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Remote sensing images are primary data sources for land use classification. High spatial resolution images enable more accurate analysis and identification of land cover types. However, a higher spatial resolution also brings new challenges to the existing classification methods. In the low-level feature spaces of remote sensing images, it is difficult to improve classification performance by modifying classifiers. Probabilistic topic models can connect low-level features and high-level semantics of remote sensing images. Latent Dirichlet allocation (LDA) models are representatives of probabilistic topic models. However, at present, probabilistic topic models are mainly adopted for scene classification and image retrieval in remote sensing image analysis only. In this study, multiscale segmentation was employed to construct bag-of-words (BoW) representations of high-resolution images. The segmented patches were then utilized as "image documents." A structural topic model was used with an LDA model to import spatial information from the image documents at two levels: topical prevalence and topical content in the form of covariates. In this way, latent topic features in image documents can be more accurately deduced. The proposed method showed more satisfactory classification performance than standard LDA models and demonstrated a certain degree of robustness against the changes in the segmentation scale. INDEX TERMS Bag-of-word model, latent topic, land cover, latent Dirichlet allocation, machine learning, probabilistic topic models.

Two obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Signal Strength(RSS). This paper proposes a method, which uses convolutional neural network (CNN) to classify the floor and location of Bluetooth RSS as well as magnetic field data to calculate the final coordinates, could apply Fingerprint Positioning into indoor environment with large areas and multiply floors. The method involves converting the collected Bluetooth RSS into the "fingerprint image" required for calculation and establishing the CNN for classification training. Subsequently, the real-time Bluetooth RSS are imported into the CNN to classify the floor and determine the transmitters' location. Additionally, the observer's coordinates are matched using the magnetic field data. Our experiments suggested that the proposed method can classify floors and transmitters' locations with predictable bunds of 0.9667 and 0.9333, respectively. At the same time, the average positioning error is less than 1.2 m, which is 43.32% and 44.67% higher than the traditional Bluetooth and magnetic field fingerprint positioning. The accuracy of dynamic positioning is also within 1.55 meters.

This study investigated whether the effects of pragmatics instruction delivered via a self-access website in a Chinese as a foreign language learning environment vary according to learners' language proficiency. The website provided learners with explicit instruction in how to express gratitude appropriately in Chinese and offered them pragmatic consciousness-raising activities for practice. Two groups of learners who differed in Chinese proficiency received the instruction over five weeks. The results showed that all learners produced more appropriate expressions of gratitude and used more varied thanking strategies in the posttest, but higher-level learners benefited more from the instruction in both pragmatic awareness and production. In their reflective e-journals, learners reported the promising possibilities of using websites as a tool for teaching pragmatics in foreign language contexts.

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People have to move between indoor and outdoor frequently in city scenarios. The global navigation satellite system signal cannot provide reliable indoor positioning services. To solve the problem, this article proposes a seamless positioning system based on an inverse global navigation satellite system signal, which can extend the global navigation satellite system service into the indoor scenario. In this method, a signal source is arranged at a key position in the room, and the inverse global navigation satellite system signal is transmitted to the global navigation satellite system receiver to obtain a preset positioning result. The indoor positioning service is continued with the inertial navigation system after leaving the key position. The inverse global navigation satellite system seamless positioning system proposed in this article can unify indoor and outdoor positioning using the same receiver. The receiver does not need to re-receive navigation information when the scene changes, which avoids the switching process. Through the design of signal layer coverage, the receiver is in a warm start state, and the users can quickly fix the position when the scenario changes, realizing quick access in a true sense. This enables the ordinary commercial global navigation satellite system receiver to obtain indoor positioning capability without modification, and the algorithm can perform accurate positioning indoors and outdoors without switching.

<p class="MsoNormal" style="margin: 0cm 0cm 0pt;"><span style="font-size: 9pt; mso-bidi-font-weight: bold;"><span style="font-family: Times New Roman;">This paper proposes a new self-adaptive genetic algorithm</span></span><span style="font-family: SimSun; font-size: 9pt; mso-bidi-font-family: SimSun; mso-hansi-font-family: 'Times New Roman';" lang="ZH-CN">。</span><span style="font-size: 9pt; mso-bidi-font-weight: bold;"><span style="font-family: Times New Roman;">This new algorithm divides the whole evolution process into three stages. At each stage, the new algorithm adopts different operation method. The main ideas are grading balance selection, continuous crossover operation. The new algorithm designs especially self-adaptive mutation probability according to the principle of searching for things. Numerical experiments show that the new algorithm is more effective than the comparative algorithm in realizing the high convergence precision, reducing the convergence generation and good at keeping the stability of the adaptive genetic algorithm. </span></span></p>

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