Established sampling protocols for 3D point cloud learning, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), have long been relied upon. However, real-world data often suffer from corruptions, such as sensor noise, which violates the benign data assumption in current protocols. As a result, these protocols are highly vulnerable to noise, posing significant safety risks in critical applications like autonomous driving. To address these issues, we propose an enhanced point cloud sampling protocol, PointSP, designed to improve robustness against point cloud corruptions. PointSP incorporates key point reweighting to mitigate outlier sensitivity and ensure the selection of representative points. It also introduces a local-global balanced downsampling strategy, which allows for scalable and adaptive sampling while maintaining geometric consistency. Additionally, a lightweight tangent plane interpolation method is used to preserve local geometry while enhancing the density of the point cloud. Unlike learning-based approaches that require additional model training, PointSP is architecture-agnostic, requiring no extra learning or modification to the network. This enables seamless integration into existing pipelines. Extensive experiments on synthetic and real-world corrupted datasets show that PointSP significantly improves the robustness and accuracy of point cloud classification, outperforming state-of-the-art methods across multiple benchmarks.

Effective sampling plays a critical role in the preprocessing of 3D point cloud data, directly impacting the performance of downstream models. Traditional Farthest Point Sampling (FPS) ensures global spatial uniformity but is highly sensitive to noise and occlusion, often leading to reduced recognition accuracy. To address this issue, we propose a robust sampling method based on a novel density-aware metric called the aggregation rate. By computing K-nearest neighbor distances for each point, the method quantifies local compactness and suppresses outliers during sampling. We integrate our approach into the PointNet++ framework and evaluate it on two challenging corrupted datasets: ModelNet40-C and PointCloud-C. Experimental results show notable improvements in robustness and classification accuracy, with gains of up to 2.0% on PointCloud-C. Parameter studies further confirm the method’s stability and effectiveness. Our approach offers a lightweight, plug-and-play solution that enhances sampling robustness, making it well-suited for real-world noisy 3D environments.

Co-representation, which co-represents samples and features, has been widely used in various machine learning tasks, such as document clustering, gene expression analysis, and recommendation systems. It not only reveals the cluster structure of both samples and features, but also reveals the sample–feature correlation. Given a tabular data matrix, co-representation usually exhibits as the co-occurrence structures of rows and columns. However, identifying such structured patterns in complex real-world data can be very challenging. To address this problem, we propose an unsupervised discriminative co-representation learning model based on multidimensional scaling (DCLMDS). The main novelty is that DCLMDS introduces a co-representation learning term to ensure the discriminability between co-occurrence structures. As a result, the co-representation learned by DCLMDS contains richer information of the underlying correlation between samples and features within data. This could subsequently enhance the capacity of machines and systems for processing complex real-world information more proficiently. Furthermore, inspired by the fuzzy set theory, we integrate fuzzy membership degree that can accurately capture the uncertainty within data, thus enabling DCLMDS to learn a more effective co-representation in a soft manner. To evaluate the performance of DCLMDS, we conduct extensive experiments on 18 datasets, and the results demonstrate that DCLMDS can generate both accurate and discriminative co-representation, which well meets our desired outcomes.

Co-clustering is to cluster samples and features simultaneously, which can also reveal the relationship between row clusters and column clusters. Therefore, lots of scientists have drawn much attention to conduct extensive research on it, and co-clustering is widely used in recommendation systems, gene analysis, medical data analysis, natural language processing, image analysis, and social network analysis. In this article, we survey the entire research aspect of co-clustering, especially the latest advances in co-clustering, and discover the current research challenges and future directions. First, due to different views from researchers on the definition of co-clustering, this article summarizes the definition of co-clustering and its extended definitions, as well as related issues, based on the perspectives of various scientists. Second, existing co-clustering techniques are approximately categorized into four classes: information-theory-based, graph-theory-based, matrix-factorization-based, and other theories-based. Third, co-clustering is applied in various aspects such as recommendation systems, medical data analysis, natural language processing, image analysis, and social network analysis. Furthermore, 10 popular co-clustering algorithms are empirically studied on 10 benchmark datasets with 4 metrics—accuracy, purity, block discriminant index, and running time, and their results are objectively reported. Finally, future work is provided to get insights into the research challenges of co-clustering.

