Personalised Visual Art Recommendation by Learning Latent Semantic Representations

In Recommender systems, data representation techniques play a great role as they have the power to entangle, hide and reveal explanatory factors embedded within datasets. Hence, they influence the quality of recommendations. Specifically, in Visual Art (VA) recommendations the complexity of the concepts embodied within paintings, makes the task of capturing semantics by machines far from trivial. In VA recommendation, prominent works commonly use manually curated metadata to drive recommendations. Recent works in this domain aim at leveraging visual features extracted using Deep Neural Networks (DNN). However, such data representation approaches are resource demanding and do not have a direct interpretation, hindering user acceptance. To address these limitations, we introduce an approach for Personalised Recommendation of Visual arts based on learning latent semantic representation of paintings. Specifically, we trained a Latent Dirichlet Allocation (LDA) model on textual descriptions of paintings. Our LDA model manages to successfully uncover non-obvious semantic relationships between paintings whilst being able to offer explainable recommendations. Experimental evaluations demonstrate that our method tends to perform better than exploiting visual features extracted using pre-trained Deep Neural Networks.Comment: Accepted at SMAP202

Operations management and collaboration in agri-food supply chains

Agri-food supply chains refers to the connections that exist across the agri-food stakeholders from farm to fork related processes. In this environment, operations like planning and control are critical to enhance agri-food supply chains performance and decisions, which are considered complex mainly because the presence of a high variety of information and variables that are required to be managed simultaneously. Hence, the use of a combination of methods, methodologies and multidisciplinary approaches are one of the key trends in research to handle these complexities. The purpose of this is to benefit the agri-food sector by identifying sustainable solutions that will enhance social opportunities, as well as livelihoods, local and national economies. From this, impacts are expected in terms of providing stakeholders with validated scenarios to improve agri-food supply chain resilience, establish agri-food decision-making guidelines to enhance agriculture quality delivery and strengthen agri-food stakeholders position in local and global supply chains. However, since the interdependencies between agri-food stakeholders are related to several agri-food activities, resources and systems, the establishment of collaborative business models across the agri-food supply chain has turned more important than ever, specially to support global agri-food supply chains, food safety and traceability in response to the sustainable global challenges. Therefore, aligned with the H2020 RUC-APS research project, this research focuses in addressing key decision-making challenges in agri-food supply chain environments by connecting key operations management methodologies to collaborative research approaches in the regions of Europe, Asia and South America. The objective is to identify the operations management situations where decisions are made difficult by uncertainty in the agri-food domain, within the study and implementation of Operations Management based approaches in agri-food supply chains

Knowledge discovering from multiple sources in agriculture value-chain

The agri-food value-chain results from the interaction of multiple stakeholders. Each stakeholder contributes with a distinct perspective and interest. The diversity in activities and work forms in the value-chain results in a wide variety of data sources, and data management practices. It is common to find information managed in databases, document repositories, or even social media. Document formats also vary (e.g., CSV, PDF, XML, etc.), and so do content types (e.g., graphics, tables, lists, images, etc.). In this context, effective decision-making relies heavily on the availability of interoperable, comprehensive, accurate, and timely information. Knowledge graphs (KG) are graphbased data models for knowledge extraction from multiple structured and unstructured sources that support multilingual integration. KG are frequently combined with knowledge discovering approaches like embedding and multi-relational data mining methods like the Formal Concept Analysis (FCA) and its extension the Relational Concept Analysis (RCA). This work proposes an automatic pipeline process to combine and align different agri-food information sources to discover new pieces of knowledge based on KG and RCA. The approach combines several research lines: (1) entities and relations detection in different sources; (2) alignment with a shared ontology description, based on GACS and AGROVOC, and (3) discovering new knowledge with Relational Concept Analysis in the shape of association rules formalized following the description logic

Agro-Knowledge Integration: Developing a FAIR data science approach for adding value to the agricultural supply chain

Farms are the engine to support rural employment making a considerable contribution to territorial development. Even though they have always been considered a cornerstone of agricultural activity in the European Union (EU) and in Latin America, this sector most often suffers from very low efficiency and effectiveness, sensitivity to weather, market disruptions and other external factors. Two different problems in knowledge sharing are present in this domain. First, the various interoperability regulations between the countries. Although some efforts are done to bypass this problem, like the EU-Mercosur signed in the summer of 2019, the different process semantics implemented in each region are a serious threat to the fulfillment of the process interoperability. Another problem is that in most of the cases, the knowledge transferred from generation to generation is paramount from a cultural point of view, but most of the time, it does not answer to the needs nor the requirements of the agri-food value chain. We aim at creating the core technology for a knowledge hub that integrates and aligns international regulations in agricultural activities, such as FAO's best practices, and possibly the last-born EU-Mercosur regulations with the local restrictions, such as national policies, allowing the small farmers to access, in an easy way, a wider market through the certification of the practices and products. In order to develop this core technology, we propose to deploy various methodologies and tools working on the domains of knowledge formalization, domain alignment and visualization. The domain of formal representation allows for the semantic alignment of rules and restrictions from different institutional regulation bodies. Simultaneously, we will propose a model for incoherence detection letting us to highlight contradictory regulations. Those knowledge atoms and constructs will be represented through some visualization information interfaces according to the users’ needs. The methods and tools that will be employed are at the same time the pillars from the multi relational data mining (MRDM), the artificial intelligence (AI), the knowledge formalization (KF) domains, but will extend the interoperability properties of those domains to become a new interesting and valuable tool for the presented problem. This abstract is issued from an accepted Stic-AmSud project that wad elaborated during the secondments of the RUC-APS project

Une contribution au Genie Automatique: le prototypage des machines et systemes automatises de production

INIST T 74949 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

EI2N 2012 PC Co-chairs Message

Click on the DOI link to access this article (may not be free)After the successful Sixth edition in 2011, the seventh edition of the Enterprise Integration, Interoperability and Networking workshop (EI2N2012) has been organised as part of the OTM2012 Federated Conferences and is co-sponsored by the IFAC Technical Committee 5.3 Enterprise Integration and Networking, the IFIP TC 8 WG 8.1 Design and Evaluation of Information Systems, the SIG INTEROP Grande-Rgion on Enterprise Systems Interoperability, the SIG INTEROP-VLab.IT on Enterprise Interoperability and the French CNRS National Research Group GDR MACS

Scenarios, shared understanding, and group decision support to foster innovation networks

Collaborative innovation involves diverse individuals and organizations working together to develop new ideas, products, or services. Successful collaboration in networked innovation projects is challenging due to the need to cross the knowledge boundaries that exist between organizations, disciplines, and cognitive frames. We propose an approach to support knowledge mobilization and learning in networked innovation projects. Scenarios, stored in a shared repository, are used to capture and share information about application and solution domains. A collaborative process guides participants to reach a shared understanding and construct shared meaning. Stakeholders engage in a collaborative decision-making process of scenario ranking that includes identifying and negotiating comparison criteria. Although the approach is presented with examples in the domain of agriculture, where validation of the constituent elements took place, it is domain independent