OpenRaiser/Intern-Atlas huggingface dataset:
Real-Time Traffic Signal Control for Modern Roundabouts by Using Particle Swarm Optimization-Based Fuzzy Controller
Venue: arXiv cs.NE 2014
Year: 2014

Due to that the existing traffic facilities can hardly be extended, developing traffic signal control methods is the most important way to improve the traffic efficiency of modern roundabouts. This paper proposes a novel traffic signal controller with two fuzzy layers for signalizing the roundabout. The outer layer of the controller computes urgency degrees of all the phase subsets and then activates the most urgent subset. This mechanism helps to instantly respond to the current traffic condition of the roundabout so as to improve real-timeness. The inner layer of the controller computes extension time of the current phase. If the extension value is larger than a threshold value, the current phase is maintained; otherwise the next phase in the running phase subset (selected by the outer layer) is activated. The inner layer adopts well-designed phase sequences, which helps to smooth the traffic flows and to avoid traffic jam. In general, the proposed traffic signal controller is capable of improving real-timeness as well as reducing traffic congestion. Moreover, an offline particle swarm optimization (PSO) algorithm is developed to optimize the membership functions adopted in the proposed controller. By using optimal membership functions, the performance of the controller can be further improved. Simulation results demonstrate that the proposed controller outperforms previous traffic signal controllers in terms of improving the traffic efficiency of modern roundabouts.
Hidden in snow, revealed in thaw: Cold boot attacks revisited
Venue: arXiv cs.CR 2014
Year: 2014

In this paper, we will provide an overview of the current state-of-the-art with regards to so-called cold boot attacks, their practical applicability and feasibility, potential counter-measures and their effectiveness.
Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network
Venue: arXiv cs.IT 2014
Year: 2014

A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.
System Analysis and Design for integrated sponsored SMS/USSD Based M-Services (A case study of Maternal Health M-Service in Tanzania)
Venue: arXiv cs.CY 2014
Year: 2014

Mobile phones have proven to be the best way of providing reliable access to information to people in low and mid income countries where other forms of communication perform poorly. As a result of the wide spread of mobile phones, there has been an increase in number of Mobile Application (M-Services) which are being used as a tool for disseminating different type information to people. M-Services of this nature are established to address informational challenges that are faced by people especially low income people. Because of this then, these projects must be sustained so that people can enjoy the benefits of it. Contrary to this, reports show that most of these M-Services are facing the challenge of cost of operating them, which in a direct way affects the sustainability of these services. In this paper therefore we present an analysis and later design of a noncommercial M-Service, which integrates advertising functionality as a tool for subsidizing the cost of operating M-Services. To achieve this we have employed some concepts of Information System Analysis and Design (ISAD) as the guiding principle towards achieving our design. A prototype of M-Health is used for the study.
Unstable markup: A template-based information extraction from web sites with unstable markup
Venue: arXiv cs.IR 2014
Year: 2014

This paper presents results of a work on crawling CEUR Workshop proceedings web site to a Linked Open Data (LOD) dataset in the framework of ESWC 2014 Semantic Publishing Challenge 2014. Our approach is based on using an extensible template-dependent crawler and DBpedia for linking extracted entities, such as the names of universities and countries.
The Mathematical Abstraction Theory, The Fundamentals for Knowledge Representation and Self-Evolving Autonomous Problem Solving Systems
Venue: arXiv cs.LO 2014
Year: 2014

