Time series data is ubiquitous in people’s daily production and life, and visualizations augmented with annotations can significantly facilitate the understanding of such data and promote downstream tasks. Consequently, numerous annotation tools have been developed to detect and narrate useful patterns within time series visualizations. However, most existing tools can only identify basic factual insights (e.g., increasing or decreasing trends) that are already present in the charts. When users need deeper insights (e.g., predicting future trends) and richer contextual information (e.g., associative patterns between dimensions within and beyond the chart), these tools often fall short. To address this challenge, we present AutoMA, a system that automatically generates multi-level annotations for time series visualizations. We introduce an LLM-based pattern extraction method that supports the identification of seven distinct temporal patterns. Furthermore, we present a multi-level annotation design space that encompasses six specific annotation tasks, aimed at delivering richer contextual information. The generated annotations span a spectrum of information, ranging from directly observable temporal patterns to deeper insights obtained through further computation, and ultimately to advanced inter-dimensional association patterns. Finally, we demonstrate the effectiveness of our approach through experiments and user evaluations. The results indicate that AutoMA significantly enhances users’ ability to comprehend and explore time series data.

The Linux kernel remains vulnerable to numerous bugs, with approximately 65% detected by Syzkaller lacking Proof-of-Concept (PoC), hampering risk mitigation efforts. These bugs, termed irreproducible kernel bugs, highlight the challenge of statefulness issue-related irreproducibility in kernel fuzzing, which is an open research without definitive solutions. Our investigation reveals that suboptimal seed quality distribution in fuzzing is the root obstacle preventing effective tracking of the states leading to crashes. Inspired by this insight, we introduce Reverse Fuzzing (RF), an innovative approach that infers hard-to- reach states by continuously reverse-oriented deriving from subsequently encountered bridge states to increase reproduction probability. RF differentiates between the “trigger” seed, which directly causes crashes, and “activator” seeds, which establish the necessary preconditions, prioritizing exploration around trigger while simultaneously regenerating and maintaining activators during fuzzing, which effectively facilitate to restructure such elusive states from “yesterday”. We implement YOME, a prototype leveraging RF to strike a balance between fuzzing efficiency and effectiveness through customized scheduling and mutation strategies, armed with a refinement mechanism to improve seed quality distribution. Our evaluations validate that YOME reproduce 110% more bugs than previous kernel fuzzers and demonstrate its practicality in real-world scenarios. YOME generated 125 PoCs (30.1% of the total) and uncovered 23 unique bugs, with 40 confirmed and 5 assigned CVEs.

Vision algorithms face challenges of limited visual presentation and unreliability in pedestrian activity assessment. In this article, we introduce LANDER, an interactive analysis system for visual exploration of pedestrian activity and uncertainty in surveillance videos. This visual analytics system focuses on three common categories of uncertainties in object tracking and action recognition. LANDER offers an overview visualization of activity and uncertainty, along with spatio-temporal exploration views closely associated with the scene. Expert evaluation and user study indicate that LANDER outperforms traditional video exploration in data presentation and analysis workflow. Specifically, compared to the baseline method, it excels in reducing retrieval time ($p< $0.01), enhancing uncertainty identification ($p< $0.05), and improving the user experience ($p< $0.05).

The narrative progression of events, evolving into a cohesive story, relies on the entity-entity relationships. Among the plethora of visualization techniques, storyline visualization has gained significant recognition for its effectiveness in offering an overview of story trends, revealing entity relationships, and facilitating visual communication. However, existing methods for storyline visualization often fall short in accurately depicting the specific relationships between entities. In this study, we present E 2 Storyline, a novel approach that emphasizes simplicity and aesthetics of layout while effectively conveying entity-entity relationships to users. To achieve this, we begin by extracting entity-entity relationships from textual data and representing them as subject-predicate-object (SPO) triplets, thereby obtaining structured data. By considering three types of design requirements, we establish new optimization objectives and model the layout problem using multi-objective optimization (MOO) techniques. The aforementioned SPO triplets, together with time and event information, are incorporated into the optimization model to ensure a straightforward and easily comprehensible storyline layout. Through a qualitative user study, we determine that a pixel-based view is the most suitable method for displaying the relationships between entities. Finally, we apply E 2 Storyline to real-world data, including movie synopses and live text commentaries. Through comprehensive case studies, we demonstrate that E 2 Storyline enables users to better extract information from stories and comprehend the relationships between entities.

Mathematical optimization is the process of determining the set of globally or locally optimal parameters in a finite or infinite search space. It has been extensively employed in the research areas of computer science, engineering, operations research, and economics. The application of mathematical optimization has also been extended to data visualization, where it can enhance data processing, structure visualization, and facilitate exploration. However, the current state of summarization in the application of mathematical optimization in data visualization remains inadequate. In this article, we review and classify the existing techniques for advanced mathematical optimization in the fields of data visualization and visual analytics. The classification is conducted based on a classical visualization pipeline, including data enhancement and transformation, representation and rendering, as well as interactive exploration and analysis. We also discuss various mathematical optimization models and their solution methods to help readers gain a better understanding of the relationship among models, visualization, and application scenarios. We additionally provide an online exploration demo, which could enable users to interactively find relevant articles. Based on the limitations and potential trends revealed in the existing literature, we define future challenges in the cross-disciplinary of mathematical optimization and data visualization.

Acoustic quality detection is vital in the manufactured products quality control field since it represents the conditions of machines or products. Recent work employed machine learning models in manufactured audio data to detect anomalous patterns. A major challenge is how to select applicable audio features to meliorate model’s accuracy and precision. To relax this challenge, we extract and analyze three audio feature types including Time Domain Feature, Frequency Domain Feature, and Cepstrum Feature to help identify the potential linear and non-linear relationships. In addition, we design a visual analysis system, namely AFExplorer, to assist data scientists in extracting audio features and selecting potential feature combinations. AFExplorer integrates four main views to present detailed distribution and relevance of the audio features, which helps users observe the impact of features visually in the feature selection. We perform the case study with AFExplore according to the ToyADMOS and MIMII Dataset to demonstrate the usability and effectiveness of the proposed system.

With the development of production technology and social needs, sectors of manufacturing are constantly improving. The use of sensors and computers has made it increasingly convenient to collect multimedia data in manufacturing. Targeted, rapid, and detailed analysis based on the type of multimedia data can make timely decisions at different stages of the entire manufacturing process. Visualization and visual analytics are frequently adopted in multimedia data analysis of manufacturing because of their powerful ability to understand, present, and analyze data intuitively and interactively. In this paper, we present a literature review of visualization and visual analytics specifically for manufacturing multimedia data. We classify existing research according to visualization techniques, interaction analysis methods, and application areas. We discuss the differences when visualization and visual analytics are applied to different types of multimedia data in the context of particular examples of manufacturing research projects. Finally, we summarize the existing challenges and prospective research directions.

Visual cryptography schemes are developed for image security, where encryption is realized by distributing a secret image into shares and decryption is done only by stacking the shares. Human eyesight is usually used to evaluate the security and performance of visual cryptography schemes. However, objective criteria for visual cryptography schemes are not yet established. In this paper, by the aid of neural networks, we propose two criteria called encryption-inconsistency and decryption-consistency for evaluating the shares and the recovered images, respectively. We also implemented the experiments for two representatives of visual cryptography schemes by applying three popular convolutional neural networks (CNN) to adopt our proposed criteria.