Reviewerly/exHarmony huggingface dataset:
HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Network for Image Classification

Quantum machine learning has been developing in recent years, demonstrating great potential in various research domains and promising applications for pattern recognition. However, due to the constraints of quantum hardware, the input qubits are restricted caused by small circuit size, and the fuzziness in all dimensions caused by the features that are difficult to be effectively mined. Besides, previous studies focus on binary classification, but multi-classification received little attention. To address the difficulty in multi-classification, this paper proposed a hybrid multi-branches quantum-classical neural network (HM-QCNN) that utilizes a multi-branch strategy to construct the convolutional part. The part consists of three branches to extract the features of different scales and morphologies. Two quantum convolutional layers apply quantum CRZ gates and rotational gates to design a random quantum circuit (RQC) with 4 qubits and full qubits measurements. The experiments on three public datasets (MNIST, Fashion MNIST, and MedMNIST) demonstrate that HM-QCNN outperforms other prevalent methods with accuracy, precision, and convergence speed. Compared with the classical CNN and the hybrid neural network without multi-branches, HM-QCNN reached 97.40% and improved the accuracy of classification by 6.45% and 1.36% on the MNIST dataset, respectively.
ESIA: An Efficient and Stable Identity Authentication for Internet of Vehicles

Decentralized, tamper-proof blockchain is regarded as a solution to a challenging authentication issue in the Internet of Vehicles (IoVs). However, the consensus time and communication overhead of blockchain increase significantly as the number of vehicles connected to the blockchain. To address this issue, vehicular fog computing has been introduced to improve efficiency. However, existing studies ignore several key factors such as the number of vehicles in the fog computing system, which can impact the consensus communication overhead. Meanwhile, there is no comprehensive study on the stability of vehicular fog composition. The vehicle movement will lead to dynamic changes in fog. If the composition of vehicular fog is unstable, the blockchain formed by this fog computing system will be unstable, which can affect the consensus efficiency. With the above considerations, we propose an efficient and stable identity authentication (ESIA) empowered by hierarchical blockchain and fog computing. By grouping vehicles efficiently, ESIA has low communication complexity and achieves high stability. Moreover, to enhance the consensus security of the hierarchical blockchain, the consensus process is from the bottom layer to the up layer (bottom-up), which we call B2UHChain. Through theoretical analysis and simulation verification, our scheme achieves the design goals of high efficiency and stability while significantly improving the IoV scalability to the power of 1.5 (^1.5) under similar security to a single-layer blockchain. In addition, ESIA has less communication and computation overhead, lower latency, and higher throughput than other baseline authentication schemes.
A runoff prediction method based on hyperparameter optimisation of a kernel extreme learning machine with multi-step decomposition

Abstract To improve the accuracy of runoff forecasting, a combined forecasting model is established by using the kernel extreme learning machine (KELM) algorithm optimised by the butterfly optimisation algorithm (BOA), combined with the variational modal decomposition method (VMD) and the complementary ensemble empirical modal decomposition method (CEEMD), for the measured daily runoff sequences at Jiehetan and Huayuankou stations and Gaochun and Lijin stations. The results show that the combined model VMD-CEEMD-BOA-KELM predicts the best. The average absolute errors are 30.02, 23.72, 25.75, 29.37, and the root mean square errors are 20.53 m 3 /s, 18.79 m 3 /s, 18.66 m 3 /s, and 21.87 m 3 /s, the decision coefficients are all above 90 percent, respectively, and the Nash efficiency coefficients are all more than 90%, from the above it can be seen that the method has better results in runoff time series prediction.
Player Perceptions of Face Validity and Fidelity in 360-Video and Virtual Reality Cricket

Virtual reality (VR) and 360° video can provide new opportunities for testing and training in sport. Both options offer different benefits in terms of efficacy for training, ease of use, and cost. This creates questions about the implementation of immersive technologies, and research is required to further understand their use. We aimed to gain initial evidence of athletes’ perceptions of face validity and fidelity in VR and 360-video. Thirty-nine international pathway cricketers experienced five overs in VR cricket and in a 360-video recording. After trying each technology, players completed questionnaires to measure perceptions of presence and task workload. Participants reported immersive experience in both methods, but higher levels of realism, possibility to act, physical effort, temporal constraints, and task control in VR. 360-video offers a better possibility to visually examine the environment, while VR offers enhanced realism and physical elements, but 360-video may still offer affordable solutions for visual tasks.
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks

Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications.
Distributing Fuzzy Inference Engines on Quantum Computers

