OpenRaiser/Intern-Atlas huggingface dataset:
Improving Network Performance with Affinity based Mobility Model in Opportunistic Network
Venue: arXiv cs.NI 2012
Year: 2012

Opportunistic network is a type of Delay Tolerant Network which is characterized by intermittent connectivity amongst the nodes and communication largely depends upon the mobility of the participating nodes. The network being highly dynamic, traditional MANET protocols cannot be applied and the nodes must adhere to store-carry-forward mechanism. Nodes do not have the information about the network topology, number of participating nodes and the location of the destination node. Hence, message transfer reliability largely depends upon the mobility pattern of the nodes. In this paper we have tried to find the impact of RWP (Random Waypoint) mobility on packet delivery ratio. We estimate mobility factors like number of node encounters, contact duration(link time) and inter-contact time which in turn depends upon parameters like playfield area (total network area), number of nodes, node velocity, bit-rate and RF range of the nodes. We also propose a restricted form of RWP mobility model, called the affinity based mobility model. The network scenario consists of a source and a destination node that are located at two extreme corners of the square playfield (to keep a maximum distance between them) and exchange data packets with the aid of mobile 'helper' nodes. The source node and the destination node are static. The mobile nodes only help in relaying the message. We prove how affinity based mobility model helps in augmenting the network reliability thereby increasing the message delivery ratio and reduce message delivery latency.
The 1-Center and 1-Highway problem
Venue: arXiv cs.CG 2012
Year: 2012

We study a variation of the 1-center problem in which, in addition to a single supply facility, we are allowed to locate a highway. This highway increases the transportation speed between any demand point and the facility. That is, given a set $S$ of points and $v>1$, we are interested in locating the facility point $f$ and the highway $h$ that minimize the expression $\max_{p\in S}d_{h}(p,f)$, where $d_h$ is the time needed to travel between $p$ and $f$. We consider two types of highways ({\em freeways} and {\em turnpikes}) and study the cases in which the highway's length is fixed by the user (or can be modified to further decrease the transportation time).
Fast Optimal Joint Tracking-Registration for Multi-Sensor Systems
Venue: arXiv cs.RO 2012
Year: 2012

Sensor fusion of multiple sources plays an important role in vehicular systems to achieve refined target position and velocity estimates. In this article, we address the general registration problem, which is a key module for a fusion system to accurately correct systematic errors of sensors. A fast maximum a posteriori (FMAP) algorithm for joint registration-tracking (JRT) is presented. The algorithm uses a recursive two-step optimization that involves orthogonal factorization to ensure numerically stability. Statistical efficiency analysis based on Cramèr-Rao lower bound theory is presented to show asymptotical optimality of FMAP. Also, Givens rotation is used to derive a fast implementation with complexity O(n) with $n$ the number of tracked targets. Simulations and experiments are presented to demonstrate the promise and effectiveness of FMAP.
Deploying SIP-based Mobile Exam Application onto Next Generation Network testbed
Venue: arXiv cs.NI 2012
Year: 2012

Over the past few years, mobile operators are faced with enormous challenges. Of such challenges, evolved user demands on personalized applications. Telecommunications industry as well as research community have paid enormous attention to Next Generation Networks (NGN) to address this challenge. NGN is perceived as a sophisticated platform where both application developers and mobile operators cooperate to develop user applications with enhanced quality of experience. The objective of this paper is twofold: first we present an introduction to state-of-the-art NGN testbed to be developed at KAU, and second we provide initial analysis for deploying a mobile application on top of the testbed.
Multiset Estimates and Combinatorial Synthesis
Venue: arXiv cs.SY 2012
Year: 2012

The paper addresses an approach to ordinal assessment of alternatives based on assignment of elements into an ordinal scale. Basic versions of the assessment problems are formulated while taking into account the number of levels at a basic ordinal scale [1,2,...,l] and the number of assigned elements (e.g., 1,2,3). The obtained estimates are multisets (or bags) (cardinality of the multiset equals a constant). Scale-posets for the examined assessment problems are presented. 'Interval multiset estimates' are suggested. Further, operations over multiset estimates are examined: (a) integration of multiset estimates, (b) proximity for multiset estimates, (c) comparison of multiset estimates, (d) aggregation of multiset estimates, and (e) alignment of multiset estimates. Combinatorial synthesis based on morphological approach is examined including the modified version of the approach with multiset estimates of design alternatives. Knapsack-like problems with multiset estimates are briefly described as well. The assessment approach, multiset-estimates, and corresponding combinatorial problems are illustrated by numerical examples.
Unfolding Prismatoids as Convex Patches: Counterexamples and Positive Results
Venue: arXiv cs.CG 2012
Year: 2012

