Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2011.4.1 | Award Amount: 2.43M | Year: 2012
Currently there are a multitude of companies offering Content Analytics and Social Internet Mining services for the purposes of Opinion Mining and Sentiment Analysis. Truly effective Sentiment Analysis is a complex NLP task in monolingual contexts alone. In multilingual contexts the complexity increase many-fold and also presents the challenge of comparison of opinion across languages and cultures. Named Entity Recognition and Classification is also key element to this challenge. As with is often the case in many innovative sectors and industries, a high percentage of SMEs are active offering niche solutions to specific segments of the market and/or domains. Acquiring or developing the base qualifying technologies require to enter this market is and expensive undertaking that redirects limited the resources of SMEs away from offering products and services that the market demands. The OpeNER project has as the goal of reuse and repurposing of exiting language resources and data sets to provide a set of underlying technologies to the broader community. OpeNER will focus on the provision of a supplementary sentiment lexicon with culturally normalised and graduated values. NERC will also be tackled leveraging Linked Data with the aim of bringing multilingual NERC up to par with state of the art in dominant languages. The scope of OpeNER will be restricted to Spanish, English, French, German, Dutch and Italian. The general research of the project will be oriented toward a generic application domain, and later, refined and validated in the Tourism domain. This will be achieved in conjunction with and End User Advisory Board composed of European Tourism Promotion Agencies, an online Tourism Portal and other interested parties. Furthermore, OpeNER will employ proven techniques from the Open Source community and develop an online development community thus ensuring the long term viability beyond the project timeframe. So that the benefits of the project are adopted and extensible to new domains and languages, OpeNER will strive to make the tools and techniques resulting from the project available under Open Source or Hybrid Licenses.
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: PHC-26-2014 | Award Amount: 4.35M | Year: 2015
PD_manager aims to build and evaluate an innovative, mhealth, patient centric ecosystem for Parkinsons disease (PD) management. The analysis of experts diagnostic behaviour and the decomposition of tasks undertaken by the various actors during the disease management will enable the validation of technology against routine clinical judgment measures . Primary motor symptoms such as tremor, bradykinesia and postural imbalance and non-motor symptoms, such as sleep, speech and cognitive disorders, will be evaluated with data captured by light, unobtrusive, co-operative, mobile devices: sensor insoles, a wristband and the patients or caregivers (the role of which is of paramount importance) smartphone. Data mining studies will lead to the implementation of a Decision Support Platform with suggestions for modifications in the medication which is the key for prolongation of independence and improved Quality of Life. Compliance with medical recommendations will also be studied; the patient will be motivated to adhere to his medication and diet, will be empowered to exercise and make physiotherapies and will be educated about occupational and speech therapy in order to self-manage his condition. The PD_manager Knowledge Management platform will be built with a cloud-based, open architecture approach based on FI-WARE that will support the use of any commercial set of sensors within the Internet of Things concept. The successful implementation of all abovementioned objectives will be evaluated in a total of 230 patients (the 30 that will be enrolled at the first phase of the project and 200 more during the pilot). In addition to the evaluation of the clinical effectiveness, acceptability and usability of the developed platform and mobile apps a detailed study for the potential of PD_manager as a new care model in terms of health outcomes, quality of life, care efficiency gains and economic benefits will also be conducted.
