Reviewerly/exHarmony huggingface dataset:
Adversarial Domain Generalization Defense for Automatic Modulation Classification

Automatic modulation classification (AMC) technology plays a vital role in the sixth generation mobile system (6G). However, deep learning (DL) based AMC models possess unexpected vulnerability against adversarial examples, which seriously affects their applications in 6G. To this end, we propose an Adversarial Domain Generalization Defense (ADGD) algorithm to improve the adversarial robustness of DL-based AMC models. Firstly, we sequentially pre-train two classifiers that classify the original signals and the adversarial examples respectively. Secondly, we extract task-relevant features of the original signals and adversarial examples and align them. Finally, we use adversarial training to enhance the adversarial robustness of the models. The comparative experiments with various defense algorithms under white-box and black-box conditions of various attack algorithms demonstrate the outstanding defense performance of the ADGD algorithm. The proposed solution is of great significance to promote the application of AMC technology in 6G.
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning

As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users. Mobile users in FL systems typically communicate with base stations (BSs) via wireless channels, where training performance could be degraded due to unreliable access caused by user mobility. However, existing work only investigates a static scenario or random initialization of user locations, which fail to capture mobility in real-world networks. To tackle this issue, we propose a practical model for user mobility in FL across multiple BSs, and develop a user scheduling and resource allocation method to minimize the training delay with constrained communication resources. Specifically, we first formulate an optimization problem with user mobility that jointly considers user selection, BS assignment to users, and bandwidth allocation to minimize the latency in each communication round. This optimization problem turned out to be NP-hard and we proposed a delay-aware greedy search algorithm (DAGSA) to solve it. Simulation results show that the proposed algorithm achieves better performance than the state-of-the-art baselines and a certain level of user mobility could improve training performance.
A Diffusion Model-Based Open Set Identification Method for Specific Emitters

This paper proposes an open set identification architecture for Specific Emitters, which consists of four modules: a diffusion module, a denoising module, an out-of-distribution (OOD) detection module, and a classifier. The diffusion module destroys the input signal into a Gaussian prior distribution, while the denoising module restores the corresponding Gaussian distribution to the original data. The OOD detection module evaluates whether a sample belongs to a known class, and a classifier is used to identify emitters within the known class. Experimental results on the ADS-B dataset demonstrate that the proposed method outperforms the OpenMax algorithm with a 0.09 improvement in macro-F1 score at 13.4% openness. These results show that the proposed method is a promising solution for SEI in the open set scenario.
A Training-time Friendly Network for Real-time Drone Detection

The rapid development of the unmanned aerial vehicle (UAV) market poses potential threats to public safety and personal privacy, and vision sensors are widely deployed to detect intruding drones due to the intuitiveness and accessibility of videos. However, the pixel area of long-distance intrusion drones is small and the morphological features are weak, which makes identification and positioning difficult. At present, a lot of work has focused on improving the detection accuracy of drones, but there is very little to improve the detection speed of drones and shorten the network training time. Therefore, we propose a drone detector with high accuracy, short training time, and fast inference speed. In this work, we choose Centernet from the consideration of a light-head, single-stage, and anchor-free design that enables fast inference. In addition, we improve the original framework of Centernet through the separation strategy of identification features and positioning features. Furthermore, encoding more training samples from annotated boxes has a similar effect to increasing the batch size, which helps to increase the learning rate and speed up the training process. To this end, we introduce a new method of Gaussian elliptic coding and propose a new loss function for it. Considering the complexity of background during flight and the small size of the drone compared to the background, a background filter is used to reduce the influence of the complex background. In this paper, the proposed model is trained and tested on the Drone-vs-Bird dataset, and its mAP reaches 83.7%, and its FPS reaches 29.6.
Enhancing User Experience in High-Resolution Collaborative Virtual Reality on Commodity Mobile Devices

As virtual reality becomes increasingly popular, people expect better experience quality, especially in collaborative VR applications. However, the limitations of network transmission and rendering performance can hinder the realization of such expectations. This paper aims to improve the quality of experience (QoE) for high-resolution collaborative VR scenarios. Considering the limited computation capacity on commodity mobile devices, we study the video frame decoding latency and introduce a fine-grained quality level for optimizing the QoE goal within a single time slot. The proposed method is explained and validated through simulations with real-world data and experiments on multiple mobile smartphones and VR devices. The results show a 94.5% improvement in QoE compared to a lower quality level (1080P) and a 67.1% improvement compared to 2K, highlighting the potential of our approach in enhancing user experiences in collaborative VR applications.
Efficient Federated Learning in Wireless Communication Systems: A Multi-Objective Optimization Perspective

