Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Reinforced Mnemonic Reader for Machine Reading Comprehension

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention mechanism is proposed to refine current attentions by directly accessing to past attentions that are temporally memorized in a multi-round alignment architecture, so as to avoid the problems of attention redundancy and attention deficiency. Second, a new optimization approach, called dynamic-critical reinforcement learning, is introduced to extend the standard supervised method. It always encourages to predict a more acceptable answer so as to address the convergence suppression problem occurred in traditional reinforcement learning algorithms. Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets.Comment: Published in 27th International Joint Conference on Artificial Intelligence (IJCAI), 201

PeerWise - The Marmite of Veterinary Student Learning

PeerWise is a free online student-centred collaborative learning tool with which students anonymously author, answer, and evaluate multiple choice questions (MCQs). Features such as commenting on questions, rating questions and comments, and appearing on leaderboards, can encourage healthy competition, engage students in reflection and debate, and enhance their communication skills. PeerWise has been used in diverse subject areas but never previously in Veterinary Medicine. The Veterinary undergraduates at the University of Glasgow are a distinct cohort; academically gifted and often highly strategic in their learning due to time pressures and volume of course material. In 2010-11 we introduced PeerWise into 1st year Veterinary Biomolecular Sciences in the Glasgow Bachelor of Veterinary Medicine and Surgery programme. To scaffold PeerWise use, a short interactive session introduced students to the tool and to the basic principles of good MCQ authorship. Students were asked to author four and answer forty MCQs throughout the academic year. Participation was encouraged by an allocation of up to 5% of the final year mark and inclusion of studentauthored questions in the first summative examination. Our analysis focuses on engagement of the class with the\ud tool and their perceptions of its use. All 141 students in the class engaged with PeerWise and the majority contributed beyond that which was stipulated. Student engagement with PeerWise prior to a summative exam was positively correlated to exam score, yielding a relationship that was highly significant (p<0.001). Student perceptions of PeerWise were predominantly positive with explicit recognition of its value as a learning and revision tool, and more than two thirds of the class in agreement that question authoring and answering reinforced their learning. There was clear polarisation of views, however, and those students who did not like PeerWise were vociferous in their dislike, the biggest criticism being lack of moderation by staff

Finding Structured and Unstructured Features to Improve the Search Result of Complex Question

-Recently, search engine got challenge deal with such a natural language questions. Sometimes, these questions are complex questions. A complex question is a question that consists several clauses, several intentions or need long answer. In this work we proposed that finding structured features and unstructured features of questions and using structured data and unstructured data could improve the search result of complex questions. According to those, we will use two approaches, IR approach and structured retrieval, QA template. Our framework consists of three parts. Question analysis, Resource Discovery and Analysis The Relevant Answer. In Question Analysis we used a few assumptions, and tried to find structured and unstructured features of the questions. Structured feature refers to Structured data and unstructured feature refers to unstructured data. In the resource discovery we integrated structured data (relational database) and unstructured data (webpage) to take the advantaged of two kinds of data to improve and reach the relevant answer. We will find the best top fragments from context of the webpage In the Relevant Answer part, we made a score matching between the result from structured data and unstructured data, then finally used QA template to reformulate the question. In the experiment result, it shows that using structured feature and unstructured feature and using both structured and unstructured data, using approach IR and QA template could improve the search result of complex questions