Does Overseas Investment Raise Corporate Environmental Protection? Evidence from Chinese A-List Companies

This paper uses the relevant data of China’s listed companies from 2010 to 2018 to test the impact of overseas investment on corporate environmental protection and further examines whether the heterogeneity of the company and the heterogeneity of the host country changes this effect. The research results show that the environmental protection of overseas investment companies is significantly higher than that of other companies. The impact of overseas investment on corporate environmental protection is dynamic, and it only helps improve corporate environmental protection after three years of investment. This article is conducive to causally identifying the logical relationship between overseas investment and corporate environmental protection. The policy significance is that the government can rationally guide companies to invest abroad, and oversea investment will help enhance corporate environmental protection.

Decision-Making Taxonomy of DevOps Success Factors Using Preference Ranking Organization Method of Enrichment Evaluation

Due to multitudes factors like rapid change in technology, customer needs, and business trends, the software organizations are facing pressure to deliver quality software on time. To address this concern, the software industry is continually looking the solution to improve processing timeline. Thus, the Development and Operations (DevOps) has gained a wide popularity in recent era, and several organizations are adopting it, to leverage its perceived benefits. However, companies are facing several problems while executing the DevOps practices. The objective of this work is to identify the DevOps success factors that will help in DevOps process improvement. To accomplish this research firstly, a systematic literature review is conducted to identify the factors having positive influence on DevOps. Secondly, success factors were mapped with DevOps principles, i.e., culture, automation, measurement, and sharing. Thirdly, the identified success factors and their mapping were further verified with industry experts via questionnaire survey. In the last step, the PROMETHEE-II method has been adopted to prioritize and investigate logical relationship of success factors concerning their criticality for DevOps process. This study’s outcomes portray the taxonomy of the success factors, which help the experts design the new strategies that are effective for DevOps process improvement.

A Study of Big-Data-Driven Data Visualization and Visual Communication Design Patterns

This paper provides an in-depth study and analysis of big-data-driven data visualization and visual communication design models. The characteristics of new media and the definition of traditional media are analyzed; the importance of the new media environment is derived through comparison; and the successful cases of new media integration today are analyzed. In this process, we will optimize the traditional science and technology intelligence service model, optimize the various components that make up the science and technology intelligence wisdom service, achieve model optimization and reflect the four characteristics of science and technology intelligence wisdom service, and reconstruct the science and technology intelligence wisdom service using the literature research method. The design based on imagery schema theory is manifest, inclusive, and somewhat innovative and at the same time has a high degree of consistency and internal logical relationship with the visual representation of multidomain heterogeneous data at the cognitive level and displays purpose. This internal logical relationship is systematically organized and deeply analyzed, and the methodology from subpattern extraction and visual interaction design to the deep integration of visual representation is proposed in combination with specific application scenarios and cases.

An Interpretation of the Play After the Fall Based on Role- and -Value Cognitive Concept

The paper aims to interpret Arthur Miller’s stream-of-consciousness play text, After the Fall, from the perspective of the cognitive concept of evolving reference, namely “role and values”. The results of the study are as follows: 1. Mutual across-time-and-space contextual embedment or entanglement is the distinctive feature of stream-of-consciousness play text, which makes it possible to present synchronically what has happened diachronically, so that the various values generated by role switching over the past years are accessible in a while. 2. This feature in turn makes characterization more natural, true-to-life, vivid and substantial, revealing not only the different aspects of the protagonist’s disposition but also the shaping process involved. 3. Despite the seemingly disordered contextual entanglement, the values through role switching are implicitly linked by the cause-effect logical relationship, which ensures the textual coherence of the play.

Analysis of Factors Influencing Audit Quality based on Grounded Theory Methods

With the gradual increase in business diversity and the increase in the degree of informatization of various industries, the level of audit quality has brought a great impact. This article collects 6 cases of my country's supervision and control commission penalties from 2010 to 2020, and adopts grounded theory methods to study the factors affecting audit quality and draw conclusions. The analysis first further summarizes the initial categories obtained from the analysis into the main categories, and then refines the core categories based on the logical relationship between the main categories. On this basis, it proposes ways to improve audit quality. factor.

