Reviewerly/exHarmony huggingface dataset:
The Overview of Elliptic Curve Cryptography (ECC)

Abstract Elliptic Curve Cryptography (ECC) is one of the strongest and most efficient cryptographic techniques in modern cryptography. This paper gives the following introduction: The introduction of cryptography’s development; the introduction of the elliptic curve; the principle of ECC; the horizontal comparison between ECC and other types of cryptography; the modern breakthrough of ECC; the applications of ECC; by using a method of literature review. The study’s findings indicate that this factor is responsible for the rapid historical development of cryptography, from the classical password to the leap to modern cryptography. Elliptic Curve Cryptography (ECC), as one of the most important modern cryptographies, is stronger than most other cryptographies both in terms of security and strength, because it uses an elliptic curve to construct and, at the same time, uses mathematical operations to encrypt and generate keys. At the same time, elliptic curve cryptography can continue to improve the speed and intensity with the improvement of accelerators, scalar multiplication, and the speed of order operation. The applications of the elliptic curve in ECDSA and SM2 are very efficient, which further illustrates the importance of elliptic curve cryptography.
AUV Path Planning in Dynamic Environment Based on Improved Artificial Potential Field Method Based on Visibility Graph

In order to solve the forced shaking problem of the Autonomous Underwater Vehicle (AUV) facing dynamic obstacles in the path planning when the Artificial Potential Field Method is used, a method of removing the shaking state based on a favorable path is proposed, and on this basis, Visibility Graph Method is used to optimize the path of the AUV to avoid dynamic obstacles. By judging the relative motion direction of AUV and dynamic obstacle, the moving direction of AUV is adjusted based on Visibility Graph Method when it is far away from the obstacle. When it is close to the obstacle, the direction conducive to approaching the target point is selected as the favorable forward direction to bypass the obstacle. The simulation results show that the dynamic obstacle avoidance path optimization method based on the Visibility Graph Method reduces the number of moving steps by about 19% compared with the traditional method when dealing with a single dynamic obstacle, and can be applied in complex environments with multiple dynamic obstacles.
Ex-Cit XR: Expert-elicitation and validation of Extended Reality visualisation and interaction techniques for disengaging and transitioning users from immersive virtual environments

This research explores visualisation and interaction techniques to disengage users from immersive virtual environments (IVEs) and transition them back to the Augmented Reality mode in the real world. To gain a better understanding and novel ideas, we invited eleven Extended Reality (XR) experts to participate in an elicitation study to design such techniques for disengagement. From the elicitation study, we elicited a total of 132 techniques for four different scenarios of IVEs: Narrative-driven, Social-platform, Adventure Sandbox, and Fast-paced Battle experiences. Through extracted keywords and thematic analysis, we classified the elicited techniques into six categories of Activities, Breaks, Cues, Degradations, Notifications, and Virtual Agents. We shared our analyses on users’ intrinsic motivation to engage in different experiences, subjective ratings of four design attributes in designing the disengagement techniques, Positive and Negative Affect Schedules, and user preference. In addition, we gave the design patterns found and illustrated the exemplary user cases of Ex-Cit XR. Finally, we conducted an online survey to preliminarily validate our design recommendations. We proposed the SPINED behavioural manipulation spectrum for XR disengagement to guide how the systems can strategically escalate to disengage users from an IVE.
A Multiscale Fusion YOLOV3-Based Model for Human Abnormal Behavior Detection in Special Scenarios

Urban public security incidents are prone to occur. Better understanding of pedestrian abnormal behavior and trajectory in crowded places is conducive to crowd management and safety monitoring. A novel pedestrian abnormal behavior detection model (PABDM) is proposed to identify crowd behavior under abnormal scenarios. This model originated from a multiscale fusion you only look once (YOLO) version 3 (V3) algorithm and was trained using the PASCAL visual object classes (VOC) in combination with an abnormal pedestrian data set (APD), denoted as VOC+APD. Compared with YOLOV3-VOC, single-stage detectors (SSD)-VOC, and SSD-VOC+APD, the proposed model has notable advantages in prediction accuracy and detection efficiency. The results show that the network loss function of the model tends to be stable after 500 epochs, and its detection accuracy is 6% higher than the average accuracy of the compared models. This proposed model also effectively solves the problem of missing detection caused by edge target, fuzzy target, and small target in abnormal state human detection. The research results are of great significance for real-time crowd monitoring in complex scenes.
Double-function enhancement algorithm for low-illumination images based on retinex theory

