Reviewerly/exHarmony huggingface dataset:
Maximum Focal Inter-Class Angular Loss with Norm Constraint for Automatic Modulation Classification

Artificial intelligence (AI) has emerged as the most promising solution expected to overcome the high degree of abstraction of radio signals and achieve accurate automatic modulation classification (AMC). To further improve the classification performance of the AMC model and enhance its interpretability, the network output layer is modeled as a decision space into which the input data is projected. In this paper, we expand the inter-class angle between the classes with the largest confusion rate to increase the decision space. In addition, we extend the perspective to the softmax layer and evaluate the negative impact of the output distribution range on the confidence difference in the AMC problem. We further propose constraining the norm of the input data to the output layer in combination with prior knowledge of the distribution of modulation signal data. Combining the above two aspects, a Maximum Focal Inter-Class Angular Loss with Norm Constraint (MFICAL-NC) scheme is proposed. The experimental results show that the method can guide the model to obtain a better fitting state and a stronger generalization ability.
Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm

Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis. In this paper, we propose a new waveform design paradigm with the knowledge graph (KG)-based intelligent recommendation system. The proposed paradigm aims to improve the design efficiency by structural characterization of existing waveforms and intelligently utilizing the knowledge learned from them. To achieve this goal, we first build a communication waveform knowledge graph (CWKG) with a first-order neighbor node, for which both structured semantic knowledge and numerical parameters of a waveform are integrated by representation learning. Based on the developed CWKG, we further propose an intelligent communication waveform recommendation system (CWRS) to generate waveform candidates. In the CWRS, an improved involution1D operator is introduced according to the characteristics of KG-based waveform representation, and the multi-head self-attention is adopted to weigh the influence of various components. Meanwhile, multilayer perceptron-based collaborative filtering is used to evaluate the matching degree between the requirement and the waveform candidate. Simulation results show that the proposed CWKG-based CWRS can recommend waveform candidates with high reliability, and utilize existing waveform knowledge much more effectively than existing methods.
Transfer Learning with Input Reconstruction Loss

Neural networks have been widely utilized for wireless communication optimizations. In most of the literature, a dedicated neural network is trained for each specific optimization problem. However, under many scenarios, several distinct objectives are worth optimizing on the same wireless environment. Instead of exhaustively training a new model for every objective, it is more efficient to exploit the correlations between these objectives to train models with shared model parameters and feature representations. In the deep learning literature, transfer learning has been proposed to encourage knowledge transfer among models solving correlated problems. Unlike a majority of transfer learning applications where the high level features are relatively easy to locate in the neural networks, this paper considers wireless communication problems, in which it is much more difficult to identify high level features transferable to correlated tasks. To address this issue, this paper proposes to add an additional reconstruction loss when training the model. This new loss is for reconstructing the problem inputs starting from a selected neural network hidden layer. This approach encourages the features learnt to be general and descriptive about the inputs, instead of being solely responsible for minimizing the specific task-based loss. When a new objective is to be optimized, these features can be readily used for transfer learning. Simulation results in device-to-device wireless network power allocation optimization suggest that the proposed approach is highly efficient in data and model complexity, resilient to over-fitting, and supports competitive optimization performances.
Modulation Classification for MIMO Systems in the Presence of Non-Gaussian Noise

Distribution test, a method to evaluate the goodness-of-fit, has been applied to automatic modulation clas-sification (AMC) in recent years. It compares the empirical cumulative distribution function (ECDF) of the sample signal with the theoretical cumulative distribution function (TCDF) under each candidate modulation format. Most existing works in MIMO systems assume the additive noise is Gaussian. However, additive noise often exhibits non-Gaussian characteristics in practical systems. The TCDF of each modulation developed for Gaussian noise can not perform well in practical system. To solve this issue, this paper proposes a practical MIMO system model, which assumes that the noise is Cauchy-Gaussian bi-parameter mixture and analyzes the TCDF corresponding to different modulation modes under this noise model. The Kolmogorov-Smirnov (KS) distribution test is used to AMC in flat-fading channel for both Quadrature Amplitude Modulation and Phase Shift Keying. Extensive simulation results demonstrate that, compared with two-sample KS test based classifier, one-sample KS test based classifier can achieve good recognition results under the non-Gaussian noise model.
MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE TRANSFER