We address the unsolved problem of unfolding prismatoids in a new context, viewing a "topless prismatoid" as a convex patch---a polyhedral subset of the surface of a convex polyhedron homeomorphic to a disk. We show that several natural strategies for unfolding a prismatoid can fail, but obtain a positive result for "petal unfolding" topless prismatoids. We also show that the natural extension to a convex patch consisting of a face of a polyhedron and all its incident faces, does not always have a nonoverlapping petal unfolding. However, we obtain a positive result by excluding the problematical patches. This then leads a positive result for restricted prismatoids. Finally, we suggest suggest studying the unfolding of convex patches in general, and offer some possible lines of investigation.
Data Dissemination And Collection Algorithms For Collaborative Sensor Networks Using Dynamic Cluster Heads
Venue: arXiv cs.NI 2012
Year: 2012

We develop novel data dissemination and collection algorithms for Wireless Sensor Networks (WSNs) in which we consider $n$ sensor nodes distributed randomly in a certain field to measure a physical phenomena. Such sensors have limited energy, shortage coverage range, bandwidth and memory constraints. We desire to disseminate nodes' data throughout the network such that a base station will be able to collect the sensed data by querying a small number of nodes. We propose two data dissemination and collection algorithms (DCA's) to solve this problem. Data dissemination is achieved through dynamical selection of some nodes. The selected nodes will be changed after a time slot $t$ and may be repeated after a period $T$.
Efficient Constrained Regret Minimization
Venue: arXiv cs.LG 2012
Year: 2012

Online learning constitutes a mathematical and compelling framework to analyze sequential decision making problems in adversarial environments. The learner repeatedly chooses an action, the environment responds with an outcome, and then the learner receives a reward for the played action. The goal of the learner is to maximize his total reward. However, there are situations in which, in addition to maximizing the cumulative reward, there are some additional constraints on the sequence of decisions that must be satisfied on average by the learner. In this paper we study an extension to the online learning where the learner aims to maximize the total reward given that some additional constraints need to be satisfied. By leveraging on the theory of Lagrangian method in constrained optimization, we propose Lagrangian exponentially weighted average (LEWA) algorithm, which is a primal-dual variant of the well known exponentially weighted average algorithm, to efficiently solve constrained online decision making problems. Using novel theoretical analysis, we establish the regret and the violation of the constraint bounds in full information and bandit feedback models.
Publishing Life Science Data as Linked Open Data: the Case Study of miRBase
Venue: arXiv cs.DB 2012
Year: 2012

This paper presents our Linked Open Data (LOD) infrastructures for genomic and experimental data related to microRNA biomolecules. Legacy data from two well-known microRNA databases with experimental data and observations, as well as change and version information about microRNA entities, are fused and exported as LOD. Our LOD server assists biologists to explore biological entities and their evolution, and provides a SPARQL endpoint for applications and services to query historical miRNA data and track changes, their causes and effects.
A Multi-Dimensional approach towards Intrusion Detection System
Venue: arXiv cs.CR 2012
Year: 2012

In this paper, we suggest a multi-dimensional approach towards intrusion detection. Network and system usage parameters like source and destination IP addresses; source and destination ports; incoming and outgoing network traffic data rate and number of CPU cycles per request are divided into multiple dimensions. Rather than analyzing raw bytes of data corresponding to the values of the network parameters, a mature function is inferred during the training phase for each dimension. This mature function takes a dimension value as an input and returns a value that represents the level of abnormality in the system usage with respect to that dimension. This mature function is referred to as Individual Anomaly Indicator. Individual Anomaly Indicators recorded for each of the dimensions are then used to generate a Global Anomaly Indicator, a function with n variables (n is the number of dimensions) that provides the Global Anomaly Factor, an indicator of anomaly in the system usage based on all the dimensions considered together. The Global Anomaly Indicator inferred during the training phase is then used to detect anomaly in the network traffic during the detection phase. Network traffic data encountered during the detection phase is fed back to the system to improve the maturity of the Individual Anomaly Indicators and hence the Global Anomaly Indicator.
Hajj and Umrah Event Recognition Datasets
Venue: arXiv cs.CV 2012
Year: 2012

In this note, new Hajj and Umrah Event Recognition datasets (HUER) are presented. The demonstrated datasets are based on videos and images taken during 2011-2012 Hajj and Umrah seasons. HUER is the first collection of datasets covering the six types of Hajj and Umrah ritual events (rotating in Tawaf around Kabaa, performing Sa'y between Safa and Marwa, standing on the mount of Arafat, staying overnight in Muzdalifah, staying two or three days in Mina, and throwing Jamarat). The HUER datasets also contain video and image databases for nine types of human actions during Hajj and Umrah (walking, drinking from Zamzam water, sleeping, smiling, eating, praying, sitting, shaving hairs and ablutions, reading the holy Quran and making duaa). The spatial resolutions are 1280 x 720 pixels for images and 640 x 480 pixels for videos and have lengths of 20 seconds in average with 30 frame per second rates.
Optimising Performance Through Unbalanced Decompositions
Venue: arXiv cs.DC 2012
Year: 2012