In order to solve the problems of noise amplification and excessive enhancement caused by low contrast and uneven illumination in the process of low-illumination image enhancement, a high-quality image enhancement algorithm is proposed in this paper. First, the total-variation model is used to obtain the smoothed V- and S-channel images, and the adaptive gamma transform is used to enhance the details of the smoothed V-channel image. Then, on this basis, the improved multi-scale retinex algorithms based on the logarithmic function and on the hyperbolic tangent function, respectively, are used to obtain different V-channel enhanced images, and the two images are fused according to the local intensity amplitude of the images. Finally, the three-dimensional gamma function is used to correct the fused image, and then adjust the image saturation. A lightness-order-error (LOE) approach is used to measure the naturalness of the enhanced image. The experimental results show that compared with other classical algorithms, the LOE value of the proposed algorithm can be reduced by 79.95% at most. Compared with other state-of-the-art algorithms, the LOE value can be reduced by 53.43% at most. Compared with some algorithms based on deep learning, the LOE value can be reduced by 52.13% at most. The algorithm proposed in this paper can effectively reduce image noise, retain image details, avoid excessive image enhancement, and obtain a better visual effect while ensuring the enhancement effect.
Multi-wavelength dual-polarization optical unitary processor using integrated multi-plane light converter

Abstract An optical unitary processor (OUP) is a programmable photonic circuit to achieve arbitrary unitary operation for various applications, including optical communication, deep learning, and quantum computing. Conventionally, OUPs are implemented by cascading 2 × 2 reconfigurable interferometers, but this scheme cannot easily be extended to multiple wavelength and polarization channels due to the strict requirement to employ 50:50 beam splitters. Here, we demonstrate that an OUP using multiport directional couplers (DCs) can realize independent unitary conversion of multiple wavelength and polarization channels simultaneously. This OUP is based on the multi-plane light conversion (MPLC) principle, which does not require a specific transformation at each layer, unlike the conventional scheme. Thanks to this unique robustness of the MPLC method and strong wavelength/polarization dependence of multiport DCs, we numerically show that independent unitary transformations can be applied to up to 16 channels (2 polarization × 4 wavelengths) using a single device.
Deep Learning-Based Modulation Recognition for Low Signal-to-Noise Ratio Environments

Automatic modulation classification (AMC), which plays a significant role in wireless communication, can recognize the modulation type of the received signal without large amounts of transmitted data and parameter information. Supported by deep learning, which is a powerful tool for functional expression and feature extraction, the development of AMC can be greatly promoted. In this paper, we propose a deep learning-based modulation classification method with 2D time-frequency signal representation. In our proposed method, signals which have been received are first analyzed by time-frequency based on continuous wavelet transform (CWT). Then, CWT images of received signals are obtained and input to the deep learning model for classifying. We create a new CWT image dataset including 12 modulation types of signals under various signal-to-noise ratio (SNR) environment to verify the effectiveness of the proposed method. The experimental results demonstrate that our proposed method can reach to a high classification accuracy over the SNR of −11 dB.
A new four-dimensional hyper-chaotic system for image encryption

&lt;span lang="EN-US"&gt;Currently, images are very important with the rapid growth of communication networks. Therefore, image encryption is a process to provide security for private information and prevent unwanted access to sensitive data by unauthorized individuals. Chaos systems provide an important role for key generation, with high randomization properties and accurate performance. In this study, a new four-dimensional hyper-chaotic system has been suggested that is used in the keys generation, which are utilized in the image encryption process to achieve permutation and substitution operations. Firstly, color bands are permuted using the index of the chaotic sequences to remove the high correlation among neighboring pixels. Secondly, dynamic S-boxes achieve the principle of substitution, which are utilized to diffuse the pixel values of the color image. The efficiency of the proposed method is tested by the key space, histogram, and so on. Security analysis shows that the proposed method for encrypting images is secure and resistant to different attacks. It contains a big key space of (2627) and a high sensitivity to a slight change in the secret key, a fairly uniform histogram, and entropy values nearby to the best value of 8. Moreover, it consumes a very short time for encryption and decryption.&lt;/span&gt;
Fundamentals

Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
The Fisher–Rao loss for learning under label noise

