Reviewerly/exHarmony huggingface dataset:
Design of Real-Time Obstacle Avoidance and Race Path Planning System for Unmanned Vehicle

Nowdays the local path planning has received considerable attention and achieved good effect in known environment. However, obstacles avoidance is a huge challenges in some unstructured environments and other factors. Meanwhile, the service efficiency of unmanned vehicle is greatly limited by the path planning efficiency. To solve these problems, a real-time obstacle avoidance and racing planning system for unmanned vehicles based on multi-sensor fusion is proposed which has a good balance between the success rate of driving safety and decision-making efficiency of unmanned vehicles in unknown environments. Combining the advantages of virtual force field and velocity obstacle, an obstacle avoidance model is applied to design a PID controller with subsection control for Ackerman chassis structure, which realizes efficient obstacle avoidance and high speed driving of unmanned vehicles. Finally, a dynamic weighted A* algorithm path planner with corner penalty is proposed for path planning in this work. Simulations and experimental results show that the obstacle avoidance ability of the unmanned vehicle which maintain a high speed is improved compared with the traditional tracking algorithm. And the path planning efficiency is improved compared with the traditional A * algorithm.
LAGIM: A Label-Aware Graph Interaction Model for Joint Multiple Intent Detection and Slot Filling

Multi-intent spoken language understanding joint model can handle multiple intents in an utterance and is closer to complicated real-world scenarios, attracting increasing attention. However, existing research (1) usually focuses on identifying implicit correlations between utterances and one-hot encoding while ignoring intuitive and explicit original label characteristics; (2) only considers the token-level intent-slot interaction, which results in the limitation of the performance. In this paper, we propose a Label-Aware Graph Interaction Model (LAGIM), which captures the correlation between utterances and explicit labels’ semantics to deliver enriched priors. Then, a global graph interaction module is constructed to model the sentence-level interaction between intents and slots. Specifically, we propose a novel framework to model the global interactive graph based on the injection of the original label semantics, which can fuse explicit original label features and provide global optimization. Experimental results show that our model outperforms existing approaches, achieving a relative improvement of 11.9% and 2.1% overall accuracy over the previous state-of-the-art model on the MixATIS and MixSnips datasets, respectively.
Accelerating Safety Verification of Neural Network Dynamical Systems Using Assured Compressed Models

In this paper, we develop a formal assured simplification method to accelerate the safety verification process of a neural network model that approximates a nonlinear dynamical system. Firstly, an assured compression error is proposed to describe the maximum output difference introduced by neural network compression. A reachability-based computational process is then developed to estimate the assured compression error. The assured compression error is used to accelerate the reachability analysis and safety verification of neural network dynamical system models, which can significantly reduce the computational time for safety verification. Finally, the proposed approach is applied to a case study of a magnetic levitation system to demonstrate its correctness and effectiveness.
Human posture recognition based on wearable sensor

In recent years, with the development of economy and technology, people were increasingly concerned about health, and at the same time, human behavior data based on wearable devices revealed great research value for human health. Currently, most human recognition study focused on video images, however, human behavior recognition using video images showed the following shortcomings: Firstly, it was necessary to install and fix the equipment in advance and the range observed by the equipment was limited; Secondly, it was the privacy controversy involved in human behavior recognition based on video images and the inconvenience caused by the joint parts obscured. Therefore, this study investigated human behavior recognition for wearable sensor data and constructed a body sensor-based behavior recognition system, which used a multilayer CNN-BiLSTM with one-dimensional convolution for extracting features, then combined a bi-directional long and short-term memory network (BiLSTM) that was good at processing time series data, and finally used a fully connected layer for classification to obtain the final The final recognition result was obtained by using a fully connected layer for classification. The system had been experimented on two publicly available datasets (MHEALTH and USCHAD), and achieved good effects, with recognition accuracy of 98.82% and 97.62% on the MHEALTH and HPAT datasets, respectively, showing that the system had good generalization capability.
TinyML optimization for activity classification on the resource-constrained body sensor BI-Vital

Addressing the demand for scalable and efficient deployment of machine learning (ML) models on mobile devices and especially microcontrollers, this research introduces a hardware-in-the-loop (HIL) deployment setup within the producer-consumer software architecture of the BI-Vital, a chest-mounted body sensor designed by our research group. The sensor provides real-time monitoring of various physiological and environmental parameters, enhanced by the integration of tiny machine learning (TinyML). Leveraging the UCI-HAR dataset for Human Activity Recognition (HAR), this study focuses on optimizing ML models’ hyperparameters to balance inference time, memory, accuracy, and power consumption. An efficiency score E is proposed to assess models in this unique context. The results illustrate the trade-offs between the models: Decision Trees offer reduced power usage, Multilayer Perceptrons ensure high accuracy with minimal memory requirements, while Convolutional Neural Networks present limitations due to extended inference times. The results emphasize the potential of TinyML in wearable physiological monitoring, especially for optimizing models for resource-limited devices in real-world applications.
Target Detection in Remote Sensing Images Based on Multi-scale Fusion Faster RCNN

