5 research outputs found
An Approach for Link Prediction in Directed Complex Networks based on Asymmetric Similarity-Popularity
Complex networks are graphs representing real-life systems that exhibit
unique characteristics not found in purely regular or completely random graphs.
The study of such systems is vital but challenging due to the complexity of the
underlying processes. This task has nevertheless been made easier in recent
decades thanks to the availability of large amounts of networked data. Link
prediction in complex networks aims to estimate the likelihood that a link
between two nodes is missing from the network. Links can be missing due to
imperfections in data collection or simply because they are yet to appear.
Discovering new relationships between entities in networked data has attracted
researchers' attention in various domains such as sociology, computer science,
physics, and biology. Most existing research focuses on link prediction in
undirected complex networks. However, not all real-life systems can be
faithfully represented as undirected networks. This simplifying assumption is
often made when using link prediction algorithms but inevitably leads to loss
of information about relations among nodes and degradation in prediction
performance. This paper introduces a link prediction method designed explicitly
for directed networks. It is based on the similarity-popularity paradigm, which
has recently proven successful in undirected networks. The presented algorithms
handle the asymmetry in node relationships by modeling it as asymmetry in
similarity and popularity. Given the observed network topology, the algorithms
approximate the hidden similarities as shortest path distances using edge
weights that capture and factor out the links' asymmetry and nodes' popularity.
The proposed approach is evaluated on real-life networks, and the experimental
results demonstrate its effectiveness in predicting missing links across a
broad spectrum of networked data types and sizes
Image Captioning based on Feature Refinement and Reflective Decoding
Image captioning is the process of automatically generating a description of
an image in natural language. Image captioning is one of the significant
challenges in image understanding since it requires not only recognizing
salient objects in the image but also their attributes and the way they
interact. The system must then generate a syntactically and semantically
correct caption that describes the image content in natural language. With the
significant progress in deep learning models and their ability to effectively
encode large sets of images and generate correct sentences, several
neural-based captioning approaches have been proposed recently, each trying to
achieve better accuracy and caption quality. This paper introduces an
encoder-decoder-based image captioning system in which the encoder extracts
spatial features from the image using ResNet-101. This stage is followed by a
refining model, which uses an attention-on-attention mechanism to extract the
visual features of the target image objects, then determine their interactions.
The decoder consists of an attention-based recurrent module and a reflective
attention module, which collaboratively apply attention to the visual and
textual features to enhance the decoder's ability to model long-term sequential
dependencies. Extensive experiments performed on Flickr30K, show the
effectiveness of the proposed approach and the high quality of the generated
captions
A Sequence-to-Sequence Approach for Arabic Pronoun Resolution
This paper proposes a sequence-to-sequence learning approach for Arabic
pronoun resolution, which explores the effectiveness of using advanced natural
language processing (NLP) techniques, specifically Bi-LSTM and the BERT
pre-trained Language Model, in solving the pronoun resolution problem in
Arabic. The proposed approach is evaluated on the AnATAr dataset, and its
performance is compared to several baseline models, including traditional
machine learning models and handcrafted feature-based models. Our results
demonstrate that the proposed model outperforms the baseline models, which
include KNN, logistic regression, and SVM, across all metrics. In addition, we
explore the effectiveness of various modifications to the model, including
concatenating the anaphor text beside the paragraph text as input, adding a
mask to focus on candidate scores, and filtering candidates based on gender and
number agreement with the anaphor. Our results show that these modifications
significantly improve the model's performance, achieving up to 81% on MRR and
71% for F1 score while also demonstrating higher precision, recall, and
accuracy. These findings suggest that the proposed model is an effective
approach to Arabic pronoun resolution and highlights the potential benefits of
leveraging advanced NLP neural models
Interactive face generation from verbal description using conceptual fuzzy sets
Abstract — In this article, a human centered approach for interactive face generation is presented. The users of the system are given the possibility to interactively generate faces from verbal description. The system generates automatically an average face from the same race of the target face using anthropometric measurements of the face and the head and displays it to the user as an initial face. This face is then transformed to the desired face according to the verbal description given by the user. The user describes each facial feature using words, and then the feature changes according to the user’s description. Depending on the race of the user and the gender of the face drawn, the relation between the size of each facial feature and the set of the linguistic words used to describe it is obtained using Conceptual Fuzzy Sets(CFS), which are used to represent the meaning of the words. CFSs can represent context dependent meaning. The context here is the race of the user and the gender of the target face. We describe the encouraging results of the experiments and discuss future directions for our face generation system. Index Terms — anthropometric measurements of the face, face generation, context dependent meaning of words, conceptual fuzzy set