OpenRaiser/Intern-Atlas huggingface dataset:
Mining Frequent Itemsets (MFI) over Data Streams: Variable Window Size (VWS) by Context Variation Analysis (CVA) of the Streaming Transactions
Venue: arXiv cs.DB 2014
Year: 2014

The challenges with respect to mining frequent items over data streaming engaging variable window size and low memory space are addressed in this research paper. To check the varying point of context change in streaming transaction we have developed a window structure which will be in two levels and supports in fixing the window size instantly and controls the heterogeneities and assures homogeneities among transactions added to the window. To minimize the memory utilization, computational cost and improve the process scalability, this design will allow fixing the coverage or support at window level. Here in this document, an incremental mining of frequent item-sets from the window and a context variation analysis approach are being introduced. The complete technology that we are presenting in this document is named as Mining Frequent Item-sets using Variable Window Size fixed by Context Variation Analysis (MFI-VWS-CVA). There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this design we have used window size change to represent the conceptual drift in an information stream. As it were, whenever there is a problem in setting window size effectively the item-set will be infrequent. The experiments that we have executed and documented proved that the algorithm that we have designed is much efficient than that of existing.
Distributed Cooperative Sensing in Cognitive Radio Networks: An Overlapping Coalition Formation Approach
Venue: arXiv cs.NI 2014
Year: 2014

Cooperative spectrum sensing has been shown to yield a significant performance improvement in cognitive radio networks. In this paper, we consider distributed cooperative sensing (DCS) in which secondary users (SUs) exchange data with one another instead of reporting to a common fusion center. In most existing DCS algorithms, the SUs are grouped into disjoint cooperative groups or coalitions, and within each coalition the local sensing data is exchanged. However, these schemes do not account for the possibility that an SU can be involved in multiple cooperative coalitions thus forming overlapping coalitions. Here, we address this problem using novel techniques from a class of cooperative games, known as overlapping coalition formation games, and based on the game model, we propose a distributed DCS algorithm in which the SUs self-organize into a desirable network structure with overlapping coalitions. Simulation results show that the proposed overlapping algorithm yields significant performance improvements, decreasing the total error probability up to 25% in the Q_m+Q_f criterion, the missed detection probability up to 20% in the Q_m/Q_f criterion, the overhead up to 80%, and the total report number up to 10%, compared with the state-of-the-art non-overlapping algorithm.
Toward Automated Discovery of Artistic Influence
Venue: arXiv cs.CV 2014
Year: 2014

Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classification methodologies for the task of fine-art style classification. A two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models, while the second level touches the features aspect of the paintings and compares semantic-level features vs. low-level and intermediate-level features present in the painting. Then, we investigate the question "Who influenced this artist?" by looking at his masterpieces and comparing them to others. We pose this interesting question as a knowledge discovery problem. For this purpose, we investigated several painting-similarity and artist-similarity measures. As a result, we provide a visualization of artists (Map of Artists) based on the similarity between their works
A Novel (k,n) Secret Sharing Scheme from Quadratic Residues for Grayscale Images
Venue: arXiv cs.CR 2014
Year: 2014

A new grayscale image encryption algorithm based on $(k,n)$ threshold secret sharing is proposed. The scheme allows a secret image to be transformed into $n$ shares, where any $k \le n$ shares can be used to reconstruct the secret image, while the knowledge of $k-1$ or fewer shares leaves no sufficient information about the secret image and it becomes hard to decrypt the transmitted image. In the proposed scheme, the pixels of the secret image are first permuted and then encrypted by using quadratic residues. In the final stage, the encrypted image is shared into n shadow images using polynomials of Shamir scheme. The proposed scheme is provably secure and the experimental results shows that the scheme performs well while maintaining high levels of quality in the reconstructed image.
A measurement based software quality framework
Venue: arXiv cs.SE 2014
Year: 2014

In this report we propose a solution to problem of the dependency on the experience of the software project quality assurance personnel by providing a transparent, objective and measurement based quality framework. The framework helps the quality assurance experts making objective and comparable decisions in software projects by defining and assessing measurable quality goals and thresholds, directly relating these to an escalation mechanism. First results of applying the proposed measurement based software quality framework in a real life case study are also addressed in this report.
JIDT: An information-theoretic toolkit for studying the dynamics of complex systems
Venue: arXiv cs.IT 2014
Year: 2014