Internal Relationship between Education on Cultural Confidence and Cultural Consciousness

Education on cultural confidence is the main way to strengthen one’s cultural confidence while cultural consciousness constitutes a base for cultural confidence. The two would be unified in the process of strengthening cultural confidence. Therefore, there is a logical relationship between them. In order to understand the relationship between education on cultural confidence and cultural consciousness, one needs to have a correct understanding of the scientific connotation of the two and a clear picture that education on cultural confidence would ensure that one would be kept steadfast in cultural consciousness because it does not only establish attitudes in cultural conscious cognition but also act as a guide for internal development.

Keyword coupling query of spatiotemporal data based on XML

With the increasing popularity of XML for data representations, there is a lot of interest in keyword query on XML. Many algorithms have been proposed for XML keyword queries. But the existing approaches fall short in their abilities to analyze the logical relationship between keywords of spatiotemporal data. To overcome this limitation, in this paper, we firstly propose the concept of query time series (QTS) according to the data revision degree. For the logical relationship of keywords in QTS, we study the intra-coupling logic relationship and the inter-coupling logic relationship separately. Then a calculation method of keyword similarity is proposed and the best parameter in the method is found through experiment. Finally, we compare this method with others. Experimental results show that our method is superior to previous approaches.

Implement multi-step-ahead forecasting with multi-point association fuzzy logical relationship for time series

In the existing high-order fuzzy logical relationship (FLR) based forecasting model, each FLR is used to describe the association between multiple premise observations and a consequent observation. Therefore, these FLRs concentrate on the one-step-ahead forecasting. In real applications, there exist another kind of association: the association between multiple premise observations and multiple consequent observations. For such association, the existing FLRs can’t express and ignored. To depict it, the high-order multi-point association FLR is raised in this study. The antecedent and consequent of a high-order multi-point association FLR are consisted of multiple observations. Thus, the proposed FLR reflects the influence of multiple premise observations on the multiple consequent observations, and can be applied for multi-step-ahead forecasting with no cumulative errors. On the basis of high-order multi-point association FLR, the high-order multi-point trend association FLR is constructed, it describes the trend association in time series. By using these two new kinds of FLRs, a fuzzy time series based multi-step-ahead forecasting model is established. In this model, the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy. The benefits of the proposed FLRs and the superior performance of the established forecasting model are demonstrated through the experimental analysis.

A prison term prediction model based on fact descriptions by capturing long historical information

The legal judgments are always based on the description of the case, the legal document. However, retrieving and understanding large numbers of relevant legal documents is a time-consuming task for legal workers. The legal judgment prediction (LJP) focus on applying artificial intelligence technology to provide decision support for legal workers. The prison term prediction(PTP) is an important task in LJP which aims to predict the term of penalty utilizing machine learning methods, thus supporting the judgement. Long-Short Term Memory(LSTM) Networks are a special type of Recurrent Neural Networks(RNN) that are capable of handling long term dependencies without being affected by an unstable gradient. Mainstream RNN models such as LSTM and GRU can capture long-distance correlation but training is time-consuming, while traditional CNN can be trained in parallel but pay more attention to local information. Both have shortcomings in case description prediction. This paper proposes a prison term prediction model for legal documents. The model adds causal expansion convolution in general TextCNN to make the model not only limited to the most important keyword segment, but also focus on the text near the key segments and the corresponding logical relationship of this paragraph, thereby improving the predicting effect and the accuracy on the data set. The causal TextCNN in this paper can understand the causal logical relationship in the text, especially the relationship between the legal text and the prison term. Since the model uses all CNN convolutions, compared with traditional sequence models such as GRU and LSTM, it can be trained in parallel to improve the training speed and can handling long term. So causal convolution can make up for the shortcomings of TextCNN and RNN models. In summary, the PTP model based on causality is a good solution to this problem. In addition, the case description is usually longer than traditional natural language sentences and the key information related to the prison term is not limited to local words. Therefore, it is crucial to capture substantially longer memory for LJP domains where a long history is required. In this paper, we propose a Causality CNN-based Prison Term Prediction model based on fact descriptions, in which the Causal TextCNN method is applied to build long effective history sizes (i.e., the ability for the networks to look very far into the past to make a prediction) using a combination of very deep networks (augmented with residual layers) and dilated convolutions. The experimental results on a public data show that the proposed model outperforms several CNN and RNN based baselines.