This paper focuses on a federated learning (FL) system that employs a base station as a central server while clients with limited computation capabilities perform local training.The limited bandwidth leads to that only a portion of clients can participate in each FL training round, and picking different clients can impact the performance of FL systems, requiring effective allocation of their computing resources.In FL systems, both model convergence and energy consumption are important performances.To this end, we formulate a multi-objective optimization problem (MOP) to simultaneously speed up model convergence and reduce energy consumption.To address the MOP, we propose a multi-objective algorithm (MOA) for FL systems to obtain a Pareto optimal solution set, where Tchebycheff approach is adopted to divide MOP into multiple singleobjective problems and optimize them by differential evolution.The extensive experiments on Fashion-MNIST dataset in both i.i.d and non-i.i.d data settings illustrates that MOA outperforms other algorithms.
MI-based Joint Source Channel Coding for Wireless Image Transmission

Compared with traditional architecture of separated source-channel coding schemes, deep joint source-channel coding (D-JSCC) has advantages in having smaller delay and better robustness to fast channel variations. However, existing D-JSCC schemes lack the capability to optimize encoded channel input according to specific channel characteristics. To design for better adaptability for different communication channels, we propose a mutual-information-based joint source channel coding scheme, called M-JSCC. The scheme improves the encoding performance by training to maximize the mutual information between channel input and output. Experimental results show that the proposed M-JSCC scheme outperforms the conventional D-JSCC scheme in additive white Gaussian noise (AWGN) channels , Rayleigh fading channels and Rician channels for multiple image-quality metrics such as peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM).
Aging-aware Incentive Mechanism Design for Federated Learning in Industrial Internet of Things

The privacy concern of the sensory data at Industrial Internet-of-Things (IIoT) devices hinders the rapid development of intelligent IIoT. Federated learning (FL) is widely adopted to preserve data privacy. However, the lack of participants jeopardizes the practicality of FL. This paper designs an efficient incentive mechanism for FL to encourage more IIoT devices to join the FL tasks. Considering the real-time demand of data processing for some IIoT applications, we first propose an aging-aware reputation evaluation scheme based on Age of Information to select high-quality devices. We then propose an efficient incentive mechanism based on contract theory with asymmetric information. The proposed approach enables the edge server to provide appropriate rewards by offering a tailored contract to each IIoT device based on their contribution. Simulation results demonstrate that with the increase of reputation values and the size of data samples, the utilities of the edge server and devices can be greatly improved respectively.
Model-heterogeneous Federated Learning with Partial Model Training

Federated Learning (FL) enables a large number of resource-limited devices to train a model collaboratively without data sharing. However, many works focus on model-homogeneous FL where the global and local models are the same size and ignore the inherently heterogeneous computation capabilities on different devices, which restricts resource-constrained devices from contributing to FL. In this paper, we consider model-heterogeneous FL and propose Federated Partial Model Training (FedPMT), where devices with small computation capabilities work on partial models (subsets of the global model) and contribute to the global model. Different from Dropout based partial model generation that removes neurons in hidden layers at random, model training in FedPMT is achieved from the back-propagation perspective. As such, all devices in FedPMT prioritize the most crucial parts of the global model. Empirical results show that FedPMT outperforms the existing benchmark FedDrop to a large extent. Meanwhile, compared to the popular model-homogeneous benchmark, FedAvg, FedPMT reaches the learning target with a shorter completion time and achieves a better trade-off between learning accuracy and completion time.
FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms.
An Efficient and Privacy-Preserving Outsourced Heavy Hitter Query Scheme with LDP Technique

The rise of the Internet of Things (IoT) and Big Data technologies has led to the widespread use of crowdsourcing in data-oriented applications. However, privacy concerns remain a significant issue in crowdsourcing. In this paper, we propose an efficient and privacy-preserving outsourced heavy hitter query scheme in crowdsourcing scenarios. The proposed scheme is characterized by employing a communication efficient S-Hist Local Differential Privacy (LDP) protocol and Paillier homomorphic encryption to achieve top-k items query. Detailed security analysis shows it can achieve not only participant user’s privacy but also query result privacy. In addition, extensive performance evaluations are conducted, and the results demonstrate its efficiency, and show it can balance the query accuracy and privacy well.
Meta-Learning Based Semi-blind Data Detection for Cell-Free Systems