Tracking ground support equipment (GSE) in a high accuracy manner is crucial for both airport safety and optimal management of airport assets but the related researches and products are scarce. Tracking a multi-carriage logistics train is obviously most challenging compared with other single-carriage GSE. In this paper, we design a real-time on-board positioning and heading system (ROPHS) to obtain the real-time status of a multi-carriage logistics train which consists of a powered leading vehicle and one or several non-powered trailing vehicles. The status includes: (a) the accurate positions and velocities of any points on this train, and (b) the alterable number and linking sequence of trailing vehicles at any time of a trip. Technically, the hardware of the system relies on real-time kinematic (RTK) to obtain geolocation of the leading vehicle, and on gyroscopes, magnetometers and accelerometers to obtain headings of all vehicles. A geometry based recurrence algorithm is afterwards presented to calculate the positions of any trailing vehicles. In the end, the multiple model based tracking algorithm is proposed to compute the precision-improved locations and real-time velocities of the whole train. Different from existing traditional GPS or RFID based positioning techniques, the proposed system can reach centimeter-level accuracy, which enables collisions detection, especially those latent collisions that cannot be easily monitored or foreseen by crews’ visual inspection.

This article proposes a new approach to sales forecasting for new products (stock-keeping units [SKUs]) with long lead time but short product life cycle. These SKUs are usually sold for one season only, without any replenishments. An exponential factorization machine (EFM) sales forecast model is developed to solve this problem which not only takes into account SKU attributes, but also pairwise interactions. The EFM model is significantly different from the original Factorization Machines (FM) from two fold: (1) the attribute-level formulation for explanatory/input variables; and (2) exponential formulation for the positive response/output/target variable. The attribute-level formation excludes infeasible intra-attribute interactions and results in more efficient feature engineering comparing with the conventional one-hot encoding, while the exponential formulation is demonstrated more effective than the log-transformation for the positive but not skewed distributed responses. In order to estimate the parameters, percentage error squares (PES) and error squares (ES) are minimized by a proposed adaptive batch gradient descent method over the training set. To overcome the over-fitting problem, a greedy forward stepwise feature selection method is proposed to select the most useful attributes and interactions. Real-world data provided by a footwear retailer in Singapore are used for testing the proposed approach. The forecasting performance in terms of both mean absolute percentage error (MAPE) and mean absolute error (MAE) compares favorably with not only off-the-shelf models but also results reported by extant sales and demand forecasting studies. The effectiveness of the proposed approach is also demonstrated by two external public datasets. Moreover, we prove the theoretical relationships between PES and ES minimization, and present an important property of the PES minimization for regression models; that it trains models to underestimate data. This property fits the situation of sales forecasting where unit-holding cost is much greater than the unit-shortage cost (e.g., perishable products).

Learning on 3D scene-based point cloud has received extensive attention as its promising application in many fields, and well-annotated and multisource datasets can catalyze the development of those data-driven approaches. To facilitate the research of this area, we present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks and also an effective learning framework for its hierarchical segmentation task. The dataset was generated via the photogrammetric processing on unmanned aerial vehicle (UAV) images of the National University of Singapore (NUS) campus, and has been point-wisely annotated with both hierarchical and instance-based labels. Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies. To solve this problem, a two-stage method including multi-task (MT) learning and hierarchical ensemble (HE) with consistency consideration is proposed. Experimental results demonstrate the superiority of the proposed method and potential advantages of our hierarchical annotations. In addition, we benchmark results of semantic and instance segmentation, which is accessible online at https://3d.dataset.site with the dataset and all source codes.

The requiring of large amounts of annotated training data has become a common constraint on various deep learning systems. In this paper, we propose a weakly supervised scene text detection method (WeText) that trains robust and accurate scene text detection models by learning from unannotated or weakly annotated data. With a "light" supervised model trained on a small fully annotated dataset, we explore semi-supervised and weakly supervised learning on a large unannotated dataset and a large weakly annotated dataset, respectively. For the unsupervised learning, the light supervised model is applied to the unannotated dataset to search for more character training samples, which are further combined with the small annotated dataset to retrain a superior character detection model. For the weakly supervised learning, the character searching is guided by high-level annotations of words/text lines that are widely available and also much easier to prepare. In addition, we design an unified scene character detector by adapting regression based deep networks, which greatly relieves the error accumulation issue that widely exists in most traditional approaches. Extensive experiments across different unannotated and weakly annotated datasets show that the scene text detection performance can be clearly boosted under both scenarios, where the weakly supervised learning can achieve the state-of-the-art performance by using only 229 fully annotated scene text images.

In this article, the authors introduce two citation‐based approaches to facilitate a multidimensional evaluation of 39 selected management journals. The first is a refined application of PageRank via the differentiation of citation types. The second is a form of mathematical manipulation to identify the roles that the selected management journals play. Their findings reveal that Academy of Management Journal, Academy of Management Review, and Administrative Science Quarterly are the top three management journals, respectively. They also discovered that these three journals play the role of a knowledge hub in the domain. Finally, when compared with Journal Citation Reports (Thomson Reuters, Philadelphia, PA), their results closely match expert opinions.