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This review explores the integration of human posture recognition and classification technologies in performing arts education, focusing on the advancements in deep learning, neural networks, and computer vision. It traces the evolution of these technologies and highlights their significance in developing personalized teaching methods for performing arts. Following this, we examine various posture recognition technologies, emphasizing data acquisition and processing methods while revealing their strengths and limitations, particularly in their adaptability to performing arts. Moreover, the review discusses posture classification methods, addressing the processing and interpretation of data, and the challenges in achieving classification accuracy and efficiency. Illustrative case studies demonstrate the application of these technologies in enhancing teaching and creativity in performing arts, significantly contributing to the skill development and expressiveness of artists and students. Concluding with a critical assessment of the current feasibility of these technologies in performing arts education, the review suggests potential improvements and future directions. It emphasizes the indispensable role of human posture recognition in advancing performing arts education and underscores the need for continued interdisciplinary research in this evolving field.

Stroke is a leading cause of global population mortality and disability, imposing burdens on patients and caregivers, and significantly affecting the quality of life of patients. Therefore, in this study, we aimed to explore the application of virtual reality technology in physical therapy by using immersive interactive training and designing rehabilitation modes for individual and group settings. We also aimed to provide patients with stroke with a comprehensive home-based rehabilitation and treatment plan, ultimately enhancing training effectiveness. Patients can engage in home-based rehabilitation through this system and undergo functional, mirror, and constraint-induced therapies tailored to different task contents. Simultaneously, using brain-computer interface technology, an emotion analysis mechanism was designed to map the patients’ brainwave signal data onto a two-dimensional space of positive-negative valence arousal; this approach can enable remote physical therapists to discern the patients’ emotional states during the rehabilitation process through virtual spaces, facilitating timely adjustments to rehabilitation tasks. Moreover, to prevent compromised effectiveness owing to improper training postures leading to compensation, the system offers real-time identification and recording, promptly issuing alerts when compensation occurs. The system provides a multiuser virtual rehabilitation space, enabling timely corrections and data observations, offering patients with stroke a home-based rehabilitation program, thereby realizing a localized aging care model.

This study addresses $\mathcal {H}_{\infty }$ filtering against DoS attacks within the framework of singular Markov jump systems. To provide a more comprehensive depiction of the issue pertaining to cyber security, DoS attacks are perceived as occurring in an unpredictable manner and can be represented by a collection of a random variable that follows the Bernoulli distribution. Consequently, the corresponding filter error system is formulated. Utilizing a set of viable linear matrix inequalities, this research establishes criteria of stochastically admissible for filter error system, ensuring a specified $\mathcal {H}_{\infty }$ performance. A filter design method under DoS attacks is proposed. Finally, the effectiveness of this method is demonstrated through a practical example involving a tunnel diode model.

In the charity sector, fundraising and transparency have long been key issues. Charity NFT (Non-Fungible Token) auctions, an emerging charity fundraising model integrating blockchain and NFT concepts, bring opportunities and challenges. Blockchain provides distributed data integrity and transparency via cryptography-linked data blocks, while NFTs enable unique digital ownership representation. This study designs a charity NFT auction platform on the Fisco Bcos blockchain, using multi-signature algorithms to ensure NFT authenticity, ECDSA (Elliptic Curve Digital Signature Algorithm) signatures for transaction integrity and traceability, NFTs and virtual currencies to reduce costs, and IPFS (InterPlanetary File System) for storage. The implemented system achieves 2104 TPS throughput with 492-millisecond latency, increasing transaction processing with low latency. Overall, the platform aims to address charity issues like opaque fund flows, high costs, and fake initiatives through the strategic application of blockchain and NFT functionality.

