Reviewerly/exHarmony huggingface dataset:
An enlarged polygon method without binary variables for obstacle avoidance trajectory optimization

In this article, an Enlarged Polygon/Polyhedron (ELP) method without binary variables is proposed to represent the Convex Polygonal/Polyhedral Obstacle Avoidance (CPOA) constraints in trajectory optimization. First, the equivalent condition of a point outside the convex set is given and proved rigorously. Then, the ELP condition describing the CPOA constraints equivalently is given without introducing binary variables, and its geometric meaning is explained. Finally, the ELP method is used to transform the CPOA trajectory optimization problem into an optimal control problem without binary variables. The effectiveness and validity of ELP method are demonstrated through simulations with both simple linear dynamic model and complex nonlinear dynamic model. Comparison indicates the computational time of ELP method is only 1%-20% of that of the traditional Mixed-Integer Programming (MIP) method.
ACLSDN: A Heuristic Face Recognition Framework with Adaptive Cascaded Deep Learning using Spectral Feature Selection

In recent times, most of the Facial Recognition framework has shown better outcomes in a constrained environment.Due to its high usage, the FR has gained increased attention in the research field.However, it has faced various issues in the real-time application, such as facial disguise designing in facial features, temporal variations, and low-quality images.Therefore, there is a requirement for significant techniques for detecting and identifying the human face.This paper has implemented an effective FR technique.The adequate dataset relevant to the FR process is initially gathered from the standard dataset.Then, the images are pre-processed with the aid of the median filtering process to obtain the pre-processed images.Then, it is given to the three level-Discrete Wavelet Transform (DWT), which is used to attain the spectral features.Then, the features from the spectral features are optimally chosen by using the Improved Horse Herd Optimization algorithm termed (IHHO) derived from Horse Herd Optimization (HHO).Further, it is subjected to the Adaptive Cascaded framework termed ACLSDN, designed using Long Short Term Memory (LSTM) and Deep Neural Network (DNN) model.It is carried out by averaging the score attained from both the LSTM and DNN models for attaining the classified outcomes in maximized accuracy.Here, the same IHHO algorithm is used for optimizing the parameters in the LSTM and DNN frameworks.Finally, the effectiveness of the designed FR model is validated using various metrics and shows maximized accuracy value.
Improving multi-label learning by modeling Local label and feature correlations

Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously, and many methods have been proposed by modeling label correlations in a global way to improve the performance of multi-label learning. However, the local label correlations and the influence of feature correlations are not fully exploited for multi-label learning. In real applications, different examples may share different label correlations, and similarly, different feature correlations are also shared by different data subsets. In this paper, a method is proposed for multi-label learning by modeling local label correlations and local feature correlations. Specifically, the data set is first divided into several subsets by a clustering method. Then, the local label and feature correlations, and the multi-label classifiers are modeled based on each data subset respectively. In addition, a novel regularization is proposed to model the consistency between classifiers corresponding to different data subsets. Experimental results on twelve real-word multi-label data sets demonstrate the effectiveness of the proposed method.
A grouping feature selection method based on feature interaction

Feature interaction is crucial in the process of feature selection. In this paper, a grouping feature selection method based on feature interaction (GFS-NPIS) is proposed. Firstly, a new evaluation function measuring feature interaction is proposed. Secondly, a grouping strategy based on approximate Markov blanket is used to remove strong redundant features. Lastly, a new feature selection method called as GFS-NPIS is given. In order to verify the effectiveness of our method, we compare GFS-NPIS with other eight representative ones on three classifiers (SVM, KNN and CART). The experimental results on fifteen public data sets show that GFS-NPIS outperforms others in terms of classification accuracy and Macro-F1.
Semantic Segmentation with High Inference Speed in Off-Road Environments

