Towards Deep Learning Interpretability: A Topic Modeling Approach

The recent development of deep learning has achieved the state-of-the-art performance in various machine learning tasks. The IS research community has started to leveraged deep learning-based text mining for analyzing textual documents. The lack of interpretability is endemic among the state-of-the-art deep learning models, constraining model improvement, limiting additional insights, and prohibiting adoption. In this study, we propose a novel text mining research framework, Neural Topic Embedding, capable of extracting useful and interpretable representations of texts through deep neural networks. Specifically, we leverage topic modeling to enrich deep learning data representations with meaning. To demonstrate the effectiveness of our proposed framework, we conducted a preliminary evaluation experiment on a testbed of fake review detection and our interpretable representations improves the state-of-the-art by almost 8 percent as measured by F1 score. Our study contributes to the IS community by opening the gate for future adoption of the state-of-the-art deep learning methods

A Comprehensive Learning-Based Model for Power Load Forecasting in Smart Grid

In the big data era, learning-based techniques have attracted more and more attention in many industry areas such as smart grid, intelligent transportation. The power load forecasting is one of the most critical issues in data analysis of smart grid. However, learning-based methods have not been widely used due to the poor data quality and computational capacity. In this paper, we propose a comprehensive learning-based model to forecast heavy and over load (HOL) accidents according to the data from various information systems. At first, we present a combined random under- and over-sampling technique for imbalanced electric data, and choose an optimal sampling rate through several experiments. Then, we reduce the attributes that have significant impact on the power load by using learning-based methods. Finally, we provide an algorithm based on the random forest method to prevent the over-fitting problem. We evaluate the proposed model and algorithms with the real-world data provided by China Grid. The experimental results show that our model works efficiently and achieves low error rates

How do servant leadership and self-esteem at work shape family performance in China? : A resource-gain-development perspective

Acknowledgements This study was supported by National Natural Science Foundation of China (Grant No. 71872139), the Humanity and Social Science Foundation of Ministry of Education of China (Grant No. 18YJC630164) and The Fundamental Research Funds for the Central Universities of China.Peer reviewedPostprin

Development of a 3-D Position Sensitive Neutron Detector Based on Organic Scintillators with Double Side SiPM Readout

A 3-D position sensitive neutron detector is being developed based on a plastic scintillator array. A double side SiPM readout is used to determine the depth of interaction (DOI) in each scintillator unit. In the preliminary test, the DOI in a 254 x 6 x 6 mm3 SP101 plastic scintillator is measured at different positions using a collimated Co-60 source. The SiPM (KETEK PM6660) signals are recorded by a 2.5 GS/s digital oscilloscope. The DOI results are calculated using both the amplitude and the temporal information. Position resolutions (FWHM) of 2.5 cm and 6.6 cm are realized, respectively. A detector based on a 2-D array is capable of recording the 3-D position information of the incident neutron. The 3-D detector is to be used together with a neutron time projection chamber as a directional fast neutron detector. According to the simulation results, the angular resolution (8 degree FWHM) is much better than that of a typical neutron scatter camera.Comment: 7 pages, 7 figures, submitted to IEEE NSS MIC 201