Reviewerly/exHarmony huggingface dataset:
Utility Optimization for Over-the-air Computation Systems with Spectrum Sharing

Wireless data aggregation (WDA) is a pivotal enabling technique for Internet of Things (IoT). Recently, an emerging WDA technique, over-the-air computation (AirComp), has been proposed to perform fast data aggregation, with enhanced spectrum utilization and shortened transmission delay. AirComp leverages the superposition property of a wireless multiple access channel to accomplish functional computation of data, which can support the model aggregation in distributed machine learning and fusion of sensing data. In this paper, we consider a multi-cell AirComp system with spectrum sharing, in which every user in a multi-cell network shares a common part of the spectrum. The mean squared error (MSE) is used as the performance metric to quantify the computation accuracy at each access point (AP). With the aim of coordinating the MSEs among multiple APs, two different objective functions, weighted sum MSE and proportional fairness, are adopted. Accordingly, to minimize the weighted sum MSE, the block coordinate descent (BCD) method is employed. The analytical solutions are also provided. To minimize the proportional fairness, a tractable solution is developed, which is based on the successive convex approximation (SCA) technique. The effectiveness of the proposed schemes is validated by our numerical results.
Lightweight Models for Traffic Classification: A Two-Step Distillation Approach

Traffic classification task is to identify different types of Internet traffic or applications. Classical traffic classification methods have the limitation that they need to predefine the features. The emergence of new applications reduces their accuracy due to the inaccurate feature design. The deep learning-based methods could extract features from raw traffic data and achieve high accuracy, but accordingly, leading to more complex models and heavier computations. The above deep models are hard to be deployed on edge nodes or resource-limited IoT devices. Therefore, in this paper, we adopt novel compressed models based on a two-step distillation approach for traffic classification. To address the trade-off problem between classification accuracy and model complexity, we first design lightweight models and then propose a novel training procedure to enhance their classification accuracy. Specifically, the response, relationship, and feature map-based knowledge of different traffic are distilled to train the small models. Experiment results demonstrate that compared to the state-of-the-art model, the minimum model using the proposed method can achieve higher accuracy, F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores, and reduce nearly 99.7% computation overhead, thus verifying the effectiveness of our method.
Leveraging MIMO Transmit Diversity for Channel-Agnostic Device Identification

The accurate identification of wireless devices is critical for enabling automated network access monitoring and authenticated data communication in large-scale networks; e.g., IoT networks. RF fingerprinting has emerged as a potential solution for device identification by leveraging the transmitter unique manufacturing impairments of the RF components. Although deep learning is proven efficient in classifying devices based on the hardware impairments, trained models perform poorly due to channel variations. That is, although training and testing neural networks using data generated during the same period achieve reliable classification, testing them on data generated at different times degrades the accuracy substantially. To the best of our knowledge, we are the first to propose to leverage MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification. For the proposed technique we show that, for Rayleigh channels, blind partial channel estimation enabled by MIMO increases the testing accuracy by up to 40% when the models are trained and tested over the same channel, and by up to 60% when the models are tested on a channel that is different from that used for training.
Multi-Satellite Tracking For The LEO Satellite Communication Network

The multi-satellite tracking (MST) for fast link switch is of great importance in guaranteeing the link quality of low earth orbit (LEO) satellite communications. In the marine satellite communication scenario, the position and attitude of the ship and the satellite are known. However, due to the noise from uninterested flight vehicles and the communication path, as well as the effect of the coupling motion between the LEO satellite and the ship, an MST system is necessary to achieve high tracking accuracy. In this work, we propose an MST algorithm for shipborne digital phased arrays, which exploits the constellation orbit characteristics and then associates the interested target states with the orbit parameters. Upon performing a coarse MST using the probabilistic multi-hypothesis tracking (PMHT), the proposed algorithm further picks interested targets and updates their states by the satellite orbit association (SOA). In the process, states of the interested targets are determined according to the orbit parameters in a designed linear constellation orbit model (LCOM), and new strategies are applied to construct and update the LCOM to ensure its effectiveness. Numerical simulations show that the proposed algorithm performs better over the conventional ones.
AT-CBDC: Achieving Anonymity and Traceability in Central Bank Digital Currency

