Efficient word representation techniques (word embeddings) with modern machine learning models have shown reasonable improvement on automatic text classification tasks. However, the effectiveness of such techniques has not been evaluated yet in terms of insufficient word vector representation for training. Convolutional Neural Network has achieved significant results in pattern recognition, image analysis, and text classification. This study investigates the application of the CNN model on text classification problems by experimentation and analysis. We trained our classification model with a prominent word embedding generation model, Fast Text on publically available datasets, six benchmark datasets including Ag News, Amazon Full and Polarity, Yahoo Question Answer, Yelp Full, and Polarity. Furthermore, the proposed model has been tested on the Twitter US airlines non-benchmark dataset as well. The analysis indicates that using Fast Text as word embedding is a very promising approach.

Cervical cancer is one of the curable cancers when it is diagnosed in the early stages. Pap smear test and visual inspection using acetic acid are the most common screening mechanism for the cervical lesion to categorize the cervical cells as normal, precancerous, or cancerous. However, most of the classification methods success depends on the accurate spotting and segmenting of cervical location. These challenges pave the way for sixty years of research in cervical cancer diagnosis, but still, accurate spotting of the cervical cell remains an open challenge. Moreover, state-of-the-art classification methods are developed based upon the extraction of manual annotations of features. In this paper, an effective hybrid deep learning technique using Small-Object Detection-Generative Adversarial Networks (SOD-GAN) with Fine-tuned Stacked Autoencoder (F-SAE) is developed to address the shortcomings mentioned above. The generator and discriminator of the SOD-GAN are developed using Region-based Convolutional Neural Network (RCNN). The model parameters are fine-tuned using F-SAE, and the hyperparameters of the SOD-GAN are normalized and optimized to make the lesion detection faster. The proposed approach automatically detects and classifies the cervical premalignant and malignant conditions based on deep features without any preliminary classification and segmentation assistance. Extensive experimentation has also been done with multivariate heterogeneous data, and the proposed approach has shown promising improvement in efficiency and reduces the time complexity.

Spray impingement on smooth and heated surfaces is a highly complex thermofluid phenomenon present in several engineering applications. The combination of phase Doppler interferometry, high-speed visualization, and time-resolved infrared thermography allows characterizing the heat transfer and fluid dynamics involved. Particular emphasis is given to the use of nanofluids in sprays due to their potential to enhance the heat transfer mechanisms. The results for low nanoparticle concentrations (up to 1 wt.%) show that the surfactant added to water, required to stabilize the nanofluids and minimize particle clustering, affects the spray’s main characteristics. Namely, the surfactant decreases the liquid surface tension leading to a larger wetted area and wettability, promoting heat transfer between the surface and the liquid film. However, since lower surface tension also tends to enhance splash near the edges of the wetted area, the gold nanospheres act to lessen such disturbances due to an increase of the solutions’ viscosity, thus increasing the heat flux removed from the spray slightly. The experimental results obtained from this work demonstrate that the maximum heat convection coefficients evaluated for the nanofluids can be 9.8% to 21.9% higher than those obtained with the base fluid and 11.5% to 38.8% higher when compared with those obtained with DI water.

In this paper, we break down some aspects of modern surveillance systems within the context of the Wide InTegration of sensor Networks to Enable Smart Surveillance (WITNESS) NATO project. A brief description of the architecture of the system is presented, as well as several use case scenarios which WITNESS will cater for.

European Higher Education Institutions (HEIs) have undertaken a path of structural reforms, outlined by the Bologna Process, to progress towards quality education and the creation of more learner-centred and engaging learning environments. As a result, HEIs have tried to foster the adoption of active learning methodologies. Among them, flipped classroom has recently attracted much attention. Flipped classroom can be defined as an instructional approach where students gain first exposure to a subject through out-of-class self-paced learning activities (readings, video lectures, etc.). Thus, class time can be devoted to deepening their learning through activities facilitated by the teacher. However, for flipped classroom to be effective, students should not skip out-of-class learning activities. Gamification, that is the use of game elements in non-game contexts, can be helpful to foster students' engagement and motivation. This doctoral research aims to design, implement and refine an instructional approach which combines flipped classroom and gamification.

