Reviewerly/exHarmony huggingface dataset:
E3CM: Epipolar-constrained cascade correspondence matching

Accurate and robust correspondence matching is of utmost importance for various 3D computer vision tasks. However, traditional explicit programming-based methods often struggle to handle challenging scenarios, and deep learning-based methods require large well-labeled datasets for network training. In this article, we introduce Epipolar-Constrained Cascade Correspondence (E3CM), a novel approach that addresses these limitations. Unlike traditional methods, E3CM leverages pre-trained convolutional neural networks to match correspondence, without requiring annotated data for any network training or fine-tuning. Our method utilizes epipolar constraints to guide the matching process and incorporates a cascade structure for progressive refinement of matches. We extensively evaluate the performance of E3CM through comprehensive experiments and demonstrate its superiority over existing methods. To promote further research and facilitate reproducibility, we make our source code publicly available at https://mias.group/E3CM/.
Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey

The severity of falls increases with age and reduced mobility. Falls are a frequent source of domestic accidents and accidental death on the part of fragile people. They produce anatomical injuries, reduce quality of life, cause dramatic psychological effects, and impose heavy financial burdens. A growing elderly population leads to a direct increase in health service costs, and indirectly to a deterioration of social life in the long term. Unsurprisingly, socioeconomic costs have triggered new scientific health research to detect falls in older people. One of the most appropriate solutions for monitoring the elderly and automatically detecting falls is computer vision. The Kinect camera plays a vital role in recognizing and detecting activities while ensuring seniors’ comfort, safety, and privacy preferences in the fall detection system. This research surveys several Kinect-based works in the literature that cover the approaches used in fall detection. In addition, we discuss the public fall benchmark based on Kinect technology. In general, the main objective of this survey is to provide a complete description of the modules making up the fall detectors and thereby guide researchers in developing fall approaches based on Kinect.
Video Summarization Based on Feature Fusion and Data Augmentation

During the last few years, several technological advances have led to an increase in the creation and consumption of audiovisual multimedia content. Users are overexposed to videos via several social media or video sharing websites and mobile phone applications. For efficient browsing, searching, and navigation across several multimedia collections and repositories, e.g., for finding videos that are relevant to a particular topic or interest, this ever-increasing content should be efficiently described by informative yet concise content representations. A common solution to this problem is the construction of a brief summary of a video, which could be presented to the user, instead of the full video, so that she/he could then decide whether to watch or ignore the whole video. Such summaries are ideally more expressive than other alternatives, such as brief textual descriptions or keywords. In this work, the video summarization problem is approached as a supervised classification task, which relies on feature fusion of audio and visual data. Specifically, the goal of this work is to generate dynamic video summaries, i.e., compositions of parts of the original video, which include its most essential video segments, while preserving the original temporal sequence. This work relies on annotated datasets on a per-frame basis, wherein parts of videos are annotated as being “informative” or “noninformative”, with the latter being excluded from the produced summary. The novelties of the proposed approach are, (a) prior to classification, a transfer learning strategy to use deep features from pretrained models is employed. These models have been used as input to the classifiers, making them more intuitive and robust to objectiveness, and (b) the training dataset was augmented by using other publicly available datasets. The proposed approach is evaluated using three datasets of user-generated videos, and it is demonstrated that deep features and data augmentation are able to improve the accuracy of video summaries based on human annotations. Moreover, it is domain independent, could be used on any video, and could be extended to rely on richer feature representations or include other data modalities.
In Situ Structural Health Monitoring of Full-Scale Wind Turbine Blades in Operation Based on Stereo Digital Image Correlation

Structural health monitoring (SHM) and the operational condition assessment of blades are greatly important for the operation of wind turbines that are at a high risk of disease in service for more than 5 years. Since certain types of blade faults only occur during wind turbine operation, it is more significant to perform in situ SHM of rotating full-scale blades than existing SHM of small-scale blades or static testing of full-scale blades. Considering that these blades are usually not prefabricated with relevant sensors, this study performed SHM and condition assessment of full-scale blades in operation with stereo digital image correlation. A self-calibration method adapted to the outdoors with a large field of view was introduced based on the speckled patterns. To accurately obtain the in- and off-plane deformation, a new reference frame is constructed at the center of the rotation of the blades. The 3D displacements of the points of interest (POIs) on the blade of a 2 MW wind turbine were characterized. Furthermore, the frequency spectrum of the measured 3D displacements of the blades was compared with the blades with the faults. The results showed that the introduced technique is a convenient and nondestructive technique that enables SHM of full-scale wind turbine blades in operation.
Face Keypoint Detection Method Based on Blaze_ghost Network

