OpenRaiser/Intern-Atlas huggingface dataset:
Real-Time Stochastic Processing Networks with Concurrent Resource Requirements
Venue: arXiv cs.NI 2012
Year: 2012

Stochastic Processing Networks (SPNs) can be used to model communication networks, manufacturing systems, service systems, etc. We consider a real-time SPN where tasks generate jobs with strict deadlines according to their traffic patterns. Each job requires the concurrent usage of some resources to be processed. The processing time of a job may be stochastic, and may not be known until the job completes. Finally, each task may require that some portion of its tasks to be completed on time. In this paper, we study the problem of verifying whether it is feasible to fulfill the requirements of tasks, and of designing scheduling policies that actually fulfill the requirements. We first address these problems for systems where there is only one resource. Such systems are analog to ones studied in a previous work, and, similar to the previous work, we can develop sharp conditions for feasibility and scheduling policy that is feasibility-optimal. We then study systems with two resources where there are jobs that require both resources to be processed. We show that there is a reduction method that turns systems with two resources into equivalent single-resource systems. Based on this method, we can also derive sharp feasibility conditions and feasibility-optimal scheduling policies for systems with two resources.
A Study of Trade-off between Opportunistic Resource Allocation and Interference Alignment in Femtocell Scenarios
Venue: arXiv cs.IT 2012
Year: 2012

One of the main problems in wireless heterogeneous networks is interference between macro- and femto-cells. Using Orthogonal Frequency-Division Multiple Access (OFDMA) to create multiple frequency orthogonal sub-channels, this interference can be completely avoided if each sub-channel is exclusively used by either macro- or a femto-cell. However, such an orthogonal allocation may be inefficient. We consider two alternative strategies for interference management, opportunistic resource allocation (ORA) and interference alignment (IA). Both of them utilize the fading fluctuations across frequency channels in different ways. ORA allows the users to interfere, but selecting the channels where the interference is faded, while the desired signal has a good channel. IA uses precoding to create interference-free transmissions; however, such a precoding changes the diversity picture of the communication resources. In this letter we investigate the interactions and the trade-offs between these two strategies.
Learning Asynchronous-Time Information Diffusion Models and its Application to Behavioral Data Analysis over Social Networks
Venue: arXiv cs.SI 2012
Year: 2012

One of the interesting and important problems of information diffusion over a large social network is to identify an appropriate model from a limited amount of diffusion information. There are two contrasting approaches to model information diffusion: a push type model known as Independent Cascade (IC) model and a pull type model known as Linear Threshold (LT) model. We extend these two models (called AsIC and AsLT in this paper) to incorporate asynchronous time delay and investigate 1) how they differ from or similar to each other in terms of information diffusion, 2) whether the model itself is learnable or not from the observed information diffusion data, and 3) which model is more appropriate to explain for a particular topic (information) to diffuse/propagate. We first show there can be variations with respect to how the time delay is modeled, and derive the likelihood of the observed data being generated for each model. Using one particular time delay model, we show the model parameters are learnable from a limited amount of observation. We then propose a method based on predictive accuracy by which to select a model which better explains the observed data. Extensive evaluations were performed. We first show using synthetic data with the network structures taken from real networks that there are considerable behavioral differences between the AsIC and the AsLT models, the proposed methods accurately and stably learn the model parameters, and identify the correct diffusion model from a limited amount of observation data. We next apply these methods to behavioral analysis of topic propagation using the real blog propagation data, and show there is a clear indication as to which topic better follows which model although the results are rather insensitive to the model selected at the level of discussing how far and fast each topic propagates from the learned parameter values.
Automatic Sampling of Geographic objects
Venue: arXiv cs.AI 2012
Year: 2012

Today, one's disposes of large datasets composed of thousands of geographic objects. However, for many processes, which require the appraisal of an expert or much computational time, only a small part of these objects can be taken into account. In this context, robust sampling methods become necessary. In this paper, we propose a sampling method based on clustering techniques. Our method consists in dividing the objects in clusters, then in selecting in each cluster, the most representative objects. A case-study in the context of a process dedicated to knowledge revision for geographic data generalisation is presented. This case-study shows that our method allows to select relevant samples of objects.
An Information Theoretic Location Verification System for Wireless Networks
Venue: arXiv cs.IT 2012
Year: 2012

As location-based applications become ubiquitous in emerging wireless networks, Location Verification Systems (LVS) are of growing importance. In this paper we propose, for the first time, a rigorous information-theoretic framework for an LVS. The theoretical framework we develop illustrates how the threshold used in the detection of a spoofed location can be optimized in terms of the mutual information between the input and output data of the LVS. In order to verify the legitimacy of our analytical framework we have carried out detailed numerical simulations. Our simulations mimic the practical scenario where a system deployed using our framework must make a binary Yes/No "malicious decision" to each snapshot of the signal strength values obtained by base stations. The comparison between simulation and analysis shows excellent agreement. Our optimized LVS framework provides a defence against location spoofing attacks in emerging wireless networks such as those envisioned for Intelligent Transport Systems, where verification of location information is of paramount importance.
The computational complexity of Minesweeper
Venue: arXiv cs.CC 2012
Year: 2012

We show that the Minesweeper game is PP-hard, when the object is to locate all mines with the highest probability. When the probability of locating all mines may be infinitesimal, the Minesweeper game is even PSPACE-complete. In our construction, the player can reveal a boolean circuit in polynomial time, after guessing an initial square with no surrounding mines, a guess that has 99 percent probability of success. Subsequently, the mines must be located with a maximum probability of success. Furthermore, we show that determining the solvability of a partially uncovered Minesweeper board is NP-complete with hexagonal and triangular grids as well as a square grid, extending a similar result for square grids only by R. Kaye. Actually finding the mines with a maximum probability of success is again PP-hard or PSPACE-complete respectively. Our constructions are in such a way that the number of mines can be computed in polynomial time and hence a possible mine counter does not provide additional information. The results are obtained by replacing the dyadic gates in [3] by two primitives which makes life more easy in this context.
Finding Small Sparse Cuts Locally by Random Walk
Venue: arXiv cs.DS 2012
Year: 2012

We study the problem of finding a small sparse cut in an undirected graph. Given an undirected graph G=(V,E) and a parameter k <= |E|, the small sparsest cut problem is to find a subset of vertices S with minimum conductance among all sets with volume at most k. Using ideas developed in local graph partitioning algorithms, we obtain the following bicriteria approximation algorithms for the small sparsest cut problem: - If there is a subset U with conductance ϕand vol(U) <= k, then there is a polynomial time algorithm to find a set S with conductance O(\sqrt{ϕ/ε}) and vol(S) <= k^{1+ε} for any ε> 1/k. - If there is a subset U with conductance ϕand vol(U) <= k, then there is a polynomial time algorithm to find a set S with conductance O(\sqrt{ϕln(k)/ε}) and vol(S) <= (1+ε)k for any ε> 2ln(k)/k. These algorithms can be implemented locally using truncated random walk, with running time almost linear to the output size. This provides a local graph partitioning algorithm with a better conductance guarantee when k is sublinear.
Model Checking with Probabilistic Tabled Logic Programming
Venue: arXiv cs.LO 2012
Year: 2012

We present a formulation of the problem of probabilistic model checking as one of query evaluation over probabilistic logic programs. To the best of our knowledge, our formulation is the first of its kind, and it covers a rich class of probabilistic models and probabilistic temporal logics. The inference algorithms of existing probabilistic logic-programming systems are well defined only for queries with a finite number of explanations. This restriction prohibits the encoding of probabilistic model checkers, where explanations correspond to executions of the system being model checked. To overcome this restriction, we propose a more general inference algorithm that uses finite generative structures (similar to automata) to represent families of explanations. The inference algorithm computes the probability of a possibly infinite set of explanations directly from the finite generative structure. We have implemented our inference algorithm in XSB Prolog, and use this implementation to encode probabilistic model checkers for a variety of temporal logics, including PCTL and GPL (which subsumes PCTL*). Our experiment results show that, despite the highly declarative nature of their encodings, the model checkers constructed in this manner are competitive with their native implementations.
What's in an `is about' link? Chemical diagrams and the Information Artifact Ontology
Venue: arXiv cs.AI 2012
Year: 2012

The Information Artifact Ontology is an ontology in the domain of information entities. Core to the definition of what it is to be an information entity is the claim that an information entity must be `about' something, which is encoded in an axiom expressing that all information entities are about some entity. This axiom comes into conflict with ontological realism, since many information entities seem to be about non-existing entities, such as hypothetical molecules. We discuss this problem in the context of diagrams of molecules, a kind of information entity pervasively used throughout computational chemistry. We then propose a solution that recognizes that information entities such as diagrams are expressions of diagrammatic languages. In so doing, we not only address the problem of classifying diagrams that seem to be about non-existing entities but also allow a more sophisticated categorisation of information entities.
Succinct Indices for Range Queries with applications to Orthogonal Range Maxima
Venue: arXiv cs.DS 2012
Year: 2012

We consider the problem of preprocessing $N$ points in 2D, each endowed with a priority, to answer the following queries: given a axis-parallel rectangle, determine the point with the largest priority in the rectangle. Using the ideas of the \emph{effective entropy} of range maxima queries and \emph{succinct indices} for range maxima queries, we obtain a structure that uses O(N) words and answers the above query in $O(\log N \log \log N)$ time. This is a direct improvement of Chazelle's result from FOCS 1985 for this problem -- Chazelle required $O(N/ε)$ words to answer queries in $O((\log N)^{1+ε})$ time for any constant $ε> 0$.
Using Belief Theory to Diagnose Control Knowledge Quality. Application to cartographic generalisation
Venue: arXiv cs.AI 2012
Year: 2012

Both humans and artificial systems frequently use trial and error methods to problem solving. In order to be effective