Reviewerly/exHarmony huggingface dataset:
Pseudorandom Number Generator Based on Three Kinds of Four-Wing Memristive Hyperchaotic System and Its Application in Image Encryption

In this paper, we propose a method to design the pseudorandom number generator (PRNG) using three kinds of four-wing memristive hyperchaotic systems (FWMHSs) with different dimensions as multientropy sources. The principle of this method is to obtain pseudorandom numbers with good randomness by coupling XOR operation on the three kinds of FWMHSs with different dimensions. In order to prove its potential application in secure communication, the security of PRNG based on this scheme is analyzed from the perspective of cryptography. In addition, PRNG has passed the NIST 800.22 and ENT test, which shows that PRNG has good statistical characteristics. Finally, an image encryption algorithm based on PRNG is adopted. In the encryption algorithm, the optimized Arnold matrix scrambling method and the diffusion processing based on XOR are used to obtain the final encrypted image. Through the evaluation of encryption performance, it is concluded that there is no direct relationship between the pristine image and encrypted image. The results show that the proposed image encryption scheme has good statistical output characteristics and security performance in line with cryptography.
A metasedimentary source of gold in Archean orogenic gold deposits

Abstract The sources of metals enriched in Archean orogenic gold deposits have long been debated. Metasedimentary rocks, which are generally accepted as the main metal source in Phanerozoic deposits, are less abundant in Archean greenstone belts and commonly discounted as a viable metal source for Archean deposits. We report ultralow-detection-limit gold and trace-element concentrations from a suite of metamorphosed sedimentary rocks from the Abitibi belt and Pontiac subprovince, Superior Province, Canada. Systematic decreases in the Au content with increasing metamorphic grade indicate that Au was mobilized during prograde metamorphism. Mass balance calculations show that over 10 t of Au, 30,000 t of As, and 600 t of Sb were mobilized from 1 km3 of Pontiac subprovince sedimentary rock metamorphosed to the sillimanite metamorphic zone. The total gold resource in orogenic gold deposits in the southern Abitibi belt (7500 t Au) is only 3% of the Au mobilized from the estimated total volume of high-metamorphic-grade Pontiac sedimentary rock in the region (25,000 km3), indicating that sedimentary rocks are a major contributor of metals to the orogenic gold deposits in the southern Abitibi belt.
Efficient Semantic Image Synthesis via Class-Adaptive Normalization

Spatially-adaptive normalization (SPADE) is remarkably successful recently in conditional semantic image synthesis in T. Park et al. 2019 which modulates the normalized activation with spatially-varying transformations learned from semantic layouts, to prevent the semantic information from being washed away. Despite its impressive performance, a more thorough understanding of the advantages inside the box is still highly demanded to help reduce the significant computation and parameter overhead introduced by this novel structure. In this paper, from a return-on-investment point of view, we conduct an in-depth analysis of the effectiveness of this spatially-adaptive normalization and observe that its modulation parameters benefit more from semantic-awareness rather than spatial-adaptiveness, especially for high-resolution input masks. Inspired by this observation, we propose class-adaptive normalization (CLADE), a lightweight but equally-effective variant that is only adaptive to semantic class. In order to further improve spatial-adaptiveness, we introduce intra-class positional map encoding calculated from semantic layouts to modulate the normalization parameters of CLADE and propose a truly spatially-adaptive variant of CLADE, namely CLADE-ICPE. Through extensive experiments on multiple challenging datasets, we demonstrate that the proposed CLADE can be generalized to different SPADE-based methods while achieving comparable generation quality compared to SPADE, but it is much more efficient with fewer extra parameters and lower computational cost. The code and pretrained models are available at https://github.com/tzt101/CLADE.git.
Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images

