Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

This work aims to improve an existing time series forecasting algorithm –LBF– by the application of frequent episodes techniques as a complementary step to the model. When real-world time series are forecasted, there exist many samples whose values may be specially unexpected. By the combination of frequent episodes and the LBF algorithm, the new procedure does not make better predictions over these outliers but, on the contrary, it is able to predict the apparition of such atypical samples with a great accuracy. In short, this work shows how to detect the occurrence of anomalous samples in time series improving, thus, the general forecasting scheme. Moreover, this hybrid approach has been successfully tested on electricity-related time series

Taking the bite out of automated naming of characters in TV video

We investigate the problem of automatically labelling appearances of characters in TV or film material with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”

Finding Faulty Functions From the Traces of Field Failures

Corrective maintenance, which rectifies field faults, consumes 30-60% time of software maintenance. Literature indicates that 50% to 90% of the field failures are rediscoveries of previous faults, and that 20% of the code is responsible for 80% of the faults. Despite this, identification of the location of the field failures in system code remains challenging and consumes substantial (30-40%) time of corrective maintenance. Prior fault discovery techniques for field traces require many pass-fail traces, discover only crashing failures, or identify faulty coarse grain code such as files as the source of faults. This thesis (which is in the integrated article format) first describes a novel technique (F007) that focuses on identifying finer grain faulty code (faulty functions) from only the failing traces of deployed software. F007 works by training the decision trees on the function-call level failed traces of previous faults of a program. When a new failed trace arrives, F007 then predicts a ranked list of faulty functions based on the probability of fault proneness obtained via the decision trees. Second, this thesis describes a novel strategy, F007-plus, that trains F007 on the failed traces of mutants (artificial faults) and previous faults. F007-plus facilitates F007 in discovering new faulty functions that could not be discovered because they were not faulty in the traces of previously known actual faults. F007 (including F007-plus) was evaluated on the Siemens suite, Space program, four UNIX utilities, and a large commercial application of size approximately 20 millions LOC. F007 (including the use of F007-plus) was able to identify faulty functions in approximately 90% of the failed traces by reviewing approximately less than 10% of the code (i.e., by reviewing only the first few functions in the ranked list). These results, in fact, lead to an emerging theory that a faulty function can be identified by using prior traces of at least one fault in that function. Thus, F007 and F007-plus can correctly identify faulty functions in the failed traces of the majority (80%-90%) of the field failures by using the knowledge of faults in a small percentage (20%) of functions

What's the Big Idea? Intellectual History and the Longue Durée

Historians of all kinds are beginning to return to temporally expansive studies after decades of aversion and neglect. There are even signs that intellectual historians are returning to the longue durée. What are the reasons for this revival of long-range intellectual history? And how might it be rendered methodologically robust as well as historically compelling? This article proposes a model of transtemporal history, proceeding via serial contextualism to create a history in ideas spanning centuries, even millennia: key examples come from work in progress on ideas of civil war from ancient Rome to the present. The article concludes with brief reflections on the potential impact of the digital humanities on the practice of intellectual history.Histor

Redefining the anthology : forms and affordances in digital culture

Alors que le modèle économique de la télévision américaine, longtemps dominant, a été mis au défi de diverses manières par les changements industriels et technologiques de ces dernières années, des formes narratives de plus en plus hétérogènes sont apparues, qui se sont ajoutées aux structures sérielles originaires. La diversité des formes télévisuelles est devenue particulièrement évidente depuis que les paysages télévisuels nationaux et locaux ont commencé à s’ouvrir aux marchés étrangers situés en dehors des États-Unis, pour finalement adopter une perspective transnationale et globale. La transition vers la télévision distribuée sur Internet a joué un rôle central dans cette fragmentation formelle et la nouvelle dynamique de la diffusion en ligne a ouvert une different perspective pour comprendre le flux mondial de contenus télévisuels, qui reflète aujourd'hui un environnement multimédia et numérique hautement interconnecté et mis en réseau. En effet, la multiplication des services de vidéo à la demande oblige la sérialité à s’adapter au paysage médiatique contemporain, donnant naissance à des produits audiovisuels pouvant être transférés en ligne et présentant des spécificités de production, de distribution et de réception. L’un des résultats de tels changements dans les séries télévisées américaines à l’aube du XXIe siècle est la série anthologique divisée en différentes saisons avec des histoires distinctes, et pourtant liées par le ton et le style. Ma recherche se situe dans un tel contexte technologique, industriel et culturel, où le contenu télévisuel est de plus en plus fragmenté. Compte tenu de cette fragmentation des contenus, cette thèse examine la manière dont les contenus télévisuels contemporains sont distribués, dans l'interaction entre les processus de recommandation basés sur des algorithmes et les pratiques éditoriales plus traditionnelles. L’objectif de ce projet est donc d’étudier la manière dont certaines structures narratives typiques de la forme de l’anthologie apparaissent dans le contexte de la sérialité de la télévision nord-américaine, à partir de conditions spécifiques de production, de distribution et de consommation dans l’industrie des médias. En se concentrant sur l'évolution (dimension temporelle et historique) et sur la circulation numérique (dimension spatiale, géographique) des séries d'anthologies américaines, et en observant les particularités de leur production et de leur style, ainsi que leurs réseaux de distribution et les modes de consommation qu'elles favorisent, cette thèse s’inscrit finalement dans une conversation plus vaste sur les études culturelles et numériques. L’objectif final est d’étudier la relation entre les formes anthologiques, les plateformes de distribution et les modèles de consommation, en proposant une approche comparative de l’anthologie qui soit à la fois cross-culturelle, crosshistorique, cross-genre et qui prenne en consideration les pratiques pre- et post-numériques pour l’organisation de contenus culturels.As the longtime dominant U.S. television business model has been challenged in various ways by industrial and technological changes in recent years, more heterogeneous narrative forms have emerged in addition to original serial structures. The diversity of televisual forms became particularly evident since national, local television landscapes started opening up to foreign markets outside of the U.S., finally embracing a transnational, global perspective and tracing alternative value-chains. The transition to internet-distributed television played a pivotal role in this formal fragmentation and new dynamics of online streaming opened up another path for understanding the flow of television content, which today reflects a highly interconnected, networked media and digital environment. Indeed, the proliferation of video-on-demand services is forcing seriality to adapt to the contemporary mediascape, giving rise to audiovisual products that can be transferred online and present specificities in production, distribution and reception. One of the outcomes of such changes in U.S. television series at the dawn of the twenty-first century is the anthology series divided in different seasons with separate stories, yet linked by tone and style. My research positions itself in such a technological, industrial and cultural context, where television content is increasingly fragmented. Given such a fragmentation, this thesis considers the ways contemporary television content is distributed in the interaction between algorithmic-driven recommendation processes and more traditional editorial practices. The aim of the project is to investigate the way certain narrative structures typical of the anthology form emerge in the context of U.S. television seriality, starting from specific conditions of production, distribution and consumption in the media industry. By focusing on the evolution (temporal, historical dimension) and on the digital circulation (spatial, geographic dimension) of U.S. anthology series, and observing the peculiarities in their production and style, as well as their distributional networks and the consumption patterns they foster, this thesis ultimately insert itself into a larger conversation on digital-cultural studies. The final purpose is to examine the relation between anthological forms, distribution platforms and consumption models, by proposing a comparative approach to the anthology that is at the same time cross-cultural, cross-historical, cross-genre and accounting for both pre- and post-digital practices for cultural content organization

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care