CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information

Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in such Open KBs are not canonicalized, leading to the storage of redundant and ambiguous facts. Recent research has posed canonicalization of Open KBs as clustering over manuallydefined feature spaces. Manual feature engineering is expensive and often sub-optimal. In order to overcome this challenge, we propose Canonicalization using Embeddings and Side Information (CESI) - a novel approach which performs canonicalization over learned embeddings of Open KBs. CESI extends recent advances in KB embedding by incorporating relevant NP and relation phrase side information in a principled manner. Through extensive experiments on multiple real-world datasets, we demonstrate CESI's effectiveness.Comment: Accepted at WWW 201

Measuring the service quality of services : tradonic servqual model

SERVQUAL and e-SERVQUAL have been considered the most effective and powerful approaches in evaluating the quality and gaps in the service delivered in traditional and electronic services, respectively, but neither SERVQUAL nor e-SERVQUAL can measure the overall service quality of the firm. Therefore, this chapter aims to propose and test a new scale that can measure the overall service quality of the firm. © 2018 by IGI Global. All rights reserved

Service research in Asia : research paradigm and productivity

This article traces the journey of service research by analyzing the articles contributed by researchers from Asia based on research productivity and research paradigm. The research analyzes top service journals from 2009 to 2016. The findings suggest high productivity of researchers from Taiwan. Survey research is widely used followed by mathematical modeling, lab experiments, interviews, conceptual modeling, theoretical modeling, and case study. Most researched topics observed such as customer satisfaction and relationship, service quality and performance, service marketing, service delivery, and service operations. The study provides valuable insights and highlights the contributions of Asian researchers to the field. © 2019, © 2019 Taylor & Francis Group, LLC

EFFECT OF PHYSICAL ACTIVITY ON SEVERAL LIPIDS, AMINO ACIDS, AND PEPTIDE-DERIVED HORMONES IN HEALTHY INDIVIDUALS

Physical activity induces many changes in the human body by increasing energy metabolism and resting energy expenditure and hormones play a major role in these changes. Hormones are chemical messengers that stimulate biochemical reactions that trigger cell activity and functions. Hormones are secreted from the glands of the endocrine system and communication between the endocrine system and nervous system regulates both internal and external changes and maintains homeostasis. Hormones are classified into lipid, amino acid, and peptide-derived hormones and they play major roles in the human body. Lipid-derived hormones perform many important functions i.e., muscle growth, neuromuscular adaptation, protein metabolism, carbohydrate metabolism, gluconeogenesis, fat oxidation, salt and water homeostasis, etc. Amino acid-derived hormones also perform many important functions like vasoconstriction, thermoregulation, tissue differentiation, fight or flight response, maintaining circadian rhythm and sleep-wake cycle, etc. Peptide-derived hormones play a major role in body fluid homeostasis, regulating appetite, gluconeogenesis, glucose production, and lipid metabolism, maintaining circadian rhythm, maintaining energy balance, reducing weight gain, delaying gastric emptying, etc. Physical activity regulates hormone levels in the body to provide major benefits and enhance the health status of healthy individuals. This review will provide a brief description of all lipid, amino acid, and peptide-derived hormones that perform many important functions and how their functions are influenced by physical activity.  Article visualizations

Quality management practices in SMEs : a comparative study between India and Namibia

Purpose: Small- and medium-sized enterprises (SMEs) have now become an important part of economy for not only developed nations but also for emerging economies. Irrespective of the benefits that can be derived, SMEs in emerging economies still lack the will to implement quality management (QM) practices. Using a comparative study, the purpose of this paper is to understand the status of QM practices in SMEs of emerging economies. Design/methodology/approach: A survey-based approach was adopted to understand the established QM practices in the SMEs. A survey instrument was designed by reviewing the literature on QM initiatives in SMEs. A sample of 270 SMEs across Southern India and 189 SMEs in Namibia was selected through stratified random sampling technique. Findings: The overall response rate was 19.52 percent for India and 26.46 percent for Namibia, respectively. There were similarities and differences in responses from SMEs in both countries. Similarities are in terms of limited implementation of QM practices, and also less use of tools and techniques. Reasons for not implementing include unknown to the authors, and the high cost of training. Differences emerged in the type of market (Indian SMEs catering to one major customer), CSFs and business performance indicators. It was interesting to find that management commitment and involvement do not have a major influence as CSF for SMEs in both the countries. Originality/value: The research is the first attempt in bringing a comparative study about QM practices in SMEs from developing countries. The insights will help emerging economies to develop policies for education and training, and thus facilitate implementation of QM practices in SMEs. © 2019, Emerald Publishing Limited

Fair Algorithms for Hierarchical Agglomerative Clustering

Hierarchical Agglomerative Clustering (HAC) algorithms are extensively utilized in modern data science, and seek to partition the dataset into clusters while generating a hierarchical relationship between the data samples. HAC algorithms are employed in many applications, such as biology, natural language processing, and recommender systems. Thus, it is imperative to ensure that these algorithms are fair -- even if the dataset contains biases against certain protected groups, the cluster outputs generated should not discriminate against samples from any of these groups. However, recent work in clustering fairness has mostly focused on center-based clustering algorithms, such as k-median and k-means clustering. In this paper, we propose fair algorithms for performing HAC that enforce fairness constraints 1) irrespective of the distance linkage criteria used, 2) generalize to any natural measures of clustering fairness for HAC, 3) work for multiple protected groups, and 4) have competitive running times to vanilla HAC. Through extensive experiments on multiple real-world UCI datasets, we show that our proposed algorithm finds fairer clusterings compared to vanilla HAC as well as other state-of-the-art fair clustering approaches