Reviewerly/exHarmony huggingface dataset:
Rate-Distortion Optimization for Adaptive Gradient Quantization in Federated Learning

Federated learning (FL) is an emerging machine learning setting designed to preserve privacy. However, constantly updating model parameters on uplink channels results in huge communication overload, which is a major challenge for FL. In this paper, we consider an adaptive gradient quantization approach based on rate-distortion optimization in FL, which consists of a non-stationary random walk model on the true global optimal model parameters. Unlike traditional quantization methods, our goal is to minimize the total communication costs when the global server reconstructs model parameters under distortion constraints. Furthermore, when considering the iterative process, we utilize the Kalman filter to reduce computational complexity. And in each iteration, a generalized water-filling algorithm is used to calculate the optimal quantization levels for each local client. Numerical results show that the proposed method outperforms conventional quantization methods in terms of reducing communication costs.
EAPS: Edge-Assisted Privacy-Preserving Federated Prediction Systems

To reduce the delay and network congestion for content delivery in wireless networks, proactive caching scheme has attracted lots of attentions from both academia and industry. However, traditional caching prediction methods require to collect user data in a centralized server, which is becoming unreliable and impractical due to regulatory restrictions. To circumvent this issue, deploying caching prediction system in a federated learning (FL) fashion becomes a promising solution. However, there still exist privacy risks, and even worse, the FL is vulnerable to low-cost attacks. To solve this problem, a novel federated prediction system (FPS) is studied to provide high robustness and privacy. Firstly, to keep a balance between further enhancing privacy protection and alleviating the performance degradation caused by additional protection schemes, we propose an edge-assisted, robust and privacy-preserving FPS framework based on the local differential privacy (LDP) scheme. Secondly, to mitigate the impact of heterogeneous data, we add a regularization term to the local loss function. Furthermore, an attention-based aggregation scheme is proposed to defend against Byzantine attacks during the training process. Finally, the experiment results are provided to show the superiority of our proposed algorithm in terms of prediction accuracy and robustness.
Environment-Aware Adaptive Transmission for Adaptive Video Streaming Based on Edge Computing in High-speed rail Scenarios

As High-speed rail becomes a popular way to travel, users have a high demand for streaming services. In High-speed rail scenarios, users move fast and base stations handover frequently. Most of the existing network bandwidth prediction algorithms and bitrate selection algorithms are proposed based on low-speed scenarios. These algorithms are difficult to adapt to high-speed mobile scenarios. To solve this problem, this paper proposes an adaptive streaming media transmission method using edge computing, High-speed rail status and cross-layer information (EHCI) in the 5G network environment. Firstly, a QoE model and a coordinated transmission architecture using edge computing, High-speed rail operation status and cross-layer information are proposed. Secondly, a media transcoding algorithm and rate selection algorithm are proposed. Finally, the simulation experiment is carried out in this paper. Simulation results demonstrate that the method proposed in this paper can well improve the QoE of High-speed rail passengers, and is helpful to the study of the optimized transmission of streaming media in High-speed rail scenarios.
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications

Federated learning (FL) provides an emerging approach for collaboratively training semantic encoder/decoder models of semantic communication systems, without private user data leaving the devices. Most existing studies on trustworthy FL aim to eliminate data poisoning threats that are produced by malicious clients, but in many cases, eliminating model poisoning attacks brought by fake servers is also an important objective. In this paper, a certificateless authentication-based trustworthy federated learning (CATFL) framework is proposed, which mutually authenticates the identity of clients and server. In CATFL, each client verifies the server’s signature information before accepting the delivered global model to ensure that the global model is not delivered by false servers. On the contrary, the server also verifies the server’s signature information before accepting the delivered model updates to ensure that they are submitted by authorized clients. Compared to PKI-based methods, the CATFL can avoid too high certificate management overheads. Meanwhile, the anonymity of clients shields data poisoning attacks, while real-name registration may suffer from user-specific privacy leakage risks. Therefore, a pseudonym generation strategy is also presented in CATFL to achieve a trade-off between identity traceability and user anonymity, which is essential to conditionally prevent from user-specific privacy leakage. Theoretical security analysis and evaluation results validate the superiority of CATFL.
Understanding Before Transmission (UBT): Hashing-based Semantic Communication Model

In Shannon’s theory, semantic aspects of communication were identified but considered irrelevant to the technical communication problems. However, semantic communication techniques have recently attracted renewed research interests in 6G because they have the capability to support an efficient interpretation of the meaning intended by the sender (or accomplishment of the goal) when dealing with multi-dimensional data such as videos, images, audio, sentences, documents etc. We propose a new hashing-based semantic communication, where our learning objective is the "semantic extraction" to produce optimal binary signatures (hash codes) by supervised learning. We evaluate the proposed framework over large image data sets and demonstrate its effectiveness in bulk transmission.
Online Scheduling of CPU-NPU Co-inference for Edge AI Tasks