Deep learning-based recognition of radio signal modulation has emerged as a current research hotspot with significant practical potential. However, in practical applications, radio modulation signal data acquisition is complicated to obtain, and label samples are costly and time-consuming to meet the data dependence of deep learning. Transfer learning allows pretrained networks to be reused on large-scale datasets, making it a kind of solution for modulation signal recognition in limited data. The method of suppressing small singular values in the feature vector is employed in this paper to realize selective knowledge transfer for modulation signal recognition, while stochastic normalization is employed to replace the batch normalization layer to avoid over-fitting. We tested the stochastic normalized selective knowledge transfer method on the RML2016.10A and RML2016.04C datasets, with an SNR of 6dB signal samples, and found that it can lead to average growth of 15.77% and 10.32% when compared to direct training, and 6.1% and 2.73% when compared to vanilla fine-tuning. In addition, we check up under a variety of SNR conditions to ensure that our method is effective.
Reducing Average Job Completion Time for DAG-style Jobs by Adding Idle Slots

Sizes of data processing jobs in cloud clusters have been growing rapidly in the big data era. It is critical to execute those jobs efficiently. The average job completion time (JCT) is a widely used metric to measure executing efficiency. JCT refers to the length of the time interval between a job's arrival to its completion. Typically, a data processing job contains multiple stages with complex precedence constraints. Carefully scheduling the processing sequence of stages within a job may significantly reduce its JCT. Our objective is to minimize the average JCT for online arrival jobs. The computation graphs of those jobs are usually directed acyclic graphs (DAGs). It makes the scheduling problem challenging. Recent works have shown that reinforcement learning (RL) agents can adaptively adjust the scheduling policies by dynamically assigning priorities for job stages. However, we notice that other factors besides stage priories may impact the JCT significantly. In particular, we observe that inserting idle slots before large jobs may reduce the waiting time of small jobs that arrive slightly later and reduce the average JCT. We analyze the benefits of inserting idle time for simple cases theoretically and show the condition in which idle slots should be inserted for two adjacent jobs. In addition, we adapt the RL-based scheduler by integrating the observation. Experiment results on both real-world and synthetic datasets show the efficiency of our scheduler. Also, a perturbation-based method is applied to demonstrate the contribution of each proposed feature.
Zero-Rating, One Big Mess: Analyzing Differential Pricing Practices of European MNOs

Zero-rating, the practice of not billing data traffic that belongs to certain applications, has become popular within the mobile ecosystem around the globe. There is an ongoing debate whether mobile operators should be allowed to differentiate traffic or whether net neutrality regulations should prevent this. Despite the importance of this issue, we know little about the technical aspects of zero-rating offers since the implementation is kept secret by mobile operators and therefore is opaque to end-users and regulatory agencies. This work aims to independently audit classification practices used for zero-rating of four popular applications at seven different mobile operators in the EU. We execute and evaluate more than 300 controlled experiments within domestic and internationally roamed environments and identify potentially problematic behavior at almost all investigated operators. With this study, we hope to increase transparency around the current practices and inform future decisions and policies.
Lifelong Autonomous Botnet Detection

Botnet-driven attacks have attracted attention due to their diversity, high potential to cause damage and massive data generation. Existing botnet detection solutions are usually specific to a type of attack behavior. This particularity makes attack detection challenging because it involves a high operational overhead for manually calibrating and managing a large set of solutions for different attacks and variations. Hence, this work presents LBDS, a botnet detection system that acts autonomously in dynamic environments. It relies on concept drift and AutoML, two main techniques that consider dynamic behavior on data distribution. The LBDS evaluation has followed a diverse set of attacks and protocols. Results demonstrate that the system detects botnets utilizing different detection techniques, indicating its ability to consider various aspects of data and attacks.
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm

