MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

A systematic literature review of blockchain-based applications: Current status, classification and open issues

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Introduction To Electrodynamics

The limits of trust-free systems : A literature review on blockchain technology and trust in the sharing economy

Overview of the MPEG-4 Standard and Error Resilience Investigations

Responding to natural or man-made disasters in a timely and effective manner can reduce deaths and injuries, contain or prevent secondary disasters, and reduce the resulting economic losses and social disruption. Appropriate IT solutions can improve this response. However, exhaustive and realistic validation of these IT solutions is difficult; proofs are not available, simulations lack realism, and drills are expensive and cannot be reproduced. This paper presents DrillSim: a simulation environment that plays out the activities of a crisis response (e.g., evacuation). It has capabilities to integrate real-life drills into a simulated response activity using an instrumented environment with sensing and communication capabilities. IT solutions can be plugged in the simulation system to study their effectiveness in disaster management and response. This way, by using a simulation coupled with an on-going drill, IT solutions can be tested in a less expensive but realistic scenario.

/pdf/drillsim-a-simulation-framework-for-emergency-response-3fnlgdu9wc.pdf

DrillSim: a simulation framework for emergency response drills

Information Technology has the potential of improving the quality and the amount of information humans receive during emergency response. Testing this technology in realistic and ∞exible environments is a non-trivial task. DrillSim is an augmented reality simulation environment for testing IT solutions. It provides an environment where scientists and developers can bring their IT solutions and test their efiectiveness on the context of disaster response. The architecture of DrillSim is based on a multi-agent simulation. The simulation of the disaster response activity is achieved by modeling each person involved as an agent. This flner granularity provides extensibility to the system since new scenarios can be deflned by deflning new agents. This paper presents the architecture of DrillSim and explains in detail how DrillSim deals with the edition and addition of agent roles.

Multi-Agent Simulation of Disaster Response

A Comparative Study of Vision Based Human Detection Techniques in People Counting Applications

A paper-based colorimetric assay for the determination of bilirubin has been developed. The method is based on the in-situ reduction of chloroauric acid to form gold nanoparticles. A chromatographic paper was patterned using a wax printer. Chloroauric acid was drop-cast onto the reagent zone. In the presence of bilirubin, gold(III) ions are reduced and form gold nanoparticles. This leads to a color change from yellow to purple. The intensity of the purple color (peak at 530 nm) increases with bilirubin concentration in the 5.0 to 1000 mg L−1 range. The detection limit is 1.0 mg L−1. For the quantification of bilirubin, images were captured using a digital camera, and data were processed with the help of machine learning-based supervised prediction using Random Forest classification. The method was applied to the determination of bilirubin in urine samples. The spiked urine samples exhibit more than 95% recovery.

Paper-based device for the colorimetric assay of bilirubin based on in-situ formation of gold nanoparticles

Keyphrases are essential meta-data that summarize the contents of an instructional video. In this paper, we present a domain independent, statistical approach for automatic keyphrase extraction from audio transcripts of video lectures. We identify new features in audio transcripts, that capture key patterns characterizing keyphrases in lecture videos. A system for keyphrase extraction is designed that uses a supervised machine learning algorithm, based on a Naive-Bayes classifier to extract relevant keyphrases. Our extensive experimental studies show that our system extracts more relevant keywords than existing approaches. The paper also evaluates the performance of the proposed keyphrase extraction method for different categories of lectures. The extracted keyphrases are used further as features for automatic topic based segmentation of the video lectures. This process of automatic keyphrase extraction and segmentation results in a section-wise annotated video lecture which can be effectively viewed in a lecture browser.

Vidhya Balasubramanian

Papers

DrillSim: a simulation framework for emergency response drills

Multi-Agent Simulation of Disaster Response

A Comparative Study of Vision Based Human Detection Techniques in People Counting Applications

Paper-based device for the colorimetric assay of bilirubin based on in-situ formation of gold nanoparticles

Automatic keyphrase extraction and segmentation of video lectures