The intention of the present study is to establish the mathematical fundamentals for automated problem solving essentially targeted for robotics by approaching the task universal algebraically introducing knowledge as realizations of generalized free algebra based nets, graphs with gluing forms connecting in- and out-edges to nodes. Nets are caused to undergo transformations in conceptual level by type wise differentiated intervening net rewriting systems dispersing problems to abstract parts, matching being determined by substitution relations. Achieved sets of conceptual nets constitute congruent classes. New results are obtained within construction of problem solving systems where solution algorithms are derived parallel with other candidates applied to the same net classes. By applying parallel transducer paths consisting of net rewriting systems to net classes congruent quotient algebras are established and the manifested class rewriting comprises all solution candidates whenever produced nets are in anticipated languages liable to acceptance of net automata. Furthermore new solutions will be added to the set of already known ones thus expanding the solving power in the forthcoming. Moreover special attention is set on universal abstraction, thereof generation by net block homomorphism, consequently multiple order solving systems and the overall decidability of the set of the solutions. By overlapping presentation of nets new abstraction relation among nets is formulated alongside with consequent alphabetical net block renetting system proportional to normal forms of renetting systems regarding the operational power. A new structure in self-evolving problem solving is established via saturation by groups of equivalence relations and iterative closures of generated quotient transducer algebras over the whole evolution.
Renewal Strings for Cleaning Astronomical Databases
Venue: arXiv cs.AI 2014
Year: 2014

Large astronomical databases obtained from sky surveys such as the SuperCOSMOS Sky Surveys (SSS) invariably suffer from a small number of spurious records coming from artefactual effects of the telescope, satellites and junk objects in orbit around earth and physical defects on the photographic plate or CCD. Though relatively small in number these spurious records present a significant problem in many situations where they can become a large proportion of the records potentially of interest to a given astronomer. In this paper we focus on the four most common causes of unwanted records in the SSS: satellite or aeroplane tracks, scratches fibres and other linear phenomena introduced to the plate, circular halos around bright stars due to internal reflections within the telescope and diffraction spikes near to bright stars. Accurate and robust techniques are needed for locating and flagging such spurious objects. We have developed renewal strings, a probabilistic technique combining the Hough transform, renewal processes and hidden Markov models which have proven highly effective in this context. The methods are applied to the SSS data to develop a dataset of spurious object detections, along with confidence measures, which can allow this unwanted data to be removed from consideration. These methods are general and can be adapted to any future astronomical survey data.
Ideal codes over separable ring extensions
Venue: arXiv cs.IT 2014
Year: 2014

This paper investigates the application of the theoretical algebraic notion of a separable ring extension, in the realm of cyclic convolutional codes or, more generally, ideal codes. We work under very mild conditions, that cover all previously known as well as new non trivial examples. It is proved that ideal codes are direct summands as left ideals of the underlying non-commutative algebra, in analogy with cyclic block codes. This implies, in particular, that they are generated by an idempotent element. Hence, by using a suitable separability element, we design an efficient algorithm for computing one of such idempotents.
Simulating Organogenesis in COMSOL: Image-based Modeling
Venue: arXiv cs.CE 2014
Year: 2014

Mathematical Modelling has a long history in developmental biology. Advances in experimental techniques and computational algorithms now permit the development of increasingly more realistic models of organogenesis. In particular, 3D geometries of developing organs have recently become available. In this paper, we show how to use image-based data for simulations of organogenesis in COMSOL Multiphysics. As an example, we use limb bud development, a classical model system in mouse developmental biology. We discuss how embryonic geometries with several subdomains can be read into COMSOL using the Matlab LiveLink, and how these can be used to simulate models on growing embryonic domains. The ALE method is used to solve signaling models even on strongly deforming domains.
PV Integration in Low-Voltage Feeders with Demand Response
Venue: arXiv cs.SY 2014
Year: 2014

Increased distributed Photo-Voltaic (PV) generation leads to an increase in voltages and unwarranted backflows into the grid. This paper investigates Demand Response (DR) with Electric Water Heaters (EWHs) as a way to increase the PV hosting capacity of a low-voltage feeder. A control strategy relying only on power measurements at the transformer is proposed. Flexible loads are optimally dispatched considering energy acquisition costs, a PV shedding penalty, and power and energy constraints. Furthermore, grouping of loads and PV plants is investigated, and switching penalties are used to reduce the unnecessary switching of loads. It is shown that this strategy can substantially increase the PV hosting capacity of a Low-Voltage (LV) feeder, even when only basic controllability is available.
When do Numbers Really Matter?
Venue: arXiv cs.AI 2014
Year: 2014

