OpenRaiser/Intern-Atlas huggingface dataset:
An original Propagator for large array
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we demonstrate that when the ratio $n$ of the number of antenna elements $N$ to the number $P$ of radiating sources is superior or equal to $2$, then it is possible to choose a propagator from a set of $n(n+1)/2-1$ operators to compute the Angles of Arrival (AoA) of the narrowband incoming waves. This new non eigenbased approach is efficient when the Signal to Noise Ratio (SNR) is moderate, and gives multitude of possibilities, that are dependent of the random data, to construct the complex sets whose columns are orthogonal to the signal subspace generated by the radiating sources. Elementary examples are given for $n=3$, $n=4$ and $n=6$. The simulation results are presented to illustrate the performance of the proposed computational methods.
Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
Venue: arXiv cs.CV 2015
Year: 2015

Tensors or multiarray data are generalizations of matrices. Tensor clustering has become a very important research topic due to the intrinsically rich structures in real-world multiarray datasets. Subspace clustering based on vectorizing multiarray data has been extensively researched. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model. In contrast to existing techniques, we propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the so-called multinomial manifold, for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.
A Multicomponent Approach to Nonrigid Registration of Diffusion Tensor Images
Venue: arXiv cs.CV 2015
Year: 2015

We propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion.
Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective
Venue: arXiv cs.CV 2015
Year: 2015

Many computer vision algorithms employ subspace models to represent data. The Low-rank representation (LRR) has been successfully applied in subspace clustering for which data are clustered according to their subspace structures. The possibility of extending LRR on Grassmann manifold is explored in this paper. Rather than directly embedding Grassmann manifold into a symmetric matrix space, an extrinsic view is taken by building the self-representation of LRR over the tangent space of each Grassmannian point. A new algorithm for solving the proposed Grassmannian LRR model is designed and implemented. Several clustering experiments are conducted on handwritten digits dataset, dynamic texture video clips and YouTube celebrity face video data. The experimental results show our method outperforms a number of existing methods.
Multi-Block ADMM for Big Data Optimization in Modern Communication Networks
Venue: arXiv cs.NA 2015
Year: 2015

In this paper, we review the parallel and distributed optimization algorithms based on the alternating direction method of multipliers (ADMM) for solving "big data" optimization problems in modern communication networks. We first introduce the canonical formulation of the large-scale optimization problem. Next, we describe the general form of ADMM and then focus on several direct extensions and sophisticated modifications of ADMM from $2$-block to $N$-block settings to deal with the optimization problem. The iterative schemes and convergence properties of each extension/modification are given, and the implementation on large-scale computing facilities is also illustrated. Finally, we numerate several applications in communication networks, such as the security constrained optimal power flow problem in smart grid networks and mobile data offloading problem in software defined networks (SDNs).
Robust real time face recognition and tracking on gpu using fusion of rgb and depth image
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents a real-time face recognition system using kinect sensor. The algorithm is implemented on GPU using opencl and significant speed improvements are observed. We use kinect depth image to increase the robustness and reduce computational cost of conventional LBP based face recognition. The main objective of this paper was to perform robust, high speed fusion based face recognition and tracking. The algorithm is mainly composed of three steps. First step is to detect all faces in the video using viola jones algorithm. The second step is online database generation using a tracking window on the face. A modified LBP feature vector is calculated using fusion information from depth and greyscale image on GPU. This feature vector is used to train a svm classifier. Third step involves recognition of multiple faces based on our modified feature vector.
Design and Evaluation of Distributed Networked Control for a Dual-Machine Power System
Venue: arXiv cs.SY 2015
Year: 2015

Oscillations between swing modes of electric machines is an important limitation in achieving a high level of transient performance and reliability in power grids. Based on the new advances in measurement and transmission of wide-area information, this work proposes a distributed networked control scheme by considering the communication delays. The results are applied to reduce the inter-area swing oscillations in a power grid. In comparison with the previous works, we provide a more realistic modeling of the resulting networked control system with data sampling and delays. The exactness of the proposed modeling allows for precise evaluation and comparison between the distributed and decentralized schema. A symmetric a dual machine power system is highly oscillatory and we focus on this case to evaluate the ability of the proposed control design in dampening of the oscillations. The design can be done either based on optimization of a quadratic cost function or a disturbance attenuation level
Image Subset Selection Using Gabor Filters and Neural Networks
Venue: arXiv cs.CV 2015
Year: 2015

