Reviewerly/exHarmony huggingface dataset:
Research on smart wearable clothing for the senior citizen

In recent years, as China has entered an aging society, the safety monitoring problem of the senior citizen still needs to be solved, and the wearable intelligent devices for the senior citizen have become a research hotspot in recent years. This paper mainly monitors the movement of the senior citizen from several modules such as Beidou Satellite Positioning System and senior citizen fall monitoring. If the system detects the fall of the senior citizen, the system will immediately send positioning information to the guardian's mobile phone Help SMS through visible light communication and use the circular voice help module in the system to ask passers-by for help, so that the senior citizen can get timely help and treatment after falling, and reduce the risk of aggravation and even death.
Few-shot Object Counting and Detection with Query-Guided Attention

The focus of this paper is on Few-Shot Counting and Detection (FSCD), a task that involves counting and localizing target objects based on a few exemplar bounding boxes. In particular, we address two major challenges in developing a FSCD model: the high cost of bounding box labeling and the large variations in object appearance. To mitigate the former issue, we propose a neighbor distance-aware mechanism for generating pseudo bounding boxes. This mechanism utilizes neighboring objects as context to estimate the location and size of the target object without requiring training. To address the challenge of appearance variation, we introduce a novel query-guided attention module that enhances the visual features of the search image by employing multi-head cross attention with query features. The module is designed to encourage attentive inspection of the search image by directing the model to focus more on regions that share similarities with the target objects. We integrate the query-guided attention module into the Faster-RCNN object detection model, resulting in a new few-shot object detector named Counting-RCNN. The proposed approach outperforms the state-of-the-art method on a large-scale FSCD147 dataset, achieving 0.60 MAE, 5.36 RMSE, and 13.01% AP50 improvement.
Multi-Resolution Diffeomorphic Image Registration with Convolutional Vision Transformer Network

In recent years, the research of medical image registration based on convolutional neural network (CNN) has attracted much attention. In particular, the deformable image registration method based on diffeomorphism seems to have achieved promising results due to its unique topology conservation and transformation reversibility. However, the results of most existing learning-based approaches are not necessarily diffeomorphic. Moreover, due to local receptive fields caused by convolutional inductive bias, CNNs usually have limitations in catching the global and remote spatial relationships between points in anatomical images. Vision Transformer (ViT) shows tremendous advantages in modeling long-term dependencies in sequential images due to its embedded self-attention mechanism. Therefore, we propose a hybrid convolution Vision Transformer Network (CViT) model based on multi-resolution diffeomorphism. The model employs a multi-resolution strategy to learn global connectivity and local context of medical images in the diffeomorphic mapping space, which can simultaneously integrate the advantages of CNN and ViT to provide a better understanding of spatial correspondence. We evaluate our approach respectively on a large scale and a small scale dataset of 3D brain MRI scans, gaining an average Dice of 0.813 on the OASIS dataset. Extensive quantitative and qualitative results show that our method achieves state-of-the-art performance while maintaining desirable diffeomorphism.
LanYUAN, a GPT large model using Curriculum Learning and Sparse Attention

In 2021, the Inspur AI Research Institute introduced the AI Megatron Model Yuan-1.0, a massive Chinese language AI model containing 245.7 billion parameters. This model surpassed OpenAI's GPT-3, making it the world's largest Chinese NLP model. Although the model was pre-trained using Nvidia's Megatron framework with model parallelism, data parallelism, and pipelining optimizations, there is still room for improvement in terms of training time, cost, and convergence. To achieve better performance, this paper investigates the impacts of batch size and learning rate on model training time and accuracy to balance model performance. We replaced the pipelining optimization with the more efficient DeepSpeed framework, and combined DeepSpeed's ZeRO-based data parallelism with Nvidia's Megatron-LM model parallelism to achieve higher performance on Nvidia GPU clusters with high-bandwidth interconnects. Additionally, we used a curriculum learning-based method and four types of sparse attention as a new optimization approaches. The results showed that the training time was reduced by 20% and the throughput increased by 20% compared to the 47 billion parameters Yuan-1.0 model. Approximately, the optimized model achieved performance improvement in downstream tasks with the same training data.
Research on small target Detection Algorithm of UAV based on Mask R-CNN

