Reviewerly/exHarmony huggingface dataset:
Brain Tumor Segmentation Leveraging Optimized U-net Models

Segmenting a brain tumor is a difficult project in the field of computer vision. It is of great importance in depicting the morphology of the tumor and hence assists clinicians to develop treatment plans. Machine learning, specifically deep learning techniques, has shown promising potential in image segmentation. By learning from large-scale datasets, the model could achieve useful information for identifying healthy and tumorous tissues. One of the most effective models is U-net. Since U-net is first proposed in 2015, it has been validated in multiple fields, especially for medical image processing. The core challenge of applying it to brain tumor segmentation is the complexity of the semantics it possesses. To seek the optimal model for the task, extensive ablation studies are conducted for searching the optimal hyperparameter combinations, measured by dice loss, IOU, and dice coefficient. Several modifications are implemented on the original built of U-net, including applying U-net++ and adding the attention module.
Self-Supervised Learning for Fusion of IR and RGB Images in Visual Teach and Repeat Navigation

With increasing computation power, longer battery life and lower prices, mobile robots are becoming a viable option for many applications. When the application requires long-term autonomy in an uncontrolled environment, it is necessary to equip the robot with a navigation system robust to environmental changes. Visual Teach and Repeat (VT&R) is one such navigation system that is lightweight and easy to use. Similarly, as other methods rely on camera input, the performance of VT&R can be highly influenced by changes in the scene's appearance. One way to address this problem is to use machine learning or/and add redundancy to the sensory input. However, it is usually complicated to collect long-term datasets for given sensory input, which can be exploited by machine learning methods to extract knowledge about possible changes in the environment from the data. In this paper, we show that we can use a dataset not containing the environmental changes to train a model processing infrared images and improve the robustness of the VT&R framework by fusion with the classic method based on RGB images. In particular, our experiments show that the proposed training scheme and fusion method can alleviate the problems arising from adverse illumination changes. Our approach can broaden the scope of possible VT&R applications that require deployment in environments with significant illumination changes.
Our first six months using Shareyourpaper.org for Libraries

&lt;em&gt;&lt;strong&gt;Making&lt;/strong&gt;&lt;/em&gt;&lt;strong&gt; time for a change.&lt;/strong&gt; Every semester we seem to run through the same lists of why “this is a really busy time”: the semester starts, holiday weekends, midterms, admissions deadlines, grant deadlines, finals.
Research on Cooperative Control Strategy of Microgrid Distributed Generators Based on Blockchain Technology

In recent years, distributed power generation has received widespread attention due to its advantages such as low power generation cost, environmental friendliness, and high efficiency. A large number of distributed generators (DGs) are connected to the microgrid through power electronic converters, which affects the safety and stability of the system. Improving the system control strategy and the reliability and economy of the microgrid have become the key. Aiming at the problems of unbalanced power distribution and frequency fluctuation in traditional droop control, this paper combines microgrid decentralized control and blockchain technology, designs two control strategies based on a consistent iterative algorithm and Practical Byzantine Fault Tolerance (PBFT) respectively. Experimental results show that both control strategies can effectively coordinate DGs in the microgrid, ensure balanced power distribution, stable frequency, and optimal system operating costs; at the same time, the antitampering, traceability, and decentralization of blockchain technology can improve the fault tolerance, security, and scalability of the system.
Legal Elements Extraction via Label Recross Attention and Contrastive Learning

Automatic extraction of legal elements (LEs) from legal documents is essential for constructing a smart court of law. While this task has met with certain success, how to distinguish similar LEs that appear infrequently in the training set remains a challenge, particularly when the number of training documents is limited and the cost of labeling additional documents is high. To overcome this obstacle, we present L-RACL, a neural-net model with label recross attention and contrastive loss to distinguish similar LE labels and capture correlations between LE labels. Extensive experiments show that L-RACL outperforms existing methods. Finally, we finetune ChatGPT to perform LE extraction and show that L-RACL surpasses it.
Multi-Task Learning for Industrial Mobile Robot Perception Using a Simulated Warehouse Dataset

Autonomous industrial mobile robots need advanced perception capabilities to operate safely and human-compliantly in shared working environments. To achieve a high-level understanding of the mobile robots' surroundings, this paper investigates Multi-Task Learning approaches to process multiple tasks simultaneously and potentially improve the generalization performance. Our work alleviates the scarcity of datasets that are relevant for industrial settings by introducing and making publicly available a simulated warehouse dataset (Warehous-eSIM) covering semantic segmentation, depth estimation and surface normals estimation tasks. We collect and examine numerous MTL task-balancing techniques for industrial mobile robot perception. Our experiments show that MTL methods that have shown superior performance on different computer vision datasets fail to improve over the single-task learning setup in our scenario. This implies that the performance of those approaches is very dependent on the considered dataset, which further highlights the value of introducing new relevant datasets focused on industrial mobile robot environments.
Multi-Formation Planning and Coordination for Object Transportation