Long Short-Term Memory (LSTM) networks are unique to exercise data in its memory cell with long-term memory as Natural Language Processing (NLP) tasks have inklings of intensive time and computational power due to their complex structures like magnitude language model Transformer required to pre-train and learn billions of data performing different NLP tasks. In this paper, a dynamic chaotic model is proposed for the objective of transforming neurons states in network with neural dynamic characteristics by restructuring LSTM as Chaotic Neural Oscillatory-Long-Short Term Memory (CNO-LSTM), where neurons in LSTM memory cells are weighed in substitutes by oscillatory neurons to speed up computational training of language model and improve text classification accuracy for real-world applications. From the implementation perspective, five popular datasets of general text classification including binary, multi classification and multi-label classification are used to compare with mainstream baseline models on NLP tasks. Results showed that the performance of CNO-LSTM, a simplified model structure and oscillatory neurons state in exercising different types of text classification tasks are above baseline models in terms of evaluation index such as Accuracy, Precision, Recall and F1. The main contributions are time reduction and improved accuracy. It achieved approximately 46.76% of the highest reduction training time and 2.55% accuracy compared with vanilla LSTM model. Further, it achieved approximately 35.86% in time reduction compared with attention model without oscillatory indicating that the model restructure has reduced GPU dependency to improve training accuracy.

Cardiomegaly is an asymptomatic disease. Symptoms, such as palpitations, chest tightness, and shortness of breath, may be the early indications of cardiac hypertrophy, which can be divided into cardiac hypertrophy and ventricular enlargement. Their causes and treatment strategies are different. The early detection of cardiomegaly can help to make decisions for administering drugs and surgical treatments. In addition, with regard to problems in manual inspection, such as time consuming and the need for human interpretations and experiences, an assistive tool is required to automatically develop and identify normal heart or enlarged hearts. Therefore, this study proposes the combination of 2D (two dimensional) and 1D (one dimensional) convolutional neural network based classifier for rapid cardiomegaly screening in clinical applications based on chest X-ray (CXR) examinations in frontal posteroanterior view. The 2D and 1D convolutional processes and multilayer connected classification network are used to enhance the original CXR image and to remove unwanted noises to increase accuracy in feature extraction and pattern recognition tasks. The training dataset and testing dataset are collected from the National Institutes of Health CXR image database, which is used to train the classifier and validate the performance of the classifier in a K-fold cross-validation manner. Experimental results indicate the potential performance for rapid cardiomegaly screening with regard to recall (%), precision (%), accuracy (%), and F1 score.

Hate speech is a form of expression that assaults a person or a community based on race, origin, religion, sexual orientation, or other attributes. Although it can be expressed in multiple ways, both online and offline, the increasing popularity of social media has exponentially increased both its use and severity. Therefore, the aim of this research is to locate and analyze the unstructured data of selected social media posts that intend to spread hate in the comment sections. To address this issue, we propose a novel framework called FADOHS, which combines data analysis and natural language processing strategies, to sensitize all social media providers to the pervasiveness of hate on social media. Specifically, we use sentiment and emotion analysis algorithms to analyze recent posts and comments on these pages. Posts suspected of containing dehumanizing words will be processed before fed to the clustering algorithm for further evaluation. According to the experimental results, the proposed FADOHS framework is able to surpass the state-of-the-art approach in terms of precision, recall, and F1 scores by approximately 10%.

Fog-driven IoT architecture located between IoT devices and the centralized cloud infrastructure is introduced to extend computing, storage and network services to the edge of the Internet and therefore resources and services of the fog nodes are available and are closer to the end user and end device for providing mobility, low latency and location awareness. However, the paradigm of fog computing due to its inherited properties from cloud as inherits its security and privacy concerns such as spoofing, message replay, impersonation, man-in-the middle and physical capturing of IoT devices etc. To address these concerns in fog computing services, in this paper, a lightweight anonymous authentication and secure communication scheme is proposed and it only used secure one-way hash function and bitwise XOR operations when cloud, fog and user mutually authenticate each other. After the successful authentication, both fog-based participants can agree on a session key to encrypt the subsequent communication messages. The security can be ensured during authentication process by using the Burrows-Abadi-Needham (BAN) logic and the performance comparisons with existing schemes demonstrate that the proposed scheme is secure and highly efficient.

How to detect influential customers to broadcast advertisements to the maximum range is a key issue in effective viral marketing. If multiple companies sell the same product in viral marketing campaigns, there was competition between them. There may exist some unwanted users who hold hostile opinions, and these users may have a negative effect upon receiving promotional information. The company does not want the advertising information to reach such unwanted users over a period of time. In such competitive advertising, how to propagate the positive influence of its product and avoid it reaching the unwanted users of competitors in the limited time is a critical problem for business product promotion. Motivated by the phenomenon, we study the influence maximization problem with limited unwanted users (IML) in the independent cascade (IC) model. To accelerate the process of the influence propagation simulation, we present a path sampling approach based on the random walk to simulate the process of influence propagation. To avoid the unwanted users, only the influences on the paths reaching the wanted users are calculated at each time step, and the influences reaching the unwanted users are ignored.To reduce the computation time, the paths of the random walk will be recorded to avoid repeated random walks in the subsequent seed selection. To find the optimal influential customers, we employ a greedy scheme to select the top-k most influential nodes as seed nodes. Experimental results over the real-world datasets show that the algorithm we presented can get wider effective influence spreading than other algorithms.

