With the exponential growth of user-generated content, policies and guidelines are not always enforced in social media, resulting in the prevalence of deviant content violating policies and guidelines. The adverse effects of deviant content are devastating and far-reaching. However, the detection of deviant content from sparse and imbalanced textual data is challenging, as a large number of stakeholders are involved with different stands and the subtle linguistic cues are highly dependent on complex context. To address this problem, we propose a multi-view attention-based deep learning system, which combines random subspace and binary particle swarm optimization (RS-BPSO) to distill content of interest (candidates) from imbalanced data, and applies the context and view attention mechanisms in convolutional neural network (dubbed as SSCNN) for the extraction of structural and semantic features. We evaluate the proposed approach on a large-scale dataset collected from Facebook, and find that RS-BPSO is able to detect whether the content is associated with marijuana with an accuracy of 87.55%, and SSCNN outperforms baselines with an accuracy of 94.50%.
The control design problem for the uncertain nonlinear system with bounded state constraint and mismatching condition is considered in this paper. The uncertainty in the system, which may be due to unknown system parameters and external disturbance, is nonlinear and time‐varying. The state of the system is constrained to be bounded. The system does not satisfy the (global) matching condition. A creative one‐to‐one state transformation is proposed by converting the bounded states into the unbounded ones. A step‐by‐step state transformation is proposed to convert the mismatched system into a matched system. The robust control is then proposed based on the transformed system. The control is demonstrated to be able to guarantee the uniform boundedness and uniform ultimate boundedness of the system in the presence of uncertainty, while the state constraint can be always guaranteed. 相似文献
In this paper, the fixed‐time synchronization for complex‐valued bidirectional associative memory (BAM) neural networks with time delays is studied. Based on the fixed‐time stability, the Lyapunov functional method and some inequality techniques, a new criterion is presented to guarantee that the addressed systems achieve synchronization in fixed time and a more accurate estimation independent of the initial conditions is given for the settling time. Meanwhile, a new nonlinear delayed controller different from the existing ones is designed. In the end, two numerical examples are provided to illustrate the effectiveness of the obtained result. 相似文献
Discarded train brake shoes mainly consist of steel-backed friction material. To be better reutilized, its essential features and its interaction in cement-based material need to be studied. Consequently, particle size analysis, SEM, IR and TGA were used to investigate two types of waste brake shoes, i e, mechanical grinding friction reclaimed material of waste brake-shoe (G-FRMWBS) and pyrolysis-friction reclaimed materials of waste brake-shoe (P-FRMWBS). The latter exhibited less organic content, larger range of particle size distribution and smaller medium particle diameter. Both types contained inorganic particles of spherical and irregular shapes, striped with steel fiber. Upon isometric substituting fine aggregates, G-FRMWBS lifted the strength of mortar effectively that was increased by 16.6% and 17.5% when the replacing rate was 5%; the value went up to 19.2% and 19.2% when the replacing rate was 10%. Moreover, inclusion of FRMWBS enhanced the chloride penetration resistance, and optimized the pore characteristic and ITZ (interfacial transition zone) as well. 相似文献