HPC centers face increasing demand for software flexibility, and there is growing consensus that Linux containers are a promising solution. However, existing container build solutions require root privileges and cannot be used directly on HPC resources. This limitation is compounded as supercomputer diversity expands and HPC architectures become more dissimilar from commodity computing resources. Our analysis suggests this problem can best be solved with low-privilege containers. We detail relevant Linux kernel features, propose a new taxonomy of container privilege, and compare two open-source implementations: mostly-unprivileged rootless Podman and fully-unprivileged Charliecloud. We demonstrate that low-privilege container build on HPC resources works now and will continue to improve, giving normal users a better workflow to securely and correctly build containers. Minimizing privilege in this way can improve HPC user and developer productivity as well as reduce support workload for exascale applications.

The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when "perfect" nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches.

Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity.Timely knowledge of the outbreak state is valuable for managing an effective response.The current state of the art is to gather this knowledge using in-person patient contact.While accurate, this is time-consuming and expensive.This has motivated inquiry into new approaches using internet activity traces, based on the theory that lay observations of health status lead to informative features in internet data.These approaches risk being deceived by activity traces having a coincidental, rather than informative, relationship to disease incidence; to our knowledge, this risk has not yet been quantitatively explored.We evaluated both simulated and real activity traces of varying deceptiveness for influenza incidence estimation using linear regression.We found that deceptiveness knowledge does reduce error in such estimates, that it may help automatically-selected features perform as well or better than features that require human curation, and that a semantic distance measure derived from the Wikipedia article category tree serves as a useful proxy for deceptiveness.This suggests that disease incidence estimation models should incorporate not only data about how internet features map to incidence but also additional data to estimate feature deceptiveness.By doing so, we may gain one more step along the path to accurate, reliable disease incidence estimation using internet data.This capability would improve public health by decreasing the cost and increasing the timeliness of such estimates. Author summaryWhile often considered a minor infection, seasonal flu kills many thousands of people every year and sickens millions more.The more accurate and up-to-date public health officials' view of what the seasonal outbreak is, the more effectively the outbreak can be addressed.Currently, this knowledge is based on collating information on patients who

Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

Supercomputing centers are seeing increasing demand for user-defined software stacks (UDSS), instead of or in addition to the stack provided by the center. These UDSS support user needs such as complex dependencies or build requirements, externally required configurations, portability, and consistency. The challenge for centers is to provide these services in a usable manner while minimizing the risks: security, support burden, missing functionality, and performance. We present Charliecloud, which uses the Linux user and mount namespaces to run industry-standard Docker containers with no privileged operations or daemons on center resources. Our simple approach avoids most security risks while maintaining access to the performance and functionality already on offer, doing so in just 800 lines of code. Charliecloud promises to bring an industry-standard UDSS user workflow to existing, minimally altered HPC resources.

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics. However, its utility is limited by missing geographic information; for example, fewer than 1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable, content-based approach to estimate the location of tweets using a novel yet simple variant of gaussian mixture models. Further, because real-world applications depend on quantified uncertainty for such estimates, we propose novel metrics of accuracy, precision, and calibration, and we evaluate our approach accordingly. Experiments on 13 million global, comprehensively multi-lingual tweets show that our approach yields reliable, well-calibrated results competitive with previous computationally intensive methods. We also show that a relatively small number of training data are required for good estimates (roughly 30,000 tweets) and models are quite time-invariant (effective on tweets many weeks newer than the training set). Finally, we show that toponyms and languages with small geographic footprint provide the most useful location signals.

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

Users have come to rely on automated route finding services for driving, public transit, walking, and bicycling. Current state of the art route finding algorithms typically rely on objective factors like time and distance; they do not consider subjective preferences that also influence route quality. This paper addresses that need. We introduce a new framework for evaluating edge rating prediction techniques in transportation networks and use it to explore ten families of prediction algorithms in Cyclopath, a geographic wiki that provides route finding services for bicyclists. Overall, we find that personalized algorithms predict more accurately than non-personalized ones, and we identify two algorithms with low error and excellent coverage, one of which is simple enough to be implemented in thin clients like web browsers. These results suggest that routing systems can generate better routes by collecting and analyzing users' subjective preferences.

