The existing quantum algorithm for association rules mining (qARM) has a significant limitation: the need for artificial setting of the quantum oracle of data when mining frequent itemsets. This becomes extremely time-consuming when dealing with large-scale data. To overcome this limitation, this study proposes an improved method based on the C^n -X gate and the extended Grover algorithm (less than oracle). We abandon the original quantum phase estimation algorithm in the process of calculating the support degree and instead use a combined gate based on the C^n -X gate. This is because its linear time complexity is more efficient than the polynomial time complexity of the quantum estimation algorithm. Furthermore, we modify the extended Grover algorithm to screen the support degree. Our method is validated using IBM’s Qiskit tool for simulation experiments. We test on data sets with sizes of 2× 2 and 4× 4 , and fragments 29-32 items of data for the dataset Market Basket Optimization. The results demonstrate that, with our method, when dealing with large-scale data sets, there is no longer a need to prepare quantum oracle for all data in advance. In the valid data, its search accuracy can reach above 90 % at the highest. This suggests that our improved method enhances the efficiency and accuracy of quantum association rules mining.

Existing consensus protocols for blockchain technology have issues with high latency and complexity. Directed Acyclic Graph(DAG)-based Byzantine Fault Tolerance(BFT) solutions have become popular to address these issues, but they often trade off security or liveness. Many employ timing assumptions to circumvent these issues, but these are not always reliable in an adversarial setting. Recent studies have found that Byzantine Reliable Broadcast(BRB) protocol can achieve asset-transfer capabilities in a deterministic, full asynchronous mode. However, BRB cannot handle generalized applications like smart-contracts, which require a global total order. A new solution called BRAPTOR has been proposed and developed that addresses these issues. BRAPTOR is a hybrid system that combines the partial-order consensusless BRB with an efficient Byzantine total order broadcast consensus component. It decouples data dissemination from metadata ordering for scalable and high throughput results. Theoretical analysis with correctness proofs and evaluation show that BRAPTOR achieves instant asset-transfer, asynchronous liveness, optimal resilience, optimum communication complexity, constant time complexity and post quantum safety.

Variational quantum algorithms (VQAs) are most promising to show quantum advantages on noisy intermediate-scale quantum devices. Variational quantum classifiers (VQCs) are widely applied to classification tasks in the quantum domain. However, VQCs cannot show advantages in high-dimensional data. The large number of features necessitates the use of a significant number of qubits in VQCs. This results in long training time and increases training difficulty, ultimately leading to poor classification performance. In this paper, in order to enhance the ability of VQCs to handle high-dimensional data, a high-efficiency variational quantum classifier (HE-VQC) is proposed. Comparative Qiskit simulations of HE-VQC and four common VQCs were conducted on the UNSW-NB15 dataset. The simulation results show that HE-VQC significantly reduces training time while delivering superior classification performance.

Quantum genetic algorithms(QGA) typically construct traditional quantum chromosomes as independent quantum bit systems, which has facilitated the enhancement of search speed and precision in genetic algorithms, demonstrating powerful problem-solving capabilities in optimization issues. Although traditional QGA have enriched population diversity, they still tend to fall into local optima, and the evolutionary update operations require rotation directions to be determined by lookup tables, a process involving multiple conditional judgments that affect algorithm efficiency. To address these issues, an improved method has been proposed to further enhance the performance of QGA. In the initial population selection, a population grouping evolution method based on microhabitat coevolution strategy is used to prevent the algorithm from getting trapped in local optima when solving complex function problems. Additionally, quantum entanglement is introduced on top of quantum super-position by implementing quantum entanglement using quantum registers, and higher-order quantum rotation gates are employed that do not involve multiple conditional judgments of lookup tables, thus improving the efficiency of quantum chromosome measurement and evolutionary updates. Finally, traditional QGA and higher-order quantum genetic algorithms were applied to test functions in multiple experiments. The results indicate that, compared to traditional QGA, the higher-order quantum genetic algorithms have achieved an increase in convergence speed, an approximate 14.3% improvement in convergence precision, and possess enhanced global search capabilities.

With the rapid development of quantum machine learning (QML), quantum convolutional neural networks (QCNN) have been proposed and shown advantages in classification problems. An intrusion detection system (IDS) based on the QML method is proven to have higher accuracy than IDS based on the traditional machine learning (ML) method. However, the multiple convolution pooling operations of QCNN will cause the loss of valuable data features, resulting in a large error in the final measurement results. In this paper, we design an IDS model of QCNN based on a variational quantum neural network (VQNN), which can effectively reduce data feature loss and improve detection accuracy. We compare this model with traditional ML models such as artificial neural network (ANN), logistic regression (LR), K-nearest neighbor (KNN) algorithm, support vector machine (SVM), and decision tree (DT). Experiment results show that the accuracy of our proposed model is 94.51%, which is higher than other classical IDS models.

