OpenRaiser/Intern-Atlas huggingface dataset:
Home Location Identification of Twitter Users
Venue: arXiv cs.SI 2014
Year: 2014

We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone or geographic region, using the content of users tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.
Probabilistic Robustness Analysis of Stochastic Jump Linear Systems
Venue: arXiv cs.SY 2014
Year: 2014

In this paper, we propose a new method to measure the probabilistic robustness of stochastic jump linear system with respect to both the initial state uncertainties and the randomness in switching. Wasserstein distance which defines a metric on the manifold of probability density functions is used as tool for the performance and the stability measures. Starting with Gaussian distribution to represent the initial state uncertainties, the probability density function of the system state evolves into mixture of Gaussian, where the number of Gaussian components grows exponentially. To cope with computational complexity caused by mixture of Gaussian, we prove that there exists an alternative probability density function that preserves exact information in the Wasserstein level. The usefulness and the efficiency of the proposed methods are demonstrated by example.
Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means
Venue: arXiv cs.CV 2014
Year: 2014

We first establish a law of large numbers and a convergence theorem in distribution to show the rate of convergence of the non-local means filter for removing Gaussian noise. We then introduce the notion of degree of similarity to measure the role of similarity for the non-local means filter. Based on the convergence theorems, we propose a patch-based weighted means filter for removing impulse noise and its mixture with Gaussian noise by combining the essential idea of the trilateral filter and that of the non-local means filter. Our experiments show that our filter is competitive compared to recently proposed methods.
The Beauty of the Commons: Optimal Load Sharing by Base Station Hopping in Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

In wireless sensor networks (WSNs), the base station (BS) is a critical sensor node whose failure causes severe data losses. Deploying multiple fixed BSs improves the robustness, yet requires all BSs to be installed with large batteries and large energy-harvesting devices due to the high energy consumption of BSs. In this paper, we propose a scheme to coordinate the multiple deployed BSs such that the energy supplies required by individual BSs can be substantially reduced. In this scheme, only one BS is selected to be active at a time and the other BSs act as regular sensor nodes. We first present the basic architecture of our system, including how we keep the network running with only one active BS and how we manage the handover of the role of the active BS. Then, we propose an algorithm for adaptively selecting the active BS under the spatial and temporal variations of energy resources. This algorithm is simple to implement but is also asymptotically optimal under mild conditions. Finally, by running simulations and real experiments on an outdoor testbed, we verify that the proposed scheme is energy-efficient, has low communication overhead and reacts rapidly to network changes.
Development of SyReC based expandable reversible logic circuits
Venue: arXiv cs.AR 2014
Year: 2014

Reversible computing is gaining high interest from researchers due to its various promises. One of the prominent advantages perceived from reversible logic is that of reduced power dissipation with many reversible gates at hand, designing a reversible circuit (combinational) has received due attention and achievement. A proposed language for description of reversible circuit, namely SyReC, is also in place. What remain are the software tools which would help in reversible circuit synthesis through simulation. Beginning with the smallest reversible circuit realizations the SyReC statements and expressions, we employ a hierarchal approach to develop a complete reversible circuit, entirely from its SyReC code. We implement this as a software tool. The tool allows a user to expand a reversible circuit of choice in terms of bit width of its inputs. The background approach of expansion of a reversible circuit has also been proposed as a part of this dissertation. Also, a user can use the tool to observe the effect of expansion on incurred costs, in terms of increase in number of lines, number of gates and quantum cost. The importance of observing the change in costs with respect to scale of expansion is important not only from analysis point of view, but also because the cost depends on the approach used for expansion. This dissertation also proposes a reversible circuit design for elevator controller (combinational) and the related costs. The aim is to emphasize use of the proposed approach is designing customized circuits.
Web-based Database Management to support Telemedicine System
Venue: arXiv cs.CY 2014
Year: 2014

The transfer of the medical care services to the patient, rather than the transport of the patient to the medical services providers is aim of the project. This is achieved by using web-based applications including Modern Medical Informatics Services which is easier, faster and less expensive. The required system implements the suitable informatics and electronics solutions efficiently for the Tele-medicine care. We proposed an approach to manage different multimedia medical databases in the telemedicine system. In order to be efficiently and effectively manage, search, and display database information, we define an information package for both of doctor and patient as a concise data set of their medical information from each visit. The methodology for accessing various types of medical records will be provided, also we will design two web-based interfaces, high-quality data and display for many medical service purposes.
Customers perception of mbanking adoption in Kingdom of Bahrain: an empirical assessment of an extended tam model
Venue: arXiv cs.CY 2014
Year: 2014

