Line charts are commonly used to visualize a series of data values. When the data are noisy, smoothing is applied to make the signal more apparent. Conventional methods used to smooth line charts, e.g., using subsampling or filters, such as median, Gaussian, or low-pass, each optimize for different properties of the data. The properties generally do not include retaining peaks (i.e., local minima and maxima) in the data, which is an important feature for certain visual analytics tasks. We present TopoLines, a method for smoothing line charts using techniques from Topological Data Analysis. The design goal of TopoLines is to maintain prominent peaks in the data while minimizing any residual error. We evaluate TopoLines for 2 visual analytics tasks by comparing to 5 popular line smoothing methods with data from 4 application domains.
TopoLines: Topological Smoothing for Line Charts
P. Rosen, A. Suh, C. Salgado, M. Hajij
EuroVis Short Papers
Peer review is a widely utilized pedagogical feedback mechanism for engaging students, which has been shown to improve educational outcomes. However, we find limited discussion and empirical measurement of peer review in visualization coursework. In addition to engagement, peer review provides direct and diverse feedback and reinforces recently-learned course concepts through critical evaluation of others’ work. In this paper, we discuss the construction and application of peer review in a computer science visualization course, including: projects that reuse code and visualizations in a feedback-guided, continual improvement process and a peer review rubric to reinforce key course concepts. To measure the effectiveness of the approach, we evaluate student projects, peer review text, and a post-course questionnaire from 3 semesters of mixed undergraduate and graduate courses. The results indicate that course concepts are reinforced with peer review—82% reported learning more because of peer review, and 75% of students recommended continuing it. Finally, we provide a road-map for adapting peer review to other visualization courses to produce more highly engaged students.
Leveraging Peer Feedback to Improve Visualization Education
Z. Beasley, A. Friedman, L. Piegl, P. Rosen
IEEE Pacific Visualization Symposium (PacificVis)
Major League Hacking named CSE students Akash Singh and Jamshidbek Mirzakhalov two of the top 50 hackers. MLH is the official student hackathon league which powers almost 2,000 competitions and each year they choose the top 50 hackers out of nearly 100,000 hackers/mentors/organizers to be in the Top 50 hackers list. This year USF was one of 10 universities to have more than one student featured in the list.
Singh’s first hackathon was MangoHacks, after which he changed his major from biology to computer science. Now, Singh is a research assistant in the USF Graphics and Visualization Lab and the president of Society for Competitive Hackers.
“Hackathons are an experience rather than a competition…We get an environment to test our skills, gain new ones and most importantly meet programmers from all over the world. The experience itself boosts your confidence and introduces you to the world waiting for you once you graduate,” Singh says. In the future he hopes to pursue his PhD in computer science with a concentration in Data Science and Data Visualization. To find out more click here.
Original Story: https://www.usf.edu/engineering/cse/newsroom/02192020-mlh.aspx
Congratulations to Ghulam Jilani Quadri would successfully passed his Major Research Area Presentation, “Modelling Effective Visualization Using Graphical Encoding Perception”, on November 18.
Junyi Tu defended is dissertation, “Efficient Algorithms and Applications in Topological Data Analysis”, on November 7, 2019!
Prof. Tu will be joining the faculty of Salisbury University, where he will teach Computer Science as a Visiting Assistant Professor in the Spring, switching to a tenure-track Assistant Professor in Fall.
We wish Prof. Tu good fortune on his future career.
See the Salisbury University Computer Science Department to learn more.
Graphs are commonly used to encode relationships among entities, yet their abstractness makes them difficult to analyze. Node-link diagrams are popular for drawing graphs, and force-directed layouts provide a flexible method for node arrangements that use local relationships in an attempt to reveal the global shape of the graph. However, clutter and overlap of unrelated structures can lead to confusing graph visualizations. This paper leverages the persistent homology features of an undirected graph as derived information for interactive manipulation of force-directed layouts. We first discuss how to efficiently extract 0-dimensional persistent homology features from both weighted and unweighted undirected graphs. We then introduce the interactive persistence barcode used to manipulate the force-directed graph layout. In particular, the user adds and removes contracting and repulsing forces generated by the persistent homology features, eventually selecting the set of persistent homology features that most improve the layout. Finally, we demonstrate the utility of our approach across a variety of synthetic and real datasets.
Persistent Homology Guided Force-Directed Graph Layouts
A. Suh, M Hajij, B. Wang, C. Scheidegger, P. Rosen
Transaction on Visualization and Computer Graphics (InfoVis)
Reproducibility has been increasingly encouraged by communities of science in order to validate experimental conclusions, and replication studies represent a significant opportunity to vision scientists wishing contribute new perceptual models, methods, or insights to the visualization community. Unfortunately, the notion of replication of previous studies does not lend itself to how we communicate research findings. Simple put, studies that re-conduct and confirm earlier results do not hold any novelty, a key element to the modern research publication system. Nevertheless, savvy researchers have discovered ways to produce replication studies by embedding them into other sufficiently novel studies. In this position paper, we define three methods–re-evaluation, expansion, and specialization–for embedding a replication study into a novel published work. Within this context, we provide a non-exhaustive case study on replications of Cleveland and McGill’s seminal work on graphical perception. As it turns out, numerous replication studies have been carried out based on that work, which have both confirmed prior findings and shined new light on our understanding of human perception. Finally, we discuss how publishing a true replication study should be avoided, while providing suggestions for how vision scientists and others can still use replication studies as a vehicle to producing visualization research publications.
You Can’t Publish Replication Studies (and How to Anyways)
G. Quadri, P. Rosen
VIS x Vision Workshop at IEEE VIS
With the popularization of Topological Data Analysis, the Reeb graph has found new applications as a summarization technique in the analysis and visualization of large and complex data, whose usefulness extends beyond just the graph itself. Pairing critical points enables forming topological fingerprints, known as persistence diagrams, that provides insights into the structure and noise in data. Although the body of work addressing the efficient calculation of Reeb graphs is large, the literature on pairing is limited. In this paper, we discuss two algorithmic approaches for pairing critical points in Reeb graphs, first a multipass approach, followed by a new single-pass algorithm, called Propagate and Pair.
Propagate and Pair: A Single-Pass Approach to Critical Point Pairing in Reeb Graphs
J. Tu, M. Hajij, P. Rosen
International Symposium on Visual Computing
Enhancement is an important step in post-processing digital images for personal use, in medical imaging, and for object recognition. Most existing manual techniques rely on region selection, similarity, and/or thresholding for editing, never really considering the topological structure of the image. In this paper, we leverage the contour tree to extract a hierarchical representation of the topology of an image. We propose 4 topology-aware transfer functions for editing features of the image using local topological properties, instead of global image properties. Finally, we evaluate our approach with grayscale and color images.
Topologically-Guided Color Image Enhancement
J. Tu, P. Rosen
International Symposium on Visual Computing
Paul Rosen, Ph.D., assistant professor in Computer Science and Engineering received a National Science Foundation Faculty Early Career Development Program (CAREER) Award.
Rosen will use the award to investigate new methods for visualizing uncertainty using Topological Data Analysis. The approach will develop topology-based techniques for extracting features from ensembles and new visual analysis approaches for investigating those features.
Rosen and his research team will use their theoretic results to assist teams of biomedical engineers investigating conditions of myocardial ischemia and energy scientists at the National Renewable Energy Laboratory in building new tools to analyze uncertainties in their domains.
In addition, Rosen plans to work with students to develop intuitive methods of communicating uncertainties in data to laypeople.