Mood of the Planet is an interactive physical-digital sculpture that has as its center-piece a large “arch” or “doorway” that emits colored light and sound as a form of visualization and sonification of the changing, live emotions expressed by people all around the Earth. It is the product of several disciplines, including the arts, computer science, linguistics and psychology. In particular, we use

The Big-5/OCEAN personality traits model, one of the central approaches to psychometrics, has been shown to have many applications over a variety of disciplines. In particular, correlations have been studied leading to effective characterization of people’s behavior, and the model has become notorious for its role in the Cambridge Analytica/Facebook scandal surrounding the 2016 US presidential elections

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social and cognitive abilities. ASD has no cure so early diagnosis is important for reducing its impact. The current behavioral observation-based subjective-diagnosis systems (e.g., DSM-5 or ICD-10) frequently misdiagnose subjects. Therefore, researchers are attempting to develop automated diagnosis systems with minimal human

Deterministic lateral displacement (DLD) is evolving as an effective passive technique for seclusion of circulating tumor cells (CTCs) functioning based on nonuniform splitting of laminar flow moving through an array of micropillars. In this research work, an unconventional approach has been presented to alter the fluidic resistance between micropillars in asymmetric DLD array for better separation

Sentiment analysis (SA) is an important research area in cognitive computation—thus, in-depth studies of patterns of sentiment analysis are necessary. At present, rich-resource data-based SA has been well-developed, while the more challenging and practical multi-source unsupervised SA (i.e., a target-domain SA by transferring from multiple source domains) is seldom studied. The challenges behind this

Picture fuzzy set (PFS) is more effective tool for handling the uncertainty and vagueness in the real world and it can contain more information than intuitionistic fuzzy set (IFS). In this paper, we proposed a new entropy measure in terms of PFSs and some of its properties are discussed in detail. An example involving linguistic variables is established to show the validity of the proposed information

In a three-valued situation table, there are different levels of importance for different issues with respect to the alliance, conflict, and neutrality relations, and it is necessary to distinguish all issues as reducible and irreducible elements to filter out the key issues for solving conflicts. However, we have not observed studies of issue reduction for three-valued situation tables in three-way

According to the psychological literature, implicit motives allow for the characterization of behavior, subsequent success, and long-term development. Contrary to personality traits, implicit motives are often deemed to be rather stable personality characteristics. Normally, implicit motives are obtained by Operant Motives, unconscious intrinsic desires measured by the Operant Motive Test (OMT). The

Spoken language identification (LID) is the process of determining and classifying natural language from a given content and dataset. Data must be processed to extract useful features to perform LID. The mel-frequency cepstral coefficient (MFCC) is one of the most popular feature extraction techniques in LID. The MFCC features are generated to serve as inputs for the classification stage. In this study

Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral ground-glass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative

Semantic segmentation is a crucial issue in the field of computer vision, and it aims to assign each pixel in an image to a semantic object category. Modern cognitive research has presented that the biological system contains hidden features and explicit features, although they both contain useful information, the hidden features need further processing to make them explicit or clear. Inspired by this

Aspect-based sentiment analysis (ABSA) can help consumers provide clear and objective sentiment recommendations through massive quantities of data and is conducive to overcoming ambiguous human weaknesses in subjective judgments. However, the robustness and accuracy of existing sentiment analysis methods must still be improved. We first propose a deep-level semiself-help sentiment annotation system

Precise nodule segmentation in thyroid ultrasound images is important for clinical quantitative analysis and diagnosis. Fully supervised deep learning method can effectively extract representative features from nodules and background. Despite the great success, deep learning–based segmentation methods still face a critical hindrance: the difficulty in acquiring sufficient training data due to high

As an important extension of decision-theoretic rough sets, three-way decision theory provides a new perspective for people to deal with uncertain problems. However, the traditional multi-granularity decision-theoretic rough sets model has limited ability in describing the risk preferences of decision-makers and the processing of intuitionistic fuzzy information. In addition, as far as we know, most

