Scanned receipts OCR and key information extraction (SROIE) represent the processeses of recognizing text from scanned receipts and extracting key texts from them and save the extracted tests to structured documents. SROIE plays critical roles for many document analysis applications and holds great commercial potentials, but very little research works and advances have been published in this area. In recognition of the technical challenges, importance and huge commercial potentials of SROIE, we organized the ICDAR 2019 competition on SROIE. In this competition, we set up three tasks, namely, Scanned Receipt Text Localisation (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). A new dataset with 1000 whole scanned receipt images and annotations is created for the competition. The competition opened on 10th February, 2019 and closed on 5th May, 2019. There are 29, 24 and 18 valide submissions received for the three competition tasks, respectively. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, submission statistics, performance of submitted methods and results analysis. According to the wide interests gained through SROIE and the healthy number of submissions from academic, research institutes and industry over different countries, we believe the competition SROIE is successful. And it is interesting to observe many new ideas and approaches are proposed for the new competition task set on key information extraction. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance. Task of key information extraction is still very challenging and can be set for many other important document analysis applications. It is hoped that this competition will help draw more attention from the community and promote research and development efforts on SROIE.

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.

The current study developed a psychometrically sound multidimensional measure of Internet addiction: the Chinese Internet Addiction Inventory (CIAI). Data were collected from 1,029 Chinese undergraduate students from 14 universities and colleges. The initial sample was split randomly into two samples (N1=516; N2=513). An exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) were conducted on the two samples respectively. Findings from the EFA suggest that this measure assesses three dimensions of Internet addiction: conflicts, mood modification, and dependence. Items in each dimension showed high internal consistency and acceptable test-retest reliability. Findings from the CFA further confirmed the three-factor measurement structure of CIAI. Test of criterion-related validity also showed good abilities for all three CIAI subscales to discriminate between an Internet addictive group and non-Internet addictive group. The theoretical and clinical implications of CIAI and its limitations are discussed.

In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instanceaware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multioriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on totaltext containing curved text which suggests effectiveness of the proposed approach.

Recurrent neural network(RNN) has been broadly applied to natural language processing(NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory(LSTM) units for Chinese word segmentation, which is a crucial preprocess task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft features from context, whereas bi-directional LSTM network(BLSTM) does not need any prior knowledge or pre-designing, and it is expert in keeping the contextual information in both directions. Experiment result shows that our approach gets stateof-the-art performance in word segmentation on both traditional Chinese datasets and simplified Chinese datasets.

Driven by the vision of Internet of Things, some research efforts have already focused on designing a network of efficient speech recognition for the development of edge computing. Other researches (such as tpool2) do not make full use of spatial and temporal information in the acoustic features of speech. In this paper, we propose a compact speech recognition network with spatio-temporal features for edge computing, named EdgeRNN. Alternatively, EdgeRNN uses 1-Dimensional Convolutional Neural Network (1-D CNN) to process the overall spatial information of each frequency domain of the acoustic features. A Recurrent Neural Network (RNN) is used to process the temporal information of each frequency domain of the acoustic features. In addition, we propose a simplified attention mechanism to enhance the portion of the network that contributes to the final identification. The overall performance of EdgeRNN has been verified on speech emotion and keywords recognition. The IEMOCAP dataset is used in speech emotion recognition, and the unweighted average recall (UAR) reaches 63.98%. Speech keywords recognition uses Google's Speech Commands Datasets V1 with a weighted average recall (WAR) of 96.82%. Compared with the experimental results of the related efficient networks on Raspberry Pi 3B+, the accuracies of EdgeRNN have been improved on both of speech emotion and keywords recognition.

This research was designed to explore the differences in behaviour, cognition and emotion between college students in Beijing and Suzhou, who were under different external stresses during the severe acute respiratory syndrome (SARS) prevalence time in China from 22 April to 23 June, 2003. A ‘Psychological responses questionnaire on SARS’ designed by the authors was filled out by subjects. A total of 268 valid cases were collected in Beijing through e‐mails or websites, and 397 valid copies were collected in Suzhou through pencil‐and‐paper tests. The two groups differed in their behaviours, cognitions and emotions. Cognitive and behavioural responses of the Beijing sample changed along with the time and the epidemic situation, and so did the negative emotions of the Suzhou sample. Path analysis of the Beijing sample found support for the mediating effects of the cognition on the relationships of stress and the emotional and behavioural responses, while the results of the Suzhou sample did not. Differences between the two samples are discussed. Copyright © 2005 John Wiley & Sons, Ltd.