Unsupervised federated learning for cross-modal retrieval has received increasing attention in recent years as it can free the requirement for annotations and avoid uploading original clients’ data to servers. Most existing methods focus on how to learn better local models and their aggregation to overcome data distribution drift across clients. Unlike prior works, we propose to address the data distribution problem by generating synthetic data, which can benefit existing federated learning methods. Specifically, we train a WGAN generator with three newly designed loss constraints on each client to improve the quality of the generated data. We first compute cluster prototypes to address the problem of lack of labels. Then, a direct contrastive loss between generated image and text features, an indirect contrastive loss with reference to cluster prototypes, and a Jensen-Shannon Divergence (JSD) loss also with reference to cluster prototypes work together to constrain the WGAN. The locally trained generators and local prototypes are sent to the server to generate and filter synthetic data with consideration of data distribution across all clients. The filtered data are used to train the aggregated global retrieval model, which is later sent to clients. The final global model becomes robust to all clients after several rounds of client-server iteration. Extensive experiments using four baselines across three datasets demonstrate that our method performs favourably against state-of-the-art methods.

Source-free domain adaptive object detection (SFOD) aims to transfer models pre-trained on the source domain to the unlabeled target domain without requiring access to the source data. Most existing SFOD methods leverage pseudo-labels for self-supervised training in the target domain. We investigate the limitations of threshold techniques to obtain high-quality pseudo-labels. In response, we design the Sequential SourceFree domain adaptive Object Detection (S-SFOD) algorithm, which enhances the quality of pseudo-labels at both the image and instance levels. At the image level, we reconstruct the training dataset, prioritizing the training of images that yield more reliable pseudo-labels to help the model acquire valuable target domain knowledge in the initial training stages. At the instance level, we introduce an adaptive local-global threshold method to balance the quality and quantity of pseudo-labels by dynamically adjusting the thresholds based on the model's learning progress. By improving the quality of pseudo-labels through these complementary techniques at both the image and instance levels, we effectively transfer knowledge from the source domain to the target domain. Extensive experiments on multiple cross-domain object detection datasets demonstrate that our proposed method outperforms current state-of-the-art SFOD algorithms. The code and model will be released.

This article investigates secure bipartite consensus control of nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks, and designs an event-based dynamic quantized sliding mode control scheme. In order to ensure the stability of MASs in the process of achieving bipartite consensus, combined with the online adjustment strategy of quantitative sensitivity parameters and the designed event-triggered mechanism, the constraints of quantitative measurement saturation parameters and event-triggered threshold parameters are given. Moreover, it is proved that Zeno behavior does not occur at the zoom-out/zoom-in stage. Then, combined with reasonable assumptions about the frequency and duration of DoS attacks, appropriate controller parameters are redesigned, and the stability of the system is proved by Lyapunov stability theory and mathematical induction. Finally, the effectiveness of the proposed method is illustrated by a simulation example.

Multi-view retrieval faces significant pressure due to the rapidly increasing multi-view information on the internet. The multi-view hashing method turns continuous features into compact information of fixed length and considerably improves retrieval efficiency. However, existing multi-view hashing methods neglect the bias produced during multi-view alignment and multi-label guidance processes. To address these issues, we introduce a novel multi-view hash method that learns compact hash codes. It first employs a multi-view double alignment module to align features from different views. Then, it utilizes a self-adjusted cross-attention fusion module to fuse these features. Finally, we propose a weighted contrastive learning module to learn more discriminative representations, smoothing the differences among all samples. Extensive experiments show that our method yields compact hash codes and outperforms state-of-the-art methods.

The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which are largely limited by the requirement of large-scale human-annotated datasets. Such methods are not able to deal with the cold-start scenarios in industrial products. To alleviate the problem, we propose FORCE, a Framework Of Rule-based Conversational rEcommender system that helps developers to quickly build CRS bots by simple configuration. We conduct experiments on two datasets in different languages and domains to verify its effectiveness and usability.

Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to maintain the dialogue logic when the scenarios get more and more complex. On the other hand, data-driven dialogue systems, usually with end-to-end structures, are popular in academic research and easier to deal with complex conversations, but such methods require plenty of training data and the behaviors are less interpretable. In this paper, we propose a method to leverages the strength of both rule-based and data-driven dialogue managers (DM). We firstly introduce the DM of Carina Dialog System (CDS, an advanced industrial dialogue system built by Microsoft). Then we propose the "model-trigger" design to make the DM trainable thus scalable to scenario changes. Furthermore, we integrate pre-trained models and empower the DM with few-shot capability. The experimental results demonstrate the effectiveness and strong few-shot capability of our method.

Conducting the spatial active noise control (ANC) in wave-domain has been shown advantageous over conventional point-based methods. In the existing schemes, signals at all error microphones are collected and processed in a centralized manner to update the secondary source driving signals. The high computational complexity of this centralized strategy represents one of the major challenges for applying multi-channel ANC systems in large-scale applications. In order to address this issue, this work presents a distributed wave-domain ANC scheme by resorting to distributed optimization techniques. The global ANC problem is formulated as a sum of local costs, and the diffusion adaptation strategy is subsequently utilized to provide a distributed solution that only requires local information exchanges. Simulation results show that the proposed algorithm achieves sufficiently good performance compared to its centralized counterpart.

Conducting the spatial active noise control (ANC) in wave-domain has been shown advantageous over conventional point-based methods. In the existing schemes, signals at all error microphones are collected and processed in a centralized manner to update the secondary source driving signals. The high computational complexity of this centralized strategy represents one of the major challenges for applying multi-channel ANC systems in large-scale applications. In order to address this issue, this work presents a distributed wave-domain ANC scheme by resorting to distributed optimization techniques. The global ANC problem is formulated as a sum of local costs, and the diffusion adaptation strategies are subsequently utilized to provide a distributed solution that only requires local information exchanges. Simulation results show that the proposed algorithms achieve sufficiently good performance compared to its centralized counterpart.