Reviewerly/exHarmony huggingface dataset:
Time Optimal Trajectory Optimization for Vertical Parking of Heavy Duty Vehicle based on Chebyshev Pseudospectral Method

Heavy duty vehicle are open-loop unstable while reversing owing to their unusual articulated construction, while vertical parking in tighter surroundings is sometimes quite problematic due to their lengthy wheelbase and significant non-holonomic dynamics. This paper theoretically analyzes the difference brought by the nonholonomic constraints for the heavy duty vehicle model, and precisely portrays the physical and related environmental constraints of the vehicle; Considering the jackknife phenomenon brought by the unique articulation characteristics of the heavy duty vehicle, the hitch angle of the vehicle in reverse is restricted with the objective of time optimality, and the state space is carried out without compromising the quality of the local optimal solution substantial pruning; A finite-dimensional nonlinear programming(NLP) is constructed for vertical parking of heavy duty vehicles with an accurate obstacle avoidance strategy and an optimal nonholonomic dynamics, boundary and path constrained discretization scheme based on Chebyshev's pseudospectral method; Matlab is used to solve the NLP problem, and the final simulation results show that: the vertical parking trajectory planning for heavy duty vehicle in narrower aisles is done accurately and less time-consumingly in a unified framework without relying on search and sampling methods.
Virtual Force Vector Control Method of Intelligent Vehicle Path Planning

Path planning is a hot topic in the field of intelligent vehicle research. In order to overcome the local minimum point problem and the target point inaccessibility problem in the traditional artificial potential field path planning method, an intelligent vehicle path planning method based on virtual force vector control is proposed. The action area of the obstacle potential field is optimized into an elliptical area, and the road boundary repulsion potential field is increased. Then the calculation formulas of the virtual force gravitational vector and the virtual force repulsion vector are derived by applying the artificial potential field theory. In addition, the virtual force vector control strategy and intelligent vehicle path planning method are designed by controlling the virtual gravity and virtual repulsion in the vertical direction, to avoid the virtual resultant force being zero. The performance of intelligent vehicle path planning method is simulated under three typical scenarios. The results show that the virtual force vector control strategy proposed in this paper can effectively avoid the intelligent vehicle falling into the local minimum point, and the comprehensive performance of path planning is better than the traditional artificial potential field method.
The 5Js in Ethiopia: Amharic Hate Speech Data Annotation Using Toloka Crowdsourcing Platform

This paper presents an Amharic hate speech annotation using the Yandex Toloka crowdsourcing platform. The dataset for this paper is collected from 5 consecutive years in Ethiopia (5Js), namely from 2018-2022, following the ‘June’ events in Ethiopia. Every June for the last five years, some events put the country in violence. Accordingly, we annotate 5,267 tweets, nearly 1k tweets every year. We explore the main challenges of crowdsourcing annotation for Amharic hate speech data collection using Toloka. We attain a Fliess kappa score of 0.34 using three independent annotators that annotate the tweets and the gold label is determined using majority voting. Using the datasets, we build different classification models using classical machine learning and deep learning approaches. The classical machine learning algorithms, LR and SVM, achieve an F1 score of 0.49, while NB archives an F1 score of 0.46. The deep learning algorithms (LSTM, BiLSTM, and CNN) achieve a similar F1 score, that is 0.44, which is the lowest performance of all models. The contextual embedding models, Am-FLAIR and Am-RoBERTa achieve F1 scores of 0.48 and 0.50 respectively. We publicly release <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> the dataset, source code, and models with a permissive license. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/uhh-lt/ethiopicmodels
Deep Active Learning with Budget Annotation

Digital data collected over the decades and data currently being produced with use of information technology is vastly the unlabeled data or data without description. The unlabeled data is relatively easy to acquire but expensive to label even with use of domain experts. Most of the recent works focus on use of active learning with uncertainty metrics measure to address this problem. Although most uncertainty selection strategies are very effective, they fail to take informativeness of the unlabeled instances into account and are prone to querying outliers. In order to address these challenges we propose an hybrid approach of computing both the uncertainty and informativeness of an instance, then automaticaly label the computed instances using budget annotator. To reduce the annotation cost, we employ the state-of-the-art pre-trained models in order to avoid querying information already contained in those models. Our extensive experiments on different sets of datasets demonstrate the efficacy of the proposed approach.
Research on the Influence of the Depth and Width of YOLOv5 Network Structure on Traffic Signal Detection Performance