Traveling to a new region has become a very common thing for people, due to work or life requirement. With the development of recommendation engine and the popularity of social media network, people are more and more used to relying on personalized Points-of-Interest (POI) recommendations. However, traditional approaches can fail if users moves to a region where they had little or no active history or even social network friends information before. Under the requirement of smart city construction, the need to give high quality personalized POI recommendation when a user travels to a new region has arisen. Fortunately, with the widespread of wireless Internet, the booming of Internet-of-Things (IoT) and the common-usage of location sensors in mobile phones, the coupling degree between social media networks and location information is ever increasing, which could leads us to a new way to solve this problem in the ear of Big Data. In this research, we presented New Place Recommendation Algorithm (N-PRA) which is designed based on Latent Factor model. Many different types of social media contexts (time-related and location-related), such as a user's interest fluctuation, different types of POIs' popularity fluctuation, types of POIs, the influence of geographical neighborhood on POIs, and user's social network friendship are taken into consideration in this approach. The algorithm presented is verified on Yelp, an open-source real urban data-set, and compared against several other baseline POI recommendation algorithms. Experimental results show that the algorithm presented in this paper could achieve a better accuracy.

Task offloading could optimize computational resource utilization in edge computing environments. However, how to assign and offload tasks for different behavior users is an essential problem since the systems dynamic, intelligent application diversity, and user personality. With user behavior prediction, this paper proposes soCoM, a semi-online Computational Offloading Model. We explore the user behaviors in sophisticated action space by reinforcement learning for catching unknown environment information. With Dueling Deep-Q Network, both the prediction accuracy of users' behaviors and the server load balance are well-considered, while increasing the computational efficiency and decreasing the resource costing. We propose a dynamic simulation environment of edge computing to demonstrate that user behavior is the critical factor for impacting system utilization. As the action space increasing, Dueling DQN performs better than state-of-art DQN and other improved strategies, and also load balance in multiple different server scenario.

Graphene has unique and outstanding properties that make it a promising material for many applications. It has triggered considerable research in fields including solar cells, photodetectors, electrodes, and supercapacitors. Despite the favorable characteristics of devices using graphene have been widely explored, issues such as low absorbance, complex processing, and limited device size remain. Hence, we present large-area CdSe quantum dots (QDs)/reduced graphene oxide (rGO) films and corresponding photodetectors through a cost-effective and simple spin-coating method. As light turns on, CdSe QDs are excited and generate excess electron-hole pairs, leading to a significantly increased on/off current ratio of 2195 at a low bias voltage of -1 V, compared to that of photodetectors without CdSe QDs. Decorating the rGO film with CdSe QDs enables the wavefunction modulation and enhances the light harvesting. Our proposed high-performance photodetector can be operated at a low voltage, which is beneficial for applications in various green and low-power consumption devices.

Identifying essential proteins is important for not only understanding cellular activity but also detecting human disease genes. A series of centrality measures have been proposed to identify essential proteins based on the protein-protein interaction (PPI) network. Although, existing studies have focused on the topological features of the PPI network and the intrinsic characteristics of biological attributes. it is still a big challenge to further improve the prediction accuracy of essential proteins. Moreover, there are substantial amounts of false-positive data in PPI networks; thus, a PPI network should be modelled as an uncertain network. How to identify essential proteins more accurately and conveniently has become a research hotspot. In this paper, we proposed a new essential protein discovery method called ETB-UPPI on uncertain PPI networks. The algorithm detects essential proteins by integrating topological features with biological information. Experimental results on four Saccharomyces cerevisiae datasets have shown that ETB-UPPI can not only improve the prediction accuracy but also outperform other prediction methods, including the most commonly-used centrality measures (DC, SC, BC, IC, EC, and NC), topology-based methods (LAC) and biological-data-integrating methods (PeC, WDC, UDONC, LBCC, TEGS, and RSG).