Widely used deep learning models are found to have poor robustness. Little noises can fool state-of-the-art models into making incorrect predictions. While there is a great deal of high-performance attack generation methods, most of them directly add perturbations to original data and measure them using L_p norms; this can break the major structure of data, thus, creating invalid attacks. In this paper, we propose a black-box attack, which, instead of modifying original data, modifies latent features of data extracted by an autoencoder; then, we measure noises in semantic space to protect the semantics of data. We trained autoencoders on MNIST and CIFAR-10 datasets and found optimal adversarial perturbations using a genetic algorithm. Our approach achieved a 100% attack success rate on the first 100 data of MNIST and CIFAR-10 datasets with less perturbation than FGSM.
Regularized Ising Formulation for Near-Optimal MIMO Detection using Quantum Inspired Solvers

Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that the quantum-inspired computing approach based on Coherent Ising Machines (CIMs) is a promising candidate for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the direct approach adopted in the existing literature on MIMO detection using Quantum Annealing. We evaluate our methods using a simplified, quantum-inspired model and show that our methods can achieve a near-optimal performance for several Large MIMO systems, like <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16\times 16,20\times 20$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$24\times 24$</tex> MIMO with BPSK modulation.
Fast Chaotic Image Encryption with Simultaneous Permutation and Diffusion for IoT Applications

The current prevalence of the internet for secret communication, alongside the development of the Internet of Things (IoT), increases the importance of cryptography for information security. Considering that the most widely shared archives are images and that traditional encryption algorithms, such as the Advanced Encryption Standard (AES), are not optimized for image encryption, researchers have investigated the use of chaos theory in image encryption schemes. However, their elevated encryption time and memory access, due to the number of scannings needed to cipher an image, have been an issue for chaotic ciphers. Considering this, a new cipher is proposed in this work in which the plain-image is divided into blocks and a simultaneous permutation-diffusion is adopted, increasing the cipher's time efficiency. The cipher is executed in a Raspberry Pi 3 device, and it presents a significant increase in throughput compared to other similar works in literature. Additionally, extensive security evaluations showed that the cipher is secure relative to statistical, differential, chosen-plaintext, brute force, noise, and occlusion attacks, which are hence suitable for the application in real-time IoT applications.
Malicious Blockchain Domain Detection Based on Heterogeneous Information Network

With the popularity of Blockchain Domain Name System (BDNS), more and more cybercriminals integrate Blockchain Domain Names (BDNs) into their infrastructure. Due to the anonymity and anti-censorship of BDNs, it is difficult to detect malicious activities based on BDNs, posing a serious threat to network security. In this paper, we propose a novel method to detect malicious BDNs. First, we extract 16 statistical features of domain names. Second, we construct a Heterogeneous Information Network (HIN) of BDNS, which can use malicious traditional domain names as supplementary data. Then we associate domain names by meta-paths in the HIN and build an association graph of domain names. To better characterize domain names, we use the graph convolutional network algorithm to fuse the statistical features of domain names in the association graph. Finally, we detect malicious BDNs by the neural network algorithm. Compared with the existing methods, the experimental results show that our method can accurately detect malicious BDNs with the F1 score of 0.9901 and discover more unknown malicious BDNs from the dataset.
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning

Deep reinforcement learning (DRL) has made sig-nificant achievements in many real-world applications. But these real-world applications typically can only provide partial ob-servations for making decisions due to occlusions and noisy sensors. However, partial state observability can be used to hide malicious behaviors for backdoors. In this paper, we explore the sequential nature of DRL and propose a novel temporal-pattern backdoor attack to DRL, whose trigger is a set of temporal constraints on a sequence of observations rather than a single observation, and effect can be kept in a controllable duration rather than in the instant. We validate our proposed backdoor attack to a typical job scheduling task in cloud computing. Numerous experimental results show that our backdoor can achieve excellent effectiveness, stealthiness, and sustainability. Our backdoor's average clean data accuracy and attack success rate can reach 97.8% and 97.5%, respectively.
Robust Semantic Transmission of Images with Generative Adversarial Networks

