A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN-NER

Within the context of the Competence Centre for the Conservation of Cultural Heritage (4CH) project, the design and deployment of a platform-as-a-service cloud infrastructure for the first European competence centre of cultural heritage (CH) has begun, and some web services have been integrated into the platform. The first integrated service is the INFN-CHNet web application for FAIR storage of scientific analysis on CH: THESPIAN-Mask. It is based on CIDOC-CRM-compatible ontology and CRMhs, describing the scientific metadata. To ease the process of metadata generation and data injection, another web service has been developed: THESPIAN-NER. It is a tool based on a deep neural network for named entity recognition (NER), enabling users to upload their Italian-written report files and obtain labelled entities. Those entities are used as keywords either to serve as (semi)automatically custom queries for the database, or to fill (part of) the metadata form as a descriptor for the file to be uploaded. The services have been made freely available in the 4CH PaaS cloud platform

System architecture of a web service for Content-Based Image Retrieval

This paper presents the system architecture of a Content- Based Image Retrieval system implemented as a web service. The proposed solution is composed of two parts, a client run- ning a graphical user interface for query formulation and a server where the search engine explores an image repository. The separation of the user interface and the search engine follows a Service as a Software (SaaS) model, a type of cloud computing design where a single core system is online and available to authorized clients. The proposed architecture follows the REST software architecture and HTTP proto- col for communications, two solutions that combined with metadata coded in RDF, make the proposed system ready for its integration in the semantic web. User queries are formulated by visual examples through a graphical inter- face and content is remotely accessed also through HTTP communication. Visual descriptors and similarity measures implemented in this work are mostly de ned in the MPEG-7 standard, while textual metadata is coded according to the Dublin Core speci cations.Peer ReviewedPostprint (published version

ElasTraS: An Elastic Transactional Data Store in the Cloud

Over the last couple of years, "Cloud Computing" or "Elastic Computing" has emerged as a compelling and successful paradigm for internet scale computing. One of the major contributing factors to this success is the elasticity of resources. In spite of the elasticity provided by the infrastructure and the scalable design of the applications, the elephant (or the underlying database), which drives most of these web-based applications, is not very elastic and scalable, and hence limits scalability. In this paper, we propose ElasTraS which addresses this issue of scalability and elasticity of the data store in a cloud computing environment to leverage from the elastic nature of the underlying infrastructure, while providing scalable transactional data access. This paper aims at providing the design of a system in progress, highlighting the major design choices, analyzing the different guarantees provided by the system, and identifying several important challenges for the research community striving for computing in the cloud.Comment: 5 Pages, In Proc. of USENIX HotCloud 200

The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps

Third party apps that work on top of personal cloud services such as Google Drive and Dropbox, require access to the user's data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google's Chrome store, we discover that the existing permission model is quite often misused: around two thirds of analyzed apps are over-privileged, i.e., they access more data than is needed for them to function. In this work, we analyze three different permission models that aim to discourage users from installing over-privileged apps. In experiments with 210 real users, we discover that the most successful permission model is our novel ensemble method that we call Far-reaching Insights. Far-reaching Insights inform the users about the data-driven insights that apps can make about them (e.g., their topics of interest, collaboration and activity patterns etc.) Thus, they seek to bridge the gap between what third parties can actually know about users and users perception of their privacy leakage. The efficacy of Far-reaching Insights in bridging this gap is demonstrated by our results, as Far-reaching Insights prove to be, on average, twice as effective as the current model in discouraging users from installing over-privileged apps. In an effort for promoting general privacy awareness, we deploy a publicly available privacy oriented app store that uses Far-reaching Insights. Based on the knowledge extracted from data of the store's users (over 115 gigabytes of Google Drive data from 1440 users with 662 installed apps), we also delineate the ecosystem for third-party cloud apps from the standpoint of developers and cloud providers. Finally, we present several general recommendations that can guide other future works in the area of privacy for the cloud