Americans’ Eastward Journey - Intercultural Communication in The Portrait of a Lady

In the late 19th century, a heat of travel abroad especially to Europe arose among Americans. With The portrait of a Lady, noted for its international theme as the research subject, this paper intends to interpret some leading causes including context, prejudice and ethnocentrism resulting in cultural conflicts. Meanwhile, a closer observation will be given to the process and types of cultural adaptation, containing culture shock, assimilation and integration. This paper, lastly, expresses that a more smooth intercultural communication is urgently needed for an ideal culture integration

A New Historicist Interpretation of Beloved

According to New Historicism, there are plural histories instead of single History. For a long time, blacks’ miserable history was marginalized. This paper thinks, Morrison, in Beloved, presented their histories in the form of eye-catching stories. Such juxtaposition of literature and history rightly accords with the idea of New Historicism that literature and history have no clear border line. Thus, under the guidance of New Historicism, this paper intends to explore the hidden African Americans’ histories by analyzing Beloved so as to reconstruct the part of the blacks’ history under slavery.

Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos

To ensure the safe and efficient navigation of autonomous vehicles and advanced driving assistance systems in complex traffic scenarios, predicting the future bounding boxes of surrounding traffic agents is crucial. However, simultaneously predicting the future location and scale of target traffic agents from the egocentric view poses challenges due to the vehicle's egomotion causing considerable field-of-view changes. Moreover, in anomalous or risky situations, tracking loss or abrupt motion changes limit the available observation time, requiring learning of cues within a short time window. Existing methods typically use a simple concatenation operation to combine different cues, overlooking their dynamics over time. To address this, this paper introduces the Fusion-Gated Recurrent Unit (Fusion-GRU) network, a novel encoder-decoder architecture for future bounding box localization. Unlike traditional GRUs, Fusion-GRU accounts for mutual and complex interactions among input features. Moreover, an intermediary estimator coupled with a self-attention aggregation layer is also introduced to learn sequential dependencies for long range prediction. Finally, a GRU decoder is employed to predict the future bounding boxes. The proposed method is evaluated on two publicly available datasets, ROL and HEV-I. The experimental results showcase the promising performance of the Fusion-GRU, demonstrating its effectiveness in predicting future bounding boxes of traffic agents

Vision Sensor based Action Recognition for Improving Efficiency and Quality under the Environment of Industry 4.0

In the environment of industry 4.0, human beings are still an important influencing factor of efficiency and quality which are the core of product life cycle management. Hence, monitoring and analyzing humans\u27 actions are essential. This paper proposes a vision sensor based method to evaluate the accuracy of operators\u27 actions. Each action of operators is recognized in real time by a Convolutional Neural Network (CNN) based classification model in which hierarchical clustering is introduced to minimize the effects of action uncertainty. Warnings are triggered when incorrect actions occur in real time and applications of action analysis of workers on a reducer assembling line show the effectiveness of the proposed method. The research is expected to provide a guidance for operators to correct their actions to reduce the cost of quality defects and improve the efficiency of workforce

A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection

Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share-Split-Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-specific features intertwined in the extracted features through feature projection and collaborative training. A base model for a group of five authorized subjects is trained and tested on the inspection keyword dataset collected by this study. The model achieved a 95.3% or higher mean accuracy in classifying the keywords of any authorized inspectors. Its mean accuracy in speaker classification is 99.2%. Due to the richer keyword representations that the model learns from the pooled training data, adapting the base model to a new inspector requires only a little training data from that inspector, like five utterances per keyword. Using the speaker classification scores for inspector verification can achieve a success rate of at least 93.9% in verifying authorized inspectors and 76.1% in detecting unauthorized ones. Further, the paper demonstrates the applicability of the proposed model to larger-size groups on a public dataset. This paper provides a solution to addressing challenges facing AI-assisted human-robot interaction, including worker heterogeneity, worker dynamics, and job heterogeneity.Comment: Accepted by Engineering Applications of Artificial Intelligence journal on Oct 31th. Upload the accepted clean versio

Impacts of exercise intervention on various diseases in rats.

BackgroundExercise is considered as an important intervention for treatment and prevention of several diseases, such as osteoarthritis, obesity, hypertension, and Alzheimer's disease. This review summarizes decadal exercise intervention studies with various rat models across 6 major systems to provide a better understanding of the mechanisms behind the effects that exercise brought.MethodsPubMed was utilized as the data source. To collect research articles, we used the following terms to create the search: (exercise [Title] OR physical activity [Title] OR training [Title]) AND (rats [Title/Abstract] OR rat [Title/Abstract] OR rattus [Title/Abstract]). To best cover targeted studies, publication dates were limited to "within 11 years." The exercise intervention methods used for different diseases were sorted according to the mode, frequency, and intensity of exercise.ResultsThe collected articles were categorized into studies related to 6 systems or disease types: motor system (17 articles), metabolic system (110 articles), cardiocerebral vascular system (171 articles), nervous system (71 articles), urinary system (2 articles), and cancer (21 articles). Our review found that, for different diseases, exercise intervention mostly had a positive effect. However, the most powerful effect was achieved by using a specific mode of exercise that addressed the characteristics of the disease.ConclusionAs a model animal, rats not only provide a convenient resource for studying human diseases but also provide the possibility for exploring the molecular mechanisms of exercise intervention on diseases. This review also aims to provide exercise intervention frameworks and optimal exercise dose recommendations for further human exercise intervention research