With the widespread use of social media platforms and people’s increasing dependence on them, social media has emerged as one of the most important channels for advertorials. However, there is currently a lack of research on detecting advertorials on social media platforms. This research focuses on detecting advertorials, a type of advertisement that frequently conceals itself within normal articles, blurring the nature of advertising and deceiving users. To effectively carry out research on advertorial detection, we have constructed a multi-topic advertorial dataset in Chinese with rich social information. This dataset is obtained from the Chinese question-answering platform ZHIHU, and it is publicly available to facilitate further research. 1 Furthermore, we propose AdDetector, a novel dual-tower model that detects advertorials by jointly leveraging the article’s textual and social information. In addition, we use fine-grained sentence-level classification labels to improve the model’s generalization capability on previously unseen topic articles. Experiment results show that our model significantly improves the \(F_1\) score by 1.29% in the intra-domain advertorial detection setting and 1.52% in the transfer setting in comparison with several strong baselines. The extensive ablation studies and thorough performance analyses also validate the complementary and beneficial values of the novel components of AdDetector. We also make our source code publicly available to facilitate future studies. 2 This research provides crucial support for user protection and advertising management.

The advancement of 3D understanding and representation is a crucial step for the next phase of autonomous driving, robotics, augmented and virtual reality, 3D gaming and 3D e-commerce products. However, existing 3D semantic representation research has primarily focused on point clouds to perceive 3D objects and scenes, overlooking the rich visual details offered by multi-view images, thereby limiting the potential of 3D semantic representation. This paper introduces OpenView, a novel representation method that integrates both point clouds and multi-view images to form a unified 3D representation. OpenView comprises a unique fusion framework, sequence-independent modeling, a cross-modal fusion encoder, and a progressive hard learning strategy. Our experiments demonstrate that OpenView outperforms the state-of-the-art by 11.5% and 5.5% on the R@1 metric for cross-modal retrieval and the Top-1 metric for zero-shot classification tasks, respectively. Furthermore, we showcase some applications of OpenView: 3D retrieval, 3D captioning and hierarchical data clustering, highlighting its generality in the field of 3D representation learning.

Telecommunication fraud refers to deceptive activities in the field of communication services. This research focuses on a category of fraud identified as ''mutation telecommunication fraud". There is currently a lack of research on mutation telecommunication fraud detection, allowing this type of fraud to persist uncaught. We identify that detecting mutation fraud requires capturing multi-source patterns, including user communication graphs and temporal-spatial Voice of Call (VOC) features. Specifically, we introduce MutationGuard, which leverages Graph Neural Networks (GNN) to capture changes in user communication graphs. For VOC records, we map call start times onto a 3D cylindrical surface, thereby representing each VOC record in spatial coordinates and utilizing proposed LFFE and TCFE modules to capture local fraud behaviors and temporal behavior changes. The proposed neural modeling approach that facilitates multi-source information fusion constitutes a significant advancement in detecting mutation fraud. Experiment results reveal a significant improvement in the AUC score by 1.52% and the F1 score by 1.36% on the proposed telecommunication fraud dataset. Particularly, our method shows a significant improvement of 13.93% in accuracy on mutation fraud data. We also validate the effectiveness of our method on the publicly available Sichuan Telecommunication Fraud dataset.

The drastic increase in language models' parameters has led to a new trend of deploying models in cloud servers, raising growing concerns about private inference for Transformer-based models. Existing two-party privacy-preserving techniques, however, only take into account natural language understanding (NLU) scenarios. Private inference in natural language generation (NLG), crucial for applications like translation and code completion, remains underexplored. In addition, previous privacy-preserving techniques suffer from convergence issues during model training and exhibit poor inference speed when used with NLG models due to the neglect of time-consuming operations in auto-regressive generations. To address these issues, we propose a fast private text generation framework for Transformer-based language models, namely MERGE. MERGE reuses the output hidden state as the word embedding to bypass the embedding computation and reorganize the linear operations in the Transformer module to accelerate the forward procedure. Extensive experiments show that MERGE achieves a 26.5x speedup to the vanilla encrypted model under the sequence length 512, and reduces 80% communication cost, with an up to 10x speedup to state-of-the-art approximated models.

Deep neural networks are widely used in retrieval systems. However, they are notoriously vulnerable to attack. Among the various forms of adversarial attacks, the patch attack is one of the most threatening forms. This type of attack can introduce cognitive biases into the retrieval system by inserting deceptive patches into images. Despite the seriousness of this threat, there are still no well-established solutions in image retrieval systems. In this paper, we propose the Pre-denosing Augmented Image Retrieval (PAIR) model, a new approach designed to protect image retrieval systems against adversarial patch attacks. The core strategy of PAIR is to dynamically and randomly reconstruct entire images based on their semantic content. This purifies well-designed patch attacks while preserving the semantic integrity of the images. Furthermore, we present a novel training strategy that incorporates a semantic discriminator. This discriminator significantly improves PAIR's ability to capture real semantics and reconstruct images. Experiments show that PAIR significantly outperforms existing defense methods. It effectively reduces the success rate of two state-of-the-art patch attack methods to below 5%, achieving a 14% improvement over current leading methods. Moreover, in defending against global perturbation attacks, PAIR also achieves competitive results.

Topic segmentation aims to reveal the latent structure of a document and divide it into multiple parts. However, current neural solutions are limited in the context modeling of sentences and feature representation of candidate boundaries. This causes the model to suffer from inefficient sentence context encoding and noise information interference. In this paper, we design a new text segmentation model SegFormer with unidirectional attention blocks to better model sentence representations. To alleviate the problem of noise information interference, SegFormer uses a novel additional context aggregator and a topic classification loss to guide the model to aggregate the information within the appropriate range. In addition, SegFormer applies an iterative prediction algorithm to search for optimal boundaries progressively. We evaluate SegFormer's generalization ability, multilingual ability, and application ability on multiple challenging real-world datasets. Experiments show that our model significantly improves the performance by 7.5% on the benchmark WIKI-SECTION compared to several strong baselines. The application of SegFormer to a real-world dataset to separate normal and advertisement segments in product marketing essays also achieves superior performance in the evaluation with other cutting-edge models.