We propose a conceptual framework that includes the antecedents and consequences of firms’ adopting and integrating robotics into their customer service operations. Drawing insights from literature on customer service, technology marketing, and computer science, our proposed framework elaborates on the concept of the degree of robotics adoption (DRA) as well as the antecedents (employee acceptance of robots and customer acceptance of robots) and multiple sequential consequences (service quality, customer long-term performance, and customer engagement) of DRA. We also discuss how the nature of the firm (Business to Consumer versus Business-to-Business, i.e. B2C vs. B2B), service characteristics (utilitarian vs. hedonic), and brand positioning (low equity vs. high equity) might moderate the relationship between DRA and service quality. Further, we provide actionable guidance for managers to adopt and integrate robotics into their customer service operations.
Fig. 1. Physically plausible spatially varying surface appearance estimated using the proposed SA-SVBRDF-net from a single photograph of planar spatially varying plastic (a,b), wood (c) and metal (d) captured unknown natural lighting, and revisualized under a novel lighting condition.We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface re ectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a su ciently large set of labeled training pairs consisting of photographs of SVBRDF samples and corresponding re ectance parameters, is a di cult and arduous process. To reduce the amount of required labeled training data, we propose to leverage the appearance information embedded in unlabeled images of spatially varying materials to self-augment the training process. Starting from an initial approximative network obtained from a small set of labeled training pairs, we estimate provisional model parameters for each unlabeled training exemplar. Given this provisional re ectance estimate, we then synthesize a novel temporary labeled training pair by rendering the exact corresponding image under a new lighting condition. After re ning the network using these additional training samples, we re-estimate the provisional model parameters for the unlabeled data and repeat the self-augmentation process until convergence. We demonstrate the e cacy of the proposed network structure on spatially varying wood, metals, and plastics, as well as thoroughly validate the e ectiveness of the self-augmentation training process.
Owing to the continuous contributions by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available, and they have been pushing Chinese MRC research forward. To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to fill the right candidate sentence into the passage that has several blanks. Moreover, to add more difficulties, we also made fake candidates that are similar to the correct ones, which requires the machine to judge their correctness in the context. The proposed dataset contains over 100K blanks (questions) within over 10K passages, which was originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on pre-trained models, and the results show that the state-of-the-art model still underperforms human performance by a large margin. We hope the release of the dataset could further accelerate the machine reading comprehension research. 1
H uman faces are used extensively in print advertisements. In prior literature, researchers have studied spokespersons in general, but few have studied faces explicitly. This paper aims to answer three questions that are important to both researchers and practitioners: (1) Do faces affect how a viewer reacts to an advertisement on the metrics that advertisers care about? (2) If faces do have an effect, is it large enough to warrant careful selection of faces when constructing print advertisements? (3) If faces do have an effect and the effect is large, what facial features elicit such differential reactions on these metrics, and are such reactions different across individuals and/or product categories? Relying on the eigenface method, a holistic approach widely used in the computer science field for face recognition, we conducted an empirical study to answer these three questions. The results show that different faces do have an effect on people's attitude toward the advertisement, attitude toward the brand, and purchase intention and that the effect is nontrivial. Multiple segments were identified and substantial differences were found among people's reactions to the faces in the ads across those segments. We also found that the effect of faces interacts with product categories and is mediated by various facial traits such as attractiveness, trustworthiness, and competence. Implications and directions for future research are discussed.
This paper presents a deep learning based method for estimating the spatially varying surface reflectance properties from a single image of a planar surface under unknown natural lighting trained using only photographs of exemplar materials without referencing any artist generated or densely measured spatially varying surface reflectance training data. Our method is based on an empirical study of Li et al.'s [LDPT17] self‐augmentation training strategy that shows that the main role of the initial approximative network is to provide guidance on the inherent ambiguities in single image appearance estimation. Furthermore, our study indicates that this initial network can be inexact (i.e., trained from other data sources) as long as it resolves the inherent ambiguities. We show that the single image estimation network trained without manually labeled data outperforms prior work in terms of accuracy as well as generality.
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