Agency: Cordis | Branch: FP7 | Program: CP-IP | Phase: SEC-2013.5.1-2 | Award Amount: 15.13M | Year: 2014
SIIP is a break-through Suspect Identification solution based on a novel Speaker-Identification (SID) engine fusing multiple speech analytic algorithms (e.g. voiceprints recognition, Gender/Age/Language/Accent ID, Keyword/ Taxonomy spotting and Voice cloning detection). This Fused Speaker Identification will result in significantly higher true-positive speaker identification, reduced False-Positives/Negatives while increasing reliability & confidence. SIIP analyzes rich metadata from voice samples and social media. SIIP provides judicial admissible evidence for identifying crime/terror suspects as well as for mapping/tracing the suspect terror/crime network. SIIP is crucial when individuals use Internet-based applications (e.g. VoIP or social media) to plan a crime or terrorist attack. SIIPs results can easily be shared with relevant authorities based on a sustainable SIIP Info Sharing Center (SISC) located at INTERPOL. SISC guarantees an increased reliability of the identification results through advanced technologies and through voice samples checked against a large centralized database of samples collected by INTERPOLs 190 members (based on standard operating/data privacy procedures). SIIP multiplies and increases the information sharing and cooperation in the LEA community and speeds up the use of Speaker Identification by LEAs in Europe not only for individual identification but also for authentication. SIIP runs on all speech sources (e.g. Internet, PSTN, Cellular and SATCOM) and uses the latest OSINT data mining applications to obtain and corroborate voice samples. The SIIP consortium consists of 17 partners bringing together end-users, SMEs, industrial and academic partners from a variety of fields including Speech analytics, Social Media Analytics and Integration. To maximize its impact, SIIP will be designed, developed and tested with INTERPOL and police forces in the UK and Portugal, taking into account the various EU legal/ethical aspects and Interpol regulations.
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2011.4.1 | Award Amount: 2.67M | Year: 2012
Due to recently approved European and National directives and laws, the subtitling demand has grown fast in the past few years throughout Europe. The path of manual subtitling is no longer feasible, due to the quantity of the demand and the cost of the process, both in terms of time and personnel. As a result, broadcasters and subtitling companies are seeking for subtitling alternatives more productive than the traditional manual process. Large Vocabulary Continuous Speech Recognition (LVCSR) is proving to be a useful technology for such a purpose. Respeaking a technique in which a professional listens to the source audio and re-speaks it to a speech recognition engine which transcribes it is consolidating as the main subtitling technique employed for live and pre-recorded broadcast productions. Another trend in use today is the application of speech recognition to automatically generate a transcript of a programmes soundtrack without the need of a respeaker, and to use this as the basis of subtitles. Unfortunately, the high cost associated to the collection and annotation of the speech and text corpora required to train each LVCSR system for respeaking and/or automatic transcription has hindered the development of new languages and application domains. However, in order to comply with the new audiovisual legal framework, European broadcasters and subtitling companies are generating speech and text corpora suitable for LVCSR developments on a daily basis. SAVAS aims to acquire, share and reuse audiovisual resources of broadcasters and subtitling companies so that high-tech European ASR companies can use the shared data to develop domain specific LVCSRs and/or LVCSRs in new languages to solve the automated subtitling needs of the media industry. Within the project, data and LVCSR technology for automated subtitling will be collected, shared and developed for the following six languages: Basque, Spanish, Italian, French, German and Portuguese.
Agency: Cordis | Branch: FP7 | Program: CP | Phase: SEC-2010.1.2-1 | Award Amount: 7.19M | Year: 2011
Organised crime use information technology systems to communicate, work or expand their influence. Current tools for the fight against organised crime have shown their limits and reflect the need to develop a scalable tool to track them more efficiently. CAPERs objective is to build a common collaborative and information sharing platform for the detection and prevention of organised crime in which the Internet is used (e.g. sale of counterfeit or stolen goods, cyber crime) and which exploits Open Source Intelligence. State intelligence agencies are becoming more inclined to using Open Source Intelligence (OSI), and particularly tools typically associated with the Social or Semantic Internet. The techniques and technologies applied by CAPER to Open Source Intelligence will also be applied to Close Source Intelligence, i.e., existing information systems in use by the LEAs. Both sets of information will be processed and exploited equally allowing one to infer on the other. The analysis modules built in the CAPER project will also give new value to existing intelligence through image, video, speech and biometric analysis. CAPER will provide Law Enforcement Agencies (LEA) with a common operational platform for Open Source Intelligence complemented by standards based interfaces sets. It will allow easy integration with legacy systems and future applications. Analysis modules for multilingual and multimedia content (multiple languages, voice, text, audio, image, video and biometrics) and application of the analysis technologies in exiting information systems (Closed Sources) will be included to gain new value and spot missed clues. Caper groups European partners which bring their technological expertises for video, speech, image, biometric analysis, Open Source information acquisition and ETL technologies, but also Law Enforcement Agencies for the definition of their needs and the integration of the system, which will guarantee the success of the project