Common wisdom has it that small distinctions in the probabilities quantifying a Bayesian network do not matter much for the resultsof probabilistic queries. However, one can easily develop realistic scenarios under which small variations in network probabilities can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytical results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They are also important for approximate inference algorithms that preprocessnetwork CPTs to eliminate small distinctions in probabilities.
Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient
Venue: arXiv cs.CV 2014
Year: 2014

Hand gesture recognition possesses extensive applications in virtual reality, sign language recognition, and computer games. The direct interface of hand gestures provides us a new way for communicating with the virtual environment. In this paper a novel and real-time approach for hand gesture recognition system is presented. In the suggested method, first, the hand gesture is extracted from the main image by the image segmentation and morphological operation and then is sent to feature extraction stage. In feature extraction stage the Cross-correlation coefficient is applied on the gesture to recognize it. In the result part, the proposed approach is applied on American Sign Language (ASL) database and the accuracy rate obtained 98.34%.
A QoS based Routing Approach using Genetic Algorithms for Bandwidth Maximization in Network
Venue: arXiv cs.NI 2014
Year: 2014

This paper addresses the path selection problem from a known source to the destination in dense networks. The proposed solution for route discovery uses the genetic algorithm approach for a QoS based network. The multi point crossover and mutation helps in determining the optimal path and alternate path when required. The input to the genetic algorithm is a learnt module which is a part of the cognitive router that takes care of four QoS parameters. Here the set of nodes selected for routing is determined by delay, jitter and loss. On this graded surface of nodes selected, the bandwidth parameter is considered for path selection. The aim of the approach is to occupy the maximized bandwidth along the forward channels and minimize the route length. The population size is considered as fixed nodes participating in the network scenario, which will be limited to a known size of topology. The simulated results show that by using genetic algorithm (GA) approach the probability of convergence to shortest path is higher.
Non-Blocking Doubly-Linked Lists with Good Amortized Complexity
Venue: arXiv cs.DC 2014
Year: 2014

We present a new non-blocking doubly-linked list implementation for an asynchronous shared-memory system. It is the first such implementation for which an upper bound on amortized time complexity has been proved. In our implementation, operations access the list via cursors. Each cursor is associated with an item in the list and is local to a process. The implementation supports two update operations, insertBefore and delete, and two move operations, moveRight and moveLeft. An insertBefore(c, x) operation inserts an item x into the list immediately before the cursor c's location. A delete(c) operation removes the item at the cursor c's location and sets the cursor to the next item in the list. The move operations move the cursor one position to the right or left. The update operations use single-word Compare&Swap instructions. The move operations only read shared memory and never change the state of the data structure. If all update operations modify different parts of the list, they run completely concurrently. Let cp(op) be the maximum number of active cursors at any one time during the operation op. The amortized complexity of each update operation op is O(cp(op)) and each move operation is O(1). We have written a detailed correctness proof and amortized analysis of our implementation.
Sensitivity analysis for finite Markov chains in discrete time
Venue: arXiv cs.AI 2014
Year: 2014

When the initial and transition probabilities of a finite Markov chain in discrete time are not well known, we should perform a sensitivity analysis. This is done by considering as basic uncertainty models the so-called credal sets that these probabilities are known or believed to belong to, and by allowing the probabilities to vary over such sets. This leads to the definition of an imprecise Markov chain. We show that the time evolution of such a system can be studied very efficiently using so-called lower and upper expectations. We also study how the inferred credal set about the state at time n evolves as n->infinity: under quite unrestrictive conditions, it converges to a uniquely invariant credal set, regardless of the credal set given for the initial state. This leads to a non-trivial generalisation of the classical Perron-Frobenius Theorem to imprecise Markov chains.
Quantum Annealing for Variational Bayes Inference
Venue: arXiv cs.LG 2014
Year: 2014

This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference. QAVB is as easy as SAVB to implement. Experiments revealed QAVB finds a better local optimum than SAVB in terms of the variational free energy in latent Dirichlet allocation (LDA).
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Venue: arXiv cs.LG 2014
Year: 2014

Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the datagenerating process of variables. Recently, it was shown that use of non-Gaussianity identifies a causal ordering of variables in a linear acyclic model without using any prior knowledge on the network structure, which is not the case with conventional methods. However, existing estimation methods are based on iterative search algorithms and may not converge to a correct solution in a finite number of steps. In this paper, we propose a new direct method to estimate a causal ordering based on non-Gaussianity. In contrast to the previous methods, our algorithm requires no algorithmic parameters and is guaranteed to converge to the right solution within a small fixed number of steps if the data strictly follows the model.
Prediction with Advice of Unknown Number of Experts
Venue: arXiv cs.LG 2014
Year: 2014

In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the Normal- Hedge bound, which mainly depends on the effective number of experts but also weakly depends on the nominal one, we obtain a bound that does not contain the nominal number of experts at all. We use the defensive forecasting method and introduce an application of defensive forecasting to multivalued supermartingales.
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
Venue: arXiv cs.LG 2014
Year: 2014

In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive crossvalidation to find the optimal regularization parameter. Moreover, the model's predictive performance may degrade dramatically with a suboptimal value of the regularization parameter (which is sometimes desirable to induce sparseness). We propose a fully Bayesian approach based on a "spike and slab" prior (similar to L0 regularization) that does not suffer from these shortcomings. We develop an approximate MCMC method combining Langevin dynamics and reversible jump MCMC to conduct inference in this model. Experiments show that the proposed model learns a good combination of the structure and parameter values without the need for separate hyper-parameter tuning. Moreover, the model's predictive performance is much more robust than L1-based methods with hyper-parameter settings that induce highly sparse model structures.
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Venue: arXiv cs.LG 2014
Year: 2014

Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size. This paper presents two parallel GP regression methods that exploit low-rank covariance matrix approximations for distributing the computational load among parallel machines to achieve time efficiency and scalability. We theoretically guarantee the predictive performances of our proposed parallel GPs to be equivalent to that of some centralized approximate GP regression methods: The computation of their centralized counterparts can be distributed among parallel machines, hence achieving greater time efficiency and scalability. We analytically compare the properties of our parallel GPs such as time, space, and communication complexity. Empirical evaluation on two real-world datasets in a cluster of 20 computing nodes shows that our parallel GPs are significantly more time-efficient and scalable than their centralized counterparts and exact/full GP while achieving predictive performances comparable to full GP.
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking
Venue: arXiv cs.LG 2014
Year: 2014

We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes 2012) in several ways. We show that they represent a distortion between a "score" and an "ordering", thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show how the LB divergences have a number of properties akin to many permutation based metrics, and in fact have as special cases forms very similar to the Kendall-tau metric. We also show how the LB divergences subsume a number of commonly used ranking measures in information retrieval, like NDCG and AUC. Unlike the traditional permutation based metrics, however, the LB divergence naturally captures a notion of "confidence" in the orderings, thus providing a new representation to applications involving aggregating scores as opposed to just orderings. We show how a number of recently used web ranking models are forms of Lovasz-Bregman rank aggregation and also observe that a natural form of Mallow's model using the LB divergence has been used as conditional ranking models for the "Learning to Rank" problem.
A Point Counting Algorithm for Cyclic Covers of the Projective Line
Venue: arXiv cs.CG 2014
Year: 2014

We present a Kedlaya-style point counting algorithm for cyclic covers $y^r = f(x)$ over a finite field $\mathbb{F}_{p^n}$ with $p$ not dividing $r$, and $r$ and $°{f}$ not necessarily coprime. This algorithm generalizes the Gaudry-Gürel algorithm for superelliptic curves to a more general class of curves, and has essentially the same complexity. Our practical improvements include a simplified algorithm exploiting the automorphism of $\mathcal{C}$, refined bounds on the $p$-adic precision, and an alternative pseudo-basis for the Monsky-Washnitzer cohomology which leads to an integral matrix when $p \geq 2r$. Each of these improvements can also be applied to the original Gaudry-Gürel algorithm. We include some experimental results, applying our algorithm to compute Weil polynomials of some large genus cyclic covers.