9,788 research outputs found
How to quantify bilingual experience? Findings from a Delphi consensus survey
While most investigations of bilingualism document participants’ language background, there is an absence of consensus on how to quantify bilingualism. The high number of different language background questionnaires used by researchers and practitioners jeopardises data comparability and cross-pollination between research and practice. Using the Delphi consensus survey method, we asked 132 panellists (researchers, speech and language therapists, teachers) from 29 countries to rate 124 statements on a 5-point agreement scale. Consensus was pre-defined as 75% agreement threshold. After two survey rounds, 79% of statements reached consensus. The need for common measures to quantify bilingualism was acknowledged by 96% of respondents. Agreement was reached to document: language exposure and use, language difficulties, proficiency (when it cannot be assessed directly), education and literacy, input quality, language mixing practices, and attitudes (towards languages and language mixing). We discuss the implications of these findings for the creation of a new tool to quantify bilingual experience
Unsupervised clustering of MDS data using federated learning
In this master thesis we developed a model for unsupervised clustering on a data set of biomedical data. This data has been collected by GenoMed4All consortium from patients affected by Myelodysplastic Syndrome (MDS), that is an haematological disease. The main focus is put on the genetic mutations collected that are used as features of the patients in order to cluster them. Clustering approaches have been used in several studies concerning haematological diseases such MDS. A neural network-based model was used to solve the task. The results of the clustering have been compared with labels from a "gold standard'' technique, i.e. hierarchical Dirichlet processes (HDP). Our model was designed to be also implemented in the context of federated learning (FL). This innovative technique is able to achieve machine learning objective without the necessity of collecting all the data in one single center, allowing strict privacy policies to be respected. Federated learning was used because of its properties, and because of the sensitivity of data. Several recent studies regarding clinical problems addressed with machine learning endorse the development of federated learning settings in such context, because its privacy preserving properties could represent a cornerstone for applying machine learning techniques to medical data. In this work will be then discussed the clustering performance of the model, and also its generative capabilities
Document Clustering as an approach to template extraction
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceA great part of customer support is done via the exchange of emails. As the number of emails exchanged daily is constantly increasing, companies need to find approaches to ensure its efficiency. One common strategy is the usage of template emails as an answer. These answers templates are usually found by a human agent through the repetitive usage of the same answer. In this work, we use a clustering approach to find these answer templates. Several clustering algorithms are researched in this work, with a focus on the k-means methodology, as well as other clustering components such as similarity measures and pre-processing steps. As we are dealing with text data, several text representation methods are also compared. Due to the peculiarity of the provided data, we are able to design methodologies to ensure the feasibility of this task and develop strategies to extract the answer templates from the clustering results
Growth trends and site productivity in boreal forests under management and environmental change: insights from long-term surveys and experiments in Sweden
Under a changing climate, current tree and stand growth information is indispensable to the carbon sink strength of boreal forests. Important questions regarding tree growth are to what extent have management and environmental change influenced it, and how it might respond in the future. In this thesis, results from five studies (Papers I-V) covering growth trends, site productivity, heterogeneity in managed forests and potentials for carbon storage in forests and harvested wood products via differing management strategies are presented. The studies were based on observations from national forest inventories and long-term experiments in Sweden. The annual height growth of Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) had increased, especially after the millennium shift, while the basal area growth remains stable during the last 40 years (Papers I-II). A positive response on height growth with increasing temperature was observed. The results generally imply a changing growing condition and stand composition. In Paper III, yield capacity of conifers was analysed and compared with existing functions. The results showed that there is a bias in site productivity estimates and the new functions give better prediction of the yield capacity in Sweden. In Paper IV, the variability in stand composition was modelled as indices of heterogeneity to calibrate the relationship between basal area and leaf area index in managed stands of Norway spruce and Scots pine. The results obtained show that the stand structural heterogeneity effects here are of such a magnitude that they cannot be neglected in the implementation of hybrid growth models, especially those based on light interception and light-use efficiency. In the long-term, the net climate benefits in Swedish forests may be maximized through active forest management with high harvest levels and efficient product utilization, compared to increasing carbon storage in standing forests through land set-asides for nature conservation (Paper V). In conclusion, this thesis offers support for the development of evidence-based policy recommendations for site-adapted and sustainable management of Swedish forests in a changing climate
Recommended from our members
Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
A History of Psychological Boredom: The Utility of Boredom in the Practice of Psychological Science
The 100-year plus history of psychologists attempting to establish boredom as a quantifiable construct provides insight into the problems associated with how psychology adopts its subject matter. By borrowing terms from the public and assuming they represent universal aspects of human nature, the discipline has spurred critical inquiry regarding the practices hidden assumptions and theory. In particular, boredom, with its associations with both existential and trivial concerns, exposes the limitations of the practice of scientific psychology and reflects the disciplines own conflicted identity. In order to facilitate an examination of these theoretical issues, this historical examination focuses on the failed attempts by 1970s personality psychology and 1990s positive psychology to domesticate the concept. With the inclusion of the publics boredom discourse during these decades, the cultural influence on these disciplines theorizing is excavated. These influences complicate attempts by psychologists to practice as a science and provide a reason to take pause amid repeated calls to unify the discipline
False textual information detection, a deep learning approach
Many approaches exist for analysing fact checking for fake news identification, which is the focus of this thesis. Current approaches still perform badly on a large scale due to a lack of authority, or insufficient evidence, or in certain cases reliance on a single piece of evidence.
To address the lack of evidence and the inability of models to generalise across domains, we propose a style-aware model for detecting false information and improving existing performance. We discovered that our model was effective at detecting false information when we evaluated its generalisation ability using news articles and Twitter corpora.
We then propose to improve fact checking performance by incorporating warrants. We developed a highly efficient prediction model based on the results and demonstrated that incorporating is beneficial for fact checking. Due to a lack of external warrant data, we develop a novel model for generating warrants that aid in determining the credibility of a claim. The results indicate that when a pre-trained language model is combined with a multi-agent model, high-quality, diverse warrants are generated that contribute to task performance improvement.
To resolve a biased opinion and making rational judgments, we propose a model that can generate multiple perspectives on the claim. Experiments confirm that our Perspectives Generation model allows for the generation of diverse perspectives with a higher degree of quality and diversity than any other baseline model.
Additionally, we propose to improve the model's detection capability by generating an explainable alternative factual claim assisting the reader in identifying subtle issues that result in factual errors. The examination demonstrates that it does indeed increase the veracity of the claim.
Finally, current research has focused on stance detection and fact checking separately, we propose a unified model that integrates both tasks. Classification results demonstrate that our proposed model outperforms state-of-the-art methods
Automatic Irony Detection using Feature Fusion and Ensemble Classifier
With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods
Homeless at Home: The figure of the proto-queer woman in cinema of communist Poland (1970s â 1980s)
- …