"Ich sehe was, was Du nicht siehst…“ – Was Rineke Dijkstra mit ihrem Video I see a woman crying (Weeping Woman) vor Augen führt

Die Autorin interpretiert das Video "I see a woman crying (Weeping Woman)" der niederländischen Künstlerin Rinke Dijkstra unter Aspekten des Bildungsprozesses von Kindern. In der sequentiellen Erschließung einzelner Szenen des Videos kann gezeigt werden, wie ein Gespräch von Kindern ein Kunstwerk thematisiert. Damit wird mit künstlerisch-ästhetischen Mitteln das Bildungsproblem inszeniert. (DIPF/Autor)

Possibilidades interativas nos vídeos de comunicação de ciência

No atual ecossistema audiovisual, os vídeos promocionais, sejam estes dirigidos ao sector empresarial ou científico, procuram ingredientes arrojados que lhes permitam ser eficientes e veicular a informação de uma forma objetiva, clara e incisiva. As possibilidades interativas, a duração e a infografia, aliados a uma publicação e promoção faseada, poderão ser alguns desses ingredientes com potencial de enriquecer um vídeo promocional de forma a capitalizar a sua eficiência. O presente estudo tem como objetivo inicial investigar e analisar os mais diversos vídeos promocionais, de forma a ser possível identificar quais os ingredientes de maior relevo que conduziram os mesmos ao sucesso pretendido. Neste contexto, esta investigação incide sobre a caraterização de vídeos promocionais interativos, mais precisamente sobre as principais caraterísticas presentes em cada um deles. Com base nessa caraterização, foi produzido um vídeo promocional com uma vertente de comunicação de ciência (relativo à aplicação 2nd Vision), passível de funcionar como use case de suporte à Investigação. A investigação fica concluída após haver um cruzamento de dados relativos a análises e também de avaliação em laboratório.

THE EFFECTIVENESS OF POINT-OF-VIEW VIDEO MODELING IN TEACHING SOCIAL INITIATION SKILLS TO CHILDREN WITH AUTISM SPECTRUM DISORDERS

Deficits in social communication and interaction have been identified as distinguishing impairments for individuals with an autism spectrum disorder (ASD). As a pivotal skill, the successful development of social communication and interaction in individuals with ASD is a lifelong objective. Point-of-view video modeling has the potential to address these deficits. This type of video involves filming the completion of a targeted skill or behavior from a first-person perspective. By presenting only what a person might see from his or her viewpoint, it has been identified to be more effective in limiting irrelevant stimuli by providing a clear frame of reference to facilitate imitation. The current study investigated the use of point-of-view video modeling in teaching social initiations (e.g., greetings). Using a multiple baseline across participants design, five kindergarten participants were taught social initiations using point-of-view video modeling and video priming. Immediately before and after viewing the entire point-of-view video model, the participants were evaluated on their social initiations with a trained, typically developing peer serving as a communication partner. Specifically, the social initiations involved participants’ abilities to shift their attention toward the peer who entered the classroom, maintain attention toward the peer, and engage in an appropriate social initiation (e.g., hi, hello). Both generalization and maintenance were tested. Overall, the data suggest point-of-view video modeling is an effective intervention for increasing social initiations in young students with ASD. However, retraining was necessary for acquisition of skills in the classroom environment. Generalization in novel environments and with a novel communication partner, and generalization to other social initiation skills was limited. Additionally, maintenance of gained social initiation skills only occurred in the intervention room. Despite the limitations of the study and variable results, there are a number of implications moving forward for both practitioners and future researchers examining point-of-view modeling and its potential impact on the social initiation skills of individuals with ASD.

Youth Expression with Video Surveillance Technology

This qualitative study examines five young Afro-Franco Caribbean males in the Diaspora and their experiences with systems of technology as a tool of oppression and liberation. The study utilized interpretive biography and participatory video research to examine the issues of identity, power/control, surveillance technology, love and freedom. The study made use of a number of data collection methods including interviews, round table discussions, and personal narratives. A hermeneutic theoretical framework is employed to develop an objective view of the problems facing Afro-Franco Caribbean males in the schools and community. The purpose of the study is to provide an environment and new media technology that Afro-Franco Caribbean males can use to engage and discuss their views on issues mentioned above and to ultimately develop a video project to share with the community. Moreover, the study sought to examine an epistemological approach (Creolization) that young black males, particularly Afro-Franco-Caribbean males, might use to communicate, document, and share their everyday experiences in the Diaspora. The findings in the study reveal that the participants are experiencing: (a) a lack of community involvement in the urban space they currently reside, (b) frustration with the perspective of their home country, Haiti, that is commonly shown in mainstream media, and (c) ridicule, shame, and violence in the spaces (school and community) that should be safe. The study provides the community (both local and scholarly) with an opportunity to hear the voices and concerns of youth in the urban space. In addition the study suggests a need for schools to create a critical pedagogical curriculum in which power can be democratically shared.

