A web assessment approach based on summarisation and visualisation

The number of Web sites has noticeably increased to roughly 224 million in last ten years. This means there is a rapid growth of information on the Internet. Although search engines can help users to filter their desired information, the searched result is normally presented in the form of a very long list, and users have to visit each Web page in order to determine the appropriateness of the result. This leads to a considerable amount of time has to be spent on finding the required information. To address this issue, this paper proposes a Web assessment approach in order to provide an overview of the information on a Website using an integration of existing summarisation and visualisation techniques, which are text summarisation, tag cloud, Document Type View, and interactive features. This approach is capable to reduce the time required to identify and search for information from the Web

Adaptation des images et des vidéos pour des utilisateurs multiples dans des environnements hétérogènes

La dernière décennie a connu l'émergence de l'utilisation des équipements mobiles comme les assistants personnels et les téléphones, ainsi que la prolifération des réseaux personnels favorisée par le développement considérable dans les technologies de communications. D'autre part, l'information véhiculée a travers le World Wide Web devient de plus en plus visuelle (images et videos) grâce à la numérisation. Afin de permettre à tous les usagers un accès universel à cette information visuelle dans un environnement caractérisé par la diversité des équipements et l'hétérogénéité des réseaux, il devient nécessaire d'adapter les documents multimédia. L'adaptation consiste à appliquer une ou plusieurs transformations sur un document multimédia. Dans ce cadre, plusieurs travaux ont été élaborés en partant de différentes formulations. Nous pensons qu'un système d'adaptation efficace doit choisir les traitements nécessaires à appliquer sur un document visuel afin de maximiser la satisfaction de l'usager. Il doit considérer conjointement les caractéristiques de cet usager ainsi que les performances de son équipement, la qualité de sa connexion et les conditions de son environnement. La majorité des travaux réalisés dans ce domaine n'ont traité que des cas limités, par exemple ajuster une vidéo pour la capacité d'un réseau donné. Dans la présente recherche, nous proposons une solution globale obtenue à l'aide d'un modèle probabiliste qui utilise les traitements des images et des vidéos et l'extraction des caractéristiques des contenus

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Can we Pretrain a SotA Legal Language Model on a Budget From Scratch?

Even though many efficient transformers have been proposed, only few such models are available for specialized domains. Additionally, since the pretraining process is extremely costly in general – but even more so as the sequence length increases – it is often only in reach of large research labs. One way of making pretraining cheaper is the Replaced Token Detection (RTD) task, by providing more signal during training compared to MLM, since the loss can be computed over all tokens. In this work, we train Longformer models with the efficient RTD task on long-context legal data to showcase that pretraining efficient LMs is possibl using less than 12 GPU days. We evaluate the trained models on challenging summarization tasks requiring the model to summarize complex long texts. We find that both the small and base models outperform their baselines on the in-domain BillSum and out-of-domain PubMed tasks in their respective parameter range. We publish our models as a resource for researcher and practitioners

Linked Data Entity Summarization

On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion

Holistic recommender systems for software engineering

The knowledge possessed by developers is often not sufficient to overcome a programming problem. Short of talking to teammates, when available, developers often gather additional knowledge from development artifacts (e.g., project documentation), as well as online resources. The web has become an essential component in the modern developer’s daily life, providing a plethora of information from sources like forums, tutorials, Q&A websites, API documentation, and even video tutorials. Recommender Systems for Software Engineering (RSSE) provide developers with assistance to navigate the information space, automatically suggest useful items, and reduce the time required to locate the needed information. Current RSSEs consider development artifacts as containers of homogeneous information in form of pure text. However, text is a means to represent heterogeneous information provided by, for example, natural language, source code, interchange formats (e.g., XML, JSON), and stack traces. Interpreting the information from a pure textual point of view misses the intrinsic heterogeneity of the artifacts, thus leading to a reductionist approach. We propose the concept of Holistic Recommender Systems for Software Engineering (H-RSSE), i.e., RSSEs that go beyond the textual interpretation of the information contained in development artifacts. Our thesis is that modeling and aggregating information in a holistic fashion enables novel and advanced analyses of development artifacts. To validate our thesis we developed a framework to extract, model and analyze information contained in development artifacts in a reusable meta- information model. We show how RSSEs benefit from a meta-information model, since it enables customized and novel analyses built on top of our framework. The information can be thus reinterpreted from an holistic point of view, preserving its multi-dimensionality, and opening the path towards the concept of holistic recommender systems for software engineering