Reviewerly/exHarmony huggingface dataset:
Physical Layer Authenticated Image Encryption for IoT Network Based on Biometric Chaotic Signature for MPFrFT OFDM System

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform–Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of 22200. The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application.
Slicing who slices: Anonymization quality evaluation on deployment, privacy, and utility in mix-zones

In the flowering of ubiquitous computing, technologies like the Internet of Things and the Internet of Vehicles have contributed to connecting objects and sharing location services in broad environments like smart cities bringing many benefits to citizens. However, these services yield massive and unrestricted mobility data of citizens that pose privacy concerns, among them recovering the identity of people with linking attacks. Although several privacy mechanisms have been proposed to solve anonymization problems, there are few studies about their behavior and analysis of the data quality anonymized by these techniques. This paper presents an anonymization quality framework for mix-zones enabling characterizing and evaluating the impacts of anonymization over time and space in mobility data. We conducted experiments with a cab mobility dataset and two positioning algorithms to explore one of the potentialities of the anonymization quality: elect mix-zones that do not consider the traffic but its operating requirements too. The results showed that the anonymization quality enabled the selection of mix-zones that yield data anonymization considering the quality, privacy, and utility analysis. This study is unique because it analyzes mix-zone coverage and quality metrics to observe the anonymization quality not found in the literature.
Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.
Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information

In the highly competitive business environment, financial fraud detection plays a crucial role in safeguarding companies and investors. However, traditional methods face challenges when assessing the complex and multidimensional nature of financial data. Cutting-edge machine learning techniques, particularly the hierarchical graph attention network (HGAT), emerge as a promising approach for financial fraud detection. The proposed approach includes encoding adjacency matrices for capturing local relationships and utilizing multi-head self-attention to propagate structural attributes across multiple layers. Node embeddings are generated by the HGAT model, which integrates both local and extensive structural information through multihead self-attention. Through learning intricate inter-entity relationships, the HGAT model can effectively identify potential financial risks. Experiments performed on a publicly available financial report dataset demonstrate the superior performance of our model compared to the existing methods in detecting financial risks.
Effective Searching of Drowning Victims in the River using Deep Learning Method and Underwater Drone

The search for drowning victims can be a time-consuming and challenging task, often hindered by limited equipment in the field. However, advancements in technology, particularly underwater drone technology and deep learning methods for object detection, present promising solutions. YOLOv5 (You Only Look Once Version 5) is a cutting-edge object detection model known for its high detection speed and accuracy. In this study, the researchers leveraged the capabilities of YOLOv5 to enhance the efficiency of searching for submerged victims. The YOLOv5s configuration model was utilized, and the model was trained for 400 epochs. The results demonstrated impressive performance, with an accuracy value of 89%, precision of 100%, and recall of 95%.
Predicting Alzheimer’s Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort

The rise of Alzheimer’s Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimer’s Disease risk.
Multimodal image matching: A scale-invariant algorithm and an open dataset

Multimodal image matching is a core basis for information fusion, change detection, and image-based navigation. However, multimodal images may simultaneously suffer from severe nonlinear radiation distortion (NRD) and complex geometric differences, which pose great challenges to existing methods. Although deep learning-based methods had shown potential in image matching, they mainly focus on same-source images or single types of multimodal images such as optical-synthetic aperture radar (SAR). One of the main obstacles is the lack of public data for different types of multimodal images. In this paper, we make two major contributions to the community of multimodal image matching: First, we collect six typical types of images, including optical-optical, optical-infrared, optical-SAR, optical-depth, optical-map, and nighttime, to construct a multimodal image dataset with a total of 1200 pairs. This dataset has good diversity in image categories, feature classes, resolutions, geometric variations, etc. Second, we propose a scale and rotation invariant feature transform (SRIF) method, which achieves good matching performance without relying on data characteristics. This is one of the advantages of our SRIF over deep learning methods. SRIF obtains the scales of FAST keypoints by projecting them into a simple pyramid scale space, which is based on the study that methods with/without scale space have similar performance under small scale change factors. This strategy largely reduces the complexity compared to traditional Gaussian scale space. SRIF also proposes a local intensity binary transform (LIBT) for SIFT-like feature description, which can largely enhance the structure information inside multimodal images. Extensive experiments on these 1200 image pairs show that our SRIF outperforms current state-of-the-arts by a large margin, including RIFT, CoFSM, LNIFT, and MS-HLMO. Both the created dataset and the code of SRIF will be publicly available in https://github.com/LJY-RS/SRIF.
Optimized Point Set Representation for Oriented Object Detection in Remote-Sensing Images

