Retrieval-augmented generation (RAG) has shown promising potential in knowledge intensive question answering (QA). However, existing approaches only consider the query itself, neither specifying the retrieval preferen...
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Document-level event extraction task has achieved significant progress based on template generation methods. However, there is no reasonable regulation and restriction in the existing template-based generation methods...
Document-level event extraction task has achieved significant progress based on template generation methods. However, there is no reasonable regulation and restriction in the existing template-based generation methods, which results in the uncontrollability of the generation results. In some scenarios, model generates entities that do not belong to the input text, or generate template content repeatedly. It is determined by the nature of the extraction task and the generation task. To this end, we propose a controllable template generation event extraction model. According to the characteristics of template generation and event extraction tasks, the model devises copy mechanism, inhibition mechanism and rejection mechanism under the appropriately constructed template. Our model achieves state-of-the-art result on MUC-4 dataset, and finally through experimental analysis, it demonstrates the effectiveness of each mechanism we proposed.
Effectively assessing the results of users' online learning and enhancing social recognition has become a major development direction for online education platforms. For computer education, this article constructs...
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Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise rati...
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Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio,these spectra often show some anomalous features that do not work well with current *** this paper,a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected,and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame(Spectra Analysis-Frame) is provided to explore their origins from different *** SA-Frame is composed of three parts:NAPC-Spec clustering,characterization and origin ***,NAPC-Spec(Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality,resulting in 13 ***,characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum,where these 13 types are characterized as regular spectra with low S/Ns,splicing problems,suspected galactic emission signals,contamination from city light and un-gregarious type ***,a preliminary analysis of their origins is made from the characteristics of the observational targets,contamination from the sky,and the working status of the *** results would be valuable for improving the overall data quality of large-scale spectral surveys.
With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreove...
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Edge computing is an emerging paradigm poised to process a substantial portion of latency-sensitive and computation-intensive tasks through edge service providers (ESPs). However, these ESPs typically operate independ...
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Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion *** approaches use directional pairwise attention or a message hub to fuse lan...
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Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion *** approaches use directional pairwise attention or a message hub to fuse language,visual,and audio ***,these fusion methods are often quadratic in complexity with respect to the modal sequence length,bring redundant information and are not *** this paper,we propose an efficient neural network to learn modality-fused representations with CB-Transformer(LMR-CBT)for multimodal emotion recognition from unaligned multi-modal ***,we first perform feature extraction for the three modalities respectively to obtain the local structure of the ***,we design an innovative asymmetric transformer with cross-modal blocks(CB-Transformer)that enables complementary learning of different modalities,mainly divided into local temporal learning,cross-modal feature fusion and global self-attention *** addition,we splice the fused features with the original features to classify the emotions of the ***,we conduct word-aligned and unaligned experiments on three challenging datasets,IEMOCAP,CMU-MOSI,and *** experimental results show the superiority and efficiency of our proposed method in both *** with the mainstream methods,our approach reaches the state-of-the-art with a minimum number of parameters.
In the Internet of Everything (IoE), due to its issues of complexity and heterogeneity, message delay cannot be guaranteed, and it is not enough to leverage a centralized model for data collaboration. By leveraging th...
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With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud computing (JCC) is promising to break through the resource constrai...
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Emerging blockchain accounting mechanism allow mutually distributed parties to transport trusted information and ensure the correctness of data. Every blockchain node stores the complete block locally. Although this m...
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