Why the General Trust of Chinese People is Higher Than That of Japanese People?

Several major international comparative surveys (World Values Survey, East Asian Social Survey) have repeatedly shown that Chinese people report higher levels of general trust towards people than Japanese people. It has been pointed out that if Chinese people recall the people around them when answering the question “Most people can be trusted?”, this could explain their high general trust (Yoshino and Osaki, 2013), but there are no empirical studies yet. Therefore, in this study, we will compare general trust between Japan and China by focusing on “Who comes to mind in response”. A total of 318 Japanese and 476 Chinese university students were asked to answer questions about general trust, and to select from eight items such as “people in general,” “people in local area,” “friends and acquaintances,” and “parents, siblings, and relatives” as to whom they imaged when answering. The results showed that (1) Consistent with previous studies, Chinese people reported higher general trust (positive answer: 75.8%) than Japanese people (51.6%). (2) The percentage of people who only recalled “people in general” or “Japanese in general/Chinese in general” was higher in Japan (54.7%) than in China (22.3%), while the percentage of people who recalled “parents, siblings, and relatives” was higher in China (54.2%) than in Japan (16.4%). (3) In both Japan and China, the main effect of “the person who recalled” was significant, with those who recalled “parents, siblings, and relatives” having a higher general trust. (4)There was a significant interaction between “the people who recalled” and “culture” on general trust, and cultural differences in general trust were found only in the “people in general or Japanese in general/Chinese in general” condition, which is higher for Chinese than for Japanese. (5) The mediation effect of “the person who recalled” on the cultural difference in general trust was examined, and a significant partial mediation effect was identified. However, even considering these media effects, the cultural difference in general trust between Japan and China is still significant, suggesting that further investigation of other factors is needed. The results for the Japanese and Chinese in this study also indicate the need to treat responses to general trust in previous social surveys with caution and pay attention to the interpretation of cultural differences

A Survey of Cross-Lingual Sentiment Analysis Based on Pre-Trained Models

With the technology development of natural language processing, many researchers have studied Machine Learning (ML), Deep Learning (DL), monolingual Sentiment Analysis (SA) widely. However, there is not much work on Cross-Lingual SA (CLSA), although it is beneficial when dealing with low resource languages (e.g., Tamil, Malayalam, Hindi, and Arabic). This paper surveys the main challenges and issues of CLSA based on some pre-trained language models and mentions the leading methods to cope with CLSA. In particular, we compare and analyze their pros and cons. Moreover, we summarize the valuable cross-lingual resources and point out the main problems researchers need to solve in the future

Latest lessons from the bankruptcy of state-owned enterprises (SOEs) in China : an interpretative structural model (ISM) approach

State-owned enterprises (SOEs) play an important role in China. During the transformation from a planned to a market economy, plenty of Chinese SOEs fell into trouble. Dalian machine tool group (DMTG) who was once a leading enterprise in the Chinese machine tool industry bankrupted in 2017. To explore the causes of its collapse, we employ the interpretative structural model (ISM) to investigate the reasons for its failures from multi-aspect and at different levels. The results indicate that the root cause of this bankruptcy is the top manager’s mismanagement; the lack of a reasonable strategic positioning and long-term product planning are also important factors of DMTG’s failure, and the problems of human resource management accelerated the bankruptcy. Findings provide lessons to be learned from the bankruptcy for SOEs and offer managerial insight into SOEs.Peer ReviewedPostprint (published version

Multimodal Sentiment Analysis Based on Deep Learning: Recent Progress

Multimodal sentiment analysis is an important research topic in the field of NLP, aiming to analyze speakers\u27 sentiment tendencies through features extracted from textual, visual, and acoustic modalities. Its main methods are based on machine learning and deep learning. Machine learning-based methods rely heavily on labeled data. But deep learning-based methods can overcome this shortcoming and capture the in-depth semantic information and modal characteristics of the data, as well as the interactive information between multimodal data. In this paper, we survey the deep learning-based methods, including fusion of text and image and fusion of text, image, audio, and video. Specifically, we discuss the main problems of these methods and the future directions. Finally, we review the work of multimodal sentiment analysis in conversation

A Study on the Effects of Local Added Masses on the Natural and the Sound Radiation Characteristics of Thin Plate Structures