In order to achieve accurate and fast recognition of traffic signals, this paper compared and evaluated the detection performance of four YOLOv5 models from the detection speed and detection accuracy, and analyzed the influence of the depth and width of different network structures on the detection performance of traffic signals. Firstly, this paper introduces the basic principle of YOLOv5 algorithm and analyzes the structure characteristics of YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x network models. Secondly, the source of the dataset, the construction method and the target category contained in the image are described briefly. After the algorithm training and testing, the corresponding detection model is obtained. Finally, the recognition speed, confusion matrix, recall rate, accuracy rate and other evaluation indexes are compared and analyzed. Experimental results show that the recognition performance of YOLOv5m model is better than the other three models when considering the detection speed and detection accuracy.
Design and Experimental Evaluation of Nighttime Traffic-Sign Detection and Classification Based on Low-Light Enhancement

This paper focuses on solving the problem of traffic-sign detection and classification at nighttime when lighting is often lower. Images taken by the camera at night are underexposed and fail to show details. Low-light enhancement method can improve the brightness of the image. By studying four different low-light enhancement methods and applying four methods to the detection and classification of traffic signs, the performance of low-light image enhancement is compared. The evaluation results show that the nighttime traffic-sign detection and classification method, which combines a robust end-to-end convolutional neural network (CNN) and Zero-Reference Deep Curve Estimation (Zero-DCE) low-light enhancement, has high precision and a higher recall rate compared to other methods.
Real-time Multiple Path Prediction and Planning for Autonomous Driving aided by FCN

Real-time path planning is the core of autonomous driving. It plans the optimal drive path for the autonomous vehicle (AV) based on concurrent perception and localization information. Due to the uncertainties in those information, it is necessary to plan multiple paths in real-time for selection, which, however, is expensive for traditional graph search methods. In recent years, path learning methods based on semantic segmentation proved that the driving area of an AV can be predicted by learning perception information and its historical paths. Inspired by these methods, this paper proposes a real-time multiple path planning method combining semantic segmentation with the traditional graph-based search. A fully convolutional neural network (FCN) was first designed to learn the optimal path area generated by an A* based path planning method in various real and simulated environments. By injecting noises into localization information, the generalization ability of the neural network is greatly enhanced facing inaccurate localization results. Then, multiple possible path areas inferred by the FCN are adopted as constraints for the following A* based path planning. The results showed that the optimal path area reduces the computational cost of subsequent path searches, which makes real-time multiple path planning possible. Besides, even if the neural network infers an unreasonable path area, the A* based path search method can still guarantee the correctness of the path searching.
Real-time Adaptive Weighted Fusion of Multi-radar's Mutual Guidance Data

A real-time adaptive weighting fusion algorithm for radar mutual guidance data is proposed to address the current situation of poor continuity and stability of mutual guidance data among radars in domestic measurement and control ground stations. The algorithm calculates the statistical characteristics of the sample points of radar mutual guidance data at the current moment, and adaptively assigns the weights to each radar mutual guidance data according to the statistical characteristics of the sample, so as to carry out weighted data fusion estimation. The numerical simulation and the application of radar measurements show that the algorithm does not require a priori information such as historical statistics, is fast in real-time calculation and has a certain degree of robustness, which can improve the accuracy and stability of radar guidance data to a large extent.
Improving Monocular Depth Estimation in Traffic Scenarios by Polarization

Depth estimation in traffic scenarios is a significant task of environment perception in autonomous driving. The traditional methods use monocular RGB images to predict depth. However, with the development of sensor technologies, polarization cameras have emerged. Since polarized images contain more information than normal RGB images, it is necessary to explore the depth estimation based on such images. In this work, we firstly establish a new dataset which collects polarized images of four different scenes including both day- and nighttime, while sparse depth values were collected at the same time. In total, our polarization dataset contains over 3k frames of polarized images. Based on experiments on this dataset, we demonstrate that the performance of monocular depth estimation can be improved by nearly 20% with the aid of polarized images, revealing the great potential of polarized images for depth estimation tasks. The code of our method will be made public at https://github.com/MandeISun/PolarDepthVal.
Selection of Architectural Patterns based on Tactics

