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The inconsistent results shown in previous group faultline research have created a need for investigating the underlying mechanisms of the faultline's effects. This study focuses on clarifying the competing mediating roles of information diversity and team conflict in the nonlinear relationship between board faultlines (BF) and decision quality.

This study is empirically tested with the questionnaire data from 105 Chinese listed companies.

This study finds: (1) an inverted U-shaped curve relationship between BF and board decision quality and (2) that the joint mediating effect of team conflict and information diversity leads to the inverted U-shaped curve relationship between BF and decision quality. Specifically, BF shows a U-shaped curve relationship with team conflict and an inverted U-shaped curve relationship with information diversity. Either too weak or too strong faultlines will inhibit the positive effects of information diversity and amplify the negative effects of team conflicts, leading to low-quality decisions.

This study contributes to the research on: (1) board governance as it clarifies the effect of BF on the board decision-making process and its quality, which helps to open the black box of board decision-making and (2) group faultlines as it reveals how information diversity and team conflict can play a joint mediating role in the functioning of team faultlines.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

A little-known “lottery fever” has spread to many parts of rural China over the past 10 years. This is driven by participation in underground lotteries with local bookies. It is called liuhecai, which is the name of the Hong Kong lottery, and is based on guessing the bonus number of the Hong Kong Mark Six lottery. Such lotteries are illegal, but are an open secret. This chapter seeks to understand the meaning of this apparently irrational lottery fever: why people participate in it, why they believe the conspiracy theory that it is rigged (and yet still participate), and why similar lotteries have emerged in both capitalist Taiwan and post-socialist China at this particular time.

The purpose of this paper is to develop the efficiency of styrene-acrylate (SA) emulsions for polymer cement waterproof coatings with improved bacteria resistance and mechanical properties.

For effective bacteria resistance and excellent mechanical properties, various concentrations of methacryloxyethylhexadecyl dimethylammonium bromide (MHDB) were synthesised and incorporated into SA emulsions. The properties of SA emulsions modified with MHDB were characterised and compared with those of unmodified ones according to the formulations of polymer cement waterproof coatings.

The SA emulsions modified with MHDB exhibited significant enhancement of bacteria resistance and mechanical properties over the unmodified ones. The positive quaternary nitrogen and long-chain alkyl groups of MHDB in SA emulsions could attract phospholipid head groups of bacterial and insert them into the cell wall, which results in biomass leak and bactericidal effect. Moreover, MHDB as a softened monomer was beneficial to the synthesis of SA copolymer with low glass-transition temperature (T_g), then the copolymer and cement would form a more compact film which was the main reason for the enhancement of mechanical properties.

The modifier MHDB was synthesised from diethylaminoethyl methacrylate (DEAM) and 1-bromohexadecane. Besides, the congeners of MHDB could be synthesised from DEAM and 1-bromododecane, 1-tetradecyl dromide, 1-octadecyl bromide, etc. In addition, the efficiency of other modifications into SA emulsions for antibacterial polymer cement waterproof coatings could be studied as well.

The method provided a practical solution for the improvement of water-based antibacterial acrylate polymer cement waterproof coatings.

The method for enhancing bacteria resistance and mechanical properties of the waterproof coating was novel and valuable.