Attention mechanism plays an important role in the perception and cognition of human beings. Among others, many machine learning models have been developed to memorize the sequential data, such as the Long Short-Term Memory (LSTM) network and its extensions. However, due to lack of the attention mechanism, they cannot pay special attention to the important parts of the sequences. In this paper, we

Different forms of verbal aggression are often present in cyberbullying, which may impair executive function skills that enable the regulation of emotions and behavior. Emotion and behavioral regulation has been associated with better social adjustment and more positive interactions between peers. This study aimed to understand if fostering emotion and behavioral regulation strategies could decrease

Developing a robust deep neural network (DNN) for a specific task is not only time-consuming but also requires lots of experienced human experts. In order to make deep neural networks easier to apply or even take the human experts out of the design of network architecture completely, a growing number of researches focus on robust automated machine learning (AutoML). In this paper, we investigated the

In today’s digital era, the use of online social media networks, such as Google, YouTube, Facebook, and Twitter, permits people to generate a massive amount of textual content. The textual content that is produced by people reveals essential information regarding their personality, with psychopathy being among these distinct personality types. This work was aimed at classifying input texts according

The ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. First, a spatial transformer-generative adversarial network

In today’s competitive business world, customer service is often at the heart of businesses that can help strengthen their brands. Resolution of customers’ complaints in a timely and efficient manner is key to improving customer satisfaction. Moreover, customers’ complaints play an important role in identifying their requirements which offer a starting point for effective and efficient planning of

A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is

The explosive growth of multimedia data on the Internet has magnified the challenge of information retrieval. Multimedia data usually emerges in different modalities, such as image, text, video, and audio. Unsupervised cross-modal hashing techniques that support searching among multi-modal data have gained importance in large-scale retrieval tasks because of the advantage of low storage cost and high

Many people use social media in their daily life for entertainment, business, personal communication, and catching up with friends. In social media marketing, sentiment analysis is one of the most popular research topics because it can be employed to perform brand or market research monitoring and to keep an eye on the competitors. Machine learning algorithms have been utilized to carry out the task

Cross-Validation (CV) is still uncommon in time series modeling. Echo State Networks (ESNs), as a prime example of Reservoir Computing (RC) models, are known for their fast and precise one-shot learning, that often benefit from good hyper-parameter tuning. This makes them ideal to change the status quo. We discuss CV of time series for predicting a concrete time interval of interest, suggest several

In this paper, we address the problem of action recognition from still images and videos. Traditional local features such as SIFT and STIP invariably pose two potential problems: 1) they are not evenly distributed in different entities of a given category and 2) many of such features are not exclusive of the visual concept the entities represent. In order to generate a dictionary taking the aforementioned

Coronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection

Depression is one of the most common mental health issues. (It affects more than 4% of the world’s population, according to recent estimates.) This article shows that the joint analysis of linguistic and acoustic aspects of speech allows one to discriminate between depressed and nondepressed speakers with an accuracy above 80%. The approach used in the work is based on networks designed for sequence

Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters can represent a combination of meaning, structure, and pronunciation. However, existing embedding learning methods focus on the structure and meaning of Chinese characters

The coronavirus disease 2019 (COVID-19) has resulted in an ongoing pandemic worldwide. Countries have adopted non-pharmaceutical interventions (NPI) to slow down the spread. This study proposes an agent-based model that simulates the spread of COVID-19 among the inhabitants of a city. The agent-based model can be accommodated for any location by integrating parameters specific to the city. The simulation

Around the world, scientists are racing hard to understand how the COVID-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications are available. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only

The dramatic growth of the Web has motivated researchers to extract knowledge from enormous repositories and to exploit the knowledge in myriad applications. In this study, we focus on natural language processing (NLP) and, more concretely, the emerging field of affective computing to explore the automation of understanding human emotions from texts. This paper continues previous efforts to utilize

Benefit from multi-scale feature pyramid methods, recently single-stage object detectors have achieved promising accuracy and fast inference speed. However, the majority of existing feature pyramid detection techniques only simply describe complex contextual relationships from different scales. Not only are there no effective modules that adaptively extend appropriate semantic information from deeper

The complexity of the socioeconomic environment means that it is challenging to make decisions that rely on cognitive information. Decision makers normally cannot obtain a precise or sufficient level of knowledge about the problem domain and hence must provide multiple answers with interval values to depict them. This makes cognizing and decision making very difficult. To address this issue, this paper

Edge artificial intelligence or edge intelligence is an ever-growing research area due to the current popularization of the Internet of Things. Unfortunately, incorporation of artificial intelligence (AI) in smart devices operating at the edge is a challenging task due to the power-hungry characteristics of deep learning implementations, such as convolutional neural networks (CNNs). As a feasible alternative

The cognitive ability to learn with experience is a hallmark of intelligent systems. The emerging transfer optimization paradigm pursues such human-like problem-solving prowess by leveraging useful information from various source tasks to enhance optimization efficiency on a related target task. The occurrence of harmful negative transfer is a key concern in this setting, paving the way for recent