Target detection in remote sensing images is widely used in civil and military applications and plays an increasingly important role. In this paper, a novel multi-scale fusion Faster RCNN model is proposed to realize target detection in optical remote sensing images. First, the preprocessing operation is performed on the dataset by using VOC format for pretreatment and data enhancement. Second, based on traditional Faster RCNN architecture, the EfficientNetb0 and FPN is combined to extract multi-scale fusion image features, so as to obtain pyramid feature and improve the ability of network. Finally, the prepared dataset is used to train the multi-scale fusion Faster RCNN model, and then the prediction results are obtained by ROI Polling. The experimental results demonstrate that the proposed method in this paper can achieve higher accuracy as high as 84.4% of target detection in remote sensing images while reducing the size of the model.
A YOLOv5-based method for detecting vehicles in overhead surveillance video

As one of the core technologies of intelligent transportation system, vehicle detection technology has high requirements for its detection accuracy. For the characteristics of small vehicle targets and complex background in overhead surveillance video, this paper proposes a novel method by improving the original YOLOv5 algorithm. First, the very small target detection layer MSD (Minimal Scale Detection) is added to YOLOv5 network to improve the detection effect of YOLOv5 network on small target vehicles in overhead surveillance video. Second, the CA (Coordinate Attention) attention mechanism is added to the YOLOv5 network to further improve the detection accuracy. Third, the CIOU (Complete Intersection over Union) focal loss function used in the original YOLOv5 is replaced with EIOU (Efficient Intersection over Union) to accelerate the convergence of the prediction box and improve the regression accuracy of the prediction box. By testing on the self-built dataset, compared with the original YOLOv5 network, the mAP (mean Average Precision) value of the proposed method is improved by 3.01%, the Precision is improved by 1.33%, and the Recall is improved by 1.12%. The overall network performance is improved comprehensively, which is more suitable for the detection of small target vehicles in overhead surveillance video.
ICRFormer: An Improving Cos-Reweighting Transformer for 3D Human Pose Estimation in Video

Monocular 3D human pose estimation is a difficult task due to depth blur and occlusion. Spatial-temporal information is used in recent methods to alleviate these problems. However, recent methods usually utilize the original Transformer architecture and ignore the impact of efficient Transformer on 3D HPE. Therefore, we propose an improving Cos-Reweighting Transformer (ICRFormer) to estimate 3D human pose with better performance. In detail, we firstly analyze the general expressions of attention and propose an efficient Transformer architecture named Enahnced Transformer (EFormer). Then, to further extract the association among different sequences, we propose a Cos-Reweighting module to strengthen the ability of correlation calculations. Abundant experiments are conducted on two commonly used datasets: Human3.6M and MPI-INF-3DHP to evaluate ICRFormer. It is shown that our model achieve competitive performance on Human3.6M and obtains the state-of-the-art result on MPI-INF-3DHP. Compared with the most recent method MHFormer, our model outperforms it 0.2% MPJPE on Human3.6M dataset and 43.4% MPJPE on MPI-INF-3DHP.
A Vision-based UAV Tracker Aiming at Aerial Targets

Autonomous unmanned aerial vehicle (UAV) tracking has been widely used in inspection, rescue, follow-up and many other fields. Compared to ground targets, aerial targets are smaller, more flexible and possess more freedom of motion, which makes the task more challenging. In this paper, we propose a vision-based UAV tracker aiming at aerial targets. Firstly, a lightweight detection model is deployed for target detection and target’s position is estimated taking advantage of detection box information and camera internal parameters. Then, a flexible motion prediction method is proposed followed by an efficient and occlusion-aware front-end path searching approach. Finally, an optimization-based multicopter trajectory planning framework is employed to generate a spatio-temporal optimal trajectory. The experimental results indicate that our tracker achieves great robustness and security in both vertical and horizontal tracking.
Strategy Implementation and Key Algorithm of NoGo Program in Computer Games

For NoGo, an efficient, feasible, easy to understand and use placement feasibility judgment algorithm is designed in the paper. It analyzes the three elements of NoGo, designs the corresponding physical structure, introduces a game strategy used in actual combat, and analyzes in detail the stacking algorithm of the position of adjacent pieces of the same color, the calculation algorithm of Qi and the judgment algorithm of landing feasibility, so as to finally realize these key algorithms in the computer game program of NoGo. The experimental results show that the key algorithm meets the requirements of chess rules, and they are safe and reliable, and the operation speed is fast.
Short-term PV power prediction model based on weather feature clustering and Adaboost-GA-BP

