Most of the recent work on machine learning-based temporal relation classification has been done by considering only a given pair of temporal entities (events or temporal expressions) at a time. Entities that have temporal connections to the pair of temporal entities under inspection are not considered even though they provide valuable clues to the prediction. In this paper, we present a new ap...

Recommender systems usually need to compare user interests and item characteristics in the context of large user and item space, making hashing based algorithms a promising strategy to speed up recommendation. Existing hashing based recommendation methods only model the users and items and dealing with the matrix data, e.g., user-item rating matrix. In practice, recommendation scenarios can be ...

OBJECTIVE Informed medical decision making requires comprehending statistical information. We aimed to improve the understanding of conveying health-related statistical information with graphical representations compared with numerical representations. First, we investigated whether the iconicity of representations (i.e., their abstractness vs. concreteness) affected comprehension and recall of...

Based on the concept of neutrosophic linguistic numbers (NLNs) in symbolic neutrosophic theory presented by Smarandache in 2015, the paper firstly proposes basic operational laws of NLNs and the expected value of a NLN to rank NLNs. Then, we propose the NLN weighted arithmetic average (NLNWAA) and NLN weighted geometric average (NLNWGA) operators and discuss their properties. Further, we establ...

Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease, making choices with re...

In this paper we present ongoing work on building a detection and diagnosis system for Services architecture. We propose a hierarchical detection and diagnosis framework instantiated in a system called the Monitor. The Monitor verifies the messages exchanged between services against an anomaly based rule set. The Monitor architecture is application neutral making it generically applicable to a ...