Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services

Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-Store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection

Using a Goal-Driven Approach in the Investigation of a Questioned Contract

Part 3: FORENSIC TECHNIQUESInternational audienceThis paper presents a systematic process for describing digital forensic investigations. It focuses on forensic goals and anti-forensic obstacles and their operationalization in terms of human and software actions. The paper also demonstrates how the process can be used to capture the various forensic and anti-forensic aspects of a real-world case involving document forgery

To share or not to share: Publication and quality assurance of research data outputs. A report commissioned by the Research Information Network

A study on current practices with respect to data creation, use, sharing and publication in eight research disciplines (systems biology, genomics, astronomy, chemical crystallography, rural economy and land use, classics, climate science and social and public health science). The study looked at data creation and care, motivations for sharing data, discovery, access and usability of datasets and quality assurance of data in each discipline

A schema-based P2P network to enable publish-subscribe for multimedia content in open hypermedia systems

Open Hypermedia Systems (OHS) aim to provide efficient dissemination, adaptation and integration of hyperlinked multimedia resources. Content available in Peer-to-Peer (P2P) networks could add significant value to OHS provided that challenges for efficient discovery and prompt delivery of rich and up-to-date content are successfully addressed. This paper proposes an architecture that enables the operation of OHS over a P2P overlay network of OHS servers based on semantic annotation of (a) peer OHS servers and of (b) multimedia resources that can be obtained through the link services of the OHS. The architecture provides efficient resource discovery. Semantic query-based subscriptions over this P2P network can enable access to up-to-date content, while caching at certain peers enables prompt delivery of multimedia content. Advanced query resolution techniques are employed to match different parts of subscription queries (subqueries). These subscriptions can be shared among different interested peers, thus increasing the efficiency of multimedia content dissemination

Discovering inconsistencies between requested permissions and application metadata by using deep learning

This is an accepted manuscript of an article published by IEEE in 2020 International Conference on Information Security and Cryptology (ISCTURKEY), available online at https://ieeexplore.ieee.org/document/9308004 The accepted version of the publication may differ from the final published version.Android gives us opportunity to extract meaningful information from metadata. From the security point of view, the missing important information in metadata of an application could be a sign of suspicious application, which could be directed for extensive analysis. Especially the usage of dangerous permissions is expected to be explained in app descriptions. The permission-to-description fidelity problem in the literature aims to discover such inconsistencies between the usage of permissions and descriptions. This study proposes a new method based on natural language processing and recurrent neural networks. The effect of user reviews on finding such inconsistencies is also investigated in addition to application descriptions. The experimental results show that high precision is obtained by the proposed solution, and the proposed method could be used for triage of Android applications

Improving the geospatial consistency of digital libraries metadata

Consistency is an essential aspect of the quality of metadata. Inconsistent metadata records are harmful: given a themed query, the set of retrieved metadata records would contain descriptions of unrelated or irrelevant resources, and may even not contain some resources considered obvious. This is even worse when the description of the location is inconsistent. Inconsistent spatial descriptions may yield invisible or hidden geographical resources that cannot be retrieved by means of spatially themed queries. Therefore, ensuring spatial consistency should be a primary goal when reusing, sharing and developing georeferenced digital collections. We present a methodology able to detect geospatial inconsistencies in metadata collections based on the combination of spatial ranking, reverse geocoding, geographic knowledge organization systems and information-retrieval techniques. This methodology has been applied to a collection of metadata records describing maps and atlases belonging to the Library of Congress. The proposed approach was able to automatically identify inconsistent metadata records (870 out of 10,575) and propose fixes to most of them (91.5%) These results support the ability of the proposed methodology to assess the impact of spatial inconsistency in the retrievability and visibility of metadata records and improve their spatial consistency

Deep Multimodal Image-Repurposing Detection

Nefarious actors on social media and other platforms often spread rumors and falsehoods through images whose metadata (e.g., captions) have been modified to provide visual substantiation of the rumor/falsehood. This type of modification is referred to as image repurposing, in which often an unmanipulated image is published along with incorrect or manipulated metadata to serve the actor's ulterior motives. We present the Multimodal Entity Image Repurposing (MEIR) dataset, a substantially challenging dataset over that which has been previously available to support research into image repurposing detection. The new dataset includes location, person, and organization manipulations on real-world data sourced from Flickr. We also present a novel, end-to-end, deep multimodal learning model for assessing the integrity of an image by combining information extracted from the image with related information from a knowledge base. The proposed method is compared against state-of-the-art techniques on existing datasets as well as MEIR, where it outperforms existing methods across the board, with AUC improvement up to 0.23.Comment: To be published at ACM Multimeda 2018 (orals