Multi-label classification tackles the problems in which each instance is associated with multiple labels. Due to the interdependence among labels, exploiting label correlations is the main means to enhance the performances of classifiers and a variety of corresponding multi-label algorithms have been proposed. Among those algorithms Classifier Chains (CC) is one of the most effective methods. It induces binary classifiers for each label, and these classifiers are linked in a chain. In the chain, the labels predicted by previous classifiers are used as additional features for the current classifier. The original CC has two shortcomings which potentially decrease classification performances: random label ordering, noise in original and additional features. To deal with these problems, we propose a novel and effective algorithm named LSF-CC, i.e. Label Specific Features based Classifier Chain for multi-label classification. At first, a feature estimating technique is employed to produce a list of most relevant features and labels for each label. According to these lists, we define a chain to guarantee that the most frequent labels that appear in these lists are top-ranked. Then, label specific features can be selected from the original feature space and label space. Based on these label specific features, corresponding binary classifiers are learned for each label. Experiments on several multi-label data sets from various domains have shown that the proposed method outperforms well-established approaches.

With the rapid development of the social economy, our lives are flooded with all kinds of counterfeit products. The public's attitude of greedy for petty and cheap has encouraged unscrupulous manufacturers to take advantage of the opportunity to provide low-cost counterfeit products, suppress the profits of legitimate manufacturers, and also make the public lose confidence in the quality of the products. At present, the most widely used anti-counterfeiting system based on QR codes on the market. However, existing traceability systems are still mostly built in a centralized manner, and the central agency provides trust guarantees, but the public still has great doubts about the credibility of the central agency. The introduction of blockchain technology can perfectly solve the lack of existing architecture and the environment. In this research, we propose an IoT-based traceable drug anti-counterfeiting management system, a comprehensive plan from drug research and development, certification, production to sales. The framework we propose meets the requirements of information security for data integrity, resistance to replay attacks, irreversible information, and non-repudiation.

In recent years, due to the rapid development of information techniques and network technologies, more and more medical documents have been replaced by electronic files for sharing and transmitting in real time. However, medical data transmitted over public communication channels may suffer from security attacks and privacy threats. Blockchain technology has been gotten many attentions in different areas due to its unique properties such as anonymity, verifiability, immutability and decentralization. In order to secure patient privacy and provide more personal healthcare services, in this paper, we propose a data aggregation scheme based on Blockchain technology for medical environments. Moreover, in order to implement remote medical monitoring, we design a group authentication mechanism for multiple authorized users (such as patient, doctors, caregivers, family and friends) to freely access patient's personal health records. The authorized group members in a group will agree on a group session key and use it to protect patient's sensitive information. In case of a new member joins the medical group or an old member leaves the medical group, the group session key needs to be updated at any time. Finally, the electronic medical system will become more secure, reliable and useful by our proposed scheme.

When the inner insulation of oil-impregnated paper bushing breaks down in service, gases decomposed by oil and paper may cause an explosion. The explosion of bushing occurs from time to time because of the lack of an effective monitoring method. In order to provide an effective online monitoring method for the bushing to avoid explosion, this paper presents a method to monitor the PD defect in bushing by measuring the inner pressure of bushing. In this paper, the relationship of variation between characteristic gases content and inner pressure is studied, the effect of inner temperature on inner pressure is analyzed and the method for reducing this effect is also presented, and the monitoring method is proved to be useful by PD tests on a real bushing. The results show that the inner pressure increases obviously due to the generation of characteristic gases by internal discharge in oil-impregnated paper bushing, and the pressure increases with the deterioration of the discharge; although the inner pressure has a great influence on the inner pressure, the influence can be reduced or eliminated greatly by the method of common-mode rejection. According to the results, the internal discharge defect of the oil-impregnated paper bushing can be detected by monitoring the pressure, and the bushing explosion can be avoided.

