When an Android mobile device starts for the first time, the Android Setup Wizard (ASW) is loaded so the user can do his initial configurations, such as Wi-Fi network, date, user account, data restore, terms and condition, and others. However, in the context of manual or automated tests, every time a device starts for the first time, this ASW should be consistently configured before the tests begin. This configuration should not affect the test measurements to make them reproducible. It is impracticable to manually pass through all the steps of the ASW before every new test since this limits scalability. Moreover, a manual process can be error-prone, since two testers might use different navigation paths in the ASW, and this can affect the output. An automated script to do this task is not trivial as steps of the ASW can change depending on the device model, Android version (N, O, P, Q, etc.) and vendor (Verizon, Global, etc.). This means that the number of steps and the actions that should be executed in each of them can vary. Therefore, we propose WIZARD, an intelligent system that navigates automatically and consistently through the steps of the ASW, allowing the specialist to customize this navigation. To do that, WIZARD uses information retrieval (IR), vector space model (VSM), and re-rank techniques. We validate our approach on a set of real-world and commercially-available Android builds of a large company that comprises 1505 unique records. In the task of predicting the action to execute, our approach achieved an average Accuracy@1 of 98,82% and an Accuracy@5 of 99,87%. To the best of our knowledge, WIZARD is the first system that automatically navigates in the ASW to prepare devices for reproducible tests.
In the frame of POR FESR 2014-2020 Programs, the SoundLight project is intended to be a support for the inpatients, to ameliorate their pre and post-surgery psychophysiological conditions. SoundLight is an adaptive system, based on an intelligent agent designed for the control and the combined administration of auditory and visual stimuli, in a clinical environment. More precisely, the agent autonomously processes the sound signal, coming from various sources, and provides real-time conversion of the signal into controlled lighting effects, that are perceived relaxing by patients. The system is based on spectral signal analysis for the characterization of the sound signal and leverages on a novel sound-to-color conversion algorithm, which allows to transform main sound characters first into a chromatic reference wavelength and finally into a set of RGB values to be sent to light sources. The paper briefly introduces some essential concepts of the spectral analysis developed and focuses on the hardware/software architecture adopted to achieve real-time processing while maintaining a very compact form-factor of the processing hardware. Some key concepts related to the ongoing validation are also presented1.
Online health forums are increasingly used by patients to share information and discuss a broad range of health conditions. In this study, we investigate the presence of comorbidity of mental and physical disorders in users of such forums. We apply natural language processing methods to identify posts where users discuss mental health problems alongside chronic physical diseases, and use data mining techniques to explore the comorbidity patterns in these posts. We compare our findings to those reported in the literature and show how the results obtained are correlated with real-life events.
The current energy transition implies fundamental changes in energy systems, in both their structure and operation. In this context, microgrids have the potential to reshape the traditional power grid, empowering the role of small-scale renewable power generators towards more sustainable, cost-effective, and reliable energy systems. Microgrids are operated by energy management systems whose development is driven mainly by the contributions of technologies for distributed energy resources and techniques for optimal planning of loads. Yet, little attention is given to the design of such systems as software tools, whose capabilities are often limited because of a monolithic structure. We postulate that such systems need to intertwine both perspectives if microgrids are going to become a realised potential. We therefore propose a novel design of an energy management system that considers both perspectives and is based on the principles of service-orientation. We implement the proposed design into a service-oriented energy management system prototype and we run simulations with real data to test its feasibility.
The major part of the success that we have had in deep learning is attributed to the proper design of the architecture of the network model. With all the hardware resources and mathematical foundation at hand, it is the clever assembling of the pieces that define the performance of that deep learning model. Almost all of the research in this field till date use the models that are designed by a human researcher using their past experiences or applying the iterative process of adding components to the model to see which one performs the best. As we strive towards the general Artificial Intelligence, this process of manually tuning the network to make it work on a specific task does not add up well for the cause. We are concerned with automating the task of network architecture design where the learning algorithm tries to discover the best performing model on itself. To do this, we employ an evolving algorithm in addition to the gradient descent to evolve and train the model. Specifically, we use a form of genetic algorithm with mutation and recombination operators to constantly change the architecture, while the commonly used gradient descent is used as a learning algorithm for each of the genetically procreated models. We propose a series of mutation operators and a method of recombination called Highest Varying k-Features Recombination(HVk-FR) to evolve the CNN models. Results show using our method of recombination on top of mutation yields the best accuracy.
