SRI Blog

 - By Suvodeep Majumder, IEEE Member, Joymallya Chakraborty, Amritanshu Agrawal, Tim Menzies, IEEE Fellow I felt executing ML or AI project is not only purely technical,l but also clear communication between team members and the size of the team also matters, Hence I picked up this topic Heroes are those who participate in 80% (or more) of the communications associated with a commit. Abstract A “hero” project is one where 80% or more of the contributions are made by the 20% of the developers. In the literature, such projects are deprecated since they might cause bottlenecks in development and communication. However, there is little empirical evidence on this matter. Further, recent studies show that such hero projects are very prevalent. Accordingly, this paper explores the effect of having heroes in project, from a code quality perspective. We identify the heroes developer communities in 1100+ open source GitHub projects. Based on the analysis, we find that (a) hero project

-By Zhiheng Ma, Xing Wei Xiaopeng Hong, Yihong Gong Research Center for Artificial Intelligence, Peng Cheng Laboratory Paper Link Abstract In crowd counting datasets, each person is annotated by a point, which is usually the center of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of-the-art methods are based on density map estimation, which convert the sparse point annotations into a “ground truth” density map through a Gaussian kernel, and then use it as the learning target to train a density map estimator. However, such a “ground-truth” density map is imperfect due to occlusions, perspective effects, variations in object shapes, etc. On the contrary, we propose Bayesian loss, a novel loss function which constructs a density contribution probability model from the point annotations. Instead of constraining the value at every pixel in the density map, the proposed training loss adopts a more reliable supervision on the count ex

 - By Haoyu Dong, Shijie Liu, Shi Han, Zhouyu Fu, Dongmei Zhang Microsoft Research, Beijing 100080, China. Beihang University, Beijing 100191, China Paper Link Abstract Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for tab

by Karol Kurach, Research Lead and Olivier Bachem, Research Scientist, Google Research, Zürich Paper Link Abstract Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner. We introduce the Google Research Football Environment, a new reinforcement learning environment where agents are trained to play football in an advanced, physics-based 3D simulator. The resulting environment is challenging, easy to use and customize, and it is available under a permissive open-source license. In addition, it provides support for multiplayer and multi-agent experiments. We propose three full-game scenarios of varying difficulty with the Football Benchmarks and report baseline results for three commonly used reinforcement algorithms (IMPALA, PPO, and Ape-X DQN). We also provide a diverse set of simpler scenarios with the Football A