Reviewerly/exHarmony huggingface dataset:
Research on unmanned platform path planning method based on homotopy algorithm

Aiming at the problems of existing unmanned platforms in path planning such as taking up many resources and easily falling into local minima, this paper was introduced the Homotopy algorithm to carry out the research of path planning method for unmanned platforms. By applying the Homotopy Continuation Method (HCM) to construct a constrained nonlinear path equation with simple graphs instead of complex obstacles, and solving the equation by using the homotopy property of the attraction tendency of the target point and the repulsion tendency of the obstacles, and finally conducting simulation experiments, the results show that the method can generate collision-free paths, which provides a simple and feasible method for unmanned platform path planning.
SDT Compression Algorithm Based on Approximate Query of Sensor Time Series Data

In order to remove the large amount of redundant sensor timing data existing in industrial systems, improve the storage efficiency of storage media and the query efficiency of data, this paper proposes an AQ-SDT algorithm based on SDT algorithm, which provides a feasible way to approximate the query sensor time-series data, and ensures the credibility of the compressed data by dynamic search for the compression accuracy and linear regression fitting of the compressed line segments, which improves the compression rate and reduces the compression error.
A Hierarchical Optimization Framework for Parking Maneuver of Automated Vehicle

In this article, the problem of optimize autonomous ground vehicle (AGV) parking trajectory is studied. To speed up the convergence of the optimization algorithm when the problem is established in a constrained environment, a hierarchical optimization framework is proposed. In the first layer, an enhanced A-star algorithm is designed to produce an reference collision-free trajectory. Then, the obtained trajectory is used as the initial guess of the gradient-based optimization algorithm in the second layer. The overall algorithm is tested to plan the optimal parking trajectory, taking account of the AGV mechanical constraints and task-related constraints. Simulation studies were conducted to verify the effectiveness of the proposed scheme. The results show that such a design is able to produce collision-free trajectories for the considered problem.
Depth Estimation with Multi-camera Array

The reduction of camera cost makes it possible and necessary to use camera array in complex scenes. The depth estimation method with multi cameras is proposed and a verification system of four camera array is designed in this paper. The projection geometry of the camera is described and the depth estimation based on least squares algorithm is derived. The random sample consensus (RANSAC) algorithm based on multi-views geometry is used to choose inliers. The result shows that the depth estimation errors of the multi-views stereo are much smaller than the binocular stereo method. More importantly, the multi-views stereo solves the spatial aliasing problem. The redundancy configuration of the camera array makes the depth estimation more robust and accurate.
GAN-based models and applications

GANs have achieved great success in image generation. Gan consists of two main parts, the generator and the discriminator, the generator tries to generate real samples that fool the discriminator, and the discriminator tries to distinguish between real samples and generated samples. Here The continuous improvement of simulation ability and generation ability under this kind of confrontation game can realize the generation of fake images. Since GAN appeared in 2014, articles of various types of GAN have been published in major journals and conferences, and the application of GAN in image generation (specifying image synthesis, text-to-image, image-to-image, video) and GAN in NLP and other areas of application are the most studied, and research in this area has demonstrated the great potential of using GANs in image synthesis. In this paper, the classical basic GAN model is introduced at first. Then, the paper analysis the differences and characteristics of the recent GAN-based models, and introduces the applications of GAN-based models in different tasks.
Efficient and Verifiable Privacy-Preserving Federal Learning Framework

To address the 3 major problems of gradient privacy leakage during local model gradient collection and global model gradient aggregation update, the non-verifiability of aggregation results, and the huge overhead of applying homomorphic encryption techniques in existing federal learning techniques. In this paper, an efficient and verifiable privacy-preserving federal learning framework is investigated and proposed by combining neural networks, batch encryption techniques and aggregation principles. The iterative algorithm in this model training process uses stochastic gradient descent + extreme value convergence method. In each iteration, the weights of each data source are first trained locally using fully connected network, Alexnet, and LSTM, and then the weights of each data source are encrypted using batch crypt technology (BatchCrypt) and delivered to the cloud server, which aggregates the encrypted weights received from all clients using the average aggregation principle and then distributes the latest The cloud server uses the average aggregation principle to aggregate the encrypted weights received from all clients and then distributes the latest encrypted weights to each client, and each client decrypts the weights using the corresponding keys to obtain the weights and update the weights of the overall model. Our BatchCrypt encryption framework greatly reduces the encryption and decryption time consumption compared with Tencent's mainstream solutions, and the cost difference between Loss and Accuracy under different Epochs is very small, and the security and stability are higher under the “poison test”. The security and stability are higher under the “poison test”.
A POI Recommendation Model with Temporal-Regional Based Graph Representation Learning