Multi-robot formations have numerous applications, such as cooperative object transportation in smart ware-houses. Here, robots must deliver objects in formation while avoiding intra- and inter-formation collisions. This requires solutions to multi-robot task assignment, formation generation, rigid formation maintenance, and route planning. In this paper, we present a cooperative multi-formation object transportation system which explicitly handles inter-formation collisions. For formation generation, we propose a distributed motion planning approach which combines artificial potential field methods and leader-follower based control. For formation planning, we present a heuristic search-based algorithm which uses convex segmentation techniques, and extend the minimum snap method to synthesise smooth trajectories while maintaining the formation. We also propose a variant of the dynamic window approach to avoid collisions between formations. We demonstrate the efficacy of our approach in simulation.
Enhanced Visual Predictive Control Scheme for Mobile Manipulator

This paper proposes a multi-camera visual predictive control strategy for a mobile manipulator allowing to position the end-effector camera with respect to a landmark. Several issues are considered: (i) the visual landmark possible loss during navigation, (ii) the realization of large displacements which implies a large prediction horizon and impacts the closed-loop stability, (iii) the robot's high redundancy which may lead to a large search space and potential non-relevant solutions, (iv) the processing time. To cope with these challenges, the proposed strategy relies on (i) the use of two complementary cameras, (ii) the definition of a cost function depending on both the vision-based task and the manipulability, (iii) the integration of constraints allowing to prioritize the former against the latter. The strategy has been simulated and compared using ROS and Gazebo, showing its efficiency.
Scalable Model-Based Decentralized Applications in the Cloud Using Certificates and Blockchains

Cloud-based applications can achieve high performance and scalability provided through the infrastructure of cloud platforms. Distributed servers and technologies such as serverless computing further allow for distribution in terms of the network and for parallel execution. Distribution, however, does not imply decentralization, which additionally requires noncentralized coordination without trusted third parties. For example, independent network nodes may verify the execution using blockchain consensus. Despite advances in consensus algorithms, the execution of decentralized applications is challenging due to scalability and, when combined with cloud computing, the issue of authenticating distributed parties and execution states. This paper extends a model-based approach for execution and instance tracking on cloud platforms with scalability and authentication through certificates and blockchains. Thereby, distributed parties monitor and track execution states using models. The approach is extended by (a) an architecture for the authenticated and scalable distribution, execution, and tracking with executable models, and (b) a prototype evaluating feasibility for Amazon Web Services (AWS) and the Ethereum blockchain. Results indicate the potential for scalability and decentralization through executable models in the cloud with certificates and a blockchain.
Research of Improved TD3 Robotic Arm Path Planning using Evolutionary Algorithm

Nowadays, application of automated intelligent robot arm devices to improve industrial production efficiency has become a popular research field in the world. The previous off-line path planning method of robotic arm has the inadequacies of low efficiency and slow speed. Although the deep reinforcement learning has accomplished many achievements in the path planning of control manipulator, there are still some problems such as long training time and low planning accuracy. To solve the abovementioned issues, we propose an improved Twin Delayed Deep Deterministic policy gradient (TD3) algorithm (Improved Cross-Entropy Method-TD3: ICEM-TD3) for the path planning of the robotic arm. First, this paper combines evolutionary strategies with TD3 to generate action networks. Then the exploration of TD3 algorithm in the action space is replaced by the exploration in the parameter space. In addition, this paper designs a new reward function to weaken the redundancy of planning and accelerate the convergence speed of training. Finally, the Gazebo simulator is adopted to verify the proposed algorithm, and the results illustrate that the proposed algorithm can greatly improve the accuracy of the path planning of the manipulator using deep reinforcement learning.
Embroidery Robotics: An Innovative Approach to AI-based Online Learning and Image Generation

