Deep Multi-stream Network for Video-based Calving Sign Detection

We have designed a deep multi-stream network for automatically detecting calving signs from video. Calving sign detection from a camera, which is a non-contact sensor, is expected to enable more efficient livestock management. As large-scale, well-developed data cannot generally be assumed when establishing calving detection systems, the basis for making the prediction needs to be presented to farmers during operation, so black-box modeling (also known as end-to-end modeling) is not appropriate. For practical operation of calving detection systems, the present study aims to incorporate expert knowledge into a deep neural network. To this end, we propose a multi-stream calving sign detection network in which multiple calving-related features are extracted from the corresponding feature extraction networks designed for each attribute with different characteristics, such as a cow's posture, rotation, and movement, known as calving signs, and are then integrated appropriately depending on the cow's situation. Experimental comparisons conducted using videos of 15 cows demonstrated that our multi-stream system yielded a significant improvement over the end-to-end system, and the multi-stream architecture significantly contributed to a reduction in detection errors. In addition, the distinctive mixture weights we observed helped provide interpretability of the system's behavior

TurkScanner: Predicting the Hourly Wage of Microtasks

Workers in crowd markets struggle to earn a living. One reason for this is that it is difficult for workers to accurately gauge the hourly wages of microtasks, and they consequently end up performing labor with little pay. In general, workers are provided with little information about tasks, and are left to rely on noisy signals, such as textual description of the task or rating of the requester. This study explores various computational methods for predicting the working times (and thus hourly wages) required for tasks based on data collected from other workers completing crowd work. We provide the following contributions. (i) A data collection method for gathering real-world training data on crowd-work tasks and the times required for workers to complete them; (ii) TurkScanner: a machine learning approach that predicts the necessary working time to complete a task (and can thus implicitly provide the expected hourly wage). We collected 9,155 data records using a web browser extension installed by 84 Amazon Mechanical Turk workers, and explored the challenge of accurately recording working times both automatically and by asking workers. TurkScanner was created using ~150 derived features, and was able to predict the hourly wages of 69.6% of all the tested microtasks within a 75% error. Directions for future research include observing the effects of tools on people's working practices, adapting this approach to a requester tool for better price setting, and predicting other elements of work (e.g., the acceptance likelihood and worker task preferences.)Comment: Proceedings of the 28th International Conference on World Wide Web (WWW '19), San Francisco, CA, USA, May 13-17, 201

Chiral primary amino alcohol organobase catalyst for the asymmetric Diels-Alder reaction of anthrones with maleimides

Simple chiral TES-amino alcohol organocatalysts containing a bulkysilyl [triethylsilyl: TES] group on oxygen atom at γ-position were designed andsynthesized as new organocatalysts for the enantioselective Diels-Alder (DA) reactionof anthrones with maleimides to produce chiral hydroanthracene DA adducts (up to99% yield with up to 94% ee)

A face/object recognition system using coarse region segmentation and dynamic-link matching

An image recognition model that combines some neural-network-based imageprocessing models is proposed. The recognition procedure consists of coarse regionsegmentation/extraction performed by a resistive-fuse network, Gabor wavelet transformation anddynamic-link matching. We have also developed a PC-based face/object recognition systemincluding FPGA implementation of the resistive-fuse network. The system has successfullyachieved real-time face recognition from a natural scene image.Invited papers of the 1st Meeting entitled Brain IT 2004, Hibikino, Kitakuyushu, Japan, 7-9 March 200