Online communities produce rich behavioral datasets, e.g., Usenet news conversations, Wikipedia edits, and Facebook friend networks. Analysis of such datasets yields important insights (like the "long tail" of user participation) and suggests novel design interventions (like targeting users with personalized opportunities and work requests). However, certain key user data typically are unavailable, specifically viewing, pre-registration, and non-logged-in activity. The absence of data makes some questions hard to answer; ac- cess to it can strengthen, extend, or cast doubt on previous results. We report on analysis of user behavior in Cyclopath, a geographic wiki and route-finder for bicyclists. With access to viewing and non-logged-in activity data, we were able to: (a) replicate and extend prior work on user lifecycles in Wikipedia, (b) bring to light some pre-registration activity, thus testing for the presence of "educational lurking," and (c) demonstrate the locality of geographic activity and how editing and viewing are geographically correlated.

Open content communities such as wikis derive their value from the work done by users. However, a key challenge is to elicit work that is sufficient and focused where needed. We address this challenge in a geographic open content commu-nity, the Cyclopath bicycle route finding system. We devised two techniques to elicit and focus user work, one using fa-miliarity to direct work opportunities and another visually highlighting them. We conducted a field experiment, find-ing that (a) the techniques succeeded in eliciting user work, (b) the distribution of work across users was highly unequal, and (c) user work benefitted the community (reducing the length of the average computed route by 1 kilometer). Author Keywords Wiki, geowiki, open content, geographic volunteer work, volunteered geographic information ACM Classification Keywords H.5.3 [Group and Organization Interfaces]: Collaborative computing, computer-supported cooperative work, web-based interaction.

We introduced tags into the Cyclopath geographic wiki for bicyclists. To promote the creation of useful tags, we made tags wiki objects, giving ownership of tag applications to the community, not to individuals. We also introduced a novel interface that lets users fine-tune their routing preferences with tags. We analyzed the Cyclopath tagging vocabulary, the relationship of tags to existing annotation techniques (notes and ratings), and the roles users take on with respect to tagging, notes, and ratings. Our findings are: two distinct tagging vocabularies have emerged, one around each of the two main types of geographic objects in Cyclopath; tags and notes have overlapping content but serve distinct purposes; users employ both ratings and tags to express their route-finding preferences, and use of the two techniques is moderately correlated; and users are highly specialized in their use of tags and notes. These findings suggest new design opportunities, including semi-automated methods to infer new annotations in a geographic context.

Many online mapping applications let users define routes, perhaps for sharing a favorite bicycle commuting route or rating several contiguous city blocks. At the UI level, defining a route amounts to selecting a fairly large number of objects - the individual segments of roads and trails that make up the route. We present a novel interaction technique for this task called path selection. We implemented the technique and evaluated it experimentally, finding that adding path selection to a state-of-the-art technique for selecting individual objects reduced route definition time by about a factor of 2, reduced errors, and improved user satisfaction. Detailed analysis of the results showed path selection is most advantageous (a) for routes with long straight segments and (b) when objects that are optimal click targets also are visually attractive.

Google Maps and its spin-offs are highly successful, but they have a major limitation: users see only pictures of geographic data. These data are inaccessible except by limited vendor-defined APIs, and associated user data are weakly linked to them. But some applications require access, specifically geowikis and computational geowikis. We present the design and implementation of a computational geowiki. We also show empirically that both geowiki and computational geowiki features are necessary for a representative domain, bicycling, because (a) cyclists have useful knowledge unavailable except from cyclists and (b) cyclist-oriented automatic route-finding is enhanced by user input. Finally, we derive design implications: for example, user contributions presented within a route description are useful, and wikis should support contribution of opinion as well as fact.

With new technology, people can share information about everyday places they go; the resulting data helps others find and evaluate places. Recent applications like Dodgeball and Sharescape repurpose everyday place information: users create local place data for personal use, and the systems display it for public use. We explore both the opportunities -- new local knowledge, and concerns -- privacy risks, raised by this implicit information sharing. We conduct two empirical studies: subjects create place data when using PlaceMail, a location-based reminder system, and elect whether to share it on Sharescape, a community map-building system. We contribute by: (1) showing location-based reminders yield new local knowledge about a variety of places, (2) identifying heuristics people use when deciding what place-related information to share (and their prevalence), (3) detailing how these decision heuristics can inform local knowledge sharing system design, and (4) identifying new uses of shared place information, notably opportunistic errand planning.

Wikipedia’s brilliance and curse is that any user can edit any of the encyclopedia entries. We introduce the notion of the impact of an edit, measured by the number of times the edited version is viewed. Using several datasets, including recent logs of all article views, we show that frequent editors dominate what people see when they visit Wikipedia, and that this domination is increasing. Similarly, using the same impact measure, we show that the probability of a typical article view being damaged is small but increasing, and we present empirically grounded classes of damage. Finally, we make policy recommendations for Wikipedia and other wikis in light of these findings.