IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde

Meningkatkan Kemampuan Berpikir Kritis dan Kemandirian Belajar Mahasiswa melalui Pembelajaran Generatif

The research aims at analyzing the quality of students' critical thinking and self-learning abilities by applying generative learning method in the subject of Algebra Structure at Mathematics Education Study Program of STKIP PGRI Lubuk Linggau. The variable of the research was the result of generative learning approach to improve critical thinking and self-learning abilities in the subject of Algebra Structure at Mathematics Education Study Program of STKIP PGRI Lubuk Linggau. The research objects were 23 students of the fifth semester class in Mathematic Education Study Program of STKIP PGRI Lubuk Linggau the year of 2009/2010. Research method applied was quasi experiment, with experiment class treated with generative learning method and without control class. The research result showed that in general, the critical thinking and self-learning abilities of Algebra Structure Subject had the mean of 75.90 (with minimum score of 70) meaning, the critical thinking and self-learning abilities of the students were generally in high level. Whereas the analysis result of variants test was t-count =3.954 and t-table (dk =22, á =1%) = 2,508. It showed that the critical thinking and self-learning abilities of the students with generative learning approach in the subject of Algebra Structure at Mathematics Education Study Program of STKIP PGRI Lubuk Linggau was above the score of 70

A Generative Model for Score Normalization in Speaker Recognition

We propose a theoretical framework for thinking about score normalization, which confirms that normalization is not needed under (admittedly fragile) ideal conditions. If, however, these conditions are not met, e.g. under data-set shift between training and runtime, our theory reveals dependencies between scores that could be exploited by strategies such as score normalization. Indeed, it has been demonstrated over and over experimentally, that various ad-hoc score normalization recipes do work. We present a first attempt at using probability theory to design a generative score-space normalization model which gives similar improvements to ZT-norm on the text-dependent RSR 2015 database

Development and evaluation of a web-based learning system based on learning object design and generative learning to improve higher-order thinking skills and learning

This research aims to design, develop and evaluate the effectiveness of a Webbased learning system prototype called Generative Object Oriented Design (GOOD) learning system. Result from the preliminary study conducted showed most of the students were at lower order thinking skills (LOTS) compared to higher order thinking skills (HOTS) based on Bloom’s Taxonomy. Based on such concern, GOOD learning system was designed and developed based on learning object design and generative learning to improve HOTS and learning. A conceptual model design of GOOD learning system, called Generative Learning Object Organizer and Thinking Tasks (GLOOTT) model, has been proposed from the theoretical framework of this research. The topic selected for this research was Computer System (CS) which focused on the hardware concepts from the first year Diploma of Computer Science subjects. GOOD learning system acts as a mindtool to improve HOTS and learning in CS. A pre-experimental research design of one group pretest and posttest was used in this research. The samples of this research were 30 students and 12 lecturers. Data was collected from the pretest, posttest, portfolio, interview and Web-based learning system evaluation form. The paired-samples T test analysis was used to analyze the achievement of the pretest and posttest and the result showed that there was significance difference between the mean scores of pretest and posttest at the significant level a = 0.05 (p=0.000). In addition, the paired-samples T test analysis of the cognitive operations from Bloom’s Taxonomy showed that there was significance difference for each of the cognitive operation of the students before and after using GOOD learning system. Results from the study showed improvement of HOTS and learning among the students. Besides, analysis of portfolio showed that the students engaged HOTS during the use of the system. Most of the students and lecturers gave positive comments about the effectiveness of the system in improving HOTS and learning in CS. From the findings in this research, GOOD learning system has the potential to improve students’ HOTS and learning

The materiality of research: ‘woven into the fabric of the text: subversive material metaphors in academic writing’ by Katie Collins

In this feature essay, Katie Collins proposes that we shift our thinking about academic writing from building metaphors – the language of frameworks, foundations and buttresses – to stitching, sewing and piecing. Needlecraft metaphors offer another way of thinking about the creative and generative practice of academic writing as decentred, able to accommodate multiple sources and with greater space for the feminine voice

Visuality and the haptic qualities of the line in generative art

The line has an important and particular relationship with the generative artwork distinct from other elements such as the ‘pixel’, ‘voxel’ or the ‘points’ that make up point clouds. The line has a dual nature as both continuous and discrete which makes it perhaps uniquely placed to straddle the analog and digital worlds. It has a haptic or felt quality as well as an inherent ambiguity that promotes a relatively active interpretive role for the audience. There is an extensive history of the line in generative systems and artworks, taking both analog and digital forms. That it continues to play an important role, alongside other more photographically inspired ‘perceptual schemas’, may be a testament to its enduring usefulness and unique character. This paper considers the particular affordances and the ‘visuality’ of the line in relation to generative artworks. This includes asking how we might account for the felt quality of lines and the socially and culturally constructed aspects that shape our relationship with them. It asks whether, in what has been described as a ‘post digital’ or even ‘post post digital’ world, the line may offer a way to re-emphasise a more human scale and a materiality that can push back, gently, against other more dominant perceptual schemas. It also asks what generative art can learn from drawing theory, many of the concerns of which parallel and intersect with those of generative art

Rich environments for active learning in action: Problem‐based learning

Rich Environments for Active Learning (REALs) are comprehensive instructional systems that are consistent with constructivist theories. They promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher‐order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning‐to‐learn within authentic contexts using realistic tasks and performances. Problem‐Based Learning (PBL) is an instructional methodology that can be used to create REALs. PBL's student‐centred approach engages students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, PBL and REALs are a response to teacher‐centred educational practices that promote the development of inert knowledge, such as conventional teacher‐to‐student knowledge dissemination activities. In this article, we compare existing assumptions underlying teacher‐directed educational practice with new assumptions that promote problem solving and higher‐level thinking by putting students at the centre of learning activities. We also examine the theoretical foundation that supports these new assumptions and the need for REALs. Finally, we describe each REAL characteristic and provide supporting examples of REALs in action using PB