232 research outputs found

    Relationship between Design Elements and Performance in Online Innovation Contests: Contest Sequence is Moderator?

    Get PDF
    As an important issue in the field of innovation contest, performance of innovation contest has been attracting the attention of both academics and practioners over recent years. This paper explores the factors influencing performance of online innovation contest from the design elements perspective. The study is based on the empirical research of the online innovation contest community - studio.Topcoder.com. We find the longer the contest duration, the higher contest performance in the one-stage contest. The results also show that too much detailed task description will reduce the performance of the one-stage contests, but will increase the number of solvers in the two-stage contests. The results also reveal that the incentive effect of first prize in the one-stage contests is stronger than that in the two-stage contests, while the incentive effect of second prize in the two-stage contests is stronger than that in the one-stage contests, and if the amount of second prize is close to the prize amount, the number of solvers and eligible solutions will raise

    Research on the Relationship between Online Reviews and Customer Purchase Intention: The Moderating Role of Personality Trait

    Get PDF
    As an important factor that affects customer purchase intention, online review has attracted the attention from both enterprises and researchers. According to persuasion theory, planned behavior theory and regulatory focus theory, combined with the three dimensions of online reviews, we construct a modified model of the influence of online reviews on customer purchase intention, and put forward relevant theoretical assumptions. Based on data from 252 samples, this paper studies the relationship between online reviews and customer purchase intention, and further reveals the moderating effect of personality traits

    Research on Comprehensive Evaluation of Food Enterprises Websites

    Get PDF
    The importance of enterprise website to promote enterprise development is becoming more and more attention by the small and medium-sized enterprises. This paper establishes an evaluation index system of food enterprises websites from the perspective of user experience based on the websites localization and the current literature, and uses analytic hierarchy process to determine the weight of each level index, and establishes the fuzzy comprehensive evaluation model of food enterprises websites, and carries out a case study with the evaluation index system. The case study shows that it is reasonable and credible to evaluate the food enterprises websites with analytic hierarchy process and fuzzy comprehensive evaluation

    Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli

    Full text link
    In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information processing mechanisms of the visual system. Although some progress has been made in modeling neural responses to natural movies with deep neural networks, the visual representations of static and dynamic information under such time-series visual stimuli remain to be further explored. In this work, considering abundant recurrent connections in the mouse visual system, we design a recurrent module based on the hierarchy of the mouse cortex and add it into Deep Spiking Neural Networks, which have been demonstrated to be a more compelling computational model for the visual cortex. Using Time-Series Representational Similarity Analysis, we measure the representational similarity between networks and mouse cortical regions under natural movie stimuli. Subsequently, we conduct a comparison of the representational similarity across recurrent/feedforward networks and image/video training tasks. Trained on the video action recognition task, recurrent SNN achieves the highest representational similarity and significantly outperforms feedforward SNN trained on the same task by 15% and the recurrent SNN trained on the image classification task by 8%. We investigate how static and dynamic representations of SNNs influence the similarity, as a way to explain the importance of these two forms of representations in biological neural coding. Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex

    Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse

    Full text link
    Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of primate and rodent. However, they highly simplify the computational properties of neurons compared to their biological counterparts. Instead, Spiking Neural Networks (SNNs) are more biologically plausible models since spiking neurons encode information with time sequences of spikes, just like biological neurons do. However, there is a lack of studies on visual pathways with deep SNNs models. In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct neural representation similarity experiments on three neural datasets collected from two species under three types of stimuli. Based on extensive similarity analyses, we further investigate the functional hierarchy and mechanisms across species. Almost all similarity scores of SNNs are higher than their counterparts of CNNs with an average of 6.6%. Depths of the layers with the highest similarity scores exhibit little differences across mouse cortical regions, but vary significantly across macaque regions, suggesting that the visual processing structure of mice is more regionally homogeneous than that of macaques. Besides, the multi-branch structures observed in some top mouse brain-like neural networks provide computational evidence of parallel processing streams in mice, and the different performance in fitting macaque neural representations under different stimuli exhibits the functional specialization of information processing in macaques. Taken together, our study demonstrates that SNNs could serve as promising candidates to better model and explain the functional hierarchy and mechanisms of the visual system.Comment: Accepted by Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23
    corecore