Alignment of the ATLAS Inner Detector in Run 2

The performance of the ATLAS Inner Detector alignment has been studied using pp collision data at $$\sqrt{s} = 13\,\hbox {TeV}$$ s = 13 TeV collected by the ATLAS experiment during Run 2 (2015–2018) of the Large Hadron Collider (LHC). The goal of the detector alignment is to determine the detector geometry as accurately as possible and correct for time-dependent movements. The Inner Detector alignment is based on the minimization of track-hit residuals in a sequence of hierarchical levels, from global mechanical assembly structures to local sensors. Subsequent levels have increasing numbers of degrees of freedom; in total there are almost 750,000. The alignment determines detector geometry on both short and long timescales, where short timescales describe movements within an LHC fill. The performance and possible track parameter biases originating from systematic detector deformations are evaluated. Momentum biases are studied using resonances decaying to muons or to electrons. The residual sagitta bias and momentum scale bias after alignment are reduced to less than $$\sim 0.1\hbox { TeV}^{-1}$$ ∼ 0.1 TeV - 1 and $$0.9\times 10^{-3}$$ 0.9 × 10 - 3 , respectively. Impact parameter biases are also evaluated using tracks within jets.

Deep Petri nets of unsupervised and supervised learning

Artificial intelligence is one of the hottest research topics in computer science. In general, when it comes to the needs to perform deep learning, the most intuitive and unique implementation method is to use neural network. But there are two shortcomings in neural network. First, it is not easy to be understood. When encountering the needs for implementation, it often requires a lot of relevant research efforts to implement the neural network. Second, the structure is complex. When constructing a perfect learning structure, in order to achieve the fully defined connection between nodes, the overall structure becomes complicated. It is hard for developers to track the parameter changes inside. Therefore, the goal of this article is to provide a more streamlined method so as to perform deep learning. A modified high-level fuzzy Petri net, called deep Petri net, is used to perform deep learning, in an attempt to propose a simple and easy structure and to track parameter changes, with faster speed than the deep neural network. The experimental results have shown that the deep Petri net performs better than the deep neural network.

Track Fitting for the Belle II Experiment

The Belle II experiment has started to take data in 2018, studying e+e- collisions at the KEK facility in Tsukuba (Japan), in a center of mass energy range of the Bottomonium states. The tracking system includes a combination of hit measurements coming from the vertex detector, made of pixel detectors and double-sided silicon strip detectors, and acentral drift chamber, inside a solenoid of 1.5 T magnetic field. Once the pattern recognition routines have identified the track candidates, hit measurements are fitted taking into account the different information coming from different detectors, the energy loss in the materials and the inhomogeneity of the magnetic field. Track fitting is performed by the generic track-fitting software GENFIT, which includes a Kalman filter improved by a deterministic annealing filter, in order to reject outlier hits coming from not correctly associated hits by the pattern recognition. Several mass hypotheses are used in the fit, in order to achieve the best track parameter estimation for each particle kind. This article presents the design of the track fitting in the Belle II software, showing results in terms of track parameter estimation as well as computing performances.

Retrofitting Existing Web Applications with Effective Dynamic Protection Against SQL Injection Attacks

This paper presents an approach for retrofitting existing Web applications with run-time protection against known, as well as unseen, SQL injection attacks (SQLIAs) without the involvement of application developers. The precision of the approach is also enhanced with a method for reducing the rate of false positives in the SQLIA detection logic, via runtime discovery of the developers’ intention for individual SQL statements made by Web applications. The proposed approach is implemented in the form of protection mechanisms for J2EE, ASP.NET, and ASP applications. Named SQLPrevent, these mechanisms intercept HTTP requests and SQL statements, mark and track parameter values originating from HTTP requests, and perform SQLIA detection and prevention on the intercepted SQL statements. The AMNESIA testbed is extended to contain false-positive testing traces, and is used to evaluate SQLPrevent. In our experiments, SQLPrevent produced no false positives or false negatives, and imposed a maximum 3.6% performance overhead with 30 milliseconds response time for the tested applications.

Retrofitting Existing Web Applications with Effective Dynamic Protection Against SQL Injection Attacks

This article presents an approach for retrofitting existing Web applications with run-time protection against known, as well as unseen, SQL injection attacks (SQLIAs) without the involvement of application developers. The precision of the approach is also enhanced with a method for reducing the rate of false positives in the SQLIA detection logic, via runtime discovery of the developers’ intention for individual SQL statements made by Web applications. The proposed approach is implemented in the form of protection mechanisms for J2EE, ASP.NET, and ASP applications. Named SQLPrevent, these mechanisms intercept HTTP requests and SQL statements, mark and track parameter values originating from HTTP requests, and perform SQLIA detection and prevention on the intercepted SQL statements. The AMNESIA testbed is extended to contain false-positive testing traces, and is used to evaluate SQLPrevent. In our experiments, SQLPrevent produced no false positives or false negatives, and imposed a maximum 3.6% performance overhead with 30 milliseconds response time for the tested applications.