224 research outputs found

    L'intertextualité dans les publications scientifiques

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    La base de données bibliographiques de l'IEEE contient un certain nombre de duplications avérées avec indication des originaux copiés. Ce corpus est utilisé pour tester une méthode d'attribution d'auteur. La combinaison de la distance intertextuelle avec la fenêtre glissante et diverses techniques de classification permet d'identifier ces duplications avec un risque d'erreur très faible. Cette expérience montre également que plusieurs facteurs brouillent l'identité de l'auteur scientifique, notamment des collectifs de chercheurs à géométrie variable et une forte dose d'intertextualité acceptée voire recherchée

    Automatic Mobile Video Remixing and Collaborative Watching Systems

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    In the thesis, the implications of combining collaboration with automation for remix creation are analyzed. We first present a sensor-enhanced Automatic Video Remixing System (AVRS), which intelligently processes mobile videos in combination with mobile device sensor information. The sensor-enhanced AVRS system involves certain architectural choices, which meet the key system requirements (leverage user generated content, use sensor information, reduce end user burden), and user experience requirements. Architecture adaptations are required to improve certain key performance parameters. In addition, certain operating parameters need to be constrained, for real world deployment feasibility. Subsequently, sensor-less cloud based AVRS and low footprint sensorless AVRS approaches are presented. The three approaches exemplify the importance of operating parameter tradeoffs for system design. The approaches cover a wide spectrum, ranging from a multimodal multi-user client-server system (sensor-enhanced AVRS) to a mobile application which can automatically generate a multi-camera remix experience from a single video. Next, we present the findings from the four user studies involving 77 users related to automatic mobile video remixing. The goal was to validate selected system design goals, provide insights for additional features and identify the challenges and bottlenecks. Topics studied include the role of automation, the value of a video remix as an event memorabilia, the requirements for different types of events and the perceived user value from creating multi-camera remix from a single video. System design implications derived from the user studies are presented. Subsequently, sport summarization, which is a specific form of remix creation is analyzed. In particular, the role of content capture method is analyzed with two complementary approaches. The first approach performs saliency detection in casually captured mobile videos; in contrast, the second one creates multi-camera summaries from role based captured content. Furthermore, a method for interactive customization of summary is presented. Next, the discussion is extended to include the role of users’ situational context and the consumed content in facilitating collaborative watching experience. Mobile based collaborative watching architectures are described, which facilitate a common shared context between the participants. The concept of movable multimedia is introduced to highlight the multidevice environment of current day users. The thesis presents results which have been derived from end-to-end system prototypes tested in real world conditions and corroborated with extensive user impact evaluation