How to represent the object more appropriately in oriented object detection is an essential problem to be solved, there are many solutions for the object represented. It is a relatively novel approach to represent objects as a number of sample points useful for both localization and recognition. However, the current point-set-based representation methods do not effectively supervise all points for learning, and the internal information of the convex hull in the point set cannot be effectively learned. Therefore, this letter proposes point set distance (PSD) loss, which learns set-to-set supervision of objects to effectively represent objects. Besides, most of the current sample selection strategies are based on the Intersection over Union (IoU), but these methods cannot comprehensively measure candidate samples quality. To select high-quality point sets, we propose to use the probability distribution of point sets to select the positive samples. Our probabilistic point set sample selection (PPSS) scheme effectively exploits the classification information, regression information, and the distribution characteristics of the point set. Experimental results on remote sensing image datasets including DOTA, DIOR-R, and HRSC2016, demonstrate the proposed method for arbitrary-oriented object detection achieves consistent and substantial improvements.
Understanding the Factors Influencing Higher Education Students’ Intention to Adopt Artificial Intelligence-Based Robots

Although there has been some progress, the integration of artificial intelligence into higher education remains far from sufficient. The demand for teachers will persist for some time; however, with the introduction of AI-based robots into classrooms, the role of teachers has reduced to a minimum. The purpose of the current study was to evaluate Chinese higher education students’ intentions to adopt AI-based robots for educational purposes. Based on the TAM3 model, the current study proposes 14 hypotheses to evaluate students’ intention to adopt AI-based robots in education. The students’ data were collected and analyzed using PLS-SEM. The study findings revealed that 12 hypotheses were accepted and two were rejected. The results indicate that students are willing to accept AI-based robots in their education. However, the findings revealed an insignificant influence of job relevance and robot anxiety on perceived usefulness and ease of use, respectively. The findings of this study will provide insight into university administrations regarding the significance of AI-based robots in education. Moreover, the findings will help robot developers, policy makers, and university administrators design and implement AI-based robots that fulfill contemporary education needs.
A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

In the last decade, users can access their applications, data, and services via the cloud from any location with an internet connection. The scale of heterogeneous cloud environments is continuously growing due to the development of computing-intensive smart devices. A data center is the central processing unit of any cloud environment, and it is made up of hardware-oriented machines known as Physical Machines (PMs) or server and software-oriented machines known as Virtual Machines (VMs). The deployment of a huge number of physical servers as a result of the exponential development in demand for cloud services has resulted in high energy consumption and ineffective resource usage. Efficient resource utilization and minimizing power consumption by any data center have become crucial challenges. Virtual machine consolidation(VMC) is a method of optimizing computing resources by consolidating multiple VMs onto a reduced number of PMs. By consolidating VMs and running fewer physical servers, VM consolidation can lead to reducing power consumption and efficient resource utilization. This review paper presents a comprehensive analysis of cloud computing virtual machine consolidation, exploring various strategies, benefits, challenges, and future trends in this domain. By examining a wide range of literature from the year 2015 to 2023, this review attempts to provide insight into the current state of VM consolidation and its possible effects on the performance and sustainability of cloud computing. The main flaw in the articles is that the various authors focused on different assessment metrics while the emphasis should have been on improving energy efficiency and quality of service for cloud systems. Future research can be aimed at developing a multi-objective system that emphasizes minimizing cloud energy usage without sacrificing service quality, preventing service level agreements with cloud users from being compromised.
An Improved Golden Jackal Optimization Algorithm Using Opposition-Based Learning for Global Optimization and Engineering Problems

Abstract Golden Jackal Optimization (GJO) is a recently developed nature-inspired algorithm that is motivated by the collaborative hunting behaviours of the golden jackals in nature. However, the GJO has the disadvantage of poor exploitation ability and is easy to get stuck in an optimal local region. To overcome these disadvantages, in this paper, an enhanced variant of the golden jackal optimization algorithm that incorporates the opposition-based learning (OBL) technique (OGJO) is proposed. The OBL technique is implemented into GJO with a probability rate, which can assist the algorithm in escaping from the local optima. To validate the efficiency of OGJO, several experiments have been performed. The experimental outcomes revealed that the proposed OGJO has more efficiency than GJO and other compared algorithms.
Coal and Gas Outburst Prediction Model Based on Miceforest Filling and PHHO–KELM

Coal and gas outbursts are some of the most serious coal mine disasters, and effective prediction of coal and gas outbursts can reduce the likelihood of accidents and fatalities. Previously conducted studies have established that machine learning has achieved results in the prediction of coal and gas outbursts, but there is a problem that the available accident data of coal and gas outbursts are diminished or missing. This paper proposes a prediction model based on multiple filling of chained equations for random forests (miceforest) and the Harris Hawk optimization algorithm with Piecewise chaos mapping (PHHO) to optimize the kernel extreme learning machine (KELM) to solve the problem of missing data in coal and gas outburst prediction and to improve prediction accuracy in the case of missing data. Firstly, the miceforest algorithm was adopted to fill missing values in the salient samples, and then the PHHO algorithm was used to optimize the parameters of KELM. Finally, the datasets before and after filling were input into the PHHO–KELM model for experimentation and comparison with other models. The results show that miceforest filling is effective in improving the salient sample accuracy and overall accuracy of predictions, but the improvement is not significant for non-salient samples. The use of the PHHO–KELM model can effectively avoid falling into a local optimum and further improve the prediction accuracy of the KELM algorithm. The salient sample accuracy and overall accuracy of the miceforest–PHHO–KELM model prediction are 96.77% and 98.50%. And an effective coal and gas outburst model has been proposed, which is the miceforest–PHHO–KELM model.
Applications of 3D scanning and digital image correlation in structural experiments

