Using neural networks to obtain indirect information about the state variables in an alcoholic fermentation process
This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg&ndash
Forecasting Electricity Consumption and Production in Smart Homes through Statistical Methods
Abstract Over the last years, a steady increase in both domestic electricity consumption and in the adoption of personal clean energy production systems has been observed worldwide. By analyzing energy consumption and production on photovoltaic panels mounted in a house, this work focuses on finding patterns in electrical energy consumption and devising a predictive model. Our goal is to find an accurate method to predict electrical energy consumption and production. Being able to anticipate how consumers will use energy in the near future, homeowners, companies and governments may optimize their behavior and the import and export of electricity. We evaluated the ARIMA and TBATS statistical…
An asynchronous covert channel using spam
AbstractCurrent Internet e-mail facilities are built onto the foundation of standard rules and protocols, which usually allow a considerable amount of “freedom” to their designers. Each of these standards has been defined based on a number of vendor specific implementations, in order to provide common inter-working procedures for cross-vendor communication. Thus, a lot of optional and redundant information is being exchanged during e-mail sessions, which is available to implement versatile covert channel mechanisms.This work exploits this possibility by presenting a simple but effective steganographic scheme that can be used to deploy robust secret communication through spam e-mails. This s…
Human capital evaluation in knowledge-based organizations based on big data analytics
Abstract Starting from a Human Capital Analysis Model, this work introduces an original methodology for evaluating the performance of employees. The proposed architecture, particularly well suited to the special needs of knowledge-based organizations, is articulated into a framework able to manage cases where data is missing and an adaptive scoring algorithm takes into account seniority, performance, and performance evolution trends, allowing employee evaluation over longer periods. We developed a flexible software tool that gathers data from organizations in an automatic way – through adapted connectors – and generates abundant results on the measurement and distribution of employees’ perf…
Performance and energy optimisation in CPUs through fuzzy knowledge representation
Abstract This paper presents an automatic design space exploration using processor design knowledge for the multi-objective optimisation of a superscalar microarchitecture enhanced with selective load value prediction (SLVP). We introduced new important SLVP parameters and determined their influence regarding performance, energy consumption, and thermal dissipation. We significantly enlarged initial processor design knowledge expressed through fuzzy rules and we analysed its role in the process of automatic design space exploration. The proposed fuzzy rules improve the diversity and quality of solutions, and the convergence speed of the design space exploration process. Experiments show tha…
A study on forecasting electricity production and consumption in smart cities and factories
Abstract The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity producti…
Digitization, Epistemic Proximity, and the Education System: Insights from a Bibliometric Analysis
Advances in IoT, AI, Cyber-Physical Systems, Computational Intelligence, and Big Data Analytics require organizations and workforce to be able and willing to learn how to interact with digital technology. In organizations, coordination and cooperation between actors with expertise in business and technology is fundamental, but integration is hard without understanding the terminology and problems of the interlocutor. Epistemic proximity becomes prominent, underlining the importance of an education focused on flexibility, willingness to cope with the unknown, and interdisciplinarity. The main goal of this work is to provide a perspective on how the education system is evolving to support org…