Reviewerly/exHarmony huggingface dataset:
Multi-Scale Constrained Lung Medical Image Registration Based on Feature Reweighting

Registration plays a key role in medical image analysis and is an important part of intelligent medicine. Traditional registration methods need a lot of iterative computation and take a long time. The registration methods based on deep learning can meet the requirements of rapidity and accuracy. To improve the registration effect of the convolution neural network on lung medical images, we present a multi-scale registration network based on feature reweighting. The mixed dilated convolution is introduced into the registration task and the receptive field of the convolution kernel is increased under the condition that the number of parameters and the size of the output characteristic graph is unchanged. An attention module is added in the skip connection between the decoder and the encoder to make the model pay more attention to the salient areas in the image. The weighted loss function at each stage trains the whole network and adopts the cumulative transformation mechanism. Experiments show that the proposed method can improve registration efficiency and meet the requirements of medical image registration speed and accuracy.
Towards Digital Immersive Experiences for Collaborative Value Co-creation in Design

The metaverse, which is often described as featuring a range of technologies for Extended Reality (XR) to consume and digest information from large and complex Digital Twins (DTs), is often discussed as one of the pillars of Industry 5.0 transformation. Immersive computer-generated environments based on DT and XR are highlighted as cornerstone technologies for the future of product and system design. While they are expected to become a commodity in the engineering toolbox in the next 20 years, little is known about how to set up and implement such environments to maximize innovation and collaboration capabilities in design. This article discusses the high-level gaps that remain before the vision of the metaverse in design is realized. It further draws a roadmap, based on four major tracks, that pinpoints research areas, methodological approaches and capabilities needed to develop and realise digital immersive experiences for collaborative value co-creation in design.
Named Entity Recognition in Industrial Processes

Natural Language Processing (NLP) tasks like relation extraction and knowledge graph are based on named entity recognition (NER). In order to improve the recognition ability of Chinese entities in industrial processes, a NER model based on BiLSTM-CRF network was proposed. Firstly, the abstract of patent of industrial processes was crawled from the Internet through crawler. After data cleaning, de-duplication and coding, it became a data set. Then the data was put into BiLSTM for bidirectional coding to obtain long sequence semantic features, which can be decoded through Conditional Random Field (CRF). By learning the dependency between tags, it obtain the optimal tag sequence. Finally, the correct entity was identified by sequence. In the self built dataset of industrial processes, the precision, recall and F1-score of the model are 97.17%, 99.41% and 98.27% respectively, which can prove the model can effectively improve the Chinese entity recognition ability of industrial processes.
Path-Follower: A High-Precision Sampling-Based Motion Planner for Inspection Robot

Mobile inspection robots operate in narrow industrial sites have large mass and size and are usually not allowed to perform autonomous global path planning or autonomous obstacle avoidance. Instead, the robots precisely follow pre-planned global paths and stop to wait when encountering obstacles. The common working mode of inspection robots is to stop at the inspection points on the global path, collect images or point cloud data, and sometimes need to follow the global path to pass through or enter cramped spaces. To ensure the inspection effect and the passing ability, the accuracy of following the global path and reaching the target pose is very important for the motion planning of the inspection robot. This paper presents a highprecision sampling-based motion planner for metro inspection robot, named Path-Follower. Different from the traditional dynamic window approach (DWA) algorithm, Path-Follower has a special velocity sampling limit near the target pose, a local trajectory reckoning process that considers actual velocity and kinematic constraints, and a trajectory evaluation function designed for path following and target pose reaching. Path-Follower can provide the capability of accurate motion planning without modeling dynamics for mobile inspection robots. The simulation results based on Gazebo in Robot Operating System (ROS) compared with DWA algorithm built in ROS demonstrate the effectiveness of the proposed algorithm. In path following, Path-Follower has a lower absolute trajectory error of 0.011m. In target pose reaching, Path-Follower has a lower absolute end-to-end error of 0.008m.
Clustering Analysis of Patients Kdigo Scoring Time Series Data Based on DTW Algorithm

Acute kidney injury (AKI) is a clinical syndrome caused by rapid decline of renal function in a short time. Although renal dysfunction is a decisive factor of AKI, the subtypes of different trajectories have not been fully studied, so it is important to determine the clinical subtypes. AKI patients in the open source database of critical medicine mimic3 were taken as the research object. A 72-hour kdigo scoring track was created. Then, dynamic time warping (DTW) is used to calculate the similarity of heterogeneous kdigo trajectories, and hierarchical clustering (HAC) is used to identify trajectory-based subtypes. Two trajectory-based subtypes of AKI were identified, namely, continuous deterioration and delayed improvement. The results of subtypes were used in multiple logistic regression analysis, and a risk nomograph was established. It was concluded that sofa, vaso, vent, chf, hypertension, resprate, spo2, creatinine, chloride, bun, ph were independent risk factors for the development of AKI patients. This study plays a reference role in the development of AKI and can provide help for clinical staff.
Object Detection For UAV Images Based On Improved YOLOX