POI recommendation aims to predict the locations that users may be interested in at next time based on the user's historical check-in sequence information. It is a key task to improve customer experience and business operations, which has aroused widespread interest in academia and industry. But it is still challenging due to the diversity of human activities and the sparseness of the available check-in records. In order to cope with these challenges, The paper proposes a recommendation method based on graph representation learning: Temporal-Regional based Graph Convolutional Network (TRGCN) to further improve the accuracy of prediction. The model first builds a multi-graph representation based on the user's check-in record, and at the same time integrates contextual information such as time period and region into the graph. After that, the model learns the representation of each node at a specific time through the graph neural network. In addition, we apply different score functions to evaluate users' preferences for POIs and regions. The experiment performance of TRGCN proves the effectiveness of constructing a multi-graph structure of user check-in records based on spatio-temporal context to learn the representation of graph nodes. In addition, there is a strong correlation between sequence data. Experiments have also proved the effectiveness of recurrent neural network (or its variants) in processing sequence data. At the same time, the attention mechanism will learn more important information from the sequence to further improve model performance
Object-oriented Domain Adaptation for Cell Detection on Pathology Image

Pathology image automated analysis has made a huge breakthrough in recent years with the advance of deep learning techniques. However, automatically detecting all small cells in a pathology image is not trivial. Small objects have tiny scales thus only limited features could be utilized for detection. Small cells in pathological images make the detection task more challenging as the cells always have a similar appearance to the background. In this paper, we adopt an effective and lightweight method of data augmentation to improve cell detection. Specifically, we introduce a novel copy-paste strategy that greatly increases the diverse representations of small cells and thus enhances the robustness of the detection model in the complex scene. To handle the distribution discrepancy at the instance level which is brought by the data augmentation, we propose an object-oriented domain adaptation method accordingly. Our designed plug-and-play object-oriented domain adaptation is incorporated into the famous Faster R-CNN and gains a significant performance on self-made pathological data set.
Application of LCF Model Based on Deep Learning in Campus Network Platforms

In the information age, schools want to develop better, they must pay more attention to multiaspect's evaluations and opinions, and the campus network platform is a good medium. We can get teachers, students and societies for the school's more real evaluation. In order to better analyze the emotional polarity of various groups for some aspect of the school, we need to build a model to analyze the emotional polarity of each comment. However, at present, most of the widely used models focus on the context of the whole text, but rarely pay attention to the linguistic context of a particular sentence, i.e., the local context. In other words, few people have paid attention to how aspectual affective polarity is actually related to the local linguistic context before. However, the advantage of the LCF mechanism lies in its ability to classify the emotions in a particular sentence from multiple perspectives while focusing on the local linguistic context. It mainly uses contextual feature dynamic masking (CDM) and contextual feature dynamic weighting (CDW) layers to mask the words outside the local context range, while in the embedding layer of the model we use the sharing mechanism to obtain the global context and local context feature information. In this paper, we apply the LCF mechanism to the field of education by applying it to a wide range of campus web platforms to obtain valid evaluation information quickly and accurately.
Underwater Bionic Visual Decontamination Design Applied in Ocean Robots Based on CBR Strategy

Robots working in an underwater dynamic environ-ment need complex visual perception to adapt to the environment, which requires emergency planning ability to deal with various visual pollution emergencies and improve the reliability of oper-ation. An emergency strategy is proposed for the visual pollution of the operating equipment, which used case-based reasoning (CBR). The correct detection and extraction of pollutant char-acteristic information are realized by similarity matching. Some general visual contaminated cases are used to build the case library of visual contamination. The image de noising strategy of case-based reasoning and the cleaning strategy of bionic cleaning equipment are analyzed and implemented. One bionic visual decontamination equipment is designed to clean the pollutants. Based on CBR for different pollution situations, the feasibility of several schemes is verified.
Methods and Datasets of Text to Image Synthesis Based on Generative Adversarial Network

Text-to-Image (T2I) synthesis refers to the computational method of translating normal human text description into images which have alike semantic meaning as text with the use of keywords or sentences. T2I has achieved great success in a few areas, especially in generating vivid, realistic visual and photographic images. Recent years, the development of deep learning has brought some new methods for unsupervised deep learning area, which provide some models to generate visually natural images with suitably trained neural network models. However, T2I now still faces some challenges. For example, current T2I models are unable to generate high resolution images with multiple objects. And it can be difficult to reproduce the results of many approaches. Since present methods are mostly based on GAN (Generative Adversarial Network) models, this paper will focus on the methods depending on it and systematically clear up the developments of T2I based on GAN. Also, datasets always play a supporting role in task development, several current T2I-related datasets will be discussed in the paper to explain more about T2I's development. A brief discussion of the future work and challenges will be showed at the end of the paper.
Fast Spectral Clustering of Multi-Relational Data

