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Moment localization in videos using natural language refers to finding the most relevant segment from the video with given a query in natural language form. In this paper, we present a new boundary-determining strategy called explicit correlation-based convolution boundary locator (ECCL), which can handle any lengths of videos and moments while leveraging fine-grained matching relationships. In this method, we first train a deep network to obtain the correlation scores between video clips and query statements. Subsequently, with the correlation scores, we utilize a convolution kernel to generate the boundary probability distribution. Finally, the start and end time indexes of the video moment are calculated with an optimization problem. Experiments on two publicly available datasets demonstrate the feasibility of ECCL.

Retrieving vehicles matching natural language descriptions from collections of videos is a novel and uniquely challenging task, requiring consideration not only of vehicle types and colors, but also of temporal relations, e.g., "A white crossover keeping straight behind a silver hatch-back." To perform this task, we propose Dual-path Temporal Matching Network (DUN). DUN uses a pre-trained CNN and GloVe to extract visual and text features, respectively, and GRUs to mine temporal relationships in videos and sentences. Furthermore, the proposed network can attain superior performance by including techniques such as re-ranking. With its simple structure, DUN achieved second place on the AI City Challenge 2021 Track 5. The codes are available at https://github.com/okzhili/AICITY2021_Track5_DUN.

Multi-sensor fusion improves the accuracy of machine tool condition monitoring system, which is the critical feedback information to the manufacture process controller. An abundant data collected by multi-sensor monitoring system need to employ attribute extraction, election, reduction and classification to form the decision knowledge. A machine tool monitoring system is built and the method of tool condition decision knowledge discovery is presented. Multiple sensors include vibration, force, acoustic emission and main spindle current. The novel approach engages rough theory as a knowledge extraction tool to work on the data that are obtained from both multi-sensor and machining parameters, then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by domain experts. By means of the knowledge acquired, the tool condition can be identified. A case study is presented to illustrate the effectiveness of the methodology.

Although authentic leadership is becoming increasingly important in the organizational science literature, research testing the consequences of authentic leadership in Chinese hospitality context has not received great attention. To help address this need, the authors conducted an empirical study in 24 hotels in China to examine the effects of authentic leadership on employees' work engagement. The results of HLM analysis revealed that both individual-level and group-level authentic leadership were positively related to employees' work engagement. The relationship between individual-level authentic leadership and employees' work engagement was mediated by employees' perception of servant leadership and employees' trust in leaders. The relationship between group-level authentic leadership and employees' work engagement was mediated by group felt trust.