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments. We present the principles behind the code design, and provide several examples to guide users.
SAT for pedestrians
Venue: arXiv cs.CC 2014
Year: 2014

The aim of this short note is mainly pedagogical. It summarizes some knowledge about Boolean satisfiability (SAT) and the P=NP? problem in an elementary mathematical language. A convenient scheme to visualize and manipulate CNF formulae is introduced. Also some results like the formulae for the number of unsatisfied clauses and the number of solutions might be unknown.
Secure Multipath Routing Scheme using Key Pre-Distribution in Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

Multipath routing in WSN has been a long wish in security scenario where nodes on next-hop may be targeted to compromise. Many proposals of Multipath routing has been proposed in ADHOC Networks but under constrained from keying environment most seems ignorant. In WSN where crucial data is reported by nodes in deployment area to their securely located Sink, route security has to be guaranteed. Under dynamic load and selective attacks, availability of multiple secure paths is a boon and increases the attacker efforts by many folds. We propose to build a subset of neighbors as our front towards destination node. We also identified forwarders for query by base station. The front is optimally calculated to maintain the security credential and avail multiple paths. According to our knowledge ours is first secure multipath routing protocol for WSN. We established effectiveness of our proposal with mathematical analysis
A fast patch-dictionary method for whole image recovery
Venue: arXiv cs.CV 2014
Year: 2014

Various algorithms have been proposed for dictionary learning. Among those for image processing, many use image patches to form dictionaries. This paper focuses on whole-image recovery from corrupted linear measurements. We address the open issue of representing an image by overlapping patches: the overlapping leads to an excessive number of dictionary coefficients to determine. With very few exceptions, this issue has limited the applications of image-patch methods to the local kind of tasks such as denoising, inpainting, cartoon-texture decomposition, super-resolution, and image deblurring, for which one can process a few patches at a time. Our focus is global imaging tasks such as compressive sensing and medical image recovery, where the whole image is encoded together, making it either impossible or very ineffective to update a few patches at a time. Our strategy is to divide the sparse recovery into multiple subproblems, each of which handles a subset of non-overlapping patches, and then the results of the subproblems are averaged to yield the final recovery. This simple strategy is surprisingly effective in terms of both quality and speed. In addition, we accelerate computation of the learned dictionary by applying a recent block proximal-gradient method, which not only has a lower per-iteration complexity but also takes fewer iterations to converge, compared to the current state-of-the-art. We also establish that our algorithm globally converges to a stationary point. Numerical results on synthetic data demonstrate that our algorithm can recover a more faithful dictionary than two state-of-the-art methods. Combining our whole-image recovery and dictionary-learning methods, we numerically simulate image inpainting, compressive sensing recovery, and deblurring. Our recovery is more faithful than those of a total variation method and a method based on overlapping patches.
Parallel generator of $q$-valued pseudorandom sequences based on arithmetic polynomials
Venue: arXiv cs.CR 2014
Year: 2014

A new method for parallel generation of $q$-valued pseudorandom sequence based on the presentation of systems generating logical formulae by means of arithmetic polynomials is proposed. Fragment consisting of $k$-elements of $q$-valued pseudorandom sequence may be obtained by means of single calculation of a single recursion numerical formula. It is mentioned that the method of the "arithmetization" of generation may be used and further developed in order to protect the encryption gears from cryptographic onset, resulting in the initiating of mass hardware failures. The achieved results may be widely applied to the realization of perspective high-performance cryptographic facilities for information protection.
Tier Association Probability and Spectrum Partitioning for Maximum Rate Coverage in Multi-tier Heterogeneous Networks
Venue: arXiv cs.IT 2014
Year: 2014

