Accurately modeling opponent behaviors and integrating strategy are key challenges for multi-issue automated negotiation. Existing approaches often isolate preference learning or trend prediction and lack a unified cognitive structure with coordinated reasoning. This paper proposes a BDI (Belief-Desire-Intention)-based opponent modeling and strategy generation framework. The framework analyzes opponent responses (Belief), predicts preference weights and the utility function (Desire), and infers utilities of future offers (Intention). Building on these predictions, we design a responsive strategy, enabling gradual concessions and balanced outcomes. Our main contributions are: D-MBUE in the Desire module, I-DABI in the Intention module, and the BDI Negotiator on top of the modeling modules. Experiments on 45 standard negotiation domains and against 12 representative opponents demonstrate the effectiveness of our BDI framework.

With the evolving generative models, generated images are closer to reality, raising concerns about information authenticity and malicious misuse. Invisible watermarks offer a practical approach to detecting and tracing them. However, while image watermarking inevitably introduces quality degradation, most existing methods primarily focus on improving watermark robustness. To address this limitation, we propose AWMA-MoE, a framework that enhances the quality of generated images while preserving strong watermark robustness. Specifically, we design an attention-based adapter that adaptively embeds watermarks with spatially varying strengths across image regions. Building upon this, we introduce an MoE architecture that leverages diverse experts to further improve image quality while retaining watermark robustness. Experiments demonstrate that AWMA-MoE can reduce the distortion of generated images and exhibit competitive watermark performance, thus striking an improved balance for watermarking generated image tasks and better linking post-hoc and in-generation methods.

Multi-view Multi-label Learning (MVML) aims to leverage multi-view information from input samples to achieve accurate predictions of multiple labels. Unfortunately, most existing MVML methods operate under the assumption of data completeness, which makes them ineffective in practical scenarios involving missing views or uncertain labels. Recent methods address incomplete data, but few approaches handle scenarios where both views and labels are missing. To address this challenge, we propose a Dual-view Feature-guided Fusion Learning (DFFL) framework. DFFL considers both view-specific unique features and inter-view consistent features. Specifically, DFFL constructs view-uniqueness contrastive learning to ensure that features within the same view maintain high semantic relevance under the condition of view missing, while the semantics between different views are different. Unlike previous methods, DFFL assumes that label relevance can be reversely mapped to high-dimensional features. By establishing View-consistency learning, the mutual information in the shared embedding space is maximized to achieve consistent feature alignment. In particular, DFFL minimizes the conditional entropy of the marginal distribution of multi-view features through dual prediction, thereby deriving the maximum joint distribution of feature fusion and combining the missing view index matrix to achieve feature fusion. This process can effectively alleviate the fusion feature suppression existing in previous methods. Finally, the missing label index matrix is combined with the fusion feature to complete the classification task. We validate the framework on five widely used datasets, and experimental results demonstrate that our approach achieves superior performance compared to state-of-the-art methods. Ablation studies further validated the effectiveness of each component in DFFL.

In this paper, we present a new algorithm named Distributed Multi-root Pipeline MCTS (DMP-MCTS), to improve real-time search efficiency in multi-machine scenarios. By utilizing root parallel technology with significance detection and pipeline pattern for parallel MCTS (3PMCTS), we not only reduce the repetitive computing tasks among subtrees but also allow for flexible operation time based resource allocation. The experiment shows this work achieves better computational performance under linear acceleration conditions, compared with other existing works.

Regulatory module mining methods divide genes into multiple gene subgroups and explore potential biological mechanisms from omics data. By transforming gene expression profile data into gene co-expression network, we transform the task of gene module detection into the problem of finding community structure in the graph, and introduce the latest network representation learning method-graph neural network to optimize this problem. In order to systematically evaluate whether the algorithm allows overlap to affect such problems, we make two variants of the output of the algorithm, Deepgmd_cluster and Deepgmd. The difference between them is whether overlap is allowed. By comparing the known modules and the modules generated by the algorithm, we can evaluate the quality of the algorithm. We use this method to compare our algorithm with some current mainstream methods. The results show that our method has greater advantages. In the end, we analyze some typical modules from the modules found by the algorithm for visualization, and use the GO database and KEGG database to perform enrichment analysis and pathway analysis on these modules.

Astounding results from transformer models with Vision-and Language Pretraining (VLP) on joint vision-and-language downstream tasks have intrigued the multi-modal community. On the one hand, these models are usually so huge that make us more difficult to fine-tune and serve real-time online applications. On the other hand, the compression of the original transformer block will ignore the difference in information between modalities, which leads to the sharp decline of retrieval accuracy.

With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the first work that considers collusion between a malicious garbled circuit evaluator and a semi-honest server while ensuring privacy and correctness. Also, it can guarantee fairness when collusion does not exist. The security analysis shows that our protocol can securely compute any function f(x, y) against different types of adversaries in the malicious model. Also, the experimental performance analysis shows that this work outperforms the previous works for at least 10 times with the same security level.