Magnetically actuated capsule endoscopic robots (MACERs) are becoming increasingly popular because they can reach deep diseased regions inside the body that are difficult or inaccessible to traditional endoscopes without the restriction of mechanical transmission medium. However, MACERs are highly nonlinear, hence achieving obstacle avoidance, safe, and stable target tracking control of MACERs remains a challenging research topic. Therefore, to satisfy the diagnosis and treatment needs of the deep diseased regions inside the body, this paper designs a MACER target tracking neural network control method with obstacle avoidance and noise-resistant capabilities. Firstly, the kinematics and obstacle avoidance model of the MACER are established, and then a moving target tracking control scheme of robot with joint motion constraints and obstacle avoidance capabilities is designed. Next, a noise-resistant neural network is designed to quickly solve the MACER’s control scheme, thereby achieving safe, obstacle avoidance, and stable target tracking control of the MACER. Finally, the effectiveness and practicability of the proposed method are checked by simulation analysis and experiment on MACER, and compared with the existing methods. The experimental results indicate that the neural network method proposed can effectively control the MACER to track the target motion along the gastric wall curve. Compared with existing methods, the designed method has stronger anti-noise interference ability, the convergence accuracy of the proposed method is improved by 1.3 times, and the computational burden is reduced by 26.7 times.

Automating code review with Large Language Models (LLMs) shows immense promise, yet practical adoption is hampered by their lack of reliability, context-awareness, and control. To address this, we propose Specification-Grounded Code Review (SGCR), a framework that grounds LLMs in human-authored specifications to produce trustworthy and relevant feedback. SGCR features a novel dual-pathway architecture: an explicit path ensures deterministic compliance with predefined rules derived from these specifications, while an implicit path heuristically discovers and verifies issues beyond those rules. Deployed in a live industrial environment at HiThink Research, SGCR’s suggestions achieved a 42% developer adoption rate—a 90.9% relative improvement over a baseline LLM (22%). Our work demonstrates that specification-grounding is a powerful paradigm for bridging the gap between the generative power of LLMs and the rigorous reliability demands of software engineering.

In this paper, a safety-oriented hierarchical personalized driving system is proposed, which aims to mitigate the preference conflict between the passengers and the intelligent vehicle control system. Firstly, experiments on driving simulator are designed to analyze both the general and individual characteristics of different drivers, and a driving risk field (DRF) model for various driving events, such as free-driving, car-following, and lane-changing, is constructed. Secondly, the HighD natural dataset is clustered to explore the real preferences of different driving styles, and the DRF is calibrated to describe the driver’s subjective risk feeling more realistically. Thirdly, a driving decision-making mechanism with consideration of safety, efficiency, and personalized tolerance on the current lane is designed to select optimal driving events. Then, multi-point visual preview longitudinal speed adjustment and lateral lane-changing trajectory planning methods based on the spatial-temporal DRF under different driving events are proposed. Finally, human-in-the-loop experiments show that the proposed real-time system can generate personalized trajectories for different passengers in changing environments.

With the increasing demand for multi-robot exploration of unknown environments, how to accomplish this problem efficiently has become a focus of research. However, in this kind of task, the formulation of strategies for frontier point detection and task allocation largely determines the overall efficiency of the system. In the task of multi-robot exploration of unknown environments, the strategies of frontier point detection and task assignment determine the overall efficiency of the system. Most of the existing methods implement frontier point detection based on the Rapidly-Exploring Random Tree (RRT) and use greedy algorithms for task allocation. However, the classical RRT algorithm is a fixed growth step, which leads to the difficulty of growing branches in narrow environments, making the efficiency and correctness of detecting frontier points lower. Meanwhile, the allocation strategy of the greedy algorithm causes each robot to consider only the exploration area with the largest gain for itself, which easily leads to repeated exploration and reduces the overall efficiency of the system. To solve these problems, we propose an adaptive RRT tree growth strategy for frontier point detection, which can adjust the step size according to the known map information and thus improve the efficiency and accuracy of detection; and introduce a Bayesian-guided evolutionary strategy(BGE) for efficient task allocation, which can utilize the current and historical information to find the optimal allocation scheme in a global perspective. We conduct a comprehensive test of the proposed strategy in the ROS system as well as in the real world, which proves the efficiency of our strategy. Our code is open-sourced and can be provided under request.

Human-driven vehicles (HDVs) and autonomous vehicles will coexist for a long time. The time-varying charac-teristics of human-driven vehicles need to be considered when designing cruise strategies for autonomous vehicles. In this paper, the variable forgetting factor recursive least squares (VFFRLS) is proposed to identify the characteristic parameters of HDV. Based on the obtained characteristic parameters, the influence of the HDV on the stability of the vehicle platoon is analyzed, and the optimal time headway of the following autonomous vehicle is selected. Then, the time-varying cooperative adaptive cruise control method is designed to reduce the acceleration perturbation caused by HDV. Based on the data collected by the driving simulator, it is verified that the parameter identifi-cation method proposed in this paper can effectively extract the driving characteristics of HDV. Finally, the numerical simulation results demonstrate that the control strategy enhances vehicle platoon stability and improves traffic efficiency in mixed traffic environments.