The accuracy and speed of facial keypoint detection are crucial factors for effectively extracting fatigue features, such as eye blinking and yawning. This paper focuses on the improvement and optimization of facial keypoint detection algorithms, presenting a facial keypoint detection method based on the Blaze_ghost network and providing more reliable support for facial fatigue analysis. Firstly, the Blaze_ghost network is designed as the backbone network with a deeper structure and more parameters to better capture facial detail features, improving the accuracy of keypoint localization. Secondly, HuberWingloss is designed as the loss function to further reduce the training difficulty of the model and enhance its generalization ability. Compared to traditional loss functions, HuberWingloss can reduce the interference of outliers (such as noise and occlusion) in model training, improve the model’s robustness to complex situations, and further enhance the accuracy of keypoint detection. Experimental results show that the proposed method achieves significant improvements in both the NME (Normal Mean Error) and FR (Failure Rate) evaluation metrics. Compared to traditional methods, the proposed model demonstrates a considerable improvement in keypoint localization accuracy while still maintaining high detection efficiency.
Tool for Game Plot Line Visualization for Designers, Testers and Players

This paper presents a tool for automatic and interactive visualization of game plots, which can be used to check whether the designers’ work meets the constraints of the world, to help testers control played game story paths. The referential designers’ game plots can be imported from any flowchart diagrams as an XML file and the actual gameplay variant is generated on the fly and applied to the original design based on detected common diagram points.
Early and late adopter effects between schools in a one-to-one computer initiative

Although there are numerous studies on the development of teachers’ professional digital competence, only a few have studied the intermediate levels of the education system and its impact over time. A structural merger of three Norwegian municipalities allowed for a natural field experiment involving two school districts. In the present study, we explored potential differences in early and late adopter effects on teachers from school districts. Survey data were collected to assess teacher-level perceptions of organisational support and access to collaborative professional communities. Teachers also rated themselves according to the Technological, Pedagogical, and Content Knowledge (TPACK) framework for digital competence and answered questions to assess their level of student-centred teaching beliefs. The data were analysed using Structural Equation Model (SEM) to explore associations between study variables and patterns of differences between school districts. The results indicate that municipality- or school district-level initiatives have influenced the behaviour of school management and in the teacher community, and possibly result in a more student-centred teaching style in the classroom. The present paper adds new insights into how teachers and schools are influenced by the intermediate levels of the education system. The findings from our study may thus be of interest to researchers exploring the professional development of digital competence for teachers and schools. Moreover, as we highlight how school districts approach and support schools in the implementation of one-to-one coverage of digital devices in schools, we bring updated knowledge to this research field.
Exploring Factors that Drive FinTech Adoption in Jordan

This study aimed to look into FinTech adoption in Jordan. Three predictors were hypothesized to test their impact on behavioral intention: perceived ease of use, financial risks, and privacy risks. The researcher applied a quantitative method. The researcher used convenience sampling to collect data. In conclusion, the sample comprised 296 respondents who were residents of Jordan and users of FinTech applications like mobile banking and mobile wallet. Additionally, statistical tools were used to analyze the data, including structural equation modeling (SEM). This study also contributes theoretically and practically, it attempts to fill a gap in the existing literature on FinTech adoption by advancing a better understanding of determinants that might drive or inhibit behavioral intention to use FinTech services. Also, in an attempt to encourage individuals to use FinTech services and to foster the establishment of the FinTech industry.
Two Computational Approaches to Visual Analogy: Task‐Specific Models Versus Domain‐General Mapping

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task-specific knowledge acquired from a wealth of prior experience, or is it based on the domain-general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three-dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task-specific knowledge to analogical reasoning. We also developed a new model using part-based comparison (PCM) by applying a domain-general mapping procedure to learned representations of cars and their component parts. Across four-term analogies (Experiment 1) and open-ended analogies (Experiment 2), the domain-general PCM model, but not the task-specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human-like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity.
PointTrans: Rethinking 3D Object Detection from a Translation Perspective with Transformer

