Reviewerly/exHarmony huggingface dataset:
FedEDB: Building a Federated and Encrypted Data Store Via Consortium Blockchains

Decentralized storage platforms based on consortium blockchains have emerged in the spotlight of research and industry communities because they are flexible, transparent, and eliminated trust in contrast to the traditional centralized data-sharing model. However, due to wide attacking surfaces in a blockchain network, this decentralized data-sharing paradigm is subject to malicious data breaches. Untrusted blockchain nodes can directly obtain sensitive information from the query processing and their local storage. Several studies have been made for solving this dilemma, but they only focus on single-user settings and cannot be directly applied to multi-owners blockchain-based data sharing scenarios. In this paper, we introduce FedEDB, a federated and encrypted data store by using consortium blockchains. Unlike existing solutions that focus on single-user settings, our proposed schemes can efficiently support privacy-preserving and reliable multi-owner queries in the decentralized setting. We start from the practical key aggregation technique to construct the multi-owner search schemes and further refine the underling building blocks to enhance the security. Besides, we integrate the smart contract with our tailored zero-knowledge proof to enforce secure and reliable result verification protocol with fairness. We implement a prototype and thorough security analysis and comprehensive evaluation results confirm the practicability of our design.
Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

We investigate the impact of wavelength choice for the classical headers in quan-tum wrapper networking with O-band quantum payload. We show that a C-band header per-forms substantially better than an O-band header.
Full Spatial Muscle Fiber Orientation Estimation From Ultrasound Images Using a Multitask Deformable Residual Neural Network

This paper proposes a multitask deformable residual neural network, for full spatial muscle fiber orientation (MFO) estimation from ultrasound (US) images. It is developed based on the state-of-the-art model of residual UNet (ResUNet), which combines the residual block and UNet for more efficient deep learning. To better capture the characteristics of curved muscle fibers in US images, deformable convolution is used to improve the conventional convolutions in ResUNet. Moreover, along with the detection of MFO, an extra task concerning muscle segmentation is assigned to the model in order to improve the detection accuracy and robustness. Experimental results on an inhouse dataset built upon 10 healthy human subjects demonstrate the superiority of the proposed model for full spatial MFO estimation from US images.
CycleGAN-Based Image to Image Translation for Realistic Surgical Training Phantoms

Training in surgery is essential for surgeons to develop skill and dexterity. Physical training phantoms provide excellent haptic feedback and tissue properties for stitching and operating with authentic instruments and are easily available. However, they lack realistic traits and fail to reflect the complex environment of a surgical scene. Generative Adversarial Networks can be used for image-to-image translation, addressing the lack of realism in physical phantoms, by mapping patterns from the intraoperative domain onto the video stream captured during training with these surgical simulators. This work aims to achieve a successful I2I translation, from intra-operatory mitral valve surgery images onto a surgical simulator, using the CycleGAN model. Different experiments are performed - comparing the Mean Square Error Loss with the Binary Cross Entropy Loss; validating the Fréchet Inception Distance as a training and image quality metric; and studying the impact of input variability on the model performance. Differences between MSE and BCE are modest, with MSE being marginally more robust. The FID score proves to be very useful in identifying the best training epochs for the CycleGAN I2I translation architecture. Carefully selecting the input images can have a great impact in the end results. Using less style variability and input images with good feature details and clearly defined characteristics enables the network to achieve better results.Clinical Relevance— This work further contributes for the domain of realistic surgical training, successfully generating fake intra operatory images from a surgical simulator of the cardiac mitral valve.
Representative Data Selection for Efficient Medical Incremental Learning

To train a deep neural network relies on a large amount of annotated data. In special scenarios like industry defect detection and medical imaging, it is hard to collect sufficient labeled data all at once. Newly annotated data may arrive incrementally. In practice, we also prefer our target model to improve its capability gradually as new data comes in by quick re-training. This work tackles this problem from a data selection prospective by constraining ourselves to always retrain the target model with a fix amount of data after new data comes in. A variational autoencoder (VAE) and an adversarial network are combined for data selection, achieving fast model retraining. This enables the target model to continually learn from a small training set while not losing the information learned from previous iterations, thus incrementally adapting itself to new-coming data. We validate our framework on the LGG Segmentation dataset for the semantic segmentation task.Clinical relevance- The proposed VAE-based data selection model combined with adversarial training can choose a representative and reliable subset of data for time-efficient medical incremental learning. Users can immediately see the improvement of the medical segmentation model whenever new annotated images are contributed (after a few minutes of model retraining).
Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.
Rejectable SoulBound Tokens for Credentials Assignment and Acceptance of Terms

