Directors and officers liability insurance coverage, tax avoidance and financial crisis

With firms facing an increasing range of exposures and the resultant surging risks, directors and officers (D&O) liability insurance are available for corporations in order to mitigate or avoid potential litigation risks. However, this behaviour may cause a “moral hazard” problem in return, because insuring D&O “misbehaviours” mitigates the supervision effect of stakeholder litigation and encourages corporate risk-taking behaviours. Based on fixed effect models, this study investigates the effect of purchasing D&O liability insurance coverage on corporate tax avoidance behaviours. We find that higher insurance coverage encourages tax avoidance behaviours in non-crisis periods, instead of crisis periods. In addition, our findings document that this negative relation becomes inapparent in companies with higher profitability and lower default risk.October 202

Bipolar Neutrosophic Driven Approach for Assessing the Teaching Quality of English Language and Literature Learning Outcomes

The quality of teaching in English Language and Literature is a crucial factor in enhancing student learning outcomes, fostering critical thinking, and promoting linguistic and literary proficiency. Nevertheless, the procedure of assessing the quality of English teaching remains a thought-provoking task since it typically depends on individual judgments, variety in criteria, and inconsistency in human observation, resulting in intrinsic uncertainty. Bipolar Neutrosophic Sets (BNS) offer an expressive way to model ambiguity in a bipolar manner using three positive and three negative memberships. Motivated by that, we propose to explore the novel approach based on BNS to improve the judgmental process of evaluating the quality of teaching of Quality of English Language and related literature. To assess the relative importance of aggregated teaching criteria, we apply the Analytic Hierarchy Process (AHP) to drive representative weights that guide the later decision-making. We drive a new weighting scheme to apply the AHP weights into a bipolar decision matrix. Following, we introduce multi-criteria decision-making (MCDM) to determine the bipolar ideal solutions in a bipolar decision matrix, and then identify the relative closeness teaching techniques. Based on a numerical study, we conduct a holistic analysis of the results of the proposed framework, which demonstrates remarkable power in making appropriate decisions about the best scenario English teaching approach that achieves the optimal benefit for students

Sustainable digital marketing under big data: an AI random forest model approach

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies

Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations

Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL for real-world problems, such as human-aligned tasks where human safety is paramount. This paper provides safety and stability definitions for such human-aligned tasks, and then proposes an algorithm that leverages neural ordinary differential equations (NODEs) to predict human and robot movements and integrates the control barrier function (CBF) and control Lyapunov function (CLF) with the actor-critic method to help to maintain the safety and stability for human-aligned tasks. Simulation results show that the algorithm helps the controlled robot to reach the desired goal state with fewer safety violations and better sample efficiency compared to other methods in a human-aligned task.Comment: Accepted at the Workshop on Human-aligned Reinforcement Learning for Autonomous Agents and Robots at ICRA 202

Synchronous multi-decadal climate variability of the whole Pacific areas revealed in tree rings since 1567

Oceanic and atmospheric patterns play a crucial role in modulating climate variability from interannual to multi-decadal timescales by causing large-scale co-varying climate changes. The brevity of the existing instrumental records hinders the ability to recognize climate patterns before the industrial era, which can be alleviated using proxies. Unfortunately, proxy based reconstructions of oceanic and atmospheric modes of the past millennia often have modest agreements with each other before the instrumental period, raising questions about the robustness of the reconstructions. To ensure the stability of climate signals in proxy data through time, we first identified tree-ring datasets from distant regions containing coherent variations in Asia and North America, and then interpreted their climate information. We found that the multi-decadal covarying climate patterns of the middle and high latitudinal regions around the northern Pacific Ocean agreed quite well with the climate reconstructions of the tropical and southern Pacific areas. This indicates a synchronous variability at the multi-decadal timescale of the past 430 years for the entire Pacific Ocean. This pattern is closely linked to the dominant mode of the Pacific sea surface temperature (SST) after removing the warming trend. This Pacific multi-decadal SST variability resembles the Interdecadal Pacific Oscillation

Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models

Online hate is an escalating problem that negatively impacts the lives of Internet users, and is also subject to rapid changes due to evolving events, resulting in new waves of online hate that pose a critical threat. Detecting and mitigating these new waves present two key challenges: it demands reasoning-based complex decision-making to determine the presence of hateful content, and the limited availability of training samples hinders updating the detection model. To address this critical issue, we present a novel framework called HATEGUARD for effectively moderating new waves of online hate. HATEGUARD employs a reasoning-based approach that leverages the recently introduced chain-of-thought (CoT) prompting technique, harnessing the capabilities of large language models (LLMs). HATEGUARD further achieves prompt-based zero-shot detection by automatically generating and updating detection prompts with new derogatory terms and targets in new wave samples to effectively address new waves of online hate. To demonstrate the effectiveness of our approach, we compile a new dataset consisting of tweets related to three recently witnessed new waves: the 2022 Russian invasion of Ukraine, the 2021 insurrection of the US Capitol, and the COVID-19 pandemic. Our studies reveal crucial longitudinal patterns in these new waves concerning the evolution of events and the pressing need for techniques to rapidly update existing moderation tools to counteract them. Comparative evaluations against state-of-the-art tools illustrate the superiority of our framework, showcasing a substantial 22.22% to 83.33% improvement in detecting the three new waves of online hate. Our work highlights the severe threat posed by the emergence of new waves of online hate and represents a paradigm shift in addressing this threat practically.Comment: To Appear in the 45th IEEE Symposium on Security and Privacy, May 20-23, 202