Reservoir computing is a growing paradigm for simplified training of recurrent neural networks, with a high potential for hardware implementations. Numerous experiments in optics and electronics yield comparable performance with digital state-of-the-art algorithms. Many of the most recent works in the field focus on large-scale photonic systems, with tens of thousands of physical nodes and arbitrary

Melanoma is one kind of dangerous cancer that has been increasing rapidly in the world. Initial diagnosis is essential to survival, but often the disease is diagnosed in the fatal stage. The rapid growth of skin cancers raises a huge demand for accurate automatic skin lesion segmentation. While deep learning techniques, i.e., convolutional neural network (CNN), have been widely used for precise segmentation

Financial investors make trades based on available information. Previous research has proved that microblogs are a useful source for supporting stock market decisions. However, the financial domain lacks specific sentiment lexicons that could be utilized to extract the sentiment from these microblogs. In this research, we investigate automatic approaches that can be used to build financial sentiment

Facial expression recognition has seen rapid development in recent years due to its wide range of applications such as human–computer interaction, health care, and social robots. Although significant progress has been made in this field, it is still challenging to recognize facial expressions with occlusions and large head-poses. To address these issues, this paper presents a cascade regression-based

Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular

The significant rise in suicides is a major cause of concern in public health domain. Depression plays a major role in increasing suicide ideation among the individuals. Although most of the suicides can be avoided with prompt intercession and early diagnosis, it has been a serious challenge to detect the at-risk individuals. Our current work focuses on learning three closely related tasks, viz. depression

Among radiological imaging data, Chest X-rays (CXRs) are of great use in observing COVID-19 manifestations. For mass screening, using CXRs, a computationally efficient AI-driven tool is the must to detect COVID-19-positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19-positive

Neural sequence to sequence models have achieved superlative performance in summarizing text. But they tend to generate generic summaries that under-represent the opinion-sensitive aspects of the document. Additionally, the sequence to sequence models are prone to test-train discrepancy (exposure-bias) arising from the differential summary decoding processes in the training and testing phases. The

In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware

Speech enhancement is a fundamental way to improve speech perception quality in adverse environment where the received speech is seriously corrupted by noise. In this paper, we propose a cognitive computing based speech enhancement model termed SETransformer which can improve the speech quality in unkown noisy environments. The proposed SETransformer takes advantages of LSTM and multi-head attention

Background Like common stocks, Bitcoin price fluctuations are non-stationary and highly noisy. Due to attractiveness of Bitcoin in terms of returns and risk, Bitcoin price prediction is attracting a growing attention from both investors and researchers. Indeed, with the development of machine learning and especially deep learning, forecasting Bitcoin is receiving a particular interest. Methods We implement

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of the elderly. In this work, we investigate the use of sensor data and deep learning

To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to have the highest binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used to make the drug for the novel coronavirus. Here, we implemented an architecture using

Computational intelligence, particularly deep learning, offers powerful tools for discriminating and generating samples such as images. Deep learning methods have been used in different artistic contexts for neural style transfer, artistic style recognition, and musical genre recognition. Using a constrained manifold analysis protocol, we discuss to what extent spaces induced by deep-learning convolutional

Cancer is a fatal disease caused due to the undesirable spread of cells. Breast carcinoma is the most invasive tumors and is the main reason for cancer deaths in females. Therefore, early diagnosis and prognosis have become necessary to increase survivability and reduce death rates in the long run. New artificial intelligence technologies are assisting radiologists in medical image scrutiny, thereby

Sentic computing relies on well-defined affective models of different complexity—polarity to distinguish positive and negative sentiment, for example, or more nuanced models to capture expressions of human emotions. When used to measure communication success, even the most granular affective model combined with sophisticated machine learning approaches may not fully capture an organisation’s strategic

In recent years, deep learning has achieved great successes in the field of image cognitive learning, and designing a well-behaved convolutional neural network (CNN)-based architecture has become a challenging and important problem. The traditional group convolution cannot effectively address the severe problem of “information blocking”; hence, this work proposes an efficient CNN-based model to achieve

The potential impact of social influence prediction has become a hot topic in the current graph data mining area. This paper proposes a deep learning framework named Multi-view Influence prediction network (MvInf) which combines multi-view learning and graph attention neural network together to address the problem of social influence prediction. MvInf takes different attribute features of users as

People often express opinions towards others in a social network, causing sentiment links to form among users. To develop effective methods for discovering implicit sentiment links among users, the extraction and exploitation of structural semantic information from heterogeneous social networks are of great importance. We propose a novel heterogeneous social network embedding-based approach for sentiment

Detection of mental disorders from textual input is an emerging field for applied machine and deep learning methods. Here, we explore the limits of automated detection of autism spectrum disorder (ASD) and schizophrenia (SCZ). We compared the performance of: (1) dedicated diagnostic tools that involve collecting textual data, (2) automated methods applied to the data gathered by these tools, and (3)