Mobile applications have been rapidly changing the way business organizations deliver their services to their customers and how customers can interact with their service providers in order to satisfy their needs. The use of mobile applications increases rapidly, and has been used in many segments including banking segment. This research aims at extending the Technology Adoption Model (TAM) to incorporate the role of factors in influencing customers perception towards mobile banking adoption. Furthermore, the extended TAM model was evaluated empirically to measure its impact on m-banking adoption in of Bahrain. The model was evaluated using a sample survey of 372 customers. The results reveal that the intention to adopt mobile banking is mainly affected by specific factors which are: Perceived Usefulness and Ease of Use. On the other hand, some factors such as perceived cost and perceived risk did not show any affect on the users' intention to use mobile banking. The result of this research is beneficial for banking service managers to consider the factors that can enforce the Mobile Banking services adoption and increase the take-up of their mobile services.
Emerging archetypes in massive artificial societies for literary purposes using genetic algorithms
Venue: arXiv cs.AI 2014
Year: 2014

The creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and plots to create an engaging narrative. This work presents a simulated environment with hundreds of characters that allows the study of coherent and interesting literary archetypes (or behaviours), plots and sub-plots. We will use this environment to perform a study about the number of profiles (parameters that define the personality of a character) needed to create two emergent scenes of archetypes: "natality control" and "revenge". A Genetic Algorithm (GA) will be used to find the fittest number of profiles and parameter configuration that enables the existence of the desired archetypes (played by the characters without their explicit knowledge). The results show that parametrizing this complex system is possible and that these kind of archetypes can emerge in the given environment.
Memory Capacity of Neural Networks using a Circulant Weight Matrix
Venue: arXiv cs.NE 2014
Year: 2014

This paper presents results on the memory capacity of a generalized feedback neural network using a circulant matrix. Children are capable of learning soon after birth which indicates that the neural networks of the brain have prior learnt capacity that is a consequence of the regular structures in the brain's organization. Motivated by this idea, we consider the capacity of circulant matrices as weight matrices in a feedback network.
Comparison Of Document Management Systems By Meta Modelling And Workforce Centric Tuning Measures
Venue: arXiv cs.SE 2014
Year: 2014

Document management software are used widely for office paper management and related workflows. However, they have some differences from a flexibility perspective for the needs of workers. Moreover, they also differentiate in their underlying conceptual design. In this paper, a set (eight) of widely used document management systems are chosen from internet reviews [5] [6] and their conceptual models are analysed with meta modeling and flexibility tuning measures. The main objective is to compare these systems by analyzing their human-orientation and flexibility. This analysis provides useful information for organizations especially looking for document management systems which are more workforce centric.
A Security Analysis of Browser Extensions
Venue: arXiv cs.CR 2014
Year: 2014

Browser Extensions (often called plugins or addons) are small pieces of code that let developers add additional functionality to the browser. However, with extensions comes a security price: the user must trust the developer. We look at ways in which this trust can be broken and malicious extensions installed. We also look at silent installations of plugins in various browsers and work on ways to make silent installations possible in browsers that work against it. We compare the browser extension mechanism among various browsers, and try to create a set of rules to maintain the principle of least privileges in the browser. We track various plugins and determine whether the least privileges required match with the privileges asked for. We also work on a survey of extensions (for various browsers) and determine the nature of attacks possible. For eg, if a developer account gets hacked, updating of a normal extension with a malicious one is possible. We look at privilege abuse and survey extensions that ask for more privileges than they use. We finally provide a solution and allow a person to check the authenticity of the extension even before they download it.
Limiting Behavior of LQ Deterministic Infinite Horizon Nash Games with Symmetric Players as the Number of Players goes to Infinity
Venue: arXiv cs.GT 2014
Year: 2014

