCAL) and cloud-edge collaborative aggregation layer (CECAL) are designed. In the ETCAL, the asynchronous federated learning model aggregation mechanism based on the business intensity model which comprehensively considering the number, the type, the reliability, as well as the arrival rate of business. While, in the CECAL, the weighted asynchronous aggregation mechanism based on raw data volume is adopted, which improves the aggregation efficiency of the FL system. The simulation results show that the proposed mechanism is superior to local, FL-NonUBP and FL-UBP algorithm in terms of model accuracy.
Thumbnail Generation from Sampled IP Packets for Uncompressed Video-transmission Monitoring

Demand for Internet Protocol (IP)-video monitoring is growing due to the increase in the number of video signals flowing in a network. In video production, it is necessary to check for both the network state and defects in transmitted videos. Video-transmission monitoring provides an overview of each video signal and is essential for finding video defects. However, visualization for multiple uncompressed videos requires high-performance equipment.This paper proposes a video-transmission monitoring method for IP-based uncompressed video transmission that can work without special equipment. This method connects the network switch and a monitoring machine via narrow-bandwidth Ethernet, and the monitoring machine acquires the stream where packet loss occurs. However, monitoring with this method results in a new image-reconstruction problem: low-resolution image reconstruction from high-resolution images containing missing pixels due to packet loss. In this problem, there are areas that cannot be reconstructed with conventional interpolation methods. We first formulate a corrupted image due to packet loss and generate pseudo-packet-loss images to generate a training dataset. The proposed method trained with these images can be used to generate high-quality overviews not only from the pseudo-packet-loss images but also from packet-loss streams captured in the experimental architecture.
Impacts of Update Frequency on User Experience in Collaborative Virtual Environments

Collaborative Virtual Environments (CVEs) are shared virtual spaces that allow geographically distributed users to interact with each other and collaborate on various tasks. To deliver a realistic user experience, CVEs applications need to frequently send update messages containing information about shared objects to other peers. In this paper, we carry out a subjective experiment to investigate the impact of update frequency on user experience in a Collaborative Virtual Environment. The findings from this study can help in designing an effective CVEs system.
A Machine Learning Based Computing Node Selection Algorithm

Under the trend of multiple big data technologies, the development of multi-node collaborative parallel computing has made great advances. Limited by data security and data permissions, federated learning has become the current mainstream method to solve the difficult situation. However, for the huge number of computing nodes based on federated learning, there are many redundancies in client nodes. Therefore, the computing resource utilization and model training efficiency can be negatively affected. In this paper, in order to improve the computing efficiency in the parallel computing scenarios such as federated learning, we study a node selection algorithm based on machine learning. In the proposed algorithm, each node implements independent training based on the current global model. According to the contribution to model training accuracy, we select the computing nodes which will be aggregated in central sever afterwards by means of LASSO(Least Absolute Shrinkage and Selection Operator). Additionally, in the experimental simulation part, we add the standard federated averaging (FedAvg) method with random selection as comparison result. Experimental simulation shows that the combination of nodes selected by our proposed algorithm can achieve higher calculation efficiency, and improve the accuracy of federated learning and the mean square error of the accuracy.
Data-free Distillation with Evolutionary Pseudo Data for Text Classification

Most knowledge distillation methods make a strong assumption that the training datasets for downstream tasks are available. However, due to the privacy issue and transmission concerns, such an assumption is sometimes impractical in realworld scenarios, which motivates the research of data-free knowledge distillation. However, few studies have investigated the efficient approaches for data-free knowledge distillation for NLP in any systematic way. To mitigate this, our paper first establishes two guidelines that contribute to data-free distillation efficiency. Motivated by the guidelines, we then develop an evolutionbased approach to construct pseudo samples and incorporate the approach with knowledge titillation, resulting a novel framework Evo-Distil. Experiments demonstrate that our proposed data-free distillation method is superior to the existing strong baselines and rivals the distillation methods with real training data. Specifically, it achieves 90%-99% performance of the regular distillation models with real training data, relying on no assumption of access to annotated training data.
Experience Estimation of Edge Cloud Network based on the Prediction of Data Volume

Statistical learning (SL), represented by machine learning (ML) and deep learning (DL), has become a research hotspot in the recent years due to its excellent performance. However, SL-enabled network models are data-driven and enough samples are required to ensure their performance. With the development of edge cloud and cloud computing, data technology, information technology and communication technology (DICT) has begun to expand into the field of industrial customers, and data acquisition is a time-consuming and costly task in relevant industries. Therefore, how much data is needed to build a statistical learning model is the focus of the industry, which directly determines whether to promote a project. Motivated by this, this paper proposes a data volume prediction scheme of customer experience in edge cloud network, where the model training with good generalization performance can be determined by the data spectrum (Fourier transform calculation). Our experimental results show that the correlation between the proposed data spectrum and the performance of the model has been improved by an average of 91% compared with the simple data size, and the simpler the model is, the better the performance will be.
Federated Learning Technology in Serial Topology for IoT Networks