The purpose of this paper is to improve the detection accuracy of UAV small target in the high-voltage tower scene. Firstly, this paper constructs the small target data set of UAV with three different models and enhances the data set. Then, Mask R-CNN is used as the network architecture and the Mask R-CNN algorithm is improved. The detection accuracy of the improved algorithm for 20×20∼32×32 small targets is increased to 84.63%, the missed detection rate is reduced to 13.34%, and the false detection rate is reduced to 16.49%.
Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss–Markov Processes

In this article, we consider the problem of remote state estimation of a Gauss–Markov process, where a sensor can, at each discrete time instant, transmit on one out of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> different communication channels. A key difficulty of the situation at hand is that the channel statistics are unknown. We study the case where both learning of the channel reception probabilities and state estimation are carried out simultaneously. Methods for choosing the channels based on techniques for multi-armed bandits are presented, and shown to provide stability. Furthermore, we define the performance notion of estimation regret, and derive bounds on how it scales with time for the considered algorithms.
Human Activity Classification Based on Data Analysis and Feature Extraction

Human behavior recognition is one of the most important research directions in the field of computer vision, and it plays an important role in the fields of rehabilitative medicine, auxiliary security, and scene entertainment. To address the shortcomings of traditional HAR recognition methods with tedious feature extraction and severe overfitting, we propose a human behavior recognition model based on XGBoost and feature simplification methods with a limited data set. The model uses the XGBoost algorithm to classify the collected sensor data to recognize human behaviors. In addition, to improve the efficiency and accuracy of the model, we also propose a feature simplification method to reduce the computational complexity and the risk of model overfitting by reducing the number of features. Experimental results show that the model has high accuracy and computational efficiency and can be applied to different human behavior recognition scenarios.
A Server-side Dynamic Bitrate Generation for Live Streaming

In live streaming, the adaptive bitrate algorithm is an effective way to improve user's quality of experience (QoE). However, the majority of existing adaptive algorithms still have problems of performance degradation in a network environment specifically. When players download chunks of video, it is difficult for the server that provides fixed bitrates to provide suitable chunks for most users. In order to allow the bitrates generated by the server to adapt to the changing network environment, we propose a server-side dynamic bitrate generation algorithm that determines the bitrates to be generated according to clients' bandwidth information. In order to reduce the complexity of the algorithm when a large number of users are online, a clustering algorithm is also used to classify the users’ network environments. Additionally, the client selects the bitrate by considering the bandwidth utilization, difference in quality, and buffered playback time. In order to test our dynamic bitrate generation algorithm, we designed an emulator that supports simulation of multi-user online activity, and our simulation results show that compared to a fixed bitrate schema, our dynamic bitrate generation algorithm can improve user's QoE in several different network environments.
Text Sentiment Analysis Method Based on Support Vector Machine And Long Short-term Memory Network

Machine learning is a hot technology today and plays a pivotal role in text sentiment analysis [1]. Text has complex properties such as semantic word order grammar and contextual relationship, so the accuracy of text sentiment analysis faces significant challenges. There are some classic methods in the industry for text sentiment analysis, such as Support Vector Machines (SVM) and Naive Bayes[2]. These methods are strongly related to feature extraction, with high complexity and average performance. With the development of neural network technology, people began to use neural network models for text sentiment analysis, but compared with traditional methods, neural network processing corpus is more accurate, but slower. Therefore, this paper adopts the method of combining classical algorithm model and neural network model for text sentiment analysis, which can improve the processing efficiency without changing the accuracy.
The development status, risks and countermeasures of digital works NFT in the metaverse

With the rapid development of the world today, the metaverse is increasingly affecting society. At the same time, NFT has continuously become a hot word in technology in recent years. This paper studies the economic cycle and its application in the virtual and real society under the background of the metaverse, the development status of digital works NFT, its role in the virtual and real society, and sorts out various problems about risks in finance, content, copyright, etc. Besides, it puts forward countermeasures from the aspects of legal supervision, technical means, management system, etc., which are of great significance to the healthy and orderly development of NFT, social governance, and digital economy planning and decision-making in the metaverse.
Research on the optimal path planning technology of waste transfer vehicle based on Dijkstra's algorithm

Abstract: In order to solve the problem that the waste transfer truck may not take the shortest route to the waste treatment plant after collection, thus affecting the efficiency of waste treatment and increasing the time and money cost of waste treatment, in this paper, a path planning model based on Dijkstra's algorithm is constructed to iteratively solve for all possible driving paths from the perspective of the shortest path as well as the lowest cost. Finally, the optimal path for the waste transfer vehicle to reach the end point, i.e., the waste disposal station, from the initial point while avoiding obstacles is derived, and the algorithm is verified using Matlab software, and the reasonableness of the model and the feasibility of the algorithm can be derived from the final results.
Video Reconstruction Method Based on Spatio-Temporal Correlation

