SocialSensor: sensing user generated input for improved media discovery and experience

SocialSensor will develop a new framework for enabling real-time multimedia indexing and search in the Social Web. The project moves beyond conventional text-based indexing and retrieval models by mining and aggregating user inputs and content over multiple social networking sites. Social Indexing will incorporate information about the structure and activity of the users‟ social network directly into the multimedia analysis and search process. Furthermore, it will enhance the multimedia consumption experience by developing novel user-centric media visualization and browsing paradigms. For example, SocialSensor will analyse the dynamic and massive user contributions in order to extract unbiased trending topics and events and will use social connections for improved recommendations. To achieve its objectives, SocialSensor introduces the concept of Dynamic Social COntainers (DySCOs), a new layer of online multimedia content organisation with particular emphasis on the real-time, social and contextual nature of content and information consumption. Through the proposed DySCOs-centered media search, SocialSensor will integrate social content mining, search and intelligent presentation in a personalized, context and network-aware way, based on aggregation and indexing of both UGC and multimedia Web content

Bi-directional representation learning for multi-label classification

Multi-label classification is a central problem in many application domains. In this paper, we present a novel supervised bi-directional model that learns a low-dimensional mid-level representation for multi-label classification. Unlike traditional multi-label learning methods which identify intermediate representations from either the input space or the output space but not both, the mid-level representation in our model has two complementary parts that capture intrinsic information of the input data and the output labels respectively under the autoencoder principle while augmenting each other for the target output label prediction. The resulting optimization problem can be solved efficiently using an iterative procedure with alternating steps, while closed-form solutions exist for one major step. Our experiments conducted on a variety of multi-label data sets demonstrate the efficacy of the proposed bi-directional representation learning model for multi-label classification

A System for Multi-label Classification of Learning Objects

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of Learning Object (LO), which provides ubiquitous access to multiple and distributed educational resources in many repositories. This article presents a system that enables the recovery and classification of LO and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives. For this classification, it is used a special multi-label data mining designed for the LO ranking tasks. According to each position, the system is responsible for presenting the results to the end user. The learning process is supervised, using two major tasks in supervised learning from multi-label data: multi-label classification and label ranking

Spare parts criticality for unplanned maintenance of industrial systems

The paper presents a methodology and Decision Support System (DSS) for the establishment of spare parts criticality with a focus on industrial unplanned maintenance needs. The obtained criticality is used to rationalise the efficiency of the plant spare parts inventory. Through a top-down Failure Modes Effects and Criticality Analysis (FMECA) that is appropriately adapted to the unplanned maintenance requirements and through the introduction of the Component Dynamic Criticality concept, the components of an industrial production facility are ranked. For those with criticality lying above a calculated threshold, additional spares are suggested to be kept in the plant spare parts inventory. An application example demonstrates the method. [Received 14 May 2007; Revised 25 July 2007; Accepted 13 September 2007]spare parts inventory; unplanned maintenance; spare parts criticality; criticality analysis; production downtime; industrial systems; decision support systems; DSS; failure modes effects and criticality analysis; FMECA; component dynamic criticality.