Reviewerly/exHarmony huggingface dataset:
CLOE: Novelty Detection via Contrastive Learning with Outlier Exposure

Novelty detection (ND) methods seek to identify anomalies within a specific dataset. Although self-supervised representation learning is commonly used in such applications, inadequate training data may reduce the effectiveness of these methods. It is thus reasonable to use external data to improve these performances. Here, we propose a simple and effective network, CLOE, for image-based novelty detection. Our method includes a pretrained ViT model as a feature extractor and employs the contrastive learning technique to train the dataset with external data. We compare the performance of two types of extra training settings: (1) The augmented data of the original dataset. (2) The fake images obtained from generative models. The demonstrated approach achieves a new state-of-the-art performance in novelty detection, as evidenced by achieving an ROC-AUC of 99.72% on the CIFAR-10 dataset.
DR-Informed-RRT* Algorithm: Efficient Path Planning for Quadruped Robots in Complex Environments

Quadruped robotics system possesses superior trafficability. However, path planning for these robots in complex environments continues to present challenges. We propose a DR-Informed-RRT* (Dual-attached and reconstructed informed-RRT*) algorithm to address issues such as poor search optimality, low sampling efficiency, and excessive planning time. The path planning process is divided into two parts: initial path generation and path optimization. In the initial path generation phase, a dual sampling point approach with multiple sampling probabilities is employed to sample random points, reducing the number of futile node expansions and providing directionality to the growth of the random tree, thereby improving the algorithm’s efficiency. Furthermore, the reconstruction of parent nodes and the elimination of redundant nodes are incorporated in the path optimization phase, introducing different types of parent nodes to optimize the path and reducing redundancy in the planned path. Our algorithm showed promising performance in simulations and successfully validated its feasibility on a quadruped robot.
An MFG Online Path Planning Algorithm Based on Upper and Lower Structure

The collision-free path planning problem for multi-agent systems becomes increasingly complex as the number of agents increases. Mean-field game theory has proven effective for solving large-scale problems but is limited by the difficulty of solving higher-dimensional equations, which must fix beginnings and terminations. This paper proposes a new algorithm combining upper and lower layers to achieve online path planning. We utilize the mean field games process as the primary action of the lower layer, while the path planning of the grid method determines the global Markov process of the upper layer. By integrating the mean field games as an act of the global Markov process, we transfer the system’s state by considering the environment. Simulation experiments verify the feasibility of this approach, providing a new and effective means for online path planning of large-scale multi-agent systems.
Accelerated Informed RRT*: Fast and Asymptotically Path Planning Method Combined with RRT*-Connect and APF

In recent years, path planning algorithms have played a crucial role in addressing complex navigation problems in various domains, including robotics, autonomous vehicles, and virtual simulations. This abstract introduces a improved path planning algorithm called Informed RRT*-connect based on APF, which combines the strengths of the fast bidirectional rapidly-exploring random tree (RRT-connect) algorithm and the informed RRT* algorithm. The proposed algorithm aims to efficiently find collision-free paths with less iterations and time while minimizing the path length. Unlike traditional RRT-based algorithms, Informed RRT*-connect based on Artificial Potential Fields (APF) incorporates a bidirectional connection and rewiring of a new sampling point to explore the search space. This enables the algorithm to connect both the start and goal nodes more effectively and quickly to find a initial solution, reducing the search time and provide a better initial heuristics sapling for the next optimal steps. Furthermore, Informed RRT*-connect introduces an informed sampling strategy that biases the sampling towards areas of the configuration space likely to yield better paths. This approach significantly reduces the exploration time to find a path and enhances the ability to discover optimal paths efficiently. To evaluate the effectiveness of the Informed RRT*-connect algorithm, we conducted the simulation experiments on two different experiment protocol. The results demonstrate that our approach outperforms existing state-of-the-art algorithms in terms of both planning efficiency and solution optimality.
A Real-Time AGV Gesture Control Method Based on Body Part Detection

