Reviewerly/exHarmony huggingface dataset:
Product Review System With BERT for Sentiment Analysis and Implementation of Administrative Privileges on Node-RED

There are many ways to collect the product review, either by using a website or by manually asking the customers to leave their comments after buying the product. The traditional product feedback process is time-consuming. Nowadays, although industries use online review system to rate their product, the process lacks modernization. In this paper, BERT sentiment analysis is used to process the product review from the customers and automatically convert the review into a numeric score. This average score value is used for processing the sentiment analysis. The system is built entirely on Node-RED as a backbone to gather the review from the users with certain administrator privileges. An interactive dashboard is created to analyze the feedback of the product review. The above method of review system also reduces malpractice that the manufacturer or service provider may indulge into increase their ratings.
Microarchitectural Side-Channel Threats, Weaknesses and Mitigations: A Systematic Mapping Study

Over the course of recent years, microarchitectural side-channel attacks emerged as one of the most novel and thought-provoking attacks to exfiltrate information from computing hardware. These attacks leverage the unintended artefacts produced as side-effects to certain architectural design choices and proved difficult to be effectively mitigated without incurring significant performance penalties. In this work, we undertake a systematic mapping study of the academic literature related to the aforementioned attacks. We, in particular, pose four research questions and study 104 primary works to answer those questions. We inquire about the origins of artefacts leading up to exploitable settings of microarchitectural side-channel attacks; the effectiveness of the proposed countermeasures; and the lessons to be learned that would help build secure systems for the future. Furthermore, we propose a classification scheme that would also serve in the future for systematic mapping efforts in this scope.
Organizational Type Mismatch in Agile SMEs

Organizational type mismatch occurs when organizations use traditional approaches to govern agile teams. It has harmful effects on organizational performance and team morale. This article explores this phenomenon at agile software development small- and medium-sized enterprises (SMEs). In particular, we aimed to evaluate the importance, frequency, and agility level of governance practices in this context. To do this, we developed a survey underpinned by CMMI-DEV V2.0 governance practices. We collected information from 50 practitioners working at 39 agile SMEs from 12 countries. Our quantitative and qualitative analysis shows that most of the surveyed agile SMEs consider that governance practices are important. However, only a few frequently implement such practices, and those that do fail to adopt an agile approach. These results suggest that agile SMEs are also prone to organizational type mismatch. We provide some recommendations to address this issue.
CCFL: Communication-Efficient Cross-Cluster Blockchain-Based Federated Learning

Federated Learning (FL) is a distributed learning framework that enables data sharing among multiple devices to protect data privacy. Blockchain is a decentralized ledger that can record data securely and reliably. The blockchain-based FL (BFL) framework has been used to share data and computing resources in multiple clusters. However, for the BFL framework among multi-institutional clusters, data sparsity in a cluster is a key issue. Most of the relevant works assume that the data in one cluster is rich enough to build a suitable model, which is not always satisfied in all scenarios. One method to address the problem is that enlarging the size of a BFL cluster that covers as many nodes as possible is one way. However, this method will increase communication overheads and reduce transaction throughput of the blockchain. To solve the above issues, we propose a communication-efficient blockchain-based FL framework called CCFL, which connects multiple BFL clusters to solve the data sparsity issue. We also design a pearson correlation coefficient-based dynamic model filtering mechanism that filters unnecessary models to reduce communication costs and exclude malicious models. Moreover, we illustrate a reliable contribution-based interactive validation reputation mechanism to prevent malicious nodes from participating in the training. We carry out some experiments to show the feasibility and efficiency of the proposed framework.
A Lightweight Radio Frequency Fingerprint Extraction Scheme for Device Identification