The pupil size is a soft biometric trait but in-depth study to analyze it for biometric purposes is lacking in the literature, as well as datasets focused on this field of research are missing. On the basis of these observations, we present an extensive study with the objective of demonstrating that pupil size and dilation over time can be potentially used to classify people by age and by gender. To do this, 14 supervised classifiers were applied on a dataset meant for gaze analysis. Measuring the right and left pupil individually and also simultaneously, the performances of the classifiers have been compared and the worst and best performing selected to support potential fusion strategies. If good results have been obtained for age classification, ranging in 79%-82% of accuracy, it cannot be said the same for gender. This is due to the dual nature of this biometric trait. Pupil size can be considered as a physical trait for the age classification and behavioral trait for the gender. The results achieved in this study suggest the need for more balanced and reliable datasets. (c) 2020 Elsevier B.V. All rights reserved.

The Sustainable Development Goals 2030 Agenda of United Nations raises the need of clean and affordable energy. In the pathway for more efficient and environmentally friendly solutions, new alternative power technologies and energy sources are developed. Among these, the use of syngas fuels for electricity generation can be a viable alternative in areas with high biomass or coal availability. This paper presents the energy, environmental and economic analyses of a modern combined cycle plant with the aim to evaluate the potential for a combined power plant running with alternative fuels. The goal is to identify the optimal design in terms of operating conditions and its environmental impact. Two possible configurations are investigated in the power plant presented: with the possibility to export or not export steam. An economic analysis is proposed to assess the plant feasibility. The effect of the different components in its performance is assessed. The impact of using four different syngases as fuel is evaluated and compared with the natural gas fuelled power cycle. The results show that a better efficiency is obtained for the syngas 1 (up to 54%), in respect to the others. Concerning pollutant emissions, the syngas with a GHG impact and lower carbon dioxide (CO2) percentage is syngas 2.

Reviews of users on social networks have been gaining rapidly interest on the usage of sentiment analysis which serve as feedback to the government, public and private companies. Text Mining has a wide variety of applications such as sentiment analysis, spam detection, sarcasm detection, and news classification. Reviews classification using user sentiments is an important and collaborative task for many organizations. During recent years, text classification is mostly studied with machine learning models and hand–crafted features which are not able to give promising results on short text classification. In this research, a deep neural network–based model Long Short Term Memory (LSTM) with word embedding features is proposed. The proposed model has been evaluated on the large dataset of Hotel reviews based on accuracy, precision, recall, and F1-score. This research is a classification study on the hotel review sentiments given by guests of the hotel. The results reveal that the proposed model performs better as compared to the existing state-of-the-art models when combined word embedding with LSTM and shows an accuracy of 97%, precision 83%, recall 71%, and F1-score 76.53%. These promising results reveal the effectiveness of the proposed model on any type of review classification tasks.

In a scenario where the whole society is more and more complex, due to the increasing of the interactions and relations created by the technological evolution, it is essential to develop training models that allow to interface with these technological and social ecosystems in a sustainable way, providing specific, transversal and interdisciplinary skills to new professionals. They should be able to identify the needs of people and society and to find new (digital and non-digital) solutions and services based on them. In this context, one of the challenges concerns how to train young generations for the achievement of these objectives, first of all in creating services and products which allow people to have an effective, efficacy and satisfying experience (taking into consideration the Quality in Interaction and User Experience criteria). This contribution discusses some experiences carried out by the authors related to the engagement of high school students in learning processes oriented to how designing solutions and services according to this vision, by also identifying some key points to take into account to spread awareness about these topics to young generations.

