18,292 research outputs found
Automatic correction of part-of-speech corpora
In this study a simple method for automatic correction of part-ofspeech corpora is presented, which works as follows: Initially two or more already available part-of-speech taggers are applied on the data.
Then a sample of differing outputs is taken to train a classifier to predict for each difference which of the taggers (if any) delivered the correct output.
As classifiers we employed instance-based learning, a C4.5 decision tree and a Bayesian classifier. Their performances ranged from 59.1 % to 67.3 %. Training on the automatically corrected data finally lead to significant improvements in tagger performance
Impairment in predictive processes during auditory mismatch negativity in ScZ: evidence from event-related fields
Patients with schizophrenia (ScZ) show pronounced dysfunctions in auditory perception but the underlying mechanisms as well as the localization of the deficit remain unclear. To examine these questions, the current study examined whether alterations in the neuromagnetic mismatch negativity (MMNm) in ScZ-patients could involve an impairment in sensory predictions in local sensory and higher auditory areas. Using a whole-head MEG-approach, we investigated the MMNm as well as P300m and N100m amplitudes during a hierarchical auditory novelty paradigm in 16 medicated ScZ-patients and 16 controls. In addition, responses to omitted sounds were investigated, allowing for a critical test of the predictive coding hypothesis. Source-localization was performed to identify the generators of the MMNm, omission responses as well as the P300m. Clinical symptoms were examined with the positive and negative syndrome scale. Event-related fields (ERFs) to standard sounds were intact in ScZ-patients. However, the ScZ-group showed a reduction in the amplitude of the MMNm during both local (within trials) and global (across trials) conditions as well as an absent P300m at the global level. Importantly, responses to sound omissions were reduced in ScZ-patients which overlapped both in latency and generators with the MMNm sources. Thus, our data suggest that auditory dysfunctions in ScZ involve impaired predictive processes that involve deficits in both automatic and conscious detection of auditory regularities
Self-adaptive exploration in evolutionary search
We address a primary question of computational as well as biological research
on evolution: How can an exploration strategy adapt in such a way as to exploit
the information gained about the problem at hand? We first introduce an
integrated formalism of evolutionary search which provides a unified view on
different specific approaches. On this basis we discuss the implications of
indirect modeling (via a ``genotype-phenotype mapping'') on the exploration
strategy. Notions such as modularity, pleiotropy and functional phenotypic
complex are discussed as implications. Then, rigorously reflecting the notion
of self-adaptability, we introduce a new definition that captures
self-adaptability of exploration: different genotypes that map to the same
phenotype may represent (also topologically) different exploration strategies;
self-adaptability requires a variation of exploration strategies along such a
``neutral space''. By this definition, the concept of neutrality becomes a
central concern of this paper. Finally, we present examples of these concepts:
For a specific grammar-type encoding, we observe a large variability of
exploration strategies for a fixed phenotype, and a self-adaptive drift towards
short representations with highly structured exploration strategy that matches
the ``problem's structure''.Comment: 24 pages, 5 figure
HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web
When users interact with the Web today, they leave sequential digital trails
on a massive scale. Examples of such human trails include Web navigation,
sequences of online restaurant reviews, or online music play lists.
Understanding the factors that drive the production of these trails can be
useful for e.g., improving underlying network structures, predicting user
clicks or enhancing recommendations. In this work, we present a general
approach called HypTrails for comparing a set of hypotheses about human trails
on the Web, where hypotheses represent beliefs about transitions between
states. Our approach utilizes Markov chain models with Bayesian inference. The
main idea is to incorporate hypotheses as informative Dirichlet priors and to
leverage the sensitivity of Bayes factors on the prior for comparing hypotheses
with each other. For eliciting Dirichlet priors from hypotheses, we present an
adaption of the so-called (trial) roulette method. We demonstrate the general
mechanics and applicability of HypTrails by performing experiments with (i)
synthetic trails for which we control the mechanisms that have produced them
and (ii) empirical trails stemming from different domains including website
navigation, business reviews and online music played. Our work expands the
repertoire of methods available for studying human trails on the Web.Comment: Published in the proceedings of WWW'1
An information assistant system for the prevention of tunnel vision in crisis management
In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions
An LLVM Instrumentation Plug-in for Score-P
Reducing application runtime, scaling parallel applications to higher numbers
of processes/threads, and porting applications to new hardware architectures
are tasks necessary in the software development process. Therefore, developers
have to investigate and understand application runtime behavior. Tools such as
monitoring infrastructures that capture performance relevant data during
application execution assist in this task. The measured data forms the basis
for identifying bottlenecks and optimizing the code. Monitoring infrastructures
need mechanisms to record application activities in order to conduct
measurements. Automatic instrumentation of the source code is the preferred
method in most application scenarios. We introduce a plug-in for the LLVM
infrastructure that enables automatic source code instrumentation at
compile-time. In contrast to available instrumentation mechanisms in
LLVM/Clang, our plug-in can selectively include/exclude individual application
functions. This enables developers to fine-tune the measurement to the required
level of detail while avoiding large runtime overheads due to excessive
instrumentation.Comment: 8 page
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