Alibaba Group

About: Alibaba Group is a company organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Terminal (electronics). The organization has 6810 authors who have published 7389 publications receiving 55653 citations. The organization is also known as: Alibaba Group Holding Limited & Alibaba Group (Cayman Islands).

Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced Testing

Abstract: Crowdsourced testing is an emerging trend, in which test tasks are entrusted to the online crowd workers. Typically, a crowdsourced test task aims to detect as many bugs as possible within a limited budget. However not all crowd workers are equally skilled at finding bugs; Inappropriate workers may miss bugs, or report duplicate bugs, while hiring them requires nontrivial budget. Therefore, it is of great value to recommend a set of appropriate crowd workers for a test task so that more software bugs can be detected with fewer workers. This paper first presents a new characterization of crowd workers and characterizes them with testing context, capability, and domain knowledge. Based on the characterization, we then propose Multi-Objective Crowd wOrker recoMmendation approach (MOCOM), which aims at recommending a minimum number of crowd workers who could detect the maximum number of bugs for a crowdsourced testing task. Specifically, MOCOM recommends crowd workers by maximizing the bug detection probability of workers, the relevance with the test task, the diversity of workers, and minimizing the test cost. We experimentally evaluate MOCOM on 532 test tasks, and results show that MOCOM significantly outperforms five commonly-used and state-of-the-art baselines. Furthermore, MOCOM can reduce duplicate reports and recommend workers with high relevance and larger bug detection probability; because of this it can find more bugs with fewer workers.

EfficientDeRain: Learning pixel-wise dilation filtering for high-efficiency single-image deraining

Abstract: Single-image deraining is rather challenging due to the unknown rain model Existing methods often make specific assumptions of the rain model, which can hardly cover many diverse circumstances in the real world, compelling them to employ complex optimization or progressive refinement This, however, significantly affects these methods' efficiency and effectiveness for many efficiency-critical applications To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, ie, EfficientDeRain, which is able to process a rainy image within 10 ms (ie, around 6 ms on average), over 80 times faster than the state-of-the-art method (ie, RCDNet), while achieving similar de-rain effects We first propose novel pixel-wise dilation filtering In particular, a rainy image is filtered with the pixel-wise kernels estimated from a kernel prediction network, by which suitable multi-scale kernels for each pixel can be efficiently predicted Then, to eliminate the gap between synthetic and real data, we further propose an effective data augmentation method (ie, RainMix) that helps to train the network for handling real rainy images We perform a comprehensive evaluation on both synthetic and real-world rainy datasets to demonstrate the effectiveness and efficiency of our method We release the model and code in https://githubcom/tsingqguo/efficientderaingit

Fighting Against Deepfake: Patch&Pair Convolutional Neural Networks (PPCNN)

Abstract: In this paper, we propose a novel Patch&Pair Convolutional Neural Networks (PPCNN) to distinguish Deepfake videos or images from real ones. Through the comprehensive evaluations on public datasets, we demonstrate that our model performs better than existing detection methods and show better generalization.

Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery

Abstract: By providing customers with conveniences such as easy access to an extensive variety of restaurants, effortless food ordering and fast delivery, on-demand food delivery (OFD) platforms have achieved explosive growth in recent years. A crucial machine learning task performed at OFD platforms is prediction of the Order Fulfillment Cycle Time (OFCT), which refers to the amount of time elapsed between a customer places an order and he/she receives the meal. The accuracy of predicted OFCT is important for customer satisfaction, as it needs to be communicated to a customer before he/she places the order, and is considered as a service promise that should be fulfilled as well as possible. As a result, the estimated OFCT also heavily influences planning decisions such as dispatching and routing. In this paper, we present the OFCT prediction model that is currently deployed at Ele.me, which is one of the world's largest OFD platforms and delivers over 10 million meals in more than 200 Chinese cities every day. By dissecting the order fulfillment cycle of a meal order, we identify key factors behind OFCT, and capture them with numerous features constructed using a wide range of data sources. These features are fed into a deep neural network (DNN), which further incorporates representations of couriers, restaurants and delivery destinations to enhance prediction efficacy. Finally, a novel post-processing layer is introduced to improve convergence speed by better accounting for the distributional mismatch between the true OFCT values and those predicted by the model at initialization. Extensive offline and online experiments demonstrate the effectiveness of our approach.

Vertical search-based query method, system and apparatus

Abstract: Various embodiments of a method, system, and apparatus related to query based on vertical search are disclosed. In one aspect, a method of query based on vertical search receives a user query. The method obtains a first category model from a category model warehouse based on the user query to generate a first query result. The first category model includes one or more commodity categories that correspond to one or more keywords in the user query. The method also obtains one or more commodity categories corresponding to the user query from a commodity warehouse to generate a second query result. The method further generates a final query result by combining the first query result and the second query result.