The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning

As the basic form of data presentation, formal contexts play an elementary and important role in formal concept analysis and in 3-way concept analysis. In fact, many data tables are similar in form to formal contexts. Therefore, these data tables can be studied collectively in a similar framework, and such a study can be significant in knowledge discovery. We propose the notion of 3-valued formal contexts

Person search aims to locate and recognize a specified person from a gallery of uncropped scene images, which combines pedestrian detection and person re-identification (re-ID). Existing methods based on Faster R-CNN have been widely used to tackle the two sub-tasks jointly, but they ignore the feature misalignment problem, i.e., re-ID feature localization is not fully aligned with the detected bounding

Working memory is a fundamental function of cognition, allowing one to hold memory “in mind” and then to guide flexible behaviors. Although the maintenance mechanisms of working memory have attracted a lot of attention, the manipulation mechanisms are less studied. To elucidate the mechanisms of working memory manipulation, we hypothesize that the prefrontal cortex (PFC) maintains information of a

Loss functions, commonly believed to be the cost of cognitive computing, are a key element in decision-making, and three-way decisions can be regarded as a cognitive computing method that seeks to minimize the overall risks involved in the decision-making process. Recently, many studies on loss functions have been conducted based on fuzzy sets, intuitionistic fuzzy sets, and interval intuitionistic

Nonnegative matrix factorization is comprehensively used in recommendation systems. In an effort to reduce the recommended cost of newly added samples, incremental nonnegative matrix factorization and its variants have been extensively studied in recommendation systems. However, the recommendation performance is incapable of particular applications in terms of data sparsity and sample diversity. In

Hate Speech and harassment are widespread in online communication, due to users' freedom and anonymity and the lack of regulation provided by social media platforms. Hate speech is topically focused (misogyny, sexism, racism, xenophobia, homophobia, etc.), and each specific manifestation of hate speech targets different vulnerable groups based on characteristics such as gender (misogyny, sexism), ethnicity

State-of-the-art deep neural network plays an increasingly important role in artificial intelligence, while the huge number of parameters in networks brings high memory cost and computational complexity. To solve this problem, filter pruning is widely used for neural network compression and acceleration. However, existing algorithms focus mainly on pruning single model, and few results are available

The single-valued neutrosophic set (SVNS) performs well in retrieving indeterminate cognitive information for decision makers to solve problems from the perspective of multiple attribute decision making (MADM). Preference relation is used in decision-making as a fundamental tool for decision makers to express their preference. In this context, the SVNS-based preference relation is one of the most useful

The purpose of structure learning is to construct a qualitative relationship of Bayesian networks. Bayesian network with interpretability and logicality is widely applied in a lot of fields. With the extensive development of high-dimensional and low sample size data in some applications, structure learning of Bayesian networks for high dimension and low sample size data becomes a challenging problem

Social media has been remarkably grown during the past few years. Nowadays, posting messages on social media websites has become one of the most popular Internet activities. The vast amount of user-generated content has made social media the most extensive data source of public opinion. Sentiment analysis is one of the techniques used to analyze user-generated data. The Persian language has specific

In clinical research, adequate use of gathered information to provide an intelligent framework to assist the doctors is a great challenge for the current biomedical research community. This study proposed a probabilistic deep neural network (PDNN) to select wart treatment method, where the layered structure of artificial neurons plays a crucial role in generating the optimal feature space. However

Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet

To understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance

Rough set theory is a field of research pertaining to human-inspired computation. Attribute reduction is an important component of rough set theory and has been extensively studied. The reduction invariant is a key concept for an attribute reduction and finding new reduction invariants is an important task of attribute reduction. This paper explores the effect of different reduction invariants on the

Aiming to reveal the potential relationships between users, link prediction has been considered as a fundamental research issue in signed social networks. The key of the link prediction is to measure the similarity between users. Many existing researches use connections between users and their common neighbors to measure the similarities, and these methods rely too much on the structure of social networks

