Reviewerly/exHarmony huggingface dataset:
Digital currencies as an alternative investment

Purpose: The study intends to examine the movement of digital currency prices over time and compare it to the movement of financial indicators in various stock exchanges to determine whether or not modern digital currencies can represent a global alternative to investing in financial stock exchanges, given their low transaction costs and information confidentiality, which makes them more appealing to some investors. Hypotheses: The research is based on testing two basic hypotheses (1) There is no statistically significant relationship between the movement of digital currency prices and stock index fluctuations. (2) Digital currencies cannot be used as a substitute for stock market investing. Methodology: The statistical study was conducted from January 1, 2010, to July 15, 2022, using a sample of thirteen MENA region countries' indices, as well as three global indices represented by Nasdaq, Dow Jones, and American Standard &amp; Poor's, to represent the fluctuations of these indices. The movements of financial stock exchanges, as well as the ten most active digital currencies in terms of transaction volume at the time of the research, were used as indicators. The STATA statistical program was also employed for correlation analyses. Results: Standard deviations of samples showed that traditional currency markets are more stable than virtual ones. Because of this, it's clear that financial stock exchange traders can't use digital currencies as an alternative investment.
Synthesis of a Quasi-Optimal System of Multiparametric Neural Network Identification of a UAV Orientation Model in a Turbulent Atmosphere

The use of unmanned aerial vehicles (UAVs) is one of the most promising areas of development in the field of monitoring and control of the engineering networks condition and structures. One of the factors limiting the use of UAVs is the difficulty of ensuring stable flight in a turbulent atmosphere due to errors in the assessment of orientation angles. This creates the need to develop new algorithms to improve the accuracy of determining the roll and pitch of the UAV. The proposed orientation estimation algorithm is based on the expression of a discrete quasi-linear stochastic model, which allows synthesizing an adaptive Kalman-type filter. A complex formalized approach to the implementation of a multi-stage procedure for identifying dynamic objects using neural networks makes it possible to implement an effective method of adapting the model from a variety of available data characterizing various modes of UAV movement. Statistical analysis of multiple implementations of the source data allowed each value of the amplitude of the input effect to match the values of the adaptation parameters, that provide the minimum value of the filtering error. The neural network was trained using an algorithm for back propagation of the error over the specified input and output data. The results of numerical modeling allow us to claim an increase in the accuracy of determining the pitch / roll angles of the UAV up to 10% when using the proposed algorithm in comparison with the classical Kalman filter. At the same time, the greatest gain in accuracy is achieved in conditions of intense wind load, characterized by high angular acceleration of the UAV.
WEBSITE PHISING DETECTION APPLICATION USING SUPPORT VECTOR MACHINE (SVM)

Phishing is an act to get someone's important information in the form of usernames, passwords, and other sensitive information by providing fake websites that are similar to the original. Phishing (fishing for important information) is a form of criminal act that intends to obtain confidential information from someone, such as usernames, passwords and credit cards, by impersonating a trusted person or business in an official electronic communication, such as electronic mail or instant messages. Along with the development of the use of electronic media, which is followed by the increase in cyber crime, such as this phishing attack. Therefore, to minimize phishing attacks, a system is needed that can detect these attacks. Machine Learning is one method that can be used to create a system that can detect phishing. The data used in this research is 11055 website data, which is divided into two classes, namely "legitimate" and "phishing". This data is then divided using 10-fold cross validation. While the algorithm used is the Support Vector Machine (SVM) algorithm which is compared with the decision tree and k-nearest neighbor algorithms by optimizing the parameters for each algorithm. From the test results in this study, the best system accuracy was 85.71% using SVM kernel polynomial with values of degree 9 and C 2.5.
Aspect-level Sentiment Analysis Based on Semantic and Affective Dependency Enhancement

Graph neural networks based on the dependency tree can use rich syntactic information widely used in aspect-based sentiment analysis (ABSA). However, most of these models focus on the syntactic dependencies of sentences and lack the modeling of affective dependencies between aspects and context, which can clearly express affective expressions related to specific aspects in a sentence. Additionally, although graph neural networks based on the dependency tree can aggregate more attribute dependency information through the graph structure, they need to pay attention to the semantic relationship between ordered words, resulting in insufficient mining of semantic information. To address those issues, we propose a semantic and affective dependency enhancement model (SADE) in this paper, which can better handle emotional context knowledge for specific aspects and context semantic information. Specifically, the SADE model proposed an aspect-aware attention mechanism combined with self-attention to learn the aspect-related semantic correlations and the global semantics of the sentence. Additionally, SADE incorporates the senticWordNet lexical resource and dynamically considers affective dependencies between specific aspect terms and contexts in a particular domain, thereby obtaining affective knowledge-enhanced dependency information. The SADE model's experimental results demonstrate that it performs best on two public benchmark datasets.
A Goal-Conditioned Reinforcement Learning Algorithm with Environment Modeling