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Semantic segmentation is an integral component in many autonomous vehicle systems used for tasks like path identification and scene understanding. Autonomous vehicles must make decisions quickly enough so they can react to their surroundings, therefore, they must be able to segment the environment at high speeds. There has been a fair amount of research on semantic segmentation, but most of this research focuses on achieving higher accuracy, using the mean intersection over union (mIoU) metric rather than higher inference speed. More so, most of these semantic segmentation models are trained and evaluated on urban areas instead of off-road environments. Because of this there is a lack of knowledge in semantic segmentation models for use in off-road unmanned ground vehicles. In this research, SwiftNet, a semantic segmentation deep learning model designed for high inference speed and accuracy on images with large dimensions, was implemented and evaluated for inference speed of semantic segmentation of off-road environments. SwiftNet was pre-trained on the ImageNet dataset, then trained on 70% of the labeled images from the Rellis-3D dataset. Rellis-3D is an extensive off-road dataset designed for semantic segmentation, containing 6234 labeled 1920x1200 images. SwiftNet was evaluated using the remaining 30% of images from the Rellis-3D dataset and achieved an average inference speed of 24 frames per second (FPS) and an mIoU score 73.8% on a Titan RTX GPU.&lt;/div&gt;&lt;/div&gt;
Human Action Recognition Using Difference of Gaussian and Difference of Wavelet

Human Action Recognition (HAR) attempts to recognize the human action from images and videos. The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments. A novel action descriptor is proposed in this study, based on two independent spatial and spectral filters. The proposed descriptor uses a Difference of Gaussian (DoG) filter to extract scale-invariant features and a Difference of Wavelet (DoW) filter to extract spectral information. To create a composite feature vector for a particular test action picture, the Discriminant of Guassian (DoG) and Difference of Wavelet (DoW) features are combined. Linear Discriminant Analysis (LDA), a widely used dimensionality reduction technique, is also used to eliminate duplicate data. Finally, a closest neighbor method is used to classify the dataset. Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy, and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well. The average accuracy of DoG + DoW is observed as 83.6635% while the average accuracy of Discrinanat of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 80.2312% and 77.4215%, respectively. The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG + DoW is observed as 62.5231% while the average accuracy of Difference of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 60.3214% and 58.1247%, respectively. From the above accuracy observations, the accuracy of Weizmann is high compared to the accuracy of UCF 11, hence verifying the effectiveness in the improvisation of recognition accuracy.
Towards intelligent policy analysis: A discourse structure parsing technique for Chinese government document

Government document analysis can help organizations to understand key policy points and make right response. To realize intelligent analysis of a volume of policies, we propose a discourse parsing technique for in-depth understanding of Chinese government documents (CGDs). Based on Superstructure Schema and Rhetorical Structure Theory (RST), we characterize the stylistic features and macrostructure patterns of CGDs and develop a discourse analysis framework to specify its functional structure and semantic system. A tree schema is adopted to portray the hierarchy of textual fragments in full text and formalize their representations in machine-based parsing, then a multi-head partition self-attention model is constructed to evaluate the discourse analysis framework. Experiment results show that our parsing model embedding with inherent CGD discourse features performs better than baselines. The highest f1-score in span and label prediction tasks researches 0.9209. The parsing model can be migrated to other discourse parsing tasks by considering specific genre features of different documents.
Geology and geochemistry of gold mineralization at the Namicupo prospect, Mozambique Belt, northeastern Mozambique

Auriferous quartz veins in the Namicupo gold prospect occur parallel to the foliation of metasedimentary host rocks of the Xixano Complex in northeastern Mozambique. The geochemistry and ore-forming mechanism of the Namicupo prospect have never been scientifically investigated. This study discusses the ore mineralogy, fluid inclusion, and stable isotope (S-O) characteristics of the prospect. The mineralization in the Namicupo prospect is divided into two stages. The Stage I, primary mineralization, is represented by electrum with Ag contents of 15–26 at.%, associated with pyrite and chalcopyrite. The Stage II is supergene mineralization, characterized by electrum with Ag contents of 39–46 at.%, intergrown with goethite, barite, and minium, which replaced primary sulfides. Quartz from the mineralized veins hosts three types of primary fluid inclusions coexisting in the same fluid inclusion assemblage. Type A inclusions are three-phase aqueous‑carbonic inclusions (aqueous liquid, CO2 liquid and vapor), with final homogenization temperature and salinity varying from 140 to 241 °C, and 1.0 to 7.8 wt% NaCl eq., respectively. Type B inclusions occur either as mono-phase (vapor or liquid) or two-phase (vapor and liquid) CO2-rich, N2-H2O-bearing inclusions, with homogenization temperature varying from +12.9 to +29.3 °C. The Type C inclusions are two-phase (liquid and vapor) aqueous inclusions, which homogenize to liquid phase at temperatures between 209 and 337 °C. The salinity of the Type C inclusions varies from 4.0 to 10.8 wt% NaCl eq. The sulfur isotopic ratios (δ34SCDT) of sulfides associated with Stage I mineralization and the oxygen isotopic ratios (δ18OSMOW) of water calculated from those of quartz from the quartz veins vary from −3.5 to +0.9 ‰ and −1.0 to +2.5 ‰, respectively. The primary mineralization at the prospect is classified as an orogenic-type gold mineralization, formed by aqueous‑carbonic metamorphic fluids. The precipitation of primary gold mineralization in the Namicupo prospect resulted dominantly from fluid immiscibility. The considerable amount of S up to 0.1 wt% and the high Ag contents of electrum intergrown with goethite, minium and barite suggest that the supergene mineralization in the Stage II was caused by oxidation of primary gold-bearing sulfides and Au-rich electrum under moderately acidic and oxidizing conditions, which resulted in Au and Ag liberation, likely by thiosulfate. Dissolution of primary mineralization and supergene gold enrichment in the Namicupo prospect appear to have occurred largely in situ, with little evidence of distant mobilization.
Application of Wireless Sensor Network Computer Technology in Financial Management System

