OpenRaiser/Intern-Atlas huggingface dataset:
Cloud Benchmarking for Performance
Venue: arXiv cs.DC 2014
Year: 2014

How can applications be deployed on the cloud to achieve maximum performance? This question has become significant and challenging with the availability of a wide variety of Virtual Machines (VMs) with different performance capabilities in the cloud. The above question is addressed by proposing a six step benchmarking methodology in which a user provides a set of four weights that indicate how important each of the following groups: memory, processor, computation and storage are to the application that needs to be executed on the cloud. The weights along with cloud benchmarking data are used to generate a ranking of VMs that can maximise performance of the application. The rankings are validated through an empirical analysis using two case study applications; the first is a financial risk application and the second is a molecular dynamics simulation, which are both representative of workloads that can benefit from execution on the cloud. Both case studies validate the feasibility of the methodology and highlight that maximum performance can be achieved on the cloud by selecting the top ranked VMs produced by the methodology.
Jointly Private Convex Programming
Venue: arXiv cs.DS 2014
Year: 2014

In this paper we present an extremely general method for approximately solving a large family of convex programs where the solution can be divided between different agents, subject to joint differential privacy. This class includes multi-commodity flow problems, general allocation problems, and multi-dimensional knapsack problems, among other examples. The accuracy of our algorithm depends on the \emph{number} of constraints that bind between individuals, but crucially, is \emph{nearly independent} of the number of primal variables and hence the number of agents who make up the problem. As the number of agents in a problem grows, the error we introduce often becomes negligible. We also consider the setting where agents are strategic and have preferences over their part of the solution. For any convex program in this class that maximizes \emph{social welfare}, there is a generic reduction that makes the corresponding optimization \emph{approximately dominant strategy truthful} by charging agents prices for resources as a function of the approximately optimal dual variables, which are themselves computed under differential privacy. Our results substantially expand the class of problems that are known to be solvable under both privacy and incentive constraints.
Fast Exact Matrix Completion with Finite Samples
Venue: arXiv cs.NA 2014
Year: 2014

Matrix completion is the problem of recovering a low rank matrix by observing a small fraction of its entries. A series of recent works [KOM12,JNS13,HW14] have proposed fast non-convex optimization based iterative algorithms to solve this problem. However, the sample complexity in all these results is sub-optimal in its dependence on the rank, condition number and the desired accuracy. In this paper, we present a fast iterative algorithm that solves the matrix completion problem by observing $O(nr^5 \log^3 n)$ entries, which is independent of the condition number and the desired accuracy. The run time of our algorithm is $O(nr^7\log^3 n\log 1/ε)$ which is near linear in the dimension of the matrix. To the best of our knowledge, this is the first near linear time algorithm for exact matrix completion with finite sample complexity (i.e. independent of $ε$). Our algorithm is based on a well known projected gradient descent method, where the projection is onto the (non-convex) set of low rank matrices. There are two key ideas in our result: 1) our argument is based on a $\ell_{\infty}$ norm potential function (as opposed to the spectral norm) and provides a novel way to obtain perturbation bounds for it. 2) we prove and use a natural extension of the Davis-Kahan theorem to obtain perturbation bounds on the best low rank approximation of matrices with good eigen-gap. Both of these ideas may be of independent interest.
The Decrits Consensus Algorithm: Decentralized Agreement without Proof of Work
Venue: arXiv cs.CR 2014
Year: 2014

Decrits is a cryptocurrency in development that makes use of a novel consensus algorithm that does not require proof-of-work. This paper describes how the Decrits Consensus Algorithm (DCA) is as trustless as a proof-of-work algorithm while offering superior transaction security at virtually no cost.
Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks
Venue: arXiv cs.IT 2014
Year: 2014

This paper proposes a novel distributed reduced--rank scheme and an adaptive algorithm for distributed estimation in wireless sensor networks. The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by a reduced-dimension parameter vector. A distributed reduced-rank joint iterative estimation algorithm is developed, which has the ability to achieve significantly reduced communication overhead and improved performance when compared with existing techniques. Simulation results illustrate the advantages of the proposed strategy in terms of convergence rate and mean square error performance.
Kullback-Leibler divergence for interacting multiple model estimation with random matrices
Venue: arXiv cs.SY 2014
Year: 2014