In public urban spaces different generations meet and eventually end up by sharing the same kind of activities. Nowadays, more occasions of encounter are generated by a widespread push to the re-appropriation of urban spaces for green, social and inclusive aims, so creating sustainable urban ecosystems. Information and Communication Technologies scattered in the urban environment boost these processes by supporting the user experience in different kinds of services. For example, in the transport sector several digital technologies allow the use of shared mobility services and sustain other kinds of more sustainable behaviors for travelers both improving their mobility experience and motivating them towards a common green goal. According to this scenario, the paper examines the physical and digital media and services that might support the user experience during a scattered cultural urban event. Indeed, events that concern different urban spaces at the same times require different kinds of efforts by the attendants, from retrieving information about the available sites, to getting the indication on how to reach them. However, different habits and needs emerge according to the different age of the attendants. The paper shows the results of a survey administered to a group of people from different ages attending a cultural event in Rome. The study analyzes the kind of media and services people used and suggested to retrieve information about the event. Then it envisions some opportunities that emerge in creating services supporting the communication and the user experience of the event, and of the city as well.

Aging population implies an increase in demand for health care services. This hopefully could be solved by e-health, even if some issues arise about technology acceptance and adoption among the elderly. In this article, the authors illustrate HomeCare4All project as a case study to apply Human Centered Design (HCD) process in the field of digital health services, aiming at design trustworthy mobile applications for elderly people to book healthcare services at home. Starting from the results achieved from the early step of design, this paper describes the following steps of the creation of a design solution by identifying use cases, defining information architecture and prototyping an app mockup. The prototypes are then evaluated through a double usability test sessions with users, implementing an iterative design process. In conclusion authors advance suggestions for designing trustworthy mobile interactions for elderly people or people unaccustomed to technology, showing the importance of involving end users in the various stages of the design process.

In a world that is constantly transformed by the evolution of digital technologies, the museum sector must evolve, enhancing and updating its offer and its identity to answer to the new needs of the visitors and to be competitive. However, the digital transformation of an organization is only possible through a cultural transformation of the professionals working inside it. In this context the Mu.SA project [1], an Erasmus +KA2, was aimed to address the shortage of digital skills identified in the museum sector and to encourage the emergence of new professional profiles through a training programme divided into three phases: MOOC, Blended Course, Work-Based Learning. The proposed paper is about the evaluation results of the second phase of the course that involved 120 students from Greece, Italy and Portugal, most of them were already museum workers wishing to update their skills. The evaluation aimed to assess the functionality, usability and accessibility of the online platform (technical level); the learning activities and content delivery (learning level); the quality of the contents and subject coverage (learning outcomes level); using a quali-quantitative approach. The results of the evaluation were generally positive and were used to improve the course.

Among the security services, like authentication, access control, key management and intrusion detection, user authentication is very much needed for a smart city environment because an external authorized user may require the real time data to be accessed directly from the deployed Internet of Things (IoT) enabled smart devices. Using the established session key between the user and an access smart device though mutual authentication and key agreement process, the real time data can be securely accessed. To deal with this issue, we propose a new user authentication scheme in smart city environment using three factors of a legal registered user (mobile device, password and biometrics). The proposed scheme is shown to be robust against a number of potential attacks needed in an IoT-based smart city deployment. The simulation study for formal security verification using the widely-accepted "Automated Validation of Internet Security Protocols and Applications (AVISPA)" tool demonstrates that the proposed scheme is also secure. Furthermore, experiments on various cryptographic primitives have been carried out using "MIRACL Cryptographic SDK: Multiprecision Integer and Rational Arithmetic Cryptographic Library" under both server and Raspberry PI 3 settings. Finally, a comprehensive comparative analysis shows the effectiveness and better security of the proposed scheme as compared with other state of art user authentication schemes. (C) 2020 Elsevier B.V. All rights reserved.

Mu.SA. - Museum Sector Alliance [1], is an EU funded project that aims to fill the gap between formal education and training and the Museums' need of competencies to drive digital transformation in order to be competitive in the digital era. The project will reach its goal building new European profiles of emerging job roles in museums, creating a training program and delivering a pilot, that will be used to test the methodology and the contents developed. In this paper, we will present the result of the evaluation of MOOC, that represents the first part of the course that has been delivered, trying to understand the strengths and weaknesses in order to improve it. The results of the evaluation of the MOOC were generally positive indeed and the level of interest shows that this strategy is considered particularly useful at sectoral level and as an opportunity for employment growth. The number of participants involved highlights the ability of the MOOC tool to attract and interest a wide audience of a heterogeneous age group covering several professional fields.