Image compression and bit transmission are con-ducted separately in most existing methods for image trans-mission, leading to possible transmission failure or a waste of communication resource for a time-varying channel condition. This paper proposes a neural network-based image transmission system trained by generative adversarial networks (GANs) aiming to achieve robust transmission. Specifically, the deep semantic of an input image is extracted and represented as bit streams at the transmitter, and the receiver reconstructs the original image based on possible bit error and the same background knowledge as the transmitter. Experimental results show that the proposed robust transmission system trained by GAN can adapt to the current communication condition, and achieve a high-quality reconstruction even with a high transmission error rate and a smaller transmission data size than engineered codecs such as JPEG.
Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.
I Can Still Observe You: Flow-level Behavior Fingerprinting for Online Social Network

The privacy of online social networks (OSNs) remains a major concern for today's Internet. Researchers have demonstrated that by analyzing inter-packet or packet-level network traffic, a third-party analyzer is able to fingerprint a user's OSN behavior information even when the traffic is encrypted. In this paper, we propose a learning-based approach that steps further to perform OSN behavior fingerprinting only through highly compressed, flow-level network traffic (e.g., NetFlow). By preprocessing flow records, segmenting traffic flows into bursts, and leveraging a long short-term memory network to classify the bursts, our approach can identify major OSN behaviors (e.g., Facebook post, Twitter Read, Weibo video, etc.) with nearly 90% accuracy. Compared with packet-level fingerprinting approaches, our approach significantly improves the fingerprinting efficiency in evaluations, making large-scale OSN usage monitoring feasible only with limited computing resources and coarse-grained network traffic. This work also reveals the huge risks facing privacy of OSN users on today's Internet today.
A HeteSim-Measured algorithm fused semantic features in heterogeneous social networks

The construction of heterogeneous social networks enables the major social platforms in the network to connect through social information. In order to ensure network security and improve downstream tasks such as user profile, knowledge graph construction and recommendation, the relevance measurement between social information has attracted extensive attention in recent years. Although HeteSim algorithm has achieved good results in measuring the relevance between heterogeneous nodes, this method only focuses on the structure features between nodes, and fails to comprehensively consider the joint impact of structure features and semantic features. Therefore, this paper proposes HeteSim-Measured algorithm that considers the fusion of structure features and semantic features for improving the accuracy of relevance measurement. The experiment is verified by measuring the relevance based on meta-path on the datasets and comparing with HeteSim algorithm.
Do Not Have Enough Data? An Easy Data Augmentation for Code Summarization

Code comments improve the readability and intelligibility of codes, which can help developers understand programs and improve the efficiency of the software maintenance and evolution process. Unfortunately, code comments are often mismatched, missing, or outdated in software projects, which negatively affects the efficiency of developers to infer the functionality from source code and affect the efficiency of software maintenance and evolution. To solve this problem, many source code summarization algorithms have been proposed. However, these methods usually try to collect a large data set which contains the mapping between code comments and source code to train models. Hence, the effectiveness of the models often rely on the quality of the training data. There are two limitations for the training sets: the insufficient data collection limitation (i.e., generate a large amount of noises-free training data) and data distribution bias limitation (i.e., generate training data for infrequently used methods). To address this issues, we have proposed a data augmentation method for code comments, named CDA-CS. Extensive experiments on Java and Python projects collected from GitHub are conducted to evaluate the performance of CDA-CS. Training models on the augmented dataset, the state-of-the-art algorithms can easily get a further 1.37% to 2.24% improvement in terms of different evaluation metrics (i.e., BLEU-4, METEOR, ROUGH_L) with no additional cost.
Multi-selection Attention for Multimodal Aspect-level Sentiment Classification

Multimodal aspect-level sentiment classification aims to utilize images to recognize the sentiment polarity of target aspects in text. To address the issues of low utilization of inter-modal complementary information and vanishing gradients, a multimodal aspect-level sentiment based on multi-selection attention mechanism is proposed. Multi-selection attention mechanism explicitly considers the contribution of different modalities to aspects and utilizes shared features and private features of image modality to enhance sentiment expression of target aspects. On this basis, inspired by residual connections in ResNet and encoder-decoders in U-Net, a simple and effective residual encoder-decoder is proposed to mine deep information and avoid vanishing gradients. The experimental results on two public sentiment datasets show that the proposed model can better utilize images to supplement textual modality and improve the accuracy of sentiment classification.