OpenRaiser/Intern-Atlas huggingface dataset:
Learning unbiased features
Venue: arXiv cs.LG 2014
Year: 2014

A key element in transfer learning is representation learning; if representations can be developed that expose the relevant factors underlying the data, then new tasks and domains can be learned readily based on mappings of these salient factors. We propose that an important aim for these representations are to be unbiased. Different forms of representation learning can be derived from alternative definitions of unwanted bias, e.g., bias to particular tasks, domains, or irrelevant underlying data dimensions. One very useful approach to estimating the amount of bias in a representation comes from maximum mean discrepancy (MMD) [5], a measure of distance between probability distributions. We are not the first to suggest that MMD can be a useful criterion in developing representations that apply across multiple domains or tasks [1]. However, in this paper we describe a number of novel applications of this criterion that we have devised, all based on the idea of developing unbiased representations. These formulations include: a standard domain adaptation framework; a method of learning invariant representations; an approach based on noise-insensitive autoencoders; and a novel form of generative model.
Iranian cashes recognition using mobile
Venue: arXiv cs.CV 2014
Year: 2014

In economical societies of today, using cash is an inseparable aspect of human life. People use cashes for marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and the necessity of knowing the monetary value of it caused one of the most challenging problems for visually impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a picture taken from cashes using some image processing and machine vision techniques. While the developed approach is very fast, it can recognize the value of cash by average accuracy of about 95% and can overcome different challenges like rotation, scaling, collision, illumination changes, perspective, and some others.
Gene Similarity-based Approaches for Determining Core-Genes of Chloroplasts
Venue: arXiv cs.NE 2014
Year: 2014

In computational biology and bioinformatics, the manner to understand evolution processes within various related organisms paid a lot of attention these last decades. However, accurate methodologies are still needed to discover genes content evolution. In a previous work, two novel approaches based on sequence similarities and genes features have been proposed. More precisely, we proposed to use genes names, sequence similarities, or both, insured either from NCBI or from DOGMA annotation tools. Dogma has the advantage to be an up-to-date accurate automatic tool specifically designed for chloroplasts, whereas NCBI possesses high quality human curated genes (together with wrongly annotated ones). The key idea of the former proposal was to take the best from these two tools. However, the first proposal was limited by name variations and spelling errors on the NCBI side, leading to core trees of low quality. In this paper, these flaws are fixed by improving the comparison of NCBI and DOGMA results, and by relaxing constraints on gene names while adding a stage of post-validation on gene sequences. The two stages of similarity measures, on names and sequences, are thus proposed for sequence clustering. This improves results that can be obtained using either NCBI or DOGMA alone. Results obtained with this quality control test are further investigated and compared with previously released ones, on both computational and biological aspects, considering a set of 99 chloroplastic genomes.
The Affine Transforms for Image Enhancement in the Context of Logarithmic Models
Venue: arXiv cs.CV 2014
Year: 2014

The logarithmic model offers new tools for image processing. An efficient method for image enhancement is to use an affine transformation with the logarithmic operations: addition and scalar multiplication. We define some criteria for automatically determining the parameters of the processing and this is done via mean and variance computed by logarithmic operations.
Solving Totally Unimodular LPs with the Shadow Vertex Algorithm
Venue: arXiv cs.DS 2014
Year: 2014

We show that the shadow vertex simplex algorithm can be used to solve linear programs in strongly polynomial time with respect to the number $n$ of variables, the number $m$ of constraints, and $1/δ$, where $δ$ is a parameter that measures the flatness of the vertices of the polyhedron. This extends our recent result that the shadow vertex algorithm finds paths of polynomial length (w.r.t. $n$, $m$, and $1/δ$) between two given vertices of a polyhedron. Our result also complements a recent result due to Eisenbrand and Vempala who have shown that a certain version of the random edge pivot rule solves linear programs with a running time that is strongly polynomial in the number of variables $n$ and $1/δ$, but independent of the number $m$ of constraints. Even though the running time of our algorithm depends on $m$, it is significantly faster for the important special case of totally unimodular linear programs, for which $1/δ\le n$ and which have only $O(n^2)$ constraints.
Buffer Overflow Analysis for C
Venue: arXiv cs.PL 2014
Year: 2014

Buffer overflow detection and mitigation for C programs has been an important concern for a long time. This paper defines a string buffer overflow analysis for C programs. The key ideas of our formulation are (a) separating buffers from the pointers that point to them, (b) modelling buffers in terms of sizes and sets of positions of null characters, and (c) defining stateless functions to compute the sets of null positions and mappings between buffers and pointers. This exercise has been carried out to test the feasibility of describing such an analysis in terms of lattice valued functions and relations to facilitate automatic construction of an analyser without the user having to write C/C++/Java code. This is facilitated by devising stateless formulations because stateful formulations combine features through side effects in states raising a natural requirement of C/C++/Java code to be written to describe them. Given the above motivation, the focus of this paper is not to build good static approximations for buffer overflow analysis but to show how given static approximations could be formalized in terms of stateless formulations so that they become amenable to automatic construction of analysers.
High Frequency Content based Stimulus for Perceptual Sharpness Assessment in Natural Images
Venue: arXiv cs.CV 2014
Year: 2014