Background Breast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. The genetic predisposition to breast cancer may be associated with a mutation in particular genes such as gene BRCA1/2. Patients who carry a germline pathogenic mutation in BRCA1/2 genes have a significantly increased risk of developing breast cancer and might benefit from targeted therapy. However, genetic testing is time consuming and costly. This study aims to predict the risk of gBRCA mutation by using the whole-slide pathology features of breast cancer H&amp;amp;E stains and the patients’ gBRCA mutation status. Methods In this study, we trained a deep convolutional neural network (CNN) of ResNet on whole-slide images (WSIs) to predict the gBRCA mutation in breast cancer. Since the dimensions are too large for slide-based training, we divided WSI into smaller tiles with the original resolution. The tile-based classification was then combined by adding the positive classification result to generate the combined slide-based accuracy. Models were trained based on the annotated tumor location and gBRCA mutation status labeled by a designated breast cancer pathologist. Four models were trained on tiles cropped at 5×, 10×, 20×, and 40× magnification, assuming that low magnification and high magnification may provide different levels of information for classification. Results A trained model was validated through an external dataset that contains 17 mutants and 47 wilds. In the external validation dataset, AUCs (95% CI) of DL models that used 40×, 20×, 10×, and 5× magnification tiles among all cases were 0.766 (0.763–0.769), 0.763 (0.758–0.769), 0.750 (0.738–0.761), and 0.551 (0.526–0.575), respectively, while the corresponding magnification slides among all cases were 0.774 (0.642–0.905), 0.804 (0.676–0.931), 0.828 (0.691–0.966), and 0.635 (0.471–0.798), respectively. The study also identified the influence of histological grade to the accuracy of the prediction. Conclusion In this paper, the combination of pathology and molecular omics was used to establish the gBRCA mutation risk prediction model, revealing the correlation between the whole-slide histopathological images and gRCA mutation risk. The results indicated that the prediction accuracy is likely to improve as the training data expand. The findings demonstrated that deep CNNs could be used to assist pathologists in the detection of gene mutation in breast cancer.
Hyperledger Fabric Access Control System for Internet of Things Layer in Blockchain-Based Applications

Blockchain-based applications are gaining traction in various application fields, including supply chain management, health care, and finance. The Internet of Things (IoT) is a critical component of these applications since it allows for data collection from the environment. In this work, we integrate the Hyperledger Fabric blockchain and IoT devices to demonstrate the access control and establish the root of trust for IoT devices. The Hyperledger Fabric is designed to be secure against unwanted access and use through encryption protocols, access restrictions, and cryptography algorithms. An attribute-based access control (ABAC) mechanism was created using Hyperledger Fabric components only to gain access to the IoT device. Single board computers based on the ARM architecture are becoming increasingly powerful and popular in automation applications. In this study, the Raspberry Pi 4 Model B based on ARM64 architecture is used as the IoT device. Because the ARM64 architecture is not supported by default, we build executable binaries and Docker images for the ARM64 architecture, using the Hyperledger Fabric source code. On an IoT device, we run the fabric node in native mode to evaluate the executable binaries generated for the ARM64 architecture. Through effective chaincode execution and testing, we successfully assess the Hyperledger fabric blockchain implementation and access control mechanism on the ARM64 architecture.
Fractional-order comprehensive learning marine predators algorithm for global optimization and feature selection

The topological structure of the search agents in the swarm is a key factor in diversifying the knowledge between the population and balancing the designs of the exploration and intensification stages. Marine Predator Algorithm (MPA) is a recently introduced algorithm that mimics the interaction between the prey and predator in ocean. MPA has a vital issue in its structure. This drawback related to the number of iterations that is divided into the algorithm phases, hence the agents do not have the adequate number of tries to discover the search landscape and exploit the optimal solutions. This situation affects the search process. Therefore, in this paper, the principle of the comprehensive learning strategy and memory perspective of the fractional calculus have been incorporated into MPA. They help to achieve an efficient sharing for the best knowledge and the historical experiences between the agents with the aim of escaping from the local solutions and avoiding the immature convergence. The developed fractional-order comprehensive learning MPA (FOCLMPA) has been examined with several multidimensional benchmarks from the CEC2017 and CEC2020 as challenging tested functions in the numerical validation part. For real-world applications, four engineering problems have been employed and a set of eighteen UCI datasets have been used to demonstrate the developed performance for feature selection optimization problem. The FOCLMPA has been compared with several well-regarded optimization algorithms via numerous statistical and non-parametric analyses to provide unbiased recommendation. The comparisons confirm the superiority and stability of FOCLMPA in handling the series of experiments with high qualified results and remarkable convergence curves.
Scalable Detection and Tracking of Geometric Extended Objects

Multiobject tracking provides situational awareness that enables new applications for modern convenience, public safety, and homeland security. This paper presents a factor graph formulation and a particle-based sum-product algorithm (SPA) for scalable detection and tracking of extended objects. The proposed method dynamically introduces states of newly detected objects, efficiently performs probabilistic multiple-measurement to object association, and jointly infers the geometric shapes of objects. Scalable extended object tracking (EOT) is enabled by modeling association uncertainty by measurement-oriented association variables and newly detected objects by a Poisson birth process. Contrary to conventional EOT methods, a fully particle-based approach makes it possible to describe different geometric object shapes. The proposed method can reliably detect, localize, and track a large number of closely-spaced extended objects without gating and clustering of measurements. We demonstrate significant performance advantages of our approach compared to the recently introduced Poisson multi-Bernoulli mixture filter. In particular, we consider a simulated scenarios with up to twenty closely-spaced objects and a real autonomous driving application where measurements are captured by a lidar sensor.
Intrusion Detection Based on Privacy-Preserving Federated Learning for the Industrial IoT