Recently, with the rapid development of IoT technology, a new type of distributed collaborative AI technology, federated learning is used on the IoT platform to solve the problems of big data and privacy protection. However, federated learning still has a long way from being practical as many key technologies still need to be broken through. This paper considers a federated learning IoT system under serial topology to solve resource consumption problems such as communication. This paper mainly studies algorithm optimization strategies including the impact of different federation aggregation methods, the balance between communication resources and computing power, and the influence of multi-level central nodes on system performance. The results show that the accuracy weighting is the optimal federation aggregation mode, and the algorithm convergence will be slower in most cases because of reducing communication consumption even if the computing power is increased.
Modulated Recognition and Function Realization Based on Deep Learning

With the development of technology, the communication is more busy and complex, and the types of interference increase, which is more important for the correct identification of the signals of the two parties in the communication, so the automatic modulation recognition technology (AMR) has been developed. Traditional automatic modulation recognition techniques are divided into two types: likelihood estimation (LB) based on hypothesis testing and feature extraction (FB). However, the traditional automatic modulation recognition technology still has many challenges and deficiencies in terms of computational complexity and accuracy. With the emergence and development of deep learning, more and more automatic modulation recognition technologies have adopted deep learning methods to improve efficiency. The correct rate of recognition and modulation has enhanced the robustness of the system, compared with the likelihood estimation method to reduce the need for a lot of prior information, and the efficiency and performance of decision-making classification have been greatly improved compared with the machine learning classification used in feature extraction. It brings rapid development and more applications of AMR. This paper mainly studies the automatic modulation recognition method based on deep learning, and proposes three network models using CNN, LSTM and CLDNN. The simulation results show that the three deep neural networks have a recognition accuracy rate of more than 90%, and LSTM has the best performance. In addition, this paper uses real signal data for identification and detection, and successfully implements and deploys the modulation identification function through the hardware platform.
Adaptive Live Streaming for Multi-user Access with Fairness Guarantee

Adaptive bit rate (ABR) algorithms are widely adopted in video streaming applications to optimize user quality of experience (QoE). However, most ABR approaches place emphasis on QoE improvement for the single user, while lacking consideration for multi-user access. In the real environment, users may access network in the meantime and multiple clients under the same bottleneck bandwidth will compete for the limited network resource. It is challenging for current ABR strategies to guarantee satisfactory QoE for each user under more constraints, e.g. latency requirement, resource competition, and limited server overhead. This paper proposes an ABR system for multi-user live streaming. In this system, clients under the same bottleneck bandwidth are clustered into several groups according to specific network conditions and user preferences. Then ABR server conducts bitrate decisions for each user group to achieve overall QoE optimization. Extensive experiments show that the proposed ABR system can guarantee fairness for multiple users with satisfactory overall QoE and acceptable resource consumption.
An Enhanced Video Compression Framework based on Rescaling Networks

Recently, enhanced video compression frameworks compatible with traditional standard codecs have achieved competitive performance especially in low-bandwidth scenarios, among which the downsampling-based preprocessing networks and super-resolution based postprocessing networks are commonly applied. Surrogate networks are further employed as the replacement of non-differentiable standard codecs during training. However, the discard of minor information such as high frequency spatial textures in the process of downsampling restricts the reconstruction quality. Moreover, existing surrogate networks merely imitate the intra-frame coding structure of standard codecs without leveraging inter-frame relations. In this paper, we propose a rescaling-based enhanced video compression framework. The main video stream preserves critical spatial structures and complete temporal information, while another lightweight segment-specific enhancement stream transmitted to the decoder side is extracted and encoded from the key frame of a video segment. The high-frequency spatial information contained in the enhancement stream is further transferred to the whole segment with the guide of decoded LR frames via a Transformer-based Reconstruction Network (TRN), thus enhancing the reconstruction quality at the expense of a small bit cost. Besides, we employ a Virtual Codec Network (VCN) during training for gradients back-propagation, which is able to imitate both inter-frame and intra-frame coding characteristics of standard codecs. Experimental results indicate the superiority of the proposed approach compared with recent downsampling-based enhanced standard compatible frameworks.
The Recognition of Remote Sensing Image based on Federated Knowledge Distillation

As a distributed Machine Learning(ML) technology, Federated Learning (FL) trains global models by federating local nodes and improves the performance of each participating client by exchanging the parameter of the participants’ local models without directly exposing the data of all parties. However, the performance of FL is at the expense of high communication overhead, thus limiting the deployment of the FL system. In this paper, a FL model named FedKD is proposed based on knowledge distillation, which distills the knowledge of the original client model to obtain a lightweight mentee model. Moreover, to reduce the risks of breach and privacy disclosure, homomorphic encryption mechanism is introduced. Combined with the FedKD model, the FedKD-HE model is proposed. The FedKD-HE model provides the potential to deploy intelligent systems efficiently in privacy-sensitive situations such as the Internet of Things(IoT), Integrated Sensing And Communication(ISAC), etc. The simulation results show that the proposed model can reduce 98.01% of the communication overhead and obtain excellent performance.
A Method to Improve the Performance of Network Data Analytics Function Based on Transfer Learning