With the rapid development of quantum machine learning, the Quantum Long Short-Term Memory (QLSTM) has been found to exhibit faster convergence characteristics in time series prediction problems. Currently, it has found applications in fields such as financial market analysis, natural language processing, and weather forecasting, but its accuracy in regression problem prediction still shows deficiencies. To improve the prediction accuracy of the model, enhanced quantum long short-term memory by using bidirectional ring variational quantum circuit (EQLSTM) is proposed. We reconsidered and optimized the types, quantities, and arrangements of quantum gates, designing a bidirectional ring variational quantum circuit composed of CRX gates (Bi-VQC) to enhance its expressibility. Bi-VQC can more accurately describe and encode complex quantum state features, enabling the EQLSTM to better learn and represent the characteristics of the input data. In addition, Bi-VQC reduces the number of optimizable parameters and lowers the circuit load of the EQLSTM model. To validate the effectiveness of the proposed model, experiments are conducted to test and evaluate the EQLSTM using the aggregated traffic in the UK academic network backbone. The experimental results confirm that the proposed EQLSTM model improves from 90.56

Unmanned mine detection vehicles (UMDVs) have been used for military missions to detect and deactivate mines and reduce military and civilian casualties. UMDVs must cover an area of several kilometers to detect mines. Hence, a central coordination system is required for these UMDVs, and it has been achieved via the development of a vehicular ad hoc network (VANET). UMDVs can communicate with each other by broadcasting mine detection messages (MDMs) to incoming vehicles in VANET without infrastructure installations. Therefore, a novel mine detection vehicle routing (MDVR) protocol has been developed to create an ad hoc communication network among UMDVs. The protocol performs cluster-based multicast communication in real time. It adapts to dynamic scenarios by proposing a priority-based cluster head election scheme (PBCHE) and introducing cluster adaptability level schemes. Network simulator results show that the MDVR protocol can reduce the overhead and delay in MDM dissemination. In addition, the MDVR protocol shows promising results in terms of throughput, packet delivery ratio, and cluster stability.

In response to the challenges encountered by existing models in network traffic prediction, particularly in terms of prediction accuracy and the difficulty of hyperparameter tuning, we propose a method that utilizes the Improved Fireworks Algorithm to optimize the hyperparameters of a combined Convolutional Neural Network and Bidirectional Gated Recurrent Units model, referred to as IFA-CNN-BiGRU. This model leverages CNN for feature extraction from data and BiGRU for capturing temporal dependencies, taking advantages of deep learning to better capture nonlinear relationships and long-term dependencies in time series data, thereby enhancing prediction accuracy. IFA algorithm realizes the automatic adjustment of model hyperparameters and enhances the generalization and adaptive ability of the model. Compared with manual hyperparameter tuning, the IFA-CNN-BiGRU model effectively optimizes the model structure. The results show that the RMSE of IFA-CNN-BiGRU is about 4.81% higher than that of FA-CNN-BiGRU and about 19.44% higher than that of CNN-BiGRU. This shows that IFA-CNN-BiGRU model has certain advantages in the task of network traffic prediction, and provides an effective solution to solve the limitations of existing models.

Quantum computing is a new discipline combining quantum mechanics and computer science, which is expected to solve technical problems that are difficult for classical computers to solve efficiently. At present, quantum algorithms and hardware continue to develop at a high speed, but due to the serious constraints of quantum devices, such as the limited numbers of qubits and circuit depth, the fault-tolerant quantum computing will not be available in the near future. Variational quantum algorithms(VQAs) using classical optimizers to train parameterized quantum circuits have emerged as the main strategy to address these constraints. However, VQAs still have many challenges, such as trainability, hardware noise, expressibility and entangling capability. The fundamental concepts and applications of VQAs are reviewed. Then, strategies are introduced to overcome the challenges of VQAs and the importance of further researching VQAs is highlighted.

Data mining has reached a state that is difficult to break through, while the scale of data is growing rapidly, due to the lack of traditional computing power and limited data storage space. Efficient and accurate extraction of valuable information from massive data has become a challenge. Researchers have combined quantum computing with data mining to address this problem, hence the concept of quantum data mining has emerged. The fundamental tenets of quantum physics are adhered to for information transmission and computing operations in quantum data mining, which use the states of minuscule particles to represent and process information. Quantum data mining are based on the characteristics of quantum computing, such as superposition and entanglement, which make the ability of computational and information extraction effectively improved. The paper discusses and summarizes the relevant literature on quantum data mining in recent 3 years. After introducing relevant basic concepts of quantum computing, quantum data mining is presented in five aspects: quantum data classification, quantum data clustering, quantum dimensionality reduction, quantum association rules, quantum linear regression, and quantum causal analysis. These approaches, based on quantum computing, offer new perspectives and tools for handling complex data mining tasks. In conclusion, the development of quantum data mining is promising and crucial to overcome the difficulties associated with large-scale data mining.

