Imtiaz Hussain Khan

Generation of Referring Expressions: Managing Structural Ambiguities

Existing algorithms for the Generation of Referring Expressions tend to generate distinguishing descriptions at the semantic level, disregarding the ways in which surface issues can affect their quality. This paper considers how these algorithms should deal with surface ambiguity, focussing on structural ambiguity. We propose that not all ambiguity is worth avoiding, and suggest some ways forward that attempt to avoid unwanted interpretations. We sketch the design of an algorithm motivated by our experimental findings.

The Clarity-Brevity Trade-off in Generating Referring Expressions

Existing algorithms for the Generation of Referring Expressions (GRE) aim at generating descriptions that allow a hearer to identify its intended referent uniquely; the length of the expression is also considered, usually as a secondary issue. We explore the possibility of making the trade-off between these two factors more explicit, via a general cost function which scores these two aspects separately. We sketch some more complex phenomena which might be amenable to this treatment.

A comparative study of EAG and PBIL on large-scale global optimization problems

The traveling salesman problem (TSP) is one of the most famous problems. Many applications and programming tools have been developed to handle TSP. However, it seems to be essential to provide easy programming tools according to state-of-theart algorithms. Therefore, we have collected and programmed new easy tools by the three object-oriented languages. In this paper, we present ADT (abstract data type) of developed tools at first; then we analyze their performance by experiments. We also design a hybrid genetic algorithm (HGA) by developed tools. Experimental results show that the proposed HGA is comparable with the recent state-of-the-art applications.

A Hearer-Oriented Evaluation of Referring Expression Generation

This paper discusses the evaluation of a Generation of Referring Expressions algorithm that takes structural ambiguity into account. We describe an ongoing study with human readers.

A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering

In this study, we investigate how the two hemispheres of the brain are involved spatiotemporally in a cognitive-based setup when people relate different colors with different concepts (for example, the color ‘blue’ associated with the word ‘dependable’ or ‘cheap’) objectively or subjectively. We developed an experimental setup using a 17-channel near-infrared spectroscopy (NIRS) device to measure the changes in brain hemoglobin concentration during a concept–color association task in a block design paradigm. The channel-wise activation data were recorded for 10 male students; after cleansing, the data were clustered using an indigenous clustering technique to identify channels having similar spatiotemporal activity. Data mining was imperative because of the big data generated by NIRS (ca. 0.1+ MB textual data captured per sec involving high volume and veracity), for which the traditional statistical techniques for data analysis could have failed to discover the patterns of interest. The results showed that it was possible to associate brain activities in the two hemispheres to study the association among linguistic concepts and colors, with most neural activity taking place in the right hemisphere of the brain characterized with intuition, subjectivity, etc. Thus, the study suggests novel application areas of neural activity analysis, such as color as marketing cue, response of obese versus lean to food intake, traditional versus neural data validation.

Introduction: Dealing with Big Data-Lessons from Cognitive Computing

Big Data analytics is an emerging area of fast-growing importance as it provides innovative ways to efficiently analyse large and complex data effectively. Many approaches have been developed to extract meaningful information and knowledge from large multidimensional data sets involving high veracity, volume, velocity, and value. Unlike traditional approaches, cognitive computing systems are not based on predetermined answers or actions to perform Big Data analytics; instead, they are trained using efficient, scalable, natural, and biologically inspired approaches, including computational intelligence and machine learning algorithms without compromising their sophistication and performance. Cognitive computing systems interact with, learn naturally from people and Big data, and thus help increase the productivity of what either humans or machines could do on their own. This Special Issue is aimed at promoting multidisciplinary research, particularly in the complementary areas of cognitive psychology, biology, and computing science for dealing with Big Data challenges across these domains. Accordingly, the articles selected in this Special Issue report a range of cognitively inspired computational approaches to deal with different aspects of Big Data analytics. Following a rigorous peer-review process, ranging from three-to-four rounds of revisions; five articles have been accepted for publication. The reviews of the final article co-authored by the guest editors were handled independently by another editorial board member. In the first article presented in this Special Issue, Wu, Pang, and Coghill propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems that could help, especially biologists, better understand natural biochemical systems. The proposed framework demonstrates how the identification of biochemical systems can be performed and evolved in an integrative manner by reusing, composing, and evolving biochemical modules qualitatively, and by mutating kinetic rates quantitatively. In the next article, Du et al. propose a novel computational method to detect specific biomarkers for different groups of cancer types. The proposed method identifies specific biomarkers for a given cancer group based on different factors, including the same survival rates, the same type, and grade. The proposed methodology is thoroughly evaluated using eight cancer types and a number of benchmark microarray gene expression data sets from public databases. In the next article, Lin et al. describe a cognitively inspired psycho-linguistically motivated approach to build a quiz generation system, which combines similarity information derived from linked data and Resource Description Framework (RDF) resources. The authors effectively exploit this similarity information to gauge the difficulty of quizzes, confirming that a higher similarity among concepts leads to a more difficult quiz formulation, and vice versa. The proposed methodology is evaluated & Ahsan Abdullah aabdullah1@kau.edu.sa

Application of Near-Infra-Red Spectroscopy for the Analysis of the Impact of Black Color on Neural Response

In a traditional survey [1] of 75 respondents, black was either the first color of choice or the second. This raises the question, is the choice of black color by chance or the choice of the respondents is cognitively influenced? To address this question, we used a multichannel NIRS (Near-Infra-Red Spectroscopy) device to analyze the neural cognitive activity of nine respondents. Using a block design paradigm, the participants were shown concept words (e.g. cheap, high quality, reliability etc.) followed by 10 colors (e.g. white, green, black etc.) with the order of black color strategically placed. Spatio-temporal clustering of the neural response identified channels having correlated neural activity. We found such correlated brain activity between left temporal lobe channels and inferior parietal lobe channels when black color was shown; thus indicating negative priming similar to Steel, et al.[2].

