Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

In the field of portfolio management using reinforcement learning, existing approaches have mainly focused on cash-only trading, overlooking the potential benefits and risks of margin trading. Incorporating margin accounts and their constraints, especially in short sale scenarios, is crucial yet often neglected. To address this gap, we make the first attempt to propose Margin Trader, an innovative and adaptive reinforcement learning framework designed for margin trading in the stock market. Margin Trader integrates margin accounts and constraints into a realistic trading environment for both long and short positions. The framework aims to balance profit maximization and risk management through the Margin Adjustment Module and the Maintenance Detection Module. Margin Trader supports various Deep Reinforcement Learning (DRL) algorithms and offers traders the flexibility to customize critical settings, such as equity allocation, margin ratios, and maintenance requirements, to suit diverse market conditions, individual preferences, and risk tolerance. Experimental results demonstrate that Margin Trader effectively learns profitable trading strategies and hedges risks in both bullish and bearish markets, outperforming other baseline models with the highest Sharpe ratio.

Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose RAGIC, a novel risk-aware framework for stock interval prediction to quantify uncertainty. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC’s generator detects the risk perception of informed investors and captures historical price trends globally and locally. Then the risk-sensitive intervals is built upon the simulated future prices from sequence generation through statistical inference, incorporating horizon-wise insights. The interval’s width is adaptively adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC’s evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.

Self-Sovereign Identity (SSI) is an evolving means of identity management that aims to give individuals more control over their digital identities and personal data rather than relying on third-party organizations or government authorities. Blockchain technology has the potential to strengthen SSI significantly by providing a secure and decentralized method towards managing and storing personal data, rendering them resistant to tampering and also enhancing their privacy. Given the diversity of blockchain-based SSI platforms, including Sovrin, uPort, and Hyperledger Indy, it is essential to have a consistent approach to evaluate the different SSI systems consistently. This paper offers guidelines for building systematic architecture and defining comprehensive internal interactions for a complete SSI system, thereby providing a framework for routine evaluation and analysis of an existing SSI platform and establishing base standards for assessing capability, compatibility and interoperability of SSI systems. Accordingly, the paper also reports on comprehensive experiments over many existing blockchain-based SSI systems using a multi-layered approach.

Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality. This multi-faceted generator informs the creation of risk-sensitive intervals through statistical inference, incorporating horizon-wise insights. The interval's width is carefully adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC's evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.