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Purpose: Because past research has investigated nonverbal behaviors in clusters, it is unclear what status value is ascribed to individual nonverbal behaviors. I test status cues theory to investigate whether response latency functions as a status cue. I explore whether it affects behavioral influence or if it only signals assertiveness and does not have status value. I also explore how one's interpretation of response latency impacts behavioral influence.

Methodology: In a two-condition laboratory experiment, I isolate response latency and test its strength independently, and then I measure behavioral influence, participants' response latency, and perceptions of assertiveness. I also conduct interviews to investigate how participants interpret their partner's response latency to understand how people ascribe different meanings to the same nonverbal behavior.

Findings: I find that response latency alone does not affect behavioral influence, in part because how people interpret it varies. However, response latency does significantly impact participants' own response latency and their perceptions of their partner's assertiveness.

Practical Implications: This research demonstrates the intricacies of nonverbal behavior and status. More specifically, this work underscores important conceptual differences between assertiveness and status, and demonstrates how the interpretation of nonverbal behavior can impact behavioral influence.

Fog computing (FC) is a new field of research and has emerged as a complement to the cloud, which can mitigate the problems inherent to the cloud computing (CC) and internet of things (IoT) model such as unreliable latency, bandwidth constraints, security and mobility. Because there is no comprehensive study on the FC in health management processing systems techniques, this paper aims at surveying and analyzing the existing techniques systematically as well as offering some suggestions for upcoming works.

The paper complies with the methodological requirements of systematic literature reviews (SLR). The present paper investigates the newest systems and studies their practical techniques in detail. The applications of FC in health management systems have been categorized into three major groups, including review articles, data analysis, frameworks and models mechanisms.

The results have indicated that despite the popularity of FC as having real-time processing, low latency, dynamic configuration, scalability, low reaction time (less than a second), high bandwidth, battery life and network traffic, a few issues remain unanswered, such as security. The most recent research has focused on improvements in remote monitoring of the patients, such as less latency and rapid response. Also, the results have shown the application of qualitative methodology and case study in the use of FC in health management systems. While FC studies are growing in the clinical field, CC studies are decreasing.

This study aims to be comprehensive, but there are some limitations. This research has only surveyed the articles that are mined, according to a keyword exploration of FC health, FC health care, FC health big data and FC health management system. Fog-based applications in the health management system may not be published with determined keywords. Moreover, the publications written in non-English languages have been ignored. Some important research studies may be printed in a language other than English.

The results of this survey will be valuable for academicians, and these can provide visions into future research areas in this domain. This survey helps the hospitals and related industries to identify FC needs. Moreover, the disadvantages and advantages of the above systems have been studied, and their key issues have been emphasized to develop a more effective FC in health management processing mechanisms over IoT in the future.

Previous literature review studies in the field of SLR have used a simple literature review to find the tasks and challenges in the field. In this study, for the first time, the FC in health management processing systems is applied in a systematic review focused on the mediating role of the IoT and thereby provides a novel contribution. An SLR is conducted to find more specific answers to the proposed research questions. SLR helps to reduce implicit researcher bias. Through the adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, SLR effectively forces researchers to search for studies beyond their subject areas and networks.

This paper has proposed a Fog architecture-based framework, which classifies dengue patients into uninfected, infected and severely infected using a data set built in 2010. The aim of this proposed framework is to developed a latency-aware system for classifying users into different categories based on their respective symptoms using Internet of Things (IoT) sensors and audio and video files.

To achieve the aforesaid aim, a smart framework is proposed, which consist of three components, namely, IoT layer, Fog infrastructure and cloud computing. The latency of the system is reduced by using network devices located in the Fog infrastructure. Data generated by IoT layer will first be processed by Fog layer devices which are in closer proximity of the user. Raw data and data generated will later be stored on cloud infrastructure, from where it will be sent to different entities such as user, hospital, doctor and government healthcare agencies.

Experimental evaluation proved the hypothesis that using the Fog infrastructure can achieve better response time for latency sensitive applications with the least effect on accuracy of the system.

The proposed Fog-based architecture can be used with IoT to directly link it with the Fog layer.

This study aims to explore whether fabric contents, specifically cotton and polyester, possess particular personality dimensions, as posited by Aaker (1997). The author examined both explicit (conscious, deliberate) and implicit (unconscious, automatic) perceptions of brand personality traits of cotton and polyester.

The convenience sample of this study included 51 students from different merchandising classes in a Midwestern university. Participants were first directed to the multidimensional implicit association test (md-IAT) and their implicit perceptions were measured for the content of the two fabrics (cotton and polyester) on five different personality attribute dimensions (sincerity, excitement, competence, sophistication and ruggedness). After the IAT, participants completed the post-IAT survey, including explicit measures of brand personality attributes of cotton and polyester.

The findings of this study show that fabric contents can be successfully described and differentiated by Aaker’s brand personality dimensions. Compared with polyester, the distinctive brand personality of cotton was the favorable association between cotton and sincerity. This association was significantly higher than all other personality dimensions in both consumers’ implicit and explicit perceptions. Neither cotton nor polyester was significantly associated with the exciting, competent and sophisticated personality dimensions.

A unique contribution of this study is that it examines implicit perceptions of the brand personality traits of cotton and polyester. The use of the md-IAT in this study allowed the assessment of consumers’ automatic associations with cotton and polyester of which they may not be aware.