This paper studies the problem of interacting multiple model (IMM) estimation for jump Markov linear systems with unknown measurement noise covariance. The system state and the unknown covariance are jointly estimated in the framework of Bayesian estimation, where the unknown covariance is modeled as a random matrix according to an inverse-Wishart distribution. For the IMM estimation with random matrices, one difficulty encountered is the combination of a set of weighted inverse-Wishart distributions. Instead of using the moment matching approach, this difficulty is overcome by minimizing the weighted Kullback-Leibler divergence for inverse-Wishart distributions. It is shown that a closed form solution can be derived for the optimization problem and the resulting solution coincides with an inverse-Wishart distribution. Simulation results show that the proposed filter performs better than the previous work using the moment matching approach.
Online SLAM with Any-time Self-calibration and Automatic Change Detection
Venue: arXiv cs.CV 2014
Year: 2014

A framework for online simultaneous localization, mapping and self-calibration is presented which can detect and handle significant change in the calibration parameters. Estimates are computed in constant-time by factoring the problem and focusing on segments of the trajectory that are most informative for the purposes of calibration. A novel technique is presented to detect the probability that a significant change is present in the calibration parameters. The system is then able to re-calibrate. Maximum likelihood trajectory and map estimates are computed using an asynchronous and adaptive optimization. The system requires no prior information and is able to initialize without any special motions or routines, or in the case where observability over calibration parameters is delayed. The system is experimentally validated to calibrate camera intrinsic parameters for a nonlinear camera model on a monocular dataset featuring a significant zoom event partway through, and achieves high accuracy despite unknown initial calibration parameters. Self-calibration and re-calibration parameters are shown to closely match estimates computed using a calibration target. The accuracy of the system is demonstrated with SLAM results that achieve sub-1% distance-travel error even in the presence of significant re-calibration events.
How to sell an app: pay-per-play or buy-it-now?
Venue: arXiv cs.GT 2014
Year: 2014

We consider pricing in settings where a consumer discovers his value for a good only as he uses it, and the value evolves with each use. We explore simple and natural pricing strategies for a seller in this setting, under the assumption that the seller knows the distribution from which the consumer's initial value is drawn, as well as the stochastic process that governs the evolution of the value with each use. We consider the differences between up-front or "buy-it-now" pricing (BIN), and "pay-per-play" (PPP) pricing, where the consumer is charged per use. Our results show that PPP pricing can be a very effective mechanism for price discrimination, and thereby can increase seller revenue. But it can also be advantageous to the buyers, as a way of mitigating risk. Indeed, this mitigation of risk can yield a larger pool of buyers. We also show that the practice of offering free trials is largely beneficial. We consider two different stochastic processes for how the buyer's value evolves: In the first, the key random variable is how long the consumer remains interested in the product. In the second process, the consumer's value evolves according to a random walk or Brownian motion with reflection at 1, and absorption at 0.
Training Design and Channel Estimation in Uplink Cloud Radio Access Networks
Venue: arXiv cs.IT 2014
Year: 2014

To decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks (C-RANs), a superimposed-segment training design is proposed. The core idea of the proposal is that each mobile station superimposes a periodic training sequence on the data signal, and each remote radio heads prepends a separate pilot to the received signal before forwarding it to the centralized base band unit pool. Moreover, a complex-exponential basis-expansion-model based channel estimation algorithm to maximize a posteriori probability is developed, where the basis-expansion-model coefficients of access links (ALs) and the channel fading of wireless backhaul links are first obtained, after which the time-domain channel samples of ALs are restored in terms of maximizing the average effective signal-to-noise ratio (AESNR). Simulation results show that the proposed channel estimation algorithm can effectively decrease the estimation mean square error and increase the AESNR in C-RANs, thus significantly outperforming the existing solutions.
Scalable Parallel Numerical CSP Solver
Venue: arXiv cs.AI 2014
Year: 2014

We present a parallel solver for numerical constraint satisfaction problems (NCSPs) that can scale on a number of cores. Our proposed method runs worker solvers on the available cores and simultaneously the workers cooperate for the search space distribution and balancing. In the experiments, we attained up to 119-fold speedup using 256 cores of a parallel computer.
Guaranteeing Positive Secrecy Capacity with Finite-Rate Feedback using Artificial Noise
Venue: arXiv cs.IT 2014
Year: 2014

