Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder

In this paper, we present a hierarchical path planning framework called SG-RL (subgoal graphs-reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By "rational", we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG-RL works in a two-level manner. At the first level, SG-RL uses a geometric path-planning method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG-RL uses an RL method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG-RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG-RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies.Comment: 20 page

Factors Influencing Purchase Intention on Mobile Shopping Web Site in China and South Korea: An Empirical Study

The research objective of this study is to analyze the factors that influence consumers\u27 perceptions of using mobile commerce services for online shopping in China and South Korea using ordered logistic regression analysis. We constructed the research model using the three dimensions of individual characteristics, shopping motivations and the characteristics of mobile shopping. We discovered that shopping frequency, utilitarianism, instant connectivity, and personalized information push positively impact the customers’ intention to use mobile phones in China. The results of the marginal effects indicated that the behavioral intentions of Chinese consumers increased when shopping frequency and instant connectivity increased. In addition, when utilitarianism and the personalized information push reach certain values, the shopping intention of online customers in China will decrease. Likewise, shopping frequency, hedonism, utilitarianism, instant connectivity, and SNS (Social Networking Services) accessibility positively affect the intention to use the Internet for m-shopping of South Korean consumers. In addition, the results regarding the marginal effects suggested that the intention to use m-shopping services on m-shopping web site of South Korean consumers increased as shopping frequency, hedonism, and instant connectivity increased. However, South Korean consumers\u27 adoption intention will decrease when utilitarianism and SNS accessibility reach certain values. These results provide important implications for mobile commerce literature and practice

OTS: A One-shot Learning Approach for Text Spotting in Historical Manuscripts

Historical manuscript processing poses challenges like limited annotated training data and novel class emergence. To address this, we propose a novel One-shot learning-based Text Spotting (OTS) approach that accurately and reliably spots novel characters with just one annotated support sample. Drawing inspiration from cognitive research, we introduce a spatial alignment module that finds, focuses on, and learns the most discriminative spatial regions in the query image based on one support image. Especially, since the low-resource spotting task often faces the problem of example imbalance, we propose a novel loss function called torus loss which can make the embedding space of distance metric more discriminative. Our approach is highly efficient and requires only a few training samples while exhibiting the remarkable ability to handle novel characters, and symbols. To enhance dataset diversity, a new manuscript dataset that contains the ancient Dongba hieroglyphics (DBH) is created. We conduct experiments on publicly available VML-HD, TKH, NC datasets, and the new proposed DBH dataset. The experimental results demonstrate that OTS outperforms the state-of-the-art methods in one-shot text spotting. Overall, our proposed method offers promising applications in the field of text spotting in historical manuscripts

Analysis of OAM Mode Purity in Phased Array Antenna

In this paper, the orbital angular momentum of different modes in electric field is decomposed, and the definition of purity of OAM mode in OAM antenna are proposed. Based on the purity theory, the purity of circular array is derived. And the effects of different parameters on the purity are analyzed. An intuitive and quantifiable dimension for comparing the OAM performance in phased array antenna is provided in this paper

Information Entropy Theory Based Recognition of the Validity of Contextual Information of Restaurants: An Empirical Study

Contextual information plays a key role in personalized recommendations. However, not all contextual information plays a positive role in personalized recommendations. Therefore, it is critical to identify the effective contextual information to realize personalized recommendations. This study aims to develop a set of feasible context importance calculation methods that can identify effective contextual information in different application scenarios. The information entropy of each contextual dimension is calculated, and the validity of the context compared according to the magnitude of its entropy is determined based on the informational entropy theory. Subsequently, this approach is applied to hotel and catering service data to determine the valid context in the dining domain. The experimental results indicate that location, work-rest condition, weather, mood and companionship considerably influence consumers’ behaviour and decisions in a catering environment, and the user preference in such contexts should be carefully considered