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Since the early 1980s, western governments are assumed to have been either moving toward post-bureaucratic models or transforming into so-called neo-Weberian bureaucracies. As different public-sector (reform) models imply different ideal typical personality traits for civil servants, the purpose of this paper is to ask the question to what extent personality requirements that governments demand from their employees have evolved over time in line with these models.

The authors analyzed the use of big-five traits in a sample of 21,003 job advertisements for local government jobs published between 1980 and 2017, applying tools for computer-assisted text analysis.

Using multilevel regression analyses, the authors conclude that, over time, there is a significant increase in the use of personality descriptors related to all big-five factors.

The authors postulate that governments nowadays are actively looking for the “renaissance bureaucrat” in line with the neo-Weberian bureaucracy paradigm. The authors end with a discussion of both positive and negative consequences of this development.

First, the authors explicitly link personality, public administration, and public management using the Abridged Big-Five-Dimensional Circumflex model of personality. Second, by linking observed trends in civil servant personality requirements to larger theories of public-sector reform models, the authors narrow the gap between public administration theories and practice. Third, the software tools that the authors use to digitalize and analyze a large number of documents (the job ads) are new to the discipline of public administration. The research can therefore serve as a guideline for scholars who want to use software tools to study large amounts of unstructured, qualitative data.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

The study aimed to clarify differences in fabric hand perceptions among Japanese and Chinese participants and implement online shopping strategies that enable consumers to easily recognize fabric texture.

Forty (20 Japanese and 20 Chinese) participants knowledgeable about clothing and fabric were recruited. Participants evaluated fabric by sight and touch in a visuotactile experiment (VTE). The stimulus material comprised 39 fabric samples representing a broad range of fabric attributes (7 fibers, 5 weaving/knitting techniques and 3 yarn thicknesses and density). A Mann–Whitney U test and a factor analysis were conducted to determine differences in responses for the different fabric variables.

The fabric hand perceptions factors were similar between both groups. Japanese participants showed a stronger preference for fabrics that felt wet. Japanese participants’ fabric hand perceptions had a 3-factor structure, while Chinese participants had a 2-factor structure. Chinese participants regarded “crisp” as perceptually and linguistically equivalent to “stretchy.”

The study’s findings suggest that Chinese people have stronger preferences in fabrics than Japanese people do. Japanese people evaluate fabric hand in a more nuanced manner than Chinese individuals, including discerning different fabric attributes, such as fiber and yarn thickness and density. Thus, nationality may influence fabric hand perceptions more than fabric knowledge does. Specifically, in evaluating “crispness,” the results required further analysis because differences in nationality may have affected evaluations regarding perception and linguistic perspectives. The findings provide design guidelines for implementing online shopping strategies adapted to each participant group.

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

This paper explores the implications of using Apple co-founder and former CEO Steve Jobs as a “paradigm case” of transformational leadership by comparing the practical metadiscourse of remembrances published at the time of his passing to the theoretical metadiscourse of transformational leadership. The authors report the frequency of transformational leadership characteristics that appeared in characterizations of Jobs in the months after his passing in October 2011. Results show that people do remember Jobs as a leader, and as one who possessed three key personal characteristics of a transformational leader: creative, passionate, and visionary. People also remembered Jobs as an innovator, which is not typically associated with transformational leadership but which does reflect the discourse of the consumer electronics industries upon which he had an impact. However, the results also show that two important interpersonal characteristics of a transformational leader were absent in the remembrance discourse: empowering and interactive. The authors discuss the implications of the two missing terms for pedagogy and theorizing, including how problematizing Jobs as a paradigm case might lead to fruitful discussions about the importance of a transformational leader’s engagement with followers.

This study aims to frame the authenticity–standardisation relationship in international gastronomy retailing and explores how and to what extent the food place of origin and the urban context in which the gastronomy stores are located shape customers' in-store experience.

This paper analyses the case of Eataly, which combines specialty grocery stores and restaurants disseminating the Italian eating style, quality food and regional traditions internationally. Facebook reviews (1,018) of four Eataly stores – New York City, Rome, Munich and Istanbul were analysed, adopting a web content mining approach.

Place of origin, quality and hosting city categories frame the gastronomic in-store experience. Standardisation elements (shared across the four analysed stores) and authenticity elements (specific to a single store) are identified towards defining three archetypical authenticity–standardisation relationships, namely originated authenticity, standardised authenticity and localised authenticity.

This study proposes original modelling that disentangles the authenticity–standardisation paradox in international gastronomy retailing. It provides evidence of the intertwining of the place of origin and the city brand in customers' in-store experience.

The paper aims to study Macau’s brand and destination personality. The data are derived from 91 semi-structured interviews with visitors to Macau at points of departure such as the airport and ferry terminals. Macau is perceived as hospitable and friendly, but complexities and ambiguities exist as the city is a fusion of Western and Asian cultures and locates gaming with a Portuguese heritage. Indeed one informant described the city as an “old person with a young spirit”. Given this, the final discussion is concerned with the concepts of anthropomorphic attributes associated with destination personality when applied to a destination, whereas managerial implications are identified with reference to a need to overcome some deficiencies in a perceived coldness in interactions with industry personnel.

This paper aims to understand what drives people – their motivations, autonomous learning attitudes and learning interests – to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.

A three-phase method was used to build volunteer mentor profiles; elicit topics of interest and establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitude toward lifelong learning and self-regulated learning strategies. The topics of interest were elicited through content analysis of answers to reflection questions. Statistical methods were applied to analyze the relationship between the interests and the mentor profiles.

Bottom-up clustering led to the identification of three mentor groups (G1 “low”; G2 “high” and G3 “medium”) based on pre-survey data. While content analysis led to identifying topics of interest: communication skills; learning AI; mentoring; prototype development; problem-solving skills; working with families. Analyzing relationships between mentor profile and the topics of interest, the group G3 “medium,” with strong intrinsic motivation, showed significantly more interest in working with families. The group with the overall highest scores (G2 “high”) evidenced also substantial interest in learning about AI, but with high variability between members of the group.

The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes toward lifelong learning. Such knowledge can help organizations shape the volunteering experience to provide more value to volunteers. Furthermore, the reflection questions can be used by volunteers as an instrument for reflection and by organizations to elicit the learning interests of volunteers.