Reviewerly/exHarmony huggingface dataset:
Real-time monitoring and feedback design for autonomous slope repair based on binocular vision

In order to cope with the special conditions of hard soil and large soil cutting volume in autonomous slope repair operations, the existing autonomous slope repair process is improved, and a real-time monitoring and feedback system is proposed to be built for the excavator using binocular vision. The system includes the estimation of the total amount of soil cut in the slope repair project, the real-time estimation of the amount of soil cut and the real-time judgement of the slope flatness. After experimental verification, the height error of the binocular camera on the slope detection can be controlled within 3cm, the distance error between the upper edge of the soil pile and the camera is within 2cm, and the error between the estimation of the volume of soil before and after the construction and the actual measurement is within 0.02m³, which meets the accuracy requirements of the autonomous slope repair construction. It meets the accuracy requirements of the slope repair construction.
Research on adjustment function model transformation of searching closed loop

Condition observation and indirect observations are the two basic models for data processing measurement adjustment. This paper applies the transformation of conditional adjustment and indirect adjustment function models to calculate the annexed route and closed error for the GPS network. We can find the relationship between the conditional equation and the coefficient matrix of the error equation, and device the information of annexed route or closed loop in GPS control network by matrix transformation, so that we can obtain the error equation directly and calculate the corresponding closure error. In this paper, we use the program and process the data of the example to verify the method can search the annexed route and the closed loop, and the closing error of GPS control network can satisfy the accuracy requirement.
Identification of mining environmental problems in high and cold plateau regions based on multisource remote sensing

The Jvhugeng mining area in the Muli Coal field is situated in the high and cold region of the Qinghai-Tibet Plateau. Years of open-pit coal mining have resulted in severe damage to the surrounding ecological environment. Therefore, it is imperative to conduct mine environmental restoration and treatment in order to achieve the goal of ecological environmental protection. To ensure the safe and efficient implementation of subsequent mine environmental restoration and treatment projects, it is essential to accurately and efficiently identify the geological environmental issues and establish clear restoration and treatment goals. Considering the unique natural conditions and geological environment of the plateau alpine mining area, multi-source technologies, such as satellite remote sensing and UAV remote sensing, are employed as the core components. These technologies are complemented by conventional geological surveying and sampling, as well as testing methods. By fully utilizing the technical characteristics of multi-spectral, INSAR, thermal infrared, and other remote sensing technologies, the identification and monitoring of geological hazards, vegetation destruction, water distribution, abnormally high temperature of residual coal on the surface, and soil control in the Jvhugeng mining area have been undertaken. The overall results of the identification process have been positive, providing a scientific basis for mine environmental restoration and control. This achievement holds great significance in ensuring the safe and efficient development of mine restoration and control projects, as well as achieving the desired effects of ecological restoration and control.
A remote sensing image segmentation model based on multi-scale feature fusion

Large-scale remote sensing images contain rich terrain information, while small-scale images capture abundant detail information. Effectively extracting and fusing these multi-scale information has grown into a major challenge. To tackle these problems, this paper recommends a novel remote sensing image segmentation model called Multi-scale Vision Transformer (MS-ViT). The MS-ViT model comprises a multi-scale feature extraction module, a feature fusion module based on multi-scale self-attention, and a decoder based on convolutional neural networks. Additionally, the model introduces spatial attention mechanism and a position encoding method considering patch sizes, as well as a feature distribution alignment method and corresponding loss functions to further enhance segmentation performance. Experimental results demonstrate significant improvements in performance of the MS-ViT model compared to several other advanced models.
Rapid retrieval system of UAV image based on deep learning

The rapid development of unmanned aerial vehicle (UAV) aerial survey technology and deep learning has revolutionized traditional aerial survey methods. With its fast measurement, low cost, good safety, and high reliability, UAVs can quickly obtain image data of a certain survey area in a relatively short time. Although UAV surveying has been used in various fields of related industries, there are still certain limitations to quickly obtaining desired information from a group of images. Therefore, this paper combines the VGG16 deep learning model to match UAV images and make it applicable to the web environment. Starting with extracting image features of UAVs from the VGG16 model, this paper implements a UAV image feature extraction and retrieval system based on VGG16. Then, the matching efficiency is compared with the matching model based on the SIFT algorithm. Finally, the image feature extraction and retrieval system based on VGG16 is combined with the web system to study the retrieval of image data of interested areas or image data without spatial information, as well as spatial information queries under web conditions.
A cloud-based solution and platform for remote sensing big data applications: challenges and opportunities