Goal-conditioned Reinforcement Learning (GcRL) has achieved remarkable success in navigating towards goals in recent years. However, learning efficiency and generalization ability remain challenging issues when dealing with uncertain motion patterns of dynamic objects in the environment. To address these issues, existing model-based GcRL algorithms with the environmental prediction have become the main stream. However, these methods are limited for capturing uncertain motion patterns. In fact, humans will consider explicit knowledge, including environmental prediction and policy priors, when making decisions. To improve navigation ability in uncertain environments, we present a novel approach that integrates the policy priors model, which is utilized to infer navigational direction based on the agent's successful historical trajectories, into the model-based GcRL algorithms. Our experimental results demonstrate that our approach is more reliable in navigating towards goals, and outperforms competitive prior works in challenging environments.
ESNet: An Efficient Segmentation Network for Medical Image Segmentation

The automatic medical image segmentation technology based on deep learning has achieved high accuracy, but there is a dilemma that the parameters are huge and difficult to clinical application. To solve this problem, we propose an Efficient Segmentation Network (ESNet) model that saves parameters and floating point of operations while ensuring segmentation accuracy. We design a powerful feature extraction module DME to construct a hierarchical encoding sub-network to enable our model to have efficient feature learning capabilities, and a feature reuse-based DCD module to design a hierarchical decoding sub-network to effectively utilize the features generated by encoding to refine segmentation. To overcome the semantic gap between encoding and decoding, a conversion module ABT is established based on the idea of dense connection to communicate the encoding sub-network and the decoding sub-network while compensating for the spatial information of the decoding sub-network input. We evaluated our model on two different medical segmentation datasets, the 2018 Lesion Boundary Segmentation challenge and the 2018 Data Science Bowl challenge, achieving the SOTA performance on both Dice and Miou.
A Study of Inter-Channel Knowledge Distillation Based on Inheritance and Exploration

Knowledge distillation is a hot research topic in the field of deep learning model compression. Knowledge distillation aims to transfer knowledge from a larger and optimised teacher network to a smaller learnable network and is important for improving the accuracy of student models. Existing KD methods typically consider privileged information about the similarity/consistency between the class distribution or intermediate feature representations of the teacher and student models. However directly driving the student model to simulate the features of the teacher model largely ignores the knowledge/features that are not found in the student model learning the teacher model. In this paper we propose a new inter-channel correlation framework for knowledge extraction (IE-ICKD) that divides the student model into two parts, inheritance and exploration, where the inheritance part has transferred existing learning knowledge from the teacher model to the student model by simulating the feature space of the teacher model, while the exploration part is assigned to learn the feature space not learned by the simulated teacher model. Extensive experiments have shown that our proposed model enables better performance of the student model on the CIFAR-100 and CIFAR-10 datasets, and it outperforms many existing methods. We achieved a Top-1 accuracy of 74.12% by choosing ResNet110 as the teacher and ResNet32 as the student, which is very close to the 74.31% of the teacher model, achieving distillation and demonstrating the effectiveness of our method.
A New Relaxative Algorithm for Coupled Riccati Matrix Equations

In this paper, an iterative algorithm for solving coupled algebraic Riccati equations is proposed that has the ad-vantage of fast convergence. Firstly, the importance and related theories of solving the Riccati equation in the control problem of Markovian jumping are introduced, and then mathematical induction is used to prove the mono-tonicity and bounded properties of iterative algorithms, and the method of obtaining the initial matrix of iterative algorithms is given. Also, numerical simulation verifies that the convergence speed of the algorithm is faster than some current algorithms.
A High-Precision Road Sign Recognition Method Based on Improved YOLOv5s

An improved traffic sign detection model based on YOLOv5s is proposed to address the problem of low accuracy of traffic sign detection in real road scenarios. The model introduces Concat connections in the SPP module to reduce the loss of feature map information and improve the feature representation capability; at the same time, the model introduces a coordinate attention mechanism in the backbone network to increase the perceptual domain of the network and improve the detection accuracy of traffic sign recognition. Experimental analysis of the CCTSDB dataset shows that the average accuracy of the improved YOLOv5s model is 91.78%, which is 10.4% higher than the original YOLOv5s. The improved YOLOv5s model can effectively identify small targets, dense and obscured traffic signs and has better detection stability. It is more suitable for high-speed traffic sign recognition in complex environments.
Safety-Critical Line-of-Sight Path Following Guidance of an Under-Actuated Maritime Autonomous Surface Ship Based on Robust Optimization

