A Gentle Introduction and Survey on Computing with Words (CWW) Methodologies

Human beings have an inherent capability to use linguistic information (LI) seamlessly even though it is vague and imprecise. Computing with Words (CWW) was proposed to impart computing systems with this capability of human beings. The interest in the field of CWW is evident from a number of publications on various CWW methodologies. These methodologies use different ways to model the semantics of the LI. However, to the best of our knowledge, the literature on these methodologies is mostly scattered and does not give an interested researcher a comprehensive but gentle guide about the notion and utility of these methodologies. Hence, to introduce the foundations and state-of-the-art CWW methodologies, we provide a concise but a wide-ranging coverage of them in a simple and easy to understand manner. We feel that the simplicity with which we give a high-quality review and introduction to the CWW methodologies is very useful for investigators or especially those embarking on the use of CWW for the first time. We also provide future research directions to build upon for the interested and motivated researchers

The Revitalization of the Community-based Management of Acute Malnutrition Program in Haiti

Severe acute malnutrition (SAM) threatens the lives of millions of children globally. In developing countries, 15% of the population is undernourished; and half of the mortality for children younger than 5 years old is associated to undernutrition (UNICEF, 2008), the most vulnerable population to malnutrition. Overall, Haiti reports 19.2% of children are undernourished, 11.4% are underweight, and 10.3% are wasted (Lutter et al., 2011; DHS, 2005, CWW-proposal, 2007). The treatment for the management of SAM has evolved over the decades (Lancet, 2006). The Community Management of Acute Malnutrition (CMAM) is an evidence-based intervention with proven effectiveness for treating children with SAM (Collins, 2007). The CMAM intervention reduces infant mortality related to SAM (Lancet, 2006, Collins, 2007; WHO, 2001; UNICEF, 2009). The CMAM intervention was validated in 2007 through the United Nations agencies for the management of SAM. Nevertheless, it has had limited reach and poor public health impact in some of the developing countries (e.g.; Haiti) where it was implemented. Concern Worldwide is a non-profit humanitarian organization, which pioneered in the creation of the CMAM intervention. Concern introduced the CMAM interventions in Haiti in October 2007 as a pilot program. The program was implemented in close to 20 health institutions in the metropolitan Port-au-Prince. As is the case with any other public health program, there were many challenges to the CMAM intervention implementation in Haiti. Concern’s CMAM intervention was not sustainable after it retracted the technical support in 2012 (UNICEF-Haiti country report, 2014). The purpose of this paper is to first review the Concern Worldwide CMAM program implementation in five communes of Port-au-Prince. Then, a suggested plan is outlined for the revitalization of the intervention’s activities and long-term sustainability once revitalized

Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Nowadays in the social network based decision making processes, as the ones involved in e-commerce and e-democracy, multiple users with di erent backgrounds may take part and diverse alternatives might be involved. This diversity enriches the process but at the same time increases the uncertainty in the opinions. This uncertainty can be considered from two di erent perspectives: (i) the uncertainty in the meaning of the words given as preferences, that is motivated by the heterogeneity of the decision makers, (ii) the uncertainty inherent to any decision making process that may lead to an expert not being able to provide all their judgments. The main objective of this contribution is to address these two type of uncertainty. To do so the following approaches are proposed: Firstly, in order to capture, process and keep the uncertainty in the meaning of the linguistic assumption the Interval Type 2 Fuzzy Sets are introduced as a way to model the experts linguistic judgments. Secondly, a measure of the coherence of the information provided by each decision maker is proposed. Finally, a consistency based completion approach is introduced to deal with the uncertainty presented in the expert judgments. The proposed approach is tested in an e-democracy decision making scenario

Who Supports Labor? The Intersection of Race and Skill in Union Campaigns

In the past half century, there has been an unprecedented decline in labor union membership, organizing ability, political effectiveness and strike activity in the United States. As a result, the ability of labor unions to influence the debate on labor standards and social reforms has experienced a significant decline. Using a mixed method approach, this research explores differences in attitudes and orientations towards labor unions across racial groups in the United States as well as organizational strategies and capacities of a labor union in a right-to-work state. Although African Americans and Latinos have been discriminated against at the hands of organized labor, the quantitative component of this research indicates that minority groups hold more positive attitudes towards unions than whites. In light of this fact, organized labor has been slow to realize that its revitalization may be contingent upon the ability of unions to organize and recruit minority populations and very little emphasis has been placed on the effects of racial differences in attitudes and orientations toward union membership and union support. Although the quantitative component of this research indicates that minority groups hold more positive attitudes towards unions, the qualitative component of this research argues that the challenges of organizing in a cross-class, cross-race union extend beyond racial and ethnic difference to issues of skill. This research, therefore, attempts to illustrate how a labor union in a right-to-work state navigates the intersection of race and skill in union campaigns

