Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT

Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Trash, Fragments, and Breaking Things: Toward a Grotesque Cripistemology for Disabled Life Writing

Despite the boom of memoirs of mental health post-1997 and the first advertisements for Prozac, most of them follow the same formula and come from the same places of privilege. This privilege is evident in the author bios on the books themselves and the careers of the writers. The popularity of these books within both abled and disabled realms has therefore created a script that those with mental illnesses are expected to abide by. Following in the example of Margaret Price, Katie Rose Guest Pryal, Merri Lisa Johnson, and others, I resituate mental illness as mental disability and place it within the world of disability studies. In doing so, this dissertation explores practical uses of Johnson and Robert McRuer’s cripistemologies, Johnson’s c/rip, and Flannery O’Connor’s and Yuan Yuan’s grotesque as methods for establishing the beginnings of a grotesque cripistemology with which those with mental disabilities might construct accessible narratives. Through a close look at zines and glitches, I seek to discover ways in which writers with mental disabilities might use fragmented writing, trash, and brokenness in order to utilize this new grotesque cripistemology in order to not construct stories of overcoming aimed at abled audiences, but rather stories of the self and of being within the hurricane which is to have a mental disability unabashedly aimed at a disabled audience

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Boys’ love, byte-sized: a qualitative exploration of queer-themed microfiction in Chinese cyberspace

This project undertakes an in-depth, qualitative investigation into queer-themed ‘Boys Love’ microfiction within the realm of Chinese cyberspace, with the aim of further understanding both the features of the genre and the motivation for production and consumption among its primarily heterosexual female user-base. Expanding upon previous studies, which have focused primarily on investigation into the consumer groups of such fiction, this project seeks to establish links between the linguistic/discursive features of queer Chinese-language microfiction and observable social phenomena or cultural frameworks. Using and developing Gee’s tools of inquiry (2014) for textual analysis, this project explores the situated meanings, figured worlds and Discourses embodied in very short fictional stories representing male same-sex intimacies and queer sexualities. In doing so, I proposes a development of Johnson’s circuits of culture model (1986), in which I hypothesize that, confronted with heteronormative social structures—constructed along a gender binary and framed through patriarchal familial and social relationships—China’s cyberspace has offered a new platform for marginalized individuals (both queer-identified and those heterosexual consumers who enjoy fantasizing about same-sex intimacies) to engage, navigate and negotiate space to tell their stories. In doing so, they find opportunities to renegotiate citizenship based on sexual identity. Therefore, this study creates a ‘circuit of queer cyberculture’ framework through which to analyse queer-themed microfiction. This framework proposes that, through an emerging form of ‘cultural self-determination’ rooted in sexual and gender identity and the declaration and negotiation of sexual citizenship, netizens who experience social marginalization in the real world through their attraction to representation of queer lives begin to indigenize circuits of popular culture observable in mainstream media platforms by creating and distributing their own works of art and fiction online. Through a combination of Critical Discourse Analysis of 40 selected works of microfiction and applied thematic analysis of 39 interviews conducted with producers and consumers of the genre in Mainland China, this project therefore assesses the development of the Boys’ Love genre into a microfiction format, distributed via a publicly visible online platform. Investigation of the defining characteristics of the genre, in combination with data gathered from interviews, allows this project to demonstrate how this new empirical data can expand our global and local knowledge of theoretical and conceptual debates regarding identity, gender, representation, queer sexualities, sexual citizenship and circuits of culture

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective