17,287 research outputs found
ACCESS: An Inception Report
Imagine a world in which all groups of citizens coming together to realize some public benefit measure and communicate the character and consequences of their work. Imagine further that all those groups have adopted a common reporting system that enables their individual reports to be compared, thus creating powerful descriptions of the relative and collective performance of citizen association for public benefit. Imagine, too, that this common measuring and reporting carries across to all forms of public-private partnership and corporate social responsibility. This is the world envisioned by ACCESS.For the past 18 months a growing number of concerned actors have been meeting, studying, and testing opinion around one of the great structural weaknesses in the world's institutional infrastructure -- inefficient and weak social investment markets. This inception report sets out the results of this enquiry in the form of a proposal to establish a reporting standard for nonprofit organizations seeking to produce social, environmental and, increasingly, financial returns. The ACCESS Reporting standard is one important contribution to redressing a major global system weakness, but it is certainly not the only one. Nor is it one that can operate in isolation from other initiatives. Accordingly, the ACCESS proposed plan of work involves convening a global dialogue on NGO transparency, accountability and performance with the objective of promoting ACCESS and other practical solutions to the challenges of social investment and civil society accountability.This report sets out the background and rationale for these proposals. You will meet the ACCESS sponsors and pilot project partners. Parts of the report are descriptive and analytical but other parts are necessarily theoretical and technical in nature. We make no apology for this. Part of the reason that in 2003 the world does not yet have a reporting standard for social actors is that the theory and technique have not been mastered. For those with a strong orientation toward strategy and action, however, these aspects are presented as well
A Similarity Measure for Material Appearance
We present a model to measure the similarity in appearance between different
materials, which correlates with human similarity judgments. We first create a
database of 9,000 rendered images depicting objects with varying materials,
shape and illumination. We then gather data on perceived similarity from
crowdsourced experiments; our analysis of over 114,840 answers suggests that
indeed a shared perception of appearance similarity exists. We feed this data
to a deep learning architecture with a novel loss function, which learns a
feature space for materials that correlates with such perceived appearance
similarity. Our evaluation shows that our model outperforms existing metrics.
Last, we demonstrate several applications enabled by our metric, including
appearance-based search for material suggestions, database visualization,
clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure
Matching Natural Language Sentences with Hierarchical Sentence Factorization
Semantic matching of natural language sentences or identifying the
relationship between two sentences is a core research problem underlying many
natural language tasks. Depending on whether training data is available, prior
research has proposed both unsupervised distance-based schemes and supervised
deep learning schemes for sentence matching. However, previous approaches
either omit or fail to fully utilize the ordered, hierarchical, and flexible
structures of language objects, as well as the interactions between them. In
this paper, we propose Hierarchical Sentence Factorization---a technique to
factorize a sentence into a hierarchical representation, with the components at
each different scale reordered into a "predicate-argument" form. The proposed
sentence factorization technique leads to the invention of: 1) a new
unsupervised distance metric which calculates the semantic distance between a
pair of text snippets by solving a penalized optimal transport problem while
preserving the logical relationship of words in the reordered sentences, and 2)
new multi-scale deep learning models for supervised semantic training, based on
factorized sentence hierarchies. We apply our techniques to text-pair
similarity estimation and text-pair relationship classification tasks, based on
multiple datasets such as STSbenchmark, the Microsoft Research paraphrase
identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments
show that the proposed hierarchical sentence factorization can be used to
significantly improve the performance of existing unsupervised distance-based
metrics as well as multiple supervised deep learning models based on the
convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page
k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)
Perhaps the most straightforward classifier in the arsenal or machine
learning techniques is the Nearest Neighbour Classifier -- classification is
achieved by identifying the nearest neighbours to a query example and using
those neighbours to determine the class of the query. This approach to
classification is of particular importance because issues of poor run-time
performance is not such a problem these days with the computational power that
is available. This paper presents an overview of techniques for Nearest
Neighbour classification focusing on; mechanisms for assessing similarity
(distance), computational issues in identifying nearest neighbours and
mechanisms for reducing the dimension of the data.
This paper is the second edition of a paper previously published as a
technical report. Sections on similarity measures for time-series, retrieval
speed-up and intrinsic dimensionality have been added. An Appendix is included
providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN
Index ordering by query-independent measures
Conventional approaches to information retrieval search through all applicable entries in an inverted file for a particular collection in order to find those documents with the highest scores. For particularly large collections this may be extremely time consuming.
A solution to this problem is to only search a limited amount of the collection at query-time, in order to speed up the retrieval process. In doing this we can also limit the loss in retrieval efficacy (in terms of accuracy of results). The way we achieve this is to firstly identify the most “important” documents within the collection, and sort documents within inverted file lists in order of this “importance”. In this way we limit the amount of information to be searched at query time by eliminating documents of lesser importance, which not only makes the search more efficient, but also limits loss in retrieval accuracy. Our experiments, carried out on the TREC Terabyte collection, report significant savings, in terms of number of postings examined, without significant loss of effectiveness when based on several measures of importance used in isolation, and in combination. Our results point to several ways in which the computation cost of searching large collections of documents can be significantly reduced
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