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2007.4.2 | Award Amount: 3.45M | Year: 2008
With the globalization of markets and communication, we are experiencing globalization of problems in the world and of the solutions to these problems. An actual example is global warming and other environmental problems related to rapid growth and economic developments. Globalization of problems and their solutions require that information and communication is supported across a wide range of languages and cultures. This requires a system that can collect distributed information that is structured differently and expressed differently in languages and represent it in a uniform way. It also means that people can access this information in their language and without cultural background knowledge, as experts or laymen. Environmental problems can be acute, requiring immediate support and information available elsewhere. However, knowledge sharing and transitions is also essential for sustainable growth and development on a longer term. In both cases, it is important that distributed information and experience can be re-used on a global scale.\n\nThe goal of Kyoto is to develop a content enabling system that provides semantic search and information access to large quantities of distributed multimedia data for both experts and the general public, over a variety of data from wide-spread sources in a range of culturally diverse languages. This is enabled through an ontology linked to wordnets for a variety of languages. Concept extraction and data mining is applied through a chain of semantic processors. The shared ontology guarantees a uniform interpretation for the diverse information from different sources and languages. The system can be maintained by field-specialists using a Wiki platform. Kyoto is a generic system offering knowledge transition across different target groups in society and across linguistic, cultural and geographic borders. Kyoto will be applied to the environmental domain and span global information across European and non-European languages.
Agency: Cordis | Branch: FP7 | Program: CP | Phase: SEC-2010.2.3-3 | Award Amount: 3.57M | Year: 2011
MOSAIC Platform will involve multi-modal data intelligence capture and analytics including video and text collaterals etc. The distributed intelligence within the platform enables decision support for automated detection, recognition, geo-location and mapping, including intelligent decision support at various levels to enhance situation awareness, surveillance targeting and camera handover; these involve level one fusion, and situation understanding to enable decision support and impact analysis at level two and three of situation assessment. Accordingly MOSAIC will develop and validate: i) A framework for capturing and interpreting the use-context requirements underpinned by a standard data ontology to facilitate the tagging, search and fusion of data from distributed multi-media sensors, sources and databases, ii) A systems architecture to support wide area surveillance with edge and central fusion and decision support capabilities, iii) Algorithms, including hardware-accelerated ones for smart cameras, which enable disparate multi-media information correlation to form a common operating picture, including representation of the temporal information and aspects, iv) Tools and techniques for the extraction of key information from video, un-controlled text and databases using pattern recognition and behaviour modelling techniques, v) Algorithms and techniques to represent decisions and actions within a mathematical framework, and how this framework can be used to simulate the effects of disturbances on the system, vi) An integrated system solution based upon the proposed systems architecture and the above developed enabling technologies including techniques for tagging different multi-media types with descriptive metadata to support multi-level fusion and correlation of surveillance and other data intelligence from distributed heterogeneous sources and networks.
Seidler P.,A e Solutions BI Ltd. |
Adderley R.,A e Solutions BI Ltd. |
Badii A.,University of Reading |
Raffaelli M.,Synthema Srl
ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining | Year: 2014
With increasing complexity of the social systems under surveillance, demand grows for automated tools which are able to support end users in making sense of situational context from the amount of available data and incoming data streams. This paper presents MOSAIC (Multi-Modal Situation Assessment and Analytics Platform), a semantically integrated system which aims at exploiting multi-modal data analysis comprising advanced tools for text and data mining, criminal network analysis, and decision support. The aim is to provide, from an enriched context, an understanding of behaviour of the system under surveillance thus supporting authorities in their decision making processes. Specific measures and algorithms have been developed to support analysts in retrieving, analysing, and disrupting criminal networks, identifying offenders that pose the greatest harm aligned with domain-specific strategies, as well as enabling the investigation of intervention strategies. A case study is provided in order to illustrate the system in practice. © 2014 IEEE.