48 research outputs found
Evolving under small disruption
We extend the edit operators of substitution, deletion, and insertion of a symbol over a word
by introducing two new operators (partial copy and partial elimination) inspired by biological
gene duplication. We de ne a disruption measure for an operator over a word and prove that
whereas the traditional edit operators are disruptive, partial copy and partial elimination are
non-disruptive. Moreover, we show that the application of only edit operators does not generate
(with low disruption) all the words over a binary alphabet, but this can indeed be done by
combining partial copy and partial elimination with the substitution operator.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
Low Disruption Transformations on Cyclic Automata
We extend the edit operators of substitution, deletion, and insertion of a symbol over a
word by introducing two new operators (partial copy and partial elimination) inspired by biological
gene duplication. We define a disruption measure for an operator over a word and prove that whereas
the traditional edit operators are disruptive, partial copy and partial elimination are non-disruptive.
Moreover, we show that the application of only edit operators does not generate (with low disruption)
all the words over a binary alphabet, but this can indeed be done by combining partial copy and
partial elimination with the substitution operator
Searching by approximate personal-name matching
We discuss the design, building and evaluation of a method to access theinformation of a person, using his name as a search key, even if it has deformations. We present a similarity function, the DEA function, based
on the probabilities of the edit operations accordingly to the involved
letters and their position, and using a variable threshold. The efficacy
of DEA is quantitatively evaluated, without human relevance judgments,
very superior to the efficacy of known methods. A very efficient
approximate search technique for the DEA function is also presented
based on a compacted trie-tree structure.Postprint (published version
Evolving Complexity and Similarity in an Artificial Life Framework based on Formal Language Theory
In this thesis, a formal framework where the evolution of biological complexity can be studied in an objective way is defined. That objectivity is due to state complexity for regular languages is used and it is a well-known and rigorous complexity measure. Such a framework is composed of a population of cyclic unary regular languages (individuals) that try to adapt to a given environment (that also consists of cyclic unary regular languages) by means of evolutionary computation
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE
A novel approach to handwritten character recognition
A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field.
First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition.
A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition.
In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules
Robust learning of acoustic representations from diverse speech data
Automatic speech recognition is increasingly applied to new domains. A key challenge is
to robustly learn, update and maintain representations to cope with transient acoustic
conditions. A typical example is broadcast media, for which speakers and environments
may change rapidly, and available supervision may be poor. The concern of this
thesis is to build and investigate methods for acoustic modelling that are robust to the
characteristics and transient conditions as embodied by such media.
The first contribution of the thesis is a technique to make use of inaccurate transcriptions as supervision for acoustic model training. There is an abundance of audio
with approximate labels, but training methods can be sensitive to label errors, and their
use is therefore not trivial. State-of-the-art semi-supervised training makes effective
use of a lattice of supervision, inherently encoding uncertainty in the labels to avoid
overfitting to poor supervision, but does not make use of the transcriptions. Existing
approaches that do aim to make use of the transcriptions typically employ an algorithm
to filter or combine the transcriptions with the recognition output from a seed model,
but the final result does not encode uncertainty. We propose a method to combine the
lattice output from a biased recognition pass with the transcripts, crucially preserving
uncertainty in the lattice where appropriate. This substantially reduces the word error
rate on a broadcast task.
The second contribution is a method to factorise representations for speakers and
environments so that they may be combined in novel combinations. In realistic scenarios,
the speaker or environment transform at test time might be unknown, or there may be
insufficient data to learn a joint transform. We show that in such cases, factorised, or
independent, representations are required to avoid deteriorating performance. Using
i-vectors, we factorise speaker or environment information using multi-condition training
with neural networks. Specifically, we extract bottleneck features from networks trained
to classify either speakers or environments. The resulting factorised representations
prove beneficial when one factor is missing at test time, or when all factors are seen,
but not in the desired combination.
The third contribution is an investigation of model adaptation in a longitudinal
setting. In this scenario, we repeatedly adapt a model to new data, with the constraint
that previous data becomes unavailable. We first demonstrate the effect of such a
constraint, and show that using a cyclical learning rate may help. We then observe
that these successive models lend themselves well to ensembling. Finally, we show
that the impact of this constraint in an active learning setting may be detrimental to
performance, and suggest to combine active learning with semi-supervised training to
avoid biasing the model.
The fourth contribution is a method to adapt low-level features in a parameter-efficient and interpretable manner. We propose to adapt the filters in a neural feature
extractor, known as SincNet. In contrast to traditional techniques that warp the
filterbank frequencies in standard feature extraction, adapting SincNet parameters is
more flexible and more readily optimised, whilst maintaining interpretability. On a task
adapting from adult to child speech, we show that this layer is well suited for adaptation
and is very effective with respect to the small number of adapted parameters