Blockchain technology has gained significant prominence in recent years due to its public, distributed, and decentration characteristics, which was widely applied in all walks of life requiring distributed trustless consensus. However, the most cryptographic protocols used in the current blockchain networks are susceptible to the quantum attack with rapid development of a sufficiently large quantum computer. In this paper, we first give an overview of the vulnerabilities of the modern blockchain networks to a quantum adversary and some potential post-quantum mitigation methods. Then, a new lattice-based signature scheme has been proposed, which can be used to secure the blockchain network over existing classical channels. Meanwhile, the public and private keys are generated by the Bonsai Trees technology with RandBasis algorithm from the root keys, which not only ensure the randomness, but also construct the lightweight nondeterministic wallets. Then, the proposed scheme can be proved secure in random oracle model, and it is also more efficient than similar literatures. In addition, we also give the detailed description of the post-quantum blockchain transaction. Furthermore, this work can help to enrich the research on the future post-quantum blockchain (PQB).

Functional tremors are clear symptoms of neurodegenerative diseases; as such, they indicate the progression of Parkinson's disease (PD). Digitized handwritten pattern analysis of Archimedes' spirals, words, and sentences can help evaluate movement discords in the upper limbs. It offers a simple, comfortable, and repeatable method of examination for clinical applications and at-home monitoring usages. Upper limb tremors can be found in PD, essential tremor (ET), and cerebellar disorders. This paper proposes a quantitative method to scale the variations of functional tremors. The deviation (in cm) and the accumulation angle (in rad) of the feature pattern in polar expression were extracted to scale the variability at different tremor levels. Then, the proposed intelligent classifier, which is used as a perceptual color representation-based classifier (PCRC) and comprises a radial Bayesian network and a color relation analysis method, was employed to screen PD or ET with perceptual color representation. An assistant tool can integrate a smart mobile device (iPad/smartphone) and PCRC into the decision support system for individualized functions to evaluate the progression of the tremor level. The proposed decision support system was validated using data collected from 50 subjects. With fivefold cross-validation, average true positive, average true negative, and hit rates of 92.02%, 88.17% and 90.44%, respectively, were obtained to quantify the performance of the proposed classifier for identifying normal controls and PD or ET.

With the recent rapid increase in the number of motor vehicles on roads, traffic accidents have increased, and emergency reporting processes have become essential. In this paper, a key agreement protocol for a real-time traffic sharing system is proposed for Vehicular Ad-Hoc Networks (VANETs), in which a broadcast center authenticates the legitimacy of a user when they report an emergency and issues a certificate of emergency to other users. This study uses a digital signatures mechanism, key agreement and authentication scheme to satisfy the security requirements for a VANET. Burrows-Abadi-Needham logic (BAN logic) is applied to prove that the proposed scheme achieves secure authentication. The proposed protocol ensures the privacy of the communication between fleets and provides a mechanism for immediate emergency reporting. The experiment results show that the proposed scheme is feasible and meets security requirements.

RF-based gesture sensing and recognition has increasingly attracted intense academic and industrial interest due to its various device-free applications in daily life, such as elder monitoring, mobile games. State-of-the-art approaches achieved accurate gesture sensing by using fine-grained RF signatures (such as CSI, Doppler effect) while could not achieve the same accuracy with coarse-grained RF signatures such as received signal strength (RSS). This paper presents rRuler, a novel feature extraction method which aims to get fine-grained human gesture features with coarse-grained RSS readings, which means rought ruler could measure fine things. In order to further verify the performance of rRuler, we further propose rRuler-HMM, a hidden Markov model (HMM) based human gesture sensing and prediction algorithm which utilizes the features extracted by rRuler as input. We implemented rRuler and rRuler-HMM using TI Sensortag platforms and off-the-shelf (CTOS) laptops in an indoor environment, extensively performance evaluations show that rRuler and rRuler-HMM stand out for their low cost and high practicability, and the average gesture sensing accuracy of rRuler-HMM can achieve 95.71% in NLoS scenario and 97.14% in LoS scenario, respectively, which is similar to the performance that fine-grained RF signatures based approaches could achieve.

This paper proposes GraspCNN, an approach to grasp detection where a feasible robotic grasp is detected as an oriented diameter circle in RGB image, using a single convolutional neural network. By detecting robotic grasps as oriented diameter circles, grasp representation is thereby simplified. In addition to our novel grasp representation, a grasp pose localization algorithm is proposed to project an oriented diameter circle back to a 6D grasp pose in point cloud. GraspCNN predicts feasible grasping circles and grasp probabilities directly from RGB image. Experiments show that GraspCNN achieves a 96.5% accuracy on the Cornell Grasping Dataset, outperforming existing one-stage detectors for grasp detection. GraspCNN is fast and stable, which can process RGB image at 50 fps and meet the requirements of real-time applications. To detect objects and locate feasible grasps simultaneously, GraspCNN is executed in parallel with YOLO, which achieves outstanding performance on both object detection and grasp detection.