A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data

Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analytic calorie-based cognitive studies, especially using non-invasive near infrared spectroscopy (NIRS) data that predict obesity using predictive data mining. Obesity is linked with neurodegenerative diseases, diabetes, and cardiovascular diseases. Thus, understanding, predicting, preventing, and managing obesity have the potential to save the lives of millions. Behavioral studies suggest that overeating in obese individuals is triggered by exaggerated brain reward center (BRC) activity to high-calorie food stimuli (Shefer et al., Neurosci Biobehav Rev 37(10):2489–503, 2013). In this paper, details of a novel research methodology are presented for a 24-month longitudinal study using a 44-channel NIRS device with the subjects in a natural environment. The proposed methodology consists of using visual stimuli of low/high calorie food items under fasting and satiated conditions for three types of subjects. The experiments consist of block design, longitudinal plan, data smoothing, BRC activation mapping, stereotactic normalization, generating paired t-test maps under fasting and non-fasting conditions and subsequently using Naïve Bayes modeling to generate obesity prediction maps for the control subjects. The simulated results consist of generation of Bayesian prediction maps using layers of paired t-test cerebral activity maps for the four BRC functional regions considered for three types of subjects, i.e., obese, control, and control subjects fed high calorie diet. We have demonstrated how cerebral functional activity data in response to visual food stimuli can be used to predict obesity in the non-obese, thus offering a non-invasive preventive measure.

A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data (Forthcoming/Available Online)

Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analytic calorie-based cognitive studies, especially using non-invasive near infrared spectroscopy (NIRS) data that predict obesity using predictive data mining. Obesity is linked with neurodegenerative diseases, diabetes, and cardiovascular diseases. Thus, understanding, predicting, preventing, and managing obesity have the potential to save the lives of millions. Behavioral studies suggest that overeating in obese individuals is triggered by exaggerated brain reward center (BRC) activity to high-calorie food stimuli (Shefer et al., Neurosci Biobehav Rev 37(10):2489–503, 2013). In this paper, details of a novel research methodology are presented for a 24-month longitudinal study using a 44-channel NIRS device with the subjects in a natural environment. The proposed methodology consists of using visual stimuli of low/high calorie food items under fasting and satiated conditions for three types of subjects. The experiments consist of block design, longitudinal plan, data smoothing, BRC activation mapping, stereotactic normalization, generating paired t-test maps under fasting and non-fasting conditions and subsequently using Naïve Bayes modeling to generate obesity prediction maps for the control subjects. The simulated results consist of generation of Bayesian prediction maps using layers of paired t-test cerebral activity maps for the four BRC functional regions considered for three types of subjects, i.e., obese, control, and control subjects fed high calorie diet. We have demonstrated how cerebral functional activity data in response to visual food stimuli can be used to predict obesity in the non-obese, thus offering a non-invasive preventive measure.

A Novel Spatiotempo ral Longitudinal Methodology fo r P redicting Obesity Using Nea r Inf ra red Spect roscopy (NIRS) Ce reb ral Functional Activity Data

Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analytic calorie-based cognitive studies, especially using non-invasive near infrared spectroscopy (NIRS) data that predict obesity using predictive data mining. Obesity is linked with neurodegenerative diseases, diabetes, and cardiovascular diseases. Thus, understanding, predicting, preventing, and managing obesity have the potential to save the lives of millions. Behavioral studies suggest that overeating in obese individuals is triggered by exaggerated brain reward center (BRC) activity to high-calorie food stimuli (Shefer et al., Neurosci Biobehav Rev 37(10):2489–503, 2013). In this paper, details of a novel research methodology are presented for a 24-month longitudinal study using a 44-channel NIRS device with the subjects in a natural environment. The proposed methodology consists of using visual stimuli of low/high calorie food items under fasting and satiated conditions for three types of subjects. The experiments consist of block design, longitudinal plan, data smoothing, BRC activation mapping, stereotactic normalization, generating paired t-test maps under fasting and non-fasting conditions and subsequently using Naïve Bayes modeling to generate obesity prediction maps for the control subjects. The simulated results consist of generation of Bayesian prediction maps using layers of paired t-test cerebral activity maps for the four BRC functional regions considered for three types of subjects, i.e., obese, control, and control subjects fed high calorie diet. We have demonstrated how cerebral functional activity data in response to visual food stimuli can be used to predict obesity in the non-obese, thus offering a non-invasive preventive measure.