Abstract Modeling emotions in negotiations is an open challenge that attracted an increasing amount of attention from researchers. Bargainers look for achieving an agreement with the opposing parties and, at the same time, try to reach their own goals. This process consists of both bargaining and (game theory) problem solving. Game theory models seek to enlighten the rational negotiations between players

Abstract An essential component of any dialogue system is understanding the language which is known as spoken language understanding (SLU). Dialogue act classification (DAC), intent detection (ID) and slot filling (SF) are significant aspects of every dialogue system. In this paper, we propose a deep learning-based multi-task model that can perform DAC, ID and SF tasks together. We use a deep bi-directional

Abstract Various types of theoretical algorithms have been proposed for 6D pose estimation, e.g., the point pair method, template matching method, Hough forest method, and deep learning method. However, they are still far from the performance of our natural biological systems, which can undertake 6D pose estimation of multi-objects efficiently, especially with severe occlusion. With the inspiration

Abstract Recently, artificial neural networks (ANNs) have been applied to various robot-related research areas due to their powerful spatial feature abstraction and temporal information prediction abilities. Decision-making has also played a fundamental role in the research area of robotics. How to improve ANNs with the characteristics of decision-making is a challenging research issue. ANNs are connectionist

Abstract Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues

Abstract In the current paper, we have proposed a new multi-modal authorship verification approach for social media texts. Authorship verification is a task of verifying whether an unknown text is written by a suspect or not. Use of social media like Facebook and Twitter is increasing day by day because of digitization. People have grown accustomed to regularly post or tweet about their everyday life

Abstract Automatic image captioning, a highly challenging research problem, aims to understand and describe the contents of the complex scene in human understandable natural language. The majority of the recent solutions are based on holistic approaches where the scene is described as a whole, potentially losing the important semantic relationship of objects in the scene. We propose Dense-CaptionNet

Abstract Every 3 seconds, someone develops dementia worldwide. Brain-training exercises, preferably involving also physical activity, have shown their potential to monitor and improve the brain function of people affected by Alzheimer disease (AD) or mild cognitive impairment (MCI). This paper presents a cognitive robotic system designed to assist mild dementia patients during brain-training sessions

Abstract The deep learning–based computer-aided diagnosis (CADx) approaches of dementia often require a lot of manual intervention. Although deep learning has a good effect on feature extraction, the current deep learning methods usually need to set a large number of parameters manually, which is time consuming. Hierarchical extreme learning machine (H-ELM) needs only less manual intervention and can

Abstract This work explores the relationship between a person’s demographic/psychological traits (e.g., gender and personality) and self-identity images and captions. We use a dataset of images and captions provided by N ≈ 1350 individuals, and we automatically extract features from both the images and captions. We identify several visual and textual properties that show reliable relationships with

Abstract A group decision-making (GDM) process is a social cognition process, which is a sub-topic of cognitive computation. The normalization of attribute values plays an important role in multi-attribute decision-making (MADM) and GDM problems. However, this research finds that the existing normalization methods are not always reasonable for GDM problems. To solve the problem of attribute normalization

Abstract Dialogue act classification (DAC) gives a significant insight into understanding the communicative intention of the user. Numerous machine learning (ML) and deep learning (DL) approaches have been proposed over the years in these regards for task-oriented/independent conversations in the form of texts. However, the affect of emotional state in determining the dialogue acts (DAs) has not been

Abstract Cognitive frameworks best represent cognitive information and knowledge. Several classical probability theory (CPT)-based cognitive frameworks were proposed in the literature. Recently, CPT has failed in explaining certain cognitive processes while quantum theories were successful in explaining the same. In this work, we integrate two cognitive frameworks namely conceptual spaces and 3-way

Abstract Deep neural networks have achieved outstanding progress in many fields, such as computer vision, speech recognition and natural language processing. However, large deep neural networks often need huge storage space and long training time, making them difficult to apply to resource restricted devices. In this paper, we propose a method for compressing the structure of deep neural networks.

Abstract In order to discriminate different spikes in an extracellular recording, a multitude of successful spike sorting algorithms has been proposed up to now. However, new implantable neuroprosthetics containing a spike sorting block necessitate the use of a real-time and a preferably unsupervised method. The aim of this article is to propose a new unsupervised spike sorting algorithm which could

Abstract In recent years, non-iterative learning approaches for kernel have received quite an attention by researchers and kernel ridge regression (KRR) approach is one of them. Recently, KRR-based Auto-Encoder is developed for the one-class classification (OCC) task and named as AEKOC. OCC is generally used for outlier or novelty detection. The brain can detect outlier just by learning from only normal

Abstract Millions of people in the world suffer from vision impairment or vision loss. Traditionally, they rely on guide sticks or dogs to move around and avoid potential obstacles. However, both guide sticks and dogs are passive. They are unable to provide conceptual knowledge or semantic contents of an environment. To address this issue, this paper presents a vision-based cognitive system to support

