A Proposed Exploratory Study of Object Detectors to Learn the Influence of Datasets on Model Performance

The quality of the images used to train the models in the field of object detection using deep learning models is critical in determining the model’s quality. However, there are very few methods for exploring these images in datasets to see what aspects in these images have a significant impact on the model’s performance. This could be one of the reasons why the models don’t match human perceptions. There is a need for more study that can suggest unique methodologies to address the topic at hand because the existing literature overlooks this line of thought. As a result, this paper provides a methodology based on exploratory sequential design, which may be used to identify several aspects of images in the dataset that influence model performance.

Research on the Construction of Online Culture Trading Platform Based on Netizens’ Wishes

According to the statistical data of the 44th China statistical report on Internet development, as of June 2019, the number of Internet users in China has reached 854 million, and the penetration rate has reached 61.2%. The number of mobile Internet users reached 8.47 million, accounting for 99.1% of the total Internet users [1]. The number of Internet users in China continued to grow steadily. With the rapid development of high-tech and Internet, the continuous expansion of Internet users, the continuous optimization of the network environment, the gradual formation of the network society, the network life tends to be a regular state, and the network culture is also formed in this environment, which has become an important part of China’s cultural system. This paper takes netizens as the core, studies the construction of network cultural data trading platform based on netizens’ behavior. Through the establishment of netizens influence model and online cultural data trading platform, this paper studies the consumption intention of online cultural products and the characteristics of online cultural data trading platform from the perspective of netizens. Based on the data of strong research results, this paper puts forward suggestions and strategies for the construction of network cultural data trading platform and the innovation of network cultural products in China.

The Dyadic Health Influence Model

Relationship partners affect one another’s health outcomes through their health behaviors, yet how this occurs is not well understood. To fill this gap, we present the Dyadic Health Influence Model (DHIM). The DHIM identifies three routes through which a person (the agent) can impact the health beliefs and behavior of their partner (the target). An agent may (a) model health behaviors and shape the shared environment, (b) enact behaviors that promote their relationship, and/or (c) employ strategies to intentionally influence the target’s health behavior. A central premise of the DHIM is that agents act based on their beliefs about their partner’s health and their relationship. In turn, their actions have consequences not only for targets’ health behavior but also for their relationship. We review theoretical and empirical research that provides initial support for the routes and offer testable predictions at the intersection of health behavior change research and relationship science.

Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices

This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning. Taking the semi-productive German noun system as example, we illustrate how decisions made about the representation of form and meaning influence model performance. We clarify that for modeling frequency effects in learning, it is essential to make use of incremental learning rather than the end-state of learning. We also discuss how the model can be set up to approximate the learning of inflected words in context. In addition, we illustrate how in this approach the wug task can be modeled. The model provides an excellent memory for known words, but appropriately shows more limited performance for unseen data, in line with the semi-productivity of German noun inflection and generalization performance of native German speakers.

School-Immigrant Family-Community Collaboration Practices: Similarities and Differences

We compared school-immigrant family-community collaboration practices based on the six dimensions of Epstein’s influence model (2001). These three groups of stakeholders (N = 54) participated in this study by answering a questionnaire on their collaboration practices. Kruskall-Wallis analyses revealed a notable difference between the three groups with regard to decision-making practices and at-home learning. A positive correlation was found between the number of years of teaching experience in the school and communication, volunteering, parenting, and decision making, as well as between the child’s grade level and parenting. Results show that although the collaboration practices followed Epstein’s involvement theory, they remained weak, with no significant difference between the three groups in terms of their use. Our findings are discussed in light of recent literature and their practical implications and avenues for future research are proposed to better understand and improve the conditions favoring school-immigrant family-community collaboration.