OpenRaiser/Intern-Atlas huggingface dataset:
Sum Rate Maximizing Multigroup Multicast Beamforming under Per-antenna Power Constraints
Venue: arXiv cs.IT 2014
Year: 2014

A multi-antenna transmitter that conveys independent sets of common data to distinct groups of users is herein considered, a model known as physical layer multicasting to multiple co-channel groups. In the recently proposed context of per-antenna power constrained multigroup multicasting, the present work focuses on a novel system design that aims at maximizing the total achievable throughput. Towards increasing the system sum rate, the available power resources need to be allocated to well conditioned groups of users. A detailed solution to tackle the elaborate sum rate maximization multigroup multicast problem under per-antenna power constraints is therefore derived. Numerical results are presented to quantify the gains of the proposed algorithm over heuristic solutions. Besides Rayleigh faded channels, the solution is also applied to uniform linear array transmitters operating in the far field, where line-ofsight conditions are realized. In this setting, a sensitivity analysis with respect to the angular separation of co-group users is included. Finally, a simple scenario providing important intuitions for the sum rate maximizing multigroup multicast solutions is elaborated.
Suffix Arrays for Spaced-SNP Databases
Venue: arXiv cs.DS 2014
Year: 2014

Single-nucleotide polymorphisms (SNPs) account for most variations between human genomes. We show how, if the genomes in a database differ only by a reasonable number of SNPs and the substrings between those SNPs are unique, then we can store a fast compressed suffix array for that database.
Dynamic Physiological Partitioning on a Shared-nothing Database Cluster
Venue: arXiv cs.DB 2014
Year: 2014

Traditional DBMS servers are usually over-provisioned for most of their daily workloads and, because they do not show good-enough energy proportionality, waste a lot of energy while underutilized. A cluster of small (wimpy) servers, where its size can be dynamically adjusted to the current workload, offers better energy characteristics for these workloads. Yet, data migration, necessary to balance utilization among the nodes, is a non-trivial and time-consuming task that may consume the energy saved. For this reason, a sophisticated and easy to adjust partitioning scheme fostering dynamic reorganization is needed. In this paper, we adapt a technique originally created for SMP systems, called physiological partitioning, to distribute data among nodes, that allows to easily repartition data without interrupting transactions. We dynamically partition DB tables based on the nodes' utilization and given energy constraints and compare our approach with physical partitioning and logical partitioning methods. To quantify possible energy saving and its conceivable drawback on query runtimes, we evaluate our implementation on an experimental cluster and compare the results w.r.t. performance and energy consumption. Depending on the workload, we can substantially save energy without sacrificing too much performance.
Expected degree of finite preferential attachment networks
Venue: arXiv cs.SI 2014
Year: 2014

We provide an analytic expression for the quantity described in the title. Namely, we perform a preferential attachment growth process to generate a scale-free network. At each stage we add a new node with $m$ new links. Let $k$ denote the degree of a node, and $N$ the number of nodes in the network. The degree distribution is assumed to converge to a power-law (for $k\geq m$) of the form $k^{-γ}$ and we obtain an exact implicit relationship for $γ$, $m$ and $N$. We verify this with numerical calculations over several orders of magnitude. Although this expression is exact, it provides only an implicit expression for $γ(m)$. Nonetheless, we provide a reasonable guess as to the form of this curve and perform curve fitting to estimate the parameters of that curve --- demonstrating excellent agreement between numerical fit, theory, and simulation.
Privacy and Anonymity
Venue: arXiv cs.CY 2014
Year: 2014

Since the beginning of the digital area, privacy and anonymity have been impacted drastically (both, positively and negatively), by the different technologies developed for communications purposes. The broad possibilities that the Internet offers since its conception, makes it a mandatory target for those entities that are aiming to know and control the different channels of communication and the information that flows through. In this paper, we address the current threats against privacy and anonymity on the Internet, together with the methods applied against them. In addition, we enumerate the publicly known entities behind those threats and their motivations. Finally, we analyze the state of the art concerning the protection of the privacy and anonymity on the Internet; introducing future lines of research.
A Dynamic Simulation-Optimization Model for Adaptive Management of Urban Water Distribution System Contamination Threats
Venue: arXiv cs.OH 2014
Year: 2014