With advances in information acquisition technologies, multi-view data are increasing dramatically in a variety of real-world applications, whereas such data is usually corrupted by noises and outliers. Many existing multi-view graph clustering (MVGC) methods usually learn a consensus affinity graph using a late-fusion scheme in semantic space, which compound the challenge of leveraging the underlying

Background Li and Chen (Cognit Comput. 2018; 10:496–505) proposed the concept of the D-intuitionistic hesitant fuzzy set as well as proposed a method for comparing two D-intuitionistic fuzzy sets. Method Li and Chen have proposed the concept of the D-intuitionistic hesitant fuzzy set by introducing the degree of belief of the decision maker regarding the opinion of an expert in the existing definition

Fuzzy rough sets are an effective tool for dealing with uncertainty information. The theory of three-way decisions provides a method of decision-making, when a two-way decision may be difficult to make. In this paper, we investigate the combination of fuzzy rough sets and three-way decisions, and construct robust fuzzy rough set models from a three-way decision perspective. In fuzzy rough sets, by

Sentic computing is a multi-disciplinary approach to sentiment analysis at the crossroads between affective computing and commonsense computing, which exploits both computer and social sciences to better recognize, interpret, and process opinions and sentiments over the Web. In the last ten years, many different models (such as the Hourglass of Emotions and Sentic Patterns), resources (such as AffectiveSpace

The objective of clustering ensemble is to fuse multiple base partitions (BPs) to find the underlying data structure. It has been observed that sample can change its neighbors in different BPs and different samples have different relationship stability of sample. This difference shows that samples may have different contributions to the detection of underlying data structure. In addition, clustering

As an effective tool for analyzing human behavior and social cognition, rule extraction is a key issue in cognitive computing. However, the existing association rules state whether an attribute is possessed only without considering the order relation of attribute values. In this paper, we propose a novel method for quantitative association rule mining based on a generalized one-sided context and solid

Similarity measure (SM) proves to be a necessary tool in cognitive decision making processes. A single-valued neutrosophic set (SVNS) is just a particular instance of neutrosophic sets (NSs), which is capable of handling uncertainty and impreciseness/vagueness with a better degree of accuracy. The present article proposes two new weighted vector SMs for SVNSs, by taking the convex combination of vector

In this work, we analyze the gender bias induced by BERT in downstream tasks. We also propose solutions to reduce gender bias. Contextual language models (CLMs) have pushed the NLP benchmarks to a new height. It has become a new norm to utilize CLM-provided word embeddings in downstream tasks such as text classification. However, unless addressed, CLMs are prone to learn intrinsic gender bias in the

Recently, the advancement of cognitive computing and three-way decisions has enabled in-depth sequential pattern understanding through temporal association analysis. The main challenge is to obtain concise patterns that express richer semantics for multivariate time series (MTS) analysis. In this paper, we propose a tri-partition state alphabet-based sequential pattern (Tri-SASP) for MTSs. First, a

A wireless sensor network consists of a set of low-cost, small, and low-powered sensor nodes. Information about the position of a sensor node is essential for many applications such as topology control, clustering, geographical routing, object tracking, and environmental monitoring. This article introduces a novel robust range-free genetic-based algorithm (RRGA) for the task of localization that is

Image restoration is a technique that utilizes the edge of a corrupted area. The information content of the damaged information area is inferred based on the remaining information of these images, and then the damaged area is filled to achieve image restoration. To solve the problem of image occlusion in practical applications, a squeeze-excitation network-deep convolution generative adversarial network

Cognitive computing has deep extents, which embrace different features of cognition. In the decision-making process, multi-criteria decision making is credited as a cognitive-based human action. However, to treat and unite the information from several resources, the most vital stage is data collection. Intuitionistic fuzzy set (IFS) is one of the most robust and trustworthy tools to accomplish the

