Reviewerly/exHarmony huggingface dataset:
A Blockchain-based Secure Healthcare Solution for Poverty-led Economy of IoMT Under Industry 5.0

This chapter proposes a new architecture to address challenges to security and the privacy in e-healthcare under Industry 5.0. With the growing needs for high-quality medical treatment and the continuously growing costs of treating global medical problems, systems and web-based medical care are regarded as innovative solutions. In particular, the new progress in Internet of Things (IoT) has led to the development of Internet of Medical Things (IoMT). The patient history data is handled and processed remotely in real-time rather than visiting any clinic and then having that data transferred for subsequent use to third parties, such as data that gets saved in the cloud. This patient data faced security threats and it is observed as major limitations of using such systems in Industry 5.0. This chapter analyzes the security and secrecy challenges, together with the necessities, the danger involved and proposed secured blockchain-based framework which is capable of future research scope in Industry 5.0. The study has described an Eye Hospital case study that stores the eye donors' details. With such critical scenario, this study addresses healthcare scenario with poverty-led agenda and social developmental features.
Entanglement-Assisted Quantum Networks: Mechanics, Enabling Technologies, Challenges, and Research Directions

Over the past few decades, significant progress has been made in quantum information technology, from theoretical studies to experimental demonstrations. Revolutionary quantum applications are now in the limelight, showcasing the advantages of quantum information technology and becoming a research hotspot in academia and industry. To enable quantum applications to have a more profound impact and wider application, the interconnection of multiple quantum nodes through quantum channels becomes essential. Building an entanglement-assisted quantum network, capable of realizing quantum information transmission between these quantum nodes, is the primary goal. However, entanglement-assisted quantum networks are governed by the unique laws of quantum mechanics, such as the superposition principle, the no-cloning theorem, and quantum entanglement, setting them apart from classical networks. Consequently, fundamental efforts are required to establish entanglement-assisted quantum networks. While some insightful surveys have paved the way for entanglement-assisted quantum networks, most of these studies focus on enabling technologies and quantum applications, neglecting critical network issues. In response, this paper presents a comprehensive survey of entanglement-assisted quantum networks. Alongside reviewing fundamental mechanics and enabling technologies, the paper provides a detailed overview of the network structure, working principles, and development stages, highlighting the differences from classical networks. Additionally, the challenges of building wide-area entanglement-assisted quantum networks are addressed. Furthermore, the paper emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization, to facilitate the implementation of future entanglement-assisted quantum networks.
A heterogeneous network embedded medicine recommendation system based on LSTM

In the healthcare sector, patient data plays a crucial role in medical diagnoses and treatment plans. However, existing techniques for finding similar patients based on Electronic Health Record (EHR) data face challenges due to high-dimensional and sparse vectors. To overcome this challenge, the paper proposes developing a novel heterogeneous network-embedded drug recommendation system. The system focuses on classifying the sentiment of drug users based on their reviews and other relevant features such as their medical condition, drug rating, and usage date. The overall framework of the system follows a step-by-step approach, starting with data exploration and preprocessing, followed by the development of a classification model based on Long-Short-Term Memory (LSTM) networks. During the data exploration phase, various visualization and statistical techniques are employed to analyze the different data types. This process helps in understanding the characteristics of the data, identifying patterns, and preparing the data to align with the research objective. Furthermore, additional variables are considered suitable for the LSTM model, a recurrent neural network (RNN) type designed to handle sequence data and long-term prediction problems. Unlike other models that process individual data points, LSTM incorporates feedback connections to process complete data sequences. This approach enhances the effectiveness of recommendation systems and enables the prediction of new drug user ratings based on existing user ratings. The developed system demonstrates promising results, achieving a classification accuracy of 92%. This indicates its ability to accurately predict the sentiment of drug users based on their reviews and other associated features. The novelty of this research lies in the integration of a heterogeneous network-embedded approach with LSTM-based classification, providing a more comprehensive and accurate drug recommendation system compared to existing methods.
Fake review detection in e-Commerce platforms using aspect-based sentiment analysis