Edge AI is an emerging paradigm that leverages edge computing to pave the last mile delivery of artificial intelligence. To satisfy the stringent timeliness and energy-efficiency requirements of emerging edge AI tasks, specialized AI accelerator of Neural Processing Units (NPU) have been widely equipped by edge nodes. Compared to the traditional centralized processing units (CPU), NPU has better performance and energy-efficiency. However, these benefits come at the cost of reduced inference accuracy. As a result, existing coarse-grained scheduling mechanisms that schedule a whole DNN task to either the CPU or NPU are unable to make the best use of NPU. To address this issue, we propose an online NPU-CPU co-inference scheduling mechanism to schedule the DNN task at the fine-grained layer level, and thus to fully utilize the performance, accuracy, and power diversities of the NPU and CPU. By applying Lyapunov optimization to schedule the network layers dynamically, our proposed online scheduling mechanism is able to ensure the real-time inference speed and cap the long-term time-averaged power consumption, while still approximately minimizes the long-term inference accuracy loss. Via rigorous theoretical analysis as well as realistic trace-driven simulations, we demonstrate the effectiveness of our proposed online scheduling mechanism.
Hierarchical Jamming Recognition with Spectrum Fusion Feature and Twin-bound SVM for Cognitive Satellite Communications

In satellite communications, signals are vulnerable to radio frequency jamming, especially in aerospace tracking telemetry and command (TT&C) systems and global navigation satellite system (GNSS). It is quite essential to jamming suppression for unknown jamming signals to ensure the safety of communication links, and the design of the jamming type recognition algorithm is a basic prerequisite of effectively anti-jamming, especially for the cognitive satellite communications. In this work, considering the limited resource of satellites, a fast jamming recognition algorithm is proposed to detect and classify the jamming signals with low time complexity in the process of data transmission. By taking advantage of the spectrum fusion feature and making full use of the anti-jamming prior information, the proposed algorithm combines the idea of the twin support vector machine (SVM) and the solution-oriented hierarchical classification to reduce the online time complexity with high recognition accuracy. Simulation results demonstrate the clear superiority of the proposed algorithm vis-à-vis other baseline competitors.
Meta-Learning for Wireless Interference Identification

Deep learning-based (DL-based) models have shown to be powerful tools for wireless interference identification (WII). However, one of the key concerns toward using these models in practical systems is that they perform poorly when they are encountered with signals coming from new sources not previously observed during the training phase. In a real-world communication system, the interference identifier will frequently face new unknown signals due to the existence of many wireless transmitters. This renders the conventional DL-based models impractical as a WII tool unless they go through a new training phase. Retraining the model is not only inefficient, but it can also be not feasible in some cases (e.g., at end-user devices) as the training phase consumes time and resources and requires large amounts of data. We present a new approach for data-driven WII systems using meta- learning to address the lack of adaptability in conventional DL-based models to new (not previously seen) signals. We show that by using meta-learning, we are able to identify signals coming from not previously observed technologies and frequencies using just a handful of new samples, a task that is not generally possible with conventional DL models. Finally, we analyze and compare the performance of the presented meta-learning model in multiple different settings using raw I/Q samples and Fast Fourier Transform of I/Q samples. Based on our experiments, we show that the proposed meta-learning scheme outperforms the conventional deep learning models for WII when there are just a few samples available for training <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Deep Autoencoder-based Z-Interference Channels

A deep autoencoder (DAE)-based communication over the two-user Z-interference channel (ZIC) is introduced in this paper. The proposed DAE-ZIC is designed to minimize the bit error rate (BER) in the presence of interference by jointly optimizing the encoders and decoders. Effectively, this is an end-to-end communication that designs new constellations for the ZIC. Normalization layers are embedded in the proposed DAE design to realize an average power constraint so that there are no regular shape restrictions on the constellation symbols. We compare the performance of the DAE-ZIC with two baseline methods, which are ZIC with regular and rotated constellations. Simulation results show a significant gain in BER reduction. On average, in weak, moderate, and strong regimes, 31%–75% BER improvement is achieved compared to the best existing methods.
Satellite Telemetry Data Anomaly Detection using Multiple Factors and Co-Attention based LSTM