An automatic method for the selection of subsets of images, both modern and historic, out of a set of landmark large images collected from the Internet is presented in this paper. This selection depends on the extraction of dominant features using Gabor filtering. Features are selected carefully from a preliminary image set and fed into a neural network as a training data. The method collects a large set of raw landmark images containing modern and historic landmark images and non-landmark images. The method then processes these images to classify them as landmark and non-landmark images. The classification performance highly depends on the number of candidate features of the landmark.
Designing a Linked Data Migrational Framework for Singapore Government Datasets
Venue: arXiv cs.CY 2015
Year: 2015

The subject area of this report is Linked Data and its application to the Government domain. Linked Data is an alternative method of data representation that aims to interlink data from varied sources through relationships. Governments around the world have started publishing their data in this format to assist citizens in making better use of public services. This report provides an eight step migrational framework for converting Singapore Government data from legacy systems to Linked Data format. The framework formulation is based on a study of the Singapore data ecosystem with help from Infocomm Development Authority (iDA) of Singapore. Each step in the migrational framework has been constructed with objectives, recommendations, best practices and issues with entry and exit points. This work builds on the existing Linked Data literature, implementations in other countries and cookbooks provided by Linked Data researchers. iDA can use this report to gain an understanding of the effort and work involved in the implementation of Linked Data system on top of their legacy systems. The framework can be evaluated by building a Proof of Concept (POC) application.
A Data Mining Approach to Predict Prospective Business Sectors for Lending in Retail Banking Using Decision Tree
Venue: arXiv cs.CY 2015
Year: 2015

A potential objective of every financial organization is to retain existing customers and attain new prospective customers for long-term. The economic behaviour of customer and the nature of the organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking. Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk; and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are many existing systems on customer retention as well as customer attrition systems in bank, these rigorous methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used records of business customers of a retail commercial bank in the city including rural and urban area of (Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for analysing the challenging issues, where pruned decision tree classification technique has been used to develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to build up a model to predict prospective business sectors in retail banking.
Towards Efficient Service Delivery: The Role Of Workflow Systems In Public Sector In Kenya
Venue: arXiv cs.CY 2015
Year: 2015

Current challenges in Electronic Government initiatives include performance management, effective and efficient means of sharing information between different stakeholders e.g. government departments in order to improve the quality of service delivery to the citizens. To address these challenges, this paper addresses an application domain of workflow systems in public institutions and proposes a framework that can be effectively used in implementing workflow systems in public institutions
Enhancing the Understanding of Computer Networking Courses through Software Tools
Venue: arXiv cs.CY 2015
Year: 2015

Computer networking is an important specialization in Information and Communication Technologies. However imparting the right knowledge to students can be a challenging task due to the fact that there is not enough time to deliver lengthy labs during normal lecture hours. Augmenting the use of physical machines with software tools help the students to learn beyond the limited lab sessions within the environment of higher Institutions of learning throughout the world. The Institutions focus mostly on effective use of available resources i.e. a lab may have few lab sessions scheduled within a day for different courses. Hence a particular lab session must begin and end on time. A slow student who did not complete his/her lab exercise must vacate the lab because another class is about to commence. Hence using free software tools such as OPNET IT guru, Packet trace and NS2 will help the student to learn beyond the College time. A Student will gain an insight into Computer networking field by repeatedly doing the same labs at the comfort of his laptop or PC at home, hence increasing deeper understanding into the subject. The main objective of this paper is to explore the software tools which will enhance the networks practical skills among higher education students at College of Applied Sciences Salalah. The paper also discusses the methodologies of implementing and executing lab sessions after the normal college hours. The limitations and suggestions for the improvements are also specified at the end of this paper.
Connectivity Preserving Multivalued Functions in Digital Topology
Venue: arXiv cs.CV 2015
Year: 2015

We study connectivity preserving multivalued functions between digital images. This notion generalizes that of continuous multivalued functions studied mostly in the setting of the digital plane $Z^2$. We show that connectivity preserving multivalued functions, like continuous multivalued functions, are appropriate models for digital morpholological operations. Connectivity preservation, unlike continuity, is preserved by compositions, and generalizes easily to higher dimensions and arbitrary adjacency relations.
BigDataBench-MT: A Benchmark Tool for Generating Realistic Mixed Data Center Workloads
Venue: arXiv cs.DC 2015
Year: 2015