Gamification strategies are often used in contexts where heterogeneous people have to accomplish heterogeneous tasks, in order to foster their motivation and to scaffold their engagement in the long term. In order to improve gamification quality, many scholars investigated the relationship among personality types, game mechanics and enjoyment of the gamified activities. This literature review analyses the main findings in this field, in order to define a starting point for future research about a framework able to enhance the gamification design through the customization of the reward system, according to personality types.

Designing the User Experience of an Internet of Things (IoT) product is a complex activity that requires multidisciplinary and specific competencies and that raises new challenges for designers. In this context the IoT Design Deck [1] is a method to help design teams to collaborate in order to design a connected product and an omnichannel service. The main strengths of the method are: to create a common language and a common knowledge base to help multidisciplinary teams working together; to focus on UX factors rather than on technological ones; to discover and use the potential of the IoT and take into account its threats; to have a service oriented approach rather than a device centrical one; to accelerate the design process. In this work we present the evolution of the method, obtained through a test campaign, made with experts and non-experts, and a redesign phase based on tests results.

Several benefits in clean energy and pollutant emissions can be offered from biomass energy application. This research analyzes a biomass gasifier installed at the University of Perugia and presents the description of a multifuel biomass energy plant with all its components: the combustion chamber and the heat exchanger, installed to supply the thermal input to the turbine, with 100 kW electric power and 1 MW thermal power. The application of a Computational Fluid Dynamics (CFD) prediction model can help to better understand the knowledge on chemical and thermofluidodynamic features linked to pollutant emissions. A numerical modelling based on Ansys Fluent code is realized with the aim to reproduce the behaviour of the gasifier. Two different stoichiometric woodchips:air ratios are taken into account in the numerical calculations. Experimental tests have been conducted to validate the results of the numerical analysis. The obtained results show that the experimental and the numerical analysis with the 1:5 stoichiometric ratio are comparable. The influence of the woodchips:air stoichiometric ratio on the temperature distribution that may be reached inside the gasifier is highlighted. The relationship between woodchips:air stoichiometric ratio, temperature and NOx emissions is considered. Furthermore, a comparison between the values of NOx and CO pollutants obtained with the numerical model and experimental tests has been done. The gasification process contributes to the production of renewable energy and it can be combined with other energy cycles (e.g. Organic Rankine Cycle). The novelty of the work consists in the definition and validation of a method for the analysis of the operating temperatures of the gasifier to verify how they can be combined with other heat exchange systems and in the analysis of the pollutants during the gasification process.

Today natural water resources are becoming scarce, both due to global climate change but also due to irresponsible behaviour of human beings. Lakes are among the most delicate aquatic systems due to their limited size. The objective of this paper is to propose a System Dynamics model, employed in a real case study regarding the city of Rome and one of its water reserves, the Bracciano Lake, for the evaluation of different strategies and policies to reduce environmental impacts, considering different climatic and context scenarios. The results indicate that, as the system is currently exposed to a high risk of ecological disaster, the situation might worsen, and the disaster effectively happen. Simulation models may help agencies and administrations to explore policies and find solutions to address this fundamental problem, that may become even worst over the next years, given the potential severe consequences deriving from the current global warming trends.

A well-posed stress-driven mixture is proposed for Timoshenko nano-beams. The model is a convex combination of local and nonlocal phases and circumvents some problems of ill-posedness emerged in strain-driven Eringen-like formulations for structures of nanotechnological interest. The nonlocal part of the mixture is the integral convolution between stress field and a bi-exponential averaging kernel function characterized by a scale parameter. The stress-driven mixture is equivalent to a differential problem equipped with constitutive boundary conditions involving bending and shear fields. Closed-form solutions of Timoshenko nano-beams for selected boundary and loading conditions are established by an effective analytical strategy. The numerical results exhibit a stiffening behavior in terms of scale parameter.