Consumers rely on internet user reviews. Existing sentiment-based detection systems fail to capture consumer feelings regarding numerous aspects of products or services which influence their purchasing decisions. Despite the growing interest in detecting false reviews, prior studies have not explored the capacity to detect fake reviews for diverse products, which require distinct consumer experience. To overcome these problems, this paper proposes a fake review detection model using aspect-based sentiment analysis (ABSA) while considering the effects of product types. Using a dataset of Amazon reviews, our ABSA model revealed that two aspects are fundamental for detecting fake reviews and suggests the need to associate the two. These are the product category and the verified purchase attribute (with the greatest contribution observed for credence and experience product types).
Towards using Few-Shot Prompt Learning for Automating Model Completion

We propose a simple yet a novel approach to improve completion in domain modeling activities.Our approach exploits the power of large language models by using few-shot prompt learning without the need to train or fine-tune those models with large datasets that are scarce in this field.We implemented our approach and tested it on the completion of static and dynamic domain diagrams.Our initial evaluation shows that such an approach is effective and can be integrated in different ways during the modeling activities.
A new paradigm of reservoir computing exploiting hydrodynamics

Nonlinear waves have played a significant role in the development of the science of complexity throughout history. More recently, they have also contributed to the emergence of a novel paradigm in reservoir computing called neuromorphic computing by waves. In this paradigm, information is transmitted through wave dynamics, which process the data to perform complex tasks with minimal energy consumption. Leveraging nonlinear hydrodynamic waves for computational purposes, we have created the Aqua-Photonic-Advantaged Computing Machine by Artificial Neural Networks (Aqua-PACMANNs). This system utilizes wave propagation in shallow water as the primary physical phenomenon, with minimal electronic components required, limited to a camera for detection purposes. As a proof of concept, we have successfully implemented an exclusive-not-or logic gate using the Aqua-PACMANN architecture. This accomplishment demonstrates the potential of fluid dynamic neuromorphic computing and opens up possibilities for a new class of computing systems.
Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach

This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, user-friendliness, and legal compliance. To address these challenges, the study developed a universal privacy model designed to efficiently manage and share patients’ personal and sensitive data across different platforms, such as MHR and NHS systems. The research employed various BERT techniques to differentiate between legitimate and illegitimate privacy policies. Among them, Distil BERT emerged as the most accurate, demonstrating the potential of our ML-based approach to effectively identify inadequate privacy policies. This paper outlines future research directions, emphasizing the need for comprehensive evaluations, testing in real-world case studies, the investigation of adaptive frameworks, ethical implications, and fostering stakeholder collaboration. This research offers a pioneering approach towards enhancing healthcare information privacy, providing an innovative foundation for future work in this field.
Network traffic classification model based on attention mechanism and spatiotemporal features

Abstract Traffic classification is widely used in network security and network management. Early studies have mainly focused on mapping network traffic to different unencrypted applications, but little research has been done on network traffic classification of encrypted applications, especially the underlying traffic of encrypted applications. To address the above issues, this paper proposes a network encryption traffic classification model that combines attention mechanisms and spatiotemporal features. The model firstly uses the long short-term memory (LSTM) method to analyze continuous network flows and find the temporal correlation features between these network flows. Secondly, the convolutional neural network (CNN) method is used to extract the high-order spatial features of the network flow, and then, the squeeze and excitation (SE) module is used to weight and redistribute the high-order spatial features to obtain the key spatial features of the network flow. Finally, through the above three stages of training and learning, fast classification of network flows is achieved. The main advantages of this model are as follows: (1) the mapping relationship between network flow and label is automatically constructed by the model without manual intervention and decision by network features, (2) it has strong generalization ability and can quickly adapt to different network traffic datasets, and (3) it can handle encrypted applications and their underlying traffic with high accuracy. The experimental results show that the model can be applied to classify network traffic of encrypted and unencrypted applications at the same time, especially the classification accuracy of the underlying traffic of encrypted applications is improved. In most cases, the accuracy generally exceeds 90%.
Automated prioritization of construction project requirements using machine learning and fuzzy Failure Mode and Effects Analysis (FMEA)