Long-running service workloads (e.g. web search engine) and short-term data analysis workloads (e.g. Hadoop MapReduce jobs) co-locate in today's data centers. Developing realistic benchmarks to reflect such practical scenario of mixed workload is a key problem to produce trustworthy results when evaluating and comparing data center systems. This requires using actual workloads as well as guaranteeing their submissions to follow patterns hidden in real-world traces. However, existing benchmarks either generate actual workloads based on probability models, or replay real-world workload traces using basic I/O operations. To fill this gap, we propose a benchmark tool that is a first step towards generating a mix of actual service and data analysis workloads on the basis of real workload traces. Our tool includes a combiner that enables the replaying of actual workloads according to the workload traces, and a multi-tenant generator that flexibly scales the workloads up and down according to users' requirements. Based on this, our demo illustrates the workload customization and generation process using a visual interface. The proposed tool, called BigDataBench-MT, is a multi-tenant version of our comprehensive benchmark suite BigDataBench and it is publicly available from http://prof.ict.ac.cn/BigDataBench/multi-tenancyversion/.
Space- and Time-Efficient Algorithm for Maintaining Dense Subgraphs on One-Pass Dynamic Streams
Venue: arXiv cs.DS 2015
Year: 2015

While in many graph mining applications it is crucial to handle a stream of updates efficiently in terms of {\em both} time and space, not much was known about achieving such type of algorithm. In this paper we study this issue for a problem which lies at the core of many graph mining applications called {\em densest subgraph problem}. We develop an algorithm that achieves time- and space-efficiency for this problem simultaneously. It is one of the first of its kind for graph problems to the best of our knowledge. In a graph $G = (V, E)$, the "density" of a subgraph induced by a subset of nodes $S \subseteq V$ is defined as $|E(S)|/|S|$, where $E(S)$ is the set of edges in $E$ with both endpoints in $S$. In the densest subgraph problem, the goal is to find a subset of nodes that maximizes the density of the corresponding induced subgraph. For any $ε>0$, we present a dynamic algorithm that, with high probability, maintains a $(4+ε)$-approximation to the densest subgraph problem under a sequence of edge insertions and deletions in a graph with $n$ nodes. It uses $\tilde O(n)$ space, and has an amortized update time of $\tilde O(1)$ and a query time of $\tilde O(1)$. Here, $\tilde O$ hides a $O(\poly\log_{1+ε} n)$ term. The approximation ratio can be improved to $(2+ε)$ at the cost of increasing the query time to $\tilde O(n)$. It can be extended to a $(2+ε)$-approximation sublinear-time algorithm and a distributed-streaming algorithm. Our algorithm is the first streaming algorithm that can maintain the densest subgraph in {\em one pass}. The previously best algorithm in this setting required $O(\log n)$ passes [Bahmani, Kumar and Vassilvitskii, VLDB'12]. The space required by our algorithm is tight up to a polylogarithmic factor.
An Optimized Hybrid Approach for Path Finding
Venue: arXiv cs.AI 2015
Year: 2015

Path finding algorithm addresses problem of finding shortest path from source to destination avoiding obstacles. There exist various search algorithms namely A*, Dijkstra's and ant colony optimization. Unlike most path finding algorithms which require destination co-ordinates to compute path, the proposed algorithm comprises of a new method which finds path using backtracking without requiring destination co-ordinates. Moreover, in existing path finding algorithm, the number of iterations required to find path is large. Hence, to overcome this, an algorithm is proposed which reduces number of iterations required to traverse the path. The proposed algorithm is hybrid of backtracking and a new technique(modified 8- neighbor approach). The proposed algorithm can become essential part in location based, network, gaming applications. grid traversal, navigation, gaming applications, mobile robot and Artificial Intelligence.
Fairness for Non-Orthogonal Multiple Access in 5G Systems
Venue: arXiv cs.IT 2015
Year: 2015

In non-orthogonal multiple access (NOMA) downlink, multiple data flows are superimposed in the power domain and user decoding is based on successive interference cancellation. NOMA's performance highly depends on the power split among the data flows and the associated power allocation (PA) problem. In this letter, we study NOMA from a fairness standpoint and we investigate PA techniques that ensure fairness for the downlink users under i) instantaneous channel state information (CSI) at the transmitter, and ii) average CSI. Although the formulated problems are non-convex, we have developed low-complexity polynomial algorithms that yield the optimal solution in both cases considered.
Lower bounds on $q$-wise independence tails and applications to min-entropy condensers
Venue: arXiv cs.CR 2015
Year: 2015

