System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using the input/output observations of such systems with or without prior knowledge of some of the system parameters. Many valid algorithms are available in the literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model with exogenous inputs (NARMAX) and its derivatives (NARX, NARMA). Different nonlinear estimators can be used for those algorithms, such as polynomials, neural networks or wavelet networks. This paper uses a different approach, named particle-Bernstein polynomials, as an estimator for SI. Moreover, unlike the mentioned algorithms, this approach does not operate in the time domain but rather in the spectral components of the signals through the use of the discrete Karhunen–Loève transform (DKLT). Some experiments are performed to validate this approach using a publicly available dataset based on ground vibration tests recorded from a real F-16 aircraft. The experiments show better results when compared with some of the traditional algorithms, especially for large, heterogeneous datasets such as the one used. In particular, the absolute error obtained with the prosed method is 63% smaller with respect to NARX and from 42% to 62% smaller with respect to various artificial neural network-based approaches. Full article

Music has an increasing impact on people’s daily lives, and a sterling music recommendation algorithm can help users find their habitual music accurately. Recent research on music recommendation directly recommends the same type of music according to the specific music in the user’s historical favorite list. However, users’ behavior towards a certain cannot reflect the preference for this type of music and possibly provides music the listener dislikes. A recommendation model, MCTA, based on “User-Point-Music” structure is proposed. By clustering users’ historical behavior, different interest points are obtained to further recommend high-quality music under interest points. Furthermore, users’ interest points will decay over time. Combined with the number of music corresponding to each interest point and the liking degree of each music, a multi-interest point attenuation model is constructed. Based on the real data after desensitization and encoding, including 100,000 users and 12,028 pieces of music, a series of experimental results show that the effect of the proposed MCTA model has improved by seven percentage points in terms of accuracy compared with existing works. It came to the conclusion that the multi-interest point attenuation model can more accurately simulate the actual music consumption behavior of users and recommend music better. Full article

Smart contracts are the key software components to realize blockchain applications, from single encrypted digital currency to various fields. Due to the immutable nature of blockchain, any bugs or errors will become permanent once published and could lead to huge economic losses. Recently, a great number of security problems have been exposed in smart contracts. It is important to verify the correctness of smart contracts before they are deployed on the blockchain. This paper aims to verify the correctness of smart contracts in Ethereum transactions, and the model checker Spin is adopted for the formal verification of smart contracts in order to ensure their execution with respect to parties’ willingness, as well as their reliable interaction with clients. In this direction, we propose a formal method to construct the models for smart contracts. Then, the method is applied to a study case in the Ethereum commodity market. Finally, a case model is implemented in Spin, which can simulate the process’s execution and verify the properties that are abstracted from the requirements. Compared with existing techniques, formal analysis can verify whether smart contracts comply with the specifications for given behaviors and strengthen the credibility of smart contracts in the transaction. Full article

Exercise games (exergames) that combine both exercise and video gaming train people in a fun and competitive manner to lead a healthy lifestyle. Exergames promote more physical exertion and help users exercise more easily and independently in any place. Many studies have been conducted to evaluate the positive effects of exergames. However, in most studies, heart rate was mainly used to measure the effect of exercise. In this study, we evaluate the effects of exercise according to the exercise type (rest, walking, tennis, and running) and gameplay mode (single, competition, and cooperation) of exergaming via quantitative measurements using electrocardiogram (ECG) and Kinect. The multiple comparison results reveal that physical activity measured with Kinect was statistically significant even in exergames that did not show statistically significant differences according to ECG. Running was statistically significant compared to other exercise types, and there was a significant difference in competition compared to other gameplay modes. Full article

Person re-identification (ReID) is the problem of cross-camera target retrieval. The extraction of robust and discriminant features is the key factor in realizing the correct correlation of targets. A model based on convolutional neural networks (CNNs) can extract more robust image features. Still, it completes the extraction of images from local information to global information by continuously accumulating convolution layers. As a complex CNN, a vision transformer (ViT) captures global information from the beginning to extract more powerful features. This paper proposes an unsupervised domain adaptive person re-identification model (ViTReID) based on the vision transformer, taking the ViT model trained on ImageNet as the pre-training weight and a transformer encoder as the feature extraction network, which makes up for some defects of the CNN model. At the same time, the combined loss function of cross-entropy and triplet loss function combined with the center loss function is used to optimize the network; the person’s head is evaluated and trained as a local feature combined with the global feature of the whole body, focusing on the head, to enhance the head feature information. The experimental results show that ViTReID exceeds the baseline method (SSG) by 14% (Market1501 → MSMT17) in mean average precision (mAP). In MSMT17 → Market1501, ViTReID is 1.2% higher in rank-1 (R1) accuracy than a state-of-the-art method (SPCL); in PersonX → MSMT17, the mAP is 3.1% higher than that of the MMT-dbscan method, and in PersonX → Market1501, the mAP is 1.5% higher than that of the MMT-dbscan method. Full article