As the unmanned aerial vehicle (UAV) image has the characteristics of a complex background and dense targets with a small scale, a real-time detection algorithm YOLOX-RFS based on improved YOLOX-S is proposed in this paper. First, because a tiny target has few features that are easily lost in the deep network, the ConvNeXt Block module is introduced to enhance the representation ability of shallow neural networks and learn spatial details sufficiently. Then, the influence of the receptive field of the feature map on the performance of object detection for UAV images is analyzed. The receptive field of the low-level output feature map is reduced by changing the stem of YOLOX and adopting an inverted residual block. Finally, deformable convolution is used to compensate for the lost semantic information due to the decrease of the receptive field of the top-level feature map. Experimental results show that the algorithm in this paper achieves 38.8 % mAP and 45.5 FPS on the VisDrone2019 dataset. Without sacrificing the reasoning time, the mAP is improved by 3 % compared with the original algorithm, which is more suitable for object detection tasks in UAV scenarios.
Dual-Branch Framework with Convolutional Attentive Block for Video Anomaly Detection

Video anomaly detection is a challenging task due to the diversity and low frequency of anomalous events. Inspired by two-stream paradigms, we propose a hybrid dual-branch framework for improving the robustness to different anomalous scenarios. We integrate the prediction branch and the reconstruction branch, where the prediction branch learns appearance features from video frames, and the reconstruction branch learns motion features from optical flows, which allows the dual-branch framework to be jointly optimized. Specially, a channel attention block is developed for our dual-branch framework to adaptively enhance feature representations by modeling the interdependence among feature channels. Experimental results on three benchmarks prove the effectiveness of our dual-branch framework for video anomaly detection.
UAV Route Planning Based on Fuzzy PID Control Ant Colony Algorithm

This paper proposes an improved ant colony algorithm combining fuzzy PID control and adaptive weight information adjustment to optimize the Unmanned Aerial Vehicle(UAV) route planning technology. Most of the existing UAV route planning still stays on a single algorithm, and the problems such as low iterative efficiency and poor robustness for route planning cannot be effectively solved. Therefore, this paper first improved the pheromone update strategy of common ant colony algorithm to further improve the pheromone update effect. Secondly, the paper introduced fuzzy control and convergence state pheromone increment to promote the global optimal convergence of the algorithm. Finally, the paper employed adaptive PID weight factor adjustment to expedite the ant colony algorithm's rapid iteration and convergence, thereby enhancing the global convergence route optimization results. Compared to the ant colony algorithm, the algorithm proposed in this paper exhibits an approximately 20% increase in the efficiency of route planning.
A Maturity Model for Collaborative Agents in Human-AI Ecosystems

AI entities lean on the aspects of their autonomy to carry out their tasks and perform intelligently. But when these entities collaborate in human-AI teams, their levels of autonomy and collaboration have to be balanced out. We present a maturity model for agents regarding this aspect of balancing. Whereas simple AI systems use pre-designed mechanisms, more advanced systems are able to learn this from experience. The maturity model is a two-dimensional matrix in which the degree of agency forms the horizontal axis, and the level of interaction the vertical axis. We validate the use of this maturity model with use-cases in the field of urban energy efficiency.
FedLoop: Heterogeneity Mitigation in Federated Learning

Federated Learning (FL), as a new privacy computing technology, has been researched and applied in many fields, such as the industrial internet of things (IIoT), intelligent media, and crowd sensing. However, clients' data easily be heterogeneous since they distribute in different environments, which causes performance degradation. To mitigate the impact of statistical heterogeneity, we proposed a novel federated learning framework FedLoop. Unlike conventional FL algorithms that aggregate client model parameters in every communication round, in FedLoop, the coordinate servers do not aggregate client models and just forward client models to others in each communication round. FedLoop only aggregates client model parameters to get a final model at the last iteration. Our framework can train client models with different client datasets without compromising user privacy. As the servers do not need to aggregate client models in each round, FedLoop has an advantage in computing costs. Experimental results demonstrate that FedLoop is competitive in mitigating the effects of statistical heterogeneity with a lower computing time cost.
Identity Re recognition Technology Based on Improved Lightweight ConvNetx Model