We present novel and sharp lower bounds for higher load moments in the classical problem of mapping $M$ balls into $N$ bins by $q$-universal hashing, specialized to the case when $M=N$. As a corollary we prove a tight counterpart for the result about min-entropy condensers due to Dodis, Pietrzak and Wichs (CRYPTO'14), which has found important applications in key derivation. It states that condensing $k$ bits of min-entropy into a $k$-bit string $ε$-close to almost full min-entropy (precisely $ k-\log\log(1/ε)$ bits of entropy) can be achieved by the use of $q$-independent hashing with $q= \log(1/ε)$. We prove that when given a source of min-entropy $k$ and aiming at entropy loss $\ell = \log\log (1/ε) - 3$, the independence level $q=(1-o(1))\log(1/ε)$ is necessary (for small values of $ε$), which almost matches the positive result. Besides these asymptotic bounds, we provide clear hard bounds in terms of Bell numbers and some numerical examples. Our technique is based on an explicit representation of the load moments in terms of Stirling numbers, some asymptotic estimates on Stirling numbers and a tricky application of the Paley-Zygmund inequality. \keywords{ min-entropy condensers, key derivation, balls and bins hashing, anti-concentration inequalities }
Periodic power spectrum with applications in detection of latent periodicities in DNA sequences
Venue: arXiv cs.DM 2015
Year: 2015

Latent periodic elements in genomes play important roles in genomic functions. Many complex periodic elements in genomes are difficult to be detected by commonly used digital signal processing (DSP). We present a novel method to compute the periodic power spectrum of a DNA sequence based on the nucleotide distributions on periodic positions of the sequence. The method directly calculates full periodic spectrum of a DNA sequence rather than frequency spectrum by Fourier transform. The magnitude of the periodic power spectrum reflects the strength of the periodicity signals, thus, the algorithm can capture all the latent periodicities in DNA sequences. We apply this method on detection of latent periodicities in different genome elements, including exons and microsatellite DNA sequences. The results show that the method minimizes the impact of spectral leakage, captures a much broader latent periodicities in genomes, and outperforms the conventional Fourier transform.
Algorithms for the workflow satisfiability problem engineered for counting constraints
Venue: arXiv cs.DS 2015
Year: 2015

The workflow satisfiability problem (WSP) asks whether there exists an assignment of authorized users to the steps in a workflow specification that satisfies the constraints in the specification. The problem is NP-hard in general, but several subclasses of the problem are known to be fixed-parameter tractable (FPT) when parameterized by the number of steps in the specification. In this paper, we consider the WSP with user-independent counting constraints, a large class of constraints for which the WSP is known to be FPT. We describe an efficient implementation of an FPT algorithm for solving this subclass of the WSP and an experimental evaluation of this algorithm. The algorithm iteratively generates all equivalence classes of possible partial solutions until, whenever possible, it finds a complete solution to the problem. We also provide a reduction from a WSP instance to a pseudo-Boolean SAT instance. We apply this reduction to the instances used in our experiments and solve the resulting PB SAT problems using SAT4J, a PB SAT solver. We compare the performance of our algorithm with that of SAT4J and discuss which of the two approaches would be more effective in practice.
Faster Algorithms for the Constrained k-means Problem
Venue: arXiv cs.DS 2015
Year: 2015

The classical center based clustering problems such as $k$-means/median/center assume that the optimal clusters satisfy the locality property that the points in the same cluster are close to each other. A number of clustering problems arise in machine learning where the optimal clusters do not follow such a locality property. Consider a variant of the $k$-means problem that may be regarded as a general version of such problems. Here, the optimal clusters $O_1, ..., O_k$ are an arbitrary partition of the dataset and the goal is to output $k$-centers $c_1, ..., c_k$ such that the objective function $\sum_{i=1}^{k} \sum_{x \in O_{i}} ||x - c_{i}||^2$ is minimized. It is not difficult to argue that any algorithm (without knowing the optimal clusters) that outputs a single set of $k$ centers, will not behave well as far as optimizing the above objective function is concerned. However, this does not rule out the existence of algorithms that output a list of such $k$ centers such that at least one of these $k$ centers behaves well. Given an error parameter $\varepsilon > 0$, let $\ell$ denote the size of the smallest list of $k$-centers such that at least one of the $k$-centers gives a $(1+\varepsilon)$ approximation w.r.t. the objective function above. In this paper, we show an upper bound on $\ell$ by giving a randomized algorithm that outputs a list of $2^{\tilde{O}(k/\varepsilon)}$ $k$-centers. We also give a closely matching lower bound of $2^{\tildeΩ(k/\sqrt{\varepsilon})}$. Moreover, our algorithm runs in time $O \left(n d \cdot 2^{\tilde{O}(k/\varepsilon)} \right)$. This is a significant improvement over the previous result of Ding and Xu who gave an algorithm with running time $O \left(n d \cdot (\log{n})^{k} \cdot 2^{poly(k/\varepsilon)} \right)$ and output a list of size $O \left((\log{n})^k \cdot 2^{poly(k/\varepsilon)} \right)$.