Software development has several stages to be followed by developers, starting from planning to deployment and maintenance. From those stages, the design phase, which produces blueprint of a system, needs an expert work since it is a creative and difficult stage that determines the behavior of the software. Hence, to design a good architecture that exhibits the desired behaviors, we need to select an appropriate architectural pattern. Selecting an architectural pattern for software depends on features like Quality Attributes (QA), functional requirements, and constraints. Due to the discrepancy of selection features architects face difficulty in the selection of the right pattern for the system. We have used tactics as a selection feature owing to the fact that tactics are building blocks of architectural patterns. In this study, we have built a model that detects tactics implemented in single software and then using custom built algorithm to recommend architectural pattern. The proposed tactic classification model used Natural Language Processing (NLP) for pre-processing of the datasets. We used Term Frequency-Inverse Document Frequency (TF-IDF) and word2vec vectorization after preprocessing to convert the textual data format to vectorized form. Furthermore, for classification, we used three different machine learning algorithms i.e., Support Vector Machine, Decision Tree, and Naive Bayes. To reach out high performance, we ran six different combinations of experiments. We selected TF-IDF with SVM combination since it performed better than the others with the accuracy value of 94%. Finally, we used a custom-built algorithm for the recommendation of an architectural pattern based on the tactics identified from the model and the second dataset. In this study we had conducted two prototypic experiment to evaluate our model along with custom-built algorithm. From thus two experiments, we had got most precise recommendation of architectural pattern for the system having specified tactics.
Binocular Infrared Depth Estimation Based On Generative Adversarial Network

Dedicated to the fulfillment of the all-weather scene perception of intelligent vehicle, our work focus on depth estimation based on binocular infrared image. First of all, considering the rareness of binocular infrared datasets, we apply a top-down attention and gradient alignment based on generative adversarial network (GAN) to convert binocular visible datasets into pseudo-infrared datasets, which was used to fine-tune the depth estimation network. In this way, the basal function of binocular infrared depth estimation could be achieved. Then, the enhancement and super-resolution (SR) preprocessing method based on high-order degradation modeling process was introduced to enhance the image edge information and enrich the image details. The experiment results showed that the image details are richer after SR, which improves the accuracy and breadth of binocular depth perception by 5%.
Research on Obstacle Recognition Methods of Unmanned Vehicles in Off-road Environment

The off-road environment is complex, and the accurate obstacle detection of unmanned vehicles in the off-road environment is difficult. In order to identify the obstacles in the off-road environment with low cost, real-time image recognition and high efficiency, the Faster R-CNN optimized by 4 methods is designed in this paper. Comparing with the original model, SSD and YOLO, our optimized model shows obvious advantages in obstacle identification in the off-road environment.
Using Gaussian Process in Clockwork Variational Autoencoder for Video Prediction

Accurately predicting long video sequences has been an open challenge for the machine learning research community, and we try to address it in this study. We present Gaussian Process Clockwork Variational Autoencoder (GPC-VAE), a generative model which combines the Gaussian Process with the Clockwork Variational Autoencoder (CW-VAE). We use this new variant of the VAE model for unsupervised learning on the video prediction task. We conduct a series of experiments on MineRL and MMNIST, two publicly available datasets consisting of video sequences. We evaluate our model using two well-known image quality metrics and discuss its performance compared to the benchmarks in video prediction. The results indicate that our model can outperform the baselines of video prediction in certain scenarios, but the generalizability of our generative model still needs to be improved.
A comparative study of quadrotor UAV motion control with experimental validation

Quadrotor UAVs have been widely used in many fields due to their merits of flexible maneuverability, vertical take-off as well as landing, and hovering. To achieve successful engineering applications, effective motion control of quadrotor UAVs should be guaranteed. This paper conducts a comparative study of motion controller designs for the quadrotor UAV. First, the commonly used attitude representation methods are compared and analyzed, and the dynamic model expression of the quadrotor UAV is established. Then, three typical motion controllers are introduced, which are the PID controller, integral backstepping controller, and fuzzy robust controller. Finally, based on the QDrone as well as the related experimental platform, the performance of these three controllers is tested. Some discussions are carried out based on the experiential results, which excepts to provide some guidance to the flight control system design for quadrotor UAVs.
A Graph-search based Trajectory Planning Approach with Longitudianl Priority

Most optimizations for trajectory planning with autonomous vehicles first decouple the lateral-longitudinal-time problem, transforming decision-making into a matter of limited choices. Potential alternatives that can lead to better performance have been ruled out once the trajectory has been predefined in spatial dimensions, such as path optimization. This paper mainly draws on the optimization method from the Apollo EM Motion Planner and proposes a simplified, graph-search based approach by putting the decision-making process as well as the speed optimization first. The approach mainly concentrates on multilane scenarios with high-speed autonomous driving in a two-layer manner. The first layer is the combination of decision-making and longitudinal planning, whose purpose is to provide optional speed profiles under different target distances and local decisions. The second layer supplements the lateral planning process depending on every achievable speed profile. Dynamic programming is selected in both two layers to search the best discrete sampling path, because the optimization mode with longitudinal priority reduces pruning effort of dynamic programming in the second layer. Compared with the EM Motion Planner, this approach makes up the possible loss with different target distances and local decisions, it also simplifies the detection of obstacles. In the simulation, the capabilities of the approach are testified under a high-speed, multilane scenario with obstacle vehicles with random speed.
Ensemble Learning for Sentiment Analysis on Twitter Data Related to Covid-19 Preventions

