34 research outputs found
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension
Incorporating prior knowledge can improve existing pre-training models in
cloze-style machine reading and has become a new trend in recent studies.
Notably, most of the existing models have integrated external knowledge graphs
(KG) and transformer-based models, such as BERT into a unified data structure.
However, selecting the most relevant ambiguous entities in KG and extracting
the best subgraph remains a challenge. In this paper, we propose the
LUKE-Graph, a model that builds a heterogeneous graph based on the intuitive
relationships between entities in a document without using any external KG. We
then use a Relational Graph Attention (RGAT) network to fuse the graph's
reasoning information and the contextual representation encoded by the
pre-trained LUKE model. In this way, we can take advantage of LUKE, to derive
an entity-aware representation; and a graph model - to exploit relation-aware
representation. Moreover, we propose Gated-RGAT by augmenting RGAT with a
gating mechanism that regulates the question information for the graph
convolution operation. This is very similar to human reasoning processing
because they always choose the best entity candidate based on the question
information. Experimental results demonstrate that the LUKE-Graph achieves
state-of-the-art performance on the ReCoRD dataset with commonsense reasoning.Comment: submitted for neurocomputing journa
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model
Despite the significant progress made by transformer models in machine
reading comprehension tasks, they still fall short in handling complex
reasoning tasks due to the absence of explicit knowledge in the input sequence.
To address this limitation, many recent works have proposed injecting external
knowledge into the model. However, selecting relevant external knowledge,
ensuring its availability, and requiring additional processing steps remain
challenging. In this paper, we introduce a novel attention pattern that
integrates reasoning knowledge derived from a heterogeneous graph into the
transformer architecture without relying on external knowledge. The proposed
attention pattern comprises three key elements: global-local attention for word
tokens, graph attention for entity tokens that exhibit strong attention towards
tokens connected in the graph as opposed to those unconnected, and the
consideration of the type of relationship between each entity token and word
token. This results in optimized attention between the two if a relationship
exists. The pattern is coupled with special relative position labels, allowing
it to integrate with LUKE's entity-aware self-attention mechanism. The
experimental findings corroborate that our model outperforms both the
cutting-edge LUKE-Graph and the baseline LUKE model on the ReCoRD dataset that
focuses on commonsense reasoning.Comment: submitted for Knowledge-Based Systems Journa
Energy conditions in gravity and compatibility with a stable de Sitter solution
We study a new type of the modified teleparallel gravity of the form
in which , the torsion scalar, is coupled with , the
trace of the stress-energy tensor. In a perturbational approach, we study the
stability of the solutions and as a special case we find a condition for
stability of the de Sitter phase. Then we adopt a suitable form for
that realizes a stable de Sitter solution so that the stability
condition creates a specific constraint on the parametric space of the model.
Finally, the energy conditions in the framework of gravity is
investigated.Comment: 16 pages, no figure, revised version with new reference
A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences
Continuous Hand Gesture Recognition (CHGR) has been extensively studied by
researchers in the last few decades. Recently, one model has been presented to
deal with the challenge of the boundary detection of isolated gestures in a
continuous gesture video [17]. To enhance the model performance and also
replace the handcrafted feature extractor in the presented model in [17], we
propose a GCN model and combine it with the stacked Bi-LSTM and Attention
modules to push the temporal information in the video stream. Considering the
breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN
model to empower the 3D hand skeleton features. Finally, the class
probabilities of each isolated gesture are fed to the post-processing module,
borrowed from [17]. Furthermore, we replace the anatomical graph structure with
some non-anatomical graph structures. Due to the lack of a large dataset,
including both the continuous gesture sequences and the corresponding isolated
gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR),
RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show
the superiority of the proposed model in dealing with isolated gesture
boundaries detection in continuous gesture sequence
A Conditional Generative Chatbot using Transformer Model
A Chatbot serves as a communication tool between a human user and a machine
to achieve an appropriate answer based on the human input. In more recent
approaches, a combination of Natural Language Processing and sequential models
are used to build a generative Chatbot. The main challenge of these models is
their sequential nature, which leads to less accurate results. To tackle this
challenge, in this paper, a novel end-to-end architecture is proposed using
conditional Wasserstein Generative Adversarial Networks and a transformer model
for answer generation in Chatbots. While the generator of the proposed model
consists of a full transformer model to generate an answer, the discriminator
includes only the encoder part of a transformer model followed by a classifier.
