Federatedlearning has garnered significant attention in the Internet of Things and healthcare applications due to its ability to train a shared global model across distributed clients. However, imbalanced data distribution leads to model discrepancies among clients. Most existing methods adopt implicit alignment strategies while overlooking explicit modeling of geometric and directional discrepancies in feature representations, which undermines local model optimization. To address this issue, we propose a method of representation deviation alignment in federated learning, which projects features onto the principal feature space to measure deviations between local and global feature representations explicitly. Specifically, Federated learning with Representation Deviation Alignment (FedRDA) employs a feature encoder to extract compact features and construct unbiased principal feature spaces for global and local models. Then, the residual projection in the feature space serves as a quantitative measure of the representation deviation, effectively capturing the latent direction differences between models. Besides, we introduce a representation consistency alignment strategy, which ensures that the distribution of local client features becomes more uniform within the global feature space. Extensive experiments on SVHN, CIFAR-10, CIFAR-100, Tiny-ImageNet, and GC10 demonstrate that FedRDA effectively reduces the classifier bias caused by representational differences.

As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of potential abnormal behaviors and have limited means to assess the contributions of individual clients and implement the necessary intervention. Visualization techniques have been introduced to facilitate the HFL process inspection, usually by providing model metrics and evaluation results as a dashboard representation. Although the existing visualization methods allow a simple examination of the HFL model performance, they cannot support the intensive exploration of the HFL process. In this article, strictly following the HFL privacy-preserving protocol, we design an exploratory visual analytics system for the HFL process termed HFLens, which supports comparative visual interpretation at the overview, communication round, and client instance levels. Specifically, the proposed system facilitates the investigation of the overall process involving all clients, the correlation analysis of clients' information in one or different communication round(s), the identification of potential anomalies, and the contribution assessment of each HFL client. Two case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps in understanding and diagnosing the HFL process better.

There is an increased use of Internet-of-Things and wearable sensing devices in the urban marathon to ensure effective response to unforeseen medical needs. However, the massive amount of real-time, heterogeneous movement and psychological data of runners impose great challenges on prompt medical incident analysis and intervention. Conventional approaches compile such data into one dashboard visualization to facilitate rapid data absorption but fail to support joint decision-making and operations in medical encounters. In this paper, we present MaraVis, a real-time urban marathon visualization and coordinated intervention system. It first visually summarizes real-time marathon data to facilitate the detection and exploration of possible anomalous events. Then, it calculates an optimal camera route with an arrangement of shots to guide offline effort to catch these events in time with a smooth view transition. We conduct a within-subjects study with two baseline systems to assess the efficacy of MaraVis.

Years of rural-urban migration has resulted in a significant population in China seeking ad-hoc work in large urban centres. At the same time, many businesses face large fluctuations in demand for manpower and require more efficient ways to satisfy such demands. This paper outlines AlgoCrowd, an artificial intelligence (AI)-empowered algorithmic crowdsourcing platform. Equipped with an efficient explainable task-worker matching optimization approach designed to focus on fair treatment of workers while maximizing collective utility, the platform provides explainable task recommendations to workers' personal work management mobile apps which are becoming popular, with the aim to address the above societal challenge.

As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.

This paper proposes a new multi-view human body tracking and model-based rendering system with a textured deformable human body model and explores its practical application to view transfer in image-based rendering (IBR). The proposed approach first reconstructs an initial 3-D model of the human subject offline using Kinect depth cameras or from a general 3-D model if such information is unavailable in the original data. The human pose of the subject is then tracked with multi-view videos using an annealed particle filter (APF)-based tracker with a new color-based likelihood function and a Bayesian-Kalman filter smoother. The previous captured model can then be deformed to the new position for model-based rendering. An immediate application of the proposed approach is to support fly-over effects for view transfer in IBR systems with limited cameras. It avoids the reconstruction of the complete dynamic 3-D model where a large number of cameras may be required. Moreover, for static background, the background can be rendered using IBR with precaptured depth maps to further enhance the user's experience. To reduce the artifacts during fly-over caused by tracking errors and model deformation, a novel morphing technique utilizing a new free-form deformation-based artifacts suppression (FFD-AS) method and other user interface design techniques are also proposed. It allows smooth transition between the original view and the model-rendered views. The performance of the proposed algorithm is evaluated using the publicly available HumanEva dataset and our captured RGB-D multi-view dataset. Experimental results show that the proposed APF-based tracker offered improved tracking performance compared with the conventional bidirectional silhouette likelihood criterion. The proposed morphing approach is also shown to be effective in mitigating the rendering artifacts during view transfer.

Ultrasonography is an important diagnostic imaging technique for visualization of tendons, which provides useful health diagnostic and fundamental information in neuromuscular studies of human motion systems. Conventional ultrasonic-based tendon studies, however, are highly dependent on subjective experience of operators due to various impairments of ultrasound images. Dynamic changes of muscle and tendon deformation in a sequence can hardly be manually processed. Consequently, there is an urgent need for automatic analysis of tendon behavior. This paper proposes an automatic ultrasonic tendon tracking algorithm to extract the shape deformation of central tendon of rectus femoris (CT-RF) from ultrasonic image sequences. The tracking problem is complicated by the highly deformable tendon, time-varying brightness, and the inconspicuousness of the target. To address this difficult tracking problem, we proposed a new intensity-compensated free-form deformation (IC-FFD)-based tracking algorithm with local shape refinement (LSR). Experimental results and comparison show that the proposed IC-FFD-LSR algorithm outperforms IC-FFD and conventional methods such as MI-FFD in CT-RF tracking.