Reviewerly/exHarmony huggingface dataset:
Self-Supervised Pre-Training Joint Framework: Assisting Lightweight Detection Network for Underwater Object Detection

In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately.
Statistical Analysis of Professors’ Assessment Regarding the Didactic Use of Virtual Reality: Engineering vs. Health

Virtual reality (VR) has proven to be an efficient didactic resource in higher education after the pandemic caused by COVID-19, mainly in the Engineering and Health Sciences degrees. In this work, quantitative research is carried out on the assessments made by Latin American professors of Health Sciences and Engineering of the didactic use of VR. Specifically, the gaps by university tenure in the assessments given by the professors of each of the two areas of knowledge analyzed are identified. For this purpose, a validated questionnaire has been used, which has been applied to a sample of 606 professors. As a result, it is shown that the professors of Engineering and Health Sciences have similar self-concepts of their digital competence, but the Engineering professors give higher values to the technical and didactic aspects of VR. Moreover, in both areas, professors from private universities rate VR technologies more highly than those from public universities, this gap being wider in Health Sciences. Finally, some recommendations are offered regarding digital training and the use of VR, derived from the results of this study.
Swarm Cooperative Navigation Using Centralized Training and Decentralized Execution

The demand for autonomous UAV swarm operations has been on the rise following the success of UAVs in various challenging tasks. Yet conventional swarm control approaches are inadequate for coping with swarm scalability, computational requirements, and real-time performance. In this paper, we demonstrate the capability of emerging multi-agent reinforcement learning (MARL) approaches to successfully and efficiently make sequential decisions during UAV swarm collaborative tasks. We propose a scalable, real-time, MARL approach for UAV collaborative navigation where members of the swarm have to arrive at target locations at the same time. Centralized training and decentralized execution (CTDE) are used to achieve this, where a combination of negative and positive reinforcement is employed in the reward function. Curriculum learning is used to facilitate the sought performance, especially due to the high complexity of the problem which requires extensive exploration. A UAV model that highly resembles the respective physical platform is used for training the proposed framework to make training and testing realistic. The scalability of the platform to various swarm sizes, speeds, goal positions, environment dimensions, and UAV masses has been showcased in (1) a load drop-off scenario, and (2) UAV swarm formation without requiring any re-training or fine-tuning of the agents. The obtained simulation results have proven the effectiveness and generalizability of our proposed MARL framework for cooperative UAV navigation.
Major Determinants of Innovation Performance in the Context of Healthcare Sector

Through the innovation network (IN) and the use of artificial intelligence (AI), this study aims to look into the innovation performance (IP) of the healthcare industry. Digital innovation (DI) is also tested as a mediator. For the collection of data, cross-sectional methods and quantitative research designs were used. To test the study hypotheses, the SEM technique and multiple regression technique were used. Results reveal that AI and the innovation network support the attainment of innovation performance. The finding demonstrates that the relationship between INs and IP links and AI adoption and IP links is mediated through DI. The healthcare industry plays a vital role in facilitating public health and improving the living standards of the people. This sector's growth and development are largely dependent on its innovativeness. This study highlights the major determinants of IP in the healthcare industry in terms of IN and AI adoption. This study adds to the literature's knowledge via an innovative proposal in which the mediation role of DI among IN-IP and AI adoption-innovation links is investigated.
Underwater Image Enhancement Based on the Improved Algorithm of Dark Channel

Enhancing underwater images presents a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore underwater images. However, they tend to degrade the underwater images, interfere with background clutter and miss the boundary details of blue regions. An improved image fusion and enhancement algorithm based on a prior dark channel is proposed in this paper based on graph theory. Image edge feature sharpening, and dark detail enhancement by homomorphism filtering in CIELab colour space are realized. In the RGB colour space, the multi-scale retinal with colour restoration (MSRCR) algorithm is used to improve colour deviation and enhance colour saturation. The contrast-limited adaptive histogram equalization (CLAHE) algorithm defogs and enhances image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by the linear fusion of multiple images and channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various data sets.
Anomaly Detection of High-Frequency Sensing Data in Transportation Infrastructure Monitoring System Based on Fine-Tuned Model

