10 research outputs found
Leveraging Semantic Annotations for Event-focused Search & Summarization
Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.Im heutigen Big Data Zeitalters existieren überwältigende Mengen an Textinformationen, die über mehrere Quellen verteilt sind und ein hohes Maß an Redundanz haben. Durch diese Gegebenheiten ist eine Retroperspektive auf vergangene Ereignisse für Konsumenten nur schwer möglich. Eine plausible Lösung ist die Verknüpfung semantisch ähnlicher, aber über mehrere Quellen verteilter Informationen, um dadurch eine Struktur zu erzwingen, die mehrere Zugriffspfade auf relevante Informationen, bietet. Vor diesem Hintergrund benutzt diese Dissertation Wikipedia und Onlinenachrichten als zwei prominente, aber dennoch grundverschiedene Informationsquellen, um die folgenden drei Probleme anzusprechen: • Wir adressieren ein Verknüpfungsproblem, um Wikipedia-Auszüge mit Nachrichtenartikeln zu verbinden und das Problem in eine Information-Retrieval-Aufgabe umzuwandeln. Unser neuartiger Ansatz integriert Zeit- und Geobezüge sowie Entitäten mit Text, um relevante Dokumente, die mit einem gegebenen Auszug verknüpft werden können, zu identifizieren. • Wir befassen uns mit einer unüberwachten Extraktionsmethode zur automatischen Zusammenfassung von Texten aus mehreren Dokumenten um Ereigniszusammenfassungen mit fester Länge zu generieren, was eine effiziente Aufnahme von Informationen aus großen Dokumentenmassen ermöglicht. Unser neuartiger Ansatz schlägt eine ganzzahlige lineare Optimierungslösung vor, die globale Inferenzen über Text, Zeit, Geolokationen und mit Ereignis-verbundenen Entitäten zieht. • Um den zeitlichen Fokus kurzer Ereignisbeschreibungen abzuschätzen, stellen wir einen semi-überwachten Ansatz vor, der die Redundanz innerhalb einer langzeitigen Dokumentensammlung ausnutzt, um genaue probabilistische Zeitmodelle abzuschätzen. Umfangreiche experimentelle Auswertungen zeigen die Wirksamkeit und Tragfähigkeit unserer vorgeschlagenen Ansätze zur Erreichung des größeren Ziels
On Feature Scaling of Recursive Feature Machines
In this technical report, we explore the behavior of Recursive Feature
Machines (RFMs), a type of novel kernel machine that recursively learns
features via the average gradient outer product, through a series of
experiments on regression datasets. When successively adding random noise
features to a dataset, we observe intriguing patterns in the Mean Squared Error
(MSE) curves with the test MSE exhibiting a decrease-increase-decrease pattern.
This behavior is consistent across different dataset sizes, noise parameters,
and target functions. Interestingly, the observed MSE curves show similarities
to the "double descent" phenomenon observed in deep neural networks, hinting at
new connection between RFMs and neural network behavior. This report lays the
groundwork for future research into this peculiar behavior
Load Balancing in Wireless Sensor Network using Divisible Load Theory
In this thesis, optimal load allocation strategies are proposed for a wireless sensor network which is connected in a star topology. The load considered here is of arbitrarily divisible kind, such that each fraction of the job can be distributed and assigned to any processor for computation purpose. Divisible Load Theory emphasizes on how to partition the load among a number of processors and links, such that the load is distributed optimally. Its objective is to partition the load in such a way so that the load can be distributed and processed in the shortest possible time. The existing strategies for both star and bus topologies are investigated. The performance of the suggested strategy is compared with the existing ones and it is found that it reduces the overall communication and processing time if allocation time is considered in the previous strategies
Overview of the INEX 2012 Linked Data Track
Abstract. This paper provides an overview of the Linked Data Track that was newly introduced to the set of INEX tracks in 2012.
Not Available
Not AvailableWith the increasing world’s population, higher demand for sustainable
food production so as to meet the requirement. It has increased tremendously due to
excessive use of agrochemicals. Since, the imbalanced application of agrochemicals
in agricultural field leads to soil and environmental degradation. Nowadays, thescientific community has shifted their focus on alternative eco-friendly management
approach. The plant growth-promoting rhizobacteria (PGPR) and mycorrhizae has
huge potential to substitute agrochemicals. These efficient eco-friendly microbes
have different plant growth-promoting (PGP) activities; hence PGPR and mycorrhizae
are gaining importance for restoring soil sustainability and agricultural
productivity. Application of these efficient microbes in the soil–plant–environment
system will be suitable strategies for improving the soil and crop productivity.Not Availabl