TAIGA -- an advanced hybrid detector complex for astroparticle physics and high energy gamma-ray astronomy

The physical motivations, present status, main results in study of cosmic rays and in the field of gamma-ray astronomy as well future plans of the TAIGA-1 (Tunka Advanced Instrument for cosmic ray physics and Gamma Astronomy) project are presented. The TAIGA observatory addresses ground-based gamma-ray astronomy and astroparticle physics at energies from a few TeV to several PeV, as well as cosmic ray physics from 100 TeV to several EeV. The pilot TAIGA-1 complex is located in the Tunka valley, ~50 km west from the southern tip of the lake Baikal.Comment: Submission to SciPost Phys. Proc., 10 pages, 2 figure

Primary Cosmic Rays Energy Spectrum and Mean Mass Composition by the Data of the TAIGA Astrophysical Complex

The corrected dependence of the mean depth of the EAS maximum $X_{max}$ on the energy was obtained from the data of the Tunka-133 array for 7 years and the TAIGA-HiSCORE array for 2 year. The parameter $\langle\ln A\rangle$, characterizing the mean mass compositon was derived from these results. The differential energy spectrum of primary cosmic rays in the energy range of $2\cdot 10^{14}$ - $2\cdot 10^{16}$\,eV was reconstructed using the new parameter $Q_{100}$ the Cherenkov light flux at the core distance 100 m.}Comment: 6 pages, 3 figures, Submitted to SciPost Phys.Pro

Development of hybrid reconstruction techniques for TAIGA

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UI Dark Patterns and Where to Find Them A Study on Mobile Applications and User Perception

A Dark Pattern (DP) is an interface maliciously crafted to deceive users into performing actions they did not mean to do. In this work, we analyze Dark Patterns in 240 popular mobile apps and conduct an online experiment with 589 users on how they perceive Dark Patterns in such apps. The results of the analysis show that 95% of the analyzed apps contain one or more forms of Dark Patterns and, on average, popular applications include at least seven different types of deceiving interfaces. The online experiment shows that most users do not recognize Dark Patterns, but can perform better in recognizing malicious designs if informed on the issue. We discuss the impact of our work and what measures could be applied to alleviate the issue