As one of the underlying tasks of computer vision, image and video reconstruction have an extensive research value. Video is interpreted as a sequence of images arranged based on a timeline. The spatio-temporal correlation leads to adjacent sequences contain amounts of complementary information. The optical flow is typically applied to estimate and align inter-frame motion in popular video super-resolution reconstruction methods. However, due to the prerequisite assumptions of optical flow algorithms, many existing video reconstruction techniques are inadequate for fast movement in complex scenes. Therefore, we propose a multi-frame motion alignment strategy based on a motion estimation method without optical flow. The temporal dimension is introduced in the motion estimation for deep convolution to extract features. The bidirectional motion compensation can obtain more complementary information from adjacent sequences. To evaluate the super-resolution improvement of our model in complex motion scenes, the publicly available video dataset at the NTIRE19 super-resolution challenge REDS is used for training and testing. We compare the proposed model accuracy with four super-resolution methods based on optical flow. Our model reaches the best precision in spatio-temporal correlation metrics.
Improved wavelet packet threshold denoising method for complex electromechanical hydraulic system

Due to the complex and changeable working environment and the long-term repeated impact of complex alternating loads in the operation process, the detection signals of complex electromechanical hydraulic systems often show the characteristics of messy waveform, wide spectrum, time-varying and non-stationary [1]. Therefore, the weak fault signal is easily submerged by various noises. How to suppress the noise, enhance the weak fault signal and the original useful vibration signal and retain them without distortion under different signal-to-noise ratios is the prerequisite for intelligent health monitoring of electromechanical and hydraulic systems. What's more, an improved wavelet packet threshold denoising method is proposed for the real-time online signal denoising requirements of the electro-hydraulic system, and the superiority of the proposed method is verified on the simulation and measured data.
Visual system design of tower crane based on improved YOLO

In view of the defects of the existing tower crane visual system in Cottonwood, this paper proposes a scheme to connect the video camera and PC to local area network through routers, switches, wireless bridges and other devices. The video data real-time transmission is realized by integrating the Web control package of the camera in the upper computer and accessing the monitor screen through IP address. At the same time, in order to solve the problem of narrow scope and big blind area of traditional direct-viewing camera, a new hook detection algorithm based on improved YOLO is proposed and applied to tower crane safety monitoring system, get the ROI of the picture. The P and T parameters of the camera are calculated and the rotation of the spherical camera is controlled to realize intelligent tracking of the hook.
Deep Reinforcement Learning Based UAV Mission Planning with Charging Module

This paper focuses on UAV mission planning in an environment with charging using deep reinforcement learning, firstly proposing an improved actor-critic algorithm and constructing a reward function related to the UAV energy as well as the mission to guide the UAV behavior to achieve the completion of the mission, as well as the penalty of the action and the smoothing of the trajectory, for improving the UAV's ability to complete the mission, and secondly adding a charging module to ensure the balance of UAV energy. The simulation results show that the method can ensure the stability of UAV energy and when the energy module is added, the UAV energy level smoothes out and the mission completion is higher than before.
Long Text Relationship Extraction Method for Complex Productse

In the data management of complex products, relationship extraction of related texts can assist in constructing product data chains. This can realize the integration and fusion of data chains through nodes with relationships between different data chains. However, due to the complexity of complex products in terms of customer requirements, product technology, and manufacturing process, many related text data contain a large number of complex sentences, and a large amount of referential information is often lost in the relationship extraction of long text in these complex sentences, resulting in poor relationship extraction results. In this paper, we propose a long-text relationship extraction method for complex products, using a pre-trained language model to encode semantic information and obtain input text word vectors, then using a Gaussian graph generator (GGG) to construct potentially directed multi-views, learning graph features more deeply with the help of densely connected graph convolutional networks, and using dynamic time-regularized pooling operations to extract more relationship-dependent indicative words to assist the relationship. The extraction task is completed by combining the graph feature learning results with semantic information embedding representation for relationship extraction. Experiments are conducted on the DialogRE dataset, and the experimental results show that the F1 values reach 66.1% and 63.3% on the validation and test sets, respectively, and the F1 values still exceed 65% when the number of text words exceeds 400, which verifies the feasibility and effectiveness of the proposed method.
Mask Multi-Head Attention with Partition Network for Vehicle Re-Identification

