Search Results (144)

Accurate body condition score (BCS) monitoring in dairy cows is essential for optimizing health, productivity, and welfare. Traditional manual scoring methods are labor-intensive and subjective, driving interest in automated imaging-based systems. This study evaluated the effectiveness of 2D imaging and deep learning for BCS classification using three camera perspectives—front, back, and top-down—to identify the most reliable viewpoint. The research involved 56 Norwegian Red milking cows at the Center for Livestock Experiments (SHF) of Norges Miljo-og Biovitenskaplige Universitet (NMBU) in Norway. Images were classified into BCS categories of 2.5, 3.0, and 3.5 using a YOLOv8 model. The back view achieved the highest classification precision (mAP@0.5 = 0.439), confirming that key morphological features for BCS assessment are best captured from this angle. Challenges included misclassification due to overlapping features, especially in Class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. Integration with precision farming tools enables continuous monitoring and early detection of health issues. This research highlights the potential of 2D imaging as a cost-effective alternative to 3D systems, particularly for small and medium-sized farms, supporting more effective herd management and improved animal welfare. Full article

Current tourism route recommendation systems often overemphasize popular destinations, thereby overlooking geographical accessibility between attractions and the experiential coherence of the journey. Leveraging multidimensional attribute perceptions derived from tourist reviews, this study proposes a Spatial–Semantic Integrated Model for Tourist Attraction Representation (SSIM-TAR), which holistically encodes the composite attributes and multifaceted evaluations of attractions. Integrating these multidimensional features with inter-attraction relationships, three relational metrics are defined and fused: spatial proximity, resonance correlation, and thematic-sentiment similarity, forming a Tourist Attraction Multidimensional Association Network (MAN-SRT). This network enables precise characterization of complex inter-attraction dependencies. Building upon MAN-SRT, the Tourism Sentiment Chain (TSC) model is proposed that incorporates geographical accessibility, associative resonance, and thematic-sentiment synergy to optimize the selection and sequential arrangement of attractions in personalized route planning. Results demonstrate that SSIM-TAR effectively captures the integrated attributes and experiential quality of tourist attractions, while MAN-SRT reveals distinct multidimensional association patterns. Compared with popular platforms such as “Qunar” and “Mafengwo”, the TSC approach yields routes with enhanced spatial efficiency and thematic-sentiment coherence. This study advances tourism route modeling by jointly analyzing multidimensional experiential quality through spatial–semantic feature fusion and by achieving an integrated optimization of geographical accessibility and experiential coherence in route design. Full article

Short informal texts are increasingly prevalent in modern communication, often containing fragmented grammar, personal opinions, and limited context. Traditional NLP tasks for the texts ordinarily focus on the subjective aspect learning, such as sentiment analysis and polarity classification. The study of learning objectivity from the texts is similarly significant, which can benefit many real-world applications including information filtering, content verification, etc. Unfortunately, this study is not being explored. This paper proposes a novel task that aims at identifying objectivity in short informal texts. Inspired by the characteristics of objective statements that normally need complete syntax structures for knowledge expression and delivery, we try to leverage the viewpoint of subjects (U), the tense of predicates (V), and the viewpoint of objects (O) as critical factors for objectivity learning. Upon that, we further propose a two-stage objectivity identification approach: (1) a UVO quantification module is implemented via a proposed OpenIE and large language model (LLM)-based triple feature quantification procedure; (2) an objectivity identification module employs pre-trained base models like BERT or RoBERTa that are constrained with the quantified UVO. The experimental result demonstrates our approach can outperform the base models up to $5.91\%$ in objective-F1 and up to $6.97\%$ in accuracy. Full article

This study presents an enhanced version of the StarGAN model, with a focus on medical applications, particularly endoscopic image augmentation. Our model incorporates novel Perspective Transformation and Viewpoint Attention Modules for StarGAN that improve image classification accuracy in a multiclass classification task. The Perspective Transformation Module enables the generation of more diverse viewing angles, while the Viewpoint Attention Module helps focus on diagnostically significant regions. We evaluate the performance of our enhanced architecture using the Kvasir v2 dataset, which contains 8000 images across eight gastrointestinal disease classes, comparing it against baseline models including VGG-16, ResNet-50, DenseNet-121, InceptionNet-V3, and EfficientNet-B7. Experimental results demonstrate that our approach achieves better performance in all models for this eight-class classification problem, increasing accuracy on average by 0.7% on VGG-16 and 0.63% on EfficientNet-B7 models. The addition of perspective transformation capabilities enables more diverse examples to augment the database and provide more samples of specific illnesses. Our approach offers a promising solution for medical image generation, enabling effective training with fewer data samples, which is particularly valuable in medical model development where data are often scarce due to challenges in acquisition. These improvements demonstrate significant potential for advancing machine learning disease classification systems in gastroenterology and medical image augmentation as a whole. Full article