Hate speech is an important problem in the management of user-generated content. To remove offensive content or ban misbehaving users, content moderators need reliable hate speech detectors. Recently, deep neural networks based on the transformer architecture, such as the (multilingual) BERT model, have achieved superior performance in many natural language classification tasks, including hate speech

The emergence of digital social networks has transformed society, social groups, and institutions in terms of the communication and expression of their opinions. Determining how language variations allow the detection of communities, together with the relevance of specific vocabulary (proposed by the National Council of Accreditation of Colombia (Consejo Nacional de Acreditación - CNA) to determine

The overall rating of an opinion can generally be considered as the aggregation of the individual ratings of all features of that opinion. Nevertheless, there are cases in which the overall rating differs substantially from the mean or weighted mean of the ratings of the individual features. These cases can be explained in terms of user mood. To address this problem, this study introduces a fuzzy framework

The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making

Embedding the ability of sentiment analysis in smart devices is especially challenging because sentiment analysis relies on deep neural networks, in particular, convolutional neural networks. The paper presents a novel hardware-friendly detector of image polarity, enhanced with the ability of saliency detection. The approach stems from a hardware-oriented design process, which trades off prediction

Sarcasm detection has been a well-studied problem for the computational linguistic researchers. However, research related to different categories of sarcasm has still not gained much attention. Self-Deprecating Sarcasm (SDS) is a special category of sarcasm in which users apply sarcasm over themselves, and it is extensively used in social media platforms, mainly as an advertising tool for the brand

Investors are constantly aware of the behaviour of stock markets. This affects their emotions and motivates them to buy or sell shares. Financial sentiment analysis allows us to understand the effect of social media reactions and emotions on the stock market and vice versa. In this research, we analyse Twitter data and important worldwide financial indices to answer the following question: How does

Sarcasm is common in social media, and people use it to express their opinions with stronger emotions indirectly. Although it belongs to a branch of sentiment analysis, traditional sentiment analysis methods cannot identify the rhetoric of irony as it requires a significant amount of background knowledge. Existing sarcasm detection approaches mainly focus on analyzing the text content of sarcasm using

A sentiment lexicon is a list of expressions annotated according to affect categories such as positive, negative, anger and fear. Lexicons are widely used in sentiment classification of tweets, especially when labeled messages are scarce. Sentiment lexicons are prone to obsolescence due to: 1) the arrival of new sentiment-conveying expressions such as #trumpwall and #PrayForParis and 2) temporal changes

Humans can detect outliers just by using only observations of normal samples. Similarly, one-class classification (OCC) uses only normal samples to train a classification model which can be used for outlier detection. This paper proposes a multi-layer architecture for OCC by stacking various graph-embedded kernel ridge regression (KRR)-based autoencoders in a hierarchical fashion. We formulate the

COVID-19 is an ongoing pandemic disease. To make more accurate diagnosis on COVID-19 than existing approaches, this paper proposed a novel method combining DenseNet and optimization of transfer learning setting (OTLS) strategy. Preprocessing was used to enhance, crop, and resize the collected chest CT images. Data augmentation method was used to increase the size of training set. A composite learning

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification

Co-clustering simultaneously performs clustering on the sample and feature dimensions of the data matrix, so it can obtain better insight into the data than traditional clustering. Adjustment learning extracts valuable information from chunklets for unsupervised cluster learning in specific scenarios, but in fact it can be easily extended to semi-supervised and supervised learning situations. In this

Market style analysis is critical when designing a stock price prediction framework. Under different market styles, stocks may show quite different behaviors; thus, predictions will vary. Consequently, incorporating market styles into stock price predictions should help improve the prediction performance. In this paper, we investigate how to characterize market styles to improve stock prediction performance

In the past several decades, many papers involving the stability and Hopf bifurcation of delayed neural networks have been published. However, the results on the stability and Hopf bifurcation for fractional order neural networks with delays and fractional order neural networks with leakage delays are very rare. This paper is concerned with the stability and the existence of Hopf bifurcation of fractional

Big data industry decision is supremely important for companies to boost the efficiency of leadership, which can vastly accelerate industrialized. With regard to big data industry decision assessment, the intrinsic problem involves enormous inexactness, fuzziness and ambiguity. Pythagorean fuzzy sets (PFSs), managing the uncertainness depicted in non-membership with membership, are a quite practical

The fast growth of social robotics (SR) has not been unidirectional, but rather towards a multidisciplinary scenario, creating a need for collaboration between different fields. This divergent expansion calls for a clear analysis of the field aimed at better orienting the research, thus paving the future of social robotics. This paper aims at understanding how the SR research field evolved in the last

Intuitionistic fuzzy set (IFS) is one of the most robust and trustworthy tools for portraying the imprecise information with the help of the membership degrees. Similarity measure, one of the information measures, plays an important role in treating imperfect and ambiguous information to reach the final decision by determining the degree of similarity between the pairs of the numbers. Motivated by