In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources.
Evolutionary multiobjective neural architecture search for organ medical image classification

In medical clinical applications, we must not only ensure the accuracy of classification, but also ensure that the size of the network can be carried into hardware devices with limited computing resources. In this paper, we propose an evolutionary multiobjective neural architecture search (EMNAS) framework for organ image classification. In EMNAS, evolutionary algorithms are used to encode and search network structures, which can achieve more flexible hierarchical extraction of scene information from organ med-ical images. In addition, we utilize the evolutionary multi-objective optimization to search for the Pareto optimal solutions for two conflicting objectives of computational complexity and classification accuracy. Compared with many hand-designed classical convolutional neural networks and other well-known NAS algorithms, the effectiveness of EMNAS is proved by the fact that EMNAS can achieve the best results.
Path planning algorithm based on hybrid A* and adaptive ant colony optimization

It is challenging to plan the optimal and smooth path using the traditional path planning algorithm. This paper proposes a hybrid path planning algorithm based on the A* algorithm with quadratic node search and an adaptive Ant Colony Algorithm (ACO). Through the quadratic node search operation of the nodes on the path, the proposed algorithm reduces the length of the path planning and the number of turns and improves the efficiency of path planning. The ACO based on pheromone adjustment overcomes the shortcomings of traditional ACO, such as long search time and slow convergence speed, by adjusting pheromone and improving transition probability. The experimental results show that the proposed hybrid path planning algorithm has a better global search ability and convergence speed and achieves better results in robot path planning.
Research on Bitcoin price prediction based on Support Vector Regression and its variant combination model

Bitcoin is one of the most successful cryptocurrencies, and research on Bitcoin price prediction is getting more and more attention. Previous studies have used traditional statistical methods and machine learning models to predict Bitcoin prices. However, previous studies also have many problems, such as too few influencing factors, lack of model optimization, and poor prediction effect. This paper selects 27 factors related to Bitcoin price changes and screens the features through the XGBoost algorithm and the Random Forest algorithm (RF). In this study, combined forecasting models based on Support Vector Regression (SVR), Least Squares Support Vector Regression (LSSVR) and Twin Support Vector Regression (TWSVR) are used to predict Bitcoin price, separately. In addition, the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) are applied for parameter tuning of the models. Expected Variance Score (EVS), Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to measure the prediction accuracy of the combined models. The CPU time is used to measure the operation speed of the combined models. The experimental results show that the combined model XGBoost-WOA-TWSVR has the best prediction effect, and the EVS score of this model is 0.9547. In addition, our research verifies that Twin Support Vector Regression has advantages in both prediction effect and computational speed.
Simulation design and control experiment of Folding quadrotor UAV

To shorten the flight time and reduce the probability of being intercepted when the flight reaches the specified area, the overall design of a spring-foldable rotorcraft is presented. Virtual modeling analysis and motion simulation were carried out by using 3D modeling and simulation software. The parameters of the torsion spring folding mechanism are designed and calculated in detail by the mathematical model. On this basis, the control algorithm of the folded rotorcraft is studied to meet the attitude control requirements of the aircraft. Further through the actual flight test, multiple experimental data show that. The control system can meet the flight requirements of folding aircraft. Furthermore, the rationality of the design and the feasibility of the control algorithm is verified.
A differential privacy federated learning framework for accelerating convergence

