An iterative approach for lexicon characterization in juridical context

In the juridical context, knowledge management applications have a central role. In order to improve the effectiveness of document management procedures, techniques for automatic comprehension of textual content are required. In this work, a methodology for semi-automatic derivation of knowledge from document collections is proposed. In order to extract relevant information from document text, a process integrating both statistical and lexical approaches is applied. Moreover, we propose a system for the evaluation of the extracted peculiar lexicon quality. The system is used for the processing of heterogeneous documents corpus issued by Italy’s juridical domain

A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks

Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach's effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on

Design of a Wearable Healthcare Emergency Detection Device for Elder Persons

Improving quality of life in geriatric patients is related to constant physical activity and fall prevention. In this paper, we propose a wearable system that takes advantage of sensors embedded in a smart device to collect data for movement identification (running, walking, falling and daily activities) of an elderly user in real-time. To provide high efficiency in fall detection, the sensor’s readings are analysed using a neural network. If a fall is detected, an alert is sent though a smartphone connected via Bluetooth. We conducted an experimental session using an Arduino Nano 33 BLE Sense board in inside and outside environments. The results of the experiment have shown that the system is extremely portable and provides high success rates in fall detection in terms of accuracy and loss. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Enabling IoT stream management in multi-cloud environment by orchestration

(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Every-Day lives are becoming increasingly instrumented by electronic devices and any kind of computer-based (distributed) service. As a result, organizations need to analyse an enormous amounts of data in order to increase their incomings or to improve their services. Anyway, setting-up a private infrastructure to execute analytics over Big Data is still expensive. The exploitation of Cloud infrastructure in IoT Stream management is appealing because of costs reductions and potentiality of storage, network and computing resources. The Cloud can consistently reduce the cost of analysis of data from different sources, opening analytics to big storages in a multi-cloud environment. Anyway, creating and executing this kind of service is very complex since different resources have to be provisioned and coordinated depending on users' needs. Orchestration is a solution to this problem, but it requires proper languages and methodologies for automatic composition and execution. In this work we propose a methodology for composition of services used for analyses of different IoT Stream and, in general, Big Data sources: in particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment.Peer ReviewedPostprint (author's final draft

Generation of game contents by social media analysis and MAS planning

In the age of pervasive computing and social networks, it has become commonplace to retrieve opinions about digital contents in games. In the case of multi-player, open world gaming, in fact even in “old-school” single players games, it is evident the need for adding new features in a game depending on users comments and needs. However this is a challenging task that usually requires considerable design and programming efforts, and more and more patches to games, with the inevitable consequence of loosing interest in the game by players over years. This is particularly a hard problem for all games that do not intend to be designed as interactive novels. Process Content Generation (PCG) of new contents could be a solution to this problem, but usually such techniques are used to design new maps or graphical contents. Here we propose a novel PCG technique able to introduce new contents in games by means of new story-lines and quests. We introduce new intelligent agents and events in the world: their attitudes and behaviors will promote new actions in the game, leading to the involvement of players in new gaming content. The whole methodology is driven by Social Media Analysis contents about the game, and by the use of formal planning techniques based on Multi-Agents modelsPeer ReviewedPostprint (author's final draft

A Model For e-Government Digital Document

The presence of a great amount of information is typical of bureaucratic processes, like the ones pertaining to public and private administrations. Such information is often recorded on paper or in different digital formats and its management is very expensive, both in terms of space used for storing documents and in terms of time spent in searching for the documents of interest. Furthermore, the manual management of these documents is absolutely not error-free. To efficiently access the information contained in very large document repositories, such as public administration archives, techniques for syntactic and semantic document management are required, so to ensure a large and intense process of document dematerialization, and eliminate, or at least reduce, the quantity of paper documents. In this work we present a novel RDF model of digital documents for improving the dematerialization effectiveness, that constitutes the starting point of an information system able to manage documental streams in the most efficient way. Such model takes into account the important need that is required in several E-Government applications which, depending on authorities or final users or time, provides different representations of the same multimedia contents

RB acute loss induces centrosome amplification and aneuploidy in murine primary fibroblasts

