The Effects of Cognitive Appraisal and Emotion on Consumer Behavior: The Critical Role of Recollection in the Luxury Cruise Setting

Abstract de la ponencia[EN] The purposes of this study were: (1) to integrate the cognitive appraisal theory and script theory; (2) to examine the bonding character of recollection; and (3) to assess the relationships between consumers ‘appraisals, positive/negative emotions, recollection, storytelling and repurchase intention. A review of previous studies revealed 14 theoretical hypotheses. The proposed hypotheses were tested utilizing data collected from 300 luxury cruise passengers. Confirmatory factor analysis and structural equation modeling were utilized to test the proposed theoretical relationships. According to the results, this work was the first to integrate the cognitive appraisal approach and script theory and also depicted a new angle from which marketers can better understand cruise travelers’ behaviorJoo, E.; Shin, H.; Kim, I.; Choi, J.; Jang, J.; Hyun, S. (2016). The Effects of Cognitive Appraisal and Emotion on Consumer Behavior: The Critical Role of Recollection in the Luxury Cruise Setting. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 167-167. https://doi.org/10.4995/CARMA2016.2015.3135OCS16716

Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM

As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight. However, one of the major challenges is the difficulty of automated acquisition of continuous movement data during sports matches. In particular, it is a conundrum to reliably track a tiny ball on a wide soccer pitch with obstacles such as occlusion and imitations. Tackling the problem, this paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. We combine Set Transformers to get permutation-invariant and equivariant representations of the multi-agent contexts with a hierarchical architecture that intermediately predicts the player ball possession to support the final trajectory inference. Also, we introduce the reality loss term and postprocessing to secure the estimated trajectories to be physically realistic. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. Lastly, we suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics

Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations

Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performance, pointing out the problem of Transformer-based approaches causing temporal information loss. However, Linear-based approach has also limitations that the model is too simple to comprehensively exploit the characteristics of the dataset. To solve these limitations, we propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics. We show that LTSF-DNODE outperforms the baselines on various real-world datasets. In addition, for each dataset, we explore the impacts of regularization in the neural ordinary differential equation (NODE) framework.Comment: Accepted at IEEE BigData 202

Real-time data-driven and multi-scale model-guided system for bioproccess digital twin platform

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Transmission Power Control with the Guaranteed Communication Reliability in WSN

In a wireless sensor network, sensor nodes are deployed in an ad hoc fashion and they deliver data packets using multihop transmission. However, transmission failures occur frequently in the multihop transmission over wireless media. Thus, a loss recovery mechanism is required to provide end-to-end reliability. In addition, because the sensor nodes are very small devices and have insufficient resources, energy-efficient data transmission is crucial for prolonging the lifetime of a wireless sensor network. This paper proposes a transmission power control mechanism for reliable data transmission, which satisfies communication reliability through recovery of lost packets. The proposed method calculates packet reception rate (PRR) of each hop to maintain end-to-end packet delivery rate (PDR), which is determined based on the desired communication reliability. Then, the transmission power is adjusted based on the PRR to reduce energy consumption. The proposed method was evaluated through extensive simulations, and the results show that it leads to more energy-efficient data transmission compared to existing methods.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Hindawi Publishing Corporation. The published article can be found at: http://www.hindawi.com/journals/ijdsn

A new emerging technology of digitally assisted RF circuits

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Delta-Sigma modulator(DSM) based polar transmitter

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Doherty power amplifiers for handset applications

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