The physical layer (PHY) security technology based on radio frequency (RF) fingerprint can effectively solve the secure access problem of wireless devices. The hardware impairments of the devices can be used to generate the unique RF fingerprint to identify different wireless devices. Fingerprint extraction as a key step in the process of identification faces the challenges of ensuring the identification accuracy with reduced sample dimension and low testing and training time. To address the above problems, we propose a lightweight RF fingerprint extraction scheme to extract the physical layer attributes and effectively reduce the data dimension and time consumption. Based on the proposed RF fingerprint, the Bayesian classifier is used to identify the wireless devices. Furthermore, a joint judgment strategy is proposed to improve the identification accuracy by using multiple segments of one signal frame. The experimental result shows that, compared to the existing RF fingerprint identification schemes, the proposed RF fingerprint identification scheme obtains the best identification accuracy with lower time and data consumption.
SCFL: An Efficient Cross-cluster Federated Learning Framework Based on State Channels

Blockchain-based Federated learning, called BFL, has attracted widespread attention to construct trust among multiple parties and solve a single point of failure of the central server while protecting privacy. Many researches utilize cluster and cross-chain technologies to improve poor model quality and interoperability between clusters. However, those researches still suffer from 1) high communication overhead when devices of clusters locate far away, and 2) high consensus latency since devices require frequent interactions on consensus. In this paper, we propose a cross-cluster federated learning framework based on state channels, called SCFL, to split devices into multiple clusters according to locations. We also propose a cross-cluster consensus algorithm based on cross-chain and state channels to improve the security and efficiency of off-chain and inter-chain interactions. And we also propose a hierarchical clustering method to make the model adaptable to the partition scenarios where the data is non-IID. Numerical results show that SCFL can effectively solve data sparse problems and improve the system efficiency in non-IID data partitioning cases.
An Efficient Deep Learning-based Spectrum Awareness Approach for Vehicular Communication

Intelligent transportation systems require a reliable exchange of information between network terminals in different vehicular communication environments. Making effective use of the dedicated spectrum is crucial to maximizing communication performance. This requires optimising the modulation order according to different channel conditions. This paper proposes a lightweight spectrum awareness methodology that uses wideband spectrum monitoring and deep learning-based modulation classification techniques to optimise the modulation order. We introduce a channel quality indicator block in which the classifier’s accuracy of detection is used as a forward indicator for the choice of the best modulation type for transmission. By using a 3D stochastic vehicular channel, we evaluate the classification performance at different channel parameter settings, including, speed, variance, and signal-to-noise ratio in urban and rural areas. The experimental analyses demonstrate the capability of the proposed approach to supporting a high detection probability for acceptable false decision-making ≤ 20%.
A Generative Adversarial Network Approach to Reflectarray Pattern Synthesis

In this paper, we improve the structure of a generative adversarial network (GAN) based on three fundamental characteristics of the reflectarray for synthesizing better reflectarray patterns. For meeting various challenging requirements of beamforming, we propose a new model, which uses different tactics for improvements in the generator part of a GAN model. First, for accommodating the error-sensitive characteristics of the reflectarray antenna, a fully connected network is used to increase model precision. Second, based on the effects of interference occurring among all elements in a reflectarray, a self-attention mechanism is introduced to better handle the relationship among pixels. Third, with the needs of representing the associated values of elements based on the actual physical distance between the feed horn and elements, interpolation is used to enlarge the image, and then additional convolutional layers are provided to adjust the details. The results show acceptable radiation patterns produced by the GAN model.
Pensieve 5G: Implementation of RL-based ABR Algorithm for UHD 4K/8K Content Delivery on Commercial 5G SA/NR-DC Network

While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a highperformance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents.Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.
Label-wise Distribution Adaptive Federated Learning on Non-IID Data