For a wireless multi-tier heterogeneous network with orthogonal spectrum allocation across tiers, we optimize the association probability and the fraction of spectrum allocated to each tier so as to maximize rate coverage. In practice, the association probability can be controlled using a biased received signal power. The optimization problem is non-convex and we are forced to explore locally optimal solutions. We make two contributions in this paper: first, we show that there exists a relation between the first derivatives of the objective function with respect to each of the optimization variables. This can be used to simplify numerical solutions to the optimization problem. Second, we explore the optimality of the intuitive solution that the fraction of spectrum allocated to each tier should be equal to the tier association probability. We show that, in this case, a closed-form solution exists. Importantly, our numerical results show that there is essentially zero performance loss. The results also illustrate the significant gains possible by jointly optimizing the user association and the resource allocation.
Robust Statistical Approach for Extraction of Moving Human Silhouettes from Videos
Venue: arXiv cs.CV 2014
Year: 2014

Human pose estimation is one of the key problems in computer vision that has been studied in the recent years. The significance of human pose estimation is in the higher level tasks of understanding human actions applications such as recognition of anomalous actions present in videos and many other related applications. The human poses can be estimated by extracting silhouettes of humans as silhouettes are robust to variations and it gives the shape information of the human body. Some common challenges include illumination changes, variation in environments, and variation in human appearances. Thus there is a need for a robust method for human pose estimation. This paper presents a study and analysis of approaches existing for silhouette extraction and proposes a robust technique for extracting human silhouettes in video sequences. Gaussian Mixture Model (GMM) A statistical approach is combined with HSV (Hue, Saturation and Value) color space model for a robust background model that is used for background subtraction to produce foreground blobs, called human silhouettes. Morphological operations are then performed on foreground blobs from background subtraction. The silhouettes obtained from this work can be used in further tasks associated with human action interpretation and activity processes like human action classification, human pose estimation and action recognition or action interpretation.
High Security Image Steganography with Modified Arnold cat map
Venue: arXiv cs.CR 2014
Year: 2014

Information security is concerned with maintaining the secrecy, reliability and accessibility of data. The main objective of information security is to protect information and information system from unauthorized access, revelation, disruption, alteration, annihilation and use. This paper uses spatial domain LSB substitution method for information embedding and modified forms of Arnold transform are applied twice in two different phases to ensure security. The system is tested and validated against a series of standard images and the results show that the method is highly secure and provides high data hiding capacity.
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
Venue: arXiv cs.LG 2014
Year: 2014

In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. It is noticeable that very few of these methods have explicitly addressed the dimensionality reduction along the time axis. This is however a major issue with regard to the use of elastic distances characterized by a quadratic complexity. To partially fill this apparent gap, we present in this paper an approach based on temporal down-sampling associated to elastic kernel machine learning. We experimentally show, on two data sets that are widely referenced in the domain of human gesture recognition, and very different in terms of quality of motion capture, that it is possible to significantly reduce the number of skeleton frames while maintaining a good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. The computational complexity reduction makes this approach eligible for real-time applications.
Cactus Graphs and Some Algorithms
Venue: arXiv cs.DM 2014
Year: 2014

A cactus graph is a connected graph in which every block is either an edge or a cycle. In this paper, we consider several problems of graph theory and developed optimal algorithms to solve such problems on cactus graphs. The running time of these algorithms is O(n), where n is the total number of vertices of the graph. The cactus graph has many applications in real life problems, especially in radio communication system.
Indexing Cost Sensitive Prediction
Venue: arXiv cs.LG 2014
Year: 2014

