ABSTRACT We propose an adapter‐regularised continual learning framework for dynamic financial sentiment encoding, addressing the dual challenge of retaining long‐term domain knowledge while adapting to transient market sentiment patterns. The proposed method augments a pre‐trained RoBERTa model with lightweight adapter modules and elastic knowledge consolidation, enabling parameter‐efficient updates without catastrophic forgetting. Each transformer layer incorporates parallel adapters with gating mechanisms, selectively blending original and adapted features to maintain stability during incremental training. Moreover, the framework dynamically distills emerging sentiment lexicon terms into the encoder through an auxiliary distillation loss, ensuring sensitivity to evolving financial language. The resulting sentiment embeddings are fused with market data via cross‐modal attention, where adapter gating outputs further modulate the contribution of textual features. Realised with a RoBERTa‐large backbone and online Fisher matrix approximation, our approach demonstrates compatibility with high‐frequency trading scenarios while outperforming static domain‐adapted models. The key innovation lies in the tight integration of adapter‐based continual learning with financial lexicon dynamics and modality‐specific gating, a combination not explored in prior work. Experimental validation across multi‐modal market datasets confirms the model's ability to simultaneously preserve financial domain knowledge and capture temporal sentiment shifts, outperforming both generic continual learning methods and specialised financial encoders. This advancement bridges the gap between stable representation learning and real‐time market adaptation, offering a principled solution for dynamic sentiment analysis in algorithmic trading systems.

The rapid advancement of blockchain platforms has significantly accelerated the growth of decentralized applications (DApps). Similar to traditional applications, DApps integrate front-end descriptions that showcase their features to attract users, and back-end smart contracts for executing their business logic. However, inconsistencies between the features promoted in front-end descriptions and those actually implemented in the contract can confuse users and undermine DApps's trustworthiness. In this paper, we first conducted an empirical study to identify seven types of inconsistencies, each exemplified by a real-world DApp. Furthermore, we introduce Hyperion, an approach designed to automatically identify inconsistencies between front-end descriptions and back-end code implementation in DApps. This method leverages a fine-tuned large language model LLaMA2 to analyze DApp descriptions and employs dataflow-guided symbolic execution for contract bytecode analysis. Finally, Hyperion reports the inconsistency based on predefined detection patterns. The experiment on our ground truth dataset consisting of 54 DApps shows that Hyperion reaches 84.06% overall recall and 92.06 % overall precision in reporting DApp inconsistencies. We also implement Hyperion to analyze 835 real-world DApps. The experimental results show that Hyperion discovers 459 real-world DApps containing at least one inconsistency.

Web3, the next generation of the Internet, represents a decentralized and democratized web. Although it has garnered significant public interest and found numerous real-world applications, there is a limited understanding of people's perceptions and experiences with Web3. In this study, we conducted an empirical study to investigate the categories of Web3 applications and their popularity, as well as the potential challenges and opportunities within this emerging landscape. Our research was carried out in two phases. In the first phase, we analyzed 200 popular Web3 projects associated with 10 leading Web3 venture capital firms. In the second phase, we collected and examined code-related data from GitHub and market-related data from blockchain browsers (e.g., Etherscan) for these projects. Our analysis revealed that the Web3 ecosystem can be categorized into two groups, i.e., Web3 infrastructure and Web3 applications, with each consisting of several subcategories or subdomains. We also gained insights into the popularity of these Web3 projects at both the code and market levels and pointed out the challenges in the Web3 ecosystem at the system, developer, and user levels, as well as the opportunities it presents. Our findings contribute to a better understanding of Web3 for researchers and developers, which in turn promotes further exploration and advancement in this innovative field.

Static Var compensator (SVC) has often been used in the power system industries to regulate the bus voltage and reactive power. With an increasing concern of the power system transient stability, the SVC has also been used to dynamically regulate the power flow for oscillation dampings in recent years. A guided surrogate-gradient-based evolutionary strategy (GSES)-based SVC control approach is proposed in this article to damp power system interarea oscillations. The GSES algorithm is used to train a reinforcement learning agent to learn the best decisions that control the SVC, with an objective of quickly damping interarea oscillations. The GSES algorithm brings many benefits such as easier, more robust, and efficient training with less required hyperparameters. Because the GSES can engage with multiple individual workers during the training process, parallel computation techniques are implemented in this research to accelerate the computational speed. To test the performance of the proposed GSES-based SVC controller, we compared this novel method with a supplementary SVC damping controller on two different test systems. Results indicate that the proposed GSES-based SVC control approach can effectively damp power system interarea oscillations. Especially, in some case studies, the proposed approach successfully saved the test system from a transient-instability-caused breaking down.

With the challenges of increased grid dynamics and more variability of power generation from renewable energy sources, rapidly increasing complexity in the grid model, and abundant data from measurements and simulations, the requirements for computational analysis have also increased dramatically. This paper proposes a novel big data analysis approach for power system cascading analysis, prevention, and remediation. The developed techniques will be capable of cascading analysis, better assessment of the systems vulnerability level, as well as proposing potential remediation. Case studies using IEEE 118- bus system and a 563-bus system, with comparisons against a commercial tool, validate the advantages of the developed big data approach: accurate prediction, and more importantly, faster and effective correction actions. The developed techniques could be further used for other power system applications.

Deriving generation dispatch is essential for efficient and secure operation of electric power systems. This is usually achieved by solving a security-constrained optimal power flow (SCOPF) problem, which is by nature non-convex, usually nonlinear and thus computationally intensive. The state-of-the-art optimization approaches are not able to solve this problem for large-scale power systems within the power system operation time window (usually 5 minutes). In this work, we developed supervised learning approaches to determine security-constrained generation dispatch within a much shorter time window. More importantly, the physical constraint of only accessing to local measurements and other information in most utilities' real-time operation can not be ignored for the predictive models. The feasibility and accuracy of utilizing only local features (measurements and grid information in one area) to predict optimal local generation dispatch (dispatch of all generators in the corresponding area) in multi-area power systems has been explored. The results showed optimal local generation dispatch can be predicted with local features with high accuracy, which is comparable to the results obtained with global features.