Digital credentials are being issued by authorized entities to facilitate the digital identification of their users. They can be classified in identity credentials, academic and professional credentials, and access credentials. Blockchain offers some inherent features that are highly advantageous for the management of credentials. Non-Fungible Tokens, or NFTs, might seem to be a perfect fit for the implementation of digital credentials. However, some crucial requirements for credentials are the non-transferability of the credential and that the authorized entity can receive explicit acceptance from the user who will own the new credential. Nevertheless, there is a new type of token, named Soulbound tokens (SBT), that has been designed to ensure the non-transferability of assets. This paper presents a protocol that is focused on the issuance of digital access credentials, with the additional ability to allow the association of terms and conditions that users must accept when receiving these credentials. We use an enhanced version of the SBTs, called RejSBTs, to represent the credentials and associated terms, providing non-repudiation of reception and origin proofs.
Private Identity-Related Attribute Verification Protocol Using SoulBound Tokens and Zero-Knowledge Proofs

The need for self-sovereign and privacy-preserving identity solutions is becoming more critical, due to increasing concerns about privacy and data security, especially when it involves highly sensitive Personally Identifiable Information (PII). In this article we propose a protocol based on blockchain technology, Soulbound tokens (SBT) and zero-knowledge proofs (ZKP) to address these challenges. By using the public key of the identity holder address to encrypt the data, our protocol can represent various identity attributes while ensuring privacy. We explore the concepts of self-sovereign and privacy-preserving identity and discuss the advantages of using blockchain-based SBTs and ZKPs in identity verification systems. Moreover, this article provides an overview of the state of the art in privacy-preserving identity using SBTs and ZKPs within the blockchain ecosystem, highlighting the potential benefits and future directions for this emerging field.
Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation.
Automatic Refactoring Candidate Identification Leveraging Effective Code Representation

The use of machine learning to automate the detection of refactoring candidates is a rapidly evolving research area. The majority of work in this direction uses source code metrics and commit messages to predict refactoring candidates and do not exploit the rich semantics of source code. This paper proposes a new approach for extract method refactoring candidates identification. First, we propose a novel mechanism to identify negative samples for the refactoring candidate identification task. We then employ a self-supervised autoencoder to acquire a compact representation of source code generated by a pre-trained large language model. Subsequently, we train a binary classifier to predict extract method refactoring candidates. Experiments show that our new approach outperforms the state of the art by 30% in terms of F1 score. The proposed work has implications for researchers and practitioners. Software developers may use the proposed automated approach to predict refactoring candidates better. This study will facilitate the development of improved refactoring candidate identification methods that the researchers in the field could use and extend.
Joint Estimation of Transmitter IQ Imbalance and Nonlinearity with Multipath in OFDM Systems

Radio frequency (RF) fingerprint has been an emerging research topic since the last decade. Numerous algorithms for recognition have been proposed. However, algorithms for accurate extraction of in-phase/quadrature phase imbalance (IQI) and power amplifier (PA) nonlinearity are hardly available especially when multipath is considered. This paper presents a joint estimation of transmitter IQI and PA nonlinearity with multipath in OFDM systems. Using sequential iterative estimation algorithm, transmitter IQI, PA nonlinearity and multipath are jointly and separately estimated efficiently. After obtaining these parameters, a compensation approach for IQI, PA nonlinearity and multipath is given via equalization operation at receiver. Simulation results show that the proposed algorithm has a significant estimation accuracy advantage compared to the other two algorithms that already exist.
Photon Counting with an Adaptive Storage Loop

Single-photon detectors can be used to resolve photon number by storing the photons in a delay loop. We consider a loop with adaptive, detection-record dependent out-coupling, finding that the dynamic range and effective speed of the detector can be enhanced compared to a purely passive setup.
LTE Base Station Synchronous Signal Based RF Fingerprints Identification Scheme

This paper investigates the extraction of radio frequency fingerprints (RFF) of long-term evolution (LTE) base station synchronous signals, aiming to achieve the identification of legitimate base stations before the access process of the user equipment (UE). To overcome the channel effects on RFF extraction, a channel compensation method based on the channel estimation of the secondary synchronization signals (SSS) is proposed. And a theoretical proof is provided to validate the capability of the proposed channel compensation method in eliminating channel effects and preserving fingerprint characteristics. Moreover, this paper uses the neural network to classify LTE base stations and validates the proposed method through simulation and experiments. The simulation results show that the proposed method can reach an accuracy rate of 90% or more in different channel environments. And the experimental results show that the classification accuracy reaches 97.8% in the line-of-sight (LOS) scenario and 92.35% in the non-line-of-sight (NLOS) scenario.
Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.
Performance comparison of genetic algorithms with traditional search techniques on the N-Queen Problem

This paper presents an analysis of solving the N-Queen problem using a genetic algorithm and compares its performance with traditional search algorithms like breadth-first search (BFS) and depth-first search (DFS). The N-Queen problem is a famous problem in the field of artificial intelligence that has been studied in depth. The genetic algorithm is an optimization algorithm inspired by the process of natural selection and evolution in living organisms. BFS and DFS are classical search algorithms that explore the search space to find a solution. This paper discusses all the main components of the genetic algorithm, such as population generation, fitness function, selection, crossover, and mutation, and also explains how it is used to solve the N-Queen problem. Traditional search methods, i.e., BFS and DFS, are also briefly discussed. The experimental results show that classical searching approaches are better for small-sized problems, but the genetic algorithm outperforms BFS and DFS in computation time for medium and large-sized problems. This paper concludes by analyzing the effect of parameter tuning on the genetic algorithm and suggests possible future work in the field.
Hate Speech in Times of the COVID-19 Pandemic: The Social Costs and Legal Implications of a Health Crisis