Choosing a suitable loss function is essential when learning by empirical risk minimisation. In many practical cases, the datasets used for training a classifier may contain incorrect labels, which prompts the interest for using loss functions that are inherently robust to label noise. In this paper, we study the Fisher–Rao loss function, which emerges from the Fisher–Rao distance in the statistical manifold of discrete distributions. We derive an upper bound for the performance degradation in the presence of label noise, and analyse the learning speed of this loss. Comparing with other commonly used losses, we argue that the Fisher–Rao loss provides a natural trade-off between robustness and training dynamics. Numerical experiments with synthetic and MNIST datasets illustrate this performance.
Improved SURF method in digital image correlation for estimation of large rotation angle

Digital image correlation (DIC) is widely used in optical measurement techniques with the advantages of high precision, robustness and expansibility. The conventional DIC, however, is highly dependent on the accuracy of initial parameters, and there are reports [1], [2] that it may fail when the rotation angle of the target specimen exceeds 7°. The combination of Speeded-Up Robust Features (SURF) with Random Sample Consensus (RANSAC) algorithm has been applied to solve this problem with limited success due to the effects of residual mismatches. An enhanced algorithm based on SURF is proposed in this study to address this problem. The transformation model between two images is obtained by RANSAC similar to existing methods. Then an angle compensation strategy is proposed to estimate the rotation angle of the deformed image to form the compensated image. Finally, an iterative process is proposed to refine the initial parameters from the reference and deformed images with a new convergence threshold. In addition, an evaluation criterion developed from a multiple ring template is proposed to compute the similarity of feature point pairs to improve the speed of convergence and sampling accuracy. Results from numerical simulation and experimental analysis show that the accuracy and robustness of the proposed method are significantly improved. Further application of the proposed method to monitor the rotating blades of a scaled model wind turbine illustrates further the capability of the proposed method.
Artificial Intelligence Technology for Python Test Simulation of Oral English Teaching Evaluation

Use human-computer interaction technology to create an environment for oral communication, use APP micro-classes and other forms to provide a micro-learning platform, and integrate technologies such as artificial intelligence into English-speaking teaching, which can enhance oral practice. Embed English spoken in non-English Professional college English classroom teaching activities to stimulate the ability of higher vocational students to learn foreign languages independently is the challenge that higher vocational colleges have to face. This paper studies how to improve the application of listening and speaking skills with the aid of multimedia courseware. The porting of speech recognition technology on the Android platform is realized; finally, the spoken English learning system is an APP application software developed on the Android platform based on the libspeech.so speech recognition library.
Pareto pairwise ranking for fairness enhancement of recommender systems

Learning to rank is an effective recommendation approach since its introduction around 2010. Famous algorithms such as Bayesian Personalized Ranking and Collaborative Less is More Filtering have left deep impact in both academia and industry. However, most learning to rank approaches focus on improving technical accuracy metrics such as AUC, MRR and NDCG. Other evaluation metrics of recommender systems like fairness have been largely overlooked until in recent years. In this paper, we propose a new learning to rank algorithm named Pareto Pairwise Ranking. We are inspired by the idea of Bayesian Personalized Ranking and power law distribution. We show that our algorithm is competitive with other algorithms when evaluated on technical accuracy metrics. What is more important, in our experiment section we demonstrate that Pareto Pairwise Ranking is the most fair algorithm in comparison with 9 other contemporary algorithms.
Research on Digital Watermarking Algorithm Based on Discrete Cosine Transform

Digital image watermark technology plays a very critical role in the fields of copyright maintenance and information dissemination. This paper proposes discrete cosine transform digital watermark algorithm based on the frequency domain digital watermark image of watermark algorithm and human visual features of digital watermark algorithm. The watermark image loading, pretreatment, embedding, extraction, and attack of the dynamic watermark test is realized, and the block embedded watermark information is reached. The algorithm is to combine the DCT block method and the experiment result shows good robustness features, contribute to copyright information protection.
MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network

