Abstract
Absenteeism is a major problem faced particularly by companies with a large number of employees. Therefore, the existence of absenteeism prediction tools is essential for such companies depending on intensive human-resources. This paper focuses on using machine learning technologies for predicting the absences of employees from work. More precisely, a few prediction models were tuned and tested with 241 features extracted from a population of 13.805 employees. This target population was sampled from the help desk work force of a major Brazilian phone company. The features were extracted from the profile of the help desk agents and then filtered by processes of correlation and feature selection. The selected features were then used to compare absenteeism prediction given by different classification algorithm (cf. Random Forest, Multilayer Perceptron, Support Vector Machine, Naive Bayes, XGBoost and Long Short Term Memory). The parameterization of these ML models was also studied to reach the classifier best suited for the prediction problem. Such parameterizations were tuned through the use of evolutionary algorithms, from which considerable precision was reached, the best being 72% (XGBoost) and 71% (Random Forest).

Abstract
We present the status and plans for the Keck All sky Precision Adaptive optics (KAPA) program. The program includes four key science projects, an upgrade to the Keck I laser guide star (LGS) adaptive optics (AO) facility to improve image quality and sky coverage, AO telemetry based point spread function (PSF) estimates for all science exposures, and an educational component focused on broadening the participation of women and underrepresented groups in instrumentation. All of these elements have pathfinder relevance for the ELTs. For the purpose of this conference we will focus on the AO facility upgrade which includes implementation of a new laser, wavefront sensor and real-time controller to support laser tomography, the laser tomography system itself, and modifications to an existing near-infrared tip-tilt sensor to support multiple natural guide star (NGS) and focus measurements.

Abstract
This work demonstrates a system that provides decision support to players in electricity market negotiations. This contribution is provided by ALBidS (Adaptive Learning strategic Bidding System), a decision support system that includes a large number of distinct market negotiation strategies, and learns which should be used in each context in order to provide the best expected response. The learning process on the best negotiation strategies to use at each moment is developed by means of several integrated reinforcement learning algorithms. ALBidS is integrated with MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), which enables the simulation of realistic market scenarios using real data.

Abstract
We propose an adaptive secrecy scheme using polar codes with random frozen bits for a general wiretap channel, in which to protect the data from a potential eavesdropper, part or all of the frozen bits are randomly generated per message. To assess the secrecy level of the proposed scheme, three types of decoding strategies are evaluated: a matching decoder which knows the positions of all inserted bits inside the blocklength and tries to estimate them using the same decoding techniques, a blind decoder which treats all the frozen bits as the same value, and a random decoder which considers those dynamic bits as random at the receiver. Results are presented in terms of the system security gap, assuming an adaptive decoding strategy. It is shown that the system achieves combined secrecy and reliability. The proposed scheme does not assume knowledge of the eavesdropper's channel when defining the indices of information and frozen bits. © 2019 IEEE.

Abstract

Abstract