Search

The ever-increasing size of images has made automatic image annotation one of the most important tasks in the fields of machine learning and computer vision. Despite continuous efforts in inventing new annotation algorithms and new models, results of the state-of-the-art image annotation methods are often unsatisfactory. In this paper, to further improve annotation refinement performance, a novel approach based on weighted mutual information to automatically refine the original annotations of images is proposed. Unlike the traditional refinement model using only visual feature, the proposed model use semantic embedding to properly map labels and visual features to a meaningful semantic space. To accurately measure the relevance between the particular image and its original annotations, the proposed model utilize all available information including image-to-image, label-to-label and image-to-label. Experimental results conducted on three typical datasets show not only the validity of the refinement, but also the superiority of the proposed algorithm over existing ones. The improvement largely benefits from our proposed mutual information method and utilizing all available information.

This study shows the influence of human resource management practices on the academic and non-academic staff and its impact on the higher education institution's goals. A questionnaire based on the Cranet survey was used to gather data from 240 employees (academic and non-academic staff) from a public higher education institution. A phi-k correlation algorithm was used to verify the underlying correlation coefficients, statistical significance, and outliers within multiple data types. This algorithm allows a more personalized, understandable approach to reveal the human resource management practices that significantly impact the teacher career development and capacity improvement. In addition, the use of background variables to identify the groups of respondents allows the algorithm to discern the multidimensional data for a more personalized human resource management approach. Human resource management practices involving training development and staff were correlated to the institution's goals. The phi-k correlation proved to be a suitable tool to shape structural models and latent trait analysis between multiple data types, which can overcome the drawbacks of Pearson and Cramer correlations when processing non-linear data. The presented research contributes to the literature by using the phi-k algorithm to process multiple data types. The proposed study with the phi-k algorithm is the first time applied to higher education institutions.

The capacity of combined gauge-satellite precipitation estimates largely depends on the characteristics of the input data such as the number, location and reliability of rain gauges, and satellite-derived precipitation quality. The objective of this study is to examine the influence of rain gauge network configuration including density and spatial distribution on the performance of the gauge-satellite merging estimation at monthly and ten-day temporal scales. Dense rain gauge observations and satellite-derived precipitation data (i.e., TMPA 3B42 Version 7 and Version 06 IMERG Final Run) in two provinces of China are used. A two-stage downscaling-integration approach is applied in the gauge-satellite precipitation estimation. Various scenarios of rain gauge density and combination are designed and their corresponding merged precipitation estimates are evaluated using statistical indices. The merged results using the TMPA and IMERG precipitation product, respectively, are compared. The results show that: 1) the influence of rain gauge network configuration on the gauge-satellite merged precipitation estimates gradually decreases with the increase in rain gauge density, and the gauge-satellite merged precipitation estimates are more sensitive to the rain gauge network density in wet season and ten-day temporal scale than in dry season and monthly scale, respectively and 2) the merged precipitation estimation using the IMERG precipitation data generally outperforms the estimation using TMPA precipitation data in the low gauge density scenarios, and the gap decreases with the increase in the rain gauge network density. In the areas with sparse rain gauges, improving the quality of satellite precipitation data would significantly improve the performance of the gauge-satellite merging estimation.