Diseño de un plan promocional que contribuya a posicionar los productos de la colchonería La Divina Providencia en el departamento de San Miguel

El presente Trabajo fue realizado en la Colchonería la Divina Providencia, que es una empresa que se encarga de fabricar y comercializar colchones y productos complementarios, la cual posee un espíritu de trabajo orientado a ofrecer un producto de calidad, con un personal especializado y con voluntad de trabajo. Pero con una limitante y es la carencia de estrategias de promoción que ocasionan un estancamiento en las ventas. Es por esa razón que se ha desarrollado el presente Plan Promocional que busca contribuir en el rubro dedicado a la realización y comercialización de colchones y productos complementarios en el departamento de San Miguel, con el fin de proporcionarles una serie de estrategias prácticas y entendibles que puedan llevarse a cabo y contribuir a enfrentar a la gran competencia que está acaparando el mercado. Para cual se elabora un diagnóstico interno y externo que ayudó a realizar un análisis situacional de los productos de la Colchonería la Divina providencia en la ciudad de San Miguel; también se realizó una investigación de mercado que permitió segmentar el mercado meta de la colchonería la Divina providencia, y se diseñaron de estrategias de promoción que permitieron posicionar los productos de la Colchonería la Divina providencia en la ciudad de San Miguel.

Identifying player profiles in video games

Tämä diplomityö tarkastelee pelaajatyyppien ja pelaajamotivaatioiden tunnistamista videopeleissä. Aiempi tutkimus tuntee monia pelaajatyyppien malleja, mutta niitä ei ole liiemmin sovellettu käytäntöön peleissä. Tässä työssä suoritetaan systemaattinen kirjallisuuskartoitus erilaisista pelaajatyyppien malleista, jonka pohjalta esitetään useita pelaajien luokittelutapoja. Lisäksi toteutetaan tapaustutkimus, jossa kirjallisuuden pohjalta valitaan pelaajien luokittelumalli ja testataan mallia käytännössä tunnistamalla pelaajatyyppejä data-analytiikan avulla reaaliaikaisessa strategiapelissä.

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.

Video lectures and online activities to engage students in a flipped classroom

In recent years there have been several proposals for alternative pedagogical practices. Most of these proposals are based in the, so called, “active learning”, in opposition to the common “passive learning”, which is centered on transmission of information inside classrooms as well as recognized as teacher-centered procedure. In an active learning pedagogical structure, students have a more participative role in the overall learning/teaching process, being encouraged to face new learning challenges like, for instance, solving problems and developing projects, in an autonomous approach trying to make them, consequently, able to build their own knowledge. The flipped or “inverted” classroom is one of these active learning pedagogical methodologies that emphasizes a learner-centered instruction. According to this approach, the first contact that students have with the content on a particular curriculum subject is not transmitted by the lecturer in the classroom, this teaching strategy requires students to assess and analyze the specific subject before attending to class, therefore the informational component from the lecture is the homework, and class time is dedicated to exercises and assignments, always with support from the instructor, who acts as a facilitator, helping students when needed and offering supplementary explanation as required. The main objective of this paper is to discuss and explore how the use of different types of instructional videos and online activities may be implemented in the flipped classroom procedure (as means of incorporating new content and teaching new competencies) and to describe students’ perceptions of this approach within a course in a Higher Education Institution (HEI), presenting some positive and negative features of this pedagogical practice.

Olkapään alueen makroskooppinen anatomia – video-opetusmateriaali anatomian opiskeluun

Anatomian opiskelu perustuu edelleen pitkälti kirjojen piirrettyihin kuviin. Opinnäytetyöni on tarkoitettu täydentämään kirjojen sisältöä ja tukemaan anatomian opiskelua. Tarkoituksena oli tuottaa havainnollinen videomateriaali olkapään alueesta käytettäväksi Turun yliopiston lääketieteellisen tiedekunnan anatomian kursseilla sekä itseopiskelumateriaalina. Videolla keskitytään kahteen erilliseen isoon kokonaisuuteen: kiertäjäkalvosimeen sekä lapaluuhun ja siihen kiinnittyviin lihaksiin. Suomenkielisen opetusvideon avulla opiskelijat voivat ymmärtää paremmin olkapään kolmiulotteista anatomiaa ja hahmottaa rakenteiden välisiä suhteita oikeassa kehossa. Ennen videon kuvaamista kertasin olkapään alueen anatomian, suunnittelin videon sisällön sekä harjoittelin preparointitekniikoita. Preparointia harjoiteltiin yhdellä formaliinivainajalla ja yhdellä tuorevainajalla. Avaussalityöskentelyssä tein yhteistyötä neljän muun opiskelijan kanssa. Kuvasimme kukin omien opinnäytetöidemme aiheet samasta tuorevainajasta yhden viikonlopun aikana 19.–22.12.2014. Video kuvattiin kokonaisuudessaan Turun yliopiston Medisiina- laitosrakennuksen anatomian opetusavaussalissa. Kuvaamisessa käytettiin Kliinisten taitojen oppimiskeskuksen Portin kameraa ja kuvausvälineistöä. Kuvausmateriaali muokattiin lopulliseksi videoksi käyttämällä Adobe Premiere Pro CC 2014 -videonmuokkausohjelmaa. Jälkikäteen videoon lisättiin ääniraita sekä anatomian käsitteitä selventämään videon kulkua. Lopullinen opetusvideo on noin 12 minuuttia kestävä tiivis kokonaisuus olkapään alueen anatomiasta. Se koostuu kolmesta osasta. Ensimmäinen osa on suurin kokonaisuus, jossa käsitellään lapaluun merkittävimmät luiset rakenteet sekä lapaluuhun kiinnittyvät lihakset erikseen edestä ja takaa. Toisessa osassa keskitytään kliinisesti merkittävään olkapään rakenteeseen, kiertäjäkalvosimeen, josta esitellään tarkasti kaikki neljä lihasta ja niiden sijainnit kuvattuna eri suunnista ja eri etäisyyksiltä. Videon lopuksi käydään läpi vielä olkapään kymmenen eri liikettä, jotka esitetään videolla.

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.