Abstract Linguistic neutrosophic number (LNN) is a specific form of neutrosophic number whose elements are expressed by linguistic terms. Maclaurin symmetric mean (MSM) operator is one of the basic collection operators in the modern knowledge fusion theory. Its most important feature is to consider the interrelationships among multiple input arguments. Multiple attribute group decision-making (MAGDM)

Abstract Friedreich’s ataxia (FRDA) is an inherited neurodegenerative disorder with the prevalence of 2–4 in every 100,000 Caucasian population. Since 2010, the European Friedreich’s Ataxia Consortium for Translational Studies (EFACTS) has endeavored to define and characterize FRDA by recruiting over 940 FRDA patients to provide baseline data in 19 study sites distributed in 9 European countries. It

Abstract Hesitant fuzzy linguistic preference relations (HFLPRs) can be used to represent cognitive complex information in a situation in which people hesitate among several possible linguistic terms for the preference degrees of pairwise comparisons over alternatives. HFLPRs have attracted growing attention owing to their efficiency in dealing with increasingly cognitive complex decision-making problems

Abstract Human interactive behavior is accountable for most of the variance between the observed and predicted energy consumption of buildings, and is accordingly acknowledged as a major field of research into limiting building-related energy consumption. A thorough understanding of occupant behavior is critical to facilitate a more reliable prediction of energy consumption and identifying means by

Abstract In recent years, deep neural networks (DNNs) have achieved great successes in many areas, such as cognitive computation, pattern recognition, and computer vision. Although many hand-crafted deep networks have been proposed in the literature, designing a well-behaved neural network for a specific application requires high-level expertise yet. Hence, the automatic architecture design of DNNs

Abstract The process of dimensionality reduction is a crucial solution to deal with the dimensionality problem that may be faced when dealing with the majority of machine learning techniques. This paper proposes an enhanced hybrid metaheuristic approach using grey wolf optimizer (GWO) and whale optimization algorithm (WOA) to develop a wrapper-based feature selection method. The main objective of the

Abstract There is a growing interest in using generative adversarial networks (GANs) to produce image content that is indistinguishable from real images as judged by a typical person. A number of GAN variants for this purpose have been proposed; however, evaluating GAN performance is inherently difficult because current methods for measuring the quality of their output are not always consistent with

Abstract Multiple-criteria decision-making is common in our daily life. The probabilistic linguistic term set is an effective tool to represent both simple and cognitive complex linguistic expressions given by individuals and groups completely. In this paper, the PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) is enhanced by integrating with a conflict analysis to solve

Abstract Electrical capacitance tomography (ECT) is a potent image-based measurement technology for monitoring industrial processes, but low-quality images generally limit its application scope and measurement reliability. To increase the precision of reconstruction, in this study, a data-driven plug-and-play prior abstracted by a deep convolutional neural network (DCNN) and the sparseness prior of

Abstract Continuous top-k query over sliding window is a fundamental problem in the domain of streaming data management, which monitors the query window and retrieves k objects with the highest scores when the window slides. The key of supporting this query is maintaining a subset of objects in the window, and try to retrieve answers from them when the window slides. The state-of-the-art approach called

Abstract In social networks, rising stars are junior individuals who may be not so charming at first but turn out to be outstanding over time. Recently, rising star evaluation has become a popular research topic in the field of social analysis, which is helpful for decision support, cognitive computation, and other practical problems. In this paper, we study the problem of rising star evaluation in

The early eye tracking studies of Yarbus provided descriptive evidence that an observer's task influences patterns of eye movements, leading to the tantalizing prospect that an observer's intentions could be inferred from their saccade behavior. We investigate the predictive value of task and eye movement properties by creating a computational cognitive model of saccade selection based on instructed

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson's disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the

From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit

This paper discusses the problems arising from the multidisciplinary nature of cognitive research and the need to conceptually unify insights from multiple fields into the phenomena that drive cognition. Specifically, the Fundamental Code Unit (FCU) is proposed as a means to better quantify the intelligent thought process at multiple levels of analysis. From the linguistic and behavioral output, FCU

In computational systems for visuo-haptic object recognition, vision and haptics are often modeled as separate processes. But this is far from what really happens in the human brain, where cross- as well as multimodal interactions take place between the two sensory modalities. Generally, three main principles can be identified as underlying the processing of the visual and haptic object-related stimuli

[This corrects the article DOI: 10.1007/s12559-017-9501-5.].