Urban water distribution systems hold a critical and strategic position in preserving public health and industrial growth. Despite the ubiquity of these urban systems, aging infrastructure, and increased risk of terrorism, decision support models for a timely and adaptive contamination emergency response still remain at an undeveloped stage. Emergency response is characterized as a progressive, interactive, and adaptive process that involves parallel activities of processing streaming information and executing response actions. This study develops a dynamic decision support model that adaptively simulates the time-varying emergency environment and tracks changing best health protection response measures at every stage of an emergency in real-time. Feedback mechanisms between the contaminated network, emergency managers, and consumers are incorporated in a dynamic simulation model to capture time-varying characteristics of an emergency environment. An evolutionary-computation-based dynamic optimization model is developed to adaptively identify time-dependant optimal health protection measures during an emergency. This dynamic simulation-optimization model treats perceived contaminant source attributes as time-varying parameters to account for perceived contamination source updates as more data stream in over time. Performance of the developed dynamic decision support model is analyzed and demonstrated using a mid-size virtual city that resembles the dynamics and complexity of real-world urban systems. This adaptive emergency response optimization model is intended to be a major component of an all-inclusive cyberinfrastructure for efficient contamination threat management, which is currently under development.
AsterixDB: A Scalable, Open Source BDMS
Venue: arXiv cs.DB 2014
Year: 2014

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.
The Design and Implementation of an ANN-based Non-linearity Compensator of LVDT Sensor
Venue: arXiv cs.SY 2014
Year: 2014

Linear variable differential transformer (LVDT) sensors are used in engineering applications due to their fine-grained measurements. However, these sensors exhibit non-linear input-output characteristics, which decrease the reliability of the sensing system. The contribution of this article is three-fold. First, it provides an experimental study of the non-linearity problem of the LVDT. Second, it proposes the design of a functional link artificial neural network (FLANN) based non-linearity compensator model for overcoming it. Finally, it validates the feasibility of the solution in simulation, and presents a proof-of-concept hardware implementation on a SPARTAN-II (PQ208)FPGA using VHDL in Xilinx. The model has been mathematically derived, and its simulation study has been presented that achieves nearly 100% linearity range. The result obtained from the FPGA implementation is in good agreement with the simulation result, which establishes its actualization as part of a general manufacturing process for linearity compensated LVDT sensors.
Braided Convolutional Codes -- A Class of Spatially Coupled Turbo-Like Codes
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we investigate the impact of spatial coupling on the thresholds of turbo-like codes. Parallel concatenated and serially concatenated convolutional codes as well as braided convolutional codes (BCCs) are compared by means of an exact density evolution (DE) analysis for the binary erasure channel (BEC). We propose two extensions of the original BCC ensemble to improve its threshold and demonstrate that their BP thresholds approach the maximum-a-posteriori (MAP) threshold of the uncoupled ensemble. A comparison of the different ensembles shows that parallel concatenated ensembles can be outperformed by both serially concatenated and BCC ensembles, although they have the best BP thresholds in the uncoupled case.
Money Walks: A Human-Centric Study on the Economics of Personal Mobile Data
Venue: arXiv cs.HC 2014
Year: 2014

In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made/received, applications, e.g. time spent on different apps, location and media, photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data.
Novelty Search in Competitive Coevolution
Venue: arXiv cs.NE 2014
Year: 2014

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty; (ii) score one population according to novelty, and the other according to fitness; and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.
Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics
Venue: arXiv cs.NE 2014
Year: 2014