    Kuvanlaatukokemuksen arvionnin instrumentit

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    This dissertation describes the instruments available for image quality evaluation, develops new methods for subjective image quality evaluation and provides image and video databases for the assessment and development of image quality assessment (IQA) algorithms. The contributions of the thesis are based on six original publications. The first publication introduced the VQone toolbox for subjective image quality evaluation. It created a platform for free-form experimentation with standardized image quality methods and was the foundation for later studies. The second publication focused on the dilemma of reference in subjective experiments by proposing a new method for image quality evaluation: the absolute category rating with dynamic reference (ACR-DR). The third publication presented a database (CID2013) in which 480 images were evaluated by 188 observers using the ACR-DR method proposed in the prior publication. Providing databases of image files along with their quality ratings is essential in the field of IQA algorithm development. The fourth publication introduced a video database (CVD2014) based on having 210 observers rate 234 video clips. The temporal aspect of the stimuli creates peculiar artifacts and degradations, as well as challenges to experimental design and video quality assessment (VQA) algorithms. When the CID2013 and CVD2014 databases were published, most state-of-the-art I/VQAs had been trained on and tested against databases created by degrading an original image or video with a single distortion at a time. The novel aspect of CID2013 and CVD2014 was that they consisted of multiple concurrent distortions. To facilitate communication and understanding among professionals in various fields of image quality as well as among non-professionals, an attribute lexicon of image quality, the image quality wheel, was presented in the fifth publication of this thesis. Reference wheels and terminology lexicons have a long tradition in sensory evaluation contexts, such as taste experience studies, where they are used to facilitate communication among interested stakeholders; however, such an approach has not been common in visual experience domains, especially in studies on image quality. The sixth publication examined how the free descriptions given by the observers influenced the ratings of the images. Understanding how various elements, such as perceived sharpness and naturalness, affect subjective image quality can help to understand the decision-making processes behind image quality evaluation. Knowing the impact of each preferential attribute can then be used for I/VQA algorithm development; certain I/VQA algorithms already incorporate low-level human visual system (HVS) models in their algorithms.Väitöskirja tarkastelee ja kehittää uusia kuvanlaadun arvioinnin menetelmiä, sekä tarjoaa kuva- ja videotietokantoja kuvanlaadun arviointialgoritmien (IQA) testaamiseen ja kehittämiseen. Se, mikä koetaan kauniina ja miellyttävänä, on psykologisesti kiinnostava kysymys. Työllä on myös merkitystä teollisuuteen kameroiden kuvanlaadun kehittämisessä. Väitöskirja sisältää kuusi julkaisua, joissa tarkastellaan aihetta eri näkökulmista. I. julkaisussa kehitettiin sovellus keräämään ihmisten antamia arvioita esitetyistä kuvista tutkijoiden vapaaseen käyttöön. Se antoi mahdollisuuden testata standardoituja kuvanlaadun arviointiin kehitettyjä menetelmiä ja kehittää niiden pohjalta myös uusia menetelmiä luoden perustan myöhemmille tutkimuksille. II. julkaisussa kehitettiin uusi kuvanlaadun arviointimenetelmä. Menetelmä hyödyntää sarjallista kuvien esitystapaa, jolla muodostettiin henkilöille mielikuva kuvien laatuvaihtelusta ennen varsinaista arviointia. Tämän todettiin vähentävän tulosten hajontaa ja erottelevan pienempiä kuvanlaatueroja. III. julkaisussa kuvaillaan tietokanta, jossa on 188 henkilön 480 kuvasta antamat laatuarviot ja niihin liittyvät kuvatiedostot. Tietokannat ovat arvokas työkalu pyrittäessä kehittämään algoritmeja kuvanlaadun automaattiseen arvosteluun. Niitä tarvitaan mm. opetusmateriaalina tekoälyyn pohjautuvien algoritmien kehityksessä sekä vertailtaessa eri algoritmien suorituskykyä toisiinsa. Mitä paremmin algoritmin tuottama ennuste korreloi ihmisten antamiin laatuarvioihin, sen parempi suorituskyky sillä voidaan sanoa olevan. IV. julkaisussa esitellään tietokanta, jossa on 210 henkilön 234 videoleikkeestä tekemät laatuarviot ja niihin liittyvät videotiedostot. Ajallisen ulottuvuuden vuoksi videoärsykkeiden virheet ovat erilaisia kuin kuvissa, mikä tuo omat haasteensa videoiden laatua arvioiville algoritmeille (VQA). Aikaisempien tietokantojen ärsykkeet on muodostettu esimerkiksi sumentamalla yksittäistä kuvaa asteittain, jolloin ne sisältävät vain yksiulotteisia vääristymiä. Nyt esitetyt tietokannat poikkeavat aikaisemmista ja sisältävät useita samanaikaisia vääristymistä, joiden interaktio kuvanlaadulle voi olla merkittävää. V. julkaisussa esitellään kuvanlaatuympyrä (image quality wheel). Se on kuvanlaadun käsitteiden sanasto, joka on kerätty analysoimalla 146 henkilön tuottamat 39 415 kuvanlaadun sanallista kuvausta. Sanastoilla on pitkät perinteet aistinvaraisen arvioinnin tutkimusperinteessä, mutta niitä ei ole aikaisemmin kehitetty kuvanlaadulle. VI. tutkimuksessa tutkittiin, kuinka arvioitsijoiden antamat käsitteet vaikuttavat kuvien laadun arviointiin. Esimerkiksi kuvien arvioitu terävyys tai luonnollisuus auttaa ymmärtämään laadunarvioinnin taustalla olevia päätöksentekoprosesseja. Tietoa voidaan käyttää esimerkiksi kuvan- ja videonlaadun arviointialgoritmien (I/VQA) kehitystyössä

    A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

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    This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter

    The Discovery of Data-Driven Temporal Dietary Patterns and a Validation of Their Description Using Energy and Time Cut-Offs