Out-of-distribution (OOD) detection is crucial for identifying samples that deviate from the training distribution, thereby enhancing the reliability of deep neural network models. However, existing OOD detection methods primarily address semantic shifts, where an image’s inherent semantics have changed, and often overlook covariate shifts, which are prevalent in real-world scenarios. For instance, variations in image contrast, lighting, or viewpoints can alter input features while keeping the semantic content intact. To address this, we propose the Full-Spectrum Contrastive Denoising (FSCD) framework, which improves OOD detection under covariate shifts. FSCD first establishes a robust semantic boundary and then refines feature representations through fine-tuning. Specifically, FSCD employs a dual-level perturbation augmentation module to simulate covariate shifts and a feature contrastive denoising module to effectively distinguish in-distribution samples from OOD samples. Extensive experiments on three benchmarks demonstrate that FSCD achieves state-of-the-art performance, with AUROC improvements of up to 0.51% on DIGITS, 0.55% on OBJECTS, and 2.09% on COVID compared to the previous best method while also maintaining the highest classification accuracy on covariate-shifted in-distribution samples. Full article

Distributed learning (DL), in which multiple nodes in an inner-connected network collaboratively induce a predictive model using their local data and some information communicated across neighboring nodes, has received significant research interest in recent years. Yet, it is challenging to achieve excellent performance in scenarios when training data instances have incomplete features and ambiguous labels. In such cases, it is essential to develop an efficient method to jointly perform the tasks of missing feature imputation and credible label recovery. Considering this, in this article, a distributed partial label missing data classification (dPMDC) algorithm is proposed. In the proposed algorithm, an integrated framework is formulated, which takes the ideas of both generative and discriminative learning into account. Firstly, by exploiting the weakly supervised information of ambiguous labels, a distributed probabilistic information-theoretic imputation method is designed to distributively fill in the missing features. Secondly, based on the imputed feature vectors, the classifier modeled by the random feature map of the ${\chi }^2$ kernel function can be learned. Two iterative steps constitute the dPMDC algorithm, which can be used to handle dispersed, distributed data with partially missing features and ambiguous labels. Experiments on several datasets show the superiority of the suggested algorithm from many viewpoints. Full article

Image-based viewpoint estimation is one of the tasks in image analysis, and another is the inverse problem of selecting the best viewpoint for displaying a three-dimensional object. Currently, two issues need further exploration in image-based viewpoint estimation research: insufficient labeled data and a limited number of evaluation methods for estimation results. To address the first issue, this paper proposes a spherical viewpoint sampling method based on a combination of analytical methods and motion adjustment, and designs a viewpoint-based projection image acquisition algorithm. Considering the difference between viewpoint inference and image classification, we propose an accuracy evaluation method with deviation angle tolerance for viewpoint estimation. Based on constructing a new dataset with viewpoint labels, the new accuracy evaluation method has been validated through experiments. The experimental results show that its estimation accuracy can reach 89% according to the new estimation evaluation indicators. Additionally, we applied our method to estimate the viewpoints of images from a furniture website and analyzed the viewpoint preferences in its furniture displays. Full article

Wind energy’s crucial role in global sustainability necessitates efficient wind turbine maintenance, traditionally hindered by labor-intensive, risky manual inspections. UAV-based inspections offer improvements yet often lack adaptability to dynamic conditions like blade pitch and wind. To overcome these limitations and enhance inspection efficacy, we introduce the Dynamic Trajectory Adaptation Method (DyTAM), a novel approach for automated wind turbine inspections using UAVs. Within the proposed DyTAM, real-time image segmentation identifies key turbine components—blades, tower, and nacelle—from the initial viewpoint. Subsequently, the system dynamically computes blade pitch angles, classifying them into acute, vertical, and horizontal tilts. Based on this classification, DyTAM employs specialized, parameterized trajectory models—spiral, helical, and offset-line paths—tailored for each component and blade orientation. DyTAM allows for cutting total inspection time by 78% over manual approaches, decreasing path length by 17%, and boosting blade coverage by 6%. Field trials at a commercial site under challenging wind conditions show that deviations from planned trajectories are lowered by 68%. By integrating advanced path models (spiral, helical, and offset-line) with robust optical sensing, the DyTAM-based system streamlines the inspection process and ensures high-quality data capture. The dynamic adaptation is achieved through a closed-loop control system where real-time visual data from the UAV’s camera is continuously processed to update the flight trajectory on the fly, ensuring optimal inspection angles and distances are maintained regardless of blade position or external disturbances. The proposed method is scalable and can be extended to multi-UAV scenarios, laying a foundation for future efforts in real-time, large-scale wind infrastructure monitoring. Full article

