Web Intelligence

Recently, studies have been performed for speech emotion recognition. However, little research focused on the emotion of the elderly, especially the lonely elderly. In this paper, we propose a six layer Wavelet Packet Coefficients Model for speech emotion recognition of the Chinese elderly. Six layer Wavelet Packet Coefficients, Mel Frequency Cepstrum Coefficient and the Fourier Parameter features are extracted from speech emotion database of Chinese elderly, respectively. Experimental results show that the six layer wavelet packet coefficients features are effective for recognizing emotions from speech. In particularly, when combining these three features, the recognition rates of the elderly can be improved.

Because of the increased lifespan, there is an immense increase in the demand of healthcare services for senior wellness. In this study, we proposed a system based on biological data, such as body temperature, heart rate and blood pressure, and activity data of the elderly living in a stable environment, such as nursing home, to determine their wellness conditions. The Radio Frequency Identification (RFID) is used to monitor and record real-time location information of the elderly. A novel framework integrating the daily activity data and the biological data for determining the wellness status of an elderly has been modeled by using support vector machine (SVM). In this study, the established model was evaluated on 5 elderly people living in the geriatrics department at the Third People’s Hospital of Lanzhou. The experimental results showed that with effective monitoring and alarm systems, the adverse effects on wellness conditions of elderly people living in a nursing home could be ameliorated to some extent, and the healthcare services for the elderly could be improved.

As the level of hospital informatization raises, it is possible to obtain huge amount of physiological data from bedside monitor and other medical instruments. The goal for this work is to recognize diseases from physiological data by unique combinations of representative patterns for different diseases. The representative patterns are clustered from the original physiological time series data, e.g. pulse, respiration rate, blood pressure, heart rate and oxygen saturation rate. Within a disease, to compose the set of representative patterns into a interrelated structure, we bring in Allen’s interval relations to describe the temporal relations between each of two neighboring patterns. We use Chinese Restaurant Process (CRP) to draw the uncertainty of every temporal relations that links two representative patterns. The two algorithms are combined into the model we use in this work, called probabilistic model. The experimental results suggests our model has potential in recognizing diseases.

Domain terminology recognition and extraction is the primary work for the construction of domain knowledge graph. Traditional method is tedious, and time-consuming, as well as low accuracy. This paper presents an improved Domain Term Extraction-Improvement (TDE-I) method based on relative co-occurrence rate, which can automatically extract domain basic terminologies and domain compound terminologies. We also present a Document Classification Value (DCV) method on the basis of calculating Domain Feature Vector (DFV) value which can implement a judgment to evaluate extraction accuracy according to the evaluation indexes. Our experimental results demonstrate that our approach is effectiveness and accuracy. The proposed method can provide with a new solution for the construction of domain knowledge graph and its applications.

Gradient descent is prevalent for large scale optimization problems in machine learning, especially its major role is computing and correcting the connection strength of neural network in deep learning. However, choosing a proper learning rate for SGD can be difficult. A too small rate may lead to painfully slow convergence, while too large one would hinder convergence. In this paper, we present a novel variance reduction technique which applies the moving average of gradient termed SMVRG. SMVRG can take a large learning rate by using variance reduction technique. And, we only need to preserve current gradient and the previous average gradient. Our method is employed to Long Short-Term Memory (LSTM). The experiment on two data sets, the IMDB (movie reviews) and SemEval-2016 (sentiment analysis in twitter) shows our method can improve the results significantly.

Automatically identifying Chinese characters that are similar in their glyph, pronunciations and meaning are important for building smart question generation tools in a computer-assisted language-learning environment. Previous research on the Chinese character similarity measurement focused on character glyph (e.g. structures, strokes and radicals) with heuristic algorithms whose parameter have preset values. This article presents a machine learning (regression) approach to measure the similarity between two Chinese characters, based on the information which not only includes the glyph, but also pronunciation (pinyin) and semantic meaning derived from HowNet. We evaluated various regression models using a testing set consisting of 2586 pairs of characters selected from elementary Chinese textbooks used. The study results showed that four regression models (M5, Support Vector Machine, Gaussian Process and Linear Regression) have similar results ( 0.617 ⩽ Mean Absolute Error ⩽ 0.641 , 0.772 ⩽ Root Mean Square Error ⩽ 0.790 ). In addition, the study implied that the performance of the regression model could be influenced by the character frequency. Moreover, we evaluated the regression model in a well-known Chinese language learning resource, called 100 pairs of the most confusing Chinese characters. The experiment results indicated that this approach has potential in the recognition and generation of confusing Chinese character pairs.