A blind approach to evaluate the perceptual sharpness present in a natural image is proposed. Though the literature demonstrates a set of variegated visual cues to detect or evaluate the absence or presence of sharpness, we emphasize in the current work that high frequency content and local standard deviation can form strong features to compute perceived sharpness in any natural image, and can be considered an able alternative for the existing cues. Unsharp areas in a natural image happen to exhibit uniform intensity or lack of sharp changes between regions. Sharp region transitions in an image are caused by the presence of spatial high frequency content. Therefore, in the proposed approach, we hypothesize that using the high frequency content as the principal stimulus, the perceived sharpness can be quantified in an image. When an image is convolved with a high pass filter, higher values at any pixel location signify the presence of high frequency content at those locations. Considering these values as the stimulus, the exponent of the stimulus is weighted by local standard deviation to impart the contribution of the local contrast within the formation of the sharpness map. The sharpness map highlights the relatively sharper regions in the image and is used to calculate the perceived sharpness score of the image. The advantages of the proposed method lie in its use of simple visual cues of high frequency content and local contrast to arrive at the perceptual score, and requiring no training with the images. The promise of the proposed method is demonstrated by its ability to compute perceived sharpness for within image and across image sharpness changes and for blind evaluation of perceptual degradation resulting due to presence of blur. Experiments conducted on several databases demonstrate improved performance of the proposed method over that of the state-of-the-art techniques.
Kickstarting High-performance Energy-efficient Manycore Architectures with Epiphany
Venue: arXiv cs.AR 2014
Year: 2014

In this paper we introduce Epiphany as a high-performance energy-efficient manycore architecture suitable for real-time embedded systems. This scalable architecture supports floating point operations in hardware and achieves 50 GFLOPS/W in 28 nm technology, making it suitable for high performance streaming applications like radio base stations and radar signal processing. Through an efficient 2D mesh Network-on-Chip and a distributed shared memory model, the architecture is scalable to thousands of cores on a single chip. An Epiphany-based open source computer named Parallella was launched in 2012 through Kickstarter crowd funding and has now shipped to thousands of customers around the world.
Statistical Modeling and Performance Characterization of an Ultrafast Digital Lightwave Communication System Using a Power-Cubic Optical Nonlinear Preprocessor (Extended Version)
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we present an analytical approach in obtaining the probability density function (pdf) of the random decision variable Y, formed at the output of power-cubic all-optical nonlinear preprocessor followed by the photodetector. Our approach can be used to accurately evaluate the performance of ultrafast pulse detection in the presence of Gaussian noise. Through rigorous Monte-Carlo simulation, the accuracy of widely used Gaussian approximation of decision variable Y is refuted. However, in this paper we show that the so called Log-Pearson type-3 probability density function (LP3 pdf) is an excellent representation for the decision variable Y . Three distinguishable parameters of the LP3 pdf are obtained through analytical derivation of three moments of the decision variable Y . Furthermore, toward a more realistic model, in addition to ASE Gaussian noise, the effects of shot and thermal noises are also included. Finally, using the presented analytical approach, it is shown that power-cubic preprocessor outperforms its quadratic counterparts, i.e., Second Harmonic Generation (SHG) and Two Photon Absorption (TPA) devices, in high power regime where shot and thermal noises can be neglected.
Feature extraction from complex networks: A case of study in genomic sequences classification
Venue: arXiv cs.CE 2014
Year: 2014

This work presents a new approach for classification of genomic sequences from measurements of complex networks and information theory. For this, it is considered the nucleotides, dinucleotides and trinucleotides of a genomic sequence. For each of them, the entropy, sum entropy and maximum entropy values are calculated.For each of them is also generated a network, in which the nodes are the nucleotides, dinucleotides or trinucleotides and its edges are estimated by observing the respective adjacency among them in the genomic sequence. In this way, it is generated three networks, for which measures of complex networks are extracted.These measures together with measures of information theory comprise a feature vector representing a genomic sequence. Thus, the feature vector is used for classification by methods such as SVM, MultiLayer Perceptron, J48, IBK, Naive Bayes and Random Forest in order to evaluate the proposed approach.It was adopted coding sequences, intergenic sequences and TSS (Transcriptional Starter Sites) as datasets, for which the better results were obtained by the Random Forest with 91.2%, followed by J48 with 89.1% and SVM with 84.8% of accuracy. These results indicate that the new approach of feature extraction has its value, reaching good levels of classification even considering only the genomic sequences, i.e., no other a priori knowledge about them is considered.
FlexDM: Enabling robust and reliable parallel data mining using WEKA
Venue: arXiv cs.MS 2014
Year: 2014