In the engineering field, there is a requirement for re recognition, including the identity recognition of cooperative and non-cooperative targets, that is, different sensors shoot targets in the same scene, and the identity recognition of targets from two perspectives is required, also known as target accurate retrieval. This method is widely used in fields such as automatic driving and military strikes. In the actual application scenario, deploying the re-recognition algorithm on the embedded processor needs to ensure the real-time computing speed and accuracy, so the complexity of the deep learning algorithm must be reduced, and pruning and lightweight processing are required. In the engineering field, when the image acquired by the sensor is subject to natural interference such as cloud and fog weather or fire and smoke, the difficulty of re-recognition increases. This paper adopts the lightweight improvement of ConvNeXt network model, and fine-tuned it on the basis of its original feature extraction function, so that it can successfully migrate to infrared image classification and also complete the extraction of infrared image target detail distinguishing features, and at the same time introduce the alignment of similar targets in infrared cross-view recognition. In the original similarity calculation, the average value is directly obtained from the spatial dimension of the output feature layer, which is replaced by the weight of the position in each space for estimation. The feature information of the multi-view marine ship target is extracted from the multi-sensor marine image, and its similarity is calculated, so as to determine the identity, thus reducing the support conditions and reconnaissance costs, and improving the accurate detection and tracking ability under the diverse dynamic reconnaissance conditions.
A Modern Physical GAN for Unsupervised Low-Light Image Enhancement via Illumination Estimation

Low-light environments can cause severe degradation in the visual quality of captured images, leading to the failure of advanced visual perception algorithms and creating hazards in areas such as unmanned vehicles, security surveillance, and visual localization. While deep learning has driven the advancement of Low-Light Image Enhancement (LLIE) techniques, the generalization capability of supervised learning methods is limited by the quality and size of paired datasets. To address this challenge, this paper proposes a novel unsupervised LLIE method that combines a physical model with a Generative Adversarial Network (GAN). The proposed method constructs a lightweight, fully convolutional GAN network that supports arbitrary resolution inputs and is based on modern convolutional blocks. Unlike the end-to-end approach, the generator takes low-light images and illumination as inputs, and combines them with physical models to generate enhanced results. Additionally, a multi-scale deep supervision mechanism is introduced in the discriminator to improve the visual quality of the generated images. The proposed method is compared with existing mainstream methods both qualitatively and quantitatively, demonstrating its lightweight, effectiveness, and superiority.
Integrated Modeling and Collaborative Decision-Making Method Based on Event Logic Knowledge Graph

Purely technocentric decision-making approach cannot respond to digital challenges in a reliable way, due to the lack of human experience and collaborative involvement. Combining decision-making approach with human experience knowledge, context components and real-time data analysis, collaborative decision-making can be achieved more effectively. Accordingly, we construct a “context-event-knowledge” three-layer integrated knowledge model (hereinafter referred to as “integrated model”) to define the concepts and transformation rules of knowledge information required for decision-making. From the perspective of event evolution, it systematically depicts the decision-making logic in a real circumstance and presents in the form of a service architecture. Finally, the effectiveness of our method is verified through a practical case of railway freight scheduling. The main contributions of our research are as follows: (1) The integrated model for collaborative decision-making is proposed with a human experience knowledge centered angle; (2) Achieved a more responsible and resilient decision-making process, which improving the decision-making efficiency; (3) Through the integration of human experience and service-oriented architecture, we can provide solutions for collaborative decision-making in different contexts. It has certain practical guiding significance for relevant enterprises and departments to carry out process control and emergency management.
Maneuvering Target Tracking Based on Hybrid Inference

This paper proposes a hybrid inference method combined with sequential Monte Carlo and variational Bayes, to approximate the intractable posterior distribution. The joint problem of nonlinear state estimation and model parameter identification is transformed into the optimization problem of maximizing the lower bound of evidence. Under the framework of sequential Monte Carlo, a parameterized variational proposed distribution is first designed, and then a closed-loop iterative mechanism of sampling particle weight and proposed distribution hyperparameters is constructed. The joint optimization of target state estimation and model parameter identification is carried out by stochastic gradient descent. Finally, the estimation accuracy of our proposed method is super to the state-of-art methods by simulated data verification.
Human Joints Auto-Calibration Method Based on SuperGlue