Telemetry data is an important resource to detect anomaly of satellites in orbit. In recent years, the telemetry data-driven satellite anomaly detection has drawn great attention from academia and industries. However, in prior arts, the time-or frequency-domain data feature is usually separately utilized; and, to reduce the compute complexity, a small portion of data dimensions are usually selected from the telemetry data manually, leading to reduction in detection accuracy. Motivated by this, we propose a novel satellite telemetry data anomaly detection approach using high-dimension telemetry data, and the Multiple Factors and Co-Attention based LSTM (MFCA-LSTM) model. Specifically, to achieve an accurate prediction of telemetry sequence, we first propose the MFCA-LSTM model, jointly considering the time- and frequency-domain data feature, and the auxiliary information, e.g., telecommand and mission planning. Then, by using the MFCA-LSTM model, we construct a two-level anomaly detection framework to efficiently detect full-dimension data; and, to improve the accuracy rate and reduce the false alarm rate, we further propose an adaptive Tukey test algorithm to determine the dynamic thresholds. Finally, we perform extensive experiments based on two real-world datasets, and the results testify the effectiveness of our proposed framework.
Communication Efficient Heterogeneous Federated Learning based on Model Similarity

Federated Learning is now widely used to train neural networks under distributed datasets. One of the main challenges in Federated Learning is to address network training under local data heterogeneity. Existing work proposes that taking similarity into account as an influence factor in federated learning can improve the speed of model aggregation. We propose a novel approach that introduces Centered Kernel Alignment (CKA) into loss function to compute the similarity of feature maps in the output layer. Compared to existing methods, our method enables fast model aggregation and improves global model accuracy in non-IID scenario by using Resnet50.
The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

The usual explanation of the efficacy of wavelet-based methods hinges on the sparsity of many real-world objects in the wavelet domain. Yet, standard wavelet-shrinkage techniques for sparse reconstruction are not competitive in practice, one reason being that the lack of shift-invariance of the wavelet transform produces blocky artifacts. The standard remedy is cycle spinning, which results in a substantial reduction of these artifacts. In this letter, we propose a theoretical investigation of the sparsity of solutions to the cycle-spinning variant of wavelet-based resolutions of linear inverse problems. We derive a representer theorem that provides a complete characterization of the solution set. Our theorem indicates that the solutions are typically not sparse, where sparsity is measured with respect to the wavelet dictionary. This exposes that the role of sparsity in the success of wavelet-based solutions of linear inverse problems requires further investigation. We corroborate our theoretical results with numerical examples for the problem of image denoising.
Deep Gradual-Conversion and Cycle Network for Single-View Synthesis

With the popular application of convolutional neural networks in computational intelligence, research on deep learning-based view synthesis has been a hot topic. Although promising performance has been achieved by the existing learning-based view synthesis methods, how to obtain a clearer target view in the single-view synthesis task is still a challenging problem. In this paper, we propose a novel deep gradual-conversion and cycle network (DGCC-Net) for single-view synthesis by jointly considering the gradual and cycle synthesis between source and target views. Specifically, a gradual conversion mechanism is designed to synthesize a clearer target view in a gradual manner, which learns the progressive rotation trend from the source to the target view by introducing the intermediate transformation. Based on the synthesized target view, a cycle synthesis mechanism is designed to further promote the learning of single-view synthesis network by mapping the synthesized target back to the source view. By utilizing the proposed gradual conversion and cycle synthesis mechanisms, the whole network achieves a cycle view synthesis mapping between source and target views to obtain a better target view. Experiments on widely used datasets indicate the proposed DGCC-Net exceeds state-of-the-art methods.
An Accurate Reading Algorithm for Substation Pointer Meters Based on Improved 2-D Gamma Function and PA-RetinaNet

Aiming at the low reading accuracy of the substation pointer meter caused by illumination and shooting angle, an accurate reading algorithm based on improved 2-D gamma function and PA-RetinaNet is proposed. First, the multiscale Gaussian function is used to extract the illumination components, and the 2-D gamma function is reconstructed to adaptively adjust pointer meter images under different illumination intensities. Then, the path augmentation (PA) is integrated into the RetinaNet to detect the dial scale digital keypoints. Meanwhile, combined with the elliptical transformation theory, the correction reference point in the perspective transformation is reselected to realize the tilt correction of the pointer meter image. Finally, the centripetal constraint is added to the line segment detector to achieve accurate positioning of the pointer, and the function between the pointer angle and the meter reading is deduced. Experimental results demonstrated that the method can effectively solve the problem of illumination and shooting angle in the reading of the substation’s pointer meter.
SGA-Net: A Sparse Graph Attention Network for Two-View Correspondence Learning

