Abstract
In order to secure wireless communications, we consider the usage of physical-layer security (PLS) mechanisms (i.e. coding for secrecy mechanisms) combined with self-interference generation. We present a prototype implementation of a scrambled coding for secrecy mechanism with interference generation by the legitimate receiver and the cancellation of the effect of self-interference (SI). Regarding the SI cancellation, two algorithms were evaluated: least mean square and recursive least squares. The prototype implementation is performed in real-world software-defined radio (SDR) devices using GNU-Radio. © 2020 IEEE.

Abstract
It is imperative that online companies have a complete in-depth understanding of online behavior in order to provide a better service to their customers. This paper proposes a model for real-time basket addition in the e-grocery sector that includes predictors inferred from anonymous clickstream data, such as a Markov page view sequence discrimination value. This model aims at anticipating the addition and the non-addition of items to customers' market basket, in order to enable marketers to act conveniently, for example recommending more appropriate items. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of anonymous clickstream data taken from the servers of a European e-retailing company is explored. The empirical results reveal the high predictive power of the model proposed, based on the explanatory variables introduced, as well as the supremacy of random forests over logistic regression.

Abstract
This article addresses the use of augmented reality content as a part of the point of sale promotion strategy. The main research question is to determine whether the use of an application with augmented reality can facilitate the visualization and localization of the promotions available at the physical point of sale and, consequently, influence the shopping experience. To answer this question, an application was created with augmented reality content, so that it can measure the influence that this technology can have on the perception of promotions within stores. The general objective of the study is to identify whether the use of this application, in the context of supermarkets, could facilitate the visualization and localization of promotions in comparison with conventional disclosure approaches. Based on a literature review with authors from different areas, this article summarizes an ongoing investigation and at the end details the future steps of the research.

Abstract
The worldwide investment in renewable energy sources is leading to the formation of local energy communities in which users can trade electric energy locally. Regulations and the required enablers for effective transactions in this new context are currently being designed. Hence, the development of software tools to support local transactions is still at an early stage and faces the challenge of constant updates to the data models and business rules. The present paper proposes a novel approach for the development of software tools to solve auction-based local electricity markets, considering the special needs of local energy communities. The proposed approach considers constrained bids that can increase the effectiveness of distributed generation use. The proposed method takes advantage of semantic web technologies, in order to provide models with the required dynamism to overcome the issues related to the constant changes in data and business models. Using such techniques allows the system to be agnostic to the data model and business rules. The proposed solution includes the proposed constraints, application ontology, and semantic rule templates. The paper includes a case study based on real data that illustrates the advantages of using the proposed solution in a community with 27 consumers.

Abstract
Concept drift detection is a crucial task in data stream evolving environments. Most of the state of the art approaches designed to tackle this problem monitor the loss of predictive models. Accordingly, an alarm is launched when the loss increases significantly, which triggers some adaptation mechanism (e.g. retrain the model). However, this modus operandi falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. These often take up to several weeks to be available. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels. We propose a novel approach to unsupervised concept drift detection, which is based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the behaviour of the main model (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of controlled experiments, we discovered that the proposed approach detects concept drift effectively. Relative to the gold standard, in which the labels are immediately available after prediction, our approach is more conservative: it signals less false alarms, but it requires more time to detect changes. We also show the competitiveness of our approach relative to other unsupervised methods. © 2020, Springer Nature Switzerland AG.

Abstract