By selectively enhancing the features extracted from convolution networks, the attention mechanism has shown its effectiveness for low-level visual tasks, especially for image super-resolution (SR). However, due to the spatiotemporal continuity of video sequences, simply applying image attention to a video does not seem to obtain good SR results. At present, there is still a lack of suitable attention

Availability of very high computational power along with the development of deep neural network (DNN) technology has enabled rapid progress of machine translation technology. The powerful representation ability of the deep neural network also enables the neural machine translation technology (NMT) to exploit the available large-scale bilingual parallel corpus as well as the computing power to provide

In 2019, the outbreak of Acute Encephalitis Syndrome (AES) outbreak occurred in the Bihar region of India. AES, a viral infection that affects the immune system of the human, is recognized as public health concern globally. The objective of this study is to monitor and prevent the spread of Encephalitis (ENCPH). Spatio-temporal-based Temporal-Recurrent Neural Network (T-RNN) prediction model is used

The farfetched certain classification of uncertain data suffers serious risks. Three-Way Decision (3WD) theory is utilized to implement uncertain data classification methods. Three-way uncertain data classification methods facilitate reducing decision risk and involving human–machine coordination through finding out uncertain cases for abstaining identification. Due to the limitation of traditional

In the brain-machine interface (BMI) system, improving the motor recovery function of the joint and enabling the joint to adequately track the target trajectory are our main concerns. In this paper, to further improve the motor recovery effectiveness, two tasks are completed. First, a classical single-joint information transmission (SJIT) model is analyzed and improved by introducing several neurons

Unsupervised feature selection plays a dominant role in the process of high-dimensional and unlabeled data. Conventional spectral-based unsupervised feature selection methods always learn the subspace based on the predefined graph which constructed by the original features. Therefore, if the data is corrupted by the noise or redundancy existing in the high-dimensional, then the graph will be incorrect

Research on sentic computing has received intensive attention in recent years, as indicated by the increased availability of academic literature. However, despite the growth in literature and researchers’ interests, there are no reviews on this topic. This study comprehensively explores the current research progress and tendencies, particularly the thematic structure of sentic computing, to provide

As modern socioeconomic decision-making problems are becoming more and more complex, it also becomes more and more difficult to appropriately depict decision makers’ cognitive information in decision-making process. In addition, in group decision-making problems, decision makers’ cognition is usually diverse, which makes it more complicated to express the overall preference information. Recently, the

Sentiment analysis is a useful method to extract user preferences from product reviews; however, it cannot explain the detailed reasons for user preferences because of the exclusion of neutral sentiment words, constituting a large proportion of the words used in reviews. In contrast, there are limitations to using root cause analysis to analyze sentiment relations using sentiment words extracted from

Melanins and sebums are two important criteria for the quality evaluation of skin, and they are capable of providing customized suggestions for skin care. Currently, their detection is heavily relied on manual process performed by specialists in laboratory. Although efficient, such a manual detection is an expensive and labor-intensive procedure, and hence, there has been great interest in developing

COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of

Speech is one of the most natural communication channels for expressing human emotions. Therefore, speech emotion recognition (SER) has been an active area of research with an extensive range of applications that can be found in several domains, such as biomedical diagnostics in healthcare and human–machine interactions. Recent works in SER have been focused on end-to-end deep neural networks (DNNs)

Convolutional neural networks (CNNs) have shown favorable performance in recent tracking benchmark datasets. Some methods extract different levels of features based on pre-trained CNNs to deal with various challenging scenarios. Despite demonstrated successes for visual tracking, utilizing features from the same network might suffer from the suboptimal performance due to limitations of CNN architecture

The dangerously contagious virus named “COVID-19” has struck the world strong and has locked down billions of people in their homes to stop the further spread. All the researchers and scientists in various fields are continually developing a vaccine and prevention methods to aid the world from this challenging situation. However, a reliable prediction of the epidemic may help control this contiguous

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