Abstract Accurate carbon price forecasting is essential for market efficiency, effective climate policy design, and strategic decision-making by firms managing carbon assets. Carbon prices reflect a dynamic interplay of emission quotas, economic conditions, and policy expectations, directly influencing emission reduction incentives and investment in green technologies. However, existing models often treat data input variables independently and struggle with the non-stationary, multi-scale nature of carbon price signals. This paper proposes SW-HyDEC, a structure-aware hybrid framework that integrates Correlation-based Feature Selection (CFS), Recursive Feature Elimination (RFE), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and a Hybrid-Kernel Extreme Learning Machine (Hybrid-KELM). To enhance the training convergence and robustness, model parameters are optimized using a Sobol-sequence enhanced Whale Optimization Algorithm (Sobol-WOA). Experimental results on real-world carbon market datasets demonstrate that SW-HyDEC significantly outperforms mainstream statistical, machine learning, and deep learning baselines in terms of accuracy, generalization, and stability.

Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods, i.e., content-based filtering, collaborative filtering, and deep learning, have achieved impressive results in recommendation systems. However, the cold-start and sparse-data scenarios are still challenging to deal with. Existing solutions either generate pseudo-interaction sequence, which often introduces redundant or noisy signals, or rely heavily on semantic similarity, overlooking dynamic shifts in user motivation. To address these limitations, this paper proposes a novel recommendation framework, termed M-LLM3REC, which leverages large language models for deep motivational signal extraction from limited user interactions. M-LLM3REC comprises three integrated modules: the Motivation-Oriented Profile Extractor (MOPE), Motivation-Oriented Trait Encoder (MOTE), and Motivational Alignment Recommender (MAR). By emphasizing motivation-driven semantic modeling, M-LLM3REC demonstrates robust, personalized, and generalizable recommendations, particularly boosting performance in cold-start situations in comparison with the state-of-the-art frameworks.

Recommender systems play a pivotal role in personalized content delivery but still face significant challenges in sparse interaction scenarios such as cold start conditions. Recent works have explored the integration of large language models (LLMs) into recommendation tasks. It has shown early promise but also reveals some limitations, such as hallucinations and overconfident outputs due to a lack of targeted modeling process. This paper introduces DAMA, a novel framework for Dual Alignment for Motivation-Aware recommendation. DAMA is the first framework to systematically incorporate LLM alignment into recommender system modeling. It employs a dual alignment mechanism that jointly optimizes semantic representations from two dimensions: user motivation modeling and product attribute modeling. By leveraging natural language feedback, DAMA facilitates a structured and interpretable alignment process. Extensive experimental results on multiple real-world datasets demonstrate that DAMA significantly improves recommendation performance, especially in cold start scenarios. We anticipate that DAMA will pioneer a new alignment-centric and semantically grounded paradigm for recommender systems.

Credit card fraud detection (CCFD) is a critical application of Machine Learning (ML) in the financial sector, where accurately identifying fraudulent transactions is essential for mitigating financial losses. ML models have demonstrated their effectiveness in fraud detection task, in particular with the tabular dataset. While adversarial attacks have been extensively studied in computer vision and deep learning, their impacts on the ML models, particularly those trained on CCFD tabular datasets, remains largely unexplored. These latent vulnerabilities pose significant threats to the security and stability of the financial industry, especially in high-value transactions where losses could be substantial. To address this gap, in this paper, we present a holistic framework that investigate the robustness of CCFD ML model against adversarial perturbations under different circumstances. Specifically, the gradient-based attack methods are incorporated into the tabular credit card transaction data in both black- and white-box adversarial attacks settings. Our findings confirm that tabular data is also susceptible to subtle perturbations, highlighting the need for heightened awareness among financial technology practitioners regarding ML model security and trustworthiness. Furthermore, the experiments by transferring adversarial samples from gradient-based attack method to non-gradient-based models also verify our findings. Our results demonstrate that such attacks remain effective, emphasizing the necessity of developing robust defenses for CCFD algorithms.