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Research that applies artificial intelligence (AI) to generate the captions for an image has been extensively studied in recent years. However, the length of these captions was short, and the number of generated captions was limited. In addition, it is unknown whether a short story can be generated based on the image, because many sentences have to be connected to create a fluent short story. As a result, this study introduces an encoder-decoder framework structure to generate a short story captioning (SSCap) using a common image caption dataset and a manually collected story corpus. This manuscript has three main contributions, which include 1) an unsupervised deep learning-based framework that combines a recurrent neural network (RNN) structure and encoder-decoder model for composing a short story for an image, 2) a huge story corpus, which includes two different genres (horror and romantic), manually collected and validated. Extensive experiments demonstrated that short stories created by the proposed model show creative content compared to existing systems that can only make concise sentences. Therefore, the demonstrated framework has the potential to motivate the development of a more robust AI story writer and motivates the integration of the suggested model into practical applications to help the story writers find a new idea.

While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inference in a deep network, requiring a single forward pass, is much faster. Taking advantage of this, we propose a novel architecture to fit a 3D garment template to a 3D body. Specifically, we build upon the recent progress in 3D point cloud processing with deep networks to extract garment features at varying levels of detail, including point-wise, patch-wise and global features. We fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions. The resulting two-stream architecture, which we call as GarNet, is trained using a loss function inspired by physics-based modeling, and delivers visually plausible garment shapes whose 3D points are, on average, less than 1 cm away from those of a PBS method, while running 100 times faster. Moreover, the proposed method can model various garment types with different cutting patterns when parameters of those patterns are given as input to the network., Accepted to ICCV 2019

A new compliant XY micropositioning stage is designed for locating a material sample during nanoindentation tester. This paper aims to design and optimize the proposed stage. The working travel of proposed stage is amplified via using the four-lever amplification mechanism. To improve the performances of the stage, the geometric parameters are optimized by an integration method of Taguchi method (TM), response surface method (RSM) and nondominated sorting genetic algorithm II (NSGA-II). Firstly, the number of experiment of four design variables were created by the TM. Subsequently, the output displacement and the first natural frequency are retrieved by finite element analysis (FEA). Then, the mathematical equations between four design variables and both responses is established by using the RSM. Eventually, based on mathematical equations, the NSGA-II is adopted to determine the optimal parameters. The results indicated that the optimized y-axis displacement was about 3.862 mm and the first natural frequency was approximately 45.983. The results of FEA validations are in a good agreement with the predicted results from the proposed approach. This result indicated that the integrated proposed approach ensures a high reliability for engineering optimization problems.

News articles serve the purpose of spreading company's information to the investors either consciously or unconsciously in their trading strategies on the stock market. Because of the immense growth of the internet in the last decade, the amount of financial articles have experienced a significant growth. It is important to analyze the information as fast as possible so they can support the investors in making the smart trading decisions before the market has had time to adjust itself to the effect of the information. This paper proposes an approach of using time series analysis and improved text mining techniques to predict daily stock market directions. Experiment results show that our system achieved high accuracy (up to 73%) in predicting the stock trends.