Academic writing in English can be challenging for non-native English speakers (NNESs). AI-powered rewriting tools can potentially improve NNESs’ writing outcomes at a low cost. However, whether and how NNESs make valid assessments of the revisions provided by these algorithmic recommendations remains unclear. We report a study where NNESs leverage an AI-powered rewriting tool, Langsmith, to polish their drafted academic essays. We examined the participants’ interactions with the tool via user studies and interviews. Our data reveal that most participants used Langsmith in combination with other tools, such as machine translation (MT), and those who used MT had different ways of understanding and evaluating Langsmith’s suggestions than those who did not. Based on these findings, we assert that NNESs’ quality assessment in AI-powered rewriting tools is influenced by the simultaneous use of multiple tools, offering valuable insights into the design of future rewriting tools for NNESs.

Recently, deep neural network (DNN) is drawing a lot of attention because of its applications. However, it requires a lot of computational resources and tremendous processes in order to setup an execution environment based on hardware acceleration such as GPGPU. Therefore, providing DNN applications to end-users is very hard. To solve this problem, we have developed an installation-free web browser-based DNN execution framework, WebDNN. WebDNN optimizes the trained DNN model to compress model data and accelerate the execution. It executes the DNN model with novel JavaScript API to achieve zero-overhead execution. Empirical evaluations show that it achieves more than two-hundred times the unusual acceleration. WebDNN is an open source framework and you can download it from https://github.com/mil-tokyo/webdnn.

Improved dense trajectories (iDT) have shown great performance in action recognition, and their combination with the two-stream approach has achieved state-of-the-art performance. It is, however, difficult for iDT to completely remove background trajectories from video with camera shaking. Trajectories in less discriminative regions should be given modest weights in order to create more discriminative local descriptors for action recognition. In addition, the two-stream approach, which learns appearance and motion information separately, cannot focus on motion in important regions when extracting features from spatial convolutional layers of the appearance network, and vice versa. In order to address the above mentioned problems, we propose a new local descriptor that pools a new convolutional layer obtained from crossing two networks along iDT. This new descriptor is calculated by applying discriminative weights learned from one network to a convolutional layer of the other network. Our method has achieved state-of-the-art performance on ordinal action recognition datasets, 92.3% on UCF101, and 66.2% on HMDB51.

In this paper, we would like to evaluate online learning algorithms for large-scale visual recognition using state-of-the-art features which are preselected and held fixed. Today, combinations of high-dimensional features and linear classifiers are widely used for large-scale visual recognition. Numerous so-called mid-level features have been developed and mutually compared on an experimental basis. Although various learning methods for linear classification have also been proposed in the machine learning and natural language processing literature, they have rarely been evaluated for visual recognition. Therefore, we give guidelines via investigations of state-of-the-art online learning methods of linear classifiers. Many methods have been evaluated using toy data and natural language processing problems such as document classification. Consequently, we gave those methods a unified interpretation from the viewpoint of visual recognition. Results of controlled comparisons indicate three guidelines that might change the pipeline for visual recognition.