BACKGROUND: Incorrect segregation of whole chromosomes or parts of chromosome leads to aneuploidy commonly observed in cancer. The correct centrosome duplication, assuring assembly of a bipolar mitotic spindle, is essential for chromosome segregation fidelity and preventing aneuploidy. Alteration of p53 and pRb functions by expression of HPV16-E6 and E7 oncoproteins has been associated with centrosome amplification. However, these last findings could be the result of targeting cellular proteins in addition to pRb by HPV16-E7 oncoprotein. To get a more detailed picture on the role of pRb in chromosomal instability and centrosome amplification, we analyzed the effects of the acute loss of retinoblastoma gene function in primary conditional Rb deficient mouse embryonic fibroblasts (MEFs). Moreover, since pRb is a transcriptional repressor, microarray analysis was done on pRb-competent and pRb-deficient MEFs to evaluate changes in expression of genes for centrosome homeostasis and for correct mitosis. RESULTS: Acute loss of pRb induces centrosome amplification and aneuploidy in the vast majority of cells analyzed. A time course analysis shows a decrease of cells with amplified centrosomes after 40 days from the adenoviral infection. At this time only 12% of cells still show amplified centrosomes. Interestingly, cells with pRb constitutive loss show a similar percentage of cells with amplified centrosomes. DNA-Chip analyses in MEFs wt (mock infected) and pRb depleted (Ad-Cre infected) cells reveal differential expression of genes controlling both centrosome duplication and mitotic progression. CONCLUSION: Our findings suggest a direct link between pRb status, centrosome amplification and chromosomal instability, and define specific mitotic genes as targets whose gene expression has to be altered to achieve or maintain aneuploidy

RNAi mediated acute depletion of Retinoblastoma protein (pRb) promotes aneuploidy in human primary cells via micronuclei formation

<p>Abstract</p> <p>Background</p> <p>Changes in chromosome number or structure as well as supernumerary centrosomes and multipolar mitoses are commonly observed in human tumors. Thus, centrosome amplification and mitotic checkpoint dysfunctions are believed possible causes of chromosomal instability. The Retinoblastoma tumor suppressor (<it>RB</it>) participates in the regulation of synchrony between DNA synthesis and centrosome duplication and it is involved in transcription regulation of some mitotic genes. Primary human fibroblasts were transfected transiently with short interfering RNA (siRNA) specific for human pRb to investigate the effects of pRb acute loss on chromosomal stability.</p> <p>Results</p> <p>Acutely pRb-depleted fibroblasts showed altered expression of genes necessary for cell cycle progression, centrosome homeostasis, kinetochore and mitotic checkpoint proteins. Despite altered expression of genes involved in the Spindle Assembly Checkpoint (SAC) the checkpoint seemed to function properly in pRb-depleted fibroblasts. In particular <it>AURORA-A </it>and <it>PLK1 </it>overexpression suggested that these two genes might have a role in the observed genomic instability. However, when they were post-transcriptionally silenced in pRb-depleted fibroblasts we did not observe reduction in the number of aneuploid cells. This finding suggests that overexpression of these two genes did not contribute to genomic instability triggered by <it>RB </it>acute loss although it affected cell proliferation. Acutely pRb-depleted human fibroblasts showed the presence of micronuclei containing whole chromosomes besides the presence of supernumerary centrosomes and aneuploidy.</p> <p>Conclusion</p> <p>Here we show for the first time that <it>RB </it>acute loss triggers centrosome amplification and aneuploidy in human primary fibroblasts. Altogether, our results suggest that pRb-depleted primary human fibroblasts possess an intact spindle checkpoint and that micronuclei, likely caused by mis-attached kinetochores that in turn trigger chromosome segregation errors, are responsible for aneuploidy in primary human fibroblasts where pRb is acutely depleted.</p

HOLMeS: eHealth in the Big Data and Deep Learning Era

Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions

Enhancing Random Forest Classification with NLP in DAMEH: A system for DAta Management in EHealth Domain

The use of pervasive IoT devices in Smart Cities, have increased the Volume of data produced in many and many field. Interesting and very useful applications grow up in number in E-health domain, where smart devices are used in order to manage huge amount of data, in highly distributed environments, in order to provide smart services able to collect data to fill medical records of patients. The problem here is to gather data, to produce records and to analyze medical records depending on their contents. Since data gathering involve very different devices (not only wearable medical sensors, but also environmental smart devices, like weather, pollution and other sensors) it is very difficult to classify data depending their contents, in order to enable better management of patients. Data from smart devices couple with medical records written in natural language: we describe here an architecture that is able to determine best features for classification, depending on existent medical records. The architecture is based on pre-filtering phase based on Natural Language Processing, that is able to enhance Machine learning classification based on Random Forests. We carried on experiments on about 5000 medical records from real (anonymized) case studies from various health-care organizations in Italy. We show accuracy of the presented approach in terms of Accuracy-Rejection curves