3D object detection provides useful information of surrounding objects in complex environment which servers as the fundation for autonomous robot and self-driving vehicles to work safely outdoors and the premise of high-level application such like robot path planning and obstacle avoiding. In this paper, we propose a two-stage 3D object detection framework PointTrans. The whole framework is composed of two stages: stage-1 for the bottom-up object-entity recognition and localization via semantic segmentation and clustering; stage2 for treating bounding boxes regression task as a translation task via Transformer architecture. Our PointTrans not only detects objects in 3D scene with state-of-art performance, but also generated dense semantic representation for 3D scene. Moreover, we creatively embedded parameters of 3D bounding box into a “word” vector which can be iteratively learned and optimized acrossing Transformer decoder layers.
Autonomous Navigation of QUAVs Under 3D Environments Based on Hierarchical Reinforcement Learning

An RL-based hierarchical architecture for QUAV autonomous navigation under 3D environments is presented in this paper. The model consists of three attitude controllers, three position controllers, and one path planner, which are trained separately under the small angle restriction. After training, the QUAV can autonomously reach the target in an end-to-end fashion. To address data imbalance, we use a technique called state saturation, clipping the state if the target is far away from the vehicle. Experiments show that with a training time of 191.6 minutes, the model successfully navigates the QUAV to the reference position without colliding with any obstacles. Moreover, saturation proves an effective way to increase path quality and reduce training time.
Small Object Detection for Drone Image Based on Advanced YOLOv7

Deep learning models for object detection on drone image have become a typical solution as consumer drone become more and more popular. Challenges in this scene, such as drone move too quick or too high, blocked by buildings or trees, make the accuracy of detection low. In response to the aforementioned issues, this paper selects YOLOv7 as a real-time object detector to improve, and designs the model's backbone network using a large convolution kernel architecture to expand the effective receptive field of convolution. Simultaneously, a BiFPN-like struct is designed for the YOLOv7 and used to process feature maps of various scales in order to solve the problem of the model's low precision in the drone scene. To support the large kernel backbone architecture, this paper uses methods such as struct reparameterization and depth-wise convolutions, so that the model has better inference performance.
AFcIHNet: Attention feature-constrained network for single image information hiding

Single image information hiding aims to obtain a stegano image by hiding a secret image within a cover image. Recently, deep neural network (DNN) based information hiding methods have been advanced extensively. However, it is difficult for them to preserve the spatial information at different scales on each channel feature map in the stegano image, largely because of a lack of effective features and feature fusion. In this paper, a bi-channel attention feature-constrained pixel-shuffle information hiding network (AFcIHNet) is proposed to efficiently enhance the detailed spatial information in the stegano image. First, the invertible neural network (INN) is employed for the information hiding task, which is adaptively constrained by the proposed bi-channel attention module (Bi-AM). The capability of the Bi-AM module is further enhanced by the introduction of a novel attention loss function. In addition, to take full advantage of the attention based feature fusion mechanism that combines the global and local contexts of the features, we used the Squeeze and Concat (SPC) module with adaptive branching factors, the model can hierarchically approach the input feature map from local to global. Extensive experiments on the DIV2K and COCO datasets show that the proposed method can produce competitive stegano images when compared with some state-of-the-art methods. Finally, we can get a secret image that is closer to the original image. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and average pixel difference (APD) were used to evaluate the experimental effects of cover and stegano images as well as secret and recovered secret images. The experimental results show that the obtained stegano and cover images are more indistinguishable, and the recovered secret image is closer to the original image. Overall, the overall performance is improved by nearly 0.8% compared to the existing baseline model.
Based on GMM-RRT* Algorithm for Path Planning Picking Kiwifruit Manipulator

Rapidly-exploring random tree (RRT) algorithm exhibits excessive random sampling and poor convergence when planning trajectory in a picking environment. An improved RRT* algorithm employing Gaussian mixture model sampling (GMM-RRT*) algorithm is proposed to optimize the disadvantages of the RRT* algorithm. To verify real effect, GMM-RRT* algorithm, RRT algorithm, RRT* algorithm, RRT* algorithm based on goal node guidance (T-RRT*) algorithm and GMM-RRT* algorithm are worked in 2D scene, 3D scene and manipulator picking simulation scene to solve optimal trajectory, respectively. Results obtained are statistically analyzed in terms of effective length, planning time and number of redundant nodes. Conduct experiments on the Elfin E05 manipulator platform to test the GMM-RRT* algorithm comparing with RRT* algorithm and RRT algorithm. Both simulation and experimental results show that the proposed GMM-RRT* fusion algorithm can effectively shorten planning path time in kiwifruit intelligent picking and improve efficiency of kiwifruit picking.
Deep Learning-Powered Multiple-Object Segmentation for Computer-Aided Diagnosis