To detect data without channel estimation in cell-free systems, we propose a model-agnostic Meta-learning based semi-blind data detection Deep neural Network (MDNet) training via Model-Agnostic Meta-Learning (MAML) algorithm. Considering the block fading model, the different channel states between blocks are regarded as different tasks. A meta-training dataset consisting of the signals sent by the previous coherent block user is used to train the Deep Neural Network (DNN), so MDNet can quickly adapt to the new channel state. Furthermore, first-order MDNet (FMDNet) is also proposed based on the first-order MAML algorithm. The simulation results show that both the proposed MDNet and FMDNet can accurately detect user-transmitted data by using a few pilots, significantly improving the symbol error rate compared with the conventional learning methods.
BiLSTM-Based Frame Synchronization for Overlapped S-AIS Signals: A Learning-Empowered Approach

In this paper, focusing on the signal detection of space-borne automatic identification system (S-AIS), two learning-empowered frame synchronization methods are proposed, which predict the accurate overlapping position of two S-AIS signals with the help of a bidirectional long short-term memory (BiLSTM) network. In particular, by regarding the frame synchronization as a binary classification issue, BiLSTM network can be utilized to find the overlapping position of the received signals accurately. Furthermore, convolutional neural network (CNN) is introduced into the proposed BiLSTM-based approach to handle the non-smooth power fluctuation. Simulation results show that our proposed learning-empowered methods outperform the conventional frame synchronization method in terms of accuracy and robustness, which can work effectively even under various communication conditions.
A Deep Semantic Autoencoder for Maritime Wireless Communications

With the rapid development of maritime transportation, ocean tourism, monitoring, surveillance and aquaculture, the maritime communication industry has encountered new opportunities. Maritime End-to-end (E2E) communication systems based on deep learning (DL) have become diverse, while semantic communication systems have become a critical research point due to their ability to understand and analyze natural language. In this paper, we propose a deep semantic autoencoder (DSA) for maritime wireless communications. The DSA consists of a semantic encoder, channel encoder, channel decoder, and semantic decoder. The DSA employs transformers and convolutional neural networks to extract and restore the meaning of sentences while minimizing semantic errors during transmission. In order to enhance the effectiveness of the DSA in transmitting maritime data, the Rician fading channel is utilized to simulate complex and varied marine environments. Furthermore, the system parameters of the DSA are optimized through training the maritime data and calculating the loss functions. Extensive simulation experiments indicate that compared to the benchmark, the DSA demonstrates superior bilingual evaluation understudy and sentence similarity performance under different signal-to-noise ratio conditions, indicating its ability to effectively extract semantics.
Semantic Communication on Multi-Hop Concatenated Relay Networks

In order to achieve reliable information delivery over long distance or high path loss attenuations, semantic communication (SC) on multi-hop relay networks is investigated. Compared to traditional communications, although SC shows an advantage in single-hop communications, the semantic attenuation, i.e., the loss of semantic information, over multiple hops remains an open problem. To characterize the effect of multi-hop relaying on the fidelity of semantic information, a new metric "Semantic Similarity" is introduced, which can quantify the similarity between the meanings represented by two messages at the sentence level. Then an optimization function is established, based on which end-to-end optimization is performed to preserve the semantic information to the maximum extent. Experimental results demonstrate the effectiveness of the proposed scheme, where superior performance is achieved by the proposed scheme at low-to-medium signal-to-noise ratio (SNR) regime and subject to high path loss. Compared to existing schemes, a much slower and smoother performance degradation is achieved by the proposed scheme as the number of relays increases.
Inter-Cell Interference Coordination for AirComp Federated Learning in Cellular Networks

