arXiv subjects

Hadi Veisi

Publications and source records attributed to Hadi Veisi.

24 records · Page 2Linked to original sources

arXiv2020

Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows

Unsteady fluid systems are nonlinear high-dimensional dynamical systems that may exhibit multiple complex phenomena both in time and space. Reduced Order Modeling (ROM) of fluid flows has been an active research topic in the recent decade with the primary goal to decompose complex flows to a set of features most important for future state prediction and control, typically using a dimensionality reduction technique. In this work, a novel data-driven technique based on the power of deep neural networks for reduced order modeling of the unsteady fluid flows is introduced. An autoencoder network is used for nonlinear dimension reduction and feature extraction as an alternative for singular value decomposition (SVD). Then, the extracted features are used as an input for long short-term memory network (LSTM) to predict the velocity field at future time instances. The proposed autoencoder-LSTM method is compared with non-intrusive reduced order models based on dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD). Moreover, an autoencoder-DMD algorithm is introduced for reduced order modeling, which uses the autoencoder network for dimensionality reduction rather than SVD rank truncation. Results show that the autoencoder-LSTM method is considerably capable of predicting fluid flow evolution, where higher values for coefficient of determination $R^{2}$ are obtained using autoencoder-LSTM compared to other models.

physics.flu-dyn↗

arXiv2020

Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network

Due to the increasing amount of data on the internet, finding a highly-informative, low-dimensional representation for text is one of the main challenges for efficient natural language processing tasks including text classification. This representation should capture the semantic information of the text while retaining their relevance level for document classification. This approach maps the documents with similar topics to a similar space in vector space representation. To obtain representation for large text, we propose the utilization of deep Siamese neural networks. To embed document relevance in topics in the distributed representation, we use a Siamese neural network to jointly learn document representations. Our Siamese network consists of two sub-network of multi-layer perceptron. We examine our representation for the text categorization task on BBC news dataset. The results show that the proposed representations outperform the conventional and state-of-the-art representations in the text classification task on this dataset.

cs.CL↗

arXiv2020

TeleCrowd: A Crowdsourcing Approach to Create Informal to Formal Text Corpora

Crowdsourcing has been widely used recently as an alternative to traditional annotations that is costly and usually done by experts. However, crowdsourcing tasks are not interesting by themselves, therefore, combining tasks with game will increase both participants motivation and engagement. In this paper, we have proposed a gamified crowdsourcing platform called TeleCrowd based on Telegram Messenger to use its social power as a base platform and facilitator for accomplishing crowdsourcing projects. Furthermore, to evaluate the performance of the proposed platform, we ran an experimental crowdsourcing project consisting of 500 informal Persian sentences in which participants were supposed to provide candidates that were the formal equivalent of sentences or qualify other candidates by upvoting or downvoting them. In this study, 2700 candidates and 21000 votes were submitted by the participants and a parallel dataset using candidates with the highest points, sum of their upvotes and downvotes, as the best candidates was built. As the evaluation, BLEU score of 0.54 was achieved on the collected dataset which shows that our proposed platform can be used to create large corpora. Also, this platform is highly efficient in terms of time period and cost price in comparison with other related works, because the whole duration of the project was 28 days at a cost of 40 dollars.

cs.SI↗

arXiv2019

Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications

Over the last few years, machine learning over graph structures has manifested a significant enhancement in text mining applications such as event detection, opinion mining, and news recommendation. One of the primary challenges in this regard is structuring a graph that encodes and encompasses the features of textual data for the effective machine learning algorithm. Besides, exploration and exploiting of semantic relations is regarded as a principal step in text mining applications. However, most of the traditional text mining methods perform somewhat poor in terms of employing such relations. In this paper, we propose a sentence-level graph-based text representation which includes stop words to consider semantic and term relations. Then, we employ a representation learning approach on the combined graphs of sentences to extract the latent and continuous features of the documents. Eventually, the learned features of the documents are fed into a deep neural network for the sentiment classification task. The experimental results demonstrate that the proposed method substantially outperforms the related sentiment analysis approaches based on several benchmark datasets. Furthermore, our method can be generalized on different datasets without any dependency on pre-trained word embeddings.

cs.CL↗

arXiv2019

Deep Learning Approach on Information Diffusion in Heterogeneous Networks

There are many real-world knowledge based networked systems with multi-type interacting entities that can be regarded as heterogeneous networks including human connections and biological evolutions. One of the main issues in such networks is to predict information diffusion such as shape, growth and size of social events and evolutions in the future. While there exist a variety of works on this topic mainly using a threshold-based approach, they suffer from the local viewpoint on the network and sensitivity to the threshold parameters. In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model. To this end, we propose a novel meta-path representation learning approach, Heterogeneous Deep Diffusion(HDD), to exploit meta-paths as main entities in networks. At first, the functional heterogeneous structures of the network are learned by a continuous latent representation through traversing meta-paths with the aim of global end-to-end viewpoint. Then, the well-known deep learning architectures are employed on our generated features to predict diffusion processes in the network. The proposed approach enables us to apply it on different information diffusion tasks such as topic diffusion and cascade prediction. We demonstrate the proposed approach on benchmark network datasets through the well-known evaluation measures. The experimental results show that our approach outperforms the earlier state-of-the-art methods.

cs.SI↗

arXiv2017

Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering

Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang-big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory based scheme as compared to its similar heuristic techniques. Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets. The experimental results show the significant superiority of the proposed method over the similar algorithms.

cs.AI↗