Federated Learning (FL) has recently drawn considerable attention, enabling multiple end devices to collaboratively learn global models without collecting device data. In reality, end devices can usually be distributed in non-correlated environments and generate non-IID data, which may lead to the Artificial Intelligence (AI) model weight divergence among devices and model accuracy degradation after aggregation. In this paper, to address the non-IID data problem for FL, we treat this problem among end devices as a distribution adaptation problem among multiple source domains and analyze the feasibility of feature augmentation, and then propose a novel method called Label-wisE Distribution Adaptive Federated Learning (LEDA-FL). First, to reduce the divergence in the label-wise feature space, we integrate the modified Conditional Variational AutoEncoder (CVAE) to align the label-wise feature distributions among clients. Second, we augment the label-wise features for FL clients to improve the FL performance (test accuracy and communication efficiency). Finally, we conduct an extensive experiment on five popular datasets, and the experimental results show that our proposed method improves the test accuracy of the global model (e.g.,6.2% test accuracy improvement on CIFAR100 compared to FedProx) and the communication efficiency of FL (e.g., about 60% reduction in communication cost on CIFAR100 compared to FedProx).
Image Segmentation Semantic Communication over Internet of Vehicles

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding methods.
Masking-enabled Data Protection Approach for Accurate Split Learning

Split learning is an emerging distributed machine learning framework for enabling edge intelligence, especially for training sophisticated AI models at resource-constrained Internet-of-Things (IoT) devices. In split learning, the full AI model is partitioned to the client-side (e.g., input/privacy-sensitive layers) and server-side (e.g., computation-intensive layers) portions to be trained collaboratively at the devices and edge server. Only intermediate data at the split layer are exchanged during the training process for data privacy. However, the intermediate data may still cause security concerns to reconstruct the raw data from the partial gradients. This paper proposes the masking-enabled data protection approach for split learning without compromising the model accuracy. The devices are designed to perturb the reported results via masks, and the adversary can only retrieve the global information of all the devices (instead of individual devices). We mathematically prove that the masking-enabled perturbation mechanism would not compromise the learning accuracy. Experimental results validate the effectiveness of the proposed approach in terms of successful data protection and up to 10% model accuracy gain, compared to vanilla split learning and differential privacy.
Deep Reinforcement Learning-based Quantization for Federated Learning

Federated learning (FL) is a promising solution to harness the advances of machine learning under the premise of privacy security, whereas the communication overhead of model exchange remains an obstacle to deploying FL in wireless networks. To tackle this challenge, we consider the non-uniform quantization of the global model in this work. By formulating the optimization of quantization intervals as a Markov decision process (MDP), we propose a deep reinforcement learning (DRL)- based approach to improve the performance of the quantizer for FL. Through crafting a compound reward function, the DRL agent is guided to reduce the quantization error and training loss simultaneously. Furthermore, a dual time-scale mechanism between FL and DRL is adopted to ensure that the actor and critic models of DRL converge more steadily. Simulations on various real-world datasets reveal that the proposed method can provide higher accuracy and faster convergence than the existing uniform quantizers, and can retain these benefits when applying the learned policy to a similar learning task.
CRC in Coded Schemes with Bounded-Distance Decoding

The undetected error probability of CRC combined with an error-correcting code with bounded-distance decoding is considered. It is shown that to minimize this probability, the CRC polynomial should be matched to the code. In particular, good CRC polynomials reported in the literature for pure CRC schemes may not be good when combined with coding. Guidelines to choose a good CRC polynomial to be combined with a given code are provided.
Statistical verification of upper and lower bounds for the security performance of wiretap channels

In this paper we show a way to check semantic security for AWGN wiretap channels. We introduce low complexity decoding methods that provide upper and lower bounds to the performance of an attack strategy that closely resembles the best attack strategy, which has the problem of large computational complexity. We show the assumptions under which these methods can be applied and compare simulation results of the bounds to the performance of the best attack strategy. We use a seeded modular coding scheme, which consists of a coding layer and a security layer. For the coding layer we use polar codes, but the method is neither restricted to the seeded modular coding scheme nor to the polar codes.
Explainable Artificial Intelligence for Energy-Efficient Radio Resource Management