The World Health Organization (WHO) declared the 2019 Coronavirus disease outbreak (Covid-19) as a pandemic and made it a trending topic on social media platforms, such as Facebook and Twitter. Unfortunately, news and opinions shared on social media affect people's mentality and create panic situations in society, but in the other hand, these opinions can be analyzed using sentiment analysis approach to generate knowledge and insight for the local government to monitor people reaction to the policies that have been issued to prevent the outbreak of Covid-19 virus. Therefore, this work aimed to propose an ensemble learning model that can classify the sentiment inside the people's opinions from Twitter. The ensemble model used Naïve Bayes Classifier, C4.5, and k-Nearest Neighbors as base learners with voting mechanism to generate the final decision. For learning, the ensemble model used a dataset containing 3884 clean data that was successfully downloaded using Twitter API related to Covid-19 outbreak prevention and processed using TF-IDF method. The dataset has two classes, i.e., ‘positive’ and ‘negative’ to represent the sentiment of the opinion in each data. The proposed model got 80.61% of accuracy, 79.49% of recall, and 81.20% of precision, after being evaluated using 10-fold Cross Validation. It also performed better when compared to several learning models using only single Machine Learning algorithm.
Multi-dimensional Attention Network for Vehicle Re-identification

Vehicle re-identification (vehicle ReID) is the key point in intelligent transportation systems, which mainly focus on extracting global features by a network of surveillance cameras with non-overlapping fields of view. However, vehicle ReID becomes a more difficult task when taking several aspects into account, e.g. inter-class similarity, intra-class variability, point-of-view variability, and Spatio-temporal uncertainty, which makes feature extraction very difficult. To solve such a problem, we introduce the attention mechanism into the Convolutional Neural Network (CNN) to improve the re-recognition ability of the module. A novel attention based CNN is proposed to calculate the attention weights of multi-channels by crosss-dimensional interaction to strengthen the connection between channel attention and spatial force, which enables the network to better extract the features of vehicles. We evaluate the performance of our network on VeRi-776 and VeRi-Wild datasets currently available. The experimental results demonstrate the effectiveness of our proposed method in vehicle re- recognition tasks.
Personalized learning paths Recommendation based on Learner Personas

With the rapid development of information and technology, internet learning resources, especially online learning platforms explosive developed in recent years. The problem of information overload and learning disorientation is particularly outstanding. Studies on personalized learning paths recommendation are important for the development of online learning. This research presents a method to form personalized learning paths based on learner personas. Learner personas are constructed based on their different characteristics. Based on the learner personas, and combined collaborative filter recommendation algorithm, the personalized learning paths consist of the path of the learning cells sequence network. Personalized learning paths will save learning time and improve learning achievements by providing learners with the most appropriate learning sequence and precise resources.
Design and Analysis of the Folding Handrail Used Analytical Method Based on Linkage Mechanism

Folding handrail device is widely used in helping the elderly, the disabled and rehabilitation training population. On the basis of analyzing the requirements of folding handrail fully, the motion conversion process of folding handrail is analyzed, then the design problem is proposed in this paper based on the linkage mechanism, the mathematical model of the linkage mechanism is established, and design and solution of analytic method for the linkage mechanism is also involved by MATLAB, so the scale synthesis is completed finally. The transmission angle and motion range of the designed linkage are analyzed and verified. The analysis shows that the designed linkage has good force transmission performance and reasonable motion range, which meets the requirements of practical use.
ARefine: Efficiently Storing Surveillance Videos via Accuracy-aware Refining Mechanism

Benefiting from the automatic video analysis technology, high-definition surveillance cameras have been increasingly deployed all over the city. The deployment of these cameras facilitate social management dramatically, but the huge amount of recorded video data puts tremendous pressure on the existing storage system. This paper proposes ARefine, an efficient accuracy-aware refining mechanism that adaptively compresses surveillance videos based on video content and the prediction accuracy of video analytics. ARefine first explores the potential content redundancy inherent in varied video slices from the perspective of video analysis. And then, ARefine designs a content-sensitive video refining method to condense video content dramatically without compromising the prediction accuracy. Experimental results show that ARefine significantly reduces the storage cost by 43.0%-75.0%, with a slight and controllable reduction in prediction accuracy (5%).