Storytelling infographics are a powerful medium for communicating data-driven stories through visual presentation. However, existing authoring tools lack support for maintaining story consistency and aligning with users’ story goals throughout the design process. To address this gap, we conducted formative interviews and a quantitative analysis to identify design needs and common story-informed layout patterns in infographics. Based on these insights, we propose a narrative-centric workflow for infographic creation consisting of three phases: story construction, visual encoding, and spatial composition. Building on this workflow, we developed InfoAlign, a human–AI co-creation system that transforms long or unstructured text into stories, recommends semantically aligned visual designs, and generates layout blueprints. Users can intervene and refine the design at any stage, ensuring their intent is preserved and the infographic creation process remains transparent. Evaluations show that InfoAlign preserves story coherence across authoring stages and effectively supports human–AI co-creation for storytelling infographic design.

We develop parameter-free algorithms for unconstrained online learning with regret guarantees that scale with the gradient variation $V_T(u) = \sum_{t=2}^T \|\nabla f_t(u)-\nabla f_{t-1}(u)\|^2$. For $L$-smooth convex losses, we provide fully-adaptive algorithms achieving regret of $\widetilde{O}(\|u\|\sqrt{V_T(u)} + L\|u\|^2+G^4)$ without requiring prior knowledge of comparator norm $\|u\|$, Lipschitz constant $G$, or smoothness $L$. The update in each round can be computed efficiently via a closed-form expression. Our results extend to dynamic regret and find immediate implications for the stochastically-extended adversarial (SEA) model, which significantly improves upon the previous best-known result (Wang et al., 2025).

In data-driven storytelling contexts such as data journalism and data videos, data visualizations are often presented alongside real-world imagery to support narrative context. However, these visualizations and contextual images typically remain separated, limiting their combined narrative expressiveness and engagement. Achieving this is challenging due to the need for fine-grained alignment and creative ideation. To address this, we present SceneLoom, a Vision-Language Model (VLM)-powered system that facilitates the coordination of data visualization with real-world imagery based on narrative intents. Through a formative study, we investigated the design space of coordination relationships between data visualization and real-world scenes from the perspectives of visual alignment and semantic coherence. Guided by the derived design considerations, SceneLoom leverages VLMs to extract visual and semantic features from scene images and data visualization, and perform design mapping through a reasoning process that incorporates spatial organization, shape similarity, layout consistency, and semantic binding. The system generates a set of contextually expressive, image-driven design alternatives that achieve coherent alignments across visual, semantic, and data dimensions. Users can explore these alternatives, select preferred mappings, and further refine the design through interactive adjustments and animated transitions to support expressive data communication. A user study and an example gallery validate SceneLoom's effectiveness in inspiring creative design and facilitating design externalization.

Visual analytics (VA) is typically applied to complex data, thus requiring complex tools. While visual analytics empowers analysts in data analysis, analysts may get lost in the complexity occasionally. This highlights the need for intelligent assistance mechanisms. However, even the latest LLM-assisted VA systems only provide help when explicitly requested by the user, making them insufficiently intelligent to offer suggestions when analysts need them the most. We propose a ProactiveVA framework in which LLM-powered UI agent monitors user interactions and delivers context-aware assistance proactively. To design effective proactive assistance, we first conducted a formative study analyzing help-seeking behaviors in user interaction logs, identifying when users need proactive help, what assistance they require, and how the agent should intervene. Based on this analysis, we distilled key design requirements in terms of intent recognition, solution generation, interpretability and controllability. Guided by these requirements, we develop a three-stage UI agent pipeline including perception, reasoning, and acting. The agent autonomously perceives users' needs from VA interaction logs, providing tailored suggestions and intuitive guidance through interactive exploration of the system. We implemented the framework in two representative types of VA systems, demonstrating its generalizability, and evaluated the effectiveness through an algorithm evaluation, case and expert study and a user study. We also discuss current design trade-offs of proactive VA and areas for further exploration.