Satellite remote sensing, as an important means of disseminating and perceiving spatio-temporal geographic information, is widely used in many fields. The accumulated satellite remote sensing data from various industries has gradually formed Remote Sensing Big Data, and its explosive growth has also brought challenges to various applications. It discusses the big data characteristics of Remote Sensing Big Data, and also the challenges posed by the lack of handling technologies in this paper. Meanwhile, it also discusses the opportunities and key technical issues from cloud computing technology to Remote Sensing Big Data handling, and provides a cloud RSBD solution and platform prototype.
Research on rapid loading and measuring method of panorama based on depth image

With the rapid development of virtual reality as well as geographic information technology, panoramic technology has achieved wide application. For the problems of slow loading speed of large panorama and the lack of measurement function of 2D images, this paper proposes a multi-level pyramid model based on spherical projection. Firstly, the 3D laser point cloud data is transformed by coordinates and projection to generate the depth image corresponding to the panorama, and the 3D application value of the panorama is explored; then, the panoramic images are compressed and sliced to form multi-level panoramic tiles and construct a grid sphere model; finally, the 2D panoramic image is mapped into the 3D grid sphere for rendering according to the vertex coordinates, and slices within the corresponding range are loaded according to the viewing area during browsing. The experimental results indicate that the sliced model significantly improves loading and display speed compared to loading the entire panoramic image, which has strong application value.
Turning inwards for change – The role of inner conditions in transformative co-design

The transformation towards eco-social sustainability is an important issue for many of us. Those involved in transformative activities often undergo continual processes of self-reflection, enhancing their awareness and capabilities. This paper focuses on co-design with transformative aims and on processes for which the ability and willingness of individuals to shift their perspectives and adopt new roles and practices is central. It looks at the ways in which we can nurture the interconnectedness of internal and external change by revisiting existing design practices that are complemented by awareness-based system change. The paper contributes to the discussions on the inner conditions in transformative co-design through three perspectives: empathy and relating, self-awareness in co-design, and experiencing connectedness. Transformative processes are not easy. Therefore, we need to pay more strategic attention to inner conditions if we are to increase the transformative potential of design to cultivate eco-social change.
Feature adaptive multi-view hash for image search

&lt;abstract&gt;&lt;p&gt;With the rapid development of network technology and small handheld devices, the amount of data has significantly increased and various kinds of data can be supplied to us at the same time. Recently, hashing technology has become popular in executing large-scale similarity search and image matching tasks. However, most of the prior hashing methods are mainly focused on the choice of the high-dimensional feature descriptor for learning effective hashing functions. In practice, real world image data collected from multiple scenes cannot be descriptive enough by using a single type of feature. Recently, several unsupervised multi-view hashing learning methods have been proposed based on matrix factorization, anchor graph and metric learning. However, large quantization error will be introduced via a sign function and the robustness of multi-view hashing is ignored. In this paper we present a novel feature adaptive multi-view hashing (FAMVH) method based on a robust multi-view quantization framework. The proposed method is evaluated on three large-scale benchmarks CIFAR-10, CIFAR-20 and Caltech-256 for approximate nearest neighbor search task. The experimental results show that our approach can achieve the best accuracy and efficiency in the three large-scale datasets.&lt;/p&gt;&lt;/abstract&gt;
Performance Evaluation of Machine Learning Nave Bayes Algorithms for Network Traffic Classification

Network Traffic Classification (NTC) is an important field for different network statistics like management, malware detection and other paramount constraints. Artificial Intelligence (AI) including Machine Learning (ML) and Deep Learning (DL), on the other hand, plays a very important field nowadays due to its significant capabilities with an extremely different fields and for complex problems. ML, specifically, provides tools in the most important network fields like traffic management, security, etc. Performance evaluation is a very important aspect of any system. This research paper provides a method for NTC using ML Nave Bayes (NB) algorithm in terms of Bernoulli, Multinominal and Gaussian for classifying captured network traffic in two different datasets and perform a performance evaluation and a comparison among these algorithms. The first dataset is a VPN-nonVPN (ISCXVPN2016) dataset while the second is a packet-captured regular Wi-Fi traffic flow dataset for video browsing on the web. Results were comparable in terms of f1-score, accuracy and processing time. Bernoulli NB provides average 93.05% accuracy with 742 ms, Multinominal NB provides average 98.78% accuracy with 78.3 ms processing time and finally, Gaussian NB provides average 69.14% with 46.85 ms processing time.
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation

Multimodal information (e.g., visual, acoustic, and textual) has been widely used to enhance representation learning for micro-video recommendation. For integrating multimodal information into a joint representation of micro-video, multimodal fusion plays a vital role in the existing micro-video recommendation approaches. However, the static multimodal fusion used in previous studies is insufficient to model the various relationships among multimodal information of different micro-videos. In this article, we develop a novel meta-learning-based multimodal fusion framework called Meta Multimodal Fusion (MetaMMF), which dynamically assigns parameters to the multimodal fusion function for each micro-video during its representation learning. Specifically, MetaMMF regards the multimodal fusion of each micro-video as an independent task. Based on the meta information extracted from the multimodal features of the input task, MetaMMF parameterizes a neural network as the item-specific fusion function via a meta learner. We perform extensive experiments on three benchmark datasets, demonstrating the significant improvements over several state-of-the-art multimodal recommendation models, like MMGCN, LATTICE, and InvRL. Furthermore, we lighten our model by adopting canonical polyadic decomposition to improve the training efficiency, and validate its effectiveness through experimental results. Codes are available at https://github.com/hanliu95/MetaMMF .
Integrated Model for the Factors Determining the Academic’s Remote Working Productivity and Engagement: Empirical Study

This empirical study aims to examine and validate the factors influencing productivity among remote academic staff in Jordanian higher education institutions during the Covid-19 crisis, with a specific focus on the mediating role of employee engagement. A comprehensive conceptual framework is developed by integrating relevant studies on remote work-from-home, productivity, and engagement, along with insights from in-depth interviews. Employing a descriptive correlational survey design, the study explores the relationships among the study factors using a quantitative approach. Data is collected from a random sample of 408 remote academic staff members who worked from home during Covid-19. The findings robustly support the proposition that organizational, individual, technological, and client-related factors significantly and positively influence academic productivity through the mediation of employee engagement. These results emphasize the importance of considering these interrelated factors holistically to enhance productivity and engagement in remote academic work. The study contributes to a deeper understanding of remote work dynamics and provides practical insights for improving productivity and engagement in the Jordanian higher education context.
Software-Hardware Cooperative Lightweight Research of Remote Sensing Target Detection Algorithms for Space-Borne Edge Computing

Real-time target detection of remote sensing image is a key technology for efficient application in the field of remote sensing. The detection rate of target detection models based on deep network is high, but this kind of models have a lot of parameters and computational load, so they are difficult to be deployed on the satellite. Lightweight optimization of such model is a problem for the current deep network target detection algorithm. Based on the YOLOv5s target detection model, this paper designs a software-hardware co-lightweight scheme. Firstly, the YOLOv5 model was improved to make it more suitable for remote sensing application, and a improved convolution kernel pruning method was used to solve the over-fitting problem of the model. Then, the model was quantified to run under the DPU of the FPGA, trained and tested on the homemade remote sensing aircraft datasets. The experimental results show that the target detection performance of the model can decline within 3% when 66% of the weights are clipped. It means a highly lightweight YOLOv5 model can be deployed to the edge device on the satellite.
Real-Time Image Super Resolution System Based on Micro-scanning Technology

Different from the recent popular super resolution system based on AI technology which needs normally massive training datasets, the micro-scanning super resolution system by integrating the high-precision mechanism and the image processing system can overpass the training datasets limit to enhance the image resolution and quality considerably in real time. Such a real-time image super resolution system named Quick Demo Station (QDS) system is presented. This system is jointly developed by Shanghai IAE, China, in cooperation with Cedrat technologies, France. The system is made of two modules, one imaging module (MicroScan_VIS_Module), the other image processing module (a portable image processing workstation). The image processing module performs imaging module’s control, as well as image acquisition, image registration, image super-resolution reconstruction, image contrast enhancement. Here the algorithm used to restore the low-resolution (LR) images is the iterative reconstruction method, which could achieve high quality super-resolution (SR) results. The output is a video stream whose frame rate is higher than 25 frames per second. The system is being tested in different applications and show the excellent super-resolution results, including low light level conditions and outdoor variable lighting scenarios. Furthermore, the algorithm in image processing module can be used not only on the workstations, but also in embedded processing system.
Satellite Imagery Superresolution Based on Optimal Frame Accumulation