Medication recommendation based on electronic medical record (EMR) is a research hot spot in smart healthcare. For developing computational medication recommendation methods based on EMR, an important challenge is the lack of a large number of longitudinal EMR data with time correlation. Faced with this challenge, this paper proposes a new EMR-based medication recommendation model called MR-KPA, which combines knowledge-enhanced pre-training with the deep adversarial network to improve medication recommendation from both feature representation and the fine-tuning process. Firstly, a knowledge-enhanced pre-training visit model is proposed to realize domain knowledge-based external feature fusion and pre-training-based internal feature mining for improving the feature representation. Secondly, a medication recommendation model based on the deep adversarial network is developed to optimize the fine-tuning process of pre-training visit model and alleviate over-fitting of model caused by the task gap between pre-training and recommendation.The experimental results on EMRs from medical and health institutions in Hainan Province, China show that the proposed MR-KPA model can effectively improve the accuracy of medication recommendation on small-scale longitudinal EMR data compared with existing representative methods.The advantages of the proposed MR-KPA are mainly attributed to knowledge enhancement based on ontology embedding, the pre-training visit model and adversarial training. Each of these three optimizations is very effective for improving the capability of medication recommendation on small-scale longitudinal EMR data, and the pre-training visit model has the most significant improvement effect. These three optimizations are also complementary, and their integration makes the proposed MR-KPA model achieve the best recommendation effect.
Stratified camera calibration algorithm based on the calibrating conic

In computer vision, camera calibration is essential for photogrammetric measurement. We propose a new stratified camera calibration method based on geometric constraints. This paper proposes several new theorems in 2D projective transformation: (1) There exists a family of lines whose parallelity remains invariable in a 2D projective transformation. These lines are parallel with the image of the infinity line. (2) There is only one line whose verticality is invariable with the family of parallel lines in a 2D projective transformation, and the principal point lies on this line. With the image of the infinite line and the dual conic of the circular points, the closed-form solution of the line passing through principal point is deduced. The angle among the target board and image plane, which influences camera calibration, is computed. We propose a new geometric interpretation of the target board’s pose and solution method. To obtain appropriate poses of the target board for camera calibration, we propose a visual pose guide (VPG) of the target board system that can guide a user to move the target board to obtain appropriate images for calibration. The expected homography is defined, and its solution method is deduced. Experimental results with synthetic and real data verify correctness and validity of the proposed method.
2S-AGCN Human Behavior Recognition Based on New Partition Strategy

Compared with behavior recognition based on RGB images and videos, bone behavior recognition is less susceptible to environmental factors and has stronger robustness. Therefore, in recent years, it has attracted extensive attention in the field of human behavior recognition. The information of adjacent nodes is only contained in the adjacency matrix defined in the traditional Graph Convolution Networks (GCNs), and the non-adjacent nodes which contain important potential information of bone posture are ignored. To solve this problem, a new division strategy of skeleton joint based on Two Stream Adaptive Graph Convolutional Network (2S-AGCN) is proposed in this paper, and the division rule is changed from the original three partitions to five partitions. Compared with the original method, the relationship between physical connection and non-physical connection between skeleton joints is strengthened by the new method. Finally, through experimenting on large data set NTU-RGB+D. The classification accuracy of Top-1 on X-Sub and X-View of NTU-RGB+D data set is 88.6% and 95.5% respectively, which is improved by 0.1% and 0.4% compared with the original model. The performance of this method is demonstrated in the experimental results.
Blockchain applications and challenges for supply chain and Industry 4.0: a literature review

Application of blockchain technology to facilitate a flexible, reliable, and efficient supply chain is an emerging phenomenon. Motivated by its potential use, researchers have started investigating blockchain's use for managing complex global supply chains. In addition to established supply chain performance criteria, Industry 4.0 requires a more data driven supply chain (SC), where data collection, transmission and processing capabilities are embedded in smart products. However, review articles of blockchain in SC primarily report descriptive statistics, but do not provide sufficient evidence on how topics are studied together. This article discusses the role of blockchain in SC and presents a methodology to study co-occurrence of blockchain topics in SC. Three most often researched topics identified are transparency/traceability, transaction related issues and tracking. Using a machine learning algorithm, we further examine trends of co-occurrence among various topics of interest and identify 'gaps' in existing literature and point to future research directions.
Color Image Encryption Using LFSR, DNA, and 3D Chaotic Maps

One of the most important challenges facing researchers is to find new methods to protect data sent over the Internet and prevent unauthorized access to it. In this paper, we present a new method for encrypting image data divided into two stages. The first stage requires redistributing the positions of the pixels by using a key of random numbers generated by linear feedback shift registers and then encrypting the data using deoxyribonucleic acid rules. The data generated in the previous stage is encrypted again using chaotic maps to increase the level of security in the second stage. Several statistical tests were implemented to verify the efficiency of the proposed method and compare the results with the work of other researchers. The results of the tests proved a reasonable safety rate compared to other techniques.