Smoothness is known to be crucial for acceleration in offline optimization, and for gradient-variation regret minimization in online learning. Interestingly, these two problems are actually closely connected --- accelerated optimization can be understood through the lens of gradient-variation online learning. In this paper, we investigate online learning with *Hölder* functions, a general class encompassing both smooth and non-smooth (Lipschitz) functions, and explore its implications for offline optimization. For (strongly) convex online functions, we design the corresponding gradient-variation online learning algorithm whose regret smoothly interpolates between the optimal guarantees in smooth and non-smooth regimes. Notably, our algorithms do not require prior knowledge of the Hölder smoothness parameter, exhibiting strong adaptivity over existing methods. Through online-to-batch conversion, this gradient-variation online adaptivity yields an optimal universal method for stochastic convex optimization under Hölder smoothness. However, achieving universality in offline strongly convex optimization is more challenging. We address this by integrating online adaptivity with a detection-based guess-and-check procedure, which, for the first time, yields a universal offline method that achieves accelerated convergence in the smooth regime while maintaining near-optimal convergence in the non-smooth one.

Understanding user profiling on social media poses significant challenges due to the intertwined complexities of network structures, user interactions, and the multi-dimensional nature of the data. To reduce visual clutter and offer complementary perspectives for engagingly exploring user profiling within these networks, we propose Social Media Island, an interactive 3D metaphoric visualization system. Our system uses 3D mountain metaphors to visualize user profiling, capturing user influence and activity, while tree metaphors visualize the information forwarding process. To support a flexible scope for users to explore in a more intriguing way, we design various interactions such as cutting the mountain to split out a subset with similarity to some extent for further exploration. By using these 3D visualizations with user interactions, Social Media Island facilitates immersion in the data, the fluid exploration of user influence, topic evolution, and the spread of information. The effectiveness of metaphors is evaluated by user studies, and that of the system is evaluated through two case studies.

Automating the generation of multiple linked view visualization is imperative for improving data analysis efficiency. Large Language Models (LLMs) offer substantial potential for enabling this automation, yet they encounter notable challenges in understanding complex queries and producing relevant interactive visualizations. To tackle these challenges, we introduce SmartMLVs, a system designed to harness LLMs for automatic interactive multiple linked views generation with human guidance. First, we analyze the challenges LLMs may encounter when designing visualizations in place of experts. To address these challenges, we gather the essential domain knowledge required for visual analysis process and propose a framework consisting of decomposition, visualization and linking. The decomposition process applies a human-AI interaction method to clarify user requirements. For each decomposed question, the generation process handles chart type selection, data processing and visualization generation. Finally, the linking process adds interactions for views and provides users with data insights. For better human-AI collaboration, we design a system for data exploration. Our system applies the entire framework, supporting users’ interactive exploration with multiple linked views, and can iteratively generate linked views based on user feedback. We examine the effectiveness of our method through usage scenarios and evaluations.

Figure 1: An overview of KinemaFX.KinemaFX supports interactive particle effect exploration and customization through three stages: (a) User Intent Input.Users can express their initial exploration intent by combining semantic input (a1) and graphical input (a2).(b) Effect Exploration.Kinematic supports particle effects searching based on controllable weights of semantic and kinematic similarity (b1).Users iteratively explore the space by selecting satisfying effects, thereby implicitly conveying their preferences(b2).(c) Effect Composition.From the explored particle effects, users can select (c1) and control individual effects' transformations and temporal features (c2) to compose effect artworks (c3).

With the proliferation of wireless communications, various communication systems increasingly share the same radio frequency bands, leading to co-channel interference. In such cases, frequency-hopping (FH) communication systems are widely used due to their short residence time in a certain frequency band. However, existing timing acquisition schemes lack a joint statistical analysis of the FH signal, wireless channel, and co-channel interference, which limits the performance of timing acquisition. To address this, we propose a robust anti-interference timing acquisition method (RATA) for FH communications. RATA mainly consists of two independent timing acquisition components, which are designed for fading channels in different scenarios. Furthermore, the optimal timing acquisition threshold and the timing acquisition probability of RATA are derived for fading channels with typical co-channel interference. Simulation results demonstrate that RATA can effectively adapt to fading channels with different Doppler shifts and obtain better performance than traditional methods in the presence of co-channel interference.

