客户的评论在在线购物中起着至关重要的作用。人们经常参考以前客户的评论或评论，以决定是否购买新产品。赶上这种行为，有些人会为骗子的客户创建不真实的评论，以了解产品的假质量。这些评论称为垃圾邮件评论，它使消费者在在线购物平台上混淆，并对在线购物行为产生负面影响。我们提出了称为Vispamreviews的数据集，该数据集具有严格的注释程序，用于检测电子商务平台上的垃圾邮件评论。我们的数据集由两个任务组成：用于检测评论是否为垃圾邮件的二进制分类任务以及用于识别垃圾邮件类型的多类分类任务。Phobert在这两个任务上均以宏平均F1分别获得了最高的结果，分别为88.93％和72.17％。

由于相似的外观产品及其各种姿势，在人类级别的精度上设计自动结帐系统为零售商店的精度而言具有挑战性。本文通过提出具有两阶段管道的方法来解决问题。第一阶段检测到类不足的项目，第二阶段专门用于对产品类别进行分类。我们还在视频帧中跟踪对象，以避免重复计数。一个主要的挑战是域间隙，因为模型经过合成数据的训练，但对真实图像进行了测试。为了减少误差差距，我们为第一阶段检测器采用域泛化方法。此外，模型集合用于增强第二阶段分类器的鲁棒性。该方法在AI City Challenge 2022 -Track 4上进行了评估，并在测试A集合中获得F1分40美元\％$。代码在链接https://github.com/cybercore-co-ltd/aicity22-track4上发布。

自我监督学习（SSL）利用基础数据结构来生成培训深网络的监督信号。这种方法提供了一种实用的解决方案，可用于学习多重免疫荧光大脑图像，其中数据通常比人类专家注释更丰富。基于对比度学习和图像重建的SSL算法表现出令人印象深刻的性能。不幸的是，这些方法是在自然图像而不是生物医学图像上设计和验证的。最近的一些作品已应用SSL来分析细胞图像。然而，这些作品均未研究SSL对多重免疫荧光脑图像的研究。这些作品还没有为采用特定的SSL方法提供明确的理论理由。在这些局限性的激励下，我们的论文介绍了从信息理论观点开发的一种自我监督的双损坏自适应掩盖自动编码器（DAMA）算法。 Dama的目标函数通过最大程度地降低像素级重建和特征级回归中的条件熵来最大化相互信息。此外，Dama还引入了一种新型的自适应掩码采样策略，以最大程度地提高相互信息并有效地学习脑细胞数据上下文信息。我们首次在多重免疫荧光脑图像上提供了SSL算法的广泛比较。我们的结果表明，Dama优于细胞分类和分割任务的其他SSL方法。 Dama还可以在Imagenet-1k上实现竞争精确度。 Dama的源代​​码可在https://github.com/hula-ai/dama上公开获得

近年来，由于其网状柔性和计算效率，近年来，部分微分方程（PDE）的深度学习方法受到了很多关注。但是，到目前为止，大多数作品都集中在时间依赖性的非线性微分方程上。在这项工作中，我们用众所周知的物理知情神经网络分析了潜在问题，用于微分方程，边界上的约束很少（即，约束仅在几个点上）。这种分析促使我们引入了一种名为Finnet的新技术，用于通过将有限的差异纳入深度学习来解决微分方程。即使我们在训练过程中使用网格，预测阶段也不是网状的。我们通过解决各种方程式的实验来说明我们方法的有效性，这表明Finnet可以求解较低的错误率，即使Pinns不能，也可以工作。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

In this work, we propose a new approach that combines data from multiple sensors for reliable obstacle avoidance. The sensors include two depth cameras and a LiDAR arranged so that they can capture the whole 3D area in front of the robot and a 2D slide around it. To fuse the data from these sensors, we first use an external camera as a reference to combine data from two depth cameras. A projection technique is then introduced to convert the 3D point cloud data of the cameras to its 2D correspondence. An obstacle avoidance algorithm is then developed based on the dynamic window approach. A number of experiments have been conducted to evaluate our proposed approach. The results show that the robot can effectively avoid static and dynamic obstacles of different shapes and sizes in different environments.

We introduce an approach for the answer-aware question generation problem. Instead of only relying on the capability of strong pre-trained language models, we observe that the information of answers and questions can be found in some relevant sentences in the context. Based on that, we design a model which includes two modules: a selector and a generator. The selector forces the model to more focus on relevant sentences regarding an answer to provide implicit local information. The generator generates questions by implicitly combining local information from the selector and global information from the whole context encoded by the encoder. The model is trained jointly to take advantage of latent interactions between the two modules. Experimental results on two benchmark datasets show that our model is better than strong pre-trained models for the question generation task. The code is also available (shorturl.at/lV567).

Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query. However, XAI also opens a door for adversaries to gain insights into the black-box models in MLaaS, thereby making the models more vulnerable to several attacks. For example, feature-based explanations (e.g., SHAP) could expose the top important features that a black-box model focuses on. Such disclosure has been exploited to craft effective backdoor triggers against malware classifiers. To address this trade-off, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that we establish a defense, called XRand, against such attacks. We show that our mechanism restricts the information that the adversary can learn about the top important features, while maintaining the faithfulness of the explanations.

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.