In this paper, we present an innovative approach to employ artificial intelligence for teaching a dual-arm robot embroidery tasks through online learning. Utilizing the principle of Teaching by Demonstration, we introduce an online learning algorithm that efficiently captures and reproduces the embroidery trajectories of a human expert on a 2D plane. The algorithm’s performance was evaluated quantitatively and qualitatively, showing a promising level of accuracy in reproducing the original embroidery patterns. We further validated the feasibility and potential of our approach through a user feedback survey involving 30 professional embroiderers. Despite certain areas identified for improvement, notably the finer details in embroidery, the overall positive response confirmed the viability of our approach. This study signifies a crucial step towards automating intricate manual tasks and broadens the horizons for applications of AI and robotics in various industries.
Integrated Task Scheduling, Action Planning and Control for Autonomous Disinfection by a Mobile Robotic Manipulator

This paper presents an approach for solving a challenging problem in the integration of low-level system sensing and control with high-level task scheduling, planning and perception in mobile robotic manipulator systems for autonomous disinfection. First, a unified event-based reference will be introduced. Then, the approach will be implemented in the task of disinfection by a mobile manipulator. The task of disinfection involves both continuous and discrete events. The interactions between discrete and continuous actions will also be considered. The disinfection task in a multiple-object venue is used to illustrate the proposed approach. The experimental results demonstrate the advantages of this method.
Application and Research of Prediction Model Based on Machine Learning Algorithm

In this paper, several machine learning prediction models based on the keratoconus dataset are developed and compared to arrive at the best algorithmic model. The algorithm can improve the accuracy of fitting, reduce the complexity of the fitting proces. In this paper, 80% of the data collected from the keratoconus dataset was used as the training set and 20% as the test set to build prediction models by five machine learning algorithms. Linear regression, SVR, decision tree, random forest and XGBoost, and four evaluation metrics, including mean square error MSE, root mean square error RMSE, mean absolute error MAE, and root mean square log error RMSLE, were used to evaluate the performance of the prediction values FK of the above algorithms and finally validate the effectiveness of the models. Compared with the other four algorithmic models, the XGBoost model has the best overall evaluation indexes and will be the most effective when applied.
Unsupervised Low-Light Enhancement Method Based on Attention Mechanism

Enhancing low-light images has been a research topic in the field of image processing. Significant advancements have been made recently using deep learning based approaches in this filed. However, obtaining the paired low/normal-light images in the same scene used for training is difficult, and the datasets lacks real-time, making its application value difficult to be verified. Meanwhile, low-light enhancement methods often ignore the local characteristics of images, which may result in local over or under exposure. To address the problems, the paper proposes a unsupervised generative adversarial network-based approach for image enhancement, incorporating attention in channel and spatial of images to improve the model generalization. Specially, our method does not require paired images as for datasets. At the generator component, we first apply channel attention into each feature map during the downsampling stage to focus on the channel information, and following the upsampling stage, the spatial attention feature map of the input image is multiplied with the generated results.we also perform a weighted summation of the input and the generated results to produce more realistic images. In discriminator component, we adopt a global-local discriminator that can enhance local regions while improving global lighting. We validate our method through extensive experiments on various benchmarks, and the results outperform the current cutting-edge methods.
Application of Factor Analysis Optimization Error Back Propagation Algorithm FA-BP Neural Network Equipment Fault Diagnosis Model

To enhance the precision of equipment fault diagnosis, a novel device fault diagnosis method based on factor analysis (FA) and an improved error back propagation (BP) algorithm has been proposed. An FA-BP neural network diagnosis model has been developed to enable intelligent diagnosis of equipment faults. Firstly, three common factors influencing motor faults were extracted using FA, which served as effective input data for the BP neural network. Subsequently, the data that was optimized by factor analysis was utilized as the input layer of the BP neural network, and the data were trained and tested by employing Matlab software. Eventually, the results of fault diagnosis prediction obtained from the FA-BP neural network model were compared with those attained using the traditional BP neural network. The experimental findings affirm that the improved model achieves significantly elevated accuracy of equipment fault prediction and swifter training speed compared to the traditional error back propagation algorithm.
Research on the Application of Computer 3D Image Vision Technology in Digital Media Art

This paper uses a new visualization method. The goal is to improve the results of 3D image reconstruction. Firstly, the hardware architecture of the system is improved. The clock of the system is extended. The combination of CY25400 micro controller and CDCVF2510 micro controller greatly speeds up the detection speed. Then the key technologies such as image parsing module, image preprocessing module and 3D visualization module are studied. The visualization elements are fed back to the 3D image to realize virtual reconstruction. Finally, the conventional 3D imaging technology is used to complete the simulation and comparison test in the same scene. Experiments show that this method can obtain high 3D image reconstruction effect. The system can clearly show the edge area of the image, and can meet the requirements of users.
Super-Resolution Reconstruction of Remote Sensing Images Based on Improved Residual Dense Network

