On event-based social platforms such as Meetup and Douban, online groups serve as more than virtual communities for users to share experiences, they also provide an essential pathway for users to discover and participate in offline events. As the number of groups grows, it imposes the need of the study of online group recommendation. Despite there being many existing approaches to solve this problem, they all ignore the phenomenon that the groups that users participate in often contain a number of similar users. This phenomenon implies that similar users play a crucial role in identifying the groups that users are likely to join. In order to exploit similar users to improve the recommendation performance, we propose an effective multi-scale contrastive learning system for online Group Recommendation services, which is with a two-Tower model in event-based social networks (Tower4GR). Specifically, we first adopt the two-tower model to capture the interactive signals within the sequences and groups. We then incorporate the features of similar users into the sequence encoder, and aggregate the relevant users’ features into the group encoder, through which the preferred groups of similar users are more likely to be discovered by the target user. Finally, we propose an effective multi-scale contrastive learning framework for the two-tower architecture. It derives self-supervision signals from both same-scale data and cross-scale data, thereby extracting more meaningful data patterns. Moreover, the framework strengthens the cooperative associations between two towers. Extensive experiments on three real-world datasets from Meetup demonstrate the superiority of our proposed model over existing state-of-the-art models.

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. Then, a precise diagnose using breast ultrasound (BUS) image would be significant useful. Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define region of interest (ROI) and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and three vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance (GDPH&SYSUCC AUC: 0.924, ACC: 0.893, Spec: 0.836, Sens: 0.926) with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given (GDPH&SYSUCC-AUC ours: 0.924 vs. reader1: 0.825 vs. reader2: 0.820).

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We propose a two-step model including a classification and a segmentation phases. In the classification phase, we propose a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieve tissue semantic segmentation by our propose Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduce a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conduct several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms five state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2% gap for MIoU and FwIoU. By comparing with manual labeling on a randomly sampled 100 patches dataset, patch-level labeling can greatly reduce the annotation time from hours to minutes. The source code and the released datasets are available at: https://github.com/ChuHan89/WSSS-Tissue.

Industrial cyber-physical systems are smart systems, which amalgamate the physical processes with computational capabilities to seamlessly capture, monitor and control the entities and scenarios in industrial environments. Among them, event-based industrial cyber-physical systems (EICPSs), such as Meetup and Plancast, have gained rapid developments. EICPSs provide event recommendation service for groups, which alleviates the information overload problem. However, existing group recommendation models in EICPSs focus on how to aggregate the preferences of group members, failing to model the complex and deep influence of contexts on groups. In this article, we propose an attention-based context-aware group event recommendation model (ACGER) in EICPSs. ACGER models the deep, nonlinear influence of contexts on users, groups, and events through multilayer neural networks. Especially, a novel attention mechanism is designed to enable the influence weights of contexts on users/groups change dynamically with the events concerned. Considering that groups may have completely different behavior patterns from group members, we acquire the preference of a group from two perspectives: indirect preference and direct preference. To obtain the indirect preference, we propose a method of aggregating preferences based on attention mechanism. Compared with existing predefined strategies, this method can flexibly adapt the strategy according to the events concerned by the group. To obtain the direct preference, we employ neural networks to learn it from group-event interactions. Furthermore, to make full use of rich user-event interactions in EICPSs, we integrate the context-aware individual recommendation task into ACGER, which enhances the accuracy of learning of user embeddings and event embeddings. Extensive experiments on three real datasets from Meetup and Douban event show that our model ACGER significantly outperforms the state-of-the-art models.

Nowadays, Radio Frequency Identification (RFID) technology has been widely employed in the fields of object positioning, tracking and monitoring. However, there are a large number of redundant data generated in RFID systems due to duplicate detection and cross detection. Since RFID data is usually streaming, uncertain and mobile data, traditional static data and data stream filtering strategies cannot be applied to filter the RFID data effectively. In the paper, we first present a three-phase filtering framework under a block-based sliding window model. Aiming to filter the temporal redundant events, we propose an approximate Probability Synthesis Bloom Filter (PSBF) and discuss its filter principle, update rules and error rate in details. Comparing with the existing RFID filters, PSBF can not only filter the redundant probabilistic events, but also can calculate object existential probabilities with temporal decaying, and handle with the situations of location movement and staying at the overlapping areas among multiple readers correctly. The experiments on the simulated dataset show that the proposed filter outperforms the state-of-the-art filtering method.