Authoring graph visualization requires advanced programming skills, expert domain knowledge, and significant workload. Existing authoring tools either support limited templates of graph visualization, or suffer from a high learning cost. We analyze the design requirements on a tool for graph visualizations, and contribute Nuwa, a user-friendly declarative authoring tool for the interactive specification of graph visualizations in terms of data, entity, change, and encoding. Our implementation empowers users to conveniently create, compare, and modulate comprehensive graph visualizations with a wide range of styles. We showcase various examples to verify the expressiveness of Nuwa. Via an expert interview and the analysis on cognitive dimensions we evaluate the usability of Nuwa.

This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.

Data imputation is an essential pre-processing task for data governance, aimed at filling in incomplete data. However, conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data, and they fail to achieve the best balance between accuracy and efficiency. In this paper, we present a novel visual analysis approach for data imputation. We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables. Then, we perform the initial imputation of incomplete data using correlated data entries from other tables. Additionally, we develop a visual analysis system to refine data imputation candidates. Our interactive system combines the multi-party data imputation approach with expert knowledge, allowing for a better understanding of the relational structure of the data. This significantly enhances the accuracy and efficiency of data imputation, thereby enhancing the quality of data governance and the intrinsic value of data assets. Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using their domain knowledge.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

Node-link diagrams are the most popular form for graph visualization. Yet, salient information of a node-link diagram cannot be fully depicted by solely presenting the visualization. We propose to augment node-link diagrams by creating textual descriptions for interested information. We conduct an expert review and a user interview to identify six requirements of generated interpretations, including three requirements for connection extraction and three requirements for visual expression. Our solution, GraphDescriptor, generates textual descriptions with two stages: feature extraction and description generation. The first one identifies and extracts features of node-link diagrams, like node connections, visual designs, and types of graph layouts. The second stage creates a group of hierarchical sentences based on a pre-defined schema. To the best of our knowledge, our approach is the first attempt to generate textual descriptions automatically. Three use cases and the in-lab user study confirm the superiority of our approach.

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

Distributed processing and analysis of large-scale graph data remain challenging because of the high-level discrepancy among graphs. This study investigates a novel subproblem: the distributed multi-task learning on the graph, which jointly learns multiple analysis tasks from decentralized graphs. We propose a federated multi-task graph learning (FMTGL) framework to solve the problem within a privacy-preserving and scalable scheme. Its core is an innovative data-fusion mechanism and a low-latency distributed optimization method. The former captures multi-source data relatedness and generates universal task representation for local task analysis. The latter enables the quick update of our framework with gradients sparsification and tree-based aggregation. As a theoretical result, the proposed optimization method has a convergence rate interpolates between \( \mathcal {O}(1/T) \) and \( \mathcal {O}(1/\sqrt {T}) \) , up to logarithmic terms. Unlike previous studies, our work analyzes the convergence behavior with adaptive stepsize selection and non-convex assumption. Experimental results on three graph datasets verify the effectiveness and scalability of FMTGL.

Graph visualization plays an important role in several fields, such as social media networks, protein–protein interaction networks, and traffic networks. A number of visualization design tools and programming toolkits have been widely used in graph-related applications. However, a key challenge remains in the high-efficiency visualization of large-scale graph data. In this study, we present NetV.js, an open-source and WebGL-based JavaScript library that supports the fast visualization of large-scale graph data (up to 50 thousand nodes and 1 million edges) at an interactive frame rate with a commodity computer. Experimental results demonstrate that our library outperforms existing toolkits (Sigma.js, D3.js, Cytoscape.js, and Stardust.js) in terms of performance.

A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire structure. In this study, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verify the effectiveness of our system.

When analyzing a visualized network, users need to explore different sections of the network to gain insight. However, effective exploration of large networks is often a challenge. While various tools are available for users to explore the global and local features of a network, these tools usually require significant interaction activities, such as repetitive navigation actions to follow network nodes and edges. In this paper, we propose a structure-based suggestive exploration approach to support effective exploration of large networks by suggesting appropriate structures upon user request. Encoding nodes with vectorized representations by transforming information of surrounding structures of nodes into a high dimensional space, our approach can identify similar structures within a large network, enable user interaction with multiple similar structures simultaneously, and guide the exploration of unexplored structures. We develop a web-based visual exploration system to incorporate this suggestive exploration approach and compare performances of our approach under different vectorizing methods and networks. We also present the usability and effectiveness of our approach through a controlled user study with two datasets.