Anomaly detection has been widely studied in previous studies during recent decades; however, there are still some challenges for high-frequency sensing data. The most challenging task is to deal with a large volume of data in an extremely short time. In previous studies, it has been proved that converting the data into pictures can improve the speed of anomaly detection. However, the training of the image recognition algorithms, such as deep learning models, still needs a long time. Fortunately, the fine-tuned convolutional neural networks (CNNs) give us opportunities to detect anomalies in high-frequency data quickly. Thus, a four-stage model is proposed for anomaly detection in high-frequency data. Using a real-world dataset, one designed CNN, four widely used fine-tuned CNN, and two popular machine learning methods are compared by the confusion matrix. Moreover, three ensemble methods are proposed to improve the accuracy of the detection. The results show that the majority of voters can improve the overall accuracy by 2.09%. Significantly, the accuracy of minor and outlier classes can be increased by ensemble learning which is a challenge in practice.
A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data

For the practical application of a classifier, it is necessary to select an optimal output probability threshold to obtain the best classification results. There are many criteria one may employ to select a threshold. However, selecting a threshold will often involve trading off performance in terms of one metric for performance in terms of another metric. In our literature review of studies involving selecting thresholds to optimize classification of imbalanced data, we find there is an opportunity to expand on previous work for an in-depth study of threshold selection. Our contribution is to present a systematic method for selecting the best threshold value for a given classification task and its desired performance constraints. Just as a machine learning algorithm is optimized on some training data set, we demonstrate how a user-defined set of performance metrics can be utilized to optimize the classification threshold. In this study we use four popular metrics to optimize thresholds: precision, Matthews’ Correlation Coefficient, f-measure and geometric mean of true positive rate, and true negative rate. Moreover, we compare classification results for thresholds optimized for these metrics with the commonly used default threshold of 0.5, and the prior probability of the positive class (also known as the minority to majority class ratio). Our results show that other thresholds handily outperform the default threshold of 0.5. Moreover, we show that the positive class prior probability is a good benchmark for finding classification thresholds that perform well in terms of multiple metrics.
A Novel Approach for Unsupervised Learning of Highly-Imbalanced Data

Typical fraud datasets lack consistent and accurate labels and, as such, are typically highly imbalanced with non-fraud examples greatly outnumbering the fraudulent ones. This presents significant challenges to machine learning researchers and practitioners. Due to these challenges, an effective approach in identifying fraudulent data points needs to handle highly-imbalanced datasets and be robust to class labeling. This paper introduces a novel unsupervised procedure for learning from imbalanced datasets without class labels by iteratively cleaning the training dataset. Our methodology uses an autoencoder as an underlying learner. We describe its fraud detection performance and compare it to a baseline unsupervised fraud detection learner. Our results show that our procedure significantly outperforms the baseline, in both AUC and TPR, when testing on a publicly available highly-imbalanced credit card fraud detection dataset.
Spatial Temporal Block Transformer Network for Skeleton-Based Action Recognition

Recently, skeleton-based action recognition has be-come very popular in areas such as autonomous driving and environmental awareness due to its small data size and high ac-curacy. Graph convolutional networks (GCN) can model skeleton data well by adapting to the graph structure of the skeleton. However, GCN lacks the ability to extract global spatial and temporal dependencies. Moreover, an action is not only formed by one single skeleton point or frame but several skeleton points and frames. In this paper, a novel spatial-temporal block transformer network based on the self-attention mechanism is proposed, which efficiently models global spatial-temporal dependencies. The model uses spatial-temporal blocks as input, and spatial-temporal feature aggregation modules are applied to enhance the information exchange between blocks. Extensive experiments on the two skeleton-based action recognition datasets show the remarkable performance of our model, which outperforms most GCN-based methods and all transformer-based methods.
Artificial Intelligence Model for Parkinson Disease Detection Using Machine Learning Algorithms