Pleural effusion is the pathologic accumulation of body fluids around the unilateral or bilateral lungs that is primarily caused by heart disease. A chest radiograph is a rapid examination technique used to provide a preliminary diagnosis of lung and heart diseases. Computer-aided diagnosis with the digitalized image is an automated approach that addresses the drawbacks of manual inspection. In this study, two corner detectors along with a two-dimensional convolution process are used to enhance the chest X-ray image for an accurate extrapolation of the bilateral lung cavities. Based on bounding box pixel analysis, the pixel ratios of the lung anatomy between normal and abnormal conditions can be estimated to identify the pleural effusion size. Next, a smart drainage monitoring system is developed to improve the current functions of the traditional drainage tool and confirm the drainage safety, including (a) drainage volume and required time detection, (b) unplanned removal warning, and (c) physiological status monitoring. The experimental result will be used to determine the feasibility of the proposed effusion volume estimation algorithm and the efficiency of the smart drainage monitoring prototyping tool. The proposed smart drainage monitoring system and the computer-aided method with digitalized images can be further applied in real clinical practice in the intensive care unit.

Nowadays, multi-label data are ubiquitous in real-world applications, in which each instance is associated with a set of labels. Multi-label learning has attracted significant attentions from researchers and plenty of algorithms have been proposed. Among those algorithms binary relevance (BR) is a widely used framework for multi-label classification. It constructs binary classifiers for each label by means of one-vs-rest style. BR approach is a simple and straight forward way of problem transformation for multi-label learning, but it ignores label correlations totally. Stacking based BR is a feasible way to tackle this problem. The key issue of stacking based BR is how to select label subset to extend the original features for each label. Existing methods of stacking based BR usually select identical label subset for all labels. It may be suboptimal as each label has its own most related label subset. In this paper, a novel stacking based method is introduced to utilize label correlations based on Pareto Optimum for improving the performance of BR. Our method builds a stack of two layers of BR classifiers. At the first layer, a group of binary classifiers are constructed, one for a label. At the second layer, for each label we employ Pareto Optimum to select most related label subset, then augment the original features by the selected label subset. The final binary classifiers for each label are constructed based on their corresponding reconstructed feature space. Comparing to other well-established stacking multi-label learning algorithms in terms of different multi-label classification criteria, experimental results on several multi-label benchmark datasets testify the superiority of the proposed methods.

Pulmonary valve diseases in children and adults include different degrees of stenosis, regurgitation, or congenital defects. Valve repair or replacement surgery is used to treat valvular dysfunction and to improve regurgitations flow for pulmonary valve pathologies. Handmade trileaflet valve designs with different ranges of diameters have been used for pulmonary valved conduit reconstruction among children or adult patients with available conditions. In this paper, we propose a multiple regression model as a cascadecorrelation-network-based estimator to determine optimal trileaflet parameters, including width, length, and upper/lower curved structures, for trileaflet valve reconstruction. The diameter of the main pulmonary artery is determined via computed tomography pulmonary angiography, and a trileaflet valve template is rapidly sketched. The actual valve is constructed using an expanded polytetrafluoroethylene material. Using an experimental pulmonary circulation loop system, design parameters and valve efficacy can be validated by the Taguchi method through calculation of signal-to-noise ratios. Experimental results indicate that in contrast to commercial valve stents, the handmade trileaflet valve exhibits good performance and is a valuable option in treatment of severe pulmonary regurgitation.