The intelligent control of Automated Guided Vehicles (AGV) has essential research significance and application in logistics loading, unmanned driving, and emergency rescue. As an idealized human-computer interaction method, gesture has tremendous expressive power. Therefore, the gesture-based AGV control method is the mainstream. However, In a complex environment, noise interference can affect the precise and real-time control of AGV. To deal with this problem, a real-time AGV gesture control method based on human body part detection is proposed. We design a simple AGV control method based on human gestures by the relative relationship between human body parts in space. We extend a new branch on the Fully Convolutional One-Stage Object Detection (FCOS), which constrains the detection range of human parts. This method subtly associates the human parts with the human body, which vastly improves the anti-interference capability of gesture recognition. We train the network end-to-end on the COCO Human Parts dataset and achieve a detection accuracy of 35.4% of human parts. In addition, We collect a small dataset for validating the gesture recognition method designed in this paper and achieves an accuracy of 96.1% with a detection speed of 17.23 FPS. Our method achieves precise and convenient control of AGVs.
Formation of professional competence in students when teaching the disciplines of tourism direction

The purpose of the article is to highlight the formation of professional competencies in education seekers while teaching tourism disciplines. The study is considered with the aim to form professional competencies in the field of geography, recreation and tourism in future specialists. Main material. The article analyzes professional competence of future specialists in the field of geography, recreation and tourism as an integrative quality, including mastery level of professional knowledge, skills, and abilities. The author considers the following forms of education: problem-based lectures, practical classes, independent work, excursions, describing the teaching methods: business (role-playing games), project method, etc.; and teaching tools (developed system of exercises and tasks, distance education). The study pays much attention to the formation of professional competences of future specialists in the field of geography, recreation and tourism, disclosing the specifics of their use. There is an example of the applied project method during the teaching course of the discipline “Local history and regional tourism”. The study is based on the results of a student survey on interest in studying tourism disciplines. Conclusions. The article analyzes pedagogical conditions for the formation of positive motivation of students regarding the study of tourism disciplines through the professional direction of their content. The article specifies the use of distance learning tools for organizing students’ independent work. The author determines development of professional and personal qualities of students by means of business games, using the project method, ensuring the problematic and creative nature of educational tasks. As a result, key professional competences of future specialists in the field of geography, recreation and tourism have been formed.
Analysis of hybrid attack and defense based on block withholding strategy

As the most influential digital cryptocurrency in the world, Bitcoin is widely recognized for its Proof of Work (PoW) consensus mechanism. However, the blockchain system based on PoW has also encountered many attacks and threats. Most of the current research focuses on analyzing the impact of a single attack on revenue while ignoring the effect of hybrid attacks on the blockchain system and attack revenue. We aim to study the impact of hybrid attacks and defensive measures. In this paper, we extend the block withholding attack model based on the existing selfish mining model and add bribery attacks to the attack model. We compare the benefits under different computing capabilities and theoretically analyze the defense model to prevent attacks. Finally, we compare the advantages of attackers under the attack model and the defense model. Our experiments show that different attack combinations can have negative effects, and the defense model with hidden target values can solve block withholding attacks well.
Deep learning-based depth map defect removal for industrial applications

A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, etc., fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by wrong sections on the depth map because of erroneous distance determination to objects. We present an approach combining defect detection and depth reconstruction algorithms. The first step for image defect detection is based on a convolutional auto-encoder (U-Net). The second step is a depth map reconstruction using a spatial reconstruction based on a geometric model with contour and texture analysis. We apply contour restoration and texture synthesis for image reconstruction. A method is proposed for restoring the boundaries of objects in an image based on constructing a composite curve by cubic splines. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on the RGB-D benchmark for evaluating manufacturing vision systems.
Planning the trajectory of an object in a confined space using stationary machine vision systems

The article proposes an approach to the formation of the trajectory of the spatial movement of a controlled object in a confined space using stationary vision systems. For its implementation, the following main steps are used in the work: 1. Preprocessing of data generated by the machine vision system. The task includes multicriteria image processing in order to minimize the noise component and determine the boundaries of objects. 2. An automated method for adaptive non-local separation of objects on borders, background and objects. 3. Execution of the task of adaptive nonlocal binarization. 4. Building a mask of stationary and current moving objects. 5. Formation of an equidistant displacement trajectory. 6. Checking the trajectory by moving in adjacent frames. 7. Prediction and remeasurement of the position of objects in the frame based on displacement vectors and correction of the object's movement trajectory. 7. Formation of a control team to move an object in a confined space using stationary vision systems. To test the effectiveness, studies were conducted on a set of test sequences. The studies were carried out on a group of cameras in the visible spectrum (1920x1080, RGB, 8 bits) covering the entire field of view. The adaptability of the application of the proposed approach in solving complex problems is showed.
Attention map-guided multi-scale haze removal method for industrial inspection system