Recently, a quantum algorithm called Quantum Fuzzy Inference Engine (QFIE) has been introduced with the main goal of providing exponential speedup in the execution of a Mamdani fuzzy inference engine. This quantum algorithm achieves this result by modeling a fuzzy rule base with a quantum oracle, a black box that is widely used to estimate functions using quantum mechanical principles. Although QFIE offers the possibility of performing efficient fuzzy computation on quantum computers, its real-world applicability is limited by the high levels of noise still present in current quantum computers. Consequently, it is necessary to introduce technological arrangements to make QFIE fully functional in practical cases until noise-free quantum computers are released. This paper addresses this issue by designing a distributed version of QFIE, based on the D-NISQ reference model, to distribute the computation of subsets of fuzzy rules across multiple quantum processors and minimize the negative impact of quantum noise. Experimental results prove that this distributed version of QFIE is able to significantly improve the accuracy of fuzzy computation on quantum devices, making QFIE applicable in real-world scenarios.
Transferability of Adversarial Attacks on Tiny Deep Learning Models for IoT Unmanned Aerial Vehicles

In the realm of miniature machine learning for Internet of Unmanned Aerial Vehicles (UAVs), the security concerns of machine learning models are obvious, especially when it comes to adversarial attacks. Models can become confused and their performance undermined by the introduction of meticulously crafted distortions. These attacks can even infiltrate a variety of models, bringing greater security risks. To understand how it works and mitigate its effects, our research focuses on scrutinizing the transferability of adversarial attacks in the expanding context of miniature machine learning for UAVs. In this paper, we introduce a formula help measure the transferability of adversarial attacks and explore ways to improve the transferability and effectiveness of adversarial attacks (e.g., a combination of attack techniques), and provide visulizations to vividly illustrate the repercussions of adversarial instances across a spectrum of attack intensities, helping facilitate more intuitive exploration and analysis of the results. For instance, our findings demonstrate that even subtle perturbations directed at specific attributes can lead to a significant decrease in model accuracy. We also evaluates the success rates of various attack algorithms and validates the proposed evaluation methodology for measuring transferability. And the outcomes unveiled in this study make noteworthy strides in fostering a profound comprehension of the transferability of adversarial attacks in the distinct realm of miniature machine learning for UAVs. Robust defense mechanisms, which ensure the impregnability of IoT-enabled UAV systems, can be cultivated by pinpointing the most efficacious attack strategies and evaluating their transferability.
Development of advanced digital ecosystems at universities: A study comparing experiences from Ukraine, Poland and Slovakia

Abstract During the war, a significant number of Ukrainian universities were tragically destroyed, while others were relocated. The challenging wartime circumstances in which universities operate in Ukraine have increased the use of distance education. University management and academic staff agree that the quality of services offered hinges on the establishment of comprehensive open electronic student campuses. These online campuses will deliver educational content, but also innovative pedagogical technologies for fostering competency development. This can be used for competency development in the use of gamification techniques, promoting entrepreneurship and start‐ups, implementing project methodologies, leveraging research and cognitive technologies, as well as using digital tools for communication and collaboration, and employing formative assessment strategies. The objective of the study on which this article reports was to determine the university ecosystem model most amenable for providing high quality digital education. We describe and analyse current digital education ecosystem models and examine their implementation at three universities: (1) Borys Grinchenko Kyiv University (BGKU), Ukraine; (2) University of Silesia (US), Poland; and (3) Constantine the Philosopher University in Nitra (UKF), Slovakia. Both strengths and limitations of online campuses are outlined. Our analysis supports the development of key performance indicators to assess the development of university digital education ecosystems and establish the necessary and sufficient conditions for implementation. Insights gained from organising education in remote learning conditions in wartime Ukraine are valuable also for educators in other countries.
A HYBRID DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS IN PRODUCT REVIEWS

Product reviews play a crucial role in providing valuable insights to consumers and producers. Analyzing the vast amount of data generated around a product, such as posts, comments, and views, can be challenging for business intelligence purposes. Sentiment analysis of this content helps both consumers and producers gain a better understanding of the market status, enabling them to make informed decisions. In this study, we propose a novel hybrid approach based on deep neural networks (DNNs) for sentiment analysis in product reviews, focusing on the classification of sentiments expressed. Our approach utilizes the recursive neural network (RNN) algorithm for sentiment classification. To address the imbalanced distribution of positive and negative samples in social network data, we employ a resampling technique that balances the dataset by increasing samples from the minority class and decreasing samples from the majority class. We evaluate our approach using Amazon data, comprising four product categories: clothing, cars, luxury goods, and household appliances. Experimental results demonstrate that our proposed approach performs well in sentiment analysis for product reviews, particularly in the context of digital marketing. Furthermore, the attention-based RNN algorithm outperforms the baseline RNN by approximately 5%. Notably, the study reveals consumer sentiment variations across different products, particularly in relation to appearance and price aspects.
Novel Ensemble Domain Adaptation Methodology for Enhanced Multi-class Fault Diagnosis of Highly-Connected Fleet of Assets

This paper proposes a novel methodology for enhancing multi-class classification accuracy in fault diagnosis problems among domains with highly-connected fleets of assets using time series data. The approach involves appending specially tailored models to an initial model and incorporating domain adaptation techniques to account for domain variations. The methodology is demonstrated through a case study on fault diagnosis of a fleet of hydraulic rock drills, which presents challenges due to variations in sensor data between different fault classes and individual machines. Results show significant improvements in classification accuracy, both in validation and testing, upon employing ensemble models and applying domain adaptation. While the study is limited to one case study, it lays the groundwork for exploring the applicability of the proposed methodology in other contexts.
Exploring Blockchain Architectures for Network Sharing: Advantages, Limitations, and Suitability

