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Found 7 papers, 0 papers with code
Emulating the Human Mind: A Neural-symbolic Link Prediction Model with Fast and Slow Reasoning and Filtered Rules
To tackle the challenge of dealing with incorrect paths or rules generated by the logical model, we propose a semi-supervised method to convert rules into sentences.
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects
Based on the given extrinsic and intrinsic analysis results, we note that compared to other theoretical-based techniques, the proposed hierarchical clustering approach can better group the users within the adaptive tree.
Cognitive-aware Short-text Understanding for Inferring Professions
Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications.
EmoDNN: Understanding emotions from short texts through a deep neural network ensemble
The latent knowledge in the emotions and the opinions of the individuals that are manifested via social networks are crucial to numerous applications including social management, dynamical processes, and public security.
SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding
Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors.
TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs
We can use the temporal and textual data of the nodes to compute the edge weights and then generate subgraphs with highly relevant nodes.
Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach
Machine-learning based intrusion detection classifiers are able to detect unknown attacks, but at the same time, they may be susceptible to evasion by obfuscation techniques.
Cryptography and Security C.2.0