Dialysis vascular accesses are critical for patients receiving hemodialysis treatment. However, dialysis access stenosis and further dysfunction are engendered by thrombosis or outflow (venous anastomosis site) stenosis and the progression of inflow (arterial anastomosis site) stenosis. Thus, any narrowed access causes vibrations, turbulent flow, and murmur sounds around stenosis sites. Auscultation and frequency-based techniques are employed to detect these sounds, and frequency components are also validated on the basis of the degree of stenosis (DOS). In this paper, a biophysical experimental model employing an in vitro arteriovenous graft model was established to produce various acoustic signals associated with single stenosis and multiple stenoses. By analyzing various combinations of stenoses, this paper selected suitable features of the frequency and power spectra using the Burg autoregressive method. A multiple regression model applying a higher number of explanatory variables and response variables, as a generalized regression neural network, was employed to identify DOS levels at inflow and outflow sites. The experimental results indicated that the proposed screening model provided a higher average hit rate of >90%, average true-positive rate of >90%, and true-negative rate of 100% in single and multiple stenosis screening, compared with the multiple linear regression model.

To fight against denial of services (DoS) attacks on vehicular ad hoc networks, which can cause congestion over networks and degrading the user's experience, a lot of detective techniques and schemes have been proposed. However, the complex ones cannot keep pace with the growth of vehicle networks. In this paper, we propose a simple but effective defense strategy scheme inspired by the port-hopping mechanism, which advantage is manifested in that the detection and filtering off of malicious packets launched by attackers can be achieved without any change in existing protocol. First, we design a dynamic defense strategy scheme to puzzle a DoS attacker, where the specific defense strategy will change according to a scheme of time. To mitigate the losses caused by an attacker whose goal is to probe the vulnerable services' ports contained in the UDP/TCP headers between vehicle-to-vehicle or vehicle-to-infrastructure, we add some security services' ports that are valueless to attackers. Second, we give the specific construction of such a defense strategy scheme reflected as a matrix and a security analysis with respect to detecting the probed ports. At last, in comparison with the non-strategy defense scheme, simulations considering some parameters are conducted, which can show that our scheme is an effective defense scheme used for protecting VANETs.

In recent years, information technology has become a focus of attention. All hardware, software, and communication technologies are growing rapidly. Nowadays, we can't live without technology. We use technology anywhere and anytime, and enjoy the convenience of technology. In particular, all kinds of network services and applications. These communication technologies and services constitute the concept of cloud computing. Cloud computing involves virtualization of service resources on the network; the entire service resources include scheduling, management, maintenance, and so on, which are carried out by specialized personnel. The goal of cloud computing is enabling users to use virtual resource pools maximally and handle large-scale computing problems anytime and anywhere via the network. Robotics is one of the rapidly developing technologies. Although there are numerous research teams working on cloud robots, research on cloud robot systems is still in its infancy and many problems have not yet been considered, such as the issues of robot privatization and communication security. Therefore, we propose a robot cloud service system and focus on four crucial issues: cloud platform central control, robot intelligence technology, robot privatization, and communication security. Finally, we use BAN logic to prove that the proposed scheme achieves mutual authentication and user anonymity. The proposed scheme can also defend against several kinds of attacks, like eavesdropping attack, impersonation attack, parallel session attack, man-in-the-middle attack, and replay attack.

Cardiac arrhythmias reveal multiple morphologies in time-domain waveforms. Abnormal beats reflect the origin and the conduction path of the ectopic heart activation pulses, including supraventricular, junctional, and ventricular arrhythmias and conduction abnormalities. This paper proposes a method for automated screening of cardiac arrhythmias using the discrete fractional-order integration (DFOI) and the meta-learning-based intelligent classifier. The DFOI process with the specific fractional order is used to extract the QRS features with the finite computations. It can deal with the irregular time-varying signals. The meta-learning-based intelligent classifier is used to identify the abnormal classes, consisting of a primary multilayer network and several agent networks. Since abnormal QRS complexes are multi-waveforms such as ventricular premature contraction, the classifier needs to retrain with new training patterns for meeting new condition in clinical applications. Thus, this model possesses the learning-to-optimization capability for retraining generalized regression neural network using the particle swarm optimization algorithm. Using incremental new training patterns, each inducer is used to track the past learning experiences through several agent networks. This pattern scheme can gradually enhance the accuracy by refining the optimal parameters of each sub-estimator for pattern recognition. Using the arrhythmia database of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database, the proposed intelligent classifier demonstrated greater efficiency and promising results in average accuracy, positive predictivity, and true negative rate for recognizing electrocardiography signals.

