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Capturing “attrition intensifying” structural traits from didactic interaction sequences of MOOC learners

Related publications (32)

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Scale-up Graph Processing in the Cloud: Challenges and Solutions

Willy Zwaenepoel, Amitabha Roy, Jasmina Malicevic

Processing large graphs is an important part of the big-data problem. Recently a number of scale-up systems such as X-Stream, Graphchi and Turbograph have been proposed for processing large graphs using secondary storage on a single machine. The design and ...

Privacy and Dynamics of Social Networks

Pedram Pedarsani

Over the past decade, investigations in different fields have focused on studying and understanding real networks, ranging from biological to social to technological. These networks, called complex networks, exhibit common topological features, such as a h ...

EPFL2013

Multiview image coding using graph-based approach

Pascal Frossard, Thomas Maugey

In this paper, we design a new approach for coding the geometry information in a multiview image scenario. As an alternative to depth-based schemes, we propose a representation that captures the dependencies between pixels in different frames in the form o ...

2013

Privacy and Dynamics of Social Networks (PhD Thesis: pre-print)

Pedram Pedarsani

2012

Graph-Based Information Processing

Amin Karbasi

We live in a world characterized by massive information transfer and real-time communication. The demand for efficient yet low-complexity algorithms is widespread across different fields, including machine learning, signal processing and communications. Mo ...

EPFL2012

Information Processing and Structure of Dynamical Networks

Ali Ajdari Rad

Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...

EPFL2011

Beyond Graphs: A New Synthesis

Dario Floreano, Claudio Mattiussi, Daniel Marbach, Peter Dürr

Artiﬁcial neural networks, electronic circuits, and gene networks are some examples of systems that can be modeled as networks, that is, as collections of interconnected nodes. In this paper we introduce the concept of terminal graph (t-graph for short), w ...

2011

Omnidirectional Object Duplicate Detection

Touradj Ebrahimi, Lutz Goldmann, Ivan Ivanov, Péter Vajda

In this paper, we extend a graph-based approach for omnidirectional object duplicate detection in still images. Objects are detected from several points of view with different distances. The goal of this work is to determine how many training images have t ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Automatic skill acquisition in Reinforcement Learning using connection graph stability centrality

Martin Hasler, Ali Ajdari Rad

Reinforcement Learning (RL) is an approach for training agent's behavior through trial-and-error interactions with a dynamic environment. An important problem of RL is that in large domains an enormous number of decisions are to be made. Hence, instead of ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Robust Duplicate Detection of 2D and 3D Objects

Touradj Ebrahimi, Jong Seok Lee, Lutz Goldmann, Ivan Ivanov, Péter Vajda

In this paper, we analyze our graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images so that an explici ...

2010