In crisis times, such as the recent health crisis brought out by the COVID-19 pandemic, society responds in a myriad of pro-social as well as negative, anti-social ways. The article starts by mentioning some broader social and regulatory responses to the pandemic that provided a backdrop to the studied phenomenon. It then looks explicitly at hate speech, unpacks the three-layered dimensions of its harmful consequences, helping to grasp the magnitude of harm caused by such pandemic-fuelled prejudice-based expression, and inspects the impact of the pandemic on such expression that is largely criminalised in Europe. The article concludes with some thoughts on the implications and lessons for the future.
Development of Interoperability Principles for Disaster and Emergency Management System of Türkiye

Due to their complex nature, disasters and emergencies require a data-intensive management system in which many actors from different sectors participate and simultaneous processes are managed. Due to this complexity, there are problems in managing and sharing process services and geographic data effectively. The key to solving these problems is the introduction of a complete interoperability model. In this context, interoperability models based on existing international standards are analyzed in this study. FEI and EIF models are taken as basis in terms of disaster and emergency management interoperability requirements. Accordingly, the interoperability reference model was created in legal, organizational, semantic and technical frameworks to meet the interoperability levels. This general framework model has been evaluated in the service and data layers that need to be fully defined in disaster and emergency management phases, and a basic model including interoperability solutions for the service and data model has been created.
Institutions and internationalization of SMEs in an emerging African economy: the moderating role of sustainable entrepreneurship

This chapter highlights the often overlooked but important nature of sustainable entrepreneurship in understanding internationalization of small and medium-sized enterprises (SMEs) in the context of an emerging African economy. Due to institutional peculiarities and influence of government in emerging economies, government institutional support was employed as a proxy for the institutional environment and integrated into the conceptual model. Institutional theory and triple bottom line theory served as the theoretical framework, and 141 valid data samples were obtained from early internationalizing new ventures in Nigeria. Confirmatory factor analysis was conducted while hierarchical moderated regression analysis was employed to test all hypothesized relationships. Findings showed that government institutional support is weak, attesting to the institutional void paradigm. However, sustainable entrepreneurship practice not only significantly predicts international performance of SMEs; it moderates the weak effect of government inefficiencies on SMEs’ internationalization. Chief executive officers’ education and firm size are also germane to SMEs’ internationalization. Implications are discussed.
Compression and Transmission of Big AI Model Based on Deep Learning

In recent years, big AI models have demonstrated remarkable performance in various artificial intelligence (AI) tasks. However, their widespread use has introduced significant challenges in terms of model transmission and training. This paper addresses these challenges by proposing a solution that involves the compression and transmission of large models using deep learning techniques, thereby ensuring the efficiency of model training. To achieve this objective, we leverage deep convolutional networks to design a novel approach for compressing and transmitting large models. Specifically, deep convolutional networks are employed for model compression, providing an effective means to reduce the size of large models without compromising their representational capacity. The proposed framework also includes carefully devised encoding and decoding strategies to guarantee the restoration of model integrity after transmission. Furthermore, a tailored loss function is designed for model training, facilitating the optimization of both the transmission and training performance within the system. Through experimental evaluation, we demonstrate the efficacy of the proposed approach in addressing the challenges associated with large model transmission and training. The results showcase the successful compression and subsequent accurate reconstruction of large models, while maintaining their performance across various AI tasks. This work contributes to the ongoing research in enhancing the practicality and efficiency of deploying large models in real-world AI applications.
How Anonymous Are Your Anonymized Data? The AnyMApp Case Study

Privacy is a Fundamental Human Right and data anonymization is an essential process that aims to render data unidentifiable, therefore, private. The aim of this work is to test the anonymity of the first data collected via AnyMApp, a platform to anonymously perform online usability tests. Due to time and resource constraints, the authors opted to identify open-source free tools, which were evaluated recently for their usability and functionalities. Even being a highly ranked anonymization tool (ARX), the use of this tool in our data setting was not straightforward and required guidance from tutorials. In the end, it was not possible to run any of the anonymization models available in ARX in our dataset because there were no quasi-identifier attributes within the collected data. There were sensitive data collected though, with medical diagnosis information, but not able to cause privacy concerns when added with all the other non-identifiable and non-sensitive attributes. Despite our results for this use-case, we will keep verifying/anonymizing collected data from AnyMApp because we will have different use-cases and health-related data that can be more complex and comprise quasi-identifiers and sensitive attributes that we nee to guarantee that are anonymized.