We survey and organize over fifty years of theoretical research on status and expectation state processes. After defining some key terms in this theoretical approach, we briefly describe theories and branches in the program.

We also focus on a few theories that illustrate distinct patterns of theory growth, using them to show the variety of ways in which the research program has grown.

The program structure developed from a single set of theories on development and maintenance of group inequality in the 1960s to six interrelated branches by 1988. Between 1988 and today, the overall structure has grown to total 19 different branches. We briefly describe each branch, identifying over 200 resources for the further study of these branches.

Although the various branches share key concepts and processes, they have been developed by different researchers, in a variety of settings from laboratories to schools to business organizations. Second, we outline some important issues for further research in some of the branches. Third, we emphasize the value of developing new research methods for testing and applying the theories.

These theories have been used to explain phenomena of gender, racial, and ethnic inequality among others, and for understanding some cases of personality attributions, deviance and control processes, and application of double standards in hiring.

Status and expectation state processes often operate to produce invidious social inequalities. Understanding these processes can enable social scientists to devise more effective interventions to reduce these inequalities.

Status and expectation state processes occupy a significant segment of research into group processes. This chapter provides an authoritative overview of ideas in the program, what is known, and what remains to be discovered.

To outline changes in selection attraction, search and assessment processes and examine and compare, using a surveillance perspective, the privacy and equity issues for applicants, organisations and testers.

The paper begins by reviewing briefly the role of surveillance and identifying the key issues of privacy, control and purpose of data collection and dissemination in an HR context. Through reviewing recent publications (1998‐2004) contrasts are made between earlier and new processes in applicant attraction, search and assessment. The impact of these potential changes on privacy and equity is then examined for three stakeholders: applicants, organisations, and testers.

Identifies the potential for misuse of data through these systems and the possibility of the perpetuation of discrimination for traditional marginalised groups. Raises the increased power brought about through such processes for organisations through data assemblage and for testers via increased control of their tools.

Calls for more critical work to examine the impact of new attraction, search and assessment practices on applicants to reveal whether the improved equity of access new technology affords is translated into increased equity of participation.

A very useful source of information about current processes and concerns they raise. Outlines future research agendas in these three areas.

This paper updates one's understanding of current practice and reveals genuine concerns about the potential applications of surveillance techniques within an HR context.

While high-stakes mobile assessment is increasing, researchers have done little to adapt traditional assessments to this new medium. The present study developed and tested a new response method for personality assessment using a mobile-first gamification design paradigm.

Participants used smartphones to “swipe” right or left to indicate agreement or disagreement with Goldberg's (1992) Big Five adjective indicators. These scores were correlated with responses to a Likert-type measure and participants provided reactions to both measures.

Each of the swipe-based measures was found to be a reliable and valid predictor of the corresponding dimensions measured using the Likert-type scale. Reactions to the swipe measure were mixed when compared to a traditional Likert-type measure. Response latencies of swipes were used as an indicator of self-schema beliefs. Transformed latency scores contributed incremental variance to the prediction of Likert responses beyond the dichotomous responses alone for some personality dimensions.

Convergent validity between the two measures was likely attenuated due to differences in scales, response methods, devices, connection speeds, and social desirability effects indicating that the present results may constitute a lower-bound estimate of convergent validity between the two measurement styles.

Designing assessments for mobile administration requires balancing trade-offs in speed, ease of use, and number of items relative to the reliability and validity of the measures.

Mobile-first designs such as swipe-based responses show potential to enhance future mobile assessment practices with further development.

One of the techniques for improving the performance of distributed systems is data replication, wherein new replicas are created to provide more accessibility, fault tolerance and lower access cost of the data. In this paper, the authors propose a community-based solution for the management of data replication, based on the graph model of communication latency between computing and storage nodes. Communities are the clusters of nodes that the communication latency between the nodes are minimum values. The purpose of this study if to, by using this method, minimize the latency and access cost of the data.

This paper used the Louvain algorithm for finding the best communities. In the proposed algorithm, by requesting a file according to the nodes of each community, the cost of accessing the file located out of the applicant’s community was calculated and the results were accumulated. On exceeding the accumulated costs from a specified threshold, a new replica of the file was created in the applicant’s community. Besides, the number of replicas of each file should be limited to prevent the system from creating useless and redundant data.

To evaluate the method, four metrics were introduced and measured, including communication latency, response time, data access cost and data redundancy. The results indicated acceptable improvement in all of them.

So far, this is the first research that aims at managing the replicas via community detection algorithms. It opens many opportunities for further studies in this area.

The purpose of this study is to verify that if applications categories are segmented and resources are allocated based on their specific category, how effective scheduling can be done?.

This paper proposes a scheduling framework for IoT application jobs, based upon the Quality of Service (QoS) parameters, which works at coarse grained level to select a fog environment and at fine grained level to select a fog node. Fog environment is chosen considering availability, physical distance, latency and throughput. At fine grained (node selection) level, a probability triad (C, M, G) is anticipated using Naïve Bayes algorithm which provides probability of newly submitted application job to fall in either of the categories Compute (C) intensive, Memory (M) intensive and GPU (G) intensive.

Experiment results showed that the proposed framework performed better than traditional cloud and fog computing paradigms.

The proposed framework combines types of applications and computation capabilities of Fog computing environment, which is not carried out to the best of knowledge of authors.