Customer value co-creation behavior : scale development and validation

This investigation reports a series of four studies leading to the development and validation of a customer value co-creation behavior scale. The scale comprises two dimensions: customer participation behavior and customer citizenship behavior, with each dimension having four components. The elements of customer participation behavior include information seeking, information sharing, responsible behavior, and personal interaction, whereas the aspects of customer citizenship behavior are feedback, advocacy, helping, and tolerance. The scale is multidimensional and hierarchical, and it exhibits internal consistency reliability, construct validity, and nomological validity. This study also shows that customer participation behavior and customer citizenship behavior exhibit different patterns of antecedents and consequences

Hierarchical Feature Embedding for Attribute Recognition

Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc. Most of previous work only consider the attribute-level feature embedding, which might perform poorly in complicated heterogeneous conditions. To address this problem, we propose a hierarchical feature embedding (HFE) framework, which learns a fine-grained feature embedding by combining attribute and ID information. In HFE, we maintain the inter-class and intra-class feature embedding simultaneously. Not only samples with the same attribute but also samples with the same ID are gathered more closely, which could restrict the feature embedding of visually hard samples with regard to attributes and improve the robustness to variant conditions. We establish this hierarchical structure by utilizing HFE loss consisted of attribute-level and ID-level constraints. We also introduce an absolute boundary regularization and a dynamic loss weight as supplementary components to help build up the feature embedding. Experiments show that our method achieves the state-of-the-art results on two pedestrian attribute datasets and a facial attribute dataset.Comment: CVPR 202

Differentiating inbred mouse strains from each other and those with single gene mutations using hair proteomics.

Mutant laboratory mice with distinctive hair phenotypes are useful for identifying genes responsible for hair diseases. The work presented here demonstrates that shotgun proteomic profiling can distinguish hair shafts from different inbred mouse strains. For this purpose, analyzing the total hair shaft provided better discrimination than analyzing the isolated solubilized and particulate (cross-linked) fractions. Over 100 proteins exhibited significant differences among the 11 strains and 5 mutant stocks across the wide spectrum of strains surveyed. Effects on the profile of single gene mutations causing hair shaft defects were profound. Since the hair shaft provides a discrete sampling of the species proteome, with constituents serving important functions in epidermal appendages and throughout the body, this work provides a foundation for non-invasive diagnosis of genetic diseases of hair and perhaps other tissues

A Role-Based Taxonomy of Human Resource Organizations

[Excerpt] An empirically-derived classification (taxonomy) of human resource departments , based on a few fundamental roles played in organizations, was developed as an alternative to the mostly speculative existing typologies. Four types emerged: the strategic partner, the strategic advisor, the operational partner, and the operational administrator. The stability of the solution and the relationships with variables not used to generate it were found satisfactory. The types show some similarities with those identified in the literature

"Be yourself or rather be your Brand"! care of the self as a control tool in a cosmetics firm

Care of the self, a technique for governing the individual in society, proves to be equally a control technique for the individual in the firm. In a firm dedicated to the cult of beauty, there is a blurring of the lines between employee and consumer individual. This blurring makes care of the self a control tool whose rising power over individuals is all the greater because it is nurtured and maintained by the individuals themselves.brand; marketing; individual behavior; human resources management

Efficient Object Annotation via Speaking and Pointing

Deep neural networks deliver state-of-the-art visual recognition, but they rely on large datasets, which are time-consuming to annotate. These datasets are typically annotated in two stages: (1) determining the presence of object classes at the image level and (2) marking the spatial extent for all objects of these classes. In this work we use speech, together with mouse inputs, to speed up this process. We first improve stage one, by letting annotators indicate object class presence via speech. We then combine the two stages: annotators draw an object bounding box via the mouse and simultaneously provide its class label via speech. Using speech has distinct advantages over relying on mouse inputs alone. First, it is fast and allows for direct access to the class name, by simply saying it. Second, annotators can simultaneously speak and mark an object location. Finally, speech-based interfaces can be kept extremely simple, hence using them requires less mouse movement compared to existing approaches. Through extensive experiments on the COCO and ILSVRC datasets we show that our approach yields high-quality annotations at significant speed gains. Stage one takes 2.3x - 14.9x less annotation time than existing methods based on a hierarchical organization of the classes to be annotated. Moreover, when combining the two stages, we find that object class labels come for free: annotating them at the same time as bounding boxes has zero additional cost. On COCO, this makes the overall process 1.9x faster than the two-stage approach.Comment: this article is an extension of arXiv:1811.09461, which was published at CVPR 201

Hierarchical fuzzy logic based approach for object tracking

In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.info:eu-repo/semantics/publishedVersio

A statistical multiresolution approach for face recognition using structural hidden Markov models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy

Dense-Captioning Events in Videos

Most natural videos contain numerous events. For example, in a video of a "man playing a piano", the video might also contain "another man dancing" or "a crowd clapping". We introduce the task of dense-captioning events, which involves both detecting and describing events in a video. We propose a new model that is able to identify all events in a single pass of the video while simultaneously describing the detected events with natural language. Our model introduces a variant of an existing proposal module that is designed to capture both short as well as long events that span minutes. To capture the dependencies between the events in a video, our model introduces a new captioning module that uses contextual information from past and future events to jointly describe all events. We also introduce ActivityNet Captions, a large-scale benchmark for dense-captioning events. ActivityNet Captions contains 20k videos amounting to 849 video hours with 100k total descriptions, each with it's unique start and end time. Finally, we report performances of our model for dense-captioning events, video retrieval and localization.Comment: 16 pages, 16 figure

The Impact Of Technology Trust On The Acceptance Of Mobile Banking Technology Within Nigeria

With advancement in the use of information technology seen as a key factor in economic development, developed countries are increasingly reviewing traditional systems, in various sectors such as education, health, transport and finance, and identifying how they may be improved or replaced with automated systems. In this study, the authors examine the role of technology trust in the acceptance of mobile banking in Nigeria as the country attempts to transition into a cashless economy. For Nigeria, like many other countries, its economic growth is linked, at least in part, to its improvement in information technology infrastructure, as well as establishing secure, convenient and reliable payments systems. Utilising the Technology Acceptance Model, this study investigates causal relationships between technology trust and other factors influencing user’s intention to adopt technology; focusing on the impact of seven factors contributing to technology trust. Data from 1725 respondents was analysed using confirmatory factor analysis and the results showed that confidentiality, integrity, authentication, access control, best business practices and non-repudiation significantly influenced technology trust. Technology trust showed a direct significant influence on perceived ease of use and usefulness, a direct influence on intention to use as well as an indirect influence on intention to use through its impact on perceived usefulness and perceived ease of use. Furthermore, perceived ease of use and perceived usefulness showed significant influence on consumer’s intention to adopt the technology. With mobile banking being a key driver of Nigeria’s cashless economy goals, this study provides quantitative knowledge regarding technology trust and adoption behaviour in Nigeria as well as significant insight on areas where policy makers and mobile banking vendors can focus strategies engineered to improve trust in mobile banking and increase user adoption of their technology