In this paper, we propose a new central bank digital currency (CBDC) system based on the two-tier architecture. The proposed system enhances the traditional bank-user framework of electronic cash and employs the commercial banks for account and coin management. The coin splitting is supported during coin withdrawal of users and the coin combination is achieved for coin deposit at the commercial banks, such that the efficiency of coin management is improved. The other distinguished feature is that the proposed system achieves the anonymity against the commercial banks, while enabling the central bank to support user tracing and double-spending prevention. Specifically, by utilizing the BBS+ signatures, the users can create bank accounts, withdraw coins, and deposit the received coins at the commercial banks without exposing their real identities. As a trusted party, the central bank is responsible for money issuing and financial regulation. In addition, to ensure the system inclusive, users can receive payments from others even they do not have bank accounts at commercial banks. Finally, we demonstrate that the proposed system achieves the desirable properties of balance, anonymity, and traceability and show the efficiency and practicality for the implementation on mobile devices.
Multi-head Self-attention Recommendation Model based on Feature Interaction Enhancement

In the recommendation system, click-through rate (CTR) prediction is a popular research direction. Aiming at the problem of excessive compression of features in Factorization Machine (FM) and its variant models, a recommendation model that combines feature interaction enhancement and multi-head self-attention is proposed. Hadamard product, feature vector splicing and multi-layer perception network methods are used for low-level feature vector interactive processing in this paper, and multi-head self-attention mechanism and residual network model for high-level feature interactive processing are used. By designing the fusion mechanism, the parallel low-order feature interaction network and the high-order feature interaction network are merged. The experimental results on the four benchmark data sets show that the multi-head self-attention model based on high-order feature interaction enhancement proposed in this paper outperforms existing models in terms of click-through rate prediction accuracy.
Federated Multi-Task Learning with Non-Stationary Heterogeneous Data

Federated multi-task learning (FMTL) is a promising edge learning framework to fit the data with non-independent and non-identical distribution (non-i.i.d.) by exploiting the correlations of personalized models. In many practical systems, the sensory data distribution in wireless systems is not only heterogeneous but also non-stationary due to the mobility of terminals and the randomness of link connections. The non-stationary heterogeneous data may lead to model divergence and staleness in the training stage and poor accuracy in the inference stage. In this paper, we design an adaptive FMTL framework, which can work in a non-stationary environment. We propose to optimize the model update scheme and cluster splitting scheme in the training stage to accelerate model convergencse when the training data are non-stationary. We further design a low-complexity model selection scheme in both the training and the inference stages to choose the best model for fitting the current data. The proposed framework is validated in two scenarios, linear regression and graph neural network (GNN)-based power control in wireless device-to-device (D2D) networks. Both sets of numerical results demonstrate that the proposed framework can accelerate the model training convergence and reduce the computation complexity while ensuring model accuracy.
Communication Knowledge Aided Neural Network for OFDM Receiver in Terahertz Band

Ultra-broadband communication in emerging spectrum, like Terahertz (THz) band, is the frontier to meet the data rate requirements of future wireless communication systems. Existing signal processing based methods are developed for sub-6 GHz band, which cannot capture the intricacies in ultra-broad THz bandwidth and non-linearities arising from hardware. To overcome these limitations, we develop neural network (NN) models for OFDM receiver, where expert knowledge of wireless communication is infused in different stages and parameters of the model to create a practical receiver that can adapt to different wireless environments. The parameters of the NN are derived from underlying theory and can be adapted to different wireless environments. Our model is designed to capture the correlation between real and imaginary components of wireless signals, that can be trained with limited data. The models are trained with over-the-air captured OFDM signals, transmitted in THz band with 10 GHz bandwidth. Our results show significant improvement in bit error rate (BER) for different modulation orders (upto 6 dB in BPSK and 1.2 dB in QAM 64) compared to existing signal processing based receiver designs.
Denoising Magnetic Resonance Images with Rician Noise Based On Deep Learning