Based on the weather feature clustering, an optimized algorithm called Adaboost-GA-BP is presented for solving the problem that BP neural network is easy to fall into local extremum and over-fitting in PV power short-term prediction. Initially, the K-means algorithm is applied to cluster the historical data into sunny, cloudy and overcast&rainy day. It is then the GA that optimize initial weights, forming the GA-BP neural network. The idea of Adaboost algorithm is applied to train GA-BP as different weak predictors so that the Adaboost-GA-BP strong predictor is combined. Finally, this paper provides a detailed performances comparison of various models. The Adaboost-GA-BP predicts a 4.4% decrease in MAE and a 4.1% decrease in RMSE for the sunny test set, a 9.0% decrease in MAE and a 13.5% decrease in RMSE for the cloudy test set, and a 2.7% decrease in MAE and a 9.1% decrease in RMSE for the rainy test set, outperforming the BP model, the GA-BP model, and the Adaboost-BP models. After comparative analysis, the model has better prediction accuracy and generalization ability especially for predicting cloudy weather.
Remote sensing image detection based on feature enhancement SSD

For lightweight networks, SSD identifies dam objects in high-resolution remote sensing images, and the complex background information redundancy and similar negative sample interfere with the serious problem; this paper proposes the SSD target detection algorithm based on feature enhancement. Firstly, the appropriate pre-selected box is obtained through k-means clustering, and the RepVGG network is introduced as the backbone network. Then, the ASPP perceptual field enhancement mechanism is added to the top of the network. Finally, the classification and regression part of the network combines the idea of FPN cross-layer connection and SE attention mechanism to obtain the final detection result. To verify the validity of the improved method, the homemade dam remote sensing data sets will improve the methods, such as SSD, FSSD, and Faster R-CNN target detection methods are compared. The experimental results show that the method's precision, recall, and mAP reached 0.8690, 0.7074, and 0.7815, respectively, and the mAP is improved by 11% compared to SSD. In summary, the improved method can extract the target from the complex background of high-resolution images more accurately.
Open-set Image Classification Via Subdomain Alignment

Domain adaptation has achieved great success in using labeled source domain samples to identify unlabeled target domain samples. Here, we aimed to solve the open-set domain adaptation, which is different from the closed-set domain adaptation in that it contains categories in target domain that do not appear in source domain. To solve this problem, this paper proposes open-set domain adaptation model based on subdomain alignment, which uses variable weights for discriminative training of unknown samples in target domain. Aiming at the distribution differences between domains, the model aligns the distributions of the category subspaces of source and target domains, enhancing the distribution similarity within the subspaces of the same category. Through experiments on different domain adaptation datasets, the results show that the model proposed in this paper effectively improves the accuracy of open-set domain adaptation classification.
A Distributed Framework for Deep Reinforcement Learning by Consensus

This paper proposes a distributed training framework for deep reinforcement learning algorithms to address large-scale problems with privacy protection. First, we design a hierarchical decentralized communication topology with a server to alleviate the heavy burden on the central server. By pushing the model updating and data storage to the edge side, it not only unleashes the computing potential of the terminal devices but also shortens the response time. Second, the consensus algorithm is applied for all agents over the decentralized topology to approach each other, where each agent only requires neighboring information to achieve consensus. Then, the distributed training framework for deep Q-networks is designed based on the consensus algorithm, which consists of local learning and global consensus. Finally, the simulation demonstrates that the proposed distributed learning deep Q-networks shows better performance than its centralized learning counterpart.
Anti-occlusion Object Tracking Algorithm using Response Template Fusion and Combined Features

The kernel correlation filtering algorithm cannot judge the occlusion by itself when the object is occluded by foreign objects, and the tracking performance is greatly affected. Aiming at this problem, this paper proposes an anti-occlusion object tracking algorithm that fuses response templates and combined features. The peak sidelobe ratio (PSR) is used to determine whether the object is occluded. When the object is partially occluded, a combined feature representation object model is introduced to enhance robustness, and a multi-response template with anti-occlusion performance is constructed to improve the performance of the object when it is occluded by foreign objects. The problem of reducing the available information, thereby improving algorithm performance. Experiments in the OTB-100 data set can prove that the success rate and accuracy of the algorithm have competitive results compared with the comparison algorithms.
An Extensive Study of Frequent Mining Algorithms for Colossal Patterns

