Context Awareness Framework Based on Contextual Graph

Nowadays computing becomes increasingly mobile and pervasive. One of the important steps in pervasive computing is context awareness. Context-aware pervasive systems rely on information about the context and user preferences to adapt their behavior. However, context-aware applications do not always behave as users desire and can cause users to feel dissatisfied with unexpected actions. To solve these problems, context-aware systems must provide mechanisms to adapt automatically when the context changes significantly. The interesting characteristic of context is its own behaviors which depend on various aspects of the surrounding contexts. This paper uses contextual graphs to solve the problem “the mutual relationships among the contexts.” We describe the most relevant work in this area, as well as ongoing research on developing context-aware system for ubiquitous computing based on contextual graph. The usage of contextual graph in context awareness is expected to make it effective for developers to develop various applications with the need of context reasoning

Reinforcement Learning Guided by Double Replay Memory

Experience replay memory in reinforcement learning enables agents to remember and reuse past experiences. Most of the reinforcement models are subject to single experience replay memory to operate agents. In this article, we propose a framework that accommodates doubly used experience replay memory, exploiting both important transitions and new transitions simultaneously. In numerical studies, the deep Q-networks (DQN) equipped with double experience replay memory are examined under various scenarios. A self-driving car requires an automated agent to figure out when to adequately change lanes on the real-time basis. To this end, we apply our proposed agent to the simulation of urban mobility (SUMO) experiments. Besides, we also verify its applicability to reinforcement learning whose action space is discrete (e.g., computer game environments). Taken all together, we conclude that the proposed framework outperforms priorly known reinforcement learning models in the virtue of double experience replay memory

Disturbed regeneration of saplings of Korean fir (Abies koreana Wilson), an endemic tree species, in Hallasan National Park, a UNESCO Biosphere Reserve, Jeju Island, Korea

Limited knowledge is available on the regeneration of Korean fir (Abies koreana Wilson), an endemic plant species, growing on the upper part of Mt. Hallasan, a volcanic mountain, located in the central part of Jeju Island, Korea. A forest stand with the size of 1 ha dominated by Korean fir trees was established and all the trees with DBH 2 cm or larger were mapped and surveyed. Initial analysis indicated that the numbers of saplings with their DBHs between 2 cm and 10 cm were very small and that there was a big gap in the frequency of the number of saplings regenerated from the forest stand. It seems clear that the regeneration of the Korean fir trees was disturbed for longer than the last two decades, potentially by the browsing of the seedlings by ungulate including Siberian roe deer and by the physical hindrance of the dwarf bamboo to the development of the saplings of the Korean fir. Urgent measures and extensive studies are needed to promote the natural regeneration of the tree species on the dynamics of the forest regeneration and the mechanism of forest development of the forests on the Mt. Hallasan, Jeju Island, Korea