Abstract The primary aim of the present study is to explore the use, advantages and potential of two advanced data acquisition techniques – 3D scanning and digital image correlation (DIC), in the testing of metallic structures. 3D scanning is a novel method for capturing the full outer geometry of an object. It is being increasingly used in experimental studies for the determination of dimensional parameters and the characterisation of local and global imperfections, examples of which are given and discussed. Digital image correlation (DIC) is a rapidly evolving technology for the full‐field measurement of the displacements and strains over a surface. A brief introduction to DIC and its workflow is firstly provided. Example applications of DIC in various scales of experiments, including material tests, structural element tests and full‐scale structural system tests, are subsequently discussed. These new‐generation data acquisition techniques enable structural researchers to gain a deeper insight into the behaviour of metallic structures in experimental research.
A quest for research and knowledge gaps in cybersecurity awareness for small and medium-sized enterprises

The proliferation of information and communication technologies in enterprises enables them to develop new business models and enhance their operational and commercial activities. Nevertheless, this practice also introduces new cybersecurity risks and vulnerabilities. This may not be an issue for large organizations with the resources and mature cybersecurity programs in place; the situation with small and medium-sized enterprises (SMEs) is different since they often lack the resources, expertise, and incentives to prioritize cybersecurity. In such cases, cybersecurity awareness can be a critical component of cyberdefense. However, research studies dealing with cybersecurity awareness or related domains exclusively for SMEs are rare, indicating a pressing need for research addressing the cybersecurity awareness requirements of SMEs. Prior to that, though, it is crucial to identify which aspects of cybersecurity awareness require further research in order to adapt or conform to the needs of SMEs. In this study, we conducted a systematic literature review that focused on cybersecurity awareness, prioritizing those performed with a particular focus on SMEs. The study seeks to analyze and evaluate such studies primarily to determine knowledge and research gaps in the cybersecurity awareness field for SMEs, thus providing a direction for future research.
A secure and efficient AKE scheme for IoT devices using PUF and cancellable biometrics

As the Internet of Things (IoT) continues to grow, there is an increasing need for secure and efficient authentication protocols for IoT devices. The design of IoT authentication schemes requires several essential features such as real two-factor security, Key Compromise Impersonation (KCI) resilience, and Perfect Forward Secrecy (PFS). In this paper, we perform a cryptanalysis of two state-of-the-art PUF-based Authentication and Key Exchange(AKE) schemes and show informally how they fail to achieve real two-factor security, PFS and suffer KCI, user traceability, and database attacks. To address these limitations, we propose an improved two-party AKE scheme between the user-device and the server based on a combination of a physically unclonable function (PUF) and a cancellable biometric (CB). Our proposed scheme provides real two-factor security, KCI resilience, PFS, and user untraceability, achieving all desired properties based on the Computational Diffie–Hellman (CDH) assumption in the random oracle model. To establish the security of our proposed scheme, we utilize formal analysis methods, including the RoR model and ProVerif, and perform an informal security analysis to demonstrate its resilience against potential attacks. Additionally, we analyze the computational and communication performance of our proposed scheme against related PUF-based schemes. Our proposed scheme exhibits a computational and communicational feasible overhead for deployment in the context of the IoT, with a lower magnitude than most previous related PUF-based schemes. Overall, our proposed scheme provides an efficient and secure authentication solution for IoT devices, with improved security and reduced communication overhead compared to previous PUF-based schemes. The combination of formal and informal analysis provides robust evidence of the strength and reliability of our proposed scheme, and its scalability makes it a viable solution for large-scale IoT deployments.
Identification effect of least square fitting method in archives management

Archives management plays an important role in the current information age. Solving the problem of identifying and classifying archives is essential for promoting the development of archives management. The Least Squares Support Vector Machine (LS-SVM) is obtained by introducing the least squares fitting method into SVM, which is good at solving nonlinear classification. A new wavelet function is used to improve the classifier. At the same time, the cross-validation method is used to optimize the kernel parameters. Finally, the fuzzy theory and LS-SVM are combined to obtain Fuzzy Least Squares Support Vector Machines (FLS-SVM). This FLS-SVM classifier can use the distance between the data points and the classification hyperplane to classify the data in the non-separable region. The performance of FLS-SVM is verified by simulation experiments. The experimental results show that the classification accuracy of FLS-SVM classifier in archive data sets is 98.7%, and the loss rate is only 0.26%. When the wavelet function is used as the kernel function, the average accuracy of the classifier reaches 98.38%. Experiments show that the proposed method has good classification performance. It verifies the feasibility and effectiveness of the least squares fitting method in file management identification and classification.