GS2 is an initial value gyrokinetic simulation code developed to study low-frequency turbulence in magnetized plasma. It is parallelised using MPI with the simulation domain decomposed across tasks. The optimal domain decomposition is non-trivial, and complicated by the different requirements of the linear and non-linear parts of the calculations. GS2 users currently choose a data layout, and are guided towards processor count that are efficient for linear calculations. These choices can, however, lead to data decompositions that are relatively inefficient for the non-linear calculations. We have analysed the performance impact of the data decompositions on the non-linear calculation and associated communications. This has helped us to optimise the decomposition algorithm by using unbalanced data layouts for the non-linear calculations whilst maintaining the existing decompositions for the linear calculations, which has completely eliminated communications for parts of the non-linear simulation and improved performance by up to 15% for a representative simulation.
Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies
Venue: arXiv cs.GT 2012
Year: 2012

We perform a simulation-based analysis of keyword auctions modeled as one-shot games of incomplete information to study a series of mechanism design questions. Our first question addresses the degree to which incentive compatibility fails in generalized second-price (GSP) auctions. Our results suggest that sincere bidding in GSP auctions is a strikingly poor strategy and a poor predictor of equilibrium outcomes. We next show that the rank-by-revenue mechanism is welfare optimal, corroborating past results. Finally, we analyze profit as a function of auction mechanism under a series of alternative settings. Our conclusions coincide with those of Lahaie and Pennock [2007] when values and quality scores are strongly positively correlated: in such a case, rank-by-bid rules are clearly superior. We diverge, however, in showing that auctions that put little weight on quality scores almost universally dominate the pure rank-by-revenue scheme.
Ordinal Boltzmann Machines for Collaborative Filtering
Venue: arXiv cs.IR 2012
Year: 2012

Collaborative filtering is an effective recommendation technique wherein the preference of an individual can potentially be predicted based on preferences of other members. Early algorithms often relied on the strong locality in the preference data, that is, it is enough to predict preference of a user on a particular item based on a small subset of other users with similar tastes or of other items with similar properties. More recently, dimensionality reduction techniques have proved to be equally competitive, and these are based on the co-occurrence patterns rather than locality. This paper explores and extends a probabilistic model known as Boltzmann Machine for collaborative filtering tasks. It seamlessly integrates both the similarity and co-occurrence in a principled manner. In particular, we study parameterisation options to deal with the ordinal nature of the preferences, and propose a joint modelling of both the user-based and item-based processes. Experiments on moderate and large-scale movie recommendation show that our framework rivals existing well-known methods.
Convexifying the Bethe Free Energy
Venue: arXiv cs.AI 2012
Year: 2012

The introduction of loopy belief propagation (LBP) revitalized the application of graphical models in many domains. Many recent works present improvements on the basic LBP algorithm in an attempt to overcome convergence and local optima problems. Notable among these are convexified free energy approximations that lead to inference procedures with provable convergence and quality properties. However, empirically LBP still outperforms most of its convex variants in a variety of settings, as we also demonstrate here. Motivated by this fact we seek convexified free energies that directly approximate the Bethe free energy. We show that the proposed approximations compare favorably with state-of-the art convex free energy approximations.
Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling
Venue: arXiv cs.AI 2012
Year: 2012

A Bayesian belief network models a joint distribution with an directed acyclic graph representing dependencies among variables and network parameters characterizing conditional distributions. The parameters are viewed as random variables to quantify uncertainty about their values. Belief nets are used to compute responses to queries; i.e., conditional probabilities of interest. A query is a function of the parameters, hence a random variable. Van Allen et al. (2001, 2008) showed how to quantify uncertainty about a query via a delta method approximation of its variance. We develop more accurate approximations for both query mean and variance. The key idea is to extend the query mean approximation to a "doubled network" involving two independent replicates. Our method assumes complete data and can be applied to discrete, continuous, and hybrid networks (provided discrete variables have only discrete parents). We analyze several improvements, and provide empirical studies to demonstrate their effectiveness.
Distributed Parallel Inference on Large Factor Graphs
Venue: arXiv cs.AI 2012
Year: 2012

As computer clusters become more common and the size of the problems encountered in the field of AI grows, there is an increasing demand for efficient parallel inference algorithms. We consider the problem of parallel inference on large factor graphs in the distributed memory setting of computer clusters. We develop a new efficient parallel inference algorithm, DBRSplash, which incorporates over-segmented graph partitioning, belief residual scheduling, and uniform work Splash operations. We empirically evaluate the DBRSplash algorithm on a 120 processor cluster and demonstrate linear to super-linear performance gains on large factor graph models.
Prediction Markets, Mechanism Design, and Cooperative Game Theory
Venue: arXiv cs.GT 2012
Year: 2012