In order for Parkinson's disease (PD) treatment and examination to be logical, a key requirement is that estimates of disease stage and severity are quantitative, reliable, and repeatable. The PD research in the past 50 years has been overwhelmed by the subjective emotional evaluation of human's understanding of disease characteristics during clinical visits. The Parkinson's disease data set contains 23 features and 197 instances, of which 8 patients are sound and 23 patients, are analyzed as PD patients. Relying on chi2 test, extra trees classifier, and correlation matrix as feature extraction strategies and relying on Decision Trees, K-Nearest Neighbors, Random Forests, Bagging, AdaBoosting, and Gradient Boosting as supervised AI calculations for permutation calculations. The calculation is based to obtain higher classifier accuracy, as well as ROC curves accuracy. Three conspicuous component selection strategies allow each of the 23 features to select 10 best performing features. The DT classifier has a higher accuracy of 94.87% in a dataset with 23 attributions, just like a dataset with 11 features. These results are also checked by ROC curve (AUC = 98.7%). This calculation significantly separates PD patients from patients at the individual level, thus ensuring the use of computer-based findings in clinical practice.
A Privacy-Preserving Attribute-Based Authenticated Key Management Scheme for Accountable Vehicular Communications

In recent years, the vehicular ad hoc network (VANET), recognized as the fundamental infrastructure of the intelligent transportation system (ITS), has played an increasingly momentous role in advancing real-time vehicular communications. With the purpose of achieving seamless and reliable connectivity, lots of research efforts have been witnessed. Nevertheless, the flexibility of access control and authenticated key management (AKM) has not been properly guaranteed. Particularly, exclusive V2V connections without third-party intervention or remote surveillance are crucial for privacy protection, while the non-repudiation and authenticity properties may be compromised in this case. In the proposed design, the attribute-based secret sharing scheme is adopted so that the flexible authentication mechanism with a dynamic access policy is provided. Moreover, independent V2V connections with anonymity are achieved. The vital vehicular data are autonomously stored in local storage, while the remote trusted authority (TA) maintains its ciphered form simultaneously. Therefore, repudiation on the historical transmissions can be prevented whenever a dispute occurs. The proofs and discussions regarding vital security features are presented, while the performance analysis follows. Advantages in terms of security and performance properties can be proved compared with the state-of-the-art.
Post Quantum Lattice-Based Secure Framework using Aggregate Signature for Ambient Intelligence Assisted Blockchain-Based IoT Applications

Many classical cryptographic techniques are breakable due to the quantum computing security threats, and it leads to design public key cryptography based on post-quantum cryptography primitives and security protocols. In recent years, Lattice-Based Cryptography (LBC) becomes a prominent post-quantum cryptographic primitive that can be applied in both traditional and emerging security domains, including encryption, key agreement, digital signature and homomorphic encryption. In this article, we first provide a LBC-based security framework using aggregate signature that can be applied in ambient intelligence-assisted blockchain-based Internet of Things (IoT) applications, called LAS-AIBIoT. In LAS-AIBIoT, the wearable/medical devices deployed in the patients' body securely send the sensing secret data (encrypted messages) with their respective lattice-based signatures to their nearby controller nodes (CN), where the CNs forward these secret messages to the attached aggregator node (Aggr). Each Aggr verifies the individual signature of the devices and constructs the aggregate signature on the received secret messages and signatures, and sends the aggregated secret messages with their aggregate signature to the cloud server(s) for block construction in the blockchain center. Through the consensus protocol, the block is then mined and added into the blokchain. We show the robustness of LAS-AIBIoT against various potential attacks including quantum computing security threats through the threat model discussed in this article. Finally, through the blockchain-based simulation study we show that LAS-AIBIoT can be applied for real-time ambient intelligence-assisted IoT applications.
Contrastive learning-based general Deepfake detection with multi-scale RGB frequency clues

Deepfake is a type of image and video face manipulation methods which could cause security and society threats. Although some related databases and detection models have been proposed for detecting face forgery media, achieving a generalizable detector for both known and unknown manipulations remains challenging. In this study, a novel deepfake detection model with high generalizability is proposed to tackle this issue. We employ supervised contrastive learning to enhance the generalizability to unknown manipulations and datasets. In addition, we design a cross-modality data augmentation method by combining SRM and RGB features to extract detection clues comprehensively. Furthermore, we propose a multi-scale feature enhancement module to enhance textural and semantic information. Extensive experiments have demonstrated that our method improves model generalization in both intra- and cross- dataset scenarios.
SoK: Modeling for Large S-boxes Oriented to Differential Probabilities and Linear Correlations