Vehicle Re-identification aims to match a specific vehicle image across different places or cameras based on the similarity among vehicles. vehicle re-id remains confronted with two severe challenges, small inter-class variability caused by a similar vehicle with a similar type and color, and dramatic intra-class variability caused by the variation of view. More recently, methods are proposed to improve performance by using additional metadata such as critical points and orientation, which all require expensive annotations. Therefore, we introduce attention mechanism to solve these two problems without considering extra annotation. In this paper, we propose a novel mask multi-head attention with partition network (MMAPN). To discover subtle differences between two similar vehicles, we propose a partition unit to discover more local detail. To extract features that are robust to both tremendous intra-class differences and subtle inter-class variability, we propose a mask multi-head attention block to extract potential features. Extensive experimental evaluations show our approach achieved state-of-the-art performance.
A Dark Transformation-Equivariant Algorithm for Dark Object Detection

With the advent of deep learning, convolution neural networks (CNN) based object detection plays a critical role in fields such as autonomous driving. Unlike the normal-lit scenario, the dark environment can be challenging for object detection due to the low lighting and dark noise. To improve object detection in low-light conditions, we introduce a novel Dark Transformation-Equivariant (DTE) algorithm to explore feature consistency between the normal-lit and low-light domains. Specifically, on the one hand, we construct a dark transformation to simulate poor lighting conditions by darkening the regular images, considering the sensor noise. On the other hand, we capture the representation invariance of the dark transformation by encouraging feature consistency. Under the design of DTE, detectors are able to learn more discriminative representations of dark images for low-light object detection. Experiments on the ExDark dataset demonstrate the effectiveness of our method in improving detection performance when suffering from a dark environment and we achieve a 3.4% higher mAP than that of YOLOv3.
Accelerating Hierarchical Federated Learning with Adaptive Aggregation Frequency in Edge Computing

Federated Learning (FL) has gained significant popularity as a means of handling large scale of data in Edge Computing (EC) applications. Due to the frequent communication between edge devices and server, the parameter server based framework for FL may suffer from the communication bottleneck and lead to a degraded training efficiency. As an alternative solution, Hierarchical Federated Learning (HFL), which leverages edge servers as intermediaries to perform model aggregation among devices in proximity, comes into being. However, the existing HFL solutions fail to perform effective training considering the constrained and heterogeneous communication resources on edge devices. In this paper, we design a communication-efficient HFL framework, named CE-HFL, to accelerate the convergence of HFL. Concretely, we propose to adjust the global and edge aggregation frequencies in HFL according to heterogeneous communication resources among edge devices. By performing multiple local updating before communication, the communication overhead on edge servers and the cloud server can be significantly reduced. The experimental results on real-world dataset demonstrate the effectiveness of the proposed method.
Research on behavior recognition algorithms in classroom scenarios

The quality of teaching has always been a primary concern in the field of education. With the introduction of the smart classroom concept and advancements in hardware and software technologies, there is a need for an automatic and unbiased tool to evaluate teaching quality. Based on the principle of computer vision, this paper designs a behavior recognition network to classify students' behaviors in class. These classification results can provide data basis for evaluating the concentration of students and the attractiveness of teachers in the classroom. Also this information is essential for teachers to analyze their courses, optimize course rhythm, and improve teaching quality.
LRFAN: a multi-scale large receptive field attention neural network

Transformer, which was originally used as a natural language processor, has rapidly gained importance in the field of computer vision since the introduction of ViT. The more efficient Transformer model challenges the dominance of convolution neural networks. In order to capture long-range dependencies, some convolution models have obtained performance gains by convolving very large kernels. However, as the size of convolution kernels grows, the computational complexity grows on the one hand, while speed begins to saturate on the other. In this paper, we propose a multi-scale large receptive field attention module (LRFA) that extracts feature information at different scales by grouping and superimposing different numbers of small-size convolutions. On the other hand, the superposition can have the effect of large kernel convolution, which reduces the computational complexity. LRFA overcomes the inability of conventional convolution neural networks to capture long-range dependencies and the inability of self-attention models to account for local feature information. We design an LRFA-based neural network, a multi-scale large receptive field attention neural network (LRFAN), which adjusts the superimposed convolution kernels size based on network depth and input feature information, and can adapt to the input feature maps to better capture long-range dependencies. Extensive experiments demonstrate that we outperform the conventional convolution neural network and the visual Transformer model in computer vision tasks such as image classification and object detection.