How to improve the correctness of network data analytics is one of the study key issues in the study of 3GPP R18. Transfer learning is a method of machine learning aiming at improving the performance and model training efficiency of target AI task, which can be used for reference in network data analytics. The main purpose of this paper is introducing a proposed method to improve the performance of Network Data Analytics function (NWDAF) drawing on the thought of transfer learning. The NWDAF stores the trained ML models in the Analytics Data Repository Function (ADRF) and the ADRF registers the model information to Network Repository Function (NRF). Specifically, to introduce the method in detail, this paper describes the fundamentals, system architecture, key procedures and key parameters of the proposed model transfer method.
Automatic Modulation Classification of Frequency-Hopping Signals Using High-Dimensional Phase Diagrams

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM); the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise.
Improved Swarm Learning with Differential Privacy for Radio Frequency Fingerprinting

As the multimedia technology evolves, the security of multimedia services becomes more and more important. Among the multimedia services components, the most important is the security of multimedia Internet of Things (IoT) device. Radio Frequency Fingerprinting (RFF) is a physical layer authentication factor with good anti-counterfeiting capability, which can be used for identity authentication to ensure the security of IoT devices. Since radio frequency fingerprinting is a crucial privacy data for IoT devices, this study utilized differential privacy to improve swarm learning technology and designs a malicious device evaluation method to enhance swarm learning’s privacy protection capability. Our goal is to improve the privacy protection capability of the swarm learning architecture so that IoT devices can combat the danger of RFF data leakage at the cost of a minimal amount of model precision, so as to lay a foundation for the security of multimedia services.
RMS: Real-time Motion Segmentation over the Internet of Vehicles

In the context of autonomous driving, moving objects such as vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of cars. Unfortunately, due to the limited detection range of sensors, some distant and blocked objects cannot be detected, leading to slow responses when some unexpected situations occur during driving. To address this problem, a real-time motion segmentation multi-task model (RMS), running on an individual vehicle, is introduced to provide motion segmentation of moving objects within its field of view. RMS consists of a shared encoder, a multi-modal fusion module, and a dual decoder. An enhanced High Definition (HD) map constructed with the proposed RMS in line with the recommendations of the 3rd Generation Partnership Project (3GPP) Vehicle-to-everything (V2X) communication standard is produced. Extensive experiments demonstrate how RMS outperforms existing state-of-the-art motion segmentation methods in terms of multiple metrics, including mean Intersection over Union (mIoU). Additionally, Internet of Vehicles (IoV) simulation experiments show how the time required to update the map is better than the times achieved when using other methods.
OLNC: Online Learning of Network Conditions for Adaptive Video Streaming

State-of-the-art adaptive bit rate (ABR) strategies, either learning-based or condition-wised approaches, resort to pre-training one or more ABR decision models offline in a simulator under a limited number of collected network traces. However, these existing ABR approaches struggle to generalize well to current heterogeneous network environments, especially unseen network conditions. In this paper, we propose OLNC, a network condition-wised hybrid ABR system, which combines an offline multi-model switching scheme and an additional online learning mechanism to improve its generalization capability to different network conditions. In the offline stage, OLNC trains an ABR model for each network condition and dynamically conducts model switching according to the identified network conditions. In the online stage, OLNC introduces an online learning mechanism, which automatically detects new network conditions and performs online learning to fine-tune existing models without redesigning or retraining them to guarantee satisfactory QoE under unseen network conditions. Both trace-driven and real-world environmental results show that OLNC outperforms state-of-the-art schemes, with QoE improvements of 33.1%-49.5% across a wide range of network conditions.
Brightness-Adjustable Low-Light Image Enhancement via Noise-Only Training Scheme

Existing no-reference low-light image enhancement methods are trained mainly by public datasets, inevitably unable to cover most scenes in the real world. If we change the point of view, Gaussian noises reserve natural image distribution, which may be an alternative to real-world data for image enhancement. In this paper, we design a brand-new Network to brighten low-light images via a curve-based Noise-Only Training scheme (NOTNet). Specifically, our model is devoted to enhancing images to continuous brightness levels by training random noises with different means and contrasts. Extensive experiments on mainstream datasets demonstrate our model’s superiority quantitatively and qualitatively. Our code is available at https://github.com/xyn1201/NOTNet.
Cross Attention Scoring Function for Multimedia Domain Knowledge Graph Completion

Multimedia-based applications and services are strongly expanded by the continuous development of communication and network technologies. Recently, with the high efficiency of knowledge graph(KG) in data modeling, various KGs in multimedia domains have been constructed to manage and utilize multimedia services. In multimedia KGs, the integrity of the link structure can significantly impact the performance of downstream applications. Numerous knowledge graph completion(KGC) models are proposed to predict lost relations between KG entities. However, these general models target at capturing different relation patterns and lack the attention of the relationship between entities and relations. In this paper, a novel KGC model named TransX is proposed to learn this complex inner relationship, which can robustly reflect the link structure of the multimedia domain KGs. TransX firstly projects entities and relations to multiple property spaces, and then a new cross attention block is designed to represent different patterns between entity and relation properties. The experiment results demonstrate that TransX has fewer parameters and better performance at the cost of introducing some sufferable computation in the cross attention block.