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 F(T,Θ)F(T,\Theta) gravity and compatibility with a stable de Sitter solution

We study a new type of the modified teleparallel gravity of the form $F(T,\,\Theta)$ in which $T$, the torsion scalar, is coupled with $\Theta$, 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 $F(T,\Theta)$ 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 $F(T,\Theta)$ 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