Federated learning (FL) has attracted significant interest given its prominent advantages and applicability in many scenarios. However, it has been demonstrated that sharing updated gradients/weights during the training process can lead to privacy concerns. In the context of the Internet of Things (IoT), this can be exacerbated due to intrusion detection systems (IDSs), which are intended to detect security attacks by analyzing the devices’ network traffic. Our work provides a comprehensive evaluation of differential privacy techniques, which are applied during the training of an FL-enabled IDS for industrial IoT. Unlike previous approaches, we deal with nonindependent and identically distributed data over the recent ToN_IoT dataset, and compare the accuracy obtained considering different privacy requirements and aggregation functions, namely FedAvg and the recently proposed Fed+. According to our evaluation, the use of Fed+ in our setting provides similar results even when noise is included in the federated training process.
Ideological and Political Education Recommendation System Based on AHP and Improved Collaborative Filtering Algorithm

Aiming to solve the problem that ideological and political education courses in universities are not targeted enough and cannot form personalized recommendations, this paper proposes an ideological and political education recommendation system based on analytic hierarchy process (AHP) and improved collaborative filtering algorithm. Firstly, considering the time effect of student scoring, the recommendation model is transformed into Markov decision process. Then, by combining the collaborative filtering algorithm with reinforcing learning rewards and punishments, an optimization model of student scoring based on timestamp information is constructed. To quantify the degree of students' preference for courses, the analytic hierarchy process is used to convert the students' behavior data into the preference value of courses. To solve the problem of data scarcity, the missing values are predicted by the prediction score rounding filling and the optimization boundary completion method. Experimental results show that the feasibility of the proposed system is verified, and the system has vital accuracy and convergence performance. The ideological and political education recommendation system proposed in this paper has important reference significance for promoting ideological and political education in the era of big data.
Research on Robot Path Perception and Optimization Technology Based on Whale Optimization Algorithm

With the development of modern sensor technology, the automatic movement of robot has become a reality, and improving the path planning performance of robot in dynamic and complex environment is an important development direction of mobile robot intelligence. This time, based on the idea of hybrid path planning, whale optimization algorithm and computer perception technology are introduced to plan the path of the robot. In order to improve the performance of the traditional whale optimization algorithm, it is optimized and improved by genetic algorithm. Through the performance simulation analysis of the improved whale optimization algorithm, it can be seen that the improved algorithm has better path optimization performance and significantly improved efficiency compared with other traditional algorithms. It can perceive and calculate an optimized path.
Current Trends of Big Data Research Using the Korean National Health Information Database

Recently, medical research using big data has become very popular, and its value has become increasingly recognized. The Korean National Health Information Database (NHID) is representative of big data that combines information obtained from the National Health Insurance Service collected for claims and reimbursement of health care services and results obtained from general health examinations provided to all Korean adults. This database has several strengths and limitations. Given the large size, various laboratory data, and questionnaires obtained from medical check-ups, their longitudinal nature, and long-term accumulation of data since 2002, carefully designed studies may provide valuable information that is difficult to obtain from other forms of research. However, consideration of possible bias and careful interpretation when defining causal relationships is also important because the data were not collected for research purposes. After the NHID became publicly available, research and publications based on this database have increased explosively, especially in the field of diabetes and metabolism. This article reviews the history, structure, and characteristics of the Korean NHID. Recent trends in big data research using this database, commonly used operational diagnosis, and representative studies have been introduced. We expect further progress and expansion of big data research using the Korean NHID.
AEA-NCS: An audio encryption algorithm based on a nested chaotic system

