Recommendation algorithms, as an extremely important information filtering and pushing tool, have been widely applied in many fields such as e-commerce, social networks, and online audio-visual entertainment. In research in recent years, multimodal recommendation algorithms that integrate multimodal information of items (such as text, images, etc.) and users' historical interaction information have achieved good results. However, most of the existing multimodal recommendation algorithms adopt the decision - level fusion method, this approach makes it difficult to capture the complementary information between modalities. Moreover, the few recommendation algorithms that adopt feature - level fusion have also failed to achieve good recommendation results. In view of this, we propose a feature-level fusion method based on cross-attention mechanism to capture the correlations between various modalities. In addition, in order to alleviate the problems of data sparsity and weak self-supervised signals faced by graph representation learning, we introduce graph contrastive learning into the model. At the same time, considering the long-tail problem of data and in order to avoid the uneven learning representation caused by the difference in graph structure between head data and tail data in traditional graph enhancement methods, we propose a long-tail graph enhancement method. We have carried out a large number of experiments on two real datasets and compared with other advanced baseline models. The results show that our model has been significantly improved in multiple evaluation indicators.

Ironic language, prevalent on social media, Existing sarcasm detection methods face three main limitations: (1) the implicit and context-dependent nature of irony, requiring advanced reasoning; (2) substantial computational overhead in locally fine-tuning large language models (LLMs); and (3) insufficient detection performance due to a narrow analysis of sarcastic expressions. To address these issues, we propose the Multi-Agent Sarcasm Detection framework with Chain-of-Thought (MASD). MASD introduces a Chain-of-Thought prompting paradigm that combines syntactic cues with deep semantic reasoning. A KNN-based sampling technique identifies semantically similar examples to produce a robust LLM emulation template. Leveraging LLM inferential capabilities, MASD enables effective sarcasm detection with minimal reliance on extensive pre-trained datasets. Moreover, its multi-agent architecture allows for the collaborative extraction of contextual, emantic and rhetorical insights. By incorporating a self-reflection mechanism, LLMs continuously refine their reasoning logic. Experiments conducted on benchmark datasets IAC-V1, IAC-V2, and MUSTARD within real-world scenarios reveal enhancements in Macro-F1 scores by 5.04%, 1.57%, and 8.82%, respectively. underscoring the framework's effectiveness and scalability, with substantial implications for advancing sarcasm detection methodologies.

The high precision measurement of binocular camera depends on the calibration accuracy. Mainstream methods focus on the calibration of intrinsic parameters and the pose relationship of two cameras in sequence. However, the transmission of error will decrease the calibration quality. It is still challenging especially in the case of an unknown imaging model due to the intervention of baffle and multimedia refraction. In this article, a general calibration method for a binocular vision system with an unknown imaging model is proposed. Based on the one-to-one mapping between 4-D image coordinates and 3-D Cartesian coordinates of spatial points, the intersection line of two calibration planes is extracted. All calibration planes are organized in the form of a three-plane group, and in each group, the poses are solved by the intersection lines. These poses are further optimized to acquire accurate 3-D coordinates of points in calibration planes. Combined with the correspondences of image pixels and points in a calibration plane, the line-plane intersection point between the projection line and this plane is calculated. Finally, projection lines are fitted based on corresponding line-plane intersection points. By modeling the relationship between image pixels and object-end projection lines, the uncertainty of the imaging model is solved. The proposed method is universal and stable for different imaging models including refraction, and the results of simulation and experiment show its effectiveness.