Predictive models are often used for real-time decision making. However, typical machine learning techniques ignore feature evaluation cost, and focus solely on the accuracy of the machine learning models obtained utilizing all the features available. We develop algorithms and indexes to support cost-sensitive prediction, i.e., making decisions using machine learning models taking feature evaluation cost into account. Given an item and a online computation cost (i.e., time) budget, we present two approaches to return an appropriately chosen machine learning model that will run within the specified time on the given item. The first approach returns the optimal machine learning model, i.e., one with the highest accuracy, that runs within the specified time, but requires significant up-front precomputation time. The second approach returns a possibly sub- optimal machine learning model, but requires little up-front precomputation time. We study these two algorithms in detail and characterize the scenarios (using real and synthetic data) in which each performs well. Unlike prior work that focuses on a narrow domain or a specific algorithm, our techniques are very general: they apply to any cost-sensitive prediction scenario on any machine learning algorithm.
On the Ice-Wine Problem: Recovering Linear Combination of Codewords over the Gaussian Multiple Access Channel
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we consider the Ice-Wine problem: Two transmitters send their messages over the Gaussian Multiple-Access Channel (MAC) and a receiver aims to recover a linear combination of codewords. The best known achievable rate-region for this problem is due to [1],[2] as $R_{i}\leq\frac{1}{2}\log\left(\frac{1}{2}+{\rm SNR}\right)$ $(i=1,2)$. In this paper, we design a novel scheme using lattice codes and show that the rate region of this problem can be improved. The main difference between our proposed scheme with known schemes in [1],[2] is that instead of recovering the sum of codewords at the decoder, a non-integer linear combination of codewords is recovered. Comparing the achievable rate-region with the outer bound, $R_{i}\leq\frac{1}{2}\log\left(1+{\rm SNR}\right)\,\,(i=1,2)$, we observe that the achievable rate for each user is partially tight. Finally, by applying our proposed scheme to the Gaussian Two Way Relay Channel (GTWRC), we show that the best rate region for this problem can be improved.
Fast Approximate Matrix Multiplication by Solving Linear Systems
Venue: arXiv cs.DS 2014
Year: 2014

In this paper, we present novel deterministic algorithms for multiplying two $n \times n$ matrices approximately. Given two matrices $A,B$ we return a matrix $C'$ which is an \emph{approximation} to $C = AB$. We consider the notion of approximate matrix multiplication in which the objective is to make the Frobenius norm of the error matrix $C-C'$ arbitrarily small. Our main contribution is to first reduce the matrix multiplication problem to solving a set of linear equations and then use standard techniques to find an approximate solution to that system in $\tilde{O}(n^2)$ time. To the best of our knowledge this the first examination into designing quadratic time deterministic algorithms for approximate matrix multiplication which guarantee arbitrarily low \emph{absolute error} w.r.t. Frobenius norm.
Object Segmentation in Images using EEG Signals
Venue: arXiv cs.CV 2014
Year: 2014

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When an image region, specifically a block of pixels, is displayed we estimate the probability of the block containing the object of interest using a score based on EEG activity. After several such blocks are displayed, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study shows that BCI and simple EEG analysis are useful in locating object boundaries in images.
Sequential Recurrence-Based Multidimensional Universal Source Coding of Lempel-Ziv Type
Venue: arXiv cs.IT 2014
Year: 2014

We define an algorithm that parses multidimensional arrays sequentially into mainly unrepeated but nested multidimensional sub-arrays of increasing size, and show that the resulting sub-block pointer encoder compresses almost every realization of any finite-alphabet ergodic process on $\mathbb{Z}_{\geq0}^d$ to the entropy, in the limit.
The Correlation among Software Complexity Metrics with Case Study
Venue: arXiv cs.SE 2014
Year: 2014

People demand for software quality is growing increasingly, thus different scales for the software are growing fast to handle the quality of software. The software complexity metric is one of the measurements that use some of the internal attributes or characteristics of software to know how they effect on the software quality. In this paper, we cover some of more efficient software complexity metrics such as Cyclomatic complexity, line of code and Hallstead complexity metric. This paper presents their impacts on the software quality. It also discusses and analyzes the correlation between them. It finally reveals their relation with the number of errors using a real dataset as a case study.
Incremental Cardinality Constraints for MaxSAT
Venue: arXiv cs.LO 2014
Year: 2014

Maximum Satisfiability (MaxSAT) is an optimization variant of the Boolean Satisfiability (SAT) problem. In general, MaxSAT algorithms perform a succession of SAT solver calls to reach an optimum solution making extensive use of cardinality constraints. Many of these algorithms are non-incremental in nature, i.e. at each iteration the formula is rebuilt and no knowledge is reused from one iteration to another. In this paper, we exploit the knowledge acquired across iterations using novel schemes to use cardinality constraints in an incremental fashion. We integrate these schemes with several MaxSAT algorithms. Our experimental results show a significant performance boost for these algo- rithms as compared to their non-incremental counterparts. These results suggest that incremental cardinality constraints could be beneficial for other constraint solving domains.