A Multi-Label Machine Learning Approach to Support Pathologist\u27s Histological Analysis

This paper proposes a new tool in the field of telemedicine, defined as a specific branch where IT supports medicine, in case distance impairs the proper care to be delivered to a patient. All the information contained into medical texts, if properly extracted, may be suitable for searching, classification, or statistical analysis. For this reason, in order to reduce errors and improve quality control, a proper information extraction tool may be useful. In this direction, this work presents a Machine Learning Multi-Label approach for the classification of the information extracted from the pathology reports into relevant categories. The aim is to integrate automatic classifiers to improve the current workflow of medical experts, by defining a Multi-Label approach, able to consider all the features of a model, together with their relationships. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Overview of the CLEF 2018 personalised information retrieval lab (PIR-CLEF 2018)

At CLEF 2018, the Personalised Information Retrieval Lab (PIR-CLEF 2018) has been conceived to provide an initiative aimed at both providing and critically analysing a new approach to the evaluation of personalization in Information Retrieval (PIR). PIR-CLEF 2018 is the first edition of this Lab after the successful Pilot lab organised at CLEF 2017. PIR CLEF 2018 has provided registered participants with the data sets originally developed for the PIR-CLEF 2017 Pilot task; the data collected are related to real search sessions over a subset of the ClueWeb12 collection, undertaken by 10 users by using a novel methodology. The data were gathered during the search sessions undertaken by 10 volunteer searchers. Activities during these search sessions included relevance assessment of a retrieved documents by the searchers. 16 groups registered to participate at PIR-CLEF 2018 and were provided with the data set to allow them to work on PIR related tasks and to provide feedback about our proposed PIR evaluation methodology with the aim to create an effective evaluation task

Blind Queries Applied to JSON Document Stores

Social Media, Web Portals and, in general, information systems offer their own Application Programming Interfaces (APIs), used to provide large data sets concerning every aspect of day-by-day life. APIs usually provide data sets as collections of JSON documents. The heterogeneous structure of JSON documents returned by different APIs constitutes a barrier to effectively query and analyze these data sets. The adoption of NoSQL document stores, such as MongoDB, is useful for gathering these data sets, but does not solve the problem of querying the final heterogeneous repository. The aim of this paper is to provide analysts with a tool, named HammerJDB, that allows for blind querying collections of JSON documents within a NoSQL document database. The idea below is that users may know the application domain but it may be that they are not aware of the real structures of the documents stored in the database&mdash;the tool for blind querying tries to bridge the gap, by adopting a query rewriting mechanism. This paper is an evolution of a technique for blind querying Open Data portals and of its implementation within the Hammer framework, presented in some previous work. In this paper, we evolve that approach in order to query a NoSQL document database by evolving the Hammer framework into the HammerJDB framework, which is able to work on MongoDB databases. The effectiveness of the new approach is evaluated on a data set (derived from a real-life one), containing job-vacancy ads collected from European job portals

A Multi-Label Machine Learning Approach to Support Pathologist\u27s Histological Analysis

This paper proposes a new tool in the field of telemedicine, defined as a specific branch where IT supports medicine, in case distance impairs the proper care to be delivered to a patient. All the information contained into medical texts, if properly extracted, may be suitable for searching, classification, or statistical analysis. For this reason, in order to reduce errors and improve quality control, a proper information extraction tool may be useful. In this direction, this work presents a Machine Learning Multi-Label approach for the classification of the information extracted from the pathology reports into relevant categories. The aim is to integrate automatic classifiers to improve the current workflow of medical experts, by defining a Multi-Label approach, able to consider all the features of a model, together with their relationships. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Enforcing role based access control model with multimedia signatures

International audienceRecently ubiquitous technology has invaded almost every aspect of the modern life. Several application domains, have integrated ubiquitous technology to make the management of resources a dynamic task. However, the need for adequate and enforced authentication and access control models to provide safe access to sensitive information remains a critical matter to address in such environments. Many security models were proposed in the literature thus few were able to provide adaptive access decisions based on the environmental changes. In this paper, we propose an approach based on our previous work [B.A. Bouna, R. Chbeir, S. Marrara, A multimedia access control language for virtual and ambient intelligence environments, In Secure Web Services (2007) 111-120] to enforce current role based access control models [M.J. Moyer, M. Ahama, Generalized role-based access control, in: Proceedings of International Conference on Distributed Computing Systems (ICDCS), Phoenix, Arizona, USA, 2001, pp. 391-398] using multimedia objects in a dynamic environment. In essence, multimedia objects tend to be complex, memory and time consuming nevertheless they provide interesting information about users and their context (user surrounding, his moves and gesture, people nearby, etc.). The idea behind our approach is to attribute to roles and permissions, multimedia signatures in which we integrate conditions based on users' context information described using multimedia objects in order to limit role activation and the abuse of permissions in a given environment. We also describe our architecture which extends the known XACML [XACML, XACML Profile for Role Based Access Control (RBAC), , 2008] terminology to incorporate multimedia signatures. We provide an overview of a possible implementation of the model to illustrate how it could be valuable once integrated in an intelligent environment

An architecture of a wavelet based approach for the approximate querying of huge sets of data in the telecommunication environment

Third generation mobile networks were designed to satisfy raising requests of performance, reliability and availability on behalf of customers all over the world. These networks have a new architecture where new Network Entities carry out complex functionalities and can serve many calls at the time. In order to grant high efficiency and the best performance of the network, such a complex scenario must be monitored and controlled by a certain number of Operation & Maintenance Centres (OMCs). Typically, these centres pose queries to the underlying DBMS that require complex operations over Gigabytes or Terabytes of disk-resident data, and thus, take a very long time to execute to completion and produce exact answers. Due to the exploratory nature of these applications, an exact answer may not be required, and a user may in fact prefer a fast, approximate answer. In this paper we propose an architecture for approximate query processing to be integrated in the OTS architecture of Siemens Mobile S.p.A. The main purpose of this paper is to propose a practical application of wavelet-based synopses techniques in order to improve the performances of the management system of real telecommunication network

On the expressive power of **Xquery** fragments

XQuery is known to be a powerful XML query language with many bells and whistles. For many common queries we do not need all the expressive power of XQuery. We investigate the effect of omitting certain features of XQuery on the expressive power of the language. We start from a simple base fragment which can be extended by several optional features being aggregation functions such as count and sum, sequence generation, node construction, position information in for loops, and recursion. In this way we obtain 64 different XQuery fragments which can be divided into 17 different equivalence classes such that two fragments can express the same functions iff they are in the same equivalence class. Moreover, we investigate the relationships between these equivalence classes