While the impact of finite-rate feedback on the capacity of fading channels has been extensively studied in the literature, not much attention has been paid to this problem under secrecy constraint. In this work, we study the ergodic secret capacity of a multiple-input multiple-output multiple-antenna-eavesdropper (MIMOME) wiretap channel with quantized channel state information (CSI) at the transmitter and perfect CSI at the legitimate receiver, under the assumption that only the statistics of eavesdropper CSI is known at the transmitter. We refine the analysis of the random vector quantization (RVQ) based artificial noise (AN) scheme in [1], where a heuristic upper bound on the secrecy rate loss, when compared to the perfect CSI case, was given. We propose a lower bound on the ergodic secrecy capacity. We show that the lower bound and the secrecy capacity with perfect CSI coincide asymptotically as the number of feedback bits and the AN power go to infinity. For practical applications, we propose a very efficient quantization codebook construction method for the two transmit antennas case.
Scientometrics and Information Retrieval - weak-links revitalized
Venue: arXiv cs.IR 2014
Year: 2014

This special issue brings together eight papers from experts of communities which often have been perceived as different once: bibliometrics, scientometrics and informetrics on the one side and information retrieval on the other. The idea of this special issue started at the workshop "Combining Bibliometrics and Information Retrieval" held at the 14th International Conference of Scientometrics and Informetrics, Vienna, July 14-19, 2013. Our motivation as guest editors started from the observation that main discourses in both fields are different, that communities are only partly overlapping and from the belief that a knowledge transfer would be profitable for both sides.
Efficient Direct Detection of M-PAM Sequences with Implicit CSI Acquisition for The FSO System
Venue: arXiv cs.IT 2014
Year: 2014

Compared to on-off keying (OOK), M-ary pulse amplitude modulation (M-PAM, M>2) is more spectrally efficient. However, to detect M-PAM signals reliably, the requirement of accurate channel state information is more stringent. Previously, for OOK systems, we have developed a receiver that requires few pilot symbols and can jointly detect the data sequence and estimate the unknown channel gain implicitly. In this paper, using the same approach, we extend our previous work and derive a generalized receiver for M-PAM systems. A Viterbi-type trellis-search algorithm coupled with a selective-store strategy is adopted, resulting in a low implementation complexity and a low memory requirement. Therefore, the receiver is efficient in terms of energy, spectra, implementation complexity and memory. Using theoretical analysis, we show that its error performance approaches that of maximum likelihood detection with perfect knowledge of the channel gain, as the observation window length increases. Also, simulation results are presented to justify the theoretical analysis.
E-Governance in India: Definitions, Challenges and Solutions
Venue: arXiv cs.CY 2014
Year: 2014

The Government of India is transcending from traditional modus operandi of governance towards technological involvement in the process of governance. Currently, the Government of India is in the transition phase and seamlessly unleashing the power of ICT in governance. The government is spending an enormous amount of finances in deployment of e-governance, but, are these efforts are going in the appropriate direction and leads towards intended results? What do the people percept from the concept of e-governance? What is the global perspective about perception of e-governance? What are the major challenges confronting the deployment of e-governance? In this attempt the authors have made an attempt to riposte aforesaid issues. Moreover, the authors have also suggested some plausible suggestions which may help in successful and sustainable deployment of e-governance in India.
Intriguing Trends in Nuclear Physics Articles Authorship
Venue: arXiv cs.DL 2014
Year: 2014

The increase in authorship of nuclear physics publications has been investigated using the large statistical samples. This has been accomplished with nuclear data mining of nuclear science references (NSR) and experimental nuclear reaction (EXFOR) databases. The results of this study will be discussed and conclusions will be given.
Systematic Review on Project Actuality
Venue: arXiv cs.SE 2014
Year: 2014

Nowadays much is written about how to manage projects, but too little on what really happens in project actuality. Project Actuality came out in the Rethinking Project Management (RPM) agenda in 2006 and it aims at understanding what really happens at project context. To be able to understand project actuality phenomenon, we first need to get a better comprehension on its definition and discover how to observe it and analyse it. This paper presents the results of the systematic review conducted to collect evidence on Project Actuality. The research focused on four search engines, in publications from 1994 to 2013. Among others, the study concludes that project actuality has been analysed by several methods and techniques, mostly on large organization and public sectors, in Northern Europe. The most common definitions, techniques, and tips were identified as well as the intent of transforming the results in knowledge.
Online Square Detection
Venue: arXiv cs.DS 2014
Year: 2014

The online square detection problem is to detect the first occurrence of a square in a string whose characters are provided as input one at a time. Recall that a square is a string that is a concatenation of two identical strings. In this paper we present an algorithm solving this problem in $O(n\logσ)$ time and linear space on ordered alphabet, where $σ$ is the number of different letters in the input string. Our solution is relatively simple and does not require much memory unlike the previously known online algorithm with the same working time. Also we present an algorithm working in $O(n\log n)$ time and linear space on unordered alphabet, though this solution does not outperform the previously known result with the same time bound.
Lovász-Schrijver SDP-operator, near-perfect graphs and near-bipartite graphs
Venue: arXiv cs.DM 2014
Year: 2014

