Trust plays a critical role in visual data communication and decision-making, yet existing visualization research employs varied trust measures, making it challenging to compare and synthesize findings across studies. In this work, we first took a bottom-up, data-driven approach to understand what visualization readers mean when they say they "trust" a visualization. We compiled and adapted a broad set of trust-related statements from existing inventories and collected responses to visualizations with varying degrees of trustworthiness. Through exploratory factor analysis, we derived an operational definition of trust in visualizations. Our findings indicate that people perceive a trustworthy visualization as one that presents credible information and is comprehensible and usable. Building on this insight, we developed an eight-item inventory: four core items measuring trust in visualizations and four optional items controlling for individual differences in baseline trust tendency. We established the inventory's internal consistency reliability using McDonald's omega, confirmed its content validity by demonstrating alignment with theoretically-grounded trust dimensions, and validated its criterion validity through two trust games with real-world stakes. Finally, we illustrate how this standardized inventory can be applied across diverse visualization research contexts. Utilizing our inventory, future research can examine how design choices, tasks, and domains influence trust, and how to foster appropriate trusting behavior in human-data interactions.

Despite two decades of research on automatic filtering systems, phishing attacks remain a serious problem. To alleviate risks from filtering failures, we design and evaluate the effectiveness of a new warning system on users’ susceptibility to phishing. Our proposed technique highlights key sentences based on an analysis of the persuasive techniques used. An online mixed-design study ($n=604$) shows that adding our highlighting technique outperforms existing warning solutions. It also identifies the relative efficacy of different appeals and the characteristics of susceptible users. Results show that adding our highlighting techniqueis useful even with false positives and false negatives. Inspired by this result, we propose an automatic warning generator. We created a small labeled dataset of suspicious sentences and used data augmentation. Our best models achieve F1 score of 99.95% in detecting phishing emails and 88% in detecting suspicious sentences.

The motives and means of explicit state censorship have been well studied, both quantitatively and qualitatively. Self-censorship by media outlets, however, has not received nearly as much attention, mostly because it is difficult to systematically detect. We develop a novel approach to identify news media self-censorship by using social media as a sensor. We develop a hypothesis testing framework to identify and evaluate censored clusters of keywords and a near-linear-time algorithm (called GraphDPD) to identify the highest scoring clusters as indicators of censorship. We evaluate the accuracy of our framework, versus other state-of-the-art algorithms, using both semi-synthetic and real-world data from Mexico and Venezuela during Year 2014. These tests demonstrate the capacity of our framework to identify self-censorship, and provide an indicator of broader media freedom. The results of this study lay the foundation for detection, study, and policy-response to self-censorship.

This paper presents a novel approach to solving optical flow problems using a discrete, tree-structured MRF derived from a hierarchical segmentation of the image. Our method can be used to find globally-optimal matching solutions even for problems involving very large motions. Experiments demonstrate that our approach is competitive on the MPI-Sintel dataset and that it can significantly outperform existing methods on problems involving large motions.

We present an approach for 2D sensor network localization when only bearing measurements are available and no global coordinate frame is known. Our work builds off of the linear constraint given in Kennedy et al. (2012) for sets of nodes that form triangles. We extend that constraint to general networks and present methods for locally optimizing the resulting cost function. We also show how these methods can be used for 3D network localization when the vertical axis is known. The algorithms are evaluated on both synthetic and real datasets, and we also show how they can be applied to the “structure from motion” problem in the field of computer vision.

We present a family of online algorithms for real-time factorization-based structure from motion, leveraging a relationship between the incremental singular value decomposition and recent work in online matrix completion. Our methods are orders of magnitude faster than previous state of the art, can handle missing data and a variable number of feature points, and are robust to noise and sparse outliers. Experiments show that they perform well in both online and batch settings. We also provide an implementation which is able to produce 3D models in real time using a laptop with a webcam.

We present an approach for sensor network localization when provided with a set of angular constraints. This problem arises in camera networks when angles between nearby points can be measured but depth measurements are not readily available. We provide contributions for two different variations on this problem. First, when each node is aware of a global coordinate frame, we present a novel method for identifying the components of the problem that are rigidly constrained. Second, in the more difficult case where only relative angles are known, we propose a novel spectral solution that achieves a globally-optimal embedding under transitively-triangular constraints, which we show encompass a wide range of real-world conditions. We demonstrate the utility of our algorithm on both synthetic data and data from quadrotor robot formations.

The problem of finding one-dimensional structures in images and videos can be formulated as a problem of searching for cycles in graphs. In, an untangling-cycle cost function was proposed for identifying persistent cycles in a weighted graph, corresponding to salient contours in an image. We have analyzed their method and give two significant improvements. First, we generalize their cost function to a contour cut criterion and give a computational solution by solving a family of Hermitian eigenvalue problems. Second, we use the idea of a graph circulation, which ensures that each node has a balanced in- and out-flow and permits a natural random-walk interpretation of our cost function. We show that our method finds far more accurate contours in images than. Furthermore, we show that our method is robust to graph compression which allows us to accelerate the computation without loss of accuracy.

MOTIVATION: Microbial community profiling is a highly active area of research, but tools that facilitate visualization of phylogenetic trees and associated environmental data have not kept up with the increasing quantity of data generated in these studies. RESULTS: TopiaryExplorer supports the visualization of very large phylogenetic trees, including features such as the automated coloring of branches by environmental data, manipulation of trees and incorporation of per-tip metadata (e.g. taxonomic labels). AVAILABILITY: http://topiaryexplorer.sourceforge.net. CONTACT: [email protected].

The Cornell Jaguar Project is exploring a variety of issues related to mobility and query processing. One broad theme is to break down the traditional client and server boundaries, leading to ubiquitous query processing. Another theme is to extend database and query processing techniques to small-scale and mobile devices. The project builds on and extends the Cornell PREDATOR database engine.