Establishing reliable correspondences between two images is a fundamental and important task in computer vision. This paper proposes a novel network called Sparse Graph Attention Network (SGA-Net), to capture rich contextual information of sparse graphs for feature matching task. Specifically, a graph attention block is proposed to enhance the representational ability of graph-structured features. The proposed block introduces a novel normalization technique for graph-structured features to embed global information into each edge feature, and it adopts the squeeze-and-excitation mechanism to capture graph-wise contextual information. Meanwhile, to further obtain interesting structural information of sparse graphs, a novel sparse graph transformer is developed based on multi-headed self-attention mechanism, while maintaining permutation-equivariance. Additionally, considering that the graph contexts in shallow layers are not fully exploited, a simple graph-context fusion block is introduced to adaptively capture topological information from different layers by implicitly modeling the interdependence between these graph contexts. The proposed SGA-Net can search dependable candidates among the putative correspondences and simultaneously estimate accurate camera poses for two-view geometry estimation. Extensive experiments on outlier removal and camera pose estimation tasks have demonstrated that the proposed SGA-Net outperforms state-of-the-art methods on both outdoor and indoor benchmarks (i.e., YFCC100M and SUN3D). SGA-Net achieves a mAP5° of 58.88% without RANSAC on the outdoor dataset, and it achieves a precision increase of 13.45% and 7.34% compared with the state-of-the-art result on outdoor and indoor datasets, respectively.
Requirements Quality vs. Process and Stakeholders’ Well-Being: A Case of a Nordic Bank

Requirements are key artefacts to describe the intended purpose of a software system. The quality of requirements is crucial for deciding what to do next, impacting the development process' effectiveness and efficiency. However, we know very little about the connection between practitioners' perceptions regarding requirements quality and its impact on the process or the feelings of the professionals involved in the development process. Objectives: This study investigates: i) How software development practitioners define requirements quality, ii) how the perceived quality of requirements impact process and stakeholders' well-being, and iii) what are the causes and potential solutions for poor-quality requirements. Method: This study was performed as a descriptive interview study at a sub-organization of a Nordic bank that develops its own web and mobile apps. The data collection comprises interviews with 20 practitioners, including requirements engineers, developers, testers, and newly employed developers, with five interviewees from each group. Results: The results show that different roles have different views on what makes a requirement good quality. Participants highlighted that, in general, they experience negative emotions, more work, and overhead communication when they work with requirements they perceive to be of poor quality. The practitioners also describe positive effects on their performance and positive feelings when they work with requirements that they perceive to be good.
Variational Augmentation for Enhancing Historical Document Image Binarization✱

Historical Document Image Binarization is a well-known seg- mentation problem in image processing. Despite ubiquity, traditional thresholding algorithms achieved limited success on severely degraded document images. With the advent of deep learning, several segmentation models were proposed that made significant progress in the field but were limited by the unavailability of large training datasets. To mitigate this problem, we have proposed a novel two-stage framework- the first of which comprises a generator that generates degraded samples using variational inference and the second being a CNN-based binarization network that trains on the generated data. We evaluated our framework on a range of DIBCO datasets, where it achieved competitive results against previous state-of-the-art methods.
Human Activity Recognition using Deep Learning

Machine learning research is heavily focused on human activity detection since it has various applications in a variety of fields, including security, entertainment, ambient supported living, and health management and monitoring. Researchers' interest in human daily activities is seen from studies on human activity recognition (HAR). As a result, the general architecture of the HAR system and a description of its key elements are described in this work. Review of the state-of-the-art in accelerometer-based human activity recognition According to this survey, the majority of recent research that employed CNN for HAR identification relied on it even though other deep learning models also showed acceptable accuracy. The paper suggests a 2 different classification depending on the kind of machine learning (conventional or deep learning) and the manner of execution (online or offline). Comparing 48 studies prediction performance, algorithms, activity categories, and used equipment. The study concludes by contrasting the difficulties and problems associated with identifying human movement based on accelerometer sensors utilizing deep learning versus conventional machine learning, as well as online versus offline.
Blockchain-based multi-authority revocable data sharing scheme in smart grid

In view of the problems of inefficient data encryption, non-support of malicious user revocation and data integrity checking in current smart grid data sharing schemes, this paper proposes a blockchain-based multi-authority revocable data sharing scheme in the smart grid. Using online/offline encryption technology with hybrid encryption technology enhances the encryption performance for the data owner. The use of user binary tree technology enables the traceability and revocability of malicious users. The introduction of multiple attribute authorization authorities eliminates the threat of collusive attacks that exist in traditional data-sharing schemes. In addition, the semi-honest problem of third-party servers is solved by uploading data verification credentials to the blockchain. The security analysis results show that the scheme can resist selective plaintext attacks and collusion attacks. The performance analysis results show that the proposed scheme has lower computational overhead and better functionality than similar schemes, which is suitable for secure data sharing in smart grids.