With the development of telecare medical information system (TMIS), doctors and patients are able to access useful medical services via 5G wireless communications without visiting the hospital in person. Unfortunately, TMIS should have the essential security properties, such as anonymity, mutual authentication, and privacy, since the patient’s data is transmitted via a public channel. Moreover, the sensing devices deployed in TMIS are resource-limited in terms of communication and computational costs. Thus, we design a physically secure privacy-preserving scheme using physical unclonable functions (PUF) in TMIS, called PUF-PSS to resolve the security requirements and efficiency of the existing related schemes. PUF-PSS prevents the security threats and also guarantees anonymity, key freshness, and authentication. We evaluate the security of PUF-PSS by performing formal and informal security analyses, including AVISPA implementation and ROR oracle model. We perform the test bed experiments utilizing well-known MIRACL based on a Raspberry PI 4 and compare the communication and computational costs of PUF-PSS with the previous schemes for TMIS. Consequently, PUF-PSS guarantees better efficiency and security than previous schemes and can be applied to TMIS environments. Full article

Intrusion detection system (IDS) has played a significant role in modern network security. A key component for constructing an effective IDS is the identification of essential features and network traffic data preprocessing to design effective classification model. This paper presents a Feature Subset Selection Hybrid Deep Belief Network based Cybersecurity Intrusion Detection (FSHDBN-CID) model. The presented FSHDBN-CID model mainly concentrates on the recognition of intrusions to accomplish cybersecurity in the network. In the presented FSHDBN-CID model, different levels of data preprocessing can be performed to transform the raw data into compatible format. For feature selection purposes, jaya optimization algorithm (JOA) is utilized which in turn reduces the computation complexity. In addition, the presented FSHDBN-CID model exploits HDBN model for classification purposes. At last, chicken swarm optimization (CSO) technique can be implemented as a hyperparameter optimizer for the HDBN method. In order to investigate the enhanced performance of the presented FSHDBN-CID method, a wide range of experiments was performed. The comparative study pointed out the improvements of the FSHDBN-CID model over other models with an accuracy of 99.57%. Full article

Chest and lung diseases are among the most serious chronic diseases in the world, and they occur as a result of factors such as smoking, air pollution, or bacterial infection, which would expose the respiratory system and chest to serious disorders. Chest diseases lead to a natural weakness in the respiratory system, which requires the patient to take care and attention to alleviate this problem. Countries are interested in encouraging medical research and monitoring the spread of communicable diseases. Therefore, they advised researchers to perform studies to curb the diseases’ spread and urged researchers to devise methods for swiftly and readily detecting and distinguishing lung diseases. In this paper, we propose a hybrid architecture of contrast-limited adaptive histogram equalization (CLAHE) and deep convolutional network for the classification of lung diseases. We used X-ray images to create a convolutional neural network (CNN) for early identification and categorization of lung diseases. Initially, the proposed method implemented the support vector machine to classify the images with and without using CLAHE equalizer. The obtained results were compared with the CNN networks. Later, two different experiments were implemented with hybrid architecture of deep CNN networks and CLAHE as a preprocessing for image enhancement. The experimental results indicate that the suggested hybrid architecture outperforms traditional methods by roughly 20% in terms of accuracy. Full article

A common requirement of embedded software in charge of safety tasks is to guarantee the identification of random hardware failures (RHFs) that can affect digital components. RHFs are unavoidable. For this reason, the functional safety standard devoted to automotive applications requires embedded software designs able to detect and eventually mitigate them. For this purpose, various software-based error detection techniques have been proposed over the years, focusing mainly on detecting control flow errors. Many control flow checking (CFC) algorithms have been proposed to accomplish this task. However, applying these approaches can be difficult because their respective literature gives little guidance on their practical implementation in high-level programming languages, and they have to be implemented in low-level code, e.g., assembly. Moreover, the current trend in the automotive industry is to adopt the so-called model-based software design approach, where an executable algorithm model is automatically translated into C or C++ source code. This paper presents two novelties: firstly, the compliance of the experimental data on the capabilities of control flow checking (CFC) algorithms with the ISO 26262 automotive functional safety standard; secondly, by implementing the CFC algorithm in the application behavioral model, the off-the-shelves code generator seamlessly produces the hardened source code of the application. The assessment was performed using a novel fault injection environment targeting a RISC-V (RV32I) microcontroller. Full article

