Hossein Amirkhani

• Amirkabir University of Technology

Background Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical...

With the considerable achievements of data-hungry deep learning methods in natural language processing tasks, a great amount of effort has been devoted to develop more diverse datasets for different languages. In this paper, we present FarsTail, a new dataset for natural language inference task in Persian language. It includes 10,367 samples which...

Several proposals have been put forward in recent years for improving out-of-distribution (OOD) performance through mitigating dataset biases. A popular workaround is to train a robust model by re-weighting training examples based on a secondary biased model. Here, the underlying assumption is that the biased model resorts to shortcut features. Hen...

Word suggestion in unsupervised sentence simplification aims to replace complex words of a given sentence with their simpler alternatives. This is mostly done without considering their context within the input sentence. In this paper, we propose a technique that brings context awareness to word suggestion by merging pre-trained BERT models with a s...

Many successful learning algorithms have been recently developed to represent graph-structured data. For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classification, graph classification, and link prediction. However, these methods are highly dependent on the quality of the input graph str...

Despite the impressive success of sequence to sequence models for generative question answering, they need a vast amount of question-answer pairs during training, which is hard and expensive to obtain, especially for low-resource languages. In this paper, we present a framework which exploits the semantic clusters among the question-answer pairs to...

Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method ba...

Recently, graph neural networks (GNN) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs’ research since 2004 when GNN papers were first published. The study aims to evaluate GNN research trends, both quantitatively and qualitatively. We provide the trend of research, distribu...

review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Automation methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonst...

Recently, graph neural networks (GNN) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs’ research since 2004, when GNN papers were first published. The study aims to evaluate GNN research trend, both quantitatively and qualitatively. We provide the trend of research, distribu...

Many successful learning algorithms have been recently developed to represent graph-structured data. For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classification, graph classification, and link prediction. However, these methods are highly dependent on the quality of the input graph str...

Nowadays, individuals spend significant time on online social networks and microblogging websites, consuming news and expressing their opinions and viewpoints on various topics. It is an excellent source of data for various data mining applications, such as sentiment analysis. Mining this type of data presents several challenges, including the post...

Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural Language Processing. The goal of this field is to develop systems for answering the questions regarding a given context. In this paper, we present a comprehensive survey on diverse aspects of MRC systems, including their approaches, structures, input/outputs, and res...

In this paper, we propose a novel framework for transferred deep learning-based anomaly detection in hyperspectral images. The proposed framework includes four main steps. Firstly, the image2̆019s spectral dimension is reduced by applying the principal component analysis (PCA) to decrease computational time. Secondly, a deep convolutional neural ne...

Existing techniques for mitigating dataset bias often leverage a biased model to identify biased instances. The role of these biased instances is then reduced during the training of the main model to enhance its robustness to out-of-distribution data. A common core assumption of these techniques is that the main model handles biased instances simil...

Recently, graph neural networks (GNN) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs’ research since 2004, when GNN papers were first published. The study aims to evaluate GNN research trend, both quantitatively and qualitatively. We provide the trend of research, distribu...

Machine Reading Comprehension (MRC) is an active field in natural language processing with many successful developed models in recent years. Despite their high in-distribution accuracy, these models suffer from two issues: high training cost and low out-of-distribution accuracy. Even though some approaches have been presented to tackle the generali...

Natural language inference (NLI) is known as one of the central tasks in natural language processing (NLP) which encapsulates many fundamental aspects of language understanding. With the considerable achievements of data-hungry deep learning methods in NLP tasks, a great amount of effort has been devoted to develop more diverse datasets for differe...

Machine Reading Comprehension (MRC) is an active field in natural language processing with many successful developed models in recent years. Despite their high in-distribution accuracy, these models suffer from two issues: high training cost and low out-of-distribution accuracy. Even though some approaches have been presented to tackle the generali...

One of the main challenges of the machine reading comprehension (MRC) models is their fragile out-of-domain generalization, which makes these models not properly applicable to real-world general-purpose question answering problems. In this paper, we leverage a zero-shot weighted ensemble method for improving the robustness of out-of-domain generali...

Powerful yet simple augmentation techniques have significantly helped modern deep learning-based text classifiers to become more robust in recent years. Although these augmentation methods have proven to be effective, they often utilize random or non-contextualized operations to generate new data. In this work, we modify a specific augmentation met...

In this paper, we propose a new method for detecting adversarial attacks on deep neural networks. Our algorithm is based on the intuition that attacking input images results in different displacement vectors for clean and adversarial classes. For example, if the input image is an adversarial example, the re-attacking process results in a displaceme...

Real-time messaging and opinion sharing in social media websites have made them valuable sources of different kinds of information. This source provides the opportunity for doing different kinds of analysis. Sentiment analysis as one of the most important of these analyses gains increasing interests. However, the research in this field is still fac...

In this paper, a novel approach is presented to identify the smart home residents. The different behavioral patterns of smart home’s inhabitants are exploited to distinguish the residents. The variation of a specific individual behavior in smart homes is a significant challenge. We introduce different features that are useful to handle this problem...

Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method ba...

Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method ba...

The health and safety of elderly and disabled patients who cannot live alone is an important issue. Timely detection of sudden events is necessary to protect these people, and anomaly detection in smart homes is an efficient approach to extracting such information. In the real world, there is a causal relationship between an occupant's behaviour an...

Machine reading comprehension is a challenging task and hot topic in natural language processing. Its goal is to develop systems to answer the questions regarding a given context. In this paper, we present a comprehensive survey on different aspects of machine reading comprehension systems, including their approaches, structures, input/outputs, and...

This article provides a bibliometric study of the sentiment analysis literature based on Web of Science (WoS) until the end of 2016 to evaluate current research trends, quantitatively and qualitatively. We concentrate on the analysis of scientific documents, distribution of subject categories, languages of documents and languages that have been mor...

Learning Bayesian network structures from data is known to be hard, mainly because the number of candidate graphs is super-exponential in the number of variables. Furthermore, using observational data alone, the true causal graph is not discernible from other graphs that model the same set of conditional independencies. In this paper, it is investi...

Some of the basic algorithms for learning the structure of Bayesian networks, such as the well-known K2 algorithm, require a prior ordering over the nodes as part of the input. It is well known that the accuracy of the K2 algorithm is highly sensitive to the initial ordering. In this paper, we introduce the aggregation of ordering information provi...

In many supervised learning problems, determining the true labels of training instances is expensive, laborious, and even practically impossible. As an alternative approach, it is much easier to collect multiple subjective (possibly noisy) labels from human labelers, especially with the crowdsourcing services such as Amazon’s Mechanical Turk. The c...

Although current blind image steganalysis systems utilize a wide variety of features and classifiers, a common shortcoming in all of them is that they almost have similar processes for all images and they do not take advantage of the content diversity of different images. In this paper, a new framework is proposed that enables us to employ the cont...