Recently, driven by hardware devices and deep learning technologies, computer-aided diagnosis systems have been widely applied, such as cancer diagnosis and early screening of autistic children. Many studies have reported extracting tumor regions from whole-slide images (WSI) in cancer diagnosis tasks, namely, image segmentation. However, doctors must re-analyze the ROI in the tumor area for some challenging diseases. Efficient segmentation algorithms are the key parts of perfecting machine diagnostic assistance systems. This paper presents a novel WSI segmentation framework (called UFINet), aiming to segment the tumor region on the liver tissue image and re-segment the region of interest in the tumor. The proposed algorithm provides a solution for applying medical human-computer interaction systems. The proposed framework was trained and tested on the liver tissue dataset and achieved a Dice of 66% on 86 WSIs. Experiments prove that the proposed UFIN et achieves top performance and meets the clinical requirements, providing an effective method for developing computer-aided diagnosis systems.
Joint Image Restoration For Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) has received extensive attention in recent years as it achieves pixel-level segmentation by only image-level labels. However, existing WSSS methods commonly extract Class Activation Maps (CAM) from a classification network as the initial localization cues, which is always narrow and fragmentary. We interpret the issue as the fact that incomplete supervision of the classification task introduces implicit disturbance to CAM. We turn to Joint Image Restoration and propose Restored Class Activation Mapping (RCAM) to eliminate disturbance during CAM generation. RCAM consists of a conventional CAM generation network and a proposed restoration network. For the restoration network, we set up Guidance and Decoder branches for feature extraction, and introduce Pixel-Adaptive Convolution for feature decoding. To train RCAM, we extract refined pixel-level supervision from CAM by applying condition random filed, termed Joint Mask. Experiments on PASCAL VOC 2012 and MS COCO 2014 show that our method achieves a great improvement for CAM and is competitive with existing state-of-the-art methods.
AI-Powered Chatbots and the Transformation of Work: Findings from a Case Study in Software Development and Software Engineering

The recent technological enhancements in the field of large language models and their integration into collaborative processes, for example, as chatbots, are perceived as key drivers for further transformations of work. However, the transformative effects of these technological enhancements have to be more thoroughly investigated in specific work contexts to benefit from the great potential of improvement. This research article provides findings of a case study research on how employees in software engineering perceive the collaboration with AI-powered chatbots, such as chatGPT. We investigate patterns employees develop to cope with the novel demands arising during the collaboration with these technologies and discuss our empirical findings regarding a conceptual framework of AI-related competences and another case study from a different industry. The findings contribute to a better understanding of human actors’ AI-related coping patterns as key prerequisites for a more responsible and sustainable usage of this technology in professional work contexts.
An Improved A-Star Algorithm for Global Path Planning of Unmanned Surface Vehicle

Conventional A-Star (A*) algorithm exists some problems, such as multiple extended nodes, slow convergence and weak real-time performance, in marine path planning of unmanned surface vehicles (USVs). To solve the aforementioned problems, this study proposes an adaptive-direction and variable-step A* algorithm. Firstly, a gridded environment model is established based on S57 electronic navigation chart. Considering the marine scenes, the obstacles and water flow constraints are added into the evaluation function. Secondly, an adaptive direction method and a variable step strategy are proposed, and the bi-directional search method is combined with the proposed method to form an improved A* algorithm. An evaluation function for assessing redundancy is designed to optimize the path to suppress the influence of redundant and non-smooth paths. Finally, some visualized simulated experiments are set to verify the performance of the proposed method. And three different scenes ranging from simple to complex are selected for path planning experiments. The results show that the improved algorithm outperforms the conventional A* algorithm, artificial potential field algorithm and particle swarm optimization algorithm. It can effectively reduce extended nodes, and has good real-time performance. In addition, it has the robustness and faster convergence that can be used in different path-planning scenes.