This paper is concerned with the path following guidance problem at kinematic level for a maritime autonomous surface ship (MASS) sailing in a constrained water with static and dynamic obstacles. A safety-critical line-of-sight (LOS) path following method is proposed based on control barrier functions. Specifically, an LOS guidance law is firstly designed to achieve the straight-line path following task. Next, collision avoidance with channel boundaries and static/dynamic obstacles are encoded in the control barrier functions, where robust optimization is used to ensure the constraint satisfaction in the presence of external disturbances. Then, safety-critical guidance signals are obtained by solving two quadratic programming problems. By using the proposed method, the MASS is capable of accomplishing the path following task with guaranteed input-to-state safety. Simulation results are given to substantiate the efficacy of the proposed path following guidance law for the MASS sailing in constrained water region subject to external disturbance.
Spatiotemporal mapping and similarity evaluation based on isomer motion

In the process of human-robot interaction, some good effects can be achieved with the help of motion similarity assessment when the demonstrator and the imitator have similar configurations. However, the motion similarity assessment method is ineffective in the isomeric situation. As the robot application range extends constantly, there is a growing need for action evaluation and demonstration of teaching among isomers. This paper presents a spatio-temporal mapping and similarity assessment method based on the movements of isomer. For the problem of motion time sequence alignment and spatial mapping between isomers, the key frames of motion sequence are obtained by improved k -means clustering method and reconstruction of joint matrix. Based on the aligned motion sequences, the Kabsch algorithm is used to calculate dynamic metric. In addition, there is a static metric to represent the shape of the isomer. Finally, the two metrics are combined by the similarity comparison algorithm to obtain the motion similarity percentage. In the comparison of movement similarity between human and robotic arm, the average percentage of similarity obtained by this method was more than 75.06% for the same movement, indicating that the movement similarity between isomers could be correctly identified, and the average performance was improved by 23.46% compared to the BPE evaluation model.
Improved Hybrid A* Algorithm Obstacle Avoidance Strategy Based on Reinforcement Learning

To address the problem that the traditional obstacle avoidance algorithm does not consider the dynamics constraints and the algorithm planning is not high in real-time, an improved Hybrid A* algorithm based on the reinforcement learning method is proposed. Firstly, the time performance of the Hybrid A* algorithm is improved by remodeling the weight coefficients of the algorithm's heuristic function. Secondly, the algorithm time optimality and performance optimality metric models are established. The corresponding state-action and reward functions are designed incombination with the deep deterministic policy gradient method to train the weight coefficients. Finally, the improved Hybrid A* algorithm is simulated and validated in static and dynamic obstacle environments respectively. The simulation results show effective improvement of the time performance while ensuring optimal path planning results.
Perception-Aware Model Predictive Visual Servoing for Fixed-Wing Unmanned Aerial Vehicles in Target Tracking

In this paper, a perception-aware model predictive control (MPC) method is proposed to improve the performance of image-based visual servoing (IBVS) controller. In this way, the dynamic constraints of UAVs and perception constraint of the camera can be taken into consideration, which cannot be solved by the classic IBVS. Besides, by integrating the control of UAV and pan-tilt camera, the target can be tracked and concurrently kept near the image center. Furthermore, the soft constraint method is used to reduce the computational complexity and avoid infeasible solution when calculating the MPC optimization problem. Finally, the effectiveness of the proposed method is verified based on software-in-the-loop simulations.
Research on Path Planning of Mobile Robot Based on DWA-IMP-A* Algorithm

Path planning is one of the most critical links in mobile robots. Its timeliness, security and accessibility are crucial to the development and wide application of mobile robots. However, in solving the problem of path planning, the most popular A* algorithm has some problems, such as heuristic function cannot be estimated accurately, node redundancy, path is not smooth, and obstacle avoidance cannot be achieved in real time. To solve these problems, A fusion algorithm of improved A* combined with reverse path and dynamic window method (DWA-IMP-A*) was proposed. The algorithm refines the heuristic function by incorporating the reverse path. The node optimization algorithm is used to further reduce the path length. The generated trajectories are smoothed by cubic spline interpolation. At the same time, it is integrated with the improved DWA algorithm to improve the efficiency and safety of robot path planning. The algorithm takes ROS mobile robot as the carrier and is tested under typical road conditions. Compared with A* algorithm, the planning time is reduced by 54.6% and the path length is reduced by 6.37%. Experimental results verify the effectiveness and robustness of the algorithm. The research results have certain reference significance for the path planning of various types of mobile robots and the research of driverless vehicles.
Virtual Sample Generation Technology for Object Detection with Few-Shot of Workpieces

