A Referral Rewards Incentive Dedign On Travel Consumer- Generated Content

User-generated content has become increasingly important to both tourism practitioners and travel consumers. Although prior studies have demonstrated how impactful UGC is and why marketing mavens employ UGC sites in their marketing campaigns, there is still scant evidence on how to successfully manipulate them. To fill this void, we conducted a two-phase experiment study. In the experiment, first, 65 tourists were invited then grouped according to three different treatments (namely, creating travel posts to achieve the maximum ‘comments’, ‘retweets’, or ‘likes’), and one will be rewarded if he/she achieves the goal. Second, for the manipulation check, we invited another group of Chinese consumers (n =268) to rate these travel posts based on their perceptions. Our experiment results indicate that this referral rewards incentive design has significant effects on consumers’ UGC perception (the credibility, interestingness, influence of postings), behavioral intentions (purchase intention, and WOM intention), and their likelihood of social media engagement (offering ‘likes’). In addition, we also discuss the implications of the results and how to exploit this design

Which User-generated Content Should Be Appreciated More? - A Study on UGC Features, Consumers\u27 Behavioral Intentions and Social Media Engagement

Despite researchers have made a great deal of effort on exploring the reasons of travel consumers’ participation in UGC sites and the roles of these sites in different phases of their travel, knowledge on what factors influence travel consumers’ behavioral intentions in social media still remains largely unknown to both scholars and practitioners. With the attempts to find out this, we conducted a two-phase study on Chinese consumers. Utilizing the two sets of data we collected (npost = 65; nratings = 1668), we develop a multiple linear regression model to assess the influential factors in UGC sites on consumers’ behavioral intentions. Our results indicate that travel consumers’ purchase intention, word-of-mouth (WOM) intention, and attitudes towards destination brands are positively affected by the UGC features (creditability and interestingness) and consumers’ social media engagement (comment, retweet, and like).Further, inconsistent with the previous finding that credibility is a major concern in consumers’ information search processes, the interestingness of UGC is found to be more important

How Referral Rewards Systems Shape What Tourists Share on Social Media

Sharing on social media not only relies on our intrinsic motivations but also can be induced by the extrinsic motivations such as referral rewards. Although our previous study demonstrated that incentivizing tourists to create postings could influence peer consumers’ behavioral intentions (i.e., purchase and word-of-mouth intentions) and social media engagement, we noticed that it was the content which was created under the incentive design drove all the impacts. Therefore, in this study, we extracted the content characteristics from the tourists’ postings we collected. Results indicated that the referral rewards systems (RRSs) we introduced could shape what tourists share, and the content characteristics such as positive emotional, utilitarian, high-level and low-level construal have different effects on peer consumers’ social media engagement and behavioral intentions. Our findings aid researchers and practitioners in understanding how to design successful RRSs and how to create viral content on social media

Exploring the Influence of User-Generated Content Factors on the Behavioral Intentions of Travel Consumers

Social media have been deemed as more and more critical to modern travel consumers. These consumers often regard social media as trustworthy source that can lower the perceived risk and uncertainty throughout their travel. Though previous studies revealed that travel consumers’ participation in social media could be explained by their functional, social-psychological and hedonic needs, the factors that impact their behavioral intentions, such as purchase intention, WOM intention, and attitudes of destination brands have not been well studied. By conducting a two-phase study on Chinese travel consumers (nposts =65; nratings=1668), we found that both the UGC features (credibility and interestingness) and the social media engagement of travel consumers (comment, retweet, like) can impact their behavioral intentions. In addition, compared to the credibility of a post, the interestingness could more positively influence the social media engagement of travel consumers. Our study gives a better understanding of connections between social media and the travel consumers’ behavioral intentions

Effects of sintering temperature on the densification of WC-6Co cemented carbides sintered by coupled multi-physical-fields activated technology

Sample parts with WC-6Co cemented carbides were manufactured successfully with a novel method called coupled multi-physical-fields (electric field, temperature field and force field) activated sintering technology, using a Gleeble-1500D thermal simulation machine. Effects of sintering temperature on the densification, microstructures and hardness of samples were investigated. It was found that densification of the samples was enhanced with the increase of the sintering temperature and a relative density of as high as 98.76% achieved when a sintering temperature of 1200 °C was used. The particle size of the WC in sintered samples increased from 1.837 μm to 2.897 μm when the temperature was increased from 1000 °C to 1200 °C, resulting in the decrease of the hardness from HRC 63.5 to HRC 61.7. The presented work shows that, potentially, coupled multi-physical-fields activated technology is able to produce hard alloys to meet the engineering applications

