In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
This manuscript presents a trust computation for international relations and its calculus, which related to Bayesian inference, Dempster-Shafer theory and subjective logic. We proposed a method that allows a trust computation which is previously subjective and incomputable. An example of case study for the trust computation is the United States of America–Great Britain relations. The method supports decision makers in a government such as foreign ministry, defense ministry, presidential or prime minister office. The Department of Defense (DoD) may use our method to determine a nation that can be known as a friendly, neutral or hostile nation.

Ex Output:
A Mathematical Trust Algebra for International Nation Relations Computation and Evaluation


Ex Input:
Despite progress in visual perception tasks such as image classification and detection, computers still struggle to understand the interdependency of objects in the scene as a whole, e.g., relations between objects or their attributes. Existing methods often ignore global context cues capturing the interactions among different object instances, and can only recognize a handful of types by exhaustively training individual detectors for all possible relationships. To capture such global interdependency, we propose a deep Variation-structured Reinforcement Learning (VRL) framework to sequentially discover object relationships and attributes in the whole image. First, a directed semantic action graph is built using language priors to provide a rich and compact representation of semantic correlations between object categories, predicates, and attributes. Next, we use a variation-structured traversal over the action graph to construct a small, adaptive action set for each step based on the current state and historical actions. In particular, an ambiguity-aware object mining scheme is used to resolve semantic ambiguity among object categories that the object detector fails to distinguish. We then make sequential predictions using a deep RL framework, incorporating global context cues and semantic embeddings of previously extracted phrases in the state vector. Our experiments on the Visual Relationship Detection (VRD) dataset and the large-scale Visual Genome dataset validate the superiority of VRL, which can achieve significantly better detection results on datasets involving thousands of relationship and attribute types. We also demonstrate that VRL is able to predict unseen types embedded in our action graph by learning correlations on shared graph nodes.

Ex Output:
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection


Ex Input:
Outlier detection in a large-scale database is a significant and complex issue in knowledge discovering field. As the data distributions are obscure and uncertain in high dimensional space, most existing solutions try to solve the issue taking into account the two intuitive points: first, outliers are extremely far away from other points in high dimensional space; second, outliers are detected obviously different in projected-dimensional subspaces. However, for a complicated case that outliers are hidden inside the normal points in all dimensions, existing detection methods fail to find such inner outliers. In this paper, we propose a method with twice dimension-projections, which integrates primary subspace outlier detection and secondary point-projection between subspaces, and sums up the multiple weight values for each point. The points are computed with local density ratio separately in twice-projected dimensions. After the process, outliers are those points scoring the largest values of weight. The proposed method succeeds to find all inner outliers on the synthetic test datasets with the dimension varying from 100 to 10000. The experimental results also show that the proposed algorithm can work in low dimensional space and can achieve perfect performance in high dimensional space. As for this reason, our proposed approach has considerable potential to apply it in multimedia applications helping to process images or video with large-scale attributes.

Ex Output:

Finding Inner Outliers in High Dimensional Space