To the best of our knowledge, this is the first time that a generative Chatbot
is proposed using the embedded transformer in both generator and discriminator
models. Relying on the parallel computing of the transformer model, the results
of the proposed model on the Cornell Movie-Dialog corpus and the Chit-Chat
datasets confirm the superiority of the proposed model compared to
state-of-the-art alternatives using different evaluation metrics
Recognition of patterns in multichannel recorded data using artificial neural networks and fuzzy rule based systems: application to daily life motor activities
A large number of people with a movement problem forms a relevant social and medical problem in all
countries. The rapidly growing number of elderly people. who inevitably experience increasing
limitations in their functioning as they grow older. is a cause of major international concern. Only in the
European Community. 10% of the population is suffering from more or less severe motor problems.
Awareness of disability costs and demographic developments have directed the poHcy of goverrunents to
quality of life problems. More than in the past, research devoted to diseases of the neuro·musculoskeletal
system is supported. This regards diagnosis. surgical and non-surgical treatment, rehabilitation
and prevention. In all of these areas biomechanics is essential for the assessment of the mechanical
functioning of healthy subjects and patients. Movement analysis is one of the most important parts of
biomechanlcal research.
Since the end of the 19th century there have been attempts to assess movement in an objective and
quantitative manner (Muybridge, 1887; Marey, 1894; Braune & Fischer, 1895). During the past 20
yearsJ regular technological developments like microelectronics and fast computational tools have made
this goal easier to achieve. Nowadays, in the field of Biomechanical Engineering more and more
sophisticated systems for movement analysis(MA) have been developed.
Significant results have been obtained, in several fields such as Rehabilitation, Ergonomics, Sport,
Biomechanics and orthopedics. However, in rehabilitation, MA has received limited clinical
acceptance, at least in Europe.
In 1989, the European Conununity approved a project on Computer Aided Movement Analysis in a
Rehabilitation Context (CAMARC). In general tenus, the purpose of the project was to render
procedures and instruments for MA useful for patients and clinical doctors through suitable refmements
of both instrumentation and software.
In other terms, the overall objective of the CAMARC project was the transfer of the ever-improving
bioengineering methodology and techniques for MA to the clinical environment.
An important cause of the gap between the labora tory and the clinic could be the fact that stance and
movement analysis procedures are generally aimed at the understanding of mechanisms at a rather basic
levelJ whereas many clinical questions require an overall assessment of motor behavior in terms of skills
instead of functions
Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine
In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey's Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets
Spoken Persian digits recognition using deep learning
Classification of isolated digits is a fundamental challenge for many speech classification systems. Previous works on spoken digits have been limited to the numbers 0 to 9. In this paper, we propose two deep learning-based models for spoken digit recognition in the range of 0 to 599. The first model is a Convolutional Neural Network (CNN) model that uses the Mel spectrogram obtained from the audio data. The second model uses the recent advances in deep sequential models, especially the Transformer model followed by a Long Short-Term Memory (LSTM) Network and a classifier. Moreover, we also collected a dataset, including audio data by a contribution of 145 people, covering the numerical range from 0 to 599. The experimental results on the collected dataset indicate a validation accuracy of 98.03%
Effects of vitamin B6 on premenstrual syndrome: A systematic review and meta-Analysis
Background and Objective: Premenstrual syndrome (PMS) refers to a range of physical and psychological symptoms which regularly occur during the luteal phase of a menstrual cycle and disappear short after menstruation starts. Considering the negative effects of PMS on women's daily life, various treatments have been developed to alleviate its symptoms. Vitamin B6 is one of the complementary therapies used to treat PMS. The present meta-Analysis aimed to investigate the effects of vitamin B6 on PMS. Methodology: Different databases including PubMed, ISI, Scopus, SID, Magiran, Science Direct, and Medlib were searched to identify studies addressing the effects of vitamin B6 on PMS. The relevant data obtained from these papers were analyzed by a random-effects model. Data were analyzed using R Ver. 3.2.3 Software and STATA. Results: There were significant reductions in the mean scores of PMS after treatment with vitamin B6 compare to control groups. Moreover, the mean PMS scores of the two groups were also significantly different after the treatment. The mean difference between the two groups was -1.19 [95% CI: -1.94,-0.44; P = 0.002]. Significant reductions were also observed in physical symptoms (P = 0.006) and psychological symptoms (P < 0.001) of PMS after the intervention. Conclusion: The results of our meta-Analysis confirmed vitamin B6 as a beneficial, inexpensive, and effective treatment for PMS symptoms. Therefore, the administration of this treatment option will enable midwives to achieve the important goal of reducing PMS symptoms