Evolutionary techniques driven by behavioural diversity, such as novelty search, have shown significant potential in evolutionary robotics. These techniques rely on priorly specified behaviour characterisations to estimate the similarity between individuals. Characterisations are typically defined in an ad hoc manner based on the experimenter's intuition and knowledge about the task. Alternatively, generic characterisations based on the sensor-effector values of the agents are used. In this paper, we propose a novel approach that allows for systematic derivation of behaviour characterisations for evolutionary robotics, based on a formal description of the agents and their environment. Systematically derived behaviour characterisations (SDBCs) go beyond generic characterisations in that they can contain task-specific features related to the internal state of the agents, environmental features, and relations between them. We evaluate SDBCs with novelty search in three simulated collective robotics tasks. Our results show that SDBCs yield a performance comparable to the task-specific characterisations, in terms of both solution quality and behaviour space exploration.
Technical Report: Estimating Reliability of Workers for Cooperative Distributed Computing
Venue: arXiv cs.DC 2014
Year: 2014

Internet supercomputing is an approach to solving partitionable, computation-intensive problems by harnessing the power of a vast number of interconnected computers. For the problem of using network supercomputing to perform a large collection of independent tasks, prior work introduced a decentralized approach and provided randomized synchronous algorithms that perform all tasks correctly with high probability, while dealing with misbehaving or crash-prone processors. The main weaknesses of existing algorithms is that they assume either that the \emph{average} probability of a non-crashed processor returning incorrect results is inferior to $\frac{1}{2}$, or that the probability of returning incorrect results is known to \emph{each} processor. Here we present a randomized synchronous distributed algorithm that tightly estimates the probability of each processor returning correct results. Starting with the set $P$ of $n$ processors, let $F$ be the set of processors that crash. Our algorithm estimates the probability $p_i$ of returning a correct result for each processor $i \in P-F$, making the estimates available to all these processors. The estimation is based on the $(ε, δ)$-approximation, where each estimated probability $\tilde{p_i}$ of $p_i$ obeys the bound ${\sf Pr}[p_i(1-ε) \leq \tilde{p_i} \leq p_i(1+ε)] > 1 - δ$, for any constants $δ>0$ and $ε>0$ chosen by the user. An important aspect of this algorithm is that each processor terminates without global coordination. We assess the efficiency of the algorithm in three adversarial models as follows. For the model where the number of non-crashed processors $|P-F|$ is linearly bounded the time complexity $T(n)$ of the algorithm is $Θ(\log{n})$, work complexity $W(n)$ is $Θ(n\log{n})$, and message complexity $M(n)$ is $Θ(n\log^2n)$.
Continuous On-line Evolution of Agent Behaviours with Cartesian Genetic Programming
Venue: arXiv cs.NE 2014
Year: 2014

Evolutionary Computation has been successfully used to synthesise controllers for embodied agents and multi-agent systems in general. Notwithstanding this, continuous on-line adaptation by the means of evolutionary algorithms is still under-explored, especially outside the evolutionary robotics domain. In this paper, we present an on-line evolutionary programming algorithm that searches in the agent design space for the appropriate behavioural policies to cope with the underlying environment. We discuss the current problems of continuous agent adaptation, present our on-line evolution testbed for evolutionary simulation.
Device-Centric Cooperation in Mobile Networks
Venue: arXiv cs.NI 2014
Year: 2014

The increasing popularity of applications such as video streaming in today's mobile devices introduces higher demand for throughput, and puts a strain especially on cellular links. Cooperation among mobile devices by exploiting both cellular and local area connections is a promising approach to meet the increasing demand. In this paper, we consider that a group of cooperative mobile devices, exploiting both cellular and local area links and within proximity of each other, are interested in the same video content. Traditional network control algorithms introduce high overhead and delay in this setup as the network control and cooperation decisions are made in a source-centric manner. Instead, we develop a device-centric stochastic cooperation scheme. Our device-centric scheme; DcC allows mobile devices to make control decisions such as flow control, scheduling, and cooperation without loss of optimality. Thanks to being device-centric, DcC reduces; (i) overhead; i.e., the number of control packets that should be transmitted over cellular links, so cellular links are used more efficiently, and (ii) the amount of delay that each packet experiences, which improves quality of service. The simulation results demonstrate the benefits of DcC.
Projecting Ising Model Parameters for Fast Mixing
Venue: arXiv cs.LG 2014
Year: 2014

Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that is guaranteed to be fast mixing, under several divergences. We find that Gibbs sampling using the projected parameters is more accurate than with the original parameters when interaction strengths are strong and when limited time is available for sampling.
A Fully Polynomial-Time Approximation Scheme for Speed Scaling with Sleep State
Venue: arXiv cs.DS 2014
Year: 2014

We study classical deadline-based preemptive scheduling of tasks in a computing environment equipped with both dynamic speed scaling and sleep state capabilities: Each task is specified by a release time, a deadline and a processing volume, and has to be scheduled on a single, speed-scalable processor that is supplied with a sleep state. In the sleep state, the processor consumes no energy, but a constant wake-up cost is required to transition back to the active state. In contrast to speed scaling alone, the addition of a sleep state makes it sometimes beneficial to accelerate the processing of tasks in order to transition the processor to the sleep state for longer amounts of time and incur further energy savings. The goal is to output a feasible schedule that minimizes the energy consumption. Since the introduction of the problem by Irani et al. [16], its exact computational complexity has been repeatedly posed as an open question (see e.g. [2,8,15]). The currently best known upper and lower bounds are a 4/3-approximation algorithm and NP-hardness due to [2] and [2,17], respectively. We close the aforementioned gap between the upper and lower bound on the computational complexity of speed scaling with sleep state by presenting a fully polynomial-time approximation scheme for the problem. The scheme is based on a transformation to a non-preemptive variant of the problem, and a discretization that exploits a carefully defined lexicographical ordering among schedules.
Spectrum Refarming: A New Paradigm of Spectrum Sharing for Cellular Networks
Venue: arXiv cs.IT 2014
Year: 2014

Spectrum refarming (SR) refers to a radio resource management technique which allows different generations of cellular networks to operate in the same radio spectrum. In this paper, an underlay SR model is proposed, in which an Orthogonal Frequency Division Multiple Access (OFDMA) system refarms the spectrum of a Code Division Multiple Access (CDMA) system through intelligently exploiting the interference margin provided by the CDMA system. We investigate the mutual effect of the two systems by evaluating the asymptotic signal-to-interference-plus-noise ratio (SINR) of the users, based on which the interference margin tolerable by the CDMA system is determined. By using the interference margin together with the transmit power constraints, the uplink resource allocation problem of OFDMA system is formulated and solved through dual decomposition method. Simulation results have verified our theoretical analysis, and validated the effectiveness of the proposed resource allocation algorithm and its capability to protect the legacy CDMA users. The proposed SR system requires the least information flow from the CDMA system to the OFDMA system, and importantly, no upgrading of legacy CDMA system is needed; thus it can be deployed by telecom operators to maximize the spectral efficiency of their cellular networks.
On the Synthesis of Mobile Robots Algorithms: the Case of Ring Gathering
Venue: arXiv cs.DC 2014
Year: 2014

RecentadvancesinDistributedComputinghighlightmodelsandalgo- rithms for autonomous swarms of mobile robots that self-organize and cooperate to solve global objectives. The overwhelming majority of works so far considers handmade algorithms and correctness proofs. This paper is the first to propose a formal framework to automatically design dis- tributed algorithms that are dedicated to autonomous mobile robots evolving in a discrete space. As a case study, we consider the problem of gathering all robots at a particular location, not known beforehand. Our contribution is threefold. First, we propose an encoding of the gathering problem as a reachability game. Then, we automatically generate an optimal distributed algorithm for three robots evolv- ing on a fixed size uniform ring. Finally, we prove by induction that the generated algorithm is also correct for any ring size except when an impossibility result holds (that is, when the number of robots divides the ring size).
A Synonym Based Approach of Data Mining in Search Engine Optimization
Venue: arXiv cs.IR 2014
Year: 2014

In todays era with the rapid growth of information on the web, makes users turn to search engines as a replacement of traditional media. This makes sorting of particular information through billions of webpages and displaying the relevant data makes the task tough for the search engine. Remedy for this is SEO i.e. having a website optimized in such a way that it will display the relevant webpages based on ranking. This is the main reason that makes search engine optimization a prominent position in online world. This paper present a synonym based data mining approach for SEO that makes the task of improving the ranking of the website much easier way and user will get answer to their query easily through any of search engine available in market.