With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.

Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Finally, we present extensive experiments and case studies to illustrate 24.7\% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.

图对比度学习（GCL）一直是图形自学学习的新兴解决方案。 GCL的核心原理是在正视图中降低样品之间的距离，但在负视图中增加样品之间的距离。在实现有希望的性能的同时，当前的GCL方法仍然受到两个局限性：（1）增强的不可控制的有效性，该图扰动可能会产生针对语义和图形数据的特征流程的无效视图； （2）不可靠的二进制对比理由，对于非欧几里得图数据而言，难以确定构造观点的积极性和负面性。为了应对上述局限性，我们提出了一个新的对比度学习范式，即图形软对比度学习（GSCL），该范例通过排名的社区无需任何增强和二进制对比符合性，在较细性的范围内进行对比度学习。 GSCL建立在图接近的基本假设上，即连接的邻居比遥远的节点更相似。具体而言，我们在配对和列表的封闭式排名中，以保留附近的相对排名关系。此外，随着邻里规模的指数增长，考虑了更多的啤酒花，我们提出了提高学习效率的邻里抽样策略。广泛的实验结果表明，我们提出的GSCL可以始终如一地在各种公共数据集上实现与GCL相当复杂的各种公共数据集的最新性能。

资金机构在很大程度上依赖于领域专家与研究建议之间的主题匹配来分配提案审查员。随着建议越来越跨学科，概述提案的跨学科性质是一项挑战，此后，找到具有适当专业知识的专家审阅者。解决这一挑战的重要步骤是准确对建议的跨学科标签进行分类。现有的方法论和申请相关文献，例如文本分类和提案分类，不足以共同解决跨学科建议数据引入的三个关键独特问题：1）提案的纪律标签的层次结构，谷物，例如，从信息科学到AI，再到AI的基础。 2）在提案中起着不同作用的各种主要文本部分的异质语义； 3）提案的数量在非学科和跨学科研究之间存在不平衡。我们可以同时解决该提案的跨学科性质时的三个问题吗？为了回答这个问题，我们提出了一个层次混音多标签分类框架，我们称之为H-Mixup。 H-Mixup利用基于变压器的语义信息提取器和基于GCN的跨学科知识提取器来解决第一期和第二个问题。 H-Mixup开发了Wold级混音，Word级cutmix，歧管混音和文档级混音的融合训练方法，以解决第三期。

The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers, so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principal investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs for learning representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

功能转换旨在通过数学转换现有功能来提取良好的表示（功能）空间。应对维度的诅咒，增强模型概括，克服数据稀疏性并扩大经典模型的可用性至关重要。当前的研究重点是基于领域的知识特征工程或学习潜在表示；然而，这些方法并非完全自动化，不能产生可追溯和最佳的表示空间。在重建机器学习任务的功能空间时，可以同时解决这些限制吗？在这项扩展研究中，我们提出了一个用于特征转化的自优化框架。为了取得更好的性能，我们通过（1）获得高级状态表示来改善初步工作，以使加强代理能够更好地理解当前功能集； （2）解决Q值高估的Q值高估，以学习无偏见和有效的政策。最后，为了使实验比初步工作更具说服力，我们结论是通过五个数据集添加异常检测任务，评估各种状态表示方法，并比较不同的培训策略。广泛的实验和案例研究表明，我们的工作更有效和更高。

联合学习（FL）在分布式客户端上培训机器学习模型，而不会暴露单个数据。与通常基于仔细组织的数据的集中培训不同，FL处理通常不混阻和不平衡的设备数据。因此，处理所有数据的传统流行训练协议同样地导致浪费本地计算资源，并减慢全局学习过程。为此，我们提出了一个系统性的FLBalancer，它积极选择客户的培训样本。我们的示例选择策略在尊重客户端的隐私和计算能力的同时优先确定更多“信息性”数据。为了更好地利用样本选择来加速全球培训，我们进一步推出了一种自适应截止日期控制方案，该方案预测每个轮的最佳截止日期，具有不同的客户端列车数据。与具有截止日期配置方法的现有流算法相比，我们对三个不同域的五个数据集的评估表明，FedBalancer将时间至准确性的性能提高1.22〜4.62倍，同时提高模型精度1.0〜3.3％。我们还表明，通过展示在与三种不同的FL算法共同运行时，FedBalancer提高了收敛速度和准确性，可以随时适用于其他流动方法。

对手补丁攻击旨在通过在输入图像的限制区域内任意修改像素来欺骗机器学习模型。这种攻击是对部署在物理世界中的模型的主要威胁，因为通过在相机视图中呈现自定义对象，可以容易地实现它们。由于补丁的任意性，防止这种攻击措施是挑战，并且现有的可提供防御遭受较差的认证准确性。本文提出了根据视觉变压器（VIV）模型的对抗对抗斑块的零点认证防御。而不是训练强大的模型来抵抗可能不可避免地牺牲精度的对抗斑块，而是在没有任何额外训练的情况下重用预纯的VIT模型，这可以通过简单地操纵Vit的注意力地图来达到清洁输入的高精度。具体地，通过用不同的关注掩模投票来测试每个输入，其中至少有一个推断被保证排除对抗修补程序。如果所有掩蔽的推迟达到共识，则预测是可疑的，这确保了不会用假阴性检测到任何对抗性贴剂。广泛的实验表明，PACKVETO能够实现高认证的准确性（例如，在Imagenet中为2％-pixel对抗性贴片的67.1％），显着优于最先进的方法。清洁精度与Vanilla Vit模型相同（想象成81.8％），因​​为模型参数直接重复使用。同时，通过简单地改变掩蔽策略，我们的方法可以灵活地处理不同的对抗斑块尺寸。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.