As wireless systems evolve, the problems of radio resource management (RRM) become harder to solve. Once the additional constraint of energy-efficient utilization of resources is factored in, these problems become even more challenging. Thus, experts started developing solutions based on complex artificial intelligence (AI) models that, unfortunately, suffer from a performance-explainability trade-off. In this work, we propose an explainable AI (XAI) methodology for addressing this tradeoff. Our methodology can be used to generate feature importance explanations of AI models through three XAI methods: (i) Kernel SHapley Additive exPlanations (SHAP), (ii) Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models (CERTIFAI), and (iii) Anchors. For Anchors, we formulate a new feature importance score based on the feature’s presence within the rules built by the method. We then use the generated explanations to improve the understanding of the model and reduce its complexity through a feature selection process. By applying our methodology to a reinforcement learning (RL) agent designed for energy-efficient RRM, we were able to reduce its complexity by approximately 27%−62% according to various metrics, without losing performance. Additionally, we show the possibility to replace the AI-based inference process with an Anchors-based inference process with similar performance and higher interpretability for humans.
Application-level performance of cross-layer scheduling for social VR in 5G

Social VR aims at enabling people located at different places to communicate and interact with each other in a natural way. It poses extremely strong throughput and latency requirements on the underlying communication networks. This paper investigates the potential of using cross-layer design approaches for radio access scheduling in order to realize these challenging requirements in (beyond) 5G networks. In particular, we provide an in-depth simulation study of the performance/capacity gains that can be achieved by exploiting the end-to-end latency budget and/or video frame type as cross-layer information in the scheduling decisions, and show how the benefits depend on the actual social VR scenario. This study further reveals the importance of using application-level metrics such as PSNR or SSIM rather than traditional network-level metrics like the packet drop rate in the performance assessment.
An Investigation on Intelligent Relay assisted Semantic Communication Networks

With the development of sixth generation (6G) networks, semantic communication is treated as an emerging technology to provide more intelligent communication between two parties for enhance the accuracy and efficiency of communications. Whereas, it demands to merge all physical layer blocks in the conventional communications, which heavily deteriorates the spectrum sparsity problem of wireless communication networks. For this sake, we develop a novel intelligent relay assisted semantic communications, by enhancing communication efficiency, which creates a paradigm for text transmission fusion in 6G networks. In this contribution, the semantic communication system we have designed combines traditional deep learning methods with intelligent semantic relays, based on which, the protocols about minimizing the semantic errors by recovering the meaning of sentences for addressing channel variations issues. Meanwhile, our designed system could be applied in the case of wireless channel deterioration or knowledge background mismatch at the transmitter and receiver, and is also a paradigm for future applications in one-to-many and many-to-many semantic communication. At last, we propose a range of research directions and open challenges for boosting the implementations of relay assisted semantic communications.
FIBFT: An Improved Byzantine Consensus Mechanism for Edge Computing

Blockchain has been widely used to solve data privacy and security issues in edge computing scenarios. However, the blockchain based on edge computing still has some performance problems, such as insufficient scalability, difficulty in balancing security and edge device power consumption, and inability to simultaneously meet low latency, high throughput, high security and privacy issues, etc. In order to solve these problems, this paper proposes a generally improved Byzantine consensus mechanism based on the K-medoids clustering algorithm - FIBFT. Considering the different performance characteristics of each node in the network, the node’s state is first abstracted into a multi-dimensional state space containing eigenvalues, and then the nodes are divided into subnets by the efficient K-medoids clustering algorithm. Each subnet uses a Byzantine consensus mechanism based on arbitration for consensus and data interaction, and the consensus data could be exchanged between the subnets without interfering with the consensus process. The research results show that FIBFT has better scalability and throughput while ensuring high security compared with the traditional Byzantine consensus algorithm.
Deeper Understanding of Software Change

Managing change is a challenging task in today’s complex software engineering. Understanding the diversity of changes and their relationship to current technologies is critical for dealing with volatile business systems. This article aims to identify and assess the state of the art toward understanding software change for the sake of providing a deeper understanding of its causes, mechanisms, and effects.