Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites

Dark patterns are user interface design choices that benefit an online service by coercing, steering, or deceiving users into making unintended and potentially harmful decisions. We present automated techniques that enable experts to identify dark patterns on a large set of websites. Using these techniques, we study shopping websites, which often use dark patterns to influence users into making more purchases or disclosing more information than they would otherwise. Analyzing ~53K product pages from ~11K shopping websites, we discover 1,818 dark pattern instances, together representing 15 types and 7 broader categories. We examine these dark patterns for deceptive practices, and find 183 websites that engage in such practices. We also uncover 22 third-party entities that offer dark patterns as a turnkey solution. Finally, we develop a taxonomy of dark pattern characteristics that describes the underlying influence of the dark patterns and their potential harm on user decision-making. Based on our findings, we make recommendations for stakeholders including researchers and regulators to study, mitigate, and minimize the use of these patterns.Comment: 32 pages, 11 figures, ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2019

Antiproton slowing Down in H2 and He and evidence of nuclear stopping power

We report stopping powers of hydrogen and helium for antiprotons of kinetic energies ranging from about 0.5 keV to 1.1 MeV. The Barkas effect, i.e., a difference in the stopping power for antiprotons and protons of the same energy in the same material, shows up clearly in either of the gases. Moreover, below ≈0.5 keV there is indirect evidence for an increase of the antiproton stopping power. This "nuclear" effect, i.e., energy losses in quasimolecular interactions, shows up in fair agreement with theoretical predictions

Experimental antiproton nuclear stopping power in H2 and D2

Data about antiprotons slowing down in gaseous targets at very low energies (E<1 keV) show that the stopping power in D2 is lower than in H2; the right way to explain this behavior seems to be through a nuclear stopping power derived from the classical Rutherford formula

Design and Implementation of a Memory Allocator to Achieve Cache Partitioning in the Linux Kernel

Predictability is one of the key properties of hard real-time systems. A system is predictable when it is possible to guarantee in advance that the timing constraints of all the tasks in the system will be met. Achieving predictability on modern multi-core systems is, however, very challenging, mainly due to shared architectural resources, such as cache memories. It has been shown that the interference caused by a shared cache can increase the task execution time by up to 40%, with even higher delay spikes that are hard to predict. The problem of mitigating the effects of cache interference has received much attention in the real-time systems community. A popular and effective approach is a software cache-partitioning technique called page coloring. With page coloring, memory is partitioned into colors. Assigning different colors to different tasks avoids cache interference between the tasks, as they do not contend for the same cache partitions. Therefore, the tasks are spatially isolated in cache. Various uses of page coloring have been proposed in the literature. However, the existing implementation efforts only take into account single page requests and do not isolate kernel processes, which can still interfere with other tasks. Conversely to previous works, this thesis targets both user-space and kernel memory. The objective of this work is to provide insights into the design and implementation of a page coloring solution for the latest Linux PREEMPT-RT kernel. The proposed implementation supports both single-page and multiple-page allocation schemes. Experimental results show significant improvements in task isolation, as our solution reduces the effects of cache interference. Interference affecting worst-case execution times is decreased from 14.6% to a maximum of 4.4%

Dark patterns at scale: Findings from a crawl of 11K shopping websites

status: Published onlin

Caveats and truths in genetic, clinical, autoimmune and autoinflammatory issues in Blau syndrome and early onset sarcoidosis

Blau syndrome (BS) and early onset sarcoidosis (EOS) are, respectively, the familial and sporadic forms of the pediatric granulomatous autoinflammatory disease, which belong to the group of monogenic autoinflammatory syndromes. Both of these conditions are caused by mutations in the NOD2 gene, which encodes the cytosolic NOD2 protein, one of the pivotal molecules in the regulation of innate immunity, primarily expressed in the antigen-presenting cells. Clinical onset of BS and EOS is usually in the first years of life with noncaseating epithelioid granulomas mainly affecting joints, skin, and uveal tract, variably associated with heterogeneous systemic features. The dividing line between autoinflammatory and autoimmune mechanisms is probably not so clear-cut, and the relationship existing between BS or EOS and autoimmune phenomena remains unclear. There is no established therapy for the management of BS and EOS, and the main treatment aim is to prevent ocular manifestations entailing the risk of potential blindness and to avoid joint deformities. Nonsteroidal anti-inflammatory drugs, corticosteroids and immunosuppressive drugs, such as methotrexate or azathioprine, may be helpful; when patients are unresponsive to the combination of corticosteroids and immunosuppressant agents, the tumor necrosis factor-α inhibitor infliximab should be considered. Data on anti-interleukin-1 inhibition with anakinra and canakinumab is still limited and further corroboration is required. The aim of this paper is to describe BS and EOS, focusing on their genetic, clinical, and therapeutic issues, with the ultimate goal of increasing clinicians' awareness of both of these rare but serious disorders