We present a new haze removal algorithm based on attention map-guided multi-scale image processing. The proposed method is based on the frequency-domain coefficient correction of a set of images followed by their fusion based on the Laplacian pyramid. A new stage is presented in obtaining a local-global estimate of high-contrast images, also used in the attention map-guided fusion model. The algorithm consists of the following steps: gamma correction with different gamma parameters; the weight map calculation by multiplying the saturation, contrast, and attention for each image; decomposition of the weight map into a Gaussian pyramid; 3-D block-rooting enhancement; decomposition of images after 3-D block-rooting and gamma correction into the Laplacian pyramid; merging by multiplying multi-scale images and weights. The experiment results on the dataset D-HAZE confirmed the high efficiency of the proposed enhancement method compared to the state-of-the-art techniques for industrial inspection systems.
Efficient GPU acceleration for phase unwrapping algorithm

Optical testing is constantly evolving, necessitating higher lateral resolution in interferometry. Achieving high resolution leads to longer processing times, significantly impacting testing efficiency. The unwrapping phase algorithm is crucial in interferometry, but its complex calculations can impede efficiency improvements. There are two types of algorithms for the unwrapping phase: path-dependent and path-independent. Path-dependent algorithms tend to be more efficient, and thus, we have chosen to utilize the accelerated path-dependent algorithm. Among these algorithms, Goldstein's algorithm is widely applied. This study uses CPU-GPU heterogeneous computing to parallelize and accelerate the Goldstein phase unwrapping algorithm while maintaining acceptable numerical error limits. Our proposal focuses on optimizing the serial Goldstein algorithm for GPU architectures by parallelizing and enhancing three key steps: residue identification, branch cutting, and integration. Specifically, our optimization approach leverages GPU shared memory and SIMD functionality. To assess the efficiency of our proposed method, we conducted tests on the unwrapped phase image with varying pixel sizes. The results demonstrate that as the pixel size increases, the performance gain from GPU computation becomes more pronounced compared to CPU computation. Using a 4096×4096 phase diagram on the RTX3070 laptop hardware, we achieved a 60x speed increase in the overall process compared to the CPU version. Therefore, employing this algorithm with the GPU can significantly expedite the phase unwrapping process and enhance the efficiency of interferometry.
A smartphone-based device for automatically calibrating wave-front systematical error

This article proposes an automatic calibration device for wavefront system error, which achieves three degrees of freedom random rotation of a spherical lens through hardware circuits and software algorithms. It is combined with tools such as a 4D interferometer to obtain wavefront information. Finally, the data is processed using MetroPro software to achieve fast and accurate error calibration. This device is easy to operate, efficient and stable, overcoming the problems of traditional manual calibration methods, and providing a reliable tool for the development of absolute measurement technology of wavefront aberration. The automated calibration motion platform in this article improves the shortcomings of traditional manual calibration and achieves rapid calibration of wavefront system errors, providing a reference for research and application in this field.
Preprocessing and classification of objects based on neural network models built using memristors' elements

The article proposes the use of three directions of development at once to solve the problem of dividing objects into classes. The first direction uses the formation of a computationally simple multi-criteria method for smoothing data in windows of complex adaptive forms, adapting the method of dividing objects into background/structure, and simplifying images. The approaches being developed are intended for implementation on low-computing devices with the ability to parallelize processes. The second direction being worked on is the formation of a model of the structure of a neuron organized on the basis of the use of memristor structures. The paper presents an approach to the formation of such structures, provides the characteristics of such devices, and describes methods for combining analog and digital parts to implement memory or control systems. The final direction discussed in the article is the formation of a neural network for the classification of simple objects based on a model of new neurons and data preprocessing. To test the approach proposed in the work, studies were carried out on a set of test data obtained by a sensor (simple sensor) system. The generated data array for evaluating efficiency is limited by a time window and has real noise (errors). The work provides assessments of effectiveness, recommendations for the selection of parameters and presents requirements for the type and form of the analyzed data.
A new scheme for orthogonal dual-polarization continuous-variable quantum key distribution

