In the field of green product consumption, consumers tend to seek detailed information to assess product efficacy. In recent years, the advent of Generative Artificial Intelligence (GenAI) tools has significantly streamlined consumers’ access to relevant information on green products. However, as emotional factors are decisive in purchase decision-making, existing studies that predominantly focus on rational decision-making frequently overlook this crucial emotional dimension. Addressing this gap, this study adopts the extended emotion heuristic theory to examine the impact of GenAI tools on green product purchase preferences. Using the partial least squares structural equation model, green product consumption data were collected from multiple regions including Chongqing, Guangdong, Hunan, Hubei, Shanghai, Beijing, and others, between January and March 2025. A total of 717 valid responses were analysed using SPSS 28, Amos 28, and Smart PLS 4.0. The results reveal that certain characteristics of GenAI-generated content—specifically, quality (content relevance, content accuracy), communication style (personalisation, anthropomorphism), and serendipity—positively influence purchase intention for green products. Furthermore, emotional connection plays a partial mediating role. These findings extend the application of emotion heuristic theory in the context of artificial intelligence and highlight the significant role of emotional factors in fostering consumption intentions via GenAI tools. The results offer insights for green product marketers and GenAI tool developers to enhance content quality, communication methods, and additional functions, while also informing regulatory policymaking related to GenAI tools.

Federated learning (FL) shows great promise in large-scale machine learning but introduces new privacy and security challenges. We propose ByITFL and LoByITFL, two novel FL schemes that enhance resilience against Byzantine users while preventing eavesdroppers from learning users’ private data. To ensure privacy and Byzantine resilience, our schemes are built on having a small representative dataset available to the federator and crafting a discriminator function allowing mitigating corrupt users’ contributions. ByITFL employs Lagrange coded computing and re-randomization, making it the first Byzantine-resilient FL scheme with perfect Information-Theoretic (IT) privacy, though at the cost of a significant communication overhead. LoByITFL, on the other hand, achieves Byzantine resilience and IT privacy at a significantly reduced communication cost, but requires a Trusted Third Party (TTP), used only before training in a one-time initialization phase. We provide theoretical guarantees of privacy and Byzantine resilience, along with convergence guarantees and experimental results validating our findings.

Personality detection is a fundamental task for user psychology research. One of the biggest challenges in personality detection lies in the quantitative limitation of labeled data collected by completing the personality questionnaire, which is very time-consuming and labor-intensive. Most of the existing works are mainly devoted to learning the rich representations of posts based on labeled data. However, they still suffer from the inherent weakness of the amount limitation of labels, which potentially restricts the capability of the model to deal with unseen data. In this paper, we construct a heterogeneous personality graph for each labeled and unlabeled user and develop a novel psycholinguistic augmented graph neural network to detect personality in a semi-supervised manner, namely Semi-PerGCN. Specifically, our model first explores a supervised Personality Graph Neural Network (PGNN) to refine labeled user representation on the heterogeneous graph. For the remaining massive unlabeled users, we utilize the empirical psychological knowledge of the Linguistic Inquiry and Word Count (LIWC) lexicon for multi-view graph augmentation and perform unsupervised graph consistent constraints on the parameters shared PGNN. During the learning process of finite labeled users, noise-invariant learning on a large scale of unlabeled users is combined to enhance the generalization ability. Extensive experiments on three real-world datasets, Youtube, PAN2015, and MyPersonality demonstrate the effectiveness of our Semi-PerGCN in personality detection, especially in scenarios with limited labeled users.