In large-scale cellular networks, inter-cell interference is a critical factor on the average learning performance in each cell for the over-the-air computation (AirComp) based federated learning (FL). In this paper, we propose a new device selection scheme in which we first establish an interference-reduction region in each cell, then select devices from this region for FL, and the unselected devices in each cell are muted on the resource blocks used for the AirComp to reduce the inter-cell interference. To investigate the performance of the proposed scheme in depth, we consider four types of network topologies with different spatial distribution properties for base stations (BSs) and devices, and build FL experiments based on a neural network and a real dataset. The results show that the proposed scheme efficiently reduces the inter-cell interference while improving the average FL prediction accuracy. And the properties of the spatial distribution of both BSs and devices play a key role on the average FL prediction accuracy.
THE IMPLEMENTATION OF ENGLISH DAY PROGRAM IN THE STUDENT'S SPEAKING SKILL AT THE ELEVENTH GRADE OF SMAN 18 MAKASSAR

ABSTRACT&#x0D; The aim of this study was to determine the implementation of the English day program in students' speaking skill. This research method is, applied a qualitative research, data were collected from observations, interviews, and documentation. The sample of this research is the students of class XI SMAN 18 Makassar who follow the English Club. the writer uses interview guidelines. The results of this study indicated that the English day program can improve students' speaking skills. However, the implementation of English day at SMAN 18 Makassar has not been carried out well because: some students still use Indonesian during English day, and some students are not confident to speak English with friends around them. It is recommended that the person in charge of the English Club can implement the English day program in the school environment, and tighten the applicable rules, because English day is an important program for international schools.&#x0D; Key words: implementation, English day, program, speaking, skill.
Penggunaan Sistem Poin Partisipasi untuk Kemampuan Berbicara Siswa pada Siswa Kelas Delapan SMPN 8 Makassar

ABSTRACT&#x0D; This research aimed at finding out the students’ speaking ability before and after using participation point system method in teaching speaking. The independent variable was using participation point system method and the dependent variable was students’ speaking achievement. The sample of the research were 36 students of class VIII6 at SMPN 8 Makassar in the second semester academic year 2021/2022. The design of this research was quantitative design and the method used in this research was pre-experimental one group pretest-posttest research.The result of the research showed that there was a difference between pre-test and post-test score of the students. Theaverage score of pre-test was 48,78 and the average score of post-test was 86,11. Based on the result can be seen that after the researcher gave treatment in post-test was higher than pre-test. Therefore, participation point system becomes an appropriate method in teaching speaking of class VIII6 at SMPN 8 Makassar.&#x0D; Key Words : Participation Point System, Speaking Ability&#x0D;
Privacy Protection for Federated Learning in OFDM Aided Over-the-Air Computation System

Federated learning (FL) an emerging distributed and privacy-protecting machine learning paradigm. In FL, training is completed through iterative local training and gradient aggregation among multiple mobile devices (MDs) and the edge server (ES). Over-the-air computation can help to achieve fast aggregation when multiple MDs need to offload their local gradients to the ES. Orthogonal frequency division modulation (OFDM) can further speed up aggregation through enabling simultaneous symbol transmission over multiple fading blocks. In this paper, we focus on the FL system with OFDM aided over-the-air computation technique implemented. To overcome the challenge of non-linear distortion at the amplifier of OFDM transmitter and protect the privacy of each individual MU, we propose a new aggregation method, which normalizes the local gradient first and then add uniformly distributed artificial noise, before it is offloaded to the ES. With our proposed aggregation method, the performance of differential privacy (DP) is analyzed, which disclose the quantization relationship between the variance of artificial noise and the level of privacy protection. We also analyze the peak power at OFDM transmitter by our proposed method, which is shown to be surely less than traditional method. Numerical results verifies the convergence and effectiveness of our proposed strategy.
Hound: A Parallel Image Distribution System for Cluster Based on Docker

Current applications, consisting of multiple replicas, are packaged into lightweight containers with their execution dependencies. Considering the dominant impact of distribution efficiency of gigantic images on container startup (e.g., distributed deep learning application), the image “warm-up” technique which prefetches images of these replicas to destination nodes in the cluster is proposed. However, the current image “warm-up” technique solely focuses on identical image distribution, which fails to take effect when distributing different images to destination nodes. To address this problem, this paper proposes Hound, a simple but efficient cluster image distribution system based on Docker. To support diverse image distribution requests of cluster nodes, Hound additionally adopts node-level parallelism (i.e., downloading images to destination nodes in parallel) to further improve the efficiency of image distribution. The experimental results demonstrate Hound outperforms Docker, kubernetes container runtime interface (CRI-O), and Docker-compose in terms of image distribution performance when cluster nodes request different images. Moreover, the high scalability of Hound is evaluated in the scenario of ten nodes.