Image quality, which deteriorates primarily due to noise and distortions, is critical for the effectiveness of magnetic resonance imaging (MRI) analysis. This distortion is caused because of poor imaging settings or transmission system interference. MRI scans are typically corrupted by Rician noise when acquisition takes place. This noise should be eliminated from MRI as a pre-processing phase. This paper proposes MRI denoising using deep neural network (DNN). To extract image features from noised image, the model employs a collection of convolutions. The model too uses an encoder-decoder arrangement to preserve important image features whereas rejecting irrelevant features. To reduce noise in MRI, this paper proposed DNN based technique and this technique was compared with various denoising techniques such as median filter, gaussian filter, average filter, nonlocal means filter, bilateral filter, wavelet smoothed filters. These approaches were compared using measures like peak signal to noise ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE). The results demonstrates that the suggested model can efficiently denoise MRI scans while preserving essential image features.
Dynamic Cat-Boost Enabled Keystroke Analysis for User Stress Level Detection

The impact of digital gadgets is enormous in the current Internet world because of the easy accessibility, flexibility and time-saving benefits for the consumers. The number of computer users is increasing every year. Meanwhile, the time spent and the computers also increased. Computer users browse the internet for various information gathering and stay on the internet for a long time without control. Nowadays working people from home also spend time with the smart devices, computers, and laptops, for a longer duration to complete professional work, personal work etc. the proposed study focused on deriving the impact factors of Smartphones by analyzing the keystroke dynamics Based on the usage pattern of keystrokes the system evaluates the stress level detection using machine learning techniques. In the proposed study keyboard users are intended for testing purposes. Volunteers of 200 members are collectively involved in generating the test dataset. They are allowed to sit for a certain frame of time to use the laptop in the meanwhile the keystroke of the Mouse and keyboard are recorded. The system reads the dataset and trains the model using the Dynamic Cat-Boost algorithm (DCB), which acts as the classification model. The evaluation metrics are framed by calculating Euclidean distance (ED), Manhattan Distance (MahD), Mahalanobis distance (MD) etc. Quantitative measures of DCB are framed through Accuracy, precision and F1Score.
Virtualization and Nested Virtualization Technology: Concept, Architecture and Attack Vector Model

In digital era, the Internet is a vital part of the life of people. Cloud services are Internet-based services that are available for clients. Virtualization is the backbone of Cloud technology. IBM invented Virtualization technology in 1960 to achieve maximum Usage of hardware resources. Virtualization is the technique by which we can create a virtual version of some resources like storage, network, servers, operating platforms, etc. Virtualization helps in optimizing resource utilization and delivering resources efficiently. This technology improves the rate of load efficiency, energy consumption in data centers, effective resource sharing, and saves cost. In traditional computer virtualization, a hypervisor operates various virtual machines concurrently. In nested virtualization, a hypervisor can manage other hypervisors with their VMs. In this paper, we discuss the virtualization concept, architecture of virtualization and nested virtualization. We also perform a comparative analysis of the virtualization tools. We present the security perspective of virtualization and nested virtualization. We present the attack vector model in a virtualized environment, which shows the source to destination direction of the attack. We expect that this research will inspire researchers to work in this direction.
Object detection of UAV power line inspection images based on federated learning

Power lines are widely distributed and scattered in power system. The inspection images collected by various power supply companies are existing in the form of data fragments and island. The data sharing is sometimes forbidden because of privacy protection. This means a single data owner (e.g. a regional power supply company) cannot always train good generalized models when dealing with inspection images by deep learning based methods. In this work, we propose a federated learning method to detect the insulators in power line inspection images which have different data distribution from different power companies by Unmanned Aerial Vehicles (UAVs). The experiment results show that through participating in the federated training a single power company can effectively improve the detection accuracy and generalization of the object detection model.
An Improved Exponential ElGamal Encryption Scheme with Additive Homomorphism

Homomorphic encryption is a special encryption scheme which allows an untrusted third party to operate directly on the encrypted data. ElGamal encryption scheme is an early homomorphic encryption scheme with multiplicative homomorphism. We analysed the security of standard ElGamal encryption scheme by constructing reasonable interaction between attacker and random oracle. By associating the quadratic residue property of plaintext with the corresponding ciphertext, we found that the ElGamal encryption scheme was not secure enough under the attack of chosen plaintext. To solve this problem, we proposed a new homomorphic encryption scheme which we called exponential ElGamal, and tested its efficiency and security
Industrial Intrusion Detection System Based on CNN-Attention -BILSTM Network