FUSE (Fuzzy Similarity Measure) - A measure for determining fuzzy short text similarity using Interval Type-2 fuzzy sets

Measurement of the semantic and syntactic similarity of human utterances is essential in developing language that is understandable when machines engage in dialogue with users. However, human language is complex and the semantic meaning of an utterance is usually dependent on context at a given time and also based on learnt experience of the meaning of the perception based words that are used. Limited work in terms of the representation and coverage has been done on the development of fuzzy semantic similarity measures. This paper proposes a new measure known as FUSE (FUzzy Similarity mEasure) which determines similarity using expanded categories of perception based words that have been modelled using Interval Type-2 fuzzy sets. The paper describes the method of obtaining the human ratings of these words based on Mendel’s methodology and applies them within the FUSE algorithm. FUSE is then evaluated on three established datasets and is compared with two known semantic similarity algorithms. Results indicate FUSE provides higher correlations to human ratings

A Perceptual Computing Approach for Learning Interpretable Unsupervised Fuzzy Scoring Systems

Scoring the driver’s behavior through the analysis of his/ her road trip data is an active area of research. However, such systems suffer from a lack of explainability, integration of expert bias in the calculated score, and ignoring the semantic uncer- tainty of various variables contributing to the score. To overcome these limitations, we have proposed a novel perceptual computing based unsupervised scoring system. The prowess of the proposed system has been exemplified in a case study of driver’s scoring from telemetry data. Our proposed approach yields scores that showed a higher significant separability between drivers performing responsible or irresponsible (aggressive or drowsy) driving behaviours, than the formal method of computing these scores (a p value of 3.94 × 10¯⁴ and 3.42 × 10¯³, respectively, in a Kolmogorov-Smirnov test). Further, the proposed method displayed higher robustness in the bootstrap test (where 30% of original data was omitted at random) by providing scores that were 90% similar to the original ones for all results within a confidence interval of 95%

Low Medicaid Spending Growth Amid Rebounding State Revenues: Results From a 50-State Medicaid Budget Survey State Fiscal Years 2006 and 2007

Examines the implementation of the new Medicare prescription drug benefit and the rate of Medicaid spending growth and enrollment in 2006. Identifies possible state level changes in eligibility requirements, program expansion, and enrollment processes

A Linear General Type-2 Fuzzy Logic Based Computing With Words Approach for Realising an Ambient Intelligent Platform for Cooking Recipes Recommendation

This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users' comfort. It is, therefore, appropriate to employ the paradigm of “computing with words” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users' feedback; the qualitative analysis presents social scienc

Linear components of quadratic classifiers

This is pre-print of an article published in Advances in Data Analysis and Classification. The final authenticated version is available online at: https://doi.org/10.1007/s11634-018-0321-6We obtain a decomposition of any quadratic classifier in terms of products of hyperplanes. These hyperplanes can be viewed as relevant linear components of the quadratic rule (with respect to the underlying classification problem). As an application, we introduce the associated multidirectional classifier; a piecewise linear classification rule induced by the approximating products. Such a classifier is useful to determine linear combinations of the predictor variables with ability to discriminate. We also show that this classifier can be used as a tool to reduce the dimension of the data and helps identify the most important variables to classify new elements. Finally, we illustrate with a real data set the use of these linear components to construct oblique classification treesThis research was supported by the Spanish MCyT grant MTM2016-78751-

AMICO galaxy clusters in KiDS-DR3: galaxy population properties and their redshift dependence

A catalogue of galaxy clusters was obtained in an area of 414 sq deg up to a redshift $z\sim0.8$ from the Data Release 3 of the Kilo-Degree Survey (KiDS-DR3), using the Adaptive Matched Identifier of Clustered Objects (AMICO) algorithm. The catalogue and the calibration of the richness-mass relation were presented in two companion papers. Here we describe the selection of the cluster central galaxy and the classification of blue and red cluster members, and analyze the main cluster properties, such as the red/blue fraction, cluster mass, brightness and stellar mass of the central galaxy, and their dependence on redshift and cluster richness. We use the Illustris-TNG simulation, which represents the state-of-the-art cosmological simulation of galaxy formation, as a benchmark for the interpretation of the results. A good agreement with simulations is found at low redshifts ($z \le 0.4$), while at higher redshifts the simulations indicate a lower fraction of blue galaxies than what found in the KiDS-AMICO catalogue: we argue that this may be due to an underestimate of star-forming galaxies in the simulations. The selection of clusters with a larger magnitude difference between the two brightest central galaxies, which may indicate a more relaxed cluster dynamical status, improves the agreement between the observed and simulated cluster mass and stellar mass of the central galaxy. We also find that at a given cluster mass the stellar mass of blue central galaxies is lower than that of the red ones.Comment: 14 pages, 16 figures, accepted for publication on MNRA