Abstract
This paper aims to provide a solution to a problem shared by online marketing platforms. Many of these platforms are exploited by spammers to ease their job of distributing spam. This can lead to platforms domains being black-listed by ISP’s, which translates to lower deliverability rates and consequently lower profits. Normally, platforms try to counter the problem by using rule-based systems, which require high-maintenance and are not easily editable. Additionally, since analysis occurs when a contact database is imported, the regular approach of judging messages’ contents directly is not an effective solution, as those do not yet exist. The proposed solution, a machine-learning based system for the classification of contact database’s importations, tries to surpass these aforementioned systems by making use of the capabilities introduced by machine-learning technologies, namely, reliability in regards to classification and ease of maintenance. Preliminary results show the legitimacy of this approach, since various algorithms can be successfully applied to it. The most proficient of the ones applied being Ada-boost and Random-forest. © 2020, Springer Nature Switzerland AG.

Abstract
Nowadays, humanity generates and contributes to form large and complex datasets, going from documents published on media outlets, posts on social media or location-based information. The generated information tends to be complex, heterogeneous (texts, images, videos, etc.) and is growing at an incredible pace, with much of this data having a strong spatial and temporal focus. This steady increase in the availability of such a volume of information, forces the development of more effective user interfaces that would assist users in efficient visualization, analysis and exploration of the data. This half-day workshop on User Interfaces for Spatial and Temporal Data Analysis (UISTDA) held in conjunction with the IUI2018 conference on March 11th, aimed at sharing the latest progress and developments, current challenges and potential applications for exploiting large amounts of spatial and temporal data. In this paper we provide an overview of the workshop goals together with its main contributions. © 2018 Copyright for the individual papers remains with the authors.

Abstract
The performance of an Adaptive Optics (AO) System relies on the accuracy of its Interaction Matrix which defines the opto-geometrical link between the Deformable Mirror (DM) and the Wave Front Sensor (WFS). Any mis-registrations (relative shifts, rotation, magnification or higher order pupil distortion) will strongly impact the performance, especially for high orders AO systems. Adaptive Telescopes provide a constraining environment for the AO calibration with large number of actuators DM, located inside the telescope with often no access to a calibration source and with a high accuracy required. The future Extremely Large Telescope (ELT) will take these constraints to another level with a longer calibration time required, no artificial calibration source and most of all, frequent updates of the calibration during the operation. To overcome these constraints, new calibration strategies have to be developed either doing it on-sky or working with synthetic models. The most promising approach seems to be the Pseudo-Synthetic Calibration. The principle is to generate the Interaction Matrix of the system in simulator, injecting the correct model alignment parameters identified from on-sky Measurements. It is currently the baseline for the Adaptive Optics Facility (AOF) at the Very Large Telescope (VLT) working with a Shack-Hartmann WFS but it remains to be investigated in the case of the Pyramid WFS.

Abstract
We investigated if a recently proposed method can differentiate yogurt bacteria trapped by a polymeric lensed fiber tip, through back-scattered signal analysis. Results suggest that it can be a valuable contribution for foodborne analysis/bacteria identification.

Abstract
With the rise of usage in digital content in education, deaf and blind communities face communication barriers which as a result makes education less inclusive. These barriers do not allow them to integrate within the larger scholarly communities as most tools used for information dissemination remain inaccessible to them. This paper presents BDC-API (Blind/Deaf Communications API), a free-to-use modular toolkit that will ease accessibility for the blind and deaf communities to digital education content. This content includes the use cases of Massive Online Open Courses and Serious Games used in education. BDC-API incorporates the use of state of the art technologies such as, 3D sign language translator, grammar translation, voice recognition and text-to-speech. This paper demonstrates in greater detail, how these technologies culminate in the creation of an API ready to use for any educational digital content and how the BDC-API can ensure higher quality of digital content.

Abstract
Geared by the increasing need for enhanced performance, both optical and computational, new dynamic control laws have been researched in recent years for next generation adaptive optics systems on current 10 m-class and extremely large telescopes up to 40 m. We provide an overview of these developments and point out prospects to making such controllers drive actual systems on-sky.