To address the problem that the traditional access-control model is no longer suitable for access control and authorization in the super business system—which has the characteristics of many businesses and complex permissions—a business entity-based access-control model (BE-BAC) is proposed in this paper. The BE-BAC model realizes the relationship between users, business entities, and business permissions. Firstly, according to the characteristics of the super business system, the concept of business entity is put forward, introducing the composition and relationship of the business entity. Secondly, the business entity is introduced into the access-control model, formally describing the basic relationship, constraint, mapping, and authorization strategy of the BE-BAC model. Finally, the access-control workflow, based on the business entity, is designed, and the security analysis and comprehensive comparison of the model are carried out. Compared with the existing access-control model, the BE-BAC model has higher security and flexibility, and better protects resources, through more secure access-request decisions. Full article

With the growing demand for smart, secure, and intelligent solutions, Industry 4.0 has emerged as the future of various applications. One of the primary sectors that are becoming more vulnerable to security assaults like ransomware is the healthcare sector. Researchers have proposed various mechanisms in smart and secure health care systems with this vision in mind. Existing systems are vulnerable to security attacks on medical data. It is required to build a real-time diagnosis device using a cyber-physical system with blockchain technology in a considerable manner. The proposed work’s main purpose is to build secure, real-time preservation and tamper-proof control of medical data. In this work, the Bayesian grey filter-based convolution neural network (BGF-CNN) approach is used to enhance accuracy and reduce time complexity and overhead. Additionally, PSO and GWO optimization techniques are used to improve network performance. As an outcome of the proposed work, the privacy preservation of medical data is improved with a high accuracy rate by a blockchain-based cyber-physical system using a deep neural network (BGF Blockchain). To summarize, the proposed system helps in the privacy preservation of medical data along with a reduction in communication overhead using the Bayesian Grey Filter–CNN. Full article

Virtual humans (VHs) are gaining increasing attention in various fields, including games and cultural heritage and technological contexts including virtual reality and augmented reality. Recently, since VHs can simulate human-like behavior, VHs have been proposed as virtual assistants (VAs) for all sorts of education and training applications, including applications focused on the improvement of quality of life (QoL) and well-being. In this research work, we consider the quality and efficiency of VHs implemented as part of the MyHealthWatcher project, which focuses on the monitoring of health-related parameters of elder users to improve their QoL and self-management of chronic conditions. To validate our hypothesis that the increased quality of the VH has a positive effect on user satisfaction and user quality of interaction with the system, we developed and integrated into the MyHealthWatcher system two VH variations. The first was developed with mainstream technologies and the second was developed using a professional pipeline. The two variations developed were assessed by representative target users through a between-subject focus group study. The development and validation process of the two variations allowed us to draw valuable conclusions, which are discussed in this paper. Full article

One of the major keywords in the current digital world is the blockchain, which plays a major part in all kinds of advanced service systems, offering more convenience and better efficiency/effectiveness by controlling system hardware intelligently in a way humans have never experienced [...] Full article

The primary source of the various power-quality-disruption (PQD) concerns in smart grids is the large number of sensors, intelligent electronic devices (IEDs), remote terminal units, smart meters, measurement units, and computers that are linked by a large network. Because real-time data exchange via a network of various sensors demands a small file size without an adverse effect on the information quality, one measure of the power-quality monitoring in a smart grid is restricted by the vast volume of the data collection. In order to provide dependable and bandwidth-friendly data transfer, the data-processing techniques’ effectiveness was evaluated for precise power-quality monitoring in wireless sensor networks (WSNs) using grayscale PQD image data and employing pretrained PQD data with deep-learning techniques, such as ResNet50, MobileNet, and EfficientNetB0. The suggested layers, added between the pretrained base model and the classifier, modify the pretrained approaches. The result shows that advanced MobileNet is a fairly good-fitting model. This model outperforms the other pretraining methods, with 99.32% accuracy, the smallest file size, and the fastest computation time. The preprocessed data’s output is anticipated to allow for reliable and bandwidth-friendly data-packet transmission in WSNs. Full article

High-precision voltage sag cause identification is significant in solving the power quality problem. It is challenging for traditional deep learning models to balance training complexity and recognition performance when processing high-dimensional staging data samples, which affects the final recognition effect. This paper proposes a voltage sag identification method that fuses a sparse auto-encoder and Attention Unet. The model uses a sparse auto-encoder to perform unsupervised feature learning on the high-dimensional voltage sag waveform data and automatically obtains the deep low-dimensional features. Attention Unet, fused with cross-layer spatial and channel attention modules, further extracts these features to obtain recognition results with high performance. Compared with other deep learning recognition methods, the noise-adding experiments and the measured data are verified, indicating that the proposed method has low training complexity, higher recall, and better noise immunity. It benefits auxiliary decision-making for power quality management and governance. Full article