Human pose comparison involves measuring the similarities in body postures between individuals to understand movement patterns and interactions, yet existing methods are often insufficiently robust and flexible. In this paper, we propose a RotJoint-based pipeline for pose similarity estimation that is both fine-grained and generalizable, as well as robust. Firstly, we developed a comprehensive benchmark for action ambiguity that intuitively and effectively evaluates the robustness of pose comparison methods against challenges such as body shape variations, viewpoint variations, and torsional poses. To address these challenges, we define a feature representation called RotJoints, which is strongly correlated with both the semantic and spatial characteristics of the pose. This parameter emphasizes the description of limb rotations across multiple dimensions, rather than merely describing orientation. Finally, we propose TemporalRotNet, a Transformer-based network, trained via supervised contrastive learning to capture spatial–temporal motion features. It achieves 93.7% accuracy on NTU-RGB+D close set action classification and 88% on the open set, demonstrating its effectiveness for dynamic motion analysis. Extensive experiments demonstrate that our RotJoint-based pipeline produces results more aligned with human understanding across a wide range of common pose comparison tasks and achieves superior performance in situations prone to ambiguity. Full article

Environmental challenges, high safety risks and operational inefficiencies are some of the issues facing the mining sector. The paper offers an integrated viewpoint to address these issues by combining swarm robotics, nature-inspired algorithms (NIAs) and other biomimicry-based technologies into a single framework. It presents a systematic classification of each methodology, emphasizing their key advantages and disadvantages as well as considering real-life mining application scenarios, including hazard detection, autonomous transportation and energy-efficient drilling. Case studies are citied to demonstrate how these methodologies work together, and an extensive comparison table considering their applications at mines, such as Boliden, Diavik Diamond Mine, Olympic Dam and others, presents a summary of their scalability and practicality. This paper highlights future directions such as multi-robot coordination and hybrid NIAs, to improve operational resilience and sustainability. It also provides a broad overview of biomimicry and critically examines unresolved issues like real-time adaptation, parameter tuning and mechanical wear. The paper aims to offer a comprehensive insight into using bio-inspired models to enhance mining efficiency, safety and environmental management, while proposing a road map for resolving the issues that continue to be a hurdle for wide adaptation of these technologies in the mining industry. Full article

Aircraft detection technology plays a vital role in civilian applications, with significant attention being devoted to research on related algorithms in recent years. However, most existing research predominantly focuses on aircraft detection from a single top–down viewpoint, which constrains the applicability of detection technology across diverse scenarios. To overcome this limitation, we propose RMVAD-YOLO, a multi-view aircraft detection model built upon YOLOv8. First, we propose a novel Robust Multi-Link Scale Interactive Feature Pyramid Network (RMSFPN), which robustly extracts features of the same aircraft category from multiple views while enhancing feature differentiation between different aircraft categories. Second, we propose the Shared Convolutional Dynamic Alignment Detection Head (SCDADH), which enhances task interaction and collaboration by sharing convolutions between the classification and localization branches while simultaneously reducing the number of parameters, enhancing the model’s ability to deal with multi-scale targets. Additionally, to further leverage background information and enhance the model’s adaptability to multi-scale target variations, we incorporate the LSK Module into the backbone network. Finally, we propose the WFMIoUv3 loss function, which strengthens the model’s focus on challenging samples and improves detection robustness. Experimental results on the newly released Multi-Perspective Aircraft Dataset (MAD) demonstrate that RMVAD-YOLO achieves an accuracy of 90.1%, a recall of 76%, 84.8% mAP@0.5, and 70.5% mAP@0.5:0.95, while reducing parameters and delivering an overall improvement in detection performance compared to the baseline YOLOv8n. RMVAD-YOLO also performed well on the VisDrone 2019 dataset, further demonstrating its reliable generalization capabilities. Full article

