Abstract

Abstract
Recently, the importance of data analysis for content marketing has become apparent. However, only a few companies use data as a source of knowledge to enrich their strategies. The application of data analysis in the development of content marketing strategies is still at an early stage of research and still little explored in the business context. However, given the research results analysed, it is a promising and differentiating area for the success of content marketing strategies. In this paper, the main existing approaches related to this theme were analysed and an empirical study was developed through a case study in a company, with the aim of optimising the content production for its blog, regarding digital marketing, using the data analysis provided by the company's software. The study was carried out following an exploratory and qualitative methodology, using content analysis as the main technique for data collection. The results obtained after this work have made it possible to verify and demonstrate the positive contribution of data analysis to the development of content marketing strategies.

Abstract
Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content - e.g. posts, news, products, comments -, but also user feedback - e.g. ratings, views, reads, clicks -, together with context data - user device, spacial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy and explainability.

Abstract

Abstract
The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases.

Abstract
Manufacturers of automated systems and their components have been allocating an enormous amount of time and effort in R&D activities, which led to the availability of prototypes demonstrating new capabilities as well as the introduction of such systems to the market within different domains. Manufacturers need to make sure that the systems function in the intended way and according to specifications. This is not a trivial task as system complexity rises dramatically the more integrated and interconnected these systems become with the addition of automated functionality and features to them. This effort translates into an overhead on the V&V (verification and validation) process making it time-consuming and costly. In this paper, we present VALU3S, an ECSEL JU (joint undertaking) project that aims to evaluate the state-of-the-art V&V methods and tools, and design a multi-domain framework to create a clear structure around the components and elements needed to conduct the V&V process. The main expected benefit of the framework is to reduce time and cost needed to verify and validate automated systems with respect to safety, cyber-security, and privacy requirements. This is done through identification and classification of evaluation methods, tools, environments and concepts for V&V of automated systems with respect to the mentioned requirements. VALU3S will provide guidelines to the V&V community including engineers and researchers on how the V&V of automated systems could be improved considering the cost, time and effort of conducting V&V processes. To this end, VALU3S brings together a consortium with partners from 10 different countries, amounting to a mix of 25 industrial partners, 6 leading research institutes, and 10 universities to reach the project goal.