Multi- Relational (MR) data appear widely in applications, and its current research is focused on supervised learning. This paper does three jobs in unsupervised learning of this type data. 1) Define the Kernel function customized to MR data structure. 2) Design a Fast Spectral Clustering algorithm (FSC) based on Schrödinger Equation. 3) Propose a Fast Spectral Clustering algorithm for MR data (FSCMR) that parameterizes Kernel function and detect clusters simultaneously. Empirical evidence on real datasets demonstrates FSC achieves higher efficiency than its peers and competitive performance compared with spectral clustering algorithms; the designed Kernel captures features of MR data more accurately than its peers; and FSCMR is of fine clustering ability. These jobs promote development of unsupervised learning of MR data.
Improved DeepLabV3+ image Semantic Segmentation Algorithm

For the existing image semantic segmentation algorithms that have some difficulties, such as small target missing segmentation, false segmentation, and imprecise boundary segmentation, an optimized DeepLabV3+ image semantic segmentation algorithm is proposed. Firstly, the backbone network selects Xception_65 and Xception_71 networks. After comparison, the Xception_71 network is selected; Then, a parallel multi-branch module (PMB) is proposed, and the hole convolution with hole rates of 6, 12, and 18 in the Atrous Spatial Pyramid Pooling (ASPP) module is changed to the hole convolution with hole rate of 4, 8, 12 and 16, to obtain low-level feature map data. The result will be better and ensure the high accuracy of image segmentation; In the decoding layer, the direct up-sampling multiple of the DeepLabV3+ algorithm will lead to the loss of pixel information in the image. Therefore, using a bilateral up-sampling decoder to increase the continuity of pixel information in the image; Channel attention is added to emphasize important features so that the model has a stronger generalization ability. The focal loss function is selected to eliminate the difficulty of sample imbalance in the segmentation target. Finally, experiments confirm that the mIoU value of the proposed method on the data set PASCAL VOC2012 reaches 85.4%, and the segmentation effect is significantly enhanced.
Data Discretized Representation based on Entity Feature Learning with BERT

The discretized representation of medical data has always been a complex process and plays an important role in the field of intelligent healthcare. However, deep learning has rarely been used for the discretized representation of medical data. In this paper, a BERT representation learning method based on medical entity features is proposed, namely EF-BERT (Entity-Feature BERT). Specifically, the medical data was first pre-processed, including data refilling, data denoising, and uni-form mapping. Then, the Transformer encoder was exploited to extract the key entity features in the medical text. Finally, the BERT model with fused entity features was used to obtain the final patient representation and implement the discretization of medical data. Extensive experiments on a real-world dataset have shown that our model achieves significantly better performance than several classical methods on the classification tasks.
A Components Measuring System Based on Computer Vision

Components with high precision fit need to be measured after machining. Generally, Coordinate Measuring machines (CMM) are used for those high precision measurement. However, it’s a huge cost of time to proceed high precision measurement with systems like CMM. And they inevitably require manual operation and false detection might occur due to human factors like fatigue. In this paper a components measuring system based on computer vision is proposed to achieve batch inspection and measurement of industrial components. Image processing (CV) part of this system is the focus of this paper while the mechanical part is ignored. Generally, a series of image processing is executed to get the measuring result step by step. Image processing algorithm flow includes image pre-processing, splicing, feature alignment, edge detection and Hough transformation. The experiment at last shows that the proposed system in this paper has nearly the same accuracy and a higher efficiency compared to a measuring process with traditional high precision system CMM. The average deviation of the proposed system is under 10 microns over the accuracy tolerance of 20 microns, and the measuring time cost reduced to 2.6 seconds per ROI.
An Improved Scheme for Proof-of-Stake Based on Block Memory

Due to concentrated and costless equity, the Proof-of-Stake (PoS) consensus mechanism is prone to bifurcation and block-out right determination concerns, which impacts the blockchain system's stability. To address the aforementioned issues, a new PoS consensus mechanism based on block-out memory is presented. To improve the block-out right competition method of PoS consensus mechanism by long-term and short-term block-out memory, the difficulty calculation method based on long-term memory workload proof, the difficulty calculation method based on short-term memory workload proof. The experimental results show that compared with the PoS consensus mechanism, The PoSaM consensus mechanism weakens the dominance of equity over nodes' block-out rights; it improves node participation, reduces the probability of high equity nodes' evil, and enhances the stability of the blockchain system.
Research about User Purchasing Behavior Analysis Based on Graph Convolutional Network and Web Crawler

