Reviewerly/exHarmony huggingface dataset:
An Attack-Resistant Federated Edge Learning Framework for Integrated Sensing, Computing and Communications System

Integrated sensing, computing and communications (ISC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) is a promising technology to enable both physical-digital spatial sensing, intelligent communication and computing. This paper studies a federated learning-assisted ISC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> system, in which edge nodes coordinate edge computing resource for model training based on their local integrated sensing and communications (ISAC) data. In the process of a completely distributed collaborative training, sharing and transmission of local parameters may lead to a serious Byzantine attack. To improve the system's anti-attack capability, we design a blockchain-federated edge learning framework, which utilizes the non-tampering and traceability features of the blockchain, and design a verification algorithm for federated aggregation. Particularly, an aggregation algorithm is designed to improve the fitting efficiency and accuracy of our model. Experiments based on the measured ISAC data show that the proposed scheme can effectively resist up to 30% of data tampering and up to 30% of model tampering attacks.
Tagging Algorithm and POS Tags for Narrator's Name in Hadith Document

Named Entity Recognition (NER) is important in many domains, such as information retrieval and text classification. Typically, NER uses machine learning (ML) or rule-based methods to recognize NER. ML works well with annotated corpora. The most commonly used annotated corpora are in English but not in Malay. Non-annotated corpora need to be tagged to be used in NER, and manual tagging requires time and effort from experts. In this work, we proposed a Natural Language Processing (NLP) technique to automate the tagging of narrators in hadith texts. Our proposed technique includes a model for tagging narrators in hadith texts. This research used POS tags to explain the detection of narrators' names in hadith texts. A total of 700 hadith texts were used to develop the tagging model using a rule-based technique. 300 hadith documents were examined to evaluate the tagging model and resulted in 93.64% for recall and 96.72% for precision. Further research is needed to investigate the automatic extraction of the narrators' names, which can be used in hadith retrieval.
An Efficient Graph-Based IOTA Tangle Generation Algorithm

IOTA is a recent distributed ledger technology that relies on Directed Acyclic Graph (DAG) for its ledger organization. To improve IOTA mechanisms, the state of the art methodology employs graph analysis and, for that, heavily relies on synthetic graph generation. Herein, the most popular generation method simulates IOTA protocol execution. Although this method produces realistic IOTA ledgers, it requires too much memory and time due to repeated random walks on the DAG. In this paper, we propose an alternative Graph Generation and Refinement (GraGR) algorithm designed to generate realistic IOTA ledgers while strongly relaxing memory and timing constraints. The evaluations show that, compared to the state of the art, GraGR can generate a ledger with the same properties with only half of memory and up to 10 times faster.
Instrument position in Immersive Audio: An empirical review of award-winning practices

Immersive audio technologies have broadened postproduction strategies for spatial audio, gaining popularity among mainstream audiences. However, there is a lack of defined procedures and critical thinking regarding audio mixing guidelines for surround sound in popular music. In this context, we conducted an empirical study to identify trends concerning instrument position, trajectories, and dynamics from surround mixings. Furthermore, we assess the degree to which they differ from their stereo renderings. Seven award-winning songs in the Grammy category for Best Immersive Album were analyzed, including surround 5.1 and stereo versions. The study found consistent instrument positions in the songs, with rhythmic instruments and bass in the center, lead vocals spread across front channels, and harmonic instruments in wider positions. Solo instruments occupied left, right, and center channels, with dynamics emphasizing lead vocals and solos. Trajectories were rarely used, indicating channel-based thinking. Limited adoption of immersive audio dimensions and reliance on stereo techniques were observed, with no notable differences between the surround and stereo versions. Identified song outliers are discussed and offer avenues for exploration, highlighting the importance of diverse musical expressions in informing immersive audio mixing.
A Hybrid Federated Learning Model for Insurance Fraud Detection

