Abstract
This paper models a novel demand response (DR) trading strategy. In this model, the DR aggregator obtains the DR from the end-users via two types of DR programs, i.e. a time-of-use (TOU) program and an incentive-based DR program. Then, it offers this DR to the wholesale market. Three consumer sectors, namely residential, commercial and industrial, are included in this problem. The DR program is dependent on their corresponding load profiles during the studied time horizon. This paper uses a mixed-integer linear programming (MILP) problem and it is solved using the CPLEX solver through a stochastic programming approach in GAMS. The risk measure chosen to represent the load uncertainty of the users who are participating in the DR program is Conditional Value-at-Risk (CVaR). The proposed problem is simulated and assessed through a case study of a test system. The results indicate that the industrial loads play a major role in the power system and this directly affects the DR program. Moreover, the risk-averse decision-maker in this model favors a reduced participation in the DR programs when compared to a decision-maker who is risk-neutral, since the risk-averse decision maker prefers to be more secure against uncertainties. In other words, an increase in risk factor results in a decrease in the participation rate of the consumers in DR programs.

Abstract

Abstract

Abstract
Throughout this paper, a competition created to enable an inter-connection between the academic and industrial paradigms is presented, using Open Hardware and Software. This competition is called Robot at Factory Lite and serves as a case study as an additional enrollment for students to apply knowledge in the fields of programming, perception, motion planning, task planning, autonomous robotic, among others. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Today, wireless networks are operating in increasingly complex environments, impacting the evaluation and validation of new networking solutions. Simulation, although fully controllable and easily reproducible, depends on simplified physical layer and channel models, which often produce optimistic results. Experimentation is also influenced by external random phenomena and limited testbed scale and availability, resulting in hardly repeatable and reproducible results. Previously, we have proposed the Trace-based Simulation (TS) approach to address the problem. TS uses traces of radio link quality and position of nodes to accurately reproduce past experiments in ns-3. Yet, in its current version, TS is not compatible with scenarios where Multiple-In-Multiple-Out (MIMO) is used. This is especially relevant since ns-3 assumes perfectly independent MIMO radio streams. In this paper, we introduce the Trace-based Wi-Fi Station Manager Model, which is capable of reproducing the Rate Adaptation of past Wi-Fi experiments, including the number of effective radio streams used. To validate the proposed model, the network throughput was measured in different experiments performed in the w-iLab.t testbed, considering Single-In-Single-Out (SISO) and MIMO operation using IEEE 802.11a/n/ac standards. The experimental results were then compared with the network throughput achieved using the improved TS and Pure Simulation (PS) approaches, validating the new proposed model and confirming its relevance to reproduce experiments previously executed in real environments.

Abstract
Privacy is becoming a crucial requirement in many machine learning systems. In this paper we introduce an efficient and secure distributed K-Means algorithm, that is robust to non-IID data. The base idea of our proposal consists in each client computing the K-Means algorithm locally, with a variable number of clusters. The server will use the resultant centroids to apply the K-Means algorithm again, discovering the global centroids. To maintain the client's privacy, homomorphic encryption and secure aggregation is used in the process of learning the global centroids. This algorithm is efficient and reduces transmission costs, since only the local centroids are used to find the global centroids. In our experimental evaluation, we demonstrate that our strategy achieves a similar performance to the centralized version even in cases where the data follows an extreme non-IID form.