Visual analytics (VA) requires analysts to iteratively propose analysis tasks based on observations and execute tasks by creating visualizations and interactive exploration to gain insights. This process demands skills in programming, data processing, and visualization tools, highlighting the need for a more intelligent, streamlined VA approach. Large language models (LLMs) have recently been developed as agents to handle various tasks with dynamic planning and tool-using capabilities, offering the potential to enhance the efficiency and versatility of VA. We propose LightVA, a lightweight VA framework that supports task decomposition, data analysis, and interactive exploration through human-agent collaboration. Our method is designed to help users progressively translate high-level analytical goals into low-level tasks, producing visualizations and deriving insights. Specifically, we introduce an LLM agent-based task planning and execution strategy, employing a recursive process involving a planner, executor, and controller. The planner is responsible for recommending and decomposing tasks, the executor handles task execution, including data analysis, visualization generation and multi-view composition, and the controller coordinates the interaction between the planner and executor. Building on the framework, we develop a system with a hybrid user interface that includes a task flow diagram for monitoring and managing the task planning process, a visualization panel for interactive data exploration, and a chat view for guiding the model through natural language instructions. We examine the effectiveness of our method through a usage scenario and an expert study.

Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.

Skin segmentation is an important and challenging task which finds use in direct applications such as image editing and downstream tasks such as face detection or hand gesture recognition. However, the availability of diverse and high-quality training data is a major challenge. Annotation of dense segmentation masks is an expensive and time consuming process. Existing skin segmentation datasets are often limited in scope: they include datasets built for specific downstream tasks that were captured under controlled conditions, with limited variability in lighting, scale, ethnicity, and age. This lack of diversity in the training data can lead to poor generalization and limited performance when applied to real-world images. To address this issue, we propose a tunable pipeline for dataset generation, the Synthetic Skin Mask Generator (S2MGen), which allows for the creation of dataset which includes a diverse range of body positions, camera angles, and lighting conditions. We explore the impact of these tunable parameters on skin segmentation performance. We also show that improvements can be made to the performance and generalizability of models trained on real world datasets, by the inclusion of synthetic data in the training pipeline.

High dynamic and interfered environments can seriously deteriorate the synchronization acquisition of the fast frequency hopping (FFH) spread spectrum system, which is particularly detrimental in the anti-interference systems. With the rapid development of the digital processing technologies, some synchronization acquisition algorithms in high dynamic environments have been proposed. However, these algorithms suffer from performance deterioration in the FFH system and do not analyze the acquisition performance under typical interferences. In this paper, a frequency hopping pulses (FHP)-differential adaptive anti-interference combining (DAAC) algorithm-based synchronization acquisition scheme is proposed. In particular, this algorithm combines the interference suppression method and the synchronization acquisition method, and the combining coefficients of the decision metric can be adaptively adjusted based on the interference type and the interference power. In addition, the adaptive anti-interference acquisition thresholds are derived through the constant false-alarm rate (CFAR) criterion. Theoretical and simulation results demonstrate that the proposed FHP-DAAC algorithm enhances the anti-interference and anti-high dynamic performances of the FFH receiver.

In response to the limitations of traditional fuzzing approaches that rely on static mutators and fail to dynamically adjust their test case mutations for deeper testing, resulting in the inability to generate targeted inputs to trigger vulnerabilities, this paper proposes a directed fuzzing methodology termed DocFuzz, which is predicated on a feedback mechanism mutator. Initially, a sanitizer is used to target the source code of the tested program and stake in code blocks that may have vulnerabilities. After this, a taint tracking module is used to associate the target code block with the bytes in the test case, forming a high‐value byte set. Then, the reinforcement learning mutator of DocFuzz is used to mutate the high‐value byte set, generating well‐structured inputs that can cover the target code blocks. Finally, utilizing the feedback mechanism of DocFuzz, when the reinforcement learning mutator converges and ceases to optimize, the fuzzer is rebooted to continue mutating toward directions that are more likely to trigger vulnerabilities. Comparative experiments are conducted on multiple test sets, including LAVA‐M, and the experimental results demonstrate that the proposed DocFuzz methodology surpasses other fuzzing techniques, offering a more precise, rapid, and effective means of detecting vulnerabilities in source code.

Open-domain question answering (OpenQA) is an essential but challenging task in natural language processing that aims to answer questions in natural language formats on the basis of large-scale unstructured passages. Recent research has taken the performance of benchmark datasets to new heights, especially when these datasets are combined with techniques for machine reading comprehension based on Transformer models. However, as identified through our ongoing collaboration with domain experts and our review of literature, three key challenges limit their further improvement: (i) complex data with multiple long texts, (ii) complex model architecture with multiple modules, and (iii) semantically complex decision process. In this paper, we present VEQA, a visual analytics system that helps experts understand the decision reasons of OpenQA and provides insights into model improvement. The system summarizes the data flow within and between modules in the OpenQA model as the decision process takes place at the summary, instance and candidate levels. Specifically, it guides users through a summary visualization of dataset and module response to explore individual instances with a ranking visualization that incorporates context. Furthermore, VEQA supports fine-grained exploration of the decision flow within a single module through a comparative tree visualization. We demonstrate the effectiveness of VEQA in promoting interpretability and providing insights into model enhancement through a case study and expert evaluation.