Telemanipulation-based object-side picking with a suction gripper often faces challenges such as occlusion of the target object or the gripper and the need for precise alignment between the suction cup and the object’s surface. These issues can significantly affect task success rates in logistics environments. To address these problems, this study proposes a multi-mode hand gesture-based virtual reality (VR) locomotion method to enable intuitive and precise viewpoint control. The system utilizes a head-mounted display (HMD) camera to capture hand skeleton data, which a multi-layer perceptron (MLP) model processes. The model classifies gestures into three modes: translation, rotation, and fixed, corresponding to fist, pointing, and unknown gestures, respectively. Translation mode moves the viewpoint based on the wrist’s displacement, rotation mode adjusts the viewpoint’s angle based on the wrist’s angular displacement, and fixed mode stabilizes the viewpoint when gestures are ambiguous. A dataset of 4312 frames was used for training and validation, with 666 frames for testing. The MLP model achieved a classification accuracy of 98.4%, with precision, recall, and F1-score exceeding 0.98. These results demonstrate the system’s ability to address the challenges of telemanipulation tasks by enabling accurate gesture recognition and seamless mode transitions. Full article

Numerous studies leverage pose estimation to extract human keypoint data and then classify sitting postures. However, employing neural networks for direct keypoint classification often yields suboptimal results. Alternatively, modeling keypoints into other data representations before classification introduces redundant information and substantially increases inference time. In addition, most existing methods perform well only under a single fixed viewpoint, limiting their applicability in complex real-world scenarios involving unseen viewpoints. To better address the first limitation, we propose KeypointNet, which employs a decoupled feature extraction strategy consisting of a Keypoint Feature Extraction module and a Multi-Scale Feature Extraction module. In addition, to enhance multi-view recognition capability, we propose the Multi-View Simulation (MVS) algorithm, which augments the viewpoint information by first rotating keypoints and then repositioning the camera. Simultaneously, we propose the multi-view sitting posture (MVSP) dataset, designed to simulate diverse real-world viewpoints. The experimental results demonstrate that KeypointNet outperforms the other state-of-the-art methods on both the proposed MVSP dataset and the other public datasets, while maintaining a lightweight and efficient design. Ablation studies demonstrate the effectiveness of MVS and all KeypointNet modules. Furthermore, additional experiments highlight the superior generalization, small-sample learning capability, and robustness to unseen viewpoints of KeypointNet. Full article

This article provides an in-depth analysis of blockchain research in the energy sector, focusing on projects funded by the U.S. Department of Energy (DOE) and comparing them with industry-funded initiatives. A total of 110 funded activities within the U.S. power industry were successfully tracked and mapped into a newly developed categorization framework. This framework is designed to help research agencies to systematically understand their funded portfolio. Such characterization is expected to help them make effective investments, identify research gaps, measure impact, and advance technological progress to meet national goals. In line with this need, the proposed framework proposes a 2-D categorization matrix to systematically classify blockchain efforts within the energy sector.Under the proposed framework, the Energy System Domain serves as the primary classification dimension, categorizing use cases into 30 distinct applications. The second dimension, Blockchain Properties, captures the specific needs and functionalities provided by Blockchain technology. The aim was to capture blockchain’s applicability and functionality: where and why blockchain? Principles behind the selection of the viewpoint dimensions were carefully defined based on consensus obtained through the Blockchain for Optimized Security and Energy Management (BLOSEM) project. The mapped results show that activities within the Grid Automation, Coordination, and Control (31.8%), Marketplaces and Trading (25.5%), Foundational Blockchain Research (19.1%), and Supply Chain Management (17.3%) domains have been actively pursued to date. The three leading specific use case applications were identified as Transactive Energy Management for Marketplaces and Trading, Asset Management for Supply Chain Management, and Fundamental Blockchain for Foundational Blockchain Research. The Marketplaces and Trading and Retail Services Enablement domains stood out as being favored by industry by a factor greater than 2 (2.3 and 2.6, respectively), yet there seemed to be little to zero investment from DOE. Approximately 76% of the total projects prioritized Immutability, Identity Management, and Decentralization and/or Disintermediation compared to Asset Digitization and/or Tokenization, Automation, and Privacy and/or Anonymity. The greatest discrepancies between DOE and industry were in Asset Digitization and/or Tokenization and Automation. The industry efforts (36% in Asset Digitization/Tokenization and 22% in Automation) was 14 times and 2.4 times, respectively, more intensive than the DOE-sponsored efforts, indicating a significant discrepancy in industry versus government priorities. Overall, quantifying DOE-sponsored projects and industry activities through mapping provides clarity on portfolio investments and opportunities for future research. Full article

We introduce a special class of cubic curves whose defining parameter satisfies a linear or quadratic equation provided by the values of a cross ratio. There are only seven such cubics and several properties of the real cubics in this class (some of them being elliptic curves) are discussed. Using the Möbius transformation, we extend this self-duality and obtain new families of remarkable complex cubics. In addition, we study (from the differential geometric viewpoint) the surface parameterized by all real cubic curves and we derive its curvature functions. As a by-product, we find a new classification of real Möbius transformations and some estimates for the number of vertices of real cubic curves. Full article