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    Objectives Data-driven methods were recently applied to create temporal dietary patterns (TDPs) incorporating timing and amount of energy intake over 24-hours; their relationships to obesity were determined. However, description of the data-driven TDPs using energy and time cut-offs were not validated against obesity. Aims were to (1) create data-driven TDPs, describe pattern characteristics using energy and time cut-offs, and determine relationships to BMI and waist circumference (WC); (2) assess the concurrent validity of TDPs derived using the cut-offs by determining relationships with BMI and WC. Methods Amount and timing of energy intake from the first day 24-hour dietary recall of 17,916 U.S. adults in NHANES 2007–2016 was used to pattern 4 TDPs. Clusters were created using data-driven methods: dynamic time warping coupled with kernel-k means clustering algorithm. Relationships with BMI and WC were assessed using multivariate regression. Heat maps plotting the cluster proportion by energy amount throughout the day were used to visualize the data and find energy and time cut-offs for mutually exclusive clusters. Next, the cut-off-based descriptions were used to create new clusters and multivariate regression determined their associations with BMI and WC. Strength to predict obesity was evaluated by comparing both inferential model results. Percent of participant overlap between data-driven and cut-off derived clusters was also calculated. Results Both cut-off and data-driven methods showed a cluster, representing a TDP with proportionally equivalent average energy consumed during three eating events throughout a day, was associated with significantly lower BMI (R2 = 0.12 for both methods) and WC (R2 = 0.17 for both methods) compared to the other 3 clusters that had one energy peak throughout a day (all P \u3c 0.0001). Participant membership of ≥ 82% overlapped between the cut-off and data-driven TDP clusters. Conclusions Four cut-off derived clusters highly overlapped with data-driven clusters and showed no differences in strength or pattern relationships with obesity. TDP discovery using a data-driven method was validated through practically interpretable descriptions of energy intake and timing cut-offs. TDPs hold promise for the prediction of obesity and translation to dietary guidance

    Hyperspectral Image Analysis of Food for Nutritional Intake

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    The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers have developed various semiautomatic or automatic dietary assessment systems; most of them are based on the conventional color images such as RGB. The main disadvantage of such systems is their inability to differentiate foods of similar color or same ingredients in various colors, or different forms such as cooked or mixed forms. Although adding features such as shape, size and texture improve the overall performance, they are sensitive to changes in the illumination, rotation, scale, etc. A balance between quality and quantity of features representation, and system efficiency must also be considered. Hyperspectral technology combines conventional imaging technology with spectroscopy in a three-dimensional data-cube to obtain both the spatial and spectral information of the objects. However, the high dimensionality of hyperspectral data in addition to the redundancy between spectral bands limits performance, especially in online or onboard data processing applications. Thus, various features selection/extraction are also used to select the optimal feature subsets. The results are promising and verify the feasibility of using hyperspectral technology in dietary assessment applications

    Facilitating Internet of Things on the Edge

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    The evolution of electronics and wireless technologies has entered a new era, the Internet of Things (IoT). Presently, IoT technologies influence the global market, bringing benefits in many areas, including healthcare, manufacturing, transportation, and entertainment. Modern IoT devices serve as a thin client with data processing performed in a remote computing node, such as a cloud server or a mobile edge compute unit. These computing units own significant resources that allow prompt data processing. The user experience for such an approach relies drastically on the availability and quality of the internet connection. In this case, if the internet connection is unavailable, the resulting operations of IoT applications can be completely disrupted. It is worth noting that emerging IoT applications are even more throughput demanding and latency-sensitive which makes communication networks a practical bottleneck for the service provisioning. This thesis aims to eliminate the limitations of wireless access, via the improvement of connectivity and throughput between the devices on the edge, as well as their network identification, which is fundamentally important for IoT service management. The introduction begins with a discussion on the emerging IoT applications and their demands. Subsequent chapters introduce scenarios of interest, describe the proposed solutions and provide selected performance evaluation results. Specifically, we start with research on the use of degraded memory chips for network identification of IoT devices as an alternative to conventional methods, such as IMEI; these methods are not vulnerable to tampering and cloning. Further, we introduce our contributions for improving connectivity and throughput among IoT devices on the edge in a case where the mobile network infrastructure is limited or totally unavailable. Finally, we conclude the introduction with a summary of the results achieved
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