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset.

With the rapid development of cloud systems, an increasing number of service workloads are deployed in the private cloud and/or public cloud. Although large cloud providers such as Azure and Google have published workload traces in the past, prior work has not focused on analyzing and characterizing the differences between private and public cloud workloads in detail. Based on our experience working with Azure, one of the most widely used cloud platforms in the world, we find that the workload characteristics are different between the private and public cloud workloads. Specifically, compared with the public cloud workloads, the private cloud workloads tend to be more homogeneous in both deployment sizes and utilization patterns, more static with occasional bursts in deployment characteristics, and more region-agnostic regarding the sensitivity to deployed regions. Our findings gain several insights and implications on cloud management and motivate us to build a centralized workload knowledge base.

Understanding and comparing the evolution of public opinions on a social media event is important. However, such a task requires summarizing rich semantic information and an in-depth comparison of semantics and dynamics at the same time, which is difficult for the analysis. To tackle these challenges, we propose ContextWing, an interactive visual analytics system to support pair-wise comparison for evolving sequential patterns of contexts between two data streams. The computational model of ContextWing generates dynamic topics and sequential patterns, and characterizes public attention and pair-wise correlations. A novel multi-layer bilateral wing metaphor is designed to intuitively visualizes sequential patterns merged by different contexts to reveal the similarities and differences in both temporal and semantic aspects between two streams. Interactive tools support the selection of a central keyword and its contexts to iteratively generate patterns for a focused exploration. The system supports analysis on both static and streaming settings that enables a wider range of application scenarios. We verify the effectiveness and usability of ContextWing from multiple facets, including three case studies, two expert interviews, and a user study.

The metaverse is a visual world that blends the physical world and digital world. At present, the development of the metaverse is still in the early stage, and there lacks a framework for the visual construction and exploration of the metaverse. In this paper, we propose a framework that summarizes how graphics, interaction, and visualization techniques support the visual construction of the metaverse and user-centric exploration. We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline. We propose a taxonomy of interaction technologies based on interaction tasks, user actions, feedback and various sensory channels, and a taxonomy of visualization techniques that assist user awareness. Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse. We hope this paper can provide a stepping stone for further research in the area of graphics, interaction and visualization in the metaverse.

The energy harvesting technologies are going to replace the chemical batteries by providing an ever-lasting power solution for future dispersive devices in the Internet of Things (IoT), in particular, wireless sensor networks (WSN) and wearable electronics. The power conditioning circuit plays an crucial role for enhancing the energy harvesting capability [1]. This live demonstration shows a vibration-powered Bluetooth wireless sensor node with an emphasis on its running power factor correction (PFC) power conditioning design. The concurrent full paper has been submitted to the regular ISCAS track [2]. The self-powered sensor node is composed of three modules: the piezoelectric transducer, the self-powered synchronized switch harvesting on inductor (SP-SSHI) circuit for the running PFC power conditioning, and the Bluetooth module. These modules are enclosed by a 3D-printed frame. The sensor node assembly and disassembly are shown in Fig. 1(a) and (b).

The kinetic energy harvesting technologies have attracted extensive research interests with the purpose to enable more distributed and wearable electronics to be powered by their surrounding mechanical vibrations or motions. The power conditioning circuit is of importance for harvesting more energy from the vibration source and better managing the energy storage and power users. This paper introduces the design and implementation of a vibration-powered Bluetooth wireless sensor node, whose power is acquired by a low-cost piezoelectric transducer and processed by a self-powered synchronized switch power conditioning circuit. Given the capacitive feature of the piezoelectric transducers and the irregular feature of most vibration sources, the power conditioning circuit realizes the running power factor correction (PFC), making the piezoelectric voltage in phase with the equivalent current source at every current zero-crossing. As a result, it can significantly enhance the energy harvesting capability. Detailed analysis on the power consumption shows the feasibility of this design.