Turning a CLIP Model into a Scene Text Detector

The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge. Scene text, which contains rich textual and visual information, has an inherent connection with a model like CLIP. Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection. In contrast to these works, this paper proposes a new method, termed TCM, focusing on Turning the CLIP Model directly for text detection without pretraining process. We demonstrate the advantages of the proposed TCM as follows: (1) The underlying principle of our framework can be applied to improve existing scene text detector. (2) It facilitates the few-shot training capability of existing methods, e.g., by using 10% of labeled data, we significantly improve the performance of the baseline method with an average of 22% in terms of the F-measure on 4 benchmarks. (3) By turning the CLIP model into existing scene text detection methods, we further achieve promising domain adaptation ability. The code will be publicly released at https://github.com/wenwenyu/TCM.Comment: CVPR202

Raptor Encoding for Low-Latency Concurrent Multi-PDU Session Transmission with Security Consideration in B5G Edge Network

In B5G edge networks, end-to-end low-latency and high-reliability transmissions between edge computing nodes and terminal devices are essential. This paper investigates the queue-aware coding scheduling transmission of randomly arriving data packets, taking into account potential eavesdroppers in edge networks. To address these concerns, we introduce SCLER, a Protocol Data Units (PDU) Raptor-encoded multi-path transmission method that overcomes the challenges of a larger attack surface in Concurrent Multipath Transfer (CMT), excessive delay due to asymmetric delay\&bandwidth, and lack of interaction among PDU session bearers. We propose a secure and reliable transmission scheme based on Raptor encoding and distribution that incorporates a queue length-aware encoding strategy. This strategy is modeled using Constrained Markov Decision Process (CMDP), and we solve the constraint optimization problem of optimal decision-making based on a threshold strategy. Numerical results indicate that SCLER effectively reduces data leakage risks while achieving the optimal balance between delay and reliability, thereby ensuring data security. Importantly, the proposed system is compatible with current mobile networks and demonstrates practical applicability

Looking and Listening: Audio Guided Text Recognition

Text recognition in the wild is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest vision and language processing are effective for scene text recognition. Yet, solving edit errors such as add, delete, or replace is still the main challenge for existing approaches. In fact, the content of the text and its audio are naturally corresponding to each other, i.e., a single character error may result in a clear different pronunciation. In this paper, we propose the AudioOCR, a simple yet effective probabilistic audio decoder for mel spectrogram sequence prediction to guide the scene text recognition, which only participates in the training phase and brings no extra cost during the inference stage. The underlying principle of AudioOCR can be easily applied to the existing approaches. Experiments using 7 previous scene text recognition methods on 12 existing regular, irregular, and occluded benchmarks demonstrate our proposed method can bring consistent improvement. More importantly, through our experimentation, we show that AudioOCR possesses a generalizability that extends to more challenging scenarios, including recognizing non-English text, out-of-vocabulary words, and text with various accents. Code will be available at https://github.com/wenwenyu/AudioOCR

Association of depressive symptoms with incident cardiovascular diseases in middle-aged and older Chinese adults

Importance: The prevalence of depressive symptoms among older adults has become an increasingly important public health priority. Elevated depressive symptoms are well documented among elderly people with cardiovascular disease (CVD), but studies conducted among Chinese adults are scarce. Objective: To estimate the association between depressive symptoms and incident CVD among middle-aged and older Chinese adults. Design, Setting, and Participants: The China Health and Retirement Longitudinal Study is an ongoing nationally representative prospective cohort study that was initiated in 2011. This cohort study included 12 417 middle-aged and older Chinese adults without heart disease and stroke at baseline. Statistical analysis was conducted from April 25, 2018, to December 13, 2018. Exposure: Depressive symptoms were assessed using the validated 10-item of Center for Epidemiologic Studies Depression Scale. Main Outcomes and Measures: Incident CVD (ie, self-reported physician-diagnosed heart disease and stroke combined) was followed-up from June 1, 2011, to June 31, 2015. The Center for Epidemiologic Studies Depression Scale total score ranges from 0 to 30, with a score of 12 or more indicating elevated depressive symptoms. Results: Of the 12 417 participants (mean [SD] age at baseline, 58.40 [9.51] years), 6113 (49.2%) were men. During the 4 years of follow-up, 1088 incident CVD cases were identified. Elevated depressive symptoms were independently associated with an increased CVD risk (adjusted hazard ratio, 1.39; 95% CI, 1.22-1.58) after adjusting for age, sex, residence, marital status, educational level, smoking status, drinking status, systolic blood pressure, and body mass index; history of diabetes, hypertension, dyslipidemia, and chronic kidney disease; and use of hypertension medications, diabetes medications, and lipid-lowering therapy. Of the 10 individual depressive symptoms measured by the Center for Epidemiologic Studies Depression Scale, only 2 symptoms, restless sleep (adjusted hazard ratio, 1.21; 95% CI, 1.06-1.39) and loneliness (adjusted hazard ratio, 1.21; 95% CI, 1.02-1.44), were significantly associated with incident CVD. Conclusions and Relevance: Elevated depressive symptoms overall and 2 individual symptoms (restless sleep and loneliness) were significantly associated with incident CVD among middle-aged and older Chinese adults