Mission-critical systems are significant for the survival of any organization. Financial systems, communication systems, and electricity grid systems are some examples of mission-critical applications. Any damage to such systems can cause business operations to fail. Insurance fraud is one of the challenges faced by mission-critical systems today. Insurance fraud detection has historically been a manual task left to claim agents, who look over the evidence and draw an inference based on their intuitions. Recently, machine learning (ML) has been predominantly used to automate the process of insurance fraud detection. Despite the success of ML models in detecting insurance fraud, privacy preservation of insured personal data continues to be a problem. To address this issue, we present a novel hybrid approach. The proposed work suggests an automated way to control the process of vehicle insurance claim fraud detection in the insurance industry. The proposed study combines Federated Learning (FL), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to incorporate the advantages of each technology. The suggested model uses GA for optimal feature subset extraction. The optimized feature subset is then fed into a Federated learning with a Particle swarm optimization (FPSO) model. The findings show that the suggested hybrid model has an accuracy of 94.47% and that it may be improved even further by using other nature-inspired algorithms that are only used for fraud detection.
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback

The recent advances in machine learning and deep neural networks have made them attractive candidates for wireless communications functions such as channel estimation, decoding, and downlink channel state information (CSI) compression. However, most of these neural networks are large and inefficient making it a barrier for deployment in practical wireless systems that require low-latency and low memory footprints for individual network functions. To mitigate these limitations, we propose accelerated and compressed efficient neural networks for massive MIMO CSI feedback. Specifically, we have thoroughly investigated the adoption of network pruning, post-training dynamic range quantization, and weight clustering to optimize CSI feedback compression for massive MIMO systems. Furthermore, we have deployed the proposed model compression techniques on commodity hardware and demonstrated that in order to achieve inference gains, specialized libraries that accelerate computations for sparse neural networks are required. Our findings indicate that there is remarkable value in applying these model compression techniques and the proposed joint pruning and quantization approach reduced model size by 86.5% and inference time by 76.2% with minimal impact to model accuracy. These compression methods are crucial to pave the way for practical adoption and deployments of deep learning-based techniques in commercial wireless systems.
Dual Aggregated Federated Learning with Depthwise Separable Convolution for Smart Healthcare

In recent years, federated learning is the most commonly used framework for collaborative training under the protection of privacy, and has been successfully applied to smart healthcare. The distribution of data in the federated network is usually non-independent and identically distributed (non-IID) and imbalanced, which worsens the performance and increases the gap between the local and global models. Additionally, the similarity of the medical images makes it challenging to identify. In this paper, we propose a dual aggregated federated learning with depthwise separable convolution for smart healthcare. Specifically, a diagnostic network based on depthwise separable convolution is designed, and the residual connection is introduced, which can make full use of the feature information in the medical image and improve the accuracy of disease diagnosis. Meanwhile, we design a dual federated aggregation algorithm to reduce the impact of parameter differences on multi-client model federated aggregation and improve the performance of the global model. Finally, the experimental results illustrate that our proposed algorithm achieves significant performance advantages compared with other existing methods.
Memory Replay For Continual Learning With Spiking Neural Networks

Two of the most impressive features of biological neural networks are their high energy efficiency and their ability to continuously adapt to varying inputs. On the contrary, the amount of power required to train top-performing deep learning models rises as they become more complex. This is the main reason for the increasing research interest in spiking neural networks, which mimic the functioning of the human brain achieving similar performances to artificial neural networks, but with much lower energy costs. However, even this type of network is not provided with the ability to incrementally learn new tasks, with the main obstacle being catastrophic forgetting. This paper investigates memory replay as a strategy to mitigate catastrophic forgetting in spiking neural networks. Experiments are conducted on the MNIST-split dataset in both class-incremental learning and task-free continual learning scenarios.
AI/ML Optimized High-Order Modulations

We propose machine learning (ML) based optimization methods and new high order modulations for reliable and high-capacity communications. The widely adopted square quadrature amplitude modulations (QAM) fundamentally exhibit a shaping loss of up to 1.53 dB to the Shannon capacity bound. The proposed modulations obtained through the ML based optimization outperform the square QAMs and other state of-the-art ones by about 1.2 dB and 0.3 dB, respectively, for 1024-ary modulation with LDPC coding. We construct the neural network architecture and training methods to reflect the desired properties of well-performing modulations. This significantly helps in the training convergence of the ML models to a desired optimal state and leads to the modulation constellation and bit to-symbol mapping that reduces the shaping loss to the Shannon capacity bound to a large extent. Moreover, the ML methods enable the development of new optimal modulations for a wide range of target SNR and modulation orders.
Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks

Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thousands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of fifth-generation/sixth-generation (5G/6G) networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands time series. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge.
DBE-voting: A Privacy-Preserving and Auditable Blockchain-Based E-Voting System

