Sentiment classification is an interesting and crucial research topic in the field of natural language processing (NLP). Data-driven methods, including machine learning and deep learning techniques, provide one direct and effective solution to solve the sentiment classification problem. However, the classification performance declines when the input includes review comments for multiple tasks. The most appropriate way of constructing a sentiment classification model under multi-tasking circumstances remains questionable in the related field. In this study, aiming at the multi-tasking sentiment classification problem, we propose a multi-task learning model based on a multi-scale convolutional neural network (CNN) and long short term memory (LSTM) for multi-task multi-scale sentiment classification (MTL-MSCNN-LSTM). The model comprehensively utilizes and properly handles global features and local features of different scales of text to model and represent sentences. The multi-task learning framework improves the encoder quality, simultaneously improving the results of emotion classification. Six different types of commodity review datasets were employed in the experiment. Using accuracy and F1-score as the metrics to evaluate the performance of the proposed model, comparing with methods such as single-task learning and LSTM encoder, the proposed MTL-MSCNN-LSTM model outperforms most of the existing methods.INDEX TERMS Sentiment classification, multi-task learning model, long short term memory, multi-scale convolutional neural network.

Federated learning (FL) is an emerging machine learning technique that aggregates model attributes from a large number of distributed devices. Several unique features such as energy saving and privacy preserving make FL a highly promising learning approach for power-limited and privacy sensitive devices. Although distributed computing can lower down the information amount that needs to be uploaded, model updates in FL can still experience performance bottleneck, especially for updates via wireless connections. In this work, we investigate the performance of FL update with mobile edge devices that are connected to the parameter server (PS) with practical wireless links, where uplink update from user to PS has very limited capacity. Different from the existing works, we apply non-orthogonal multiple access (NOMA) together with gradient compression in the wireless uplink. Simulation results show that our proposed scheme can significantly reduce aggregation latency while achieving similar accuracy.

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It is inevitable that defects happen to key components of the long-running high-speed trains. Thus as an effective inspection approach for defects, image detection becomes significantly important for operation and maintenance in the railway industry. However, a massive number of images collected by inspection devices challenge traditional methods based on manual effort. To address this issue, this paper proposed an automatic detection method, termed as multi-stage pipeline for defect detection (MPDD). MPDD includes two stages, component detection stage improves RPN anchor mechanism and way of feature fusion to promote detection performance, defect classification stage combines super-resolution strategy with CNN to improve defect classification performance. Experiments on high-speed train defect dataset shown that MPDD can reach the highest mAP of 0.792. The mAP on NEU surface defect database reached to 0.765 at the speed of 203ms per image.