A Linear Quadratic Deterministic Continuous Time Game with many symmetric players is considered and the Linear Feedback Nash strategies are studied as the number of players goes to infinity. We show that under some conditions the limit of the solutions exists and can be used to approximate the case with a finite but large number of players. It is shown that in the limit each player acts as if he were faced with one player only, who represents the average behavior of the others.
The Relativized Second Eigenvalue Conjecture of Alon
Venue: arXiv cs.DM 2014
Year: 2014

We prove a relativization of the Alon Second Eigenvalue Conjecture for all $d$-regular base graphs, $B$, with $d\ge 3$: for any $ε>0$, we show that a random covering map of degree $n$ to $B$ has a new eigenvalue greater than $2\sqrt{d-1}+ε$ in absolute value with probability $O(1/n)$. Furthermore, if $B$ is a Ramanujan graph, we show that this probability is proportional to $n^{-{η_{\rm \,fund}}(B)}$, where ${η_{\rm \,fund}}(B)$ is an integer depending on $B$, which can be computed by a finite algorithm for any fixed $B$. For any $d$-regular graph, $B$, ${η_{\rm \,fund}}(B)$ is greater than $\sqrt{d-1}$. Our proof introduces a number of ideas that simplify and strengthen the methods of Friedman's proof of the original conjecture of Alon. The most significant new idea is that of a ``certified trace,'' which is not only greatly simplifies our trace methods, but is the reason we can obtain the $n^{-{η_{\rm \,fund}}(B)}$ estimate above. This estimate represents an improvement over Friedman's results of the original Alon conjecture for random $d$-regular graphs, for certain values of $d$.
Performance Benefits of DataMPI: A Case Study with BigDataBench
Venue: arXiv cs.PF 2014
Year: 2014

Apache Hadoop and Spark are gaining prominence in Big Data processing and analytics. Both of them are widely deployed on Internet companies. On the other hand, high-performance data analysis requirements are causing academical and industrial communities to adopt state-of-the-art technologies in HPC to solve Big Data problems. Recently, we have proposed a key-value pair based communication library, DataMPI, which is extending MPI to support Hadoop/Spark-like Big Data Computing jobs. In this paper, we use BigDataBench, a Big Data benchmark suite, to do comprehensive studies on performance and resource utilization characterizations of Hadoop, Spark and DataMPI. From our experiments, we observe that the job execution time of DataMPI has up to 55% and 39% speedups compared with those of Hadoop and Spark, respectively. Most of the benefits come from the high-efficiency communication mechanisms in DataMPI. We also notice that the resource (CPU, memory, disk and network I/O) utilizations of DataMPI are also more efficient than those of the other two frameworks.
Sparse Polynomial Interpolation Codes and their decoding beyond half the minimal distance
Venue: arXiv cs.SC 2014
Year: 2014

We present algorithms performing sparse univariate polynomial interpolation with errors in the evaluations of the polynomial. Based on the initial work by Comer, Kaltofen and Pernet [Proc. ISSAC 2012], we define the sparse polynomial interpolation codes and state that their minimal distance is precisely the length divided by twice the sparsity. At ISSAC 2012, we have given a decoding algorithm for as much as half the minimal distance and a list decoding algorithm up to the minimal distance. Our new polynomial-time list decoding algorithm uses sub-sequences of the received evaluations indexed by a linear progression, allowing the decoding for a larger radius, that is, more errors in the evaluations while returning a list of candidate sparse polynomials. We quantify this improvement for all typically small values of number of terms and number of errors, and provide a worst case asymptotic analysis of this improvement. For instance, for sparsity T = 5 with up to 10 errors we can list decode in polynomial-time from 74 values of the polynomial with unknown terms, whereas our earlier algorithm required 2T (E + 1) = 110 evaluations. We then propose two variations of these codes in characteristic zero, where appropriate choices of values for the variable yield a much larger minimal distance: the length minus twice the sparsity.
Mixed-norm Regularization for Brain Decoding
Venue: arXiv cs.LG 2014
Year: 2014

This work investigates the use of mixed-norm regularization for sensor selection in Event-Related Potential (ERP) based Brain-Computer Interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multi-task learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multi-task learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multi-task approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.
On the continuous Fermat-Weber problem for a convex polygon using Euclidean distance
Venue: arXiv cs.CG 2014
Year: 2014

