Guiling Wang

Jiayi Chen, Xuan Zhang, Guiling Wang

The increasing penetration of renewable energy has introduced substantial volatility into wholesale electricity markets, complicating the optimal bidding strategies for power producers. Traditional Reinforcement Learning (RL) approaches often struggle to balance profit maximization with risk management, frequently overfitting to specific market conditions or failing to account for the stochastic spread between Day-Ahead (DA) and Real-Time (RT) settlements. To address these challenges, this paper makes two primary contributions. First, we introduce and open-source a high-fidelity gymnasium environment for two-settlement electricity market bidding. Grounded in extensive empirical data from the PJM Interconnection, the environment explicitly models the interplay between DA commitments and RT deviations, providing a standardized testbed for general and risk-sensitive agents. Second, we propose MARS-DA (Multi-Agent Regime-Switching for Day-Ahead markets), a novel hierarchical framework that orchestrates distinct sub-policies for risk management and profit seeking. MARS-DA utilizes a top-level Meta-Controller to dynamically blend the actions of two specialized base agents: a "Safe Agent" that optimizes for reliable DA allocation and a "Speculator Agent" that targets volatile RT arbitrage opportunities. Extensive experiments demonstrate that MARS-DA achieves superior risk-adjusted returns compared to state-of-the-art baselines while maintaining robust regime alignment during periods of extreme market volatility.

Yufeng Wu, Xin Liao, Baowei Wang et al.

Unauthorized screen capturing and dissemination pose severe security threats such as data leakage and information theft. Several studies propose robust watermarking methods to track the copyright of Screen-Camera (SC) images, facilitating post-hoc certification against infringement. These techniques typically employ heuristic mathematical modeling or supervised neural network fitting as the noise layer, to enhance watermarking robustness against SC. However, both strategies cannot fundamentally achieve an effective approximation of SC noise. Mathematical simulation suffers from biased approximations due to the incomplete decomposition of the noise and the absence of interdependence among the noise components. Supervised networks require paired data to train the noise-fitting model, and it is difficult for the model to learn all the features of the noise. To address the above issues, we propose Simulation-to-Real (S2R). Specifically, an unsupervised noise layer employs unpaired data to learn the discrepancy between the modeled simulated noise distribution and the real-world SC noise distribution, rather than directly learning the mapping from sharp images to real-world images. Learning this transformation from simulation to reality is inherently simpler, as it primarily involves bridging the gap in noise distributions, instead of the complex task of reconstructing fine-grained image details. Extensive experimental results validate the efficacy of the proposed method, demonstrating superior watermark robustness and generalization compared to state-of-the-art methods.

Jiayi Chen, Jing Li, Guiling Wang

Reinforcement Learning (RL) has shown significant promise in automated portfolio management; however, effectively balancing risk and return remains a central challenge, as many models fail to adapt to dynamically changing market conditions. In this paper, we propose Meta-controlled Agents for a Risk-aware System (MARS), a novel RL framework designed to explicitly address this limitation through a multi-agent, risk-aware approach. Instead of a single monolithic model, MARS employs a Heterogeneous Agent Ensemble where each agent possesses a unique, intrinsic risk profile. This profile is enforced by a dedicated Safety-Critic network and a specific risk-tolerance threshold, allowing agents to specialize in behaviors ranging from capital preservation to aggressive growth. To navigate different market regimes, a high-level Meta-Adaptive Controller (MAC) learns to dynamically orchestrate the ensemble. By adjusting its reliance on conservative versus aggressive agents, the MAC effectively lowers portfolio volatility during downturns and seeks higher returns in bull markets, thus minimizing maximum drawdown and enhancing overall stability. This two-tiered structure allows MARS to generate a disciplined and adaptive portfolio that is robust to market fluctuations. The framework achieves a superior balance between risk and return by leveraging behavioral diversity rather than explicit market-feature engineering. Experiments on major international stock indexes, including periods of significant financial crisis, demonstrate the efficacy of our framework on risk-adjusted criteria, significantly reducing maximum drawdown and volatility while maintaining competitive returns.

Yuan Lu, Qiang Tang, Guiling Wang

With the rapid popularity of blockchain, decentralized human intelligence tasks (HITs) are proposed to crowdsource human knowledge without relying on vulnerable third-party platforms. However, the inherent limits of blockchain cause decentralized HITs to face a few "new" challenges. For example, the confidentiality of solicited data turns out to be the sine qua non, though it was an arguably dispensable property in the centralized setting. To ensure the "new" requirement of data privacy, existing decentralized HITs use generic zero-knowledge proof frameworks (e.g. SNARK), but scarcely perform well in practice, due to the inherently expensive cost of generality. We present a practical decentralized protocol for HITs, which also achieves the fairness between requesters and workers. At the core of our contributions, we avoid the powerful yet highly-costly generic zk-proof tools and propose a special-purpose scheme to prove the quality of encrypted data. By various non-trivial statement reformations, proving the quality of encrypted data is reduced to efficient verifiable decryption, thus making decentralized HITs practical. Along the way, we rigorously define the ideal functionality of decentralized HITs and then prove the security due to the ideal-real paradigm. We further instantiate our protocol to implement a system called Dragoon, an instance of which is deployed atop Ethereum to facilitate an image annotation task used by ImageNet. Our evaluations demonstrate its practicality: the on-chain handling cost of Dragoon is even less than the handling fee of Amazon's Mechanical Turk for the same ImageNet HIT.

Yuan Lu, Qiang Tang, Guiling Wang

We conduct a systematic study on the light client of permissionless blockchains, in the setting where the full nodes and the light clients are rational. Under such a game-theoretic model, we design a superlight-client protocol to enable a client to employ some relaying full nodes (e.g. two or one) to read the blockchain. The protocol is "generic", i.e., it can be deployed disregarding the underlying consensuses, and also "superlight", i.e., the computational cost of the light client to predicate the (non)existence of a transaction in the blockchain becomes a small constant. Since our protocol resolves a fundamental challenge of broadening the usage of blockchain technology, it captures a wide variety of important use-cases such as multi-chain wallets, DApp browsers and more.