30,623 research outputs found
Full Reference Objective Quality Assessment for Reconstructed Background Images
With an increased interest in applications that require a clean background
image, such as video surveillance, object tracking, street view imaging and
location-based services on web-based maps, multiple algorithms have been
developed to reconstruct a background image from cluttered scenes.
Traditionally, statistical measures and existing image quality techniques have
been applied for evaluating the quality of the reconstructed background images.
Though these quality assessment methods have been widely used in the past,
their performance in evaluating the perceived quality of the reconstructed
background image has not been verified. In this work, we discuss the
shortcomings in existing metrics and propose a full reference Reconstructed
Background image Quality Index (RBQI) that combines color and structural
information at multiple scales using a probability summation model to predict
the perceived quality in the reconstructed background image given a reference
image. To compare the performance of the proposed quality index with existing
image quality assessment measures, we construct two different datasets
consisting of reconstructed background images and corresponding subjective
scores. The quality assessment measures are evaluated by correlating their
objective scores with human subjective ratings. The correlation results show
that the proposed RBQI outperforms all the existing approaches. Additionally,
the constructed datasets and the corresponding subjective scores provide a
benchmark to evaluate the performance of future metrics that are developed to
evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated
Database:
https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing
(Email for permissions at: ashrotreasuedu
On the Interaction between TCP and the Wireless Channel in CDMA2000 Networks
In this work, we conducted extensive active measurements on a large nationwide CDMA2000 1xRTT network in order to characterize the impact of both the Radio Link Protocol and more importantly, the wireless scheduler, on TCP. Our measurements include standard TCP/UDP logs, as well as detailed RF layer statistics that allow observability into RF dynamics. With the help of a robust correlation measure, normalized mutual information, we were able to quantify the impact of these two RF factors on TCP performance metrics such as the round trip time, packet loss rate, instantaneous throughput etc. We show that the variable channel rate has the larger impact on TCP behavior when compared to the Radio Link Protocol. Furthermore, we expose and rank the factors that influence the assigned channel rate itself and in particular, demonstrate the sensitivity of the wireless scheduler to the data sending rate. Thus, TCP is adapting its rate to match the available network capacity, while the rate allocated by the wireless scheduler is influenced by the sender's behavior. Such a system is best described as a closed loop system with two feedback controllers, the TCP controller and the wireless scheduler, each one affecting the other's decisions. In this work, we take the first steps in characterizing such a system in a realistic environment
Organic Food Quality: Can it exist without Measurement?
Abstract - Consumers who purchase organic foods are motivated primarily by the health benefits. What assurances are there that defining organic products based on inputs, methods, and ingredients will meet or exceed consumer expectations if there are no re-quirements to define, measure, and meet product quality standards? The future of the organic industry will depend on understanding and quantifying con-sumer expectations and then defining metrics for assessing organic product quality that satisfies these expectations. After establishing quantifiable product quality targets, the next challenge will be to correlate farming and processing methods so that the product quality criteria and consumer expectations are satis-fied
Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses
Massive Open Online Courses (MOOCs) offer a new scalable paradigm for
e-learning by providing students with global exposure and opportunities for
connecting and interacting with millions of people all around the world. Very
often, students work as teams to effectively accomplish course related tasks.
However, due to lack of face to face interaction, it becomes difficult for MOOC
students to collaborate. Additionally, the instructor also faces challenges in
manually organizing students into teams because students flock to these MOOCs
in huge numbers. Thus, the proposed research is aimed at developing a robust
methodology for dynamic team formation in MOOCs, the theoretical framework for
which is grounded at the confluence of organizational team theory, social
network analysis and machine learning. A prerequisite for such an undertaking
is that we understand the fact that, each and every informal tie established
among students offers the opportunities to influence and be influenced.
Therefore, we aim to extract value from the inherent connectedness of students
in the MOOC. These connections carry with them radical implications for the way
students understand each other in the networked learning community. Our
approach will enable course instructors to automatically group students in
teams that have fairly balanced social connections with their peers, well
defined in terms of appropriately selected qualitative and quantitative network
metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of
Digital Information & Web Technologies (ICADIWT), India, February 2014 (6
pages, 3 figures
Bio-inspired call-stack reconstruction for performance analysis
The correlation of performance bottlenecks and their associated source code has become a cornerstone of performance analysis. It allows understanding why the efficiency of an application falls behind the computer's peak performance and enabling optimizations on the code ultimately. To this end, performance analysis tools collect the processor call-stack and then combine this information with measurements to allow the analyst comprehend the application behavior. Some tools modify the call-stack during run-time to diminish the collection expense but at the cost of resulting in non-portable solutions. In this paper, we present a novel portable approach to associate performance issues with their source code counterpart. To address it, we capture a reduced segment of the call-stack (up to three levels) and then process the segments using an algorithm inspired by multi-sequence alignment techniques. The results of our approach are easily mapped to detailed performance views, enabling the analyst to unveil the application behavior and its corresponding region of code. To demonstrate the usefulness of our approach, we have applied the algorithm to several first-time seen in-production applications to describe them finely, and optimize them by using tiny modifications based on the analyses.We thankfully acknowledge Mathis Bode for giving us access to the Arts CF binaries, and Miguel Castrillo and Kim Serradell for their valuable insight regarding Nemo. We would like to thank Forschungszentrum Jülich for the computation time on their Blue Gene/Q system. This research has been partially funded by the CICYT under contracts No. TIN2012-34557 and TIN2015-65316-P.Peer ReviewedPostprint (author's final draft
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