Incremental and Modular Context-sensitive Analysis

Context-sensitive global analysis of large code bases can be expensive, which can make its use impractical during software development. However, there are many situations in which modifications are small and isolated within a few components, and it is desirable to reuse as much as possible previous analysis results. This has been achieved to date through incremental global analysis fixpoint algorithms that achieve cost reductions at fine levels of granularity, such as changes in program lines. However, these fine-grained techniques are not directly applicable to modular programs, nor are they designed to take advantage of modular structures. This paper describes, implements, and evaluates an algorithm that performs efficient context-sensitive analysis incrementally on modular partitions of programs. The experimental results show that the proposed modular algorithm shows significant improvements, in both time and memory consumption, when compared to existing non-modular, fine-grain incremental analysis techniques. Furthermore, thanks to the proposed inter-modular propagation of analysis information, our algorithm also outperforms traditional modular analysis even when analyzing from scratch.Comment: 56 pages, 27 figures. To be published in Theory and Practice of Logic Programming. v3 corresponds to the extended version of the ICLP2018 Technical Communication. v4 is the revised version submitted to Theory and Practice of Logic Programming. v5 (this one) is the final author version to be published in TPL

An Approach to Static Performance Guarantees for Programs with Run-time Checks

Instrumenting programs for performing run-time checking of properties, such as regular shapes, is a common and useful technique that helps programmers detect incorrect program behaviors. This is specially true in dynamic languages such as Prolog. However, such run-time checks inevitably introduce run-time overhead (in execution time, memory, energy, etc.). Several approaches have been proposed for reducing such overhead, such as eliminating the checks that can statically be proved to always succeed, and/or optimizing the way in which the (remaining) checks are performed. However, there are cases in which it is not possible to remove all checks statically (e.g., open libraries which must check their interfaces, complex properties, unknown code, etc.) and in which, even after optimizations, these remaining checks still may introduce an unacceptable level of overhead. It is thus important for programmers to be able to determine the additional cost due to the run-time checks and compare it to some notion of admissible cost. The common practice used for estimating run-time checking overhead is profiling, which is not exhaustive by nature. Instead, we propose a method that uses static analysis to estimate such overhead, with the advantage that the estimations are functions parameterized by input data sizes. Unlike profiling, this approach can provide guarantees for all possible execution traces, and allows assessing how the overhead grows as the size of the input grows. Our method also extends an existing assertion verification framework to express "admissible" overheads, and statically and automatically checks whether the instrumented program conforms with such specifications. Finally, we present an experimental evaluation of our approach that suggests that our method is feasible and promising.Comment: 15 pages, 3 tables; submitted to ICLP'18, accepted as technical communicatio

Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362

Dynamic simulations in SixTrack

The DYNK module allows element settings in SixTrack to be changed on a turn-by-turn basis. This document contains a technical description of the DYNK module in SixTrack. It is mainly intended for a developer or advanced user who wants to modify the DYNK module, for example by adding more functions that can be used to calculate new element settings, or to add support for new elements that can be used with DYNK.Comment: Submission to CERN yellow report / conference proceeding, the 2015 collimation tracking code worksho

Bilateral Internuclear Ophthalmoplegia in a Patient with Devic's Neuromyelitis Optica

An unusual presentation of Devic's neuromyelitis optica (NMO) disease associated with bilateral internuclear ophthalmoplegia (INO) is described. A 32-year-old pregnant patient was diagnosed with NMO. First symptoms were headache and sudden visual loss in her right eye (RE). Eighteen months ago, she reported other neurologic symptoms such as paresthesia. Based on her visual field, fundoscopy and Ishihara test, she was diagnosed with retrobulbar neuritis of the RE. After delivery, new neurologic symptoms resembling transverse myelitis appeared. She was treated with methylprednisolone and plasmapheresis, which improved her visual acuity; however, a sudden bilateral INO appeared, with adduction defect and nystagmus with abduction in both eyes. No improvement was obtained after treatment with azathioprine and rituximab. Paresis of the legs and the right arm persisted, but double vision and OIN gradually disappeared. At the end, the patient had a residual exophoria in the RE and nystagmus with abduction in the left eye. Prevalence of NMO is lower than one case per one million inhabitants, and it is not likely to affect the encephalic trunk; furthermore, bilateral INO in NMO is rare. Two major criteria and at least two of the three minor ones are required to confirm a NMO diagnosis, and our patient fulfilled these diagnosis criteria