Abstract
Time expressions embedded in text are important for many downstream tasks in NLP and IR. They have been, for example, utilized for timeline summarization, named entity recognition, temporal information retrieval, question answering and others. In this paper, we introduce a novel analytical approach to analyzing characteristics of time expressions in diachronic text collections. Based on a collection of news articles published over a 33-years' long time span, we investigate several aspects of time expressions with a focus on their interplay with publication dates of containing documents. We utilize a graph-based representation of temporal expressions to represent them through their co-occurring named entities. The proposed approach results in several observations that could be utilized in automatic systems that rely on processing temporal signals embedded in text. It could be also of importance for professionals (e.g., historians) who wish to understand fluctuations in collective memories and collective expectations based on large-scale, diachronic document collections.

Abstract
We live in a world where data is becoming increasingly valuable and increasingly abundant in volume. All companies produce data from sales, sensors, and various other sources. The main challenges are how can we extract insights from such a rich data environment and if Deep Learning is capable of circumventing Big Data's challenges. To reach a conclusion, Social Network data is used as a case study for predicting sentiment changes in the Stock Market. The main objective of this paper is to develop a computational study and analyze its performance. Deep Learning was able to handle some challenges of Big Data, allowing results to be obtained and compared with real world situations. The outputs contribute to understand Deep Learning's usage with Big Data and how it acts in Sentiment Analysis.

Abstract
Energy storage system (ESS) has great importance in saving energy in new power systems. Optimum selection of these elements poses a new challenge to improve the energy management and prevent cost increases in the system. Also, renewable energy resources have been increasingly used in microgrids. The uncertainty and variation of renewable distributed generation (DG) affect the performance of power systems. In this paper, ESS implementations and photovoltaic (PV) power prediction are used to improve voltage/power profile of the system and reduce the total cost of the microgrid. The purpose of this paper is the optimal installation of ESSs in a microgrid to minimize the total cost where quantile nearest neighbour forecasting is utilized for PV output power prediction as an efficient approach. Gathering data of the last samples in time duration can be used for an effective prediction of PV output in this method, which can overcome PV uncertainty due to changes in solar irradiation and other parameters. Artificial neural networks combined with multi-layer perceptron and genetic algorithm are used for optimizing the size and location of ESSs in the system. Simulation results show that the proposed method improves the power profile as 14%, 21% and 28%, relatively to the scenarios of optimal ESS installation without PV prediction, using PV prediction but with no optimal ESS implementation and not using PV-no ESS implementation, respectively. Moreover, the accuracy of the proposed prediction method is more than the gradient-descent and RNN methods by about 12% and 5%, respectively, as shown in the simulation results. Also, the cost reduction of proposed method is enhanced as 24% and 31% relatively to the cases of optimal ESS installation without PV prediction and PV prediction without optimal ESS implementation, respectively.

Abstract
This study explores the relevance of different soft skills for entrepreneurs, focusing on entrepreneurs from Portugal and Serbia. The study identifies a total of 38 soft skills titative methodology in the analysis of data collected from a questionnaire completed by entrepreneurs from these two countries. The findings reveal that soft skills competencies play a key role in the entrepreneur's activity, highlighting emotional intelligence, resilience, and persistence as fundamental attributes that an entrepreneur should possess. Furthermore, the respondents' answers have revealed notable differences in their perception of the importance of a majority of soft skills (27 out of 38 skills), which indicates that an entrepreneur's geographic area is a relevant factor in the perception of the relative importance of soft skills.

Abstract
Bin picking is a highly researched topic, due to the need for automated procedures in industrial environments. A general bin picking system requires a highly structured process, starting with data acquisition, and ending with pose estimation and grasping. A high number of bin picking problems are being presently solved, through deep learning networks, combined with distinct procedures. This study provides a comprehensive review of deep learning approaches, implemented in bin picking problems. Throughout the review are described several approaches and learning methods based on specific domains, such as gripper oriented and object oriented, as well as summarized several methodologies, in order to solve bin picking issues. Furthermore, are introduced current strategies used to simplify particular cases and at last, are presented peculiar means of detecting object poses.

Abstract
In the upcoming years, European countries have to make a strong bet on solar energy. Small photovoltaic systems are able to provide energy for several applications like housing, traffic and street lighting, among others. This field is expected to have a big growth, thus taking advantage of the largest renewable energy source existing on the planet, the sun. This paper proposes a computational model able to simulate the behavior of a stand-alone photovoltaic system. The developed model allows to predict PV systems behavior, constituted by the panels, storage system, charge controller and inverter, having as input data the solar radiation and the temperature of the installation site. Several tests are presented that validates the reliability of the developed model.