The increasing demand for mobile data services has led to a need for efficient and cost-effective network sharing solutions. Blockchain technology has emerged as a promising solution for addressing the challenges associated with network sharing, such as interoperability, trust, and accountability. This paper presents a comprehensive classification and categorization of blockchain-based network sharing scenarios, highlighting their advantages and limitations. We have identified seven network sharing scenarios, ranging from centralized network sharing to fully decentralized spectrum sharing. For each scenario, the suitability of some of the selected blockchain architectures, from public, private, sidechain, and hybrid, is evaluated through extensive evaluations. We also identify gaps and opportunities of blockchain-based network sharing solution and present future research directions at the end of paper. Our analysis and results reveal that a single blockchain architecture is not suitable for all network sharing scenarios but careful analysis should be performed when selecting the suitable blockchain network in network sharing.
Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database

Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse, because of their narrative structure. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis.To develop an NLP-based approach for extracting and categorising data from echocardiography reports by accurately converting continuous (e.g., LVOT VTI, AV VTI and TR Vmax) and discrete (e.g., regurgitation severity) outcomes in a semi-structured narrative format into a structured and categorised format, allowing for future research or clinical use.135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and was iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated.Continuous outcomes including LVOT VTI, AV VTI and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC = 1.00, p < 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. A good level (ICC = 0.75-0.9, p < 0.05) of inter-rater reliability was observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance.The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to a structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.
Hierarchical Controlled Hybrid Quantum Communication Based on Six-Qubit Entangled States in IoT

The rapid development and extensive application of the Internet of Things (IoT) have brought new challenges and opportunities to the field of communication. By integrating quantum secure communication with the IoT, we can provide a higher level of security and privacy protection to counteract security threats in the IoT. In this paper, a hybrid quantum communication scheme using six-qubit entangled states as a channel is proposed for specific IoT application scenarios. This scheme achieves hierarchical control of communication protocols on a single quantum channel. In the proposed scheme, device A transmits data to device B through quantum teleportation, while device B issues control commands to device A through remote quantum state preparation technology. These two tasks are controlled by control nodes C and D, respectively. The transmission of information from device A to device B is a relatively less important task, which can be solely controlled by control node C. On the other hand, issuing control commands from device B to device A is a more crucial task requiring joint control from control nodes C and D. This paper describes the proposed scheme and conducts simulation experiments using IBM’s Qiskit Aer quantum computing simulator. The results demonstrate that the fidelity of the quantum teleportation protocol (QTP) and the remote state preparation protocol (RSP) reach an impressive value of 0.999, fully validating the scheme’s feasibility. Furthermore, the factors affecting the fidelity of the hybrid communication protocol in an IoT environment with specific quantum noise are analyzed. By combining the security of quantum communication with the application scenarios of the IoT, this paper presents a new possibility for IoT communication.
One-Shot Non-Catalytic Distributed Purity Distillation

In this paper we revisit the problem of distributed purity distillation in the one-shot setting. The problem considers a bipartite quantum state ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AB</sup> shared between two parties Alice and Bob, who are allowed local unitary operations and one-way classical communication. The goal is to extract pure qubit states using these given resources, in their respective laboratories. A caveat of this problem is that all known protocols which recover the optimal number of pure qubit states require some pure qubits at the beginning at the protocol, to be used as ancilla. In this paper, we present a one-shot purity distillation protocol which requires a supply of ancilla qubits at 0 rate in the asymptotic iid setting, and little to no ancilla in the one-shot setting. We show that our protocol outperforms the best known purity distillation algorithms in terms of requirement of additional ancilla, while maintaining the same rate of pure states distilled.
Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body

Detecting posture changes of athletes in sports is an important task in teaching and training competitions, but its detection remains challenging due to the diversity and complexity of sports postures. This paper introduces a single-stage pose estimation algorithm named yolov8-sp. This algorithm enhances the original yolov8 architecture by incorporating the concept of multi-dimensional feature fusion and the attention mechanism for automatically capturing feature importance. Furthermore, in this paper, angle extraction is conducted for three crucial motion joints in the motion scene, with polynomial corrections applied across successive frames. In comparison with the baseline yolov8, the improved model significantly outperforms it in AP50 (average precision) aspects. Specifically, the model’s performance improves from 84.5 AP to 87.1 AP, and the performance of AP50–95, APM, and APL aspects also shows varying degrees of improvement; the joint angle detection accuracy under different sports scenarios is tested, and the overall accuracy is improved from 73.2% to 89.0%, which proves the feasibility of the method for posture estimation of the human body in sports and provides a reliable tool for the analysis of athletes’ joint angles.