Federated Learning (FL) is a special distributed machine learning environment. It is jointly trained by many clients under the coordination of a central server. And differential privacy can provide privacy guarantee for FL. While, federated learning, compared with centralized learning, converges at slower speed. And differential privacy exacerbates this trend. In this paper, we propose a novel FL algorithm named DP-FedADMM to solve these problems. We merge differential privacy (DP) into the FedADMM algorithm and propose a method to handle noisy gradients. Under the guidance of the help of the latest results in differential privacy theory, we provide a privacy proof of DP-FedADMM. Through extensive experiments on datasets, it is demonstrated that DP-FedADMM outperforms the currently popular DP-FedAvg algorithm in terms of model convergence speed.
Student Score Prediction Based on Deep Learning

With the development of data mining technology, In order to predict the student's next semester results, students' evaluations of the educational quality information were evaluated. In order to realize the end-to-end prediction method, in this paper, we propose a convolutional neural network (CABLSTM) attentional mechanism model to predict time series. CABLSTM is different from the two kinds of network connections. The model established in this paper is a convolution operation in the element state of BiLSTM. First The input original data information is used to obtain the relevant feature information through CNN. Then, by inputting the obtained feature information into the biLSTM network model with convolution operation and attention mechanism, the time-frequency information can be obtained. BiLSTM network model can obtain long time series information in data set. In addition, the attention mechanism in the model will affect the weights of different feature information and highlight the features with higher correlation, so as to obtain better prediction result.
Nighttime Image Dehazing based on Bright and Dark Channel Prior and Gaussian Mixture Model

A new method based on bright and dark channel prior and gaussian mixture model is presented for nighttime dehazing from a single image. Firstly, the nighttime image is divided into the sky area and other area using the Gaussian mixture model. At the same time, two different channel maps are obtained for the input image using the bright and dark channels. For the sky area, the prior transmittance of the bright channel is selected, and for other area, the prior transmittance of the dark channel is selected. The two are fused to obtain a new transmittance. Then, the restored image can be obtained from the atmospheric scattering model. At the same time, in order to enhance the brightness of the image. The final result is obtained by adaptive contrast enhancement of the brightness component of the restored image. The experimental results show that the proposed method is superior to several existing dehazing removal techniques in terms of luminous artifacts, color shift, overexposure and noise amplification. It can be effectively improved in dealing with similar problems such as blurring of photos taken by night traffic and outdoor monitoring.
Attention-enhanced Relation Network for Few-shot Image Classification

Traditional deep learning models firmly rely on a large amount of labeled data during pre-training. Whereas it lacks generalization in the face of unfamiliar categories. Recently, few-shot learning is a hot topic in computer vision to classify unseen classes with limited labels. A representative approach is to extract features from the support and query sets, respectively, and compare similarities via metric learning. However, convolutional neural networks often focus only on a local region and ignore the global region, which severely reduces the accuracy of the matching. Specifically, in this paper, we pile lightweight attention-based blocks in the embedding module, which combines an adaptive kernel size 2D convolutional network with a cross-channel attention mechanism to encode multi-scale features and implicitly increase the receptive field. The SE-relation module chooses to construct learnable non-linear comparators to compare the relationship utilizing channel information. Finally, we show experimental results on standard few-shot testing benchmarks such as mini-ImageNet and tiered-ImageNet to demonstrate effectiveness.
Complementary Label Queries for Efficient Active Learning

Many active learning methods are based on the assumption that a learner simply asks for the true labels of some training data from annotators. Unfortunately, it is expensive to exactly annotate instances in real-world classification tasks. To mitigate this problem, we propose a novel active learning setting, named active learning with complementary labels (ALCL). The ALCL learners only ask yes/no questions in some classes. After receiving answers from annotators, ALCL learners get a few supervised data and more training instances with complementary labels, which only specify one of the classes to which the pattern does not belong. There are two challenging issues in ALCL: the first is how to learn from these complementary labels, and another is how to sample instances to be queried. For the first issue, we propose a novel method that redistributes the weights of instances based on the balance of category contribution to learn from ordinary labels and complementary labels. For the second issue, we propose a weighting mechanism to improve existing uncertainty-based sampling strategies under this novel setup. Experiments on various datasets demonstrate the feasibility of ALCL and the superiority of our approaches.