Camera calibration is a fundamental and crucial issue in computer vision and image processing. It entails obtaining the camera's internal and external parameters. Nevertheless, the current widely used calibration methods rely on the usage of calibrators, which is time-consuming and inconvenient. A high-precision multi-camera relative posture estimation approach based on human joints is suggested in this paper. Without using specialized calibration tools like checkerboards, only one person needs to enter the calibration scene to calibrate the camera. Firstly, the traditional feature matching approach is replaced by a novel neural network called SuperGlue, which is based on a graph convolutional neural network and an attention-based flexible contextual aggregation mechanism. Secondly, the human joints detection algorithm has been upgraded compared to our earlier work. Finally, the internal and external parameters of the camera are iteratively optimized to obtain the optimal camera parameters. According to the experiment results, the calibration efficiency of the multi -camera system is improved and can be applied in many computer vision tasks. This approach greatly shortens the calibration procedure's time compared to Zhang's calibration method.
Research on Object Positioning Algorithm Based on Hybrid Monocular-Binocular

When multiple aircraft are locating and tracking the multi-targets, it is difficult to accurately locate the target. This paper proposes the monocular-binocular hybrid localization algorithm, and designs the monocular-binocular hybrid positioning strategy for the multi-aircraft detection target, that is, the positioning method switches according to the distance between the aircraft and the object. Considering the inconsistency in time and space between the target information and the aircraft state information of multi-aircraft cooperative detection. This paper applies the spatiotemporal synchronization method of the multi-aircraft data to solve this problem, and forms a collaborative positioning method with the dynamic baseline. Since the physical dimension of target needs to be known for monocular positioning, the paper proposes to estimate the dimension by binocular positioning as the input of monocular positioning. In addition, the positioning mode will be switched when the aircraft reaches a certain distance from the object. Experimental results show that the single-binocular hybrid positioning algorithm has good positioning precision and robustness.
Facial Expression Synthesis and Recognition with Pre-Trained StyleGAN

Deep learning based facial expression recognition requires large-scale training data. However, the existing available expression datasets don't have enough labelled data. To overcome the above issue, we propose an expression synthesis model which uses the StyleGAN pre-trained on a large-scale face dataset to synthesize expression images with new identities, so as to increase the image diversity of expression datasets and improve the performance of expression recognition. First, expression images are projected into the latent space of StyleGAN by GAN inversion to obtain their latent codes. Second, the identity information learned by StyleGAN from the large-scale face dataset is transferred into expression images by manipulating the latent codes. An intra-class loss is introduced to reduce the data bias between synthesized images and real expression images, and the label smoothing regularization is introduced into cross entropy loss to consider the distribution of non-ground truth classes in synthesized images, so as to improve the performance of expression recognition. Experiments on both in-the-lab and in-the-wild datasets demonstrate that our approach is competitive to several state-of-the-art methods.
Multi-Source Domain Adaptation Method of Mill Load Based on Common and Special Characteristics

In the grinding industry, accurate prediction of the mill load is the key to increasing mill income and reducing mill failure. It is difficult to improve the prediction accuracy of the model due to insufficient information on single-source domain data and distribution differences among different data. A multi-source domain unsupervised domain adaptation method based on common and special features is proposed. Multi-source domain data has both common and special characteristics. If only common features are emphasized, some useful information will be discarded. If only special features are used, the model generalization is not good. To solve this problem, a common feature extraction block is used to extract the common domain invariant representation of multiple source domains and target domains, and special features are obtained through the special feature extraction block. After the features are fused and input into the common regressor, the multi-source domain predicted values are obtained. Finally, the predicted values of multiple source domains are added and averaged to get the final prediction result. The effectiveness of this method is proved by cross-experiments on the ball mill data set collected in the laboratory.
A Multi-Agent Actor-Critic Based Approach Applied to the Snake Game

Multi-agent reinforcement learning algorithm has been proposed, demonstrated, and improved on tasks which require a team of agents to cooperate or compete to complete. Sometimes the agents are caught in some dilemmas caused by incomplete observation of environmental information, using the existing q-learning algorithms. Thus, our team come up with the idea of introducing an early warning mechanism to tackle this problem. Actor-critic is a special method to achieve early warning mechanism, guiding the actors' follow-up actions through the existence of critic agent. This kind of algorithm is applicable not only in shared rewards settings, but also in individualized settings where the agents are not able to know the global state. Nowadays, it has become a broad consensus to use the game simulation environment as a research and verification environment for artificial intelligence. In this paper, the Snake Game real-time battle platform is selected as the research environment of the multi-agent reinforcement learning algorithm and the performance of the q-learning algorithm and the actor-critic algorithm is compared on the platform. To further improve the actor-critic algorithm, a better way of experience replay is introduced and its performance is also compared in this environment.