Percutaneous pulmonary valve implantation is an improved technique that is used to treat narrowed pulmonary valves or leaky pulmonary valves in patients with congenital heart disease. This technique represents a promising strategy to reduce surgical risk and operation. In clinical cases, commercial valve stents are sometimes not available for children or special subjects due to restrictions in stent size. Hence, the handmade pulmonary valved conduit provides a strategy to design stents with customized size for valve replacement. In this paper, we propose a meta-learning-based intelligent model to train an estimator (including two sub-estimators) to determine optimal trileaflet parameters for customized trileaflet valve reconstruction. This estimation model overcomes the problem of empirical parameter determination. The meta-learning model possesses learning-to-optimization capability for training generalized regression neural network by particle swarm optimization algorithm. Through incremental training patterns, this scheme can gradually enhance optimization to provide refined parameters for customized designs that can be applicable to individuals of all age groups. The customized handmade pulmonary valved conduit was validated by assessing the regurgitation fraction and the heart pump efficiency using an experimental cardiopulmonary circulation loop system.

Nowadays, blockchain has become one of the most cutting-edge technologies, which has been widely concerned and researched. However, the quantum computing attack seriously threatens the security of blockchain, and related research is still less. Targeting at this issue, in this paper, we present the definition of post-quantum blockchain (PQB) and propose a secure cryptocurrency scheme based on PQB, which can resist quantum computing attacks. First, we propose a signature scheme based on lattice problem. We use lattice basis delegation algorithm to generate secret keys with selecting a random value, and sign message by preimage sampling algorithm. In addition, we design the first-signature and last-signature in our scheme, which are defined as double-signature. It is used to reduce the correlation between the message and the signature. Second, by combining the proposed signature scheme with blockchain, we construct the PQB and propose this cryptocurrency scheme. Its security can be reduced to the lattice short integer solution (SIS) problem. At last, through our analysis, the proposed cryptocurrency scheme is able to resist the quantum computing attack and its signature satisfies correctness and one-more unforgeability under the lattice SIS assumption. Furthermore, compared with previous signature schemes, the sizes of signature and secret keys are relatively shorter than that of others, which can decrease the computational complexity. These make our cryptocurrency scheme more secure and efficient.

With its explosive development, the Internet has come to offer an increasing multitude of online service applications. In order to better facilitate such services, a cloud computing environment, composed of a large number of processors and memories, high-speed networks, and various applications, has been developed, and continues to grow, providing convenient and quick network services. In this cloud computing environment, each server processor in the environment must cooperate with other processors to satisfy various user demands. As a result, the issue of fault-tolerance needs to be revised in order to ensure the reliability of cloud computing environments. One of the most important issue of fault-tolerance is the Byzantine agreement (BA), which requires that, even if some components are damaged, a set of fault-free service processors are able to agree on a common value. Furthermore, the faulty service processors must be detected and eliminated. Therefore, a fault diagnosis agreement (FDA) issue for such environments must also be revised simultaneously. The goal of FDA is to enable each fault-free service processor to detect/locate a common set of faulty service processors. However, a lot of messages need to be collected to solve the BA and FDA problem in the previous works. Thus, this paper also uses the concept of an early stopping protocol (ESP) to allow its participants to obtain common values early, during different rounds. Furthermore, the result of ESP can then be used to detect/locate the maximum number of faulty service processors with dual failure modes efficiently, using the minimum number of rounds. As a result, the early diagnosis cloud agreement protocol can be proposed to solve the BA, ESP, and FDA problems simultaneously to provide greater computing abilities by enhancing the reliability of a cloud computing environment.

Parkinson's disease (PD) is a progressive neurodegenerative motor system disorder. Early diagnosis of PD is important to control the symptoms appropriately. Recent voice and speech recognition techniques provide alternative solutions for PD screening. In this paper, an optimal support vector machine (SVM) based on bacterial foraging optimization (BFO) was established to predict PD effectively. The effectiveness of the proposed method, BFO-SVM, was validated on a PD data set based on vocal measurements. The proposed method was compared with two of the most frequently used parameter optimization methods, including an SVM based on the grid search method and an SVM based on particle swarm optimization. Additionally, to further boost the prediction accuracy, the relief feature selection was employed prior to the BFO-SVM method, consequently the RF-BFO-SVM was proposed. The experimental results have demonstrated that the proposed framework exhibited excellent classification performance with a superior classification accuracy of 97.42%.