We consider the continuous Fermat-Weber problem, where the customers are continuously (uniformly) distributed along the boundary of a convex polygon. We derive the closed-form expression for finding the average distance from a given point to the continuously distributed customers along the boundary. A Weiszfeld-type procedure is proposed for this model, which is shown to be linearly convergent. We also derive a closed-form formula to find the average distance for a given point to the entire convex polygon, assuming a uniform distribution. Since the function is smooth, convex, and explicitly given, the continuous version of the Fermat-Weber problem over a convex polygon can be solved easily by numerical algorithms.
Reset thresholds of automata with two cycle lengths
Venue: arXiv cs.FL 2014
Year: 2014

We present several series of synchronizing automata with multiple parameters, generalizing previously known results. Let p and q be two arbitrary co-prime positive integers, q > p. We describe reset thresholds of the colorings of primitive digraphs with exactly one cycle of length p and one cycle of length q. Also, we study reset thresholds of the colorings of primitive digraphs with exactly one cycle of length q and two cycles of length p.
A Queueing Characterization of Information Transmission over Block Fading Rayleigh Channels in the Low SNR
Venue: arXiv cs.IT 2014
Year: 2014

Unlike the AWGN (additive white gaussian noise) channel, fading channels suffer from random channel gains besides the additive Gaussian noise. As a result, the instantaneous channel capacity varies randomly along time, which makes it insufficient to characterize the transmission capability of a fading channel using data rate only. In this paper, the transmission capability of a buffer-aided block Rayleigh fading channel is examined by a constant rate input data stream, and reflected by several parameters such as the average queue length, stationary queue length distribution, packet delay and overflow probability. Both infinite-buffer model and finite-buffer model are considered. Taking advantage of the memoryless property of the service provided by the channel in each block in the the low SNR (signal-to-noise ratio) regime, the information transmission over the channel is formulated as a \textit{discrete time discrete state} $D/G/1$ queueing problem. The obtained results show that block fading channels are unable to support a data rate close to their ergodic capacity, no matter how long the buffer is, even seen from the application layer. For the finite-buffer model, the overflow probability is derived with explicit expression, and is shown to decrease exponentially when buffer size is increased, even when the buffer size is very small.
Using the Business Process Model and Notation for Modeling Enterprise Integration Patterns
Venue: arXiv cs.SE 2014
Year: 2014

Enterprise Integration Patterns (EIP) are a collection of widely used stencils for integrating enterprise applications and business processes. These patterns represent a "de-facto" standard reference for design decisions when integrating enterprise applications. For each of these patterns we present the integration semantics (model) and the conceptual translation (syntax) to the Business Process Model and Notation (BPMN), which is a "de-facto" standard for modelling business process semantics and their runtime behavior.
Computer Vision Accelerators for Mobile Systems based on OpenCL GPGPU Co-Processing
Venue: arXiv cs.DC 2014
Year: 2014

In this paper, we present an OpenCL-based heterogeneous implementation of a computer vision algorithm -- image inpainting-based object removal algorithm -- on mobile devices. To take advantage of the computation power of the mobile processor, the algorithm workflow is partitioned between the CPU and the GPU based on the profiling results on mobile devices, so that the computationally-intensive kernels are accelerated by the mobile GPGPU (general-purpose computing using graphics processing units). By exploring the implementation trade-offs and utilizing the proposed optimization strategies at different levels including algorithm optimization, parallelism optimization, and memory access optimization, we significantly speed up the algorithm with the CPU-GPU heterogeneous implementation, while preserving the quality of the output images. Experimental results show that heterogeneous computing based on GPGPU co-processing can significantly speed up the computer vision algorithms and makes them practical on real-world mobile devices.
Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit
Venue: arXiv cs.NA 2014
Year: 2014

We investigate the methods that simultaneously enforce sparsity and low-rank structure in a matrix as often employed for sparse phase retrieval problems or phase calibration problems in compressive sensing. We propose a new approach for analyzing the trade off between the sparsity and low rank constraints in these approaches which not only helps to provide guidelines to adjust the weights between the aforementioned constraints, but also enables new simulation strategies for evaluating performance. We then provide simulation results for phase retrieval and phase calibration cases both to demonstrate the consistency of the proposed method with other approaches and to evaluate the change of performance with different weights for the sparsity and low rank structure constraints.