During the last decade of research, a lot of focus has been placed on the subject of frequent pattern mining (FPM). A profitable data set with a large sum of transactions and only a few items in each transaction has been used to develop numerous FPM algorithms. Because of the rise of bioinformatics, a new sort of data set called a high-dimensional data set has emerged, with fewer transactions but a greater sum of elements in each. The execution time of classical algorithms grows with deal length. High-dimensional data sets can’t be processed by existing algorithms. But when applied to large data sets with a lot of dimensions, mining algorithms generate a huge amount of data, much of which is useless to scientists because of the little and medium-sized patterns they include. As a way to lessen the number of output patterns for mining patterns, colossal pattern mining is discussed. Since small and mid-sized patterns aren’t mined, mining algorithms for enormous patterns run faster. In this work, an extensive study of colossal patterns, existing mining algorithms with its drawback is mentioned. The definitions of FPM, high utility mining and relation of colossal patterns with others are also explained. Pattern-Fusion is the first algorithm, which is developed for colossal patterns that is described briefly in this work.
Machine Learning-Based Identification as Well as Classification of Functional and Non-functional Requirements

In software engineering, it has become necessary to divide needs into functional and non-functional categories. This article identifies and categorizes functional (F) and non-functional (NF) needs and subclasses of NF requirements. Additionally, the subclasses of NF requirements will be discussed. A chunk of the data was gathered from various resources found on the internet. In this investigation, we began by cleaning the data by employing the normalization processes, and we then moved on to the succeeding steps, which included text preparation and vectorization. The topics covered included confusion matrix, Bag of Words, Term Frequency-Inverse Document, Featurization and Machine Learning Models, ROC and AUC curves, Bi-Grams and n-Grams in Python, and Word2Vec. The early discovery of NFRs allows us to make preliminary design choices. We used machine learning algorithms to detect and categorize both functional and non-functional needs for application software development, based on the user requirements provided to us. This article aims to assist in the application development process to various software professionals, including software developers, software designers, software testers, and so on. This paper is also beneficial for developing software more quickly and delivering it to customers. Taking this step makes it easier to create an SRS document and helps the requirement analysis phase proceed more smoothly by reducing the amount of unneeded labor and complexity.
An Integrated Deep Learning Deepfakes Detection Method (IDL-DDM)

Deepfakes have fascinated enormous attention in recent times ascribable to the consequences of threats in video manipulation. Consequently, such manipulation via intelligent algorithm contributes to more crucial circumstances as electronic media integrity become a challenging concern. Furthermore, such unauthentic content is being composed and outstretched across social media platforms as detecting deepfakes videos is becoming harder nowadays. Nevertheless, various detection methods for deepfakes have been schemed, and the accuracy of such detection models still emerges as an open issue, particularly for research communities. We proposed an integrated deep learning deepfakes detection model namely IDL-DDM to overcome ongoing criticism, i.e., difficulties in identifying the fake videos more accurately. The proposed IDL-DDM comprises side-by-side deep learning algorithms such as Multilayer Perceptron and Convolutional Neural Network (CNN). In addition, the Long Short-Term Memory (LSTM) approach is applied consecutively after CNN in order to grant sequential processing of data and overcome learning dependencies. Using this learning algorithm, several facial region characteristics such as eyes, nose, and mouth are extracted and further transformed into numerical form with the intention to identify video frames more precisely. The experiments were performed via different datasets such as the Deepfakes Detection Challenge Dataset (DFDC) and Unseen (YouTube Live) videos which comprise a wealth of original and fake videos. The experimental results represent a higher achievement for the IDL-DDM in contrast to other previous similar works.
Transformer Model for Human Activity Recognition Using IoT Wearables

Human activity recognition plays a vital role in the modern life of its immense applications in medical care, sports, detection and prediction of unpredicted events and in biometrics field. HAR is the field of study for recognizing human activities from time-series data collected from video, images or from multimodal sensors equipped with smartphone, IoT wearables. A wide area of research in the field of HAR has been presented till now from machine learning and deep learning. Deep learning methods utilize the temporal view of the data, while shallow methods utilize the handcrafted features, statistical view. Most of the deep learning methods utilized CNN, LSTM architecture. Now its time to use the power of transformers in this area. For sequence analysis tasks, transformers were shown to outperform the deep learning methods. Transformers are proved to be best in finding relevant time steps and has the potential to model long term dependencies. In this work proposed, a novel method that employs a transformer-based model to classify human activities from data collected using Intertial measurement Units (IMU). This approach works by leveraging self-attention mechanisms in transformers to explore complex patterns from time-series data. In this paper, a transformer-based approach is experimented for HAR and its evaluation parameters are presented for two data sets USCHAD and WISDM. From the results, we can analyse the improvement in performance matrix and efficiency of the transformer-based model.