Understanding ship following behavior in sea ice conditions is essential for safe navigation in the Arctic. In order to unveil the ship following behavior and dominated impact factors, this research studied the ship following navigation characteristics and behavior in the Arctic through a systematic data mining approach. Firstly, a data mining mechanism was developed to extract comprehensive ship following data from the 2022 Arctic AIS dataset, enabling a comprehensive analysis of ship-following behavior. Then, Sea Ice Thickness (SIT) and Sea Ice Concentration (SIC) data are extracted and spatiotemporally linked to the ship following data to reveal the influence of sea ice on the ship following speed and distance within formations. Finally, a quantitative analysis of ship following speeds and distances under varying ice conditions were analyzed. A total of 76 formations are recorded, with formations involving icebreaker being more common, predominantly from January to May in the central Kara Sea and southeast East Siberian Sea. Results revealed that SIT mainly affects ship following speed, while SIC predominantly influences following distance, with significant negative correlations observed in formations with or without an icebreaker. This research elucidates the ship formation characteristics and the influence of sea ice on ship following behavior, providing theoretical guidance for safe formation in the Arctic waters.

Abstract The prevalence of short texts on the Web has made mining the latent topic structures of short texts a critical and fundamental task for many applications. However, due to the lack of word co-occurrence information induced by the content sparsity of short texts, it is challenging for traditional topic models like latent Dirichlet allocation (LDA) to extract coherent topic structures on short texts. Incorporating external semantic knowledge into the topic modeling process is an effective strategy to improve the coherence of inferred topics. In this paper, we develop a novel topic model—called biterm correlation knowledge-based topic model (BCK-TM)—to infer latent topics from short texts. Specifically, the proposed model mines biterm correlation knowledge automatically based on recent progress in word embedding, which can represent semantic information of words in a continuous vector space. To incorporate external knowledge, a knowledge incorporation mechanism is designed over the latent topic layer to regularize the topic assignment of each biterm during the topic sampling process. Experimental results on three public benchmark datasets illustrate the superior performance of the proposed approach over several state-of-the-art baseline models.

Abstract Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchasers. Although recommending co-purchasers for a target buyer (co-purchase initiator) on the group buying is an interesting problem, existing studies have paid few attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weak similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weak similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has high recommendation performance. In addition, considering that co-purchase may last longer, the total recommendation result can be generated in multiple stages and adjust the current recommendation list based on the feedback from the recommendation of previous stages. It is a trick for all co-purchaser recommendation methods to make the total result better.

In the Adaptive Cruise Control (ACC) system, the switching between throttle and brake is critical, and the threshold switching logic is used in most existing references, which will possibly cause the frequent switching, resulting in the mechanical damage of vehicle components, fluctuations of the vehicle dynamics, increased fuel consumption, un-comfort and un-satisfaction from passengers and car owners. In this paper, the integrated ACC controller that optimizes the switching between throttle and brake is designed. The ACC system, which consists of the continuous-time vehicle longitudinal dynamics (throttle and brake models) and the discrete-event dynamics (logic switching rule between throttle and brake), is modeled in Mixed Logic Dynamical (MLD) approach. Then the ACC controller is designed in Model Predictive Control (MPC) framework, and the requirements of spacing control, rear-end collision avoidance (safety), optimal switching between throttle and brake, and vehicle capabilities are considered as the control objectives and constraints, respectively. In MPC framework, the ACC controller design is finally transformed to an online Mixed-Integer Quadratic Programming (MIQP). The simulation results show that the proposed ACC controller shows good ability in following the preceding vehicle, prevents rear-end collisions, and it outperforms the traditional ACC controller with the threshold switching rule for providing the smoother vehicle dynamics, less switching between throttle and brake, and more comfortable traveling.

We investigate an e-commerce stages of growth model in a cross-cultural business context for small Chinese firms in New Zealand. Research findings from 14 case studies show that the Chinese owners/managers of these small firms have a high power distance, and their attitude toward e-commerce technology directly influences their firms’ e-commerce growth process. It was found that the higher the stage of e-commerce adoption, the greater the need for owners having a more positive attitude toward e-commerce, more innovativeness and enthusiasm, and more technology literacy. The higher the tolerance for ambiguity and the higher the risk-taking propensity, the higher the stage of e-commerce adoption achieved. In addition, firms at lower growth stages of e-commerce adoption are highly rated on individualism, while those firms at higher growth stages of commerce adoption are highly rated on collectivism. The research has implications for small business managers operating in a cross-cultural business context as they move through the different stage of e-commerce adoption.

A multiscale coder for curves and boundaries is presented. It utilizes a multiscale structure--beamlets--that is designed primarily for linear and curvilinear features. The coder is composed of three main components: 1) a rate-distortion optimized beamlet-based representation, 2) a tree-based coding from a beamlet representation to a symbol stream, and 3) an entropy coder. This coder is named "JBEAM." Taking advantage of its multiscale property, we utilized tree-based coding to make it progressive. The derived coder has a low order of computational complexity. Simulations demonstrate an advantage over the state-of-the-art industrial standard: JBIG 2. A software package, which includes an implementation of JBEAM, is made available. Variations and potential improvements of this method will be discussed. This work may inspire more activities in this line of research, improving curve coding.

Beamlets are combined with zero-tree coding algorithm to create a new coding method, particularly suitable for lines and curves. Beamlets are a multiscale collection of line segments at a range of scales, location, having a variety of lengths and orientations. The new coding scheme - named JBEAM is more efficient - in terms of bit rates - in coding binary curvy images in simulations.

Detection of inhomogeneous regions in a homogeneous background (e.g. textures) is considered. The underlying assumption is that samples from the homogeneous background reside on an underlying manifold, while samples that intersect with the embedded object (i.e. the inhomogeneous region) are 'away' from this manifold. The empirical distance from each sample (which is specified in the paper) to the manifold is a quantity used to determine the likelihood of a sample's overlapping with an embedded object. This result can consequently be integrated with the 'significant runs algorithms', to predict the presence of embedded structures. A 'local projection' algorithm is designed to estimate the distances between samples and the manifold. Simulation results for features embedded in textural imageries show promise. This work can be extended to a formal theoretical framework for underlying feature detection. It is particularly suitable for textural images.

A quad-tree-based hierarchical contour representation and coding method is studied. This method is based on multi scale line segments—beamlets. Simulations are reported to evaluate the effectiveness of such an approach. This is a proof-of-concept study. The reported compression ratios are not the “best”. However, the idea of tree-based coding is novel; and this idea has good potential to realize a progressive contour coding, which is important in applications such as content-based video transmission.