Performing massive data mining experiments with multiple datasets and methods is a common task faced by most bioinformatics and computational biology laboratories. WEKA is a machine learning package designed to facilitate this task by providing tools that allow researchers to select from several classification methods and specific test strategies. Despite its popularity, the current WEKA environment for batch experiments, namely Experimenter, has four limitations that impact its usability: the selection of value ranges for methods options lacks flexibility and is not intuitive; there is no support for parallelisation when running large-scale data mining tasks; the XML schema is difficult to read, necessitating the use of the Experimenter's graphical user interface for generation and modification; and robustness is limited by the fact that results are not saved until the last test has concluded. FlexDM implements an interface to WEKA to run batch processing tasks in a simple and intuitive way. In a short and easy-to-understand XML file, one can define hundreds of tests to be performed on several datasets. FlexDM also allows those tests to be executed asynchronously in parallel to take advantage of multi-core processors, significantly increasing usability and productivity. Results are saved incrementally for better robustness and reliability. FlexDM is implemented in Java and runs on Windows, Linux and OSX. As we encourage other researchers to explore and adopt our software, FlexDM is made available as a pre-configured bootable reference environment. All code, supporting documentation and usage examples are also available for download at http://sourceforge.net/projects/flexdm.
Image Dynamic Range Enhancement in the Context of Logarithmic Models
Venue: arXiv cs.CV 2014
Year: 2014

Images of a scene observed under a variable illumination or with a variable optical aperture are not identical. Does a privileged representant exist? In which mathematical context? How to obtain it? The authors answer to such questions in the context of logarithmic models for images. After a short presentation of the model, the paper presents two image transforms: one performs an optimal enhancement of the dynamic range, and the other does the same for the mean dynamic range. Experimental results are shown.
Gray level image enhancement using the Bernstein polynomials
Venue: arXiv cs.CV 2014
Year: 2014

This paper presents a method for enhancing the gray level images. This presented method takes part from the category of point operations and it is based on piecewise linear functions. The interpolation nodes of these functions are calculated using the Bernstein polynomials.
Contour Detection Using Contrast Formulas in the Framework of Logarithmic Models
Venue: arXiv cs.CV 2014
Year: 2014

In this paper we use a new logarithmic model of image representation, developed in [1,2], for edge detection. In fact, in the framework of the new model we obtain the formulas for computing the "contrast of a pixel" and the "contrast" image is just the "contour" or edge image. In our setting the range of values is preserved and the quality of the contour is good for high as well as for low luminosity regions. We present the comparison of our results with the results using classical edge detection operators.
Identifying Codes of Degree 4 Cayley Graphs over Abelian Groups
Venue: arXiv cs.IT 2014
Year: 2014

In this paper a wide family of identifying codes over regular Cayley graphs of degree four which are built over finite Abelian groups is presented. Some of the codes in this construction are also perfect. The graphs considered include some well-known graphs such as tori, twisted tori and Kronecker products of two cycles. Therefore, the codes can be used for identification in these graphs. Finally, an example of how these codes can be applied for adaptive identification over these graphs is presented.
Replacing ANSI C with other modern programming languages
Venue: arXiv cs.CY 2014
Year: 2014

Replacing ANSI C language with other modern programming languages such as Python or Java may be an actual debate topic in technical universities. Researchers whose primary interests are not in programming area seem to prefer modern and higher level languages. Keeping standard language ANSI C as a primary tool for engineers and for microcontrollers programming, robotics and data acquisition courses is another strong different opinion trend. Function oriented versus object oriented languages may be another highlighted topic in actual debates.
On families of anticommuting matrices
Venue: arXiv cs.CC 2014
Year: 2014

Let $e_{1},\dots, e_{k}$ be complex $n\times n$ matrices such that $e_{i}e_{j}=-e_{j}e_{i}$ whenever $i\not=j$. We conjecture that $\hbox{rk}(e_{1}^{2})+\hbox{rk}(e_{2}^{2})+\cdots+\hbox{rk}(e_{k}^{2})\leq O(n\log n)$, and prove some results in this direction.
A Tutorial on Network Security: Attacks and Controls
Venue: arXiv cs.CR 2014
Year: 2014

With the phenomenal growth in the Internet, network security has become an integral part of computer and information security. In order to come up with measures that make networks more secure, it is important to learn about the vulnerabilities that could exist in a computer network and then have an understanding of the typical attacks that have been carried out in such networks. The first half of this paper will expose the readers to the classical network attacks that have exploited the typical vulnerabilities of computer networks in the past and solutions that have been adopted since then to prevent or reduce the chances of some of these attacks. The second half of the paper will expose the readers to the different network security controls including the network architecture, protocols, standards and software/ hardware tools that have been adopted in modern day computer networks.
Automatic Training Data Synthesis for Handwriting Recognition Using the Structural Crossing-Over Technique
Venue: arXiv cs.CV 2014
Year: 2014