In this article, we propose a pilot alternately assisted scheme of orthogonal dual-polarization and time multiplexing for the local local oscillator continuous-variable quantum key distribution (LLO CV-QKD). Our scheme utilizes time multiplexing and dual-polarization multiplexing techniques to dramatically isolate the quantum signal from the pilot light. To analyze the influence mechanism of time-domain diffusion and polarization perturbation on the key parameters, such as the channel transmittance and excess noise, of the studied system, a general LLO excess noise model based on polarization extinction ratio (PER) and time-domain pulse extinction ratio (TER) is established. We mainly focus on the photon-leakage noise from the reference path to the quantum signal path, which is first analyzed in the dual polarization LLO regime. Furthermore, we conduct a series of simulations to verify the proposed dual polarization and time multiplexing model. Results show that it maintains a low level of excess noise and a secure key rate (SKR) of 10.25 Mbps@25km can be obtained under the finite-size effect. We achieved 0.93Mbps@25km SKR under a relatively low PER of 17 dB in the nanosecond level pulse width. Our work greatly extends the application scenarios of the dual-polarization division multiplexing CV-QKD system and provides a theoretical and representative framework for the study of improving the performance of the dual-polarization CV-QKD system.
Generating Gaussian error in lattice cryptography with quantum random number generator

The Gaussian Error, represented by random numbers with a Gaussian distribution, plays a critical role in the security of modern lattice-based cryptography. Lattice-based cryptography is the most important category of post-quantum cryptography capable of resisting attacks from both classical and quantum.While current schemes of generating Gaussian Error used in lattice cryptography face severe challenges. On the one hand, the current scheme produces an approximate Gaussian distribution rather than the rigorously proven Gaussian distribution required in lattice cryptography, posing a security risk. Meanwhile, the Gaussian Error of current schemes is obtained by using algorithms to compute uniformly distributed random numbers, which lack provable randomness and also pose security risks. On the other hand, the generation rate of Gaussian Error is not high enough for many applications of cryptographic systems in current schemes. To address these challenges, this paper proposes a novel scheme that uses a quantum random number generator(QRNG) to generate the Gaussian Error, which follows a theoretically proven Gaussian distribution and satisfies rigorous security justification in lattice cryptography. Furthermore, based on the quantum mechanical process of measuring vacuum fluctuations and the principle of minimum entropy under classical noise conditions, the Gaussian Error obtained by our scheme is theoretically unpredictable, further enhancing the security of lattice cryptography compared to the current schemes. Finally, an experiment for generating Gaussian Error was constructed, and our experimental results demonstrate that the Gaussian Error generation rate is 1 G/s, which achieves higher speed among existing works.
Modulation noise model for multi-carrier Gaussian modulated continuous-variable quantum key distribution based on OFDM technology

In this paper, a multi-carrier Gaussian modulated continuous variable quantum key distribution (CV-QKD) scheme has been proposed based on orthogonal frequency division multiplexing (OFDM) for distributing multiplexing independent secret keys encoded on N subcarriers within a single fiber channel. However, the performance of the system will be significantly influenced by the extra modulation noise in the multi-carrier quantum state preparation. Therefore, a modulation noise model is analyzed in more compact for multi-carrier Gaussian modulated CV-QKD system. Specifically, the gain imbalance and quadrature skew in IQ modulation and the third-order intermodulation effect in N subcarrier modulation are systematically analyzed in the OFDM-based multi-carrier CV-QKD with Gaussian modulation. That is, the IQ imbalance noise and the intermodulation noise are modeled as the modulation noise of the multi-carrier Gaussian modulated CV-QKD system. Moreover, the secure performances of the multi-carrier Gaussian modulated CVQKD are evaluated based on the proposed modulation noise model. Besides, the simulation results show the SKRs are greatly increased by N independent quantum state preparation, which indicates that the multi-carrier CV-QKD system gets rid of the asymptotic SKR limit of single-carrier CV-QKD system for future high-rate CV-QKD deployment in broadband access network.
Generation of entangled coherent states in cavity QED and its nonclassicality

Cavity quantum electrodynamics is the intersection of quantum physics and microcavity optics research. In this paper, we presented a two-mode cavity coupled to a classical field driven by N three-level atoms to prepare entangled coherent states based on cavity QED. In this scheme, we adopt the interaction between the three-level atoms and the two-mode optical cavity, The initial state of one optical field is an odd-even coherent state, while the initial state of another optical field is a coherent state. After considering the time evolution of the initial states with the system, we obtained the specific form of the time evolution of the quantum states and prepared the entangled coherent state successfully. We discussed and analysed some of the nonclassical properties of the optical field and quantum states, including the second-order correlation function and the Wigner function. The results show that when calculating the second-order correlation function, the photons are found to exhibit a sub-Poisson distribution at some points in time. In addition, when observing the Wigner function of the quantum states evolving with time, it is found that there is a negative part of the Wigner distribution at some moments, which implies that the quantum state exhibits nonclassical properties at these certain moments.