Audio information strongly correlates in adjacent times, and the data type of the audio is float, so the traditional encryption algorithms for the image are unsuitable for audio encryption. This paper proposes an audio encryption algorithm based on Chaos, named AEA-NCS. Most 1D maps have a control parameter, and the parameter space in the chaotic state is small. Therefore, a 2D-Logistic-nested-infinite-collapse (2D-LNIC) is proposed by combining an infinite collapse map (1D-ICM) and a logistic map. There are two control parameters in 2D-LNIC, and it exhibits good chaotic performance through the Lyapunov exponent and attractor phase diagram . In the audio encryption algorithm, 2D-LNIC generates the keystream, and the encryption algorithm is a process of scrambling and diffusion simultaneously. This structure increases the security of the algorithm. We evaluate AEA-NCS in ESC-50, and the evaluation results show that AEA-NCS exhibits good performance, significantly reducing the correlation of audio information in adjacent times. • A 2D-LNIC is proposed and its dynamic behavior is analyzed. • An audio encryption algorithm, named AEA-NCS is proposed based on 2D-LNIC. • Experimental analysis show that AEA-NCS exhibits good performance.
Multi-level feature fusion for multimodal human activity recognition in Internet of Healthcare Things

Human Activity Recognition (HAR) has become a crucial element for smart healthcare applications due to the fast adoption of wearable sensors and mobile technologies. Most of the existing human activity recognition frameworks deal with a single modality of data that degrades the reliability and recognition accuracy of the system for heterogeneous data sources. In this article, we propose a multi-level feature fusion technique for multimodal human activity recognition using multi-head Convolutional Neural Network (CNN) with Convolution Block Attention Module (CBAM) to process the visual data and Convolutional Long Short Term Memory (ConvLSTM) for dealing with the time-sensitive multi-source sensor information. The architecture is developed to be able to analyze and retrieve channel and spatial dimension features through the use of three branches of CNN along with CBAM for visual information. The ConvLSTM network is designed to capture temporal features from the multiple sensors’ time-series data for efficient activity recognition. An open-access multimodal HAR dataset named UP-Fall detection dataset is utilized in experiments and evaluations to measure the performance of the developed fusion architecture. Finally, we deployed an Internet of Things (IoT) system to test the proposed fusion network in real-world smart healthcare application scenarios. The findings from the experimental results reveal that the developed multimodal HAR framework surpasses the existing state-of-the-art methods in terms of multiple performance metrics.
Classifier Personalization for Activity Recognition Using Wrist Accelerometers

Intersubject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this paper, we propose an approach for personalizing classification rules to a single person. We demonstrate that the method improves activity detection from wrist-worn accelerometer data on a four-class recognition problem of interest to the exercise science community, where classes are ambulation, cycling, sedentary, and other. We extend a previously published activity classification method based on support vector machines so that it estimates classification uncertainty. Uncertainty is used to drive data label requests from the user, and the resulting label information is used to update the classifier. Two different datasets- one from 33 adults with 26 activity types, and another from 20 youth with 23 activity types-were used to evaluate the method using leave-one-subject-out and leave-one-groupout cross validation. The new method improved overall recognition accuracy up to 11% on average, with some large person-specific improvements (ranging from -2% to +36%). The proposed method is suitable for online implementation supporting real-time recognition systems.
Using 360° virtual reality as a decision-making assessment tool in sport

Objectives To examine the reliability, construct validity and ecological validity of 360° VR and match broadcast footage for off-field decision-making assessment in Australian football umpires. Design Validation assessments with test re-test reliability. Methods Two video-based tests of 60 clips each were developed to assess Australian football umpire decision-making, including 360° video of small-sided Australian football games and match broadcast footage of AFL games. Elite (n = 13) and amateur (n = 15) umpires participated in two testing sessions, in a randomised, counterbalanced design. Test re-test reliability was assessed using Cohen’s Kappa for individual clips and Intraclass Correlation Coefficients for test scores. Video tests were assessed for construct validity. Ecological validity of the decision-making processes was assessed for each method. Results 31 clips met the minimum Kappa criteria for the 360° VR test and 28 clips for match broadcast. Results indicated strong reliability for the 360° VR (ICC = 0.89) and match broadcast (ICC = 0.89) tests. For both video modes, elite umpires performed significantly better in decision-making accuracy than amateur (p < 0.05). For ecological validity of the decision-making processes, 360° VR was rated significantly higher than match broadcast vision (p < 0.05) overall. Conclusions This is the first study to examine the reliability and validity of 360° VR footage as an off-field decision-making assessment tool in sport. As match broadcast vision is commonly used to assess decision-making in athletes and officials, results suggest that 360° VR is also an appropriate assessment tool. Although both video modes demonstrate similar reliability and construct validity, 360° VR was considered more specific to in-game decision-making processes, suggesting stronger ecological validity.
Solving a dynamic assignment problem in the socio-economic system

The deterministic variant of the dynamic assignment problem is considered. The task of finding the path of maximum cost is investigated. Examples of solution the dynamic assignment problem and solving the problem of finding the path of maximum cost are given.