In the context of Industry 4.0, intelligent manufacturing services put forward the requirement of rapid response in the remaining useful life (RUL) of machinery. To achieve fast-responding and highly accurate RUL prediction services while mining the intra-kernel correlations of the sensor monitoring data, a deep learning-based cloud–edge collaboration framework was proposed in this article. We encapsulated a cloud prediction engine (Cloud-PE) with a deep prediction model in the cloud service layer and an edge prediction engine (Edge-PE) with a shallow prediction model in the edge service layer. The Cloud-PE assisted the Edge-PE in achieving fast and highly accurate RUL prediction by sharing depth model parameters. Both prediction models were constructed on the basis of a novel blueprint separable convolution neural network. To continuously improve the performance of Edge-PE in a context-aware manner, we adopted an update method for the Edge-PE with the assistance of the Cloud-PE. The experimental results demonstrated that the proposed framework can provide more accurate RUL prediction than existing data-driven prediction methods, and the training time of the prediction model is also significantly reduced.

MOTIVATION: Environmental dissemination of antibiotic resistance genes (ARGs) has become an increasing concern for public health. Metagenomics approaches can effectively detect broad profiles of ARGs in environmental samples; however, the detection and subsequent classification of ARG-like sequences are time consuming and have been severe obstacles in employing metagenomic methods. We sought to accelerate quantification of ARGs in metagenomic data from environmental samples. RESULTS: A Structured ARG reference database (SARG) was constructed by integrating ARDB and CARD, the two most commonly used databases. SARG was curated to remove redundant sequences and optimized to facilitate query sequence identification by similarity. A database with a hierarchical structure (type-subtype-reference sequence) was then constructed to facilitate classification (assigning ARG-like sequence to type, subtype and reference sequence) of sequences identified through similarity search. Utilizing SARG and a previously proposed hybrid functional gene annotation pipeline, we developed an online pipeline called ARGs-OAP for fast annotation and classification of ARG-like sequences from metagenomic data. We also evaluated and proposed a set of criteria important for efficiently conducting metagenomic analysis of ARGs using ARGs-OAP. AVAILABILITY AND IMPLEMENTATION: Perl script for ARGs-OAP can be downloaded from https://github.com/biofuture/Ublastx_stageone ARGs-OAP can be accessed through http://smile.hku.hk/SARGs CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract The impact of frame semantic enrichment of texts on the task of factoid question answering (QA) is studied in this paper. In particular, we consider different techniques for answer processing with frame semantics: the level of semantic class identification and role assignment to texts, and the fusion of frame semantic‐based answer‐processing approaches with other methods used in the Text REtrieval Conference (TREC). The impact of each of these aspects on the overall performance of a QA system is analyzed in this paper. The TREC 2004 and TREC 2006 factoid question sets were used for the experiments. These demonstrate that the exploitation of encapsulated frame semantics in FrameNet in a shallow semantic parsing process can enhance answer‐processing performance in factoid QA systems. This improvement is dependent on the level of semantic annotation, the frame semantic alignment method, and the method of fusing frame semantic‐based answer‐processing models with other existing models. A more comprehensively annotated environment with all different part‐of‐speech target predicates provides a higher chance of correct factoid answer retrieval where semantic alignment is based on both semantic classes and a relaxed set of semantic roles for answer span identification. Our experiments on fusion techniques of frame semantic‐based and entity‐based answer‐processing models show that merging answer lists with respect to their scores and redundancy by exploiting a fusion function leads to a more effective overall factoid QA system compared to the use of individual models.

This paper presents a new two-phase pattern (2PP) discovery technique for information extraction. 2PP consists of orthographic pattern discovery (OPD) and semantic pattern discovery (SPD) where the OPD determines the structural features from an identified region of a document and the SPD discovers a dominant semantic pattern for the region via inference, apposition and analogy. Then the discovered pattern is applied back into the region to extract required data items through pattern matching. We evaluated 2PP using 6500 data items and obtained effective result.

This paper describes a novel approach to document classification that uses decision-tree machine learning based on a succinct vector of important terms in each document. The succinct vector itself is generated by a machine-learning approach which builds parsers that can identify significant features in a document by partitioning it into regions based on low-level document characteristics. The fact that the feature vector is succinct overcomes the problem of very large term vectors, which have hindered the application of conventional machine learning to document classification. The fact that the parser can be trained to extract only important terms from documents means that small training sets can be used to achieve the same classification accuracy as with conventional approaches.