Towards an Automated Semantic Data-driven Decision Making Employing Human Brain

[EN] Decision making is time-consuming and costly, as it requires direct intensive involvement of the human brain. The variety of expertise of highly qualified experts is very high, and the available experts are mostly not available on a short notice: they might be physically remotely located, and/or not being able to address all the problems they could address time-wise. Further, people tend to base more of their intellectual labour on rapidly increasing volumes of online data, content and computing resources, and the lack of corresponding scaling in availability of the human brain resources poses a bottleneck in the intellectual labour. We discuss enabling direct interoperability between the Internet and the human brain, developing "Internet of Brains", similar to "Internet of Things", where one can semantically model, interoperate and control real life objects. The Web, "Internet of Things" and "Internet of Brains" will be connected employing the same kind of semantic structures, and work in interoperation. Applying Brain Computer Interfaces (BCIs), psychology and behavioural science, we discuss the feasibility of a possible decion making infrastructure for semantic transfer of human thoughts, thinking processes, communication directly to the InternetThis work has been partially funded by project DALICC, supported by the Austrian Research Promotion Agency (FFG) within the program “Future ICT”.Fensel, A. (2018). Towards an Automated Semantic Data-driven Decision Making Employing Human Brain. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 167-175. https://doi.org/10.4995/CARMA2018.2018.8338OCS16717

Emotions ontology for collaborative modelling and learning of emotional responses

Emotions-aware applications are getting a lot of attention as a way to improve the user experience, and also thanks to increasingly affordable Brain Computer Interfaces (BCI). Thus, projects collecting emotion-related data are proliferating, like social networks sentiment analysis or tracking students" engagement to reduce Massive Online Open Courses (MOOCs) drop out rates. All them require a common way to represent emotions so it can be more easily integrated, shared and reused by applications improving user experience. Due to the complexity of this data, our proposal is to use rich semantic models based on ontology. EmotionsOnto is a generic ontology for describing emotions and their detection and expression systems taking contextual and multimodal elements into account. The ontology has been applied in the context of EmoCS, a project that collaboratively collects emotion common sense and models it using the EmotionsOnto and other ontologies. Currently, emotion input is provided manually by users. However, experiments are being conduced to automatically measure users"s emotional states using Brain Computer Interfaces

SOLAR: Social Link Advanced recommendation system

In today’s information society, precise descriptions of the massive volume of online content available are crucial for responding to user needs adequately and efficiently. The Semantic Web Paradigm has recently advanced across many domains for the assignment of metadata to Internet content, in order to define it with explicit, machine-readable meaning. This content has become so extensive that it must be refined according to user preferences to avoid information overload. The current paper proposes a framework for the association of semantic data to webpage links based on a specific domain ontology, additionally permitting the user to express his opinion regarding his emotions about the content of the link. This data is further exploited to suggest additional links to the user, based on the semantic metadata and the level of user satisfaction with previously viewed content. A comprehensive evaluation of the tool has demonstrated a high level of user satisfaction with the features of the system.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI-020100-2008-564) and SONAR2 (TSI-020100-2008-665), under the PIBES project of the Spanish Committee of Education & Science (TEC2006-12365-C02-01) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140-C03-02)

SEMO: a framework for customer social networks analysis based on semantics

The increasing importance of the Internet in most domains has brought about a paradigm change in consumer relations. The influence of Social Networks has entered the Customer Relationship Management domain under the coined term CRM 2.0. In this context, the need to understand and classify the interactions of customers by means of new platforms has emerged as a challenge for both researchers and professionals world-wide. This is the perfect scenario for the use of SEMO, a platform for Customer Social Networks Analysis based on Semantics and emotion mining. The platform benefits from both semantic annotation and classification and text analysis, relying on techniques from the Natural Language Processing domain. The results of the evaluation of the experimental implementation of SEMO reveal a promising and viable platform from a technical perspective.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148), SONAR2 (TSI-020100-2008-665) and GO2 (TSI-020400-2009-127)Publicad

TONE: A 3-Tiered ONtology for Emotion analysis

Emotions have played an important part in many sectors, including psychology, medicine, mental health, computer science, and so on, and categorizing them has proven extremely useful in separating one emotion from another. Emotions can be classified using the following two methods: (1) The supervised method's efficiency is strongly dependent on the size and domain of the data collected. A categorization established using relevant data from one domain may not work well in another. (2) An unsupervised method that uses either domain expertise or a knowledge base of emotion types already exists. Though this second approach provides a suitable and generic categorization of emotions and is cost-effective, the literature doesn't possess a publicly available knowledge base that can be directly applied to any emotion categorization-related task. This pushes us to create a knowledge base that can be used for emotion classification across domains, and ontology is often used for this purpose. In this study, we provide TONE, an emotion-based ontology that effectively creates an emotional hierarchy based on Dr. Gerrod Parrot's group of emotions. In addition to ontology development, we introduce a semi-automated vocabulary construction process to generate a detailed collection of terms for emotions at each tier of the hierarchy. We also demonstrate automated methods for establishing three sorts of dependencies in order to develop linkages between different emotions. Our human and automatic evaluation results show the ontology's quality. Furthermore, we describe three distinct use cases that demonstrate the applicability of our ontology

Résumé automatique de textes d'opinion

International audienceIn this paper, we present a summarization system that is specifically designed to process blog posts, where factual information is mixed with opinions on the discussed facts. Our approach combines redundancy analysis with new information tracking and is enriched by a module that computes the polarity of textual fragments in order to summarize blog posts more efficiently. The system is evaluated against English data, especially through the participation in TAC (Text Analysis Conference), an international evaluation framework for automatic summarization, in which our system obtained interesting results.Nous présentons dans cet article un système de résumé automatique tourné vers l'analyse de blogs, où sont exprimées à la fois des informations factuelles et des prises de position sur les faits considérés. Notre système de résumé est fondé sur une approche nouvelle qui mêle analyse de la redondance et repérage des informations nouvelles dans les textes ; ce système générique est en outre enrichi d'un module de calcul de la polarité de l'opinion véhiculée afin de traiter de façon appropriée la subjectivité qui est le propre des billets de blogs. Le système est évalué sur l'anglais, à travers la participation à la campagne d'évaluation internationale TAC (Text Analysis Conference) où notre système a obtenu des performances satisfaisantes

EmotionsOnto: an Ontology for Developing Affective Applications

Abstract: EmotionsOnto is a generic ontology for describing emotions and their detection and expression systems taking contextual and multimodal elements into account. The ontology is proposed as a way to develop an easily computerizable and flexible formal model. Moreover, it is based on the Web Ontology Language (OWL) standard, which also makes ontologies easily shareable and extensible. Once formalized as an ontology, the knowledge about emotions can be used in order to make computers more personalised and adapted to users' needs. The ontology has been validated and evaluated by means of an applications based on a emotionsaware Tangible User Interface (TUI). The TUI is guided by emotion knowledge previously gathered using the same TUI and modelled using EmotionsOnto

Automatic Recognition of Facial Displays of Unfelt Emotions

Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datas