Multitasking is common in everyday life, but its effect on activities of daily living is not well understood. Critical appraisal of performance for both healthy individuals and patients is required. Motor activities during meal preparation were monitored in healthy individuals with a wearable sensor network during single and multitask conditions. Motor performance was quantified by the median frequencies

Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reaction. A chemical reaction is seen as a process of transforming the reactants (or molecules) through a

Continuous-time recurrent neural networks are widely used as models of neural dynamics and also have applications in machine learning. But their dynamics are not yet well understood, especially when they are driven by external stimuli. In this article, we study the response of stable and unstable networks to different harmonically oscillating stimuli by varying a parameter ρ, the ratio between the

Predictive coding has been proposed as a model of the hierarchical perceptual inference process performed in the cortex. However, results demonstrating that predictive coding is capable of performing the complex inference required to recognise objects in natural images have not previously been presented. This article proposes a hierarchical neural network based on predictive coding for performing visual

Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen tip to the writing surface. This is due to the fact that current

The plots of stories are known to follow general patterns in terms of their overall structure. This was the basic tenet of structuralist approaches to narratology. Vladimir Propp proposed a procedure for the generation of new tales based on his semi-formal description of the structure of Russian folk tales. This is one of the first existing instances of a creative process described procedurally. The

With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present

Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar research, this paper extracts three distinct sets of features from a newly constructed corpus of narratives

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural

In this paper we look at the phenomenon that is the Turing test. We consider how Turing originally introduced his imitation game and discuss what this means in a practical scenario. Due to its popular appeal we also look into different representations of the test as indicated by numerous reviewers. The main emphasis here, however, is to consider what it actually means for a machine to pass the Turing

This paper considers two emerging interdisciplinary, but related topics that are likely to create tipping points in advancing the engineering and science areas. Trusted Autonomy (TA) is a field of research that focuses on understanding and designing the interaction space between two entities each of which exhibits a level of autonomy. These entities can be humans, machines, or a mix of the two. Cognitive

The invention of fictional ideas (ideation) is often a central process in the creative production of artefacts such as poems, music and paintings, but has barely been studied in the computational creativity community. We present here a general approach to automated fictional ideation that works by manipulating facts specified in knowledge bases. More specifically, we specify a number of constructions

This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting primarily of the three fields of neural networks, evolutionary computing and fuzzy

In this paper, we present a semi-automatic system (Sherlock) for quiz generation using linked data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its generic framework for domain-independent quiz generation as well as in the ability of controlling the difficulty level of the generated quizzes. Difficulty scaling is non-trivial, and it is

Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between

The paper presents and discusses a system (xDriver) which uses an Intelligent System of Decision-making (ISD) for the task of car driving. The principal subject is the implementation, simulation and testing of the ISD system described earlier in our publications (Kowalczuk and Czubenko in artificial intelligence and soft computing lecture notes in computer science, lecture notes in artificial intelligence

A new model of the reticular formation of the brainstem is proposed. It refers to the neuronal and glial cell systems. Thus, it is biomimetically founded. The reticular formation generates modes of behavior (sleeping, eating, etc.) and commands all behavior according to the most appropriate environmental information. The reticular formation works on an abductive logic and is dominated by a redundancy

If robots are to successfully interact with humans, they need to measure, quantify and respond to the emotions we produce. Similar to humans, the perceptual cue inputs to any modelling that allows this will be based on behavioural expression and body activity features that are prototypical of each emotion. However, the likely employment of such robots in different cultures necessitates the tuning of

This article describes the prototyping of human-robot interactions in the University of Hertfordshire (UH) Robot House. Twelve participants took part in a long-term study in which they interacted with robots in the UH Robot House once a week for a period of 10 weeks. A prototyping method using the narrative framing technique allowed participants to engage with the robots in episodic interactions that

This paper looks at consensus algorithms for agent cooperation with unmanned aerial vehicles. The foundation is the consensus-based bundle algorithm, which is extended to allow multi-agent tasks requiring agents to cooperate in completing individual tasks. Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments. Using the behaviours observed in

In this paper, we revisit the work of John G Taylor on neural 'bubble' dynamics in two-dimensional neural field models. This builds on original work of Amari in a one-dimensional setting and makes use of the fact that mathematical treatments are much simpler when the firing rate function is chosen to be a Heaviside. In this case, the dynamics of an excited or active region, defining a 'bubble', reduce

Convolutive and under-determined blind audio source separation from noisy recordings is a challenging problem. Several computational strategies have been proposed to address this problem. This study is concerned with several modifications to the expectation-minimization-based algorithm, which iteratively estimates the mixing and source parameters. This strategy assumes that any entry in each source

Decision-making often requires taking into consideration immediate gains as well as delayed rewards. Studies of behavior have established that anticipated rewards are discounted according to a decreasing hyperbolic function. Although mathematical explanations for reward delay discounting have been offered, little has been proposed in terms of neural network mechanisms underlying discounting. There

Recently, consciousness research has gained much attention. Indeed, the question at stake is significant: why is the brain not just a computing device, but generates a perception from within? Ambitious endeavors trying to simulate the entire human brain assume that the algorithm will do the trick: as soon as we assemble the brain in a computer and increase the number of operations per time, consciousness

Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential