This study investigates the problem of keeping information up-to-date when crawling data sources that change over time (e.g., websites or location data). Traditional crawling methods often treat data sources independently, making it difficult to capture relationships and propagate updates efficiently. We propose using graph structures to model these relationships and show that, unfortunately, finding the theoretically optimal solution can be intractable. To address this, we introduce a specific graphical model (the latent Bernoulli process model) and demonstrate the complexity of even simple tasks within this framework. We tackle the crawling problem using a reinforcement learning-based algorithm and demonstrate its superiority over traditional baselines on both real and synthetic data. This work highlights the power of graph-structured crawling in helping users stay informed within a dynamic information landscape.

Deep neural network (DNN) mutation analysis is a promising approach to evaluating test set adequacy. Due to the large number of generated mutants that must be tested on large datasets, mutation analysis is costly. In this paper, we present a technique, named DM#, for accelerating DNN mutation testing using Fourier analysis. The key insight is that DNN outputs are real-valued functions suitable for Fourier analysis that can be leveraged to quantify mutant behavior using only a few data points. DM# uses the quantified mutant behavior to cluster the mutants so that the ones with similar behavior fall into the same group. A representative from each group is then selected for testing, and the result of the test, e.g., whether the mutant is killed or survived, is reused for all other mutants represented by the selected mutant, obviating the need for testing other mutants. 14 DNN models of sizes ranging from thousands to millions of parameters, trained on different datasets, are used to evaluate DM# and compare it to several baseline techniques. Our results provide empirical evidence on the effectiveness of DM# in accelerating mutation testing by 28.38%, on average, at the average cost of only 0.72% error in mutation score. Moreover, on average, DM# incurs 11.78, 15.16, and 114.36 times less mutation score error compared to random mutant selection, boundary sample selection, and random sample selection techniques, respectively, while generally offering comparable speed-up.

We introduce the ParClusterers Benchmark Suite (PCBS)---a collection of highly scalable parallel graph clustering algorithms and benchmarking tools that streamline comparing different graph clustering algorithms and implementations. The benchmark includes clustering algorithms that target a wide range of modern clustering use cases, including community detection, classification, and dense subgraph mining. The benchmark toolkit makes it easy to run and evaluate multiple instances of different clustering algorithms with respect to both the running time and quality. We evaluate the PCBS algorithms empirically and find that they deliver both the state of the art quality and the running time. In terms of the running time, they are on average over 4x faster than the fastest library we compared to. In terms of quality, the correlation clustering algorithm [Shi et al., VLDB'21] optimizing for the LambdaCC objective, which does not have a direct counterpart in other libraries, delivers the highest quality in the majority of datasets that we used.

The application of computer vision methods to nuanced, subjective concepts is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a "zebra"), it now falters on tasks where there is substantial subjectivity in the concept (such as identifying "gourmet tuna"). However, empowering any user to develop a classifier for their concept is technically difficult: users are neither machine learning experts nor have the patience to label thousands of examples. In reaction, we introduce the problem of Agile Modeling: the process of turning any subjective visual concept into a computer vision model through real-time user-in-the-loop interactions. We instantiate an Agile Modeling prototype for image classification and show through a user study (N=14) that users can create classifiers with minimal effort in under 30 minutes. We compare this user driven process with the traditional crowdsourcing paradigm and find that the crowd’s notion often differs from that of the user’s, especially as the concepts become more subjective. Finally, we scale our experiments with simulations of users training classifiers for ImageNet21k categories to further demonstrate the efficacy of the approach.

We aim to find the geographical coordinates of (geolocate) a large number of old building facades extracted from historical photographs. We can acquire the geo-coordinates of some of these facades either through crowdsourcing or exploring their metadata. Using these "seed" buildings and through spatial reasoning within and across the historical pictures, in this paper, we show how we infer the geolocation of the other facades. We propose a probabilistic inference approach that first constructs a graph with facades as nodes and their spatial distances as edges, and then through probabilistic inference on this graph, geolocates the facades. Our experiments show that with 10% of the building geolocated as seed buildings, we can quite accurately geolocate the rest of the buildings in our dataset.

Historical maps contain detailed geographic information difficult to find elsewhere covering long-periods of time (e.g., 125 years for the historical topographic maps in the US). However, these maps typically exist as scanned images without searchable metadata. Existing approaches making historical maps searchable rely on tedious manual work (including crowd-sourcing) to generate the metadata (e.g., geolocations and keywords). Optical character recognition (OCR) software could alleviate the required manual work, but the recognition results are individual words instead of location phrases (e.g., "Black'' and "Mountain'' vs. "Black Mountain''). This paper presents an end-to-end approach to address the real-world problem of finding and indexing historical map images. This approach automatically processes historical map images to extract their text content and generates a set of metadata that is linked to large external geospatial knowledge bases. The linked metadata in the RDF (Resource Description Framework) format support complex queries for finding and indexing historical maps, such as retrieving all historical maps covering mountain peaks higher than 1,000 meters in California. We have implemented the approach in a system called mapKurator. We have evaluated mapKurator using historical maps from several sources with various map styles, scales, and coverage. Our results show significant improvement over the state-of-the-art methods. The code has been made publicly available as modules of the Kartta Labs project at https://github.com/kartta-labs/Project.