14 research outputs found
Whois? Deep Author Name Disambiguation using Bibliographic Data
As the number of authors is increasing exponentially over years, the number
of authors sharing the same names is increasing proportionally. This makes it
challenging to assign newly published papers to their adequate authors.
Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in
digital libraries. This paper proposes an Author Name Disambiguation (AND)
approach that links author names to their real-world entities by leveraging
their co-authors and domain of research. To this end, we use a collection from
the DBLP repository that contains more than 5 million bibliographic records
authored by around 2.6 million co-authors. Our approach first groups authors
who share the same last names and same first name initials. The author within
each group is identified by capturing the relation with his/her co-authors and
area of research, which is represented by the titles of the validated
publications of the corresponding author. To this end, we train a neural
network model that learns from the representations of the co-authors and
titles. We validated the effectiveness of our approach by conducting extensive
experiments on a large dataset.Comment: Accepted for publication @ TPDL202
Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
Physical motion models offer interpretable predictions for the motion of
vehicles. However, some model parameters, such as those related to aero- and
hydrodynamics, are expensive to measure and are often only roughly approximated
reducing prediction accuracy. Recurrent neural networks achieve high prediction
accuracy at low cost, as they can use cheap measurements collected during
routine operation of the vehicle, but their results are hard to interpret. To
precisely predict vehicle states without expensive measurements of physical
parameters, we propose a hybrid approach combining deep learning and physical
motion models including a novel two-phase training procedure. We achieve
interpretability by restricting the output range of the deep neural network as
part of the hybrid model, which limits the uncertainty introduced by the neural
network to a known quantity. We have evaluated our approach for the use case of
ship and quadcopter motion. The results show that our hybrid model can improve
model interpretability with no decrease in accuracy compared to existing deep
learning approaches
An end-to-end approach for extracting and segmenting high-variance references from PDF documents
This paper addresses the problem of extracting and segmenting references from PDF documents. The novelty of the presented approach lies in its capability to discover highly varying references mainly in terms of content, length and location in the document. Unlike existing works, the proposed method does not follow the classical pipeline that consists of sequential phases. It rather learns the different characteristics of references to be used in a coherent scheme that reduces the error accumulation by following a probabilistic approach. Contrary to conventional references, mentioning the sources of information in some publications, such as those of social science, is not subject to the same specifications such as being located in a unique reference section. Therefore, the proposed method aims to extract references of highly varying reference characteristics by relaxing the restrictions of existing methods. Additionally, we present in this paper a new challenging dataset of annotated references in German social science publications. The main purpose of this work is to serve the indexation of missing references by extracting them from challenging publications such as those of German social science. The effectiveness of the presented methods in terms of both extraction and segmentation is evaluated on different datasets, including the German social science set
COIN: Counterfactual Image Generation for Visual Question Answering Interpretation
Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models’ behaviour
LaHAR: Latent Human Activity Recognition using LDA
Processing sequential multi-sensor data becomes important in many tasks due
to the dramatic increase in the availability of sensors that can acquire
sequential data over time. Human Activity Recognition (HAR) is one of the
fields which are actively benefiting from this availability. Unlike most of the
approaches addressing HAR by considering predefined activity classes, this
paper proposes a novel approach to discover the latent HAR patterns in
sequential data. To this end, we employed Latent Dirichlet Allocation (LDA),
which is initially a topic modelling approach used in text analysis. To make
the data suitable for LDA, we extract the so-called "sensory words" from the
sequential data. We carried out experiments on a challenging HAR dataset,
demonstrating that LDA is capable of uncovering underlying structures in
sequential data, which provide a human-understandable representation of the
data. The extrinsic evaluations reveal that LDA is capable of accurately
clustering HAR data sequences compared to the labelled activities
Fractal approach for determining the optimal number of topics in the field of topic modeling
In this paper we apply multifractal formalism to the analysis of statistical behaviour of topic models under condition of varying number of topics. Our analysis reveals the existence of two self-similar regions and one transition region in the function of density-of-states depending on the number of topics. As earlier a function that can be expressed through density-of-states was successfully used to determine the optimal number of topics, we test the applicability of the density-of-states function for the same purpose. We provide numerical results for three topic models (PLSA, ARTM, and LDA Gibbs sampling) on two marked-up collections containing texts in two different languages. Our experiments show that the "true" number of topics, as determined by the human mark-up, occurs in the transition region