As panel-like structures are widely used in industrial products such as high speed trains, automobiles, and ships, the effects of additional attachments (e.g. lumped mass, rib-stiffeners) to the panels on their dynamic/acoustic characteristics have been investigated analytically, numerically, experimentally or combining two or all the methods in the past decades. The present study focuses on highlighting the differences among local mass effects on the vibration and the radiation behaviour of flexible modes of the flat panel structures. A simple model comprising a local mass attached to a rectangular plate surface is set up, allowing us a deep insight into how the local mass affects the inherent mode parameters and the corresponding vibration and radiation characteristics of panel structures. The influential phenomena are first investigated analytically and then verified using FE-numerical simulations. The results show that: (1) the dynamic modal parameters of flat panel structures show different sensitivity to the values of the added mass and its locations; (2) the vibration and radiation characteristics of elastic modes with the same order can be affected in quite different degrees by the same local mass attachment; and (3) the modal acoustic interactions of thin plates can be significantly affected by the local mass attachments

A Simple Estimation of Coupling Loss Factors for Two Flexible Subsystems Connected via Discrete Interfaces

A simple formula is proposed to estimate the Statistical Energy Analysis (SEA) coupling loss factors (CLFs) for two flexible subsystems connected via discrete interfaces. First, the dynamic interactions between two discretely connected subsystems are described as a set of intermodal coupling stiffness terms. It is then found that if both subsystems are of high modal density and meanwhile the interface points all act independently, the intermodal dynamic couplings become dominated by only those between different subsystem mode sets. If ensemble- and frequency-averaged, the intermodal coupling stiffness terms can simply reduce to a function of the characteristic dynamic properties of each subsystem and the subsystem mass, as well as the number of interface points. The results can thus be accommodated within the theoretical frame of conventional SEA theory to yield a simple CLF formula. Meanwhile, the approach allows the weak coupling region between the two SEA subsystems to be distinguished simply and explicitly. The consistency and difference of the present technique with and from the traditional wave-based SEA solutions are discussed. Finally, numerical examples are given to illustrate the good performance of the present technique

Remote Sensing Evidence for Significant Variations in the Global Gross Domestic Product during the COVID-19 Epidemic

Coronavirus disease 2019 (COVID-19) has been spreading rapidly and is still threatening human health currently. A series of measures for restraining epidemic spreading has been adopted throughout the world, which seriously impacted the gross domestic product (GDP) globally. However, details of the changes in the GDP and its spatial heterogeneity characteristics on a fine scale worldwide during the pandemic are still uncertain. We designed a novel scheme to simulate a 0.1° × 0.1° resolution grid global GDP map during the COVID-19 pandemic. Simulated nighttime-light remotely sensed data (SNTL) was forecasted via a GM(1, 1) model under the assumption that there was no COVID-19 epidemic in 2020. We constructed a geographically weighted regression (GWR) model to determine the quantitative relationship between the variation of nighttime light (ΔNTL) and the variation of GDP (ΔGDP). The scheme can detect and explain the spatial heterogeneity of ΔGDP at the grid scale. It is found that a series of policies played an obvious role in affecting GDP. This work demonstrated that the global GDP, except for in a few countries, represented a remarkably decreasing trend, whereas the ΔGDP exhibited significant differences

Multiresolution Feature Guidance Based Transformer for Anomaly Detection

Anomaly detection is represented as an unsupervised learning to identify deviated images from normal images. In general, there are two main challenges of anomaly detection tasks, i.e., the class imbalance and the unexpectedness of anomalies. In this paper, we propose a multiresolution feature guidance method based on Transformer named GTrans for unsupervised anomaly detection and localization. In GTrans, an Anomaly Guided Network (AGN) pre-trained on ImageNet is developed to provide surrogate labels for features and tokens. Under the tacit knowledge guidance of the AGN, the anomaly detection network named Trans utilizes Transformer to effectively establish a relationship between features with multiresolution, enhancing the ability of the Trans in fitting the normal data manifold. Due to the strong generalization ability of AGN, GTrans locates anomalies by comparing the differences in spatial distance and direction of multi-scale features extracted from the AGN and the Trans. Our experiments demonstrate that the proposed GTrans achieves state-of-the-art performance in both detection and localization on the MVTec AD dataset. GTrans achieves image-level and pixel-level anomaly detection AUROC scores of 99.0% and 97.9% on the MVTec AD dataset, respectively