Prediction markets are designed to elicit information from multiple agents in order to predict (obtain probabilities for) future events. A good prediction market incentivizes agents to reveal their information truthfully; such incentive compatibility considerations are commonly studied in mechanism design. While this relation between prediction markets and mechanism design is well understood at a high level, the models used in prediction markets tend to be somewhat different from those used in mechanism design. This paper considers a model for prediction markets that fits more straightforwardly into the mechanism design framework. We consider a number of mechanisms within this model, all based on proper scoring rules. We discuss basic properties of these mechanisms, such as incentive compatibility. We also draw connections between some of these mechanisms and cooperative game theory. Finally, we speculate how one might build a practical prediction market based on some of these ideas.
Intelligent learning environments within blended learning for ensuring effective C programming course
Venue: arXiv cs.CY 2012
Year: 2012

This paper describes a blended learning implementation and experience supported with intelligent learning environments included in a learning management system (LMS) called @KU-UZEM. The blended learning model is realized as a combination of face to face education and e-learning. The intelligent learning environments consist of two applications named CTutor, ITest. In addition to standard e-learning tools, students can use CTutor to resolve C programming exercises. CTutor is a problem-solving environment, which diagnoses students' knowledge level but also gives feedbacks and tips to help them to understand the course subject, overcome their misconceptions and reinforce learnt concepts. ITest provides an assessment environment in which students can take quizzes that were prepared according to their learning levels. The realized model was used for two terms in the "C Programming" course given at Afyon Kocatepe University. A survey was conducted at the end of the course to find out to what extent the students were accepting the blended learning model supported with @KU-UZEM and to discover students' attitude towards intelligent learning environments. Additionally, an experiment formed with an experimental group who took an active part in the realized model and a control group who only took the face to face education was performed during the first term of the course. According to the results, students were satisfied with intelligent learning environments and the realized learning model. Furthermore, the use of intelligent learning environments improved the students' knowledge about C programming.
Evaluation of Proactive, Reactive and Hybrid Ad hoc Routing Protocol for various Battery models in VANET using Qualnet
Venue: arXiv cs.PF 2012
Year: 2012

In VANET high speed is the real characteristics which leads frequent breakdown, interference etc. In this paper we studied various Ad hoc routing protocols, Reactive, Proactive & Hybrid, taking into consideration various VANET parameters like speed, altitude etc in real traffic scenario and evaluated them for various battery models for energy conservation.. The AODV and DYMO (Reactive), OLSR (Proactive) and ZRP (hybrid) protocols are compared for battery models Duracell AA(MX- 1500),Duracell AAA(MN-2400),Duracell AAA(MX-2400), Duracell C-MN(MN-1400),Panasonic AA standard using Qualnet as a Simulation tool. Since Energy conservation is main focus area nowadays. Hence performance of the protocols with various battery models counts and helps to make a right selection. Varying parameters of VANET shows that in the real traffic scenarios proactive protocol performs more efficiently for energy conservation.
Improving Schema Matching with Linked Data
Venue: arXiv cs.DB 2012
Year: 2012

With today's public data sets containing billions of data items, more and more companies are looking to integrate external data with their traditional enterprise data to improve business intelligence analysis. These distributed data sources however exhibit heterogeneous data formats and terminologies and may contain noisy data. In this paper, we present a novel framework that enables business users to semi-automatically perform data integration on potentially noisy tabular data. This framework offers an extension to Google Refine with novel schema matching algorithms leveraging Freebase rich types. First experiments show that using Linked Data to map cell values with instances and column headers with types improves significantly the quality of the matching results and therefore should lead to more informed decisions.
Risks and remedies in e-learning system
Venue: arXiv cs.CY 2012
Year: 2012

One of the most effective applications of Information and Communication Technology (ICT) is the emergence of E-Learning. Considering the importance and need of E-Learning, recent years have seen a drastic change of learning methodologies in Higher Education. Undoubtedly, the three main entities of E-Learning system can be considered as Student, Teacher & Controlling Authority and there will be different level, but a good E-Learning system needs total integrity among all entities in every level. Apart from integrity enforcement, security enforcement in the whole system is the other crucial way to organize the it. As internet is the backbone of the entire system which is inherently insecure, during transaction of message in E-Learning system, hackers attack by utilising different loopholes of technology. So different security measures are required to be imposed on the system. In this paper, emphasis is given on different risks called e-risks and their remedies called e-remedies to build trust in the minds of all participants of E-Learning system.