Blockchain technology can construct a distributed and trusted ledger, which can be used for electronic voting (E-voting) systems to ensure the security of voting data and improve government credibility. However, existing blockchain-based solutions cannot fully fulfill five core requirements in E-voting, i.e., auditability, privacy, authentication, correctness, and unreusability, which make them unpractical in the reality. In this paper, we propose a Double Blockchain-based E-voting (DBE-voting) system, which consists of a private blockchain and a public blockchain. In the proposed system, the voter information is only recorded in the private blockchain for further auditing and the voting results are recorded in both blockchains. The voter's privacy can be protected in the private blockchain while the voting results can be queried in the public blockchain for verifying the correctness of the election process. Moreover, the ballot recorded in both blockchains is signed with a valid linkable ring signature to ensure authentication and unreusability. We propose an on-chain and off-chain hybrid storage mechanism to ensure the consistency and correctness of voting data in two blockchains. Experimental results demonstrate that the throughput of our system can reach 29 transactions per second when the block size is 512 KB. The security analysis shows that the DBE-voting is the first blockchain-based system that can meet all five requirements simultaneously.
Device Scheduling for Over-the-Air Federated Learning with Differential Privacy

In this paper, we propose a device scheduling scheme for differentially private over-the-air federated learning (DP-OTA-FL) systems, referred to as S-DPOTAFL, where the privacy of the participants is guaranteed by channel noise.In S-DPOTAFL, the gradients are aligned by the alignment coefficient and aggregated via over-the-air computation (AirComp).The scheme schedules the devices with better channel conditions in the training to avoid the problem that the alignment coefficient is limited by the device with the worst channel condition in the system.We conduct the privacy and convergence analysis to theoretically demonstrate the impact of device scheduling on privacy protection and learning performance.To improve the learning accuracy, we formulate an optimization problem with the goal to minimize the training loss subjecting to privacy and transmit power constraints.Furthermore, we present the condition that the S-DPOTAFL performs better than the DP-OTA-FL without considering device scheduling (NoS-DPOTAFL).The effectiveness of the S-DPOTAFL is validated through simulations.
iDeepRx Enabled 100 Gb/s DFT-s-OFDM Data Transmission Over 220 GHz Testbed

Deep learning (DL) based receiver (DeepRx) has been proven to be able to greatly improve the data transmission performance compared to conventional OFDM (orthogonal frequency-division multiplexing) receivers. In order to accommodate DFT-s-OFDM (discrete Fourier transform-spread OFDM), which is a widely used single carrier waveform for uplink transmission characterized with low peak to average power ratio (PAPR), the internal structure of DeepRx needs to be redesigned. In this paper, we propose a novel deep neural network based receiver, referred to as IDFT-deprecoding embedded deep receiver (iDeepRx) customized for DFT-s-OFDM. By embedding an untrainable functionality of IDFT-deprecoding between two trainable neural network structures, the proposed iDeepRx can support DFT-s-OFDM transmission and mitigate implicit link and hardware-induced channel impairments. We demonstrate a single-layer 100 Gb/s error-free data transmission using iDeepRx over 20 GHz bandwidth at 220 GHz carrier frequency and observe 2 dB+ relative performance gain over the conventional receiver. Moreover, intermediate output of iDeepRx has been extracted with constellation-like patterns for visualization and performance monitoring during training and inference, which helps in making the internal working mechanism of iDeepRx more explainable.
RelativeRFF: Multi-Antenna Device Identification in Multipath Propagation Scenarios

Radio frequency fingerprinting (RFF) is a promising solution for realizing secure and efficient device authentication. The multipath channel overshadows and disrupts the RFF extraction, which causes difficulties in training new models in the presence of fading. Existing approaches attempt to deal with this challenge by traversing channels through simulated channel models. However, this solution requires a large amount of data for training and it is difficult to guarantee that the training covers all possible channels. To mitigate the multipath channel effect on RFF with less training data, we propose a new method in a multi-antenna system, named Relative-RFF (R-RFF), which utilizes channel state information (CSI) feedback to counteract the multipath channel. The RFF imperfection relation between the different antenna chains of the device is proved to be retained after the counteraction of the multipath channel. Numerical results demonstrate that the proposed R-RFF can achieve an identification accuracy of 95.9% for 30 UEs in Tapped Delay Line channel with a signal-to-noise ratio of 20 dB.
PBFT with Gated Prioritized Block Cycles

