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This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers.

One of the most important functions of concepts is that of producing classifications; and since there are at least two different types of such things, we better give a preliminary short description of them both. The first kind of classification is based on the existence of a property common to all the things that fall under a concept. The second, instead, relies on similarities between the objects belonging to a certain class A and certain elements of a subclass AS of A, the so-called ‘stereotypes.’ In what follows, we are going to call ‘proto-concepts’ all those concepts whose power of classification depends on stereotypes, leaving the term ‘concepts’ for all the others. The main aim of this article is showing that, if a proto-concept is given simply in terms of the ability to make the appropriate distinctions, then there are stimulus-response cognitive systems — whose way of manipulating information is based on Neural Networks (NN) — able to make the appropriate distinctions typical of proto-concepts in the absence of high-level cognitive features such as consciousness, understanding, representation, and intentionality. This, of course, implies that either proto-concepts cannot be given simply in terms of the ability to make the appropriate distinctions, or that we need to modify our traditional conception of mind, because the induction-like procedure followed by a NN in producing its classifications, far from being the ultimate product of a ‘linguistic mind,’ is, rather, inscribed in the nuts and bolts of the system’s biology/electronics to which the NN belongs.

The work presented here illustrates an humanoid robot capable of interacting with an human user within the Cultural Heritage domain. Two different and complementary AI approaches, namely sub-symbolic and symbolic, have been implemented and combined together to design the framework of a robot having both rational and intuitive capabilities. Furthermore, the robot is capable of providing information expressively and of adapting its behavior according to the emotional content of the artworks descriptions. This could make the robot more effective in providing information and entertaining the users.

The paper illustrates a system that endows an humanoid robot with the capability to mimic the motion of a human user in real time, serving as a basis for further gesture based human-robot interactions. The described approach uses the Microsoft Kinect as a low cost alternative to expensive motion capture devices.

Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results show that a higher detail level of the classification makes the ATE problem much harder to resolve, showing the weaknesses of the model to accomplish the task correctly.

Producing a mathematical model of concepts is a very important issue in artificial intelligence, because if such a model were found this, besides being a very interesting result in its own right, would also contribute to the emergence of what we could call the ‘mathematics of thought.’ One of the most interesting attempts made in this direction is P. Gardenfors’ theory of conceptual spaces, a ¨ theory which is mostly presented by its author in an informal way. The main aim of the present article is contributing to Gardenfors’ theory of conceptual spaces ¨ by discussing some of the advantages which derive from the possibility of representing concepts in high-dimensional linear spaces.

In the present work a system able to classify the indoor action is presented. The data are recorded with multiple kind of sensor collecting the position of the joints of the person in the room, the acceleration recorded on the person wrist and the presence or absence in a specific room. The latent semantic analysis, based on the principal component search, allows to estimate the probability of a given action according the sampled values.

Integration of climate change measures in local development planning and delivery of Climate Compatible Development (CCD) at local levels is widely advocated to assist achievement of the Sustainable Development Goals. Such integration is not straightforward, and limited knowledge on multi-level governance of CCD exists. To progress this critical enquiry, we build on the concept of Climate Policy Integration (CPI) to assess how climate change measures are integrated in local development planning. We investigate the existing institutional arrangements across local levels (village to district) and sectors, and the barriers to and opportunities for CCD within local government authorities (LGAs). Particularly, we draw on qualitative analysis of local development policy and interviews conducted with LGAs in Muheza District of Tanzania. Findings show that several barriers, including under-resourcing, unreliable information on climate impacts, and the lack of guidelines for climate-development in key national policies are hindering the process towards CCD in local development planning. This study suggests that building capacity both within LGAs and at the national level are required to achieve CCD at sub-national levels. Currently, measures to address climate change are mostly implemented by development partners, so coordination between LGAs and these external stakeholders is essential to ensure consistency between climate and development objectives. Finally, our findings support a flexible approach to CCD, where emphasis on each of the three components of CCD—mitigation, adaptation, and development—can vary across time and space.

Pilato, Giovanni, Sabella, Giorgio, Lisi, Oscar (2016): Two new species of Milnesium (Tardigrada: Milnesiidae). Zootaxa 4132 (4): 575-587, DOI: http://doi.org/10.11646/zootaxa.4132.4.9

In this paper we illustrate the architecture of an intelligent advisor agent aimed at limiting, or as far as possible preventing, the damages caused by catastrophic events, such as floods and landslides. The agent models the domain and makes forecasting by exploiting both ontology models and belief network models. Furthermore, it uses a monitoring network to recommend preventive measures and giving alerts, if necessary, before that the event happens. The monitoring network can be implemented through both physical and soft sensors: this choice makes the measurements more adequate and available also in case of failure of some of the physical sensors. The front-end of the agent is made by a chat-bot, capable to interact with human users using natural language.

This paper shows the application of a soft sensor network for the detection of meteorological events. A set of hard (real) sensor are placed in a territory, where they measure heterogeneous quantities. Starting from their measurements, a soft sensor network provides useful information coming from the data. In this contribution we show how prediction and validation of data can be done through machine learning approach by collecting data from the historical series. Furthermore, we show how the cluster based on correlation analysis among the data achieved by the sensors can be sensibly different from the ones simply drawn on geographical distance. Keywords Soft sensor Monitoring Multivariate regression Environmental risk Wireless sensor network Intelligent Interactive Multimedia Systems and Services 2016Intelligent Interactive Multimedia Systems and Services 2016 Look Inside Reference tools Export citation Add to Papers Other actions About this Book Reprints and Permissions Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn

Pilato, Giovanni, Sabella, Giorgio, Lisi, Oscar (2015): Two new freshwater eutardigrade species from Sicily. Zootaxa 3918 (2): 273-284, DOI: http://dx.doi.org/10.11646/zootaxa.3918.2.8