Targeted Annotation of Scientific Literature and Data Resources in Invenio Digital Libraries

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Integration of JINR Information Resources

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Repository content - lost in Discovery?

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Packaging Content for Aggregated and Federated Repositories through APTrust

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Preserve your content with LOCKSS-O-Matic

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

DSpace REST API: an Interface for Reusing DSpace Content, and Practical Applications

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Usability comparison of open source content management systems

The purpose of this thesis is to study, investigate and compare usability of open source cms. The thesis examines and compares usability aspect of some open source cms. The research is divided into two complementary parts –theoretical part and analytical part. The theoretical part mainly describes open source web content management systems, usability and the evaluation methods. The analytical part is to compare and analyze the results found from the empirical research. Heuristic evaluation method was used to measure usability problems in the interfaces. The study is fairly limited in scope; six tasks were designed and implemented in each interface for discovering defects in the interfaces. Usability problems were rated according to their level of severity. Time it took by each task, level of problem’s severity and type of heuristics violated will be recorded, analyzed and compared. The results of this study indicate that the comparing systems provide usable interfaces, and WordPress is recognized as the most usable system.

User-generated experiences: how automated platforms for content creation and sharing has transformed information access

Esitys KDK-käytettävyystyöryhmän järjestämässä seminaarissa: Miten käyttäjien toiveet haastavat metatietokäytäntöjämme? / How users' expectations challenge our metadata practices? 30.9.2014.

The Role of Video Based Content Marketing as a Part of Modern Marketing Communications

The thesis studies the role of video based content marketing as a part of modern marketing communications.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

How to increase brand quality perception with audiovisual content? : Exploring the role of emotions in B2C and B2B contexts

This study discusses how audiovisual content can influence brand quality perceptions. The purpose of this study is to explore how audiovisual content creation can increase brand quality perceptions. This research problem is addressed with three sub questions, which aim at clarifying the role of emotions between content marketing and brand quality perception, explaining how different functions of audiovisual content can increase brand quality perception, and by identifying and comparing the key differences in content creation in business-to-consumer and business-to-businesscontexts. The theoretical background of the study is in brand personality, consumer emotions, consumerbrand relationships, content marketing and B2B branding literature. The empirical research part includes a single-case study. The case company was a Swiss startup that wished to build a highquality brand for both B2C and B2B segments. The empirical data was collected in September 2014. Eight interviews were conducted; seven with target segment representatives and one with an existing customer of the case company. The empirical findings were analyzed with thematic analysis and finally a 5-stage framework was created based on the findings of the research, offering a guideline for high-quality content creation. This study finds that emotions play an important role in brand quality perceptions. Psychological processes, emotion, cognition and conation, influence the engagement process of the target segment which ultimately can lead to activation and electronic word-of-mouth. Brand quality perception is the result of the overall emotion of the brand. The overall emotion derives from brand personality, brand concept, product attributes and utilitarian benefits of the brand. The entertaining and educational functions of the audiovisual content can target and evoke these emotional processes, and result in increased quality perceptions. In the B2B context, emotions are found to play a relatively smaller role in the quality perception processes. However, the significance of emotions cannot be ignored, since they can emphasize the value for the buying organization, and build on the trust and loyalty among the potential customers. The final framework presents five stages of content creation that ultimately improve brand quality perceptions. These stages help marketers to design and implement their content and evoke positive emotions in their target segment as part of a quality-based marketing strategy. Further research is warranted to quantitatively test the generalizability of the framework. Further research is also suggested to make the framework adaptable to different stages of the brand life cycle.

Evaluating Performance of Content Marketing: YouTube Marketing in Finnish Food and Drink Industry

The purpose of this research was to define content marketing and to discover how content marketing performance can be measured especially on YouTube. Further, the aim was to find out what companies are doing to measure content marketing and what kind of challenges they face in the process. In addition, preferences concerning the measurement were examined. The empirical part was conducted through multiple-case study and cross-case analysis methods. The qualitative data was collected from four large companies in Finnish food and drink industry through semi-structured phone interviews. As a result of this research, a new definition for content marketing was derived. It is suggested that return on objective, or in this case, brand awareness and engagement are used as the main metrics of content marketing performance on YouTube. The major challenge is the nature of the industry, as companies cannot connect the outcome directly to sales.

