The identification of argument relation is an important subtask of argumentation mining. Its purpose is to identify the support or attack relationship between two argument components, so that people can understand the argument process in the argumentative text more deeply. This paper proposes a research method based on Scibert and BILSTM-CRF model. First, the pre-trained language model Scibert dynamically obtains word vectors, and then combines with the BiLSTM network to fine-tune downstream tasks to obtain contextual information. Finally, the argument relation is identified by conditional random field. Experiments were conducted on two argumentation mining datasets, Persuasive Essays and UKP Sentential Corpus, which were published in Germany. The experimental results show that our method is better than the baseline method.
A decision model of maneuver schemes of multiple targets is proposed in this paper, and a multi-objective optimization algorithm is used to solve the model. There are two problems to be solved in this model. One is to quantitatively evaluate the scheme from the ship to the target, and the other is to select the optimal one from the scheme matrix formed by the combination of multiple ships and multiple targets. Therefore, the prediction results of the two types of maneuver schemes approach maneuver and occupy position maneuver are used as the basic scheme unit of the ship to the target in this decision model. In the analysis process, the opinion of experts is used to evaluate the ships, targets, and decision-making objectives which starting from the four factors: distance, time, angle, and the priority of every single scheme. Then, multi-objective optimization is used to solve the scheme matrix formed by the combination of multiple ships and multiple targets. Finally, we find the global optimal scheme combination that meets the experts’ preferences from the scheme matrix.
A new impulsive noise removal algorithm, the selective adaptive weighted median filter (SAWMF), is introduced. The proposed solution is a class of adaptive weighted median filters with incorporation of a switching mechanism. Using a median-based comparison technique to classify each image pixel as an impulse or a noise-free one, the new algorithm employs a weighted median filter where the weights are adaptively selected from two fixed values to restore the detected noisy pixels and keep the noise-free ones unchanged. The experimental results indicate that the SAWMF provides a significant performance improvement over many of the existing filtering techniques in suppressing impulsive noise with different contamination ratios.
Consensus building in group support systems relies on the mutual-question and mutual-elicitation of experts, so a
feedback mechanism is required to conduct experts to converge their thinking by visualizing the individual opinion and
the consistent state of the group. This paper proposes a new feedback mechanism, which first clusters the experts'
preferences into a set of subgroups, and then uses different line-types or line-colors to display the clustered opinions in
parallel coordinate. By using this mechanism, the group consistency is analyzed and the group discussion is conducted
efficiently. One of the characteristics of the proposed method is that it can protect the minority views automatically. An
example is presented to illustrate the application of the method.
KEYWORDS: Image filtering, Digital filtering, Optical filters, Switching, RGB color model, Image quality, Color difference, Visualization, Image processing, Space operations
This paper introduces a new class of switching vector median filter. The proposed algorithm first uses four directional
masks to analyze the color difference between the central pixel and its neighborhood pixels in the RGB color space and
classify each color pixel into noisy pixel or noise-free one, and then employs the standard vector median filtering
operations in the detected noisy locations to restore the corrupted pixels and leave the noise-free ones unchanged. The
simulation results show that the proposed method excellently suppresses impulsive noise as well as preserving the image
details well, and significantly outperforms the existing vector filtering solutions in terms of both the objective measures
and the perceptual visual quality.
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