An improved residual dense network (IRDN)-based remote sensing images super-resolution reconstruction technique is suggested in order to address the issues with single feature extraction and equalization of feature processing in the current remote sensing images super-resolution reconstruction algorithm. By strengthening the structure of the original residual dense model, adding a branch to extract more varied and rich features, and establishing the channel and position relationship of features by Coordinate Attention (CA), the algorithm enhances the performance of the model's feature extraction and representation. The strategy can effectively increase the reconstruction performance while significantly lowering the number of model parameters, according to experiments using the publicly accessible remote sensing datasets UC MERCED and NWPU-RESISC45. On the UC MERCED dataset with a scale factor of 4, the objective performance evaluation indexes of peak signal to noise ratio and structure similarity achieve 29.3126dB and 0.7987, respectively, and good visual effects are obtained. It offers a solid foundation for the future remote sensing images' additional analysis and processing.
Research on Performance Improvement Method of Dynamic Object Detection Based on Spatio-temporal Attention Mechanism

This paper proposes a method based on a spatio-temporal attention mechanism to improve detection performance aiming at the challenges in dynamic object detection. First, definitions, applications, and challenges of existing methods for dynamic object detection are introduced. Second, the concepts and advantages of the spatio-temporal attention mechanism are presented. Next, the main technical and fundamental issues of spatial attention mechanisms in dynamic object detection will be discussed. Then, the dynamic object detection model's modeling, application, and optimization based on the spatio-temporal attention mechanism are described in detail. Finally, the main content of this paper is summarized, and the potential effects of dynamic object detection methods based on spatio-temporal attention mechanisms are highlighted.
Predictive Analysis of Wordle Games Based on BP Neural Networks and K-Means Clusters

Wordle is a popular daily puzzle game currently offered by The New York Times in of which players can guess words and receive feedback on each guess. Wordle games are currently growing in popularity, but research into Wordle games is still very limited and there are many issues that need to be examined in depth.First,We need to build models for different word attributes and predict the distribution of the results reported for any one word, giving the predicted results for the word “EERIE” on March 1, 2023. Here we first processed the data to get the final outcome data, then we introduced a BP neural network prediction, we introduced a BP Neural Network prediction model and performed specific training, where the training accuracy reached over 98%, which is very accurate. We then filtered the features using XGBoost and we created a difficulty classification based on a K-means clustering analysis that divided the difficulty into two categories: difficult and easy, we then performed specific clustering and obtained the final clustering results for evaluation. Secondly, we created a linear discriminant analysis of classification accuracy and performed statistics for each word attribute, which turned out to be very consistent with our previous analysis. The more common the word is in life, the lower the difficulty. The more the word repeated letters, the higher the difficulty. Finally, we introduced the EERIE words into the clustering model and found them to be of medium difficulty and got better results.
STPID-Model : A novel approach to Perimeter Intrusion Detection

The internet of things (IoT) has been embedded in many aspects of our lives and is evolving in almost all sectors such as (smart homes, smart cities, smart hospitals, etc.). As security is the main concern everywhere in daily routine due to the increase in cases of crimes and vulnerabilities. Intrusion detection systems (IDS) are extremely important to make the specific location secure from unauthorized access but still the perimeter intrusion detection systems (PIDS) are facing issues in real intrusion detection and false alarm rate (FAR) that are caused by the environmental intrusion. In order to solve these problems in perimeter intrusion detection systems (PIDS). In this paper, we proposed a new machine learning-based model named STPID-Model where we used the Imagery library for intelligent detection systems (i-LIDS) dataset, we derived images from the recordings available in i-LIDS dataset and then we applied enhanced algorithm I-DBSCAN for intrusion detection and distinguish between fake and real intrusion. Our proposed model performed better than the others.
Optical-ISAR Image Translation via Denoising Diffusion Implicit Model

Sufficient training data play a crucial role in deep-learning techniques, however, available training samples are not always accessible for specific targets, such as satellites. Producing synthetic data through generative model is a major solution for the lack of training data. In this paper, we propose a Denoising Diffusion Implicit Model (DDIM) to generate the Inverse Synthetic Aperture Radar (ISAR) satellite images from the synthesized optical counterparts. Efficient and abbreviated feature extraction method is proposed in optical-ISAR image translation. A domain-cross generative model is then established with respect to the generative task and feature learning modules obtained from the translation model. Extensive experiments validate the feasibility and performance of the proposed approach, which outperforms the existing generative models such as CycleGAN in terms of inception score and structural similarity index.