In view of the existing intrusion detection system for real-time control, the problem of accuracy is not high, this algorithm with improved genetic algorithm to optimize the input vector of belief network, after dimension convolution layer is utilized to extract local features, recycling attention mechanism to explore the characteristics of the different impact on the weights of attack types of prediction, finally USES two-way LSTM extraction sequence. In this paper, the model based on CNN-attention-BILSTM network is compared with the model based on convolutional neural network and attention-BILSTM network and other technologies, and the performance is evaluated from the perspectives of accuracy, false alarm rate, processing performance and completeness. Finally, the experimental results show that, compared with the other models, the model based on CNN-attention-BILSTM network has the best performance on the KDD 99 test set. Using belief network dimension reduction can effectively reduce program running time.
Research and Design of Electric Power Engineering Project Management System Bsed on Blockchain Technology

The characteristics of electric power engineering projects, such as multiple types, multiple links, multiple related parties, and decentralized construction have formed complex contracts and employment relationships. Numerous stakeholders make the owner's ability to control all parties involved in the project very limited, and the problems of information islands still exist. Firstly, electric power engineering project management problems, such as qualifications of contractors or subcontractors, subcontracting in breach of contract, lack of transparency in the payment of workers' wages, arrears of wages, withholding and embezzlement of contract funds, and malicious debt collection, were analysed. Then, the data deposit, enterprise and personnel credit management, contract management, project progress management, and funds management based on the blockchain technology were studied. Finally, the blockchian based electric power engineering project management system was designed.
Application of machine learning in optimal allocation of quantum communication resources

In the application of quantum communication networks, it is an important task to realize the optimal allocation of resources according to the current situation. For example, We need to select the optimal quantum key distribution (QKD) protocol and parameters. Traditionally, the most commonly implemented method is the local search algorithm (LSA), which costs a lot of resources. Here in this work, we propose a machine learning based scheme, in which the regression machine learning is used to simultaneously select the optimal protocol and corresponding parameters. In addition, we make comparisons among a few machine learning models including random forest (RF), K-nearest neighbor (KNN) and logistic regression. Simulation results show that the new scheme takes much less time than the LSA scheme, and the RF achieves the best performance. In addition, through the RF residual analysis, we find that the machine learning scheme has good robustness. In conclusion, this work may play an important role in promoting the practical application of quantum communication networks.
English Teaching Evaluation Model Based on Association Rule Algorithm and Machine Learning

At present, Chinese colleges and universities have a clear understanding of the importance of teaching quality evaluation. They regard the evaluation of teaching quality as an important part of teaching management. This paper aims to study how to analyze and study English teaching evaluation based on association rule algorithm and machine learning and study the model. This paper raises the question of English teaching evaluation. This question is based on modelling studies. So it expounds the concepts and related algorithms of association rule algorithm and machine learning. This paper designs and analyzes a case study of the English teaching evaluation model. The experimental results show that taking a university as the empirical object for specific analysis, according to the evaluation system established in the research, the final score of the questionnaire is 89.2 points, and the English teaching evaluation result is a good grade.
Possibilities of mapping neotectonic elements based on the interpretation of space images: A study of Fergana Depression

For the current geological mapping of individual territories, remote sensing and geographic information system (GIS) technologies are widely used. Taking the Fergana depression as an example, this paper aims to determine the possibilities of using remote sensing materials for mapping structural and geomorphological elements. Landsat-8 satellite imagery obtained on 11 spectral channels was used to highlight the decoding features. Fault zones, lineament zones, various uplifts, depressions, and structural material complexes of different ages were identified. Based on the in-depth analysis of geological and geophysical data, a geological schematic map of the Fergana depression was developed. Using geological landscape and automated methods for deciphering space images, the main neotectonic elements of the Fergana depression were identified: the southern step, northern marginal slope, central graben-syncline, flexure-fault zones, local anticlinal zones, individual uplifts/depressions, and local shear zones. The results show a neotectonic map of the study area, which can be used as a tectonic basis for prospecting, seismic zoning mapping, and geological engineering development.