CSCD-IME: Correcting Spelling Errors Generated by Pinyin IME

Chinese Spelling Correction (CSC) is a task to detect and correct spelling mistakes in texts. In fact, most of Chinese input is based on pinyin input method, so the study of spelling errors in this process is more practical and valuable. However, there is still no research dedicated to this essential scenario. In this paper, we first present a Chinese Spelling Correction Dataset for errors generated by pinyin IME (CSCD-IME), including 40,000 annotated sentences from real posts of official media on Sina Weibo. Furthermore, we propose a novel method to automatically construct large-scale and high-quality pseudo data by simulating the input through pinyin IME. A series of analyses and experiments on CSCD-IME show that spelling errors produced by pinyin IME hold a particular distribution at pinyin level and semantic level and are challenging enough. Meanwhile, our proposed pseudo-data construction method can better fit this error distribution and improve the performance of CSC systems. Finally, we provide a useful guide to using pseudo data, including the data scale, the data source, and the training strategy

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency

Preparation and Self-assembly of Functionalized Nanocomposites and Nanomaterials – Relationship Between Structures and Properties

The recent progress in nanocomposites and nanomaterials is varied and occupies various fields. Nanocomposites can be prepared with a variety of special physical, thermal, and other unique properties. On the other hand, self-assembly technique is playing an important role in preparing well-defined multilevel nanostructures and the functionalized surface with the designed and controlled properties. In this chapter, various kinds of nanocomposites including gold nanoparticles, inorganic-organic hybrid composites, graphene oxide nanocomposites, and supramolecular gels via functionalized imide amphiphiles/binary mixtures have all been investigated and analyzed. We summarize main research contributions in recent years in three sections: preparation and self-assembly of some functionalized hybrid nanostructures; preparation and self-assembly of some graphene oxide nanocomposites; preparation and self-assembly of supramolecular gels based on some functionalized imide amphiphiles/binary mixtures. The above work may give the potential perspective for the design and fabrication of nanomaterials and composites. New nanocomposites and nanomaterials are emerging as sensitive study platforms based on unique optical and electrical properties. Future research on preparation of nanocomposites and nanomaterials will depend on the less-expensive processes in order to produce low-cost nanomaterials and devices

Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification

An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition

Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms

Feasibility study on posterior laminar screw fixation techniques in the axis

AbstractObjectiveTo get morphologic parameters of Chinese adults through observation and measurement on axial laminas, to evaluate the feasibility of placing axial laminar screws and to introduce the technique.MethodsRelative parameters of 28 sets of fresh Chinese adults' axial specimens, including distance from the superior and inferior entry points of axial laminar screws to the superior margins of axial laminas, superior, middle, inferior thickness and height of the axial laminas, length and angle of the axial laminar screw trajectories, distance from the entry points of axial laminar screws to the transverse foramen and central points of the inferior articular process, were measured with a digital caliper and a goniometer. Data were statistically analyzed.ResultsAveragely, distance from the superior and inferior entry points of axial laminar screws to the superior margins of axial laminas was 5 mm and 9 mm, superior, middle, inferior thickness and the height of the axial laminas were 3.2 mm, 6.7 mm, 5.5 mm and 12.8 mm respectively, and the length of the superior and inferior axial laminar screw trajectories was 26.2 mm and 25.5 mm, respectively.ConclusionsIt is feasible and reliable to apply posterior laminar screw fixation techniques to the axes of Chinese adults. Also the C2 laminar screw fixation technique can be taken as a supplementary to conventional posterior screw fixations of C2