In view of the lack of functional software oriented to “low price” in the current market, this paper recommends air tickets, hotels and appropriate tourist cities, and uses crawlers to capture and collect data on Ctrip and Qunar's air tickets, hotels and scenic spots, and then extracts their key features through graph convolutional neural network. Through the analysis of the obtained data and information, to achieve the season special recommendation and popular attractions recommendation. It fills the gap of “affordable” for large travel websites and provides browsing directions for users who want to plan their own travel routes.
Ship Target Detection in SAR Image Based on Data Enhancement Algorithm

Maritime transportation is one of China's basic modes of transportation. Quickly and accurately detecting maritime ships has also become a key concern. This paper proposes an enhanced object detection algorithm for ship object detection. First, we compare the performance of different deep learning algorithms in SAR image ship target detection and then comprehensively use a variety of data enhancement algorithms to expand the number of samples and systematically analyze the generalization performance of Synthetic aperture radar (SAR) image datasets on different data enhancement algorithms. Based on the HRSID dataset, the results show that the You Only Look Once version 3 (YOLOv3) has the best performance among different deep learning algorithms, with mAP of 80.55%, and translation enhancement algorithm, mAP of 89.16%, which is 8% higher than the original dataset. Besides the random cropping method, the other data enhancement methods improve the detection accuracy to some extent, proving the effectiveness of this method and having a specific reference value for selecting the SAR image data enhancement method.
Implementation Artificial Intelligence System Using Automated Object Detection (Identification, Monitoring, and Live Reporting) for Sustainability Asset Management on 5 Different Power Plant in Indonesia

Computer vision has been expanding at a rapid pace over the last decade to reach the equivalent of human vision level. However, even now, it is possible to emulate human vision for performing complex visual tasks faster and even more effectively than humans do. This paper discusses a novel approach to implementing computer vision in power plant asset management to detect unsafe acts and conditions. This approach involves enhancements for sense (media detection), think (algorithm), and act (notification and reporting) which can be tailored to our needs. The case study involves implementation on 5 major power plants in Indonesia and more in 2022–2023 with a multi-billion-dollar asset base and spread over various locations. With the system we developed, we can build an application that can detect various hazards and automatically report them in real-time to ensure that they are managed, controlled, or eliminated. So the risk of losing assets can be maintained appropriately. Furthermore, it is not hard to think of other domains in asset management where the ability of computer vision can be leveraged. For example, certain areas like preventive and predictive maintenance, condition base monitoring, and visual inspection can be improved to help our organization improve its asset value.
Forecasting Solar Irradiance using Hybrid Stationary Wavelet Transform- Quaternion Valued Neural Network with a Softplus AMSGrad Learning Algorithm

Efficient use of solar energy requires reliable forecasting values. In this paper, we propose a forecasting system combining the stationary wavelet transform and quaternion-valued neural networks (SWT-QVNN) and use it to forecast solar irradiance. In addition, a quaternion variant of the softplus AMSGrad learning algorithm is developed and used in optimizing the developed neural network. The proposed system was tested using irradiance data from two cities Abu Dhabi, UAE, and Tamanrasset, Algeria. It did reduce Root Mean Squared Error (RMSE) by more than 60% compared to the baseline model that uses a real-valued neural network.
RoSym

Internet of Things (IoT) firmware upgrade has turned out to be a challenging task with respect to security. While Over-The-Air (OTA) software upgrade possibility is an essential feature to achieve security, it is also most sensitive to attacks and lots of different firmware upgrade attacks have been presented in the literature. Several security solutions exist to tackle these problems. We observe though that most prior art solutions are public key-based, they are not flexible with respect to firmware image distribution principles and it is challenging to make a design with good Denial-Of-Service (DoS) attacks resistance. Apart from often being rather resource demanding, a limitation with current public key-based solutions is that they are not quantum computer resistant. Hence, in this paper, we take a new look into the firmware upgrade problem and propose RoSym, a secure, firmware distribution principle agnostic, and DoS protected upgrade mechanism purely based on symmetric cryptography. We present an experimental evaluation on a real testbed environment for the scheme. The results show that the scheme is efficient in comparison to other state of the art solutions. We also make a formal security verification of RoSym showing that it is robust against different attacks.