As actual spatial resolution continues to be a primary bottleneck for satellite imagery interpretation, the aim of resolution enhancement remains very urgent. The resolution enhancement is especially important for medium- and high-resolution satellite systems targeted at small-size objects observation. One of the efficient methods for satellite imagery spatial resolution enhancement is superresolution, when several subpixel-shifted relative to each other low-resolution frames are combined into a single image that accumulates information from all input ones. Our team has previous experience implementing this kind of superresolution in satellite imagery preprocessing. A twofold formal and 48% actual resolution enhancement has been achieved by combining two to four frames into a single image. However, the potential of the formal resolution duplication model is exhausted now. Therefore, we propose scaling up the discretization of the joint subpixel grid over the input imageset from one-half to one-third of the pixel. Accordingly, a ninefold increase in the number of subpixels requires additional information, which is obtained from a larger number of input frames. At the moment, some remote sensing satellite systems can generate many highly-overlapping frames, such as SkySat-1/2, Gaofen-1/6, Gaofen-4/13, Jilin-1, etc. As a result, too many frames are available, which leads to the problem of selecting their optimal subset. We solve this problem by optimizing the joint subpixel grid position and achieving the maximum signal-to-noise ratio in the resulting image using the matrix of mutual subpixel shifts of the input frameset.
Automatic Decision Undertaking on Expedience of Image Denoising Based on Filter Efficiency Prediction

Image denoising (filtering) is one of the most important procedures in image pre-processing chain. The aim of applying denoising is to reduce the noise while preserving image details and features. However, in practice, there are quite many cases when image denoising can lead to degradation of image quality which affects further thematic (high-level) image processing steps. Thus, the benefit of applying denoising is not always observed. In these cases, it is better to skip image denoising. This paper discusses decision-making on image denoising expedience based on denoising efficiency prediction for several modern filters. Analysis of visual quality for denoised images using subjective evaluation is carried out. Performance evaluation of existing image quality measures in the context of their application to assess the quality of denoised images is conducted. Finally, an algorithm for decision-making on image denoising expedience is proposed. It is shown that reliable decision on image denoising expedience can be undertaken in advance without carrying out denoising itself.
Starting from the Sampling Imaging System, A Comprehensive Review on the Remote Sensing Image Super-Resolution Technology

High resolution is the eternal pursuit of remote sensing satellites. At present, the highest resolution of American lock-eye satellite KH-12 has reached 10 cm, but due to the limitation of the diffraction limit of the remote sensing satellite optical system, the development of a higher resolution remote sensing satellite has encountered a bottleneck. The satellites in orbit, such as Pleiades-NEO, have found another way to super-resolve 30 cm images to 15 cm image products using super-resolution technology based on a convolutional neural network. This method of restoring low-resolution images to high-resolution images through image super-resolution techniques has attracted a lot of attention since the middle and late 20th century. The physical continuous graphic signal is sampled and quantized into discrete digital arrays by CCD or CMOS camera, while the diffraction limit of the optical system and many other factors of degradation also exacerbated this resolution degradation phenomenon. This paper writes from the optical sampling imaging system to induce the model of remote sensing image degradation and analyze the causes of resolution degradation from the source; Further, this paper investigates the image super-resolution techniques of about the last two decades and categorizes them into two categories: traditional algorithm-based, and learning-based. This paper analyzes in detail the key algorithms in the history of super-resolution, and focuses on today’s deep learning-based algorithms, clarifying the problems targeted by each type of algorithm, analyzing their design ideas and implementation principles, and how these algorithms can be adapted for super-resolution algorithms for remote sensing images. Then this paper compares the basic features of the main remote sensing image super-resolution algorithms and their advantages and disadvantages. It also introduces the current more widely used super-resolution effect evaluation metrics. Finally, this paper lists and analyzes the latest and most complete various remote sensing super-resolution datasets publicly available on the Internet, looks forward to the possible future development trends, and points out that joined with imaging systems on satellites, unsupervised learning, and multi-source remote sensing image fusion are the development directions of future remote sensing image super-resolution technologies.