Due to the emergence of risk-based verification practices, requirement prioritization has gained importance as a task in project requirements management. However, it is challenging due to the voluminous paper-based requirements, the reliance on manual content analysis and interviews, the limited focus on specific requirement types, and the neglect of the non-conformance detectability factor. This paper leverages historical data of risk priority numbers (RPN) for many written requirements to develop a novel machine learning model for requirement prioritization. The training data includes requirement statements manually annotated with professionals' ratings for three risk factors severity, probability, and non-detectability. The overall fuzzy RPN data was aggregated using fuzzy logic to tackle uncertainty in subjective ratings. The model was trained using a Convolutional Neural Network (CNN). The study is anticipated to greatly aid professionals in prioritizing extensive textual project requirements essential for effective resource allocation that can eventually eliminate costly consequences of high-risk non-compliance.
Multi-view cost-sensitive kernel learning for imbalanced classification problem

Multi-view imbalanced learning concentrates on recognizing valuable patterns from multi-view imbalanced data. There are numerous algorithm-level multi-view imbalanced learning methods including multi-view ensemble learning methods and multi-view cost-sensitive learning methods. However, these approaches have two drawbacks. Firstly, from the multi-view representation perspective, they either ignore the agreement among different views, or fail to fully exploit the complementarity information contained in multi-view data. Secondly, from the class-imbalanced perspective, multi-view ensemble learning first needs to design a specific data pre-processing scheme to generate views and then fuses the local and global information with multi-view ensemble schemes. Such models are closely related to practical problems and lack universality. In contrast, multi-view cost-sensitive learning methods possess concise formulations. But the success of such methods depends on imposing appropriate misclassification cost to the majority and minority classes. Inspired by elegant merits of asymmetric LINEX loss function, we propose a multi-view cost-sensitive kernel learning method with LINEX loss termed as MVCSKL. It not only fully exploits the consensus and complementary information of multiple views, but also adaptively learns and adjusts the misclassification cost of different classes through the asymmetric LINEX loss function. Besides, the introduction of the kernel function enables the MVCSKL model to handle complex nonlinear problem and further guarantees its prediction performance. Furthermore, we adopt the alternating direction method of multipliers and gradient descent algorithm to solve MVCSKL. Based on the Rademacher complexity, we analyze the generalization capability of MVCSKL. Extensive experiments are conducted to verify the effectiveness of the proposed model.
RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%–30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies.
The missing piece: the link between blockchain and public policy design

Through a bibliometric analysis, this article researches the central topics that link blockchain to public policy design and investigates the key policy priorities for the use of blockchain technology in the public sector. The analysis points out six thematic foci in the current literature: (1) business and strategic management, (2) technology adoption, (3) system infrastructure, (4) cryptocurrency and decentralized economy, (5) regulations and geopolitics, and (6) governance. The analysis demonstrates a high degree of co-occurrence between “barriers” and “blockchain adoption” confirming that blockchain adoption in the public sector domain is perceived as challenging. The association of the term “barrier” with other key terms suggests theoretical, technological, resource-based, and managerial challenges as the main showstoppers. The bibliometric analysis also reveals the underrepresentation of social and political sciences in our knowledge base, despite the thematic relevance of these disciplines to understand the underlying challenges. More research from these disciplines is warranted to understand better how this technology can be best integrated into the public policy processes.
Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer

The number of existing global solar radiation (GSR) observation stations is limited, and it is challenging to meet the demand for scientific research and production. Different forecasting horizons of solar radiation correspond to various applications. Therefore, it is critical to design realistic models to predict the GSR of varying sequence lengths. This study proposes a prediction model based on the analysis of the Pearson correlation between GSR and each input parameter and the establishment of different input combinations related to the result of Pearson analysis in four high-quality datasets. The proposed Informer model compares the results with five classical machine learning models on four datasets with R2, RMSE, and skill score (S) as evaluation metrics. This study examines the proposed model's prediction performance for five prediction lengths, four climate zones, three sampling frequencies, and two input types. The results showed that the Informer model performs well with the clearness index and pressure as the input. Besides, the RRMSE values are less than 10% under optimal input in long sequence forecasting. The findings suggested that the proposed advanced Informer model is a reliable alternative for GSR prediction due to its high predictive accuracy under diverse prediction lengths, sampling frequencies, climate zones, and the number of input parameters.
Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model