Deep learning models rely heavily on large amounts of data for training. This paper proposes a virtual sample generation technique (VSG) aimed at addressing the problem of few-shot object detection in the industrial field. The technique first obtains a new sample dataset by performing operations such as image rotation, cropping, and splicing on the original samples, and then generates a virtual sample dataset through CycleGAN to expand the sample dataset of industrial components, thus improving the performance of deep learning object detection networks. Finally, experiments are conducted and compared on YOLOv3 and YOLOv5. The mean average precision (mAP) obtained by this method is 2.3% and 2.4% higher than the two benchmark networks, respectively. The experimental results show that the proposed virtual sample generation technique for few-shot object detection in the industrial field is feasible, effective, and practical value.
Speech files cryptography using image key and indices key

Protecting speech files from being hacked is a vital issue. In this paper's research, a new and simple method of speech files cryptography will be introduced. The method will simplify the encryption and decryption functions by using a simple replacement operation. The method will provide a high level of security based on using two secret private keys: the image_key and the image position, these two keys will be used to generate an indices key, which will be used to apply speech sample replacements in the encryption and decryption phases. The introduced method will be efficiently used to encrypt-decrypt mono and stereo speeches. The method will be very flexible; the changes of the private keys will be an easy process, without the need to make any changes in the method algorithm. The proposed meth will be tested and implemented using various speech files, the obtained results will be used to prove the sensitivity quality, and efficiency of the proposed method.
Sign Language Translation from Instructional Videos

The advances in automatic sign language translation (SLT) to spoken languages have been mostly benchmarked with datasets of limited size and restricted domains. Our work advances the state of the art by providing the first baseline results on How2Sign, a large and broad dataset. We train a Transformer over I3D video features, using the reduced BLEU as a reference metric for validation, instead of the widely used BLEU score. We report a result of 8.03 on the BLEU score, and publish the open source implementation to promote further advances.
UAV-Ground Visual Tracking: A Unified Dataset and Collaborative Learning Approach

Visual tracking from the ground view and the UAV view has received increasing attention due to its wide range of practical applications. These two tasks have strong complementary benefits in the description of the target object, such as detailed appearance in the ground view and global motion information in the UAV view, and their combination has the potential to allow the tracking system to be more robust. However, no work has studied this problem in-depth, and it is challenging to accurately combine the ground view information and the UAV view information. To fill the gap and address the challenge, we propose a new computer vision task called UAV-Ground visual tracking. Considering the lack of relevant data and methods, we first propose a unified video dataset called UGVT, which includes 210 pairs of UAV and ground high-resolution video sequences with a total of more than 204K frames, which can be used as a comprehensive evaluation platform for relevant tracking methods. Secondly, based on the newly constructed dataset, we propose a co-learning method called MvCL to fuse the information of ground and UAV views. It first associates the same tracking target in the two views based on cross-attention operation and then fuses the complementary information of the two views. In particular, as a plug-and-play module based on Transformer structure, this method can be flexibly embedded into different tracking frameworks. Extensive experiments are conducted on the newly created dataset. The results demonstrate the effectiveness of the proposed method in improving the robustness of the tracking system compared with 10 state-of-the-art tracking methods and also indicate the prospect and significance of potential UAV-Ground visual tracking research. The dataset is available at: https://github.com/mmic-lcl/Datasets-and-benchmark-code/.
Lightweight 3D hand pose estimation by cascading CNNs with reinforcement learning

This paper proposes a novel strategy for lightweight 3D hand pose estimation. The strategy decomposes the estimation process into feature extraction and feature exploitation, where feature extraction performs dimension reduction on the original input and outputs feature vectors. Feature exploitation is further analyzed and considered as a path optimization problem, and reinforcement learning (RL) is proved to be capable of tackling the problem accurately. A framework cascading convolutional neural networks (CNNs) and RL is next introduced to validate the effectiveness of the proposed strategy, where two different backbones are used to extract features, and RL is extended into continuous space to enhance accuracy. Ablation studies and experiments are carried out on NYU and ICVL datasets using the proposed strategy with continuous RL. The results show that the accuracy of continuous RL exceeds discrete RL, and the rapidity and accuracy leads the backbones. Comparative studies show the strategy achieves leading rapidity and accuracy in single-view depth-based methods.