Archives for 2014

Today I read a paper titled “Improving Web Page Readability by Plain Language”

The abstract is:
In today’s world anybody who wants to access any information the first choice is to use the web because it is the only source to provide easy and instant access to information

However web readers face many hurdles from web which includes load of web pages, text size, finding related information, spelling and grammar etc

However understanding of web pages written in English language creates great problems for non native readers who have basic knowledge of English

In this paper, we propose a plain language for a local language (Urdu) using English alphabets for web pages in Pakistan

For this purpose we developed two websites, one with a normal English fonts and other in a local language text scheme using English alphabets

We also conducted a questionnaire from 40 different users with a different level of English language fluency in Pakistan to gain the evidence of the practicality of our approach

The result shows that the proposed plain language text scheme using English alphabets improved the reading comprehension for non native English speakers in Pakistan

Today I read a paper titled “The Role of Vertex Consistency in Sampling-based Algorithms for Optimal Motion Planning”

The abstract is:
Motion planning problems have been studied by both the robotics and the controls research communities for a long time, and many algorithms have been developed for their solution

Among them, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), and the Probabilistic Road Maps (PRMs) have become very popular recently, owing to their implementation simplicity and their advantages in handling high-dimensional problems

Although these algorithms work very well in practice, the quality of the computed solution is often not good, i.e., the solution can be far from the optimal one

A recent variation of RRT, namely the RRT* algorithm, bypasses this drawback of the traditional RRT algorithm, by ensuring asymptotic optimality as the number of samples tends to infinity

Nonetheless, the convergence rate to the optimal solution may still be slow

This paper presents a new incremental sampling-based motion planning algorithm based on Rapidly-exploring Random Graphs (RRG), denoted RRT# (RRT “sharp”) which also guarantees asymptotic optimality but, in addition, it also ensures that the constructed spanning tree of the geometric graph is consistent after each iteration

In consistent trees, the vertices which have the potential to be part of the optimal solution have the minimum cost-come-value

This implies that the best possible solution is readily computed if there are some vertices in the current graph that are already in the goal region

Numerical results compare with the RRT* algorithm

Today I read a paper titled “Visual Tracking with Similarity Matching Ratio”

The abstract is:
This paper presents a novel approach to visual tracking: Similarity Matching Ratio (SMR)

The traditional approach of tracking is minimizing some measures of the difference between the template and a patch from the frame

This approach is vulnerable to outliers and drastic appearance changes and an extensive study is focusing on making the approach more tolerant to them

However, this often results in longer, corrective algo- rithms which do not solve the original problem

This paper proposes a novel approach to the definition of the tracking problems, SMR, which turns the differences into a probability measure

Only pixel differences below a threshold count towards deciding the match, the rest are ignored

This approach makes the SMR tracker robust to outliers and points that dramaticaly change appearance

The SMR tracker is tested on challenging video sequences and achieved state-of-the-art performance

Today I read a paper titled “Impact of Gender and Age on performing Search Tasks Online”

The abstract is:
More and more people use the Internet to work on duties of their daily work routine

To find the right information online, Web search engines are the tools of their choice

Apart from finding facts, people use Web search engines to also execute rather complex and time consuming search tasks

So far search engines follow the one-for-all approach to serve its users and little is known about the impact of gender and age on people’s Web search behavior

In this article we present a study that examines (1) how female and male web users carry out simple and complex search tasks and what are the differences between the two user groups, and (2) how the age of the users impacts their search performance

The laboratory study was done with 56 ordinary people each carrying out 12 search tasks

Our findings confirm that age impacts behavior and search performance significantly, while gender influences were smaller than expected

Today I read a paper titled “A List of Household Objects for Robotic Retrieval Prioritized by People with ALS (Version 092008)”

The abstract is:
This technical report is designed to serve as a citable reference for the original prioritized object list that the Healthcare Robotics Lab at Georgia Tech released on its website in September of 2008

It is also expected to serve as the primary citable reference for the research associated with this list until the publication of a detailed, peer-reviewed paper

The original prioritized list of object classes resulted from a needs assessment involving 8 motor-impaired patients with amyotrophic lateral sclerosis (ALS) and targeted, in-person interviews of 15 motor-impaired ALS patients

All of these participants were drawn from the Emory ALS Center

The prioritized object list consists of 43 object classes ranked by how important the participants considered each class to be for retrieval by an assistive robot

We intend for this list to be used by researchers to inform the design and benchmarking of robotic systems, especially research related to autonomous mobile manipulation

Today I read a paper titled “Relativistic virtual worlds: an emerging framework”

The abstract is:
In this paper, I will attempt to establish a framework for representation in virtual worlds that may allow for input data from many different scales and virtual physics to be merged

For example, a typical virtual environment must effectively handle user input, sensor data, and virtual world physics all in real- time

Merging all of these data into a single interactive system requires that we adapt approaches from topological methods such as n-dimensional relativistic representation

A number of hypothetical examples will be provided throughout the paper to clarify technical challenges that need to be overcome to realize this vision

The long-term goal of this work is that truly invariant representations will ultimately result from establishing formal, inclusive relationships between these different domains

Using this framework, incomplete information in one or more domains can be compensated for by parallelism and mappings within the virtual world representation

To introduce this approach, I will review recent developments in embodiment, virtual world technology, and neuroscience relevant to the control of virtual worlds

The next step will be to borrow ideas from fields such as brain science, applied mathematics, and cosmology to give proper perspective to this approach

A simple demonstration will then be given using an intuitive example of physical relativism

Finally, future directions for the application of this method will be considered

Today I read a paper titled “Image Retargeting by Content-Aware Synthesis”

The abstract is:
Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them

In this paper, we design a new framework based on content-aware synthesis to enhance content-aware image retargeting

By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped

This method enables the manipulation of textural & non-textural regions with different strategy since they have different natures

We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators

To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions

We adjust the saliency of the image according to the features of the textural regions

To validate the proposed method, comparisons with state-of-the-art image targeting techniques and a user study were conducted

Convincing visual results are shown to demonstrate the effectiveness of the proposed method

Today I read a paper titled “Complex Eigenvalues for Binary Subdivision Schemes”

The abstract is:
Convergence properties of binary stationary subdivision schemes for curves have been analyzed using the techniques of z-transforms and eigenanalysis

Eigenanalysis provides a way to determine derivative continuity at specific points based on the eigenvalues of a finite matrix

None of the well-known subdivision schemes for curves have complex eigenvalues

We prove when a convergent scheme with palindromic mask can have complex eigenvalues and that a lower limit for the size of the mask exists in this case

We find a scheme with complex eigenvalues achieving this lower bound

Furthermore we investigate this scheme numerically and explain from a geometric viewpoint why such a scheme has not yet been used in computer-aided geometric design

Today I read a paper titled “Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization”

The abstract is:
As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications

The success of sparse representation owes to the development of l1-norm optimization techniques, and the fact that natural images are intrinsically sparse in some domain

The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image

Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a pre-collected dataset of example image patches, and then for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain

We further introduce two adaptive regularization terms into the sparse representation framework

First, a set of autoregressive (AR) models are learned from the dataset of example image patches

The best fitted AR models to a given patch are adaptively selected to regularize the image local structures

Second, the image non-local self-similarity is introduced as another regularization term

In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance

Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception

Today I read a paper titled “How to build a DNA search engine like Google?”

The abstract is:
This paper proposed a new method to build the large scale DNA sequences search system based on web search engine technology

We give a very brief introduction for the methods used in search engine first

Then how to build a DNA search system like Google is illustrated in detail

Since there is no local alignment process, this system is able to provide the ms level search services for billions of DNA sequences in a typical server