Accurately forecasting solar radiation is of great significance to solar energy utilization. To forecast the spatial and temporal distributions of solar radiation simultaneously, a deep neural network model named MRE-UNet is proposed, the solar radiation data in Heilongjiang province is taken as an example to test the forecasting performance in different periods ahead forecasting cases. According to the evaluation results, an 16 h historical solar radiation data was determined to be the best choice for input, and the minimum of MSE can reach 6.47 × 10−4, 1.38 × 10−3 and 2.69 × 10−3 for 1 h, 3 h and 6 h ahead forecasting cases, respectively. The transferability of the MRE-UNet is tested by performing the solar radiation nowcasting in Hubei province, China using the pre-trained MRE-UNet trained by the solar radiation data in Heilongjiang province. The robustness of MRE-UNet is tested by monitoring the effects of adding different level of noise, and MSE keeps to be 6.27 × 10−4 even though the measuring noise increase to be 50%. For further demonstration on the effectiveness of MRE-UNet in spatiotemporal forecasting, the performance in total cloud cover forecasting is also tested, and satisfactory forecasting results are obtained. Finally, spatiotemporal correlation analysis on solar radiation and total cloud cover data is carried out, a potential reason for satisfying forecasting performance of MRE-UNet is given. From this work, MRE-UNet proposed can be provided as an efficient tool for dealing with further solar radiation spatiotemporal forecasting problem, and the spatiotemporal correlation characteristics can be employed as the basis for further developing effective solar radiation forecasting approach to a degree.
MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform

As quantum computing is becoming increasingly popular, the underlying quantum computing platforms are growing both in ability and complexity. Unfortunately, testing these platforms is challenging due to the relatively small number of existing quantum programs and because of the oracle problem, i.e., a lack of specifications of the expected behavior of programs. This paper presents MorphQ, the first metamorphic testing approach for quantum computing platforms. Our two key contributions are (i) a program generator that creates a large and diverse set of valid (i.e., non-crashing) quantum programs, and (ii) a set of program transformations that exploit quantum-specific metamorphic relationships to alleviate the oracle problem. Evaluating the approach by testing the popular Qiskit platform shows that the approach creates over 8k program pairs within two days, many of which expose crashes. Inspecting the crashes, we find 13 bugs, nine of which have already been confirmed. MorphQ widens the slim portfolio of testing techniques of quantum computing platforms, helping to create a reliable software stack for this increasingly important field.
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study

Testing Deep Learning (DL) based systems inherently requires large and representative test sets to evaluate whether DL systems generalise beyond their training datasets. Diverse Test Input Generators (TIGs) have been proposed to produce artificial inputs that expose issues of the DL systems by triggering misbehaviours. Unfortunately, such generated inputs may be invalid, i.e., not recognisable as part of the input domain, thus providing an unreliable quality assessment. Automated validators can ease the burden of manually checking the validity of inputs for human testers, although input validity is a concept difficult to formalise and, thus, automate. In this paper, we investigate to what extent TIGs can generate valid inputs, according to both automated and human validators. We conduct a large empirical study, involving 2 different automated validators, 220 human assessors, 5 different TIGs and 3 classification tasks. Our results show that 84% artificially generated inputs are valid, according to automated validators, but their expected label is not always preserved. Automated validators reach a good consensus with humans (78% accuracy), but still have limitations when dealing with feature-rich datasets.
Demystifying Exploitable Bugs in Smart Contracts

Exploitable bugs in smart contracts have caused significant monetary loss. Despite the substantial advances in smart contract bug finding, exploitable bugs and real-world attacks are still trending. In this paper we systematically investigate 516 unique real-world smart contract vulnerabilities in years 2021–2022, and study how many can be exploited by malicious users and cannot be detected by existing analysis tools. We further categorize the bugs that cannot be detected by existing tools into seven types and study their root causes, distributions, difficulties to audit, consequences, and repair strategies. For each type, we abstract them to a bug model (if possible), facilitating finding similar bugs in other contracts and future automation. We leverage the findings in auditing real world smart contracts, and so far we have been rewarded with $102,660 bug bounties for identifying 15 critical zero-day exploitable bugs, which could have caused up to $22.52 millions monetary loss if exploited.