6,227 research outputs found
Detect rumors on Twitter by promoting information campaigns with generative adversarial learning
Design for Motor Controller in Hybrid Electric Vehicle Based on Vector Frequency Conversion Technology
Motor and its control technology are one of the main components of Hybrid Electric Vehicle (HEV). To meet HEV's fast torque response, vector control algorithm based on rotor flux-oriented and simulation model is concerned and modular designs for controller's hardware and software are presented in the paper in order to build a platform to achieve the vector control of asynchronous induction motor. Analyze the controller's electromagnetic compatibility, introduce the corresponding antijamming measures to assure the normal operation of the electromagnetic sensitive devices such as CAN bus; experiment proves that the measure is practical and feasible. On the basis of the control logic correct, such as improving CAN bus communication reliability, assuring power-on sequence and fault treatment, carry on the motor bench experiment, test its static properties, and adjust the controller parameters. The experimental results show that the designed driving system has the performance of low speed and high torque, a wide range of variable speed and high comprehensive efficiency
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning
The diffusion of rumors on microblogs generally follows a propagation tree
structure, that provides valuable clues on how an original message is
transmitted and responded by users over time. Recent studies reveal that rumor
detection and stance detection are two different but relevant tasks which can
jointly enhance each other, e.g., rumors can be debunked by cross-checking the
stances conveyed by their relevant microblog posts, and stances are also
conditioned on the nature of the rumor. However, most stance detection methods
require enormous post-level stance labels for training, which are
labor-intensive given a large number of posts. Enlightened by Multiple Instance
Learning (MIL) scheme, we first represent the diffusion of claims with
bottom-up and top-down trees, then propose two tree-structured weakly
supervised frameworks to jointly classify rumors and stances, where only the
bag-level labels concerning claim's veracity are needed. Specifically, we
convert the multi-class problem into a multiple MIL-based binary classification
problem where each binary model focuses on differentiating a target stance or
rumor type and other types. Finally, we propose a hierarchical attention
mechanism to aggregate the binary predictions, including (1) a bottom-up or
top-down tree attention layer to aggregate binary stances into binary veracity;
and (2) a discriminative attention layer to aggregate the binary class into
finer-grained classes. Extensive experiments conducted on three Twitter-based
datasets demonstrate promising performance of our model on both claim-level
rumor detection and post-level stance classification compared with
state-of-the-art methods.Comment: Accepted by SIGIR 202
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