Leveraging big visual data to predict severe weather

NEWS SOURCEs [www.sciencedaily.com/releases/2017/06/170621145133.htm www.eurekalert.org/pub_releases/2017-06/ps-nir062117.php phys.org/news/2017-06-leverages-big-severe-weather.html sciencenewsline.com/news/2017062217300053.html] Every year, severe weather endangers millions of people and causes billions of dollars in damage worldwide. But new research from Penn State’s College of Information Sciences and Technology (IST) and AccuWeather has found a way to better predict some of these threats by harnessing the power of big data. […]

Discovering Triangles in Portraits and Landscapes

ABSTRACT Incorporating the concept of triangles in photos is an effective composition method used by professional photographers for making pictures more interesting or dynamic. Information on the locations of the embedded triangles is valuable for comparing the composition of portrait photos, which can be further leveraged by a retrieval system or used by photographers. This […]

Skeleton Matching for Severe Weather Detection

Title: Skeleton Matching with Applications in Severe Weather Detection Authors: Mohammad Mahdi Kamani, Farshid Farhat, Stephen Wistar and James Z. Wang. Elsevier Journal: Applied Soft Computing, ~27 pages, May 2017. Abstract: Severe weather conditions cause an enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of […]

Shape matching for automated bow echo detection

Abstract: Severe weather conditions cause enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive thunderstorm conditions such as damaging winds, hail and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these patterns with high […]

Towards Stochastically Optimizing Data Computing Flows

Abstract: With rapid growth in the amount of unstructured data produced by memory-intensive applications, large scale data analytics has recently attracted increasing interest. Processing, managing and analyzing this huge amount of data poses several challenges in cloud and data center computing domain. Especially, conventional frameworks for distributed data analytics are based on the assumption of […]

Blind detection of low-rate embedding

Abstract: Steganalysis of least significant bit (LSB) embedded images in spatial domain has been investigated extensively over the past decade and most well-known LSB steganography methods have been shown to be detectable. However, according to the latest findings in the area, two major issues of very low-rate (VLR) embedding and content-adaptive steganography have remained hard […]

Image Steganalysis of Low Rate Embedding in Spatial Domain

Abstract LSB embedding in spatial domain with very low rate can be easily performed and its detection in spite of many researches is very hard, while BOSS competition has been held to break an adaptive embedding algorithm with low rate. Thus, proposing powerful steganalyzer of very low rate in spatial domain is highly requested. In […]

Game-theoretic model to mitigate packet dropping

Abstract: Performance of routing is severely degraded when misbehaving nodes drop packets instead of properly forwarding them. In this paper, we propose a Game-Theoretic Adaptive Multipath Routing (GTAMR) protocol to detect and punish selfish or malicious nodes which try to drop information packets in routing phase and defend against collaborative attacks in which nodes try […]

Secure Multipath Adaptive Routing Protocol

Abstract Locally multipath adaptive routing (LMAR) protocol, classified as a new reactive distance vector routing protocol for MANETs is proposed in this paper. LMAR can find an ad-hoc path without selfish nodes and wormholes using a random search algorithm in polynomial-time. Also when the primary path fails, it discovers an alternative safe path if network […]