We study the Lovász-Schrijver lift-and-project operator ($LS_+$) based on the cone of symmetric, positive semidefinite matrices, applied to the fractional stable set polytope of graphs. The problem of obtaining a combinatorial characterization of graphs for which the $LS_+$-operator generates the stable set polytope in one step has been open since 1990. We call these graphs $LS_+$-perfect. In the current contribution, we pursue a full combinatorial characterization of $LS_+$-perfect graphs and make progress towards such a characterization by establishing a new, close relationship among $LS_+$-perfect graphs, near-bipartite graphs and a newly introduced concept of full-support-perfect graphs.
Educational data mining using jmp
Venue: arXiv cs.CY 2014
Year: 2014

Educational Data Mining is a growing trend in case of higher education. The quality of the Educational Institute may be enhanced through discovering hidden knowledge from the student databases/ data warehouses. Present paper is designed to carry out a comparative study with the TDC (Three Year Degree) Course students of different colleges affiliated to Dibrugarh University. The study is conducted with major subject wise, gender wise and category/caste wise. The experimental results may be visualized with Scatterplot3D, Bubble Plot, Fit Y by X, Run Chart, Control Chart etc. of the SAS JMP Software.
Critical Regression Analysis of Real Time Industrial Web Data Set Using Data Mining Tool
Venue: arXiv cs.CY 2014
Year: 2014

In todays fast pacing, highly competing,volatile and challenging world, companies highly rely on data analysis obtained from both offline as well as online way to make their future strategy, to sustain in the market. This paper reviews the regression technique analysis on a real time web data to analyse different attributes of interest and to predict possible growth factors for the company, so as to enable the company to make possible strategic decisions for the growth of the company.
Impatience in mobile networks and its application to data pricing
Venue: arXiv cs.NI 2014
Year: 2014

We consider in this paper an import Quality of Experience (QoE) indicator in mobile networks that is reneging of users due to impatience. We specifically consider a cell under heavy load conditions and compute the reneging probability by using a fluid limit analysis. By solving the fixed point equation, we obtain a new QoE perturbation metric quantifying the impact of reneging on the performance of the system. This metric is then used to devise a new pricing scheme accounting of reneging. We specifically propose several flavors of this pricing around the idea of having a flat rate for accessing the network and an elastic price related to the level of QoE perturbation induced by communications.
Fast Mesh-Based Medical Image Registration
Venue: arXiv cs.CV 2014
Year: 2014

In this paper a fast triangular mesh based registration method is proposed. Having Template and Reference images as inputs, the template image is triangulated using a content adaptive mesh generation algorithm. Considering the pixel values at mesh nodes, interpolated using spline interpolation method for both of the images, the energy functional needed for image registration is minimized. The minimization process was achieved using a mesh based discretization of the distance measure and regularization term which resulted in a sparse system of linear equations, which due to the smaller size in comparison to the pixel-wise registration method, can be solved directly. Mean Squared Difference (MSD) is used as a metric for evaluating the results. Using the mesh based technique, higher speed was achieved compared to pixel-based curvature registration technique with fast DCT solver. The implementation was done in MATLAB without any specific optimization. Higher speeds can be achieved using C/C++ implementations.
Yesquel: scalable SQL storage for Web applications
Venue: arXiv cs.DC 2014
Year: 2014

Based on a brief history of the storage systems for Web applications, we motivate the need for a new storage system. We then describe the architecture of such a system, called Yesquel. Yesquel supports the SQL query language and offers performance similar to NOSQL storage systems.
Public Domain Rank: Identifying Notable Individuals with the Wisdom of the Crowd
Venue: arXiv cs.DL 2014
Year: 2014

Identifying literary, scientific, and technical works of enduring interest is challenging. Few are able to name significant works across more than a handful of domains or languages. This paper introduces an automatic method for identifying authors of notable works throughout history. Notability is defined using the record of which works volunteers have made available in public domain digital editions. A significant benefit of this bottom-up approach is that it also provides a novel and reproducible index of notability for all individuals with Wikipedia pages. The method promises to supplement the work of cultural organizations and institutions seeking to publicize the availability of notable works and prioritize works for preservation and digitization.
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Venue: arXiv cs.CY 2014
Year: 2014

Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region.Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.