149,803 research outputs found
Deeper Text Understanding for IR with Contextual Neural Language Modeling
Neural networks provide new possibilities to automatically learn complex
language patterns and query-document relations. Neural IR models have achieved
promising results in learning query-document relevance patterns, but few
explorations have been done on understanding the text content of a query or a
document. This paper studies leveraging a recently-proposed contextual neural
language model, BERT, to provide deeper text understanding for IR. Experimental
results demonstrate that the contextual text representations from BERT are more
effective than traditional word embeddings. Compared to bag-of-words retrieval
models, the contextual language model can better leverage language structures,
bringing large improvements on queries written in natural languages. Combining
the text understanding ability with search knowledge leads to an enhanced
pre-trained BERT model that can benefit related search tasks where training
data are limited.Comment: In proceedings of SIGIR 201
Interactive exploration of population scale pharmacoepidemiology datasets
Population-scale drug prescription data linked with adverse drug reaction
(ADR) data supports the fitting of models large enough to detect drug use and
ADR patterns that are not detectable using traditional methods on smaller
datasets. However, detecting ADR patterns in large datasets requires tools for
scalable data processing, machine learning for data analysis, and interactive
visualization. To our knowledge no existing pharmacoepidemiology tool supports
all three requirements. We have therefore created a tool for interactive
exploration of patterns in prescription datasets with millions of samples. We
use Spark to preprocess the data for machine learning and for analyses using
SQL queries. We have implemented models in Keras and the scikit-learn
framework. The model results are visualized and interpreted using live Python
coding in Jupyter. We apply our tool to explore a 384 million prescription data
set from the Norwegian Prescription Database combined with a 62 million
prescriptions for elders that were hospitalized. We preprocess the data in two
minutes, train models in seconds, and plot the results in milliseconds. Our
results show the power of combining computational power, short computation
times, and ease of use for analysis of population scale pharmacoepidemiology
datasets. The code is open source and available at:
https://github.com/uit-hdl/norpd_prescription_analyse
Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity
This paper presents a new approach for unsupervised Spoken Term Detection
with spoken queries using multiple sets of acoustic patterns automatically
discovered from the target corpus. The different pattern HMM
configurations(number of states per model, number of distinct models, number of
Gaussians per state)form a three-dimensional model granularity space. Different
sets of acoustic patterns automatically discovered on different points properly
distributed over this three-dimensional space are complementary to one another,
thus can jointly capture the characteristics of the spoken terms. By
representing the spoken content and spoken query as sequences of acoustic
patterns, a series of approaches for matching the pattern index sequences while
considering the signal variations are developed. In this way, not only the
on-line computation load can be reduced, but the signal distributions caused by
different speakers and acoustic conditions can be reasonably taken care of. The
results indicate that this approach significantly outperformed the unsupervised
feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT
corpus.Comment: Accepted by ICASSP 201
Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context
An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data
Deriving OCL Optimization Patterns from Benchmarks
Writing queries and navigation expressions in OCL is an important part of the task of developing a model transformation definition. When such queries are complex and the size of the models is significant, performance issues cannot be neglected.
In this paper we present five patterns intended to optimize the performance of model transformations when OCL queries are involved. For each pattern we will give an example as well as several implementation alternatives. Experimental data gathered by running benchmarks is also shown to compare the alternatives. The model transformation benchmark framework developed to obtain these results is also described
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to
adversarial examples---perturbed inputs specifically designed to produce
intentional errors in the learning algorithms at test time. Existing
input-agnostic adversarial perturbations exhibit interesting visual patterns
that are currently unexplained. In this paper, we introduce a structured
approach for generating Universal Adversarial Perturbations (UAPs) with
procedural noise functions. Our approach unveils the systemic vulnerability of
popular DCN models like Inception v3 and YOLO v3, with single noise patterns
able to fool a model on up to 90% of the dataset. Procedural noise allows us to
generate a distribution of UAPs with high universal evasion rates using only a
few parameters. Additionally, we propose Bayesian optimization to efficiently
learn procedural noise parameters to construct inexpensive untargeted black-box
attacks. We demonstrate that it can achieve an average of less than 10 queries
per successful attack, a 100-fold improvement on existing methods. We further
motivate the use of input-agnostic defences to increase the stability of models
to adversarial perturbations. The universality of our attacks suggests that DCN
models may be sensitive to aggregations of low-level class-agnostic features.
These findings give insight on the nature of some universal adversarial
perturbations and how they could be generated in other applications.Comment: 16 pages, 10 figures. In Proceedings of the 2019 ACM SIGSAC
Conference on Computer and Communications Security (CCS '19
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