Automatic methods for differential and linear characteristic search are well-established at the moment. Typically, the designers of novel ciphers also give preliminary analytical findings for analysing the differential and linear properties using automatic techniques. However, neither MILP-based nor SAT/SMT-based approaches have fully resolved the problem of searching for actual differential and linear characteristics of ciphers with large S-boxes. To tackle the issue, we present three strategies for developing SAT models for 8-bit S-boxes that are geared toward differential probabilities and linear correlations. While these approaches cannot guarantee a minimum model size, the time needed to obtain models is drastically reduced. The newly proposed SAT model for large S-boxes enables us to establish that the upper bound on the differential probability for 14 rounds of SKINNY-128 is 2−131, thereby completing the unsuccessful work of Abdelkhalek et al. We also analyse the seven AES-based constructions C1 - C7 designed by Jean and Nikolić and compute the minimum number of active S-boxes necessary to cause an internal collision using the SAT method. For two constructions C3 and C5, the current lower bound on the number of active S-boxes is increased, resulting in a more precise security analysis for these two structures.
AAA interpolation of equispaced data

Abstract We propose AAA rational approximation as a method for interpolating or approximating smooth functions from equispaced samples. Although it is always better to approximate from large numbers of samples if they are available, whether equispaced or not, this method often performs impressively even when the sampling grid is coarse. In most cases it gives more accurate approximations than other methods. We support this claim with a review and discussion of nine classes of existing methods in the light of general properties of approximation theory as well as the “impossibility theorem” for equispaced approximation. We make careful use of numerical experiments, which are summarized in a sequence of nine figures. Among our new contributions is the observation, summarized in Fig. 7, that methods such as polynomial least-squares and Fourier extension may be either exponentially accurate and exponentially unstable, or less accurate and stable, depending on implementation.
Hyperspectral Video Tracker Based on Spectral Deviation Reduction and a Double Siamese Network

The advent of hyperspectral cameras has popularized the study of hyperspectral video trackers. Although hyperspectral images can better distinguish the targets compared to their RGB counterparts, the occlusion and rotation of the target affect the effectiveness of the target. For instance, occlusion obscures the target, reducing the tracking accuracy and even causing tracking failure. In this regard, this paper proposes a novel hyperspectral video tracker where the double Siamese network (D-Siam) forms the basis of the framework. Moreover, AlexNet serves as the backbone of D-Siam. The current study also adopts a novel spectral–deviation-based dimensionality reduction approach on the learned features to match the input requirements of the AlexNet. It should be noted that the proposed dimensionality reduction method increases the distinction between the target and background. The two response maps, namely the initial response map and the adjacent response map, obtained using the D-Siam network, were fused using an adaptive weight estimation strategy. Finally, a confidence judgment module is proposed to regulate the update for the whole framework. A comparative analysis of the proposed approach with state-of-the-art trackers and an extensive ablation study were conducted on a publicly available benchmark hyperspectral dataset. The results show that the proposed tracker outperforms the existing state-of-the-art approaches against most of the challenges.
Blockchain-Driven Image Encryption Process with Arithmetic Optimization Algorithm for Security in Emerging Virtual Environments

The real world is bounded by people, hospitals, industries, buildings, businesses, vehicles, cognitive cities, and billions of devices that offer various services and interact with the world. Recent technologies, including AR, VR, XR, and the digital twin concept, provide advanced solutions to create a new virtual world. Due to the ongoing development of information communication technologies and broadcast channels, data security has become a major concern. Blockchain (BC) technology is an open, decentralized, and transparent distributed database that can be maintained by the group. BC’s major features are high credibility, decentralization, transparency, versatility, autonomy, traceability, anonymity, intelligence, reward mechanisms, and irreversibility. This study presents a blockchain-driven image encryption technique using arithmetic optimization with a fractional-order Lorenz system (BDIE-AOFOLS). The BDIE-AOFOLS technique uses the FOLS method, which integrates the Arnold map, tent map, and fractional Lorenz system. Besides this, an arithmetic optimization algorithm (AOA) was carried out for the optimum key generation process to achieve the maximum PSNR value. The design of an AOA-based optimal generation of keys for the FOLS technique determines the novelty of the current work. Moreover, the cryptographical pixel values of the images can be stored securely in the BC, guaranteeing image security. We compared the outcomes of the proposed BDIE-AOFOLS technique against benchmark color images. The comparative analysis demonstrated the improved security efficiency of the BDIE-AOFOLS technique over other approaches, with a mean square error of 0.0430 and a peak signal-to-noise ratio of 61.80 dB.