In this paper we propose a gated cycle scheme for prioritized block access in a PBFT-like consensus mechanism suitable for blockchain-based IoT applications. Blocks submitted by clients are processed in cycles of variable length, in the order determined according to their priorities which can be assigned according to the block length or orderer's stake. We investigate the performance of this scheme using an analytical model and show that it indeed allows for clear differentiation of traffic of different priorities whilst retaining good performance, esp. under higher traffic load, compared to the original PBFT.
A Blockchain-Based Data-Sharing Framework for Cloud Based Internet of Things Systems with Efficient Smart Contracts

As the Internet of Things (IoT) has advanced, data sharing has become a crucial function of cloud computing. However, data security remains a significant challenge in this field. This research proposes a blockchain-based data-sharing system that prioritizes data security and efficiency. The system includes efficient smart contracts and security gateways that record data in the cloud using blockchain. If suspicious behaviour is detected, the blockchain is checked by the centralized cloud, and the responsible party for any malicious gateway behaviour is held accountable. Authentication and data exchange algorithms are used to ensure data security. Additionally, to reduce the burden on end-users, smart contracts in blockchain use highly complex partial decryption algorithms. To satisfy data restriction safety criteria, blockchain achieves traceability of historical actions through open and transparent supervision. Experimental findings demonstrate that the proposed technique is effective in ensuring the safety and efficiency of information exchange between various clients.
FedMC: Federated Learning with Mode Connectivity Against Distributed Backdoor Attacks

Federated learning (FL) has become a hot research domain due to its privacy protection for model collaboratively training in edge computing systems. However, recent studies indicated that most FL algorithms have desperately suffered from backdoor attacks. Although many backdoor defence FL algorithms were proposed, their effects were highly related to the ratio of malicious clients (RMC) of all participated edge nodes. To be more specific, most of them only set RMC around 10% to 30% in their experiments, and their results also showed that the rate of successful backdoor defence seriously drops when RMC increases. In the paper, we propose a novel federated learning scheme with mode connectivity (FedMC) to defend against backdoor attacks, mitigating the sharp defence effect degradation as RMC increases. Conventional mode connectivity mainly focuses on training a connecting curve between two end models, which is inapplicable in distributed multiple clients FL situations. We extend the two-ends mode connectivity to multi-ends by introducing a scalable regularization term consisting of the edge clients' models to involve their knowledge in the connective model training. In each communication round, the FL-Server aggregates and absorbs the contribution of clients by training a connective model based on a small set of clean samples, which builds a pathway to accurately connect all edge clients' models and mitigates the backdoor triggers of models. Extensive experiments and results demonstrate that FedMC can effectively defend against backdoor attacks while maintaining the accuracy on untampered test data.
A Probabilistic Semi-Supervised Approach with Triplet Markov Chains

Triplet Markov chains are general generative models for sequential data which take into account three kinds of random variables: (noisy) observations, their associated discrete labels and latent variables which aim at strengthening the distribution of the observations and their associated labels. However, in practice, we do not have at our disposal all the labels associated to the observations to estimate the parameters of such models. In this paper, we propose a general framework based on a variational Bayesian inference to train parameterized triplet Markov chain models in a semi-supervised context. The generality of our approach enables us to derive semisupervised algorithms for a variety of generative models for sequential Bayesian classification.
An Efficient Post-Quantum Multi-Signature Scheme for the Internet of Vehicles

Multi-signature scheme is a unique type of digital signature where a group of participants are capable of producing a signature interactively on a shared message, thus significantly reducing the signature size. This is especially important for Internet of Vehicles (IoV) systems where higher efficiency and lower costs are required during the communication. Most approaches so far, however, are developed by traditional methods such as the integer factoring assumption, which result in potential vulnerability to quantum computing attacks. Although a few lattice-based multi-signature candidates have been proposed, they either rely on hash-and-sign process with higher costs or may be compromised by larger size of public key and signature. Motivated by the Bimodal Lattice Signature Scheme (BLISS) model [1], we propose a new lattice-based multi-signature scheme (Multi-BLISS, MB) in this paper. Our scheme can also be transformed into an aggregate signature scheme (Aggregate MB, AMB) with similar level of performance. We evaluate both schemes by setting security levels of 128, 160 and 192 bits in the experiments, and the results demonstrate significant improvement on security and efficiency comparing to existing lattice-based multi-signature schemes.