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Truth discovery methods aim to identify which piece of information is trustworthy from multi-sourced data. Most existing truth discovery methods, however, are designed for structured data and fail to meet the strong need to extract trustworthy information from raw text data. More specifically, existing methods ignore the semantic information of text answers, i.e., answers may contain multiple factors, the word usages may be diverse, and the answers may be partially correct. In addition, ubiquitous long-tail phenomenon exists in the tasks, i.e., most users provide only a few answers and only a few users provide plenty of answers, which causes the user reliability estimation for small users to be unreasonable. To tackle these challenges, we propose a Graph Convolutional Network (GCN) based truth discovery model to automatically discover trustworthy information from text data. Firstly, Smooth Inverse Frequency (SIF) is utilized to learn real-valued vector representations for answers. Then, we construct undirected graph with these vectors to capture the structural information of answers. Finally, the GCN is utilized to store and update the reliability of these answers, and sums up all the feature vectors of all neighboring answers to improve the accuracy and efficiency of truth discovery. Different from traditional methods, we use vectors to store the reliability of answers which have higher representation capability compared with real numbers, and network is utilized to capture complex relationships among answers rather than simplified functions. The experiment results on real datasets show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art truth discovery methods.

Collaborative filtering recommendation with implicit feedbacks (e.g., clicks, views, and plays) is regarded as one of the most challenging issues in both academia and industry. From implicit feedbacks, we can only get a small fraction of observed data (positive examples), and the massive unobserved data are the mixture of negative examples and unlabeled positive examples. However, most of the existing efforts either treat unobserved data equally by assigning a uniform weight or uniformly weight observed data while ignoring the hidden information (i.e., visit frequency) in implicit feedbacks. This assumption may not hold in real-life scenarios since they cannot distinguish the contributions of the whole data and it easily leads to prediction bias. Besides, those approaches still suffer from low-efficiency issue. To this end, we propose an all-weighted matrix factorization and fast optimization strategy for effective and efficient recommendation. We first design a frequency-aware weighting scheme for observed data and a user-oriented weighting scheme for unobserved data nonuniformly. Then, the weighting schemes of both observed and unobserved data are combined in a unified way to form an all-weighted matrix factorization model. Afterwards, we present a surrogate objective function and develop a fast optimization strategy to enhance the efficiency. Extensive experimental results on real-world datasets demonstrate that our method outperforms the competitive baselines on several evaluation metrics.

An (e, 2e+ion) Electron momentum spectrometer (EMS) upgraded from the third-generation (e, 2e) spectrometer is constructed in the University of Science and Technology of China (USTC). In the EMS, the energy-momentum density of electrons is derived through the charge measurement using the signals of the position sensitive detector (PSD). The information of ion fragments is derived through the time measurement and that information will be used for more research on collision dynamics. A PXI-based data acquisition (DAQ) system was designed. The system mainly consists of a 6-channel charge digital module (CDM) and a 9-channel time digital module (TDM). Shaping amplifiers (SAs) are also designed for charge measurement with CDM. According to the design specification, the CDM with SAs needs to measure charge from 100 to 900 fC with an accuracy of better than 1%. The TDM needs to measure the flight time from 0 to 15 us with a resolution of better than 100 ps. The TDM and CDM can be combined to run synchronously using a coincidence unit which also rejects the background events based on the tracking information of the electrons and the ions. Test results indicate that, within the dynamic range, the accuracy of the CDM is better than 0.4%. The RMS time resolution of better than 30 ps is achieved by using the method of time-to-digital converter (TDC) implemented in a field-programmable gate arrays (FPGA). The DAQ system has been assembled and works well with the EMS detectors.