Portfolio as an Indicator of Young Learners’ English Proficiency in Mainstream Language Instruction (EFL) and Bilingual Content Instruction (CLIL)

Tämä soveltavan kielitieteen ja kielitaidon arvioinnin toimintatutkimus tarkasteli kieliportfolion ominaisuuksia ja mahdollisuuksia nuorten oppijoiden englannin kielen arvioinnissa kahdessa eri oppimiskontekstissa: englanti oppiaineena (EFL) ja kaksikielinen sisällönopetus (CLIL). Tutkielman itsenäiset, kahteen eri englannin kielen rekisteriin (arkikieli ja akateeminen kieli) kohdistuneet portfoliokokeilut olivat erillisiä tapaustutkimuksia. Molemmat portfoliot perustuivat väljästi Eurooppalaiseen kielisalkkumalliin, ja ne olivat osa tutkielmantekijän luokkaopetusta ja -toimintaa. EFL -portfoliokokeilu 9-10-vuotiaille kolmasluokkalaisille toteutettiin marraskuun 2011 ja toukokuun 2012 välisenä aikana, kun CLIL -portfoliokokeilu n. 7-9-vuotiallle ensimmäisen ja toisen luokan oppilaille kesti kaksi lukuvuotta 2012–2014. Molemmissa kokeiluissa myös oppilaiden vanhemmat kuuluivat tutkimusjoukkoon, samoin CLIL -portfolion toteutuksessa avustaneet ja opettajanäkökulmaa edustaneet opettajaopiskelijat. Portfoliokokeilun aloitti myös kaksi muuta CLIL -opettajaa, mutta kumpikin kokeilu päättyi alkuvaiheeseensa. Tarkemman tarkastelun kohteina olivat tutkimuksen osallistujien kokemukset ja mielipiteet portfoliokokeiluista. Erityisesti tavoitteena oli selvittää, miten informatiivisena englannin kielitaidon indikaattorina kieliportfoliota pidettiin. Myös kehitysehdotuksia kerättiin. Trianguloitu aineisto koottiin sekä puolistrukturoiduin kyselyin että vapaaehtoisin teemahaastatteluin, jotka äänitettiin. EFL -aineisto koostui 18 oppilaskyselystä, 17 huoltajakyselystä ja 7 oppilashaastattelusta. CLIL -aineistoon sisältyi 19 oppilaskyselyä, 18 huoltajakyselyä, 7 oppilashaastattelua ja yksi opettajaopiskelijoiden (N=3) ryhmähaastattelu. Aineisto analysoitiin pääosin kvalitatiivisin menetelmin temaattisen sisältöanalyysin keinoin, mutta myös laskien frekvenssejä ja prosenttisosuuksia. Osallistujien mielipiteet ja kokemukset olivat hyvin samankaltaiset ja positiiviset kummassakin portfoliokokeilussa. Merkittävä enemmistö sekä oppilaista että huoltajista koki, että portfolion avulla on mahdollista osoittaa englannin kielitaitoa ja sen kehittymistä. Oppilaat kuvailivat portfoliotyötä hauskaksi ja kivaksi, ja heidän mielestään portfoliotehtävien pitäisi olla tarpeeksi haastavia, sisältää taiteellisia ja luovia elementtejä sekä kohdistua tuttuihin, mielenkiintoisiin aiheisiin. He totesivat, että portfolion avulla voi oppia lisää kieltä. Vanhempien mielestä portfolio kertoo koulun vieraiksi jääneistä oppisisällöistä, auttaa ymmärtämään lapsen ajatusmaailmaa ja motivaatiotasoa sekä paljastaa heidän kielitaidostaan uusia ulottuvuuksia. Opettajaopiskelijat havaitsivat, että portfolion avulla voi tutustua oppilaiden kieli- ja kulttuuritaustoihin sekä kartoittaa heidän kielellisiä tarpeitaan. Tämän tutkielman teoreettisen tarkastelun ja tulosten mukaan kieliportfolio tukee erinomaisesti uuden Perusopetuksen Opetussuunnitelman (NCC 2014) tavoitteita ja arvioinnin uudistuspyrkimyksiä sekä lainsäädännön arvioinnille asettamia edellytyksiä. Portfolio on erittäin suositeltava nuorten oppijoiden kielitaidon arviointimenetelmä perinteisten rinnalle.

Digital content and making it viral in social media

Tutkimuksen tavoite on selvittää digitaalisen sisällön ominaisuuksia, jotka vaikuttavat ryhtyvätkö kuluttajat jakamaan, tykkäämään ja kommentoimaan sitä sosiaalisessa mediassa. Tällä pyritään auttamaan yrityksiä ymmärtämään paremmin viraalisuutta, jotta he pystyisivät tuottamaan ja julkaisemaan nettisivuillaan ja sosiaalisessa mediassa parempaa sisältöä, jota kuluttajat jakaisivat enemmän. Tutkimus toteutetaan muodostamalla hypoteeseja mahdollisista ominaisuuksista kirjallisuuden perusteella ja testaamalla niitä regressioanalyyseillä empiirisessä osiossa. Tulokset paljastavat yhdeksän piirrettä, jotka lisäävät viraalisuutta: kiinnostavuus, neutraalisuus, yllättävyys, viihdyttävyys, epäkäytännöllisyys, artikkelin ja Facebook julkaisun pituus, eri sisältö taktiikoiden käyttö (erityisesti blogit ja kuvat lisäävät viraalisuutta) sekä kun mielipidevaikuttaja tai kuuluisuus jakaa sisällön.