This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients' health. This paper details ongoing research towards individualized health monitoring using wearable gadgets. The goal of improving healthcare facilities and improvement of the quality of life of citizens naturally brings up Internet of Things (IoT) technologies for consideration. Health observation is exceptionally critical in terms of avoidance, especially since the early determination of illnesses can minimize trouble and treatment costs. The cornerstones of intelligent, integrated, and individualized healthcare are continuous monitoring of physical signs and evaluation of medical data. To build a more reliable and robust IoMT model, the study will monitor the application of blockchain technology in federated learning (FL). A viable way to address the heterogeneity problem in federated learning is to design the system, data, and model tiers to lessen heterogeneity and produce a high-quality, tailored model for each endpoint. Blockchain-based federated learning allows for smarter simulations, lower latency, and lower power consumption while maintaining privacy at the same time. This solution provides another immediate benefit: in addition to having a shared model upgrade, the updated model on phones will now be used automatically, giving personalized knowledge about the phone is used.

In the era of Noisy Intermediate-Scale Quantum(NISQ), Variational Quantum Algorithm (VQA) is the most promising method to achieve quantum dominance. The VQA is a quantum classical-hybrid algorithm that typically uses a classical optimizer to train a parameterized quantum circuit (PQC). However, the barren plateau phenomenon will occur during training, reducing the training of quantum circuits. In this paper we design a quantum circuit structure, which combines the tree tensor network (TTN) and the parameterized quantum circuit, calling this circuit the tree parameterized quantum circuit (TPQC). We found that the TPQC had better expressiveness and was also effective in alleviating the barren plateau. The quantum classifier of TPQC is used for network attack intrusion detection, and compared with the quantum classifier of universal parameterized quantum circuit, we demonstrate that TPQC has higher accuracy for binary classification tasks.

With the rapid evolution of interactive technology, the popularity of mobile augmented reality (MAR) as a learning aid has continued to grow. However, several studies have revealed that research on the impact of AR in the educational domain is both insufficient and in an early phase. More studies are required to evaluate the effectiveness of utilizing MAR in this domain. The purpose of this study was to measure the effect of a mobile training course designed using MAR on trainees' motivation. We reviewed the associated concepts, highlighted the importance and effectiveness of MAR and explained the benefits and challenges of employing MAR in the educational domain. This study drew on John Keller's motivational model components and emphasized the significance of intrinsic motivation. We used a quantitative approach and designed a mobile training course that uses MAR to train government employees in Oman. A total of 32 employees were randomly divided into an experimental group and a control group. The experimental group used the designed application, and the control group took a training course online via computers. A motivational survey was conducted, and SPSS statistical software was used for data analysis. The results revealed that there was a significant difference in the mean motivation value for the experimental group: the trainees from the experimental group were more motivated than those from the control group. This study confirms that learners are motivated to participate in mobile training courses designed using MAR, which can contribute to the development of human resources in various domains.

The modern innovation called the Internet of Things (IoT) empowers individuals to connect to anybody and anything at any point, wherever. The application of the IoT in smart cities concerning smart healthcare management can improve patient welfare, user acceptance, the standard of living, and accurate illness monitoring. Powered wheelchairs (PW) with sensors, computers, and other connected assistive technologies are called smart wheelchairs. Smart wheelchairs with sensing abilities are intended to offer universal connectivity using cloud and edge computing technology. Numerous outstanding people were impacted by paralyzing phenomena, including Stephen Hawking and Max Brito. The issue of legitimacy is one of the most important difficulties in e-health applications, because of how sensitive the technology is, and this needs to be appropriately handled. To safeguard the data transport, usage, and interchange between sensor nodes/smart wheelchairs and servers, e-health applications require an authentication method. As all conversations use wireless channels, e-health apps are exposed to various vulnerabilities. Additionally, the IoT has limited computational and power capacity limitations. To combat the various security risks, the present research offers a user authentication technique that is efficient and ensures anonymity. The suggested method creates a safe connection for the authorized entity and forbids unauthorized entities from accessing the Internet of Things sensor nodes. The suggested approach has lower communication and computation overheads than the traditional techniques, making it more effective. In addition, the security verification of the presented protocol is scrutinized through AVISPA. The proposed scheme, on average, requires only 12.4% more computation cost to execute. Compared to the existing approaches, the suggested protocol’s extra computational cost can be compensated for by its enhanced security, while the suggested method’s communication cost is 46.3% smaller.

Open source software (OSS) has achieved popularity, however there are various software product quality problems, security issues and certain challenges confronting the OSS growth that need to be identified and addressed. The main focus of this research is to identify the risk factors associated with open-source software and the practices for those risks which will help software development companies and individuals to mitigate the risks. A systematic literature review (SLR) is employed for the identification of potential risk factors in OSS whereas questionnaire survey is used to validate the findings of the SLR from the relevant expert community. In the second round another SLR is carried out to identify the practices for softening the effect of risk-factors in OSS development. A total of 14 risk factors from the developers' perspective are identified via SLR in OSS. Amid the risk factors identified bugs, insufficient product documentation, and lack of communication and coordination among developers are considered the most important Further, we performed a secondary SLR to identify the practices for mitigating the effects of the risk factors in OSS. Therefore, a total of 31 practices for mitigating and addressing the risk factors in OSS were identified. In this work, we identified 14 risk factors and 31 practices for mitigating the critical risk factors, through SLR for adapting OSS development from developers' perspectives. We argue that focusing on the identified risk factors would minimize the risks associated with OSS. We also recommend that OSS developers should diligently consider all the risk factors that have been identified in the study for increased software productivity and distribution of reliable and robust source code.

Because mobile technology and the widespread usage of mobile devices have swiftly and radically evolved, several training centers have started to offer mobile training (m-training) via mobile devices. Thus, designing suitable m-training course content for training employees via mobile device applications has become an important professional development issue to allow employees to obtain knowledge and improve their skills in the rapidly changing mobile environment. Previous studies have identified challenges in this domain. One important challenge is that no solid theoretical framework serves as a foundation to provide instructional design guidelines for interactive m-training course content that motivates and attracts trainees to the training process via mobile devices. This study proposes a framework for designing interactive m-training course content using mobile augmented reality (MAR). A mixed-methods approach was adopted. Key elements were extracted from the literature to create an initial framework. Then, the framework was validated by interviewing experts, and it was tested by trainees. This integration led us to evaluate and prove the validity of the proposed framework. The framework follows a systematic approach guided by six key elements and offers a clear instructional design guideline checklist to ensure the design quality of interactive m-training course content. This study contributes to the knowledge by establishing a framework as a theoretical foundation for designing interactive m-training course content. Additionally, it supports the m-training domain by assisting trainers and designers in creating interactive m-training courses to train employees, thus increasing their engagement in m-training. Recommendations for future studies are proposed.

Distributed denial of service (DDoS) attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network (DNN) model for the detection of DDoS attacks in the Software-Defined Networking (SDN) paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control, SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network (DDNN) model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network (DDNN) model takes a large number of feature values as compared to previously proposed Machine Learning (ML) models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76% with a low false-positive rate and 0.065% low loss rate.The accuracy increases to 99.80% as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoT-optimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP's trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers' (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trust-based service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system.

The exponentially growing concern of cyber-attacks on extremely dense underwater sensor networks (UWSNs) and the evolution of UWSNs digital threat landscape has brought novel research challenges and issues. Primarily, varied protocol evaluation under advanced persistent threats is now becoming indispensable yet very challenging. This research implements an active attack in the Adaptive Mobility of Courier Nodes in Threshold-optimized Depth-based Routing (AMCTD) protocol. A variety of attacker nodes were employed in diverse scenarios to thoroughly assess the performance of AMCTD protocol. The protocol was exhaustively evaluated both with and without active attacks with benchmark evaluation metrics such as end-to-end delay, throughput, transmission loss, number of active nodes and energy tax. The preliminary research findings show that active attack drastically lowers the AMCTD protocol’s performance (i.e., active attack reduces the number of active nodes by up to 10%, reduces throughput by up to 6%, increases transmission loss by 7%, raises energy tax by 25%, and increases end-to-end delay by 20%).

Mobile edge computing is a promising paradigm that provides edge users with dependable computing services. However, due to the dynamic nature of mobile users and the limited resources of edge servers, it is essential to emphasize the load balancing of edge servers and the cooperation of heterogeneous computing resources. This paper proposes a Dynamic Resource Allocation (DRA) scheme based on a Quantum Approximate Optimization Algorithm (QAOA). The DRA is composed of the two components listed below. Firstly, we apply generative adversarial network to predict the future user density in various regions, which is an effective resource allocation aid. Secondly, QAOA is utilized to pre-allocate edge servers resources based on an advanced model of user density. The simulation results demonstrate that the efficient application of DRA ensures the load balancing of edge servers and simultaneously alleviates communication latency.