With the popularization of mobile applications and the timely acquisition of fresh data, real-time clustering and its evolution analysis have become the primary operations for data processing and knowledge discovery. Such continuous queries on massive objects are computation-intensive tasks in dynamic scenarios. However, existing clustering techniques are incompetent to achieve decent performance when computation-intensive operations frequently occur in streaming scenarios, which is caused by two challenges: (i) uncertainty of the clustering frequency; (ii) unpredictable distribution evolution. Hence, it is critical to find a lightweight model that can cluster the high-speed dynamic instances while exploiting the evolution amid different clustering results. This paper focuses on the problem of real-time clustering on streaming data in computation-intensive and high-dynamics tasks, through a framework Ocean, consisting of the Online clustering algorithm and evolution analysis. Particularly, the framework conceives a flexible composite window to augment the knowledge mining, achieving a proper real-time response in various scenarios. The evolution analysis supports full life-cycle detection, improving the adaptability to dynamic concept drifts and multiple patterns. Inspired by the grid partition strategy, this framework adopts grid feature vectors to capture the significant changes in streaming data. Furthermore, we propose an optimization that removes sparse grids timely and performs the online clustering adaptively for space and time efficiency. It is proven to be effective both theoretically and experimentally. This strategy enables real-time clustering for dynamic streaming data without degrading the clustering quality or increasing the computation cost. Experiments on real datasets and synthetic datasets verify the accuracy and effectiveness of Ocean compared to the state-of-the-art approaches, as well as the superior ability to perform clustering in a real-time manner.

Federated learning (FL) shows great promise in large-scale machine learning but brings new risks in terms of privacy and security. We propose ByITFL, a novel scheme for FL that provides resilience against Byzantine users while keeping the users' data private from the federator and private from other users. Our scheme builds on the preexisting non-private FLTrust scheme, which tolerates malicious users through trust scores (TS) that attenuate or amplify the users' gradient updates. The trust scores are based on the ReLU function, which we approximate by a polynomial. The distributed and privacy-preserving computation in ByITFL is designed using a combination of Lagrange coded computing, verifiable secret sharing and re-randomization steps. ByITFL is the first Byzantine resilient scheme for FL with full information-theoretic privacy.

Partial domain adaptation (PDA) assumes that target domain class label set is a subset of that of source domain, while this problem setting is close to the actual scenario. At present, there are mainly two methods to solve the overfitting of source domain in PDA, namely the entropy minimization and the weighted self-training. However, the entropy minimization method may make the distribution prediction sharp but inaccurate for samples with relatively average prediction distribution, and cause the model to learn more error information. While the weighted self-training method will introduce erroneous noise information in the self-training process due to the existence of noise weights. Therefore, we address these issues in our work and propose self-training contrastive partial domain adaptation method (STCPDA). We present two modules to mine domain information in STCPDA. We first design self-training module based on simple samples in target domain to address the overfitting to source domain. We divide the target domain samples into simple samples with high reliability and difficult samples with low reliability, and the pseudo-labels of simple samples are selected for self-training learning. Then we construct the contrastive learning module for source and target domains. We embed contrastive learning into feature space of the two domains. By this contrastive learning module, we can fully explore the hidden information in all domain samples and make the class boundary more salient. Many experimental results on five datasets show the effectiveness and excellent classification performance of our method.

The matching of incorrect pairs affects the precision of the SLAM/VO system. Because the calculation is complex and time-consuming, the various limitation methods struggle to meet the system's real-time requirements. FMS is a novel approach that adapts well to both sparse and dense mapping processes, allowing for the verification of matched pairs of features. By displacing features between two consecutive frames, the projection formula contacts motion patterns. This classifier's overall characteristics demonstrated that it was extremely effective at applying suitable matched pairings from mismatched features. The proposed algorithm outperformed the raw technique in terms of accuracy and stability when compared to the ORB-SLAM technique.

We propose a binocular stereo matching algorithm CS-ELAS. This is a cross-scale ELAS algorithm that improves the accuracy and robustness of parallax in weakly textured regions and edge regions. Our approach focuses on improving the accuracy and number of support point sets. We uniformly sample the stereo images to obtain candidate support point sets and determine robustly matched support point sets based on an adaptive cross skeleton. In this way a richer and more accurate set of support points can be obtained in the weakly textured areas near the edges. In addition, our method uses the parallax and confidence maps of low-resolution images as a priori for high-resolution images and adds high-confidence pixel information to the set of high-resolution support points. In this way, it not only increases the number of support points in weak texture regions, but also narrows the search range of candidate support points and reduces the computational cost.