The paper presents a novel technique called "Structural Crossing-Over" to synthesize qualified data for training machine learning-based handwriting recognition. The proposed technique can provide a greater variety of patterns of training data than the existing approaches such as elastic distortion and tangent-based affine transformation. A couple of training characters are chosen, then they are analyzed by their similar and different structures, and finally are crossed over to generate the new characters. The experiments are set to compare the performances of tangent-based affine transformation and the proposed approach in terms of the variety of generated characters and percent of recognition errors. The standard MNIST corpus including 60,000 training characters and 10,000 test characters is employed in the experiments. The proposed technique uses 1,000 characters to synthesize 60,000 characters, and then uses these data to train and test the benchmark handwriting recognition system that exploits Histogram of Gradient (HOG) as features and Support Vector Machine (SVM) as recognizer. The experimental result yields 8.06% of errors. It significantly outperforms the tangent-based affine transformation and the original MNIST training data, which are 11.74% and 16.55%, respectively.
Combinatorial Structure of the Deterministic Seriation Method with Multiple Subset Solutions
Venue: arXiv cs.AI 2014
Year: 2014

Seriation methods order a set of descriptions given some criterion (e.g., unimodality or minimum distance between similarity scores). Seriation is thus inherently a problem of finding the optimal solution among a set of permutations of objects. In this short technical note, we review the combinatorial structure of the classical seriation problem, which seeks a single solution out of a set of objects. We then extend those results to the iterative frequency seriation approach introduced by Lipo (1997), which finds optimal subsets of objects which each satisfy the unimodality criterion within each subset. The number of possible solutions across multiple solution subsets is larger than $n!$, which underscores the need to find new algorithms and heuristics to assist in the deterministic frequency seriation problem.
Very Low Cost Entropy Source Based on Chaotic Dynamics Retrofittable on Networked Devices to Prevent RNG Attacks
Venue: arXiv cs.CR 2014
Year: 2014

Good quality entropy sources are indispensable in most modern cryptographic protocols. Unfortunately, many currently deployed networked devices do not include them and may be vulnerable to Random Number Generator (RNG) attacks. Since most of these systems allow firmware upgrades and have serial communication facilities, the potential for retrofitting them with secure hardware-based entropy sources exists. To this aim, very low-cost, robust, easy to deploy solutions are required. Here, a retrofittable, sub 10$ entropy source based on chaotic dynamics is illustrated, capable of a 32 kbit/s rate or more and offering multiple serial communication options including USB, I2C, SPI or USART. Operation is based on a loop built around the Analog to Digital Converter (ADC) hosted on a standard microcontroller.
Image enhancement using the mean dynamic range maximization with logarithmic operations
Venue: arXiv cs.CV 2014
Year: 2014

In this paper we use a logarithmic model for gray level image enhancement. We begin with a short presentation of the model and then, we propose a new formula for the mean dynamic range. After that we present two image transforms: one performs an optimal enhancement of the mean dynamic range using the logarithmic addition, and the other does the same for positive and negative values using the logarithmic scalar multiplication. We present the comparison of the results obtained by dynamic ranges optimization with the results obtained using classical image enhancement methods like gamma correction and histogram equalization.
Compressing Deep Convolutional Networks using Vector Quantization
Venue: arXiv cs.CV 2014
Year: 2014

Deep convolutional neural networks (CNN) has become the most promising method for object recognition, repeatedly demonstrating record breaking results for image classification and object detection in recent years. However, a very deep CNN generally involves many layers with millions of parameters, making the storage of the network model to be extremely large. This prohibits the usage of deep CNNs on resource limited hardware, especially cell phones or other embedded devices. In this paper, we tackle this model storage issue by investigating information theoretical vector quantization methods for compressing the parameters of CNNs. In particular, we have found in terms of compressing the most storage demanding dense connected layers, vector quantization methods have a clear gain over existing matrix factorization methods. Simply applying k-means clustering to the weights or conducting product quantization can lead to a very good balance between model size and recognition accuracy. For the 1000-category classification task in the ImageNet challenge, we are able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN.
Spread Unary Coding
Venue: arXiv cs.NE 2014
Year: 2014

Unary coding is useful but it is redundant in its standard form. Unary coding can also be seen as spatial coding where the value of the number is determined by its place in an array. Motivated by biological finding that several neurons in the vicinity represent the same number, we propose a variant of unary numeration in its spatial form, where each number is represented by several 1s. We call this spread unary coding where the number of 1s used is the spread of the code. Spread unary coding is associated with saturation of the Hamming distance between code words.