In the big data era, integrating diverse data modalities poses significant
challenges, particularly in complex fields like healthcare. This paper
introduces a new process model for multimodal Data Fusion for Data Mining,
integrating embeddings and the Cross-Industry Standard Process for Data Mining
with the existing Data Fusion Information Group model. Our model aims to
decrease computational costs, complexity, and bias while improving efficiency
and reliability. We also propose "disentangled dense fusion", a novel embedding
fusion method designed to optimize mutual information and facilitate dense
inter-modality feature interaction, thereby minimizing redundant information.
We demonstrate the model's efficacy through three use cases: predicting
diabetic retinopathy using retinal images and patient metadata, domestic
violence prediction employing satellite imagery, internet, and census data, and
identifying clinical and demographic features from radiography images and
clinical notes. The model achieved a Macro F1 score of 0.92 in diabetic
retinopathy prediction, an R-squared of 0.854 and sMAPE of 24.868 in domestic
violence prediction, and a macro AUC of 0.92 and 0.99 for disease prediction
and sex classification, respectively, in radiological analysis.
These results underscore the Data Fusion for Data Mining model's potential to
significantly impact multimodal data processing, promoting its adoption in
diverse, resource-constrained settings.

在大数据时代，将多样化的数据模态融合在一起对于像医疗保健等复杂领域来说是一项巨大的挑战。本文介绍了一种新的多模态数据融合数据挖掘过程模型，将嵌入和跨行业标准数据挖掘流程与现有的数据融合信息团组合模型相结合。我们的模型旨在降低计算成本、复杂性和偏差，同时提高效率和可靠性。我们还提出了一种名为 “分离的密集融合” 的新型嵌入融合方法，旨在优化互信息和促进稠密的跨模态特征交互，从而最小化冗余信息。我们通过三个案例展示了该模型的有效性：使用视网膜图像和患者元数据预测糖尿病性视网膜病变、使用卫星图像、互联网和人口普查数据预测家庭暴力，以及从放射影像和临床笔记中识别临床和人口统计特征。该模型在糖尿病性视网膜病变预测中的宏 F1 分数为 0.92，在家庭暴力预测中的 R 方为 0.854，sMAPE 为 24.868，在放射学分析中的宏 AUC 分别为 0.92 和 0.99，这些结果凸显了数据挖掘模型在多模态数据处理中的潜力，推动其在不同的资源受限环境中的应用。

DF-DM：人工智能时代的多模态数据融合的基础过程模型

DF-DM: A foundational process model for multimodal data fusion in the  artificial intelligence era

Semi-supervised learning (SSL) seeks to enhance task performance by training
on both labeled and unlabeled data. Mainstream SSL image classification methods
mostly optimize a loss that additively combines a supervised classification
objective with a regularization term derived solely from unlabeled data. This
formulation neglects the potential for interaction between labeled and
unlabeled images. In this paper, we introduce InterLUDE, a new approach to
enhance SSL made of two parts that each benefit from labeled-unlabeled
interaction. The first part, embedding fusion, interpolates between labeled and
unlabeled embeddings to improve representation learning. The second part is a
new loss, grounded in the principle of consistency regularization, that aims to
minimize discrepancies in the model's predictions between labeled versus
unlabeled inputs. Experiments on standard closed-set SSL benchmarks and a
medical SSL task with an uncurated unlabeled set show clear benefits to our
approach. On the STL-10 dataset with only 40 labels, InterLUDE achieves 3.2%
error rate, while the best previous method reports 14.9%.

本研究提出了一种新的半监督学习方法 InterLUDE，通过嵌入融合和一种新的损失函数（基于一致性正则化原则），在标记和未标记数据之间实现相互作用，以提高 SSL 图像分类的性能。实验结果表明，InterLUDE 在标准闭集 SSL 基准测试和医学 SSL 任务中表现出明显的优势。在只有 40 个标签的 STL-10 数据集上，InterLUDE 的错误率为 3.2％，而最佳先前方法的错误率为 14.9％。

InterLUDE：标记数据与未标记数据之间的交互以增强半监督学习

InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance  Semi-Supervised Learning

Aspect-based sentiment analysis plays an essential role in natural language
processing and artificial intelligence. Recently, researchers only focused on
aspect detection and sentiment classification but ignoring the sub-task of
detecting user opinion span, which has enormous potential in practical
applications. In this paper, we present a new Vietnamese dataset (UIT-ViSD4SA)
consisting of 35,396 human-annotated spans on 11,122 feedback comments for
evaluating the span detection in aspect-based sentiment analysis. Besides, we
also propose a novel system using Bidirectional Long Short-Term Memory (BiLSTM)
with a Conditional Random Field (CRF) layer (BiLSTM-CRF) for the span detection
task in Vietnamese aspect-based sentiment analysis. The best result is a 62.76%
F1 score (macro) for span detection using BiLSTM-CRF with embedding fusion of
syllable embedding, character embedding, and contextual embedding from
XLM-RoBERTa. In future work, span detection will be extended in many NLP tasks
such as constructive detection, emotion recognition, complaint analysis, and
opinion mining. Our dataset is freely available at
this https URL for research purposes.

本研究提出了一种基于 BiLSTM-CRF 的新颖系统，利用收集的词汇和语境特征，针对基于方面的情感分析中的波及用户观点的判定问题，利用越南语语言数据集进行评估，最终实现了 62.76% 的 F1 分数的最优解。