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
User modeling and user adaptive interaction has become a central research issue to understand users as they interact with technology. The importance of the development of well adapted interfaces to several kinds of users and the differences that characterize them is the basis of the successful interaction. User Personas is a technique that allows the discovery and definition of the archetype users of a system. With that knowledge, the system should shape itself, inferring the user expertise to provide its users with the best possible experience. In this paper, an architecture that combines User Personas and a dynamic, evolving system is proposed, along with an evaluation by its target users. The proposed system is able to infer the user and its matching Persona, and keeps shaping itself in parallel with the user's discovery of the system.

Abstract
In the current marketplace, enterprises face enormous competitive pressures. Global competition for customers that demand customized products with shorter due dates and the advancement in information technologies, marked the introduction of the Extended Enterprise. In these EMEs (Extended Manufacturing Environments), lean, virtual, networked and distributed enterprises, form MO (Meta-Organizations), which collaborate to respond to the dynamic marketplace. MO members share resources, customers and information. In this paper we present a hybrid framework based on a DKBS (Distributed Knowledge Base System), which includes information about scheduling methods for collaborative enterprises sharing their problems. A core component of this system includes an inference engine as well as two indexes, to help in the classification of the usefulness of the information about the problems and solving methods. A more structured approach for expanding the MO concept is presented, with the HO (Hyper-Organization). The manner in which MO-DSS can communicate, cooperate and share information, in the context of the HO is also detailed.

Abstract
In this paper a scheduling meta-model is proposed for supporting hybrid collaboration, regarding machine-machine and human-machine scheduling interactions, based on a scheduling ontology. The utilization of the proposed scheduling ontology-based meta-model is illustrated through an example, which is further analysed, and some main features and advantages of each kind of collaborative interaction are discussed. © 2020, Springer Nature Switzerland AG.

Abstract
Renewable energy sources such as solar and wind are now competitive with traditional fossil and nuclear power when generating but that is just the challenge. When not generating can be a problem for grid integration and the main challenge to the widespread acceptance and dissemination of solar and wind, and the focus of research for the next generation of energy engineers. Intermittent, the adjective most associated with solar and wind energy has been and continues to be the focus of research by power engineers, AI professionals, and system scientists from the late 20th century and is the critical factor in the design of the future power grids, The most obvious solution is energy storage but then the choice of the storage method and size are complex problems. Will best solutions involve pumped hydro, Li-Ion batteries, or hydrogen generation? Or will next-generation ultra-capacitors, or high-speed flywheels gyros, or some yet to be discovered device will be the dominating technologies? The primary objective of the storage designs will be based on what's best for the reliability and efficiency of the grid, and simultaneously optimizing cost and environmental impact functions. Socio-economic and geopolitical considerations must also be considered to satisfy local or regional constraints. There is also the question of purpose: will it be sized for grid stability, or medium, or long-term storage. This factor will depend on the specific grid requirements. The focus of this paper is to study multi-source renewable energy systems that include storage called HRES or Hybrid Renewable Energy with Storage. This study describes the development of a behind-the-meter Energy Management System (EMS) for an HRES, under the assumption that Real-Time Pricing (RTP) is offered by a utility supplying power to a medium-size office complex. A cost function to be minimized is introduced to optimize the contribution of each energy source. Also, this work develops the basis of a platform for decision-makers to evaluate the viability of the optimized system in conjunction with the financial feasibility analysis.

Abstract
Health Remote Monitoring Systems (HRMS) offer the ability to address health-care human resource concerns. In developing nations, where pervasive mobile networks and device access are linking people like never before, HRMS are of special relevance. A fundamental aim of this research work is the realization of technological-based solution to triage and follow-up people living with dementias so as to reduce pressure on busy staff while doing this from home so as to avoid all unnecessary visits to hospital facilities, increasingly perceived as dangerous due to COVID-19 but also raising nosocomial infections, raising alerts for abnormal values. Sensing approaches are complemented by advanced predictive models based on Machine Learning (ML) and Artificial Intelligence (AI), thus being able to explore novel ways of demonstrating patient-centered predictive measures. Low-cost IoT devices composing a network of sensors and actuators aggregated to create a digital experience that will be used and exposure to people to simultaneously conduct several tests and obtain health data that can allow screening of early onset dementia and to aid in the follow-up of selected cases. The best ML for predicting AD was logistic regression with an accuracy of 86.9%. This application as demonstrated to be essential for caregivers once they can monitor multiple patients in real-time and actuate when abnormal values occur.