高职数据库(VF)课要突出高职特色

从教学大纲、制作课件、教材、教学等方面论述了高职数据库（VF）课程改革的具体做法，以及对将来教学的展望．     

谈职业教育中《工程力学》教学思想的变革

在力学教学中，透过现象揭示本质，展示力学美，渗透人文文化，通过这些方法激发学生的学习兴趣，培养学生的创造意识。     

Circuit state analysis using chaotic signal excitation

A new testing method for analog circuit is proposed in this paper. A low-pass Butterworth filter is taken as the typical system under test (SUT) since the analog circuits in different types of electronic systems can be regarded as the low-, band- or high-pass active (passive) filters. The chaotic signal, which is generated by an improved Chua's circuit, is employed as the excitation signal of SUT. The SUT is a “narrowband” system compared with the bandwidth of input signal, whose state is analyzed with an error-tracking approach. The experimental result depicts that this testing method can efficiently detect the change of the circuit parameter. Besides, another eight features are extracted from the output signal of SUT for analyzing the SUT states. A discussion is made for comparing the effectiveness of each feature according to the testing results.     

Fault diagnosis for analog circuits based on chaotic signal excitation

A fault diagnosis method for analog circuit is proposed in this paper. An all-purpose amplifier is taken as the typical circuit under test (CUT). The chaotic signal, which is generated by an improved Chua's circuit, is employed as the excitation signal of CUT. The algorithm for phase-space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Γ-test. The circuit state is estimated based on detecting the geometric change of Chua's attractor with a data-mining approach. For the purpose of information fusion, another eight features are extracted from the testing data to comprehensively determine the CUT states. A discussion is made for comparing the effectiveness of each feature according to the testing results.     

Contextual feature selection for text classification

We present a simple approach for the classification of “noisy” documents using bigrams and named entities. The approach combines conventional feature selection with a contextual approach to filter out passages around selected features. Originally designed for call for tender documents, the method can be useful for other web collections that also contain non-topical contents. Experiments are conducted on our in-house collection as well as on the 4-Universities data set, Reuters 21578 and 20 Newsgroups. We find a significant improvement on our collection and the 4-Universities data set (10.9% and 4.1%, respectively). Although the best results are obtained by combining bigrams and named entities, the impact of the latter is not found to be significant.     

Demand forecasting model using hotel clustering findings for hospitality industry

Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.     

"形美、音美、意美"的完美结合——浅析George Wither的诗Farewell, Sweet Grove

本文根据许渊冲先生提出的"三美论",运用话语分析的方法,对乔治.威瑟的诗歌《哦再见,可爱的林木》进行了分析,论证了该诗歌的"形美"、"音美"和"意美"。     

浅谈饭店客房特色服务的经营

饭店业的竞争越来越激烈，发展客房特色经营是走创新之路的必然趋势。本文根据饭店客房经营表现出的各种特征，探讨了饭店客房特色经营的基本方向与设计的基本原则，为饭店改善经营管理并在市场竞争中站稳脚跟提供理论指导。     

基于视频的数据挖掘系统的研究

研究了一种视频数据挖掘的系统结构.介绍了该系统中各主要模块的功能;描述了视频数据预处理和视频数据立方体;讨论了视频数据挖掘方法并探讨了典型的视频挖掘技术.     

浅析中学历史教学中的审美教育

本文对美和审美的定义和作用做适当阐述，结合课程改革中对审美教育的要求，阐述了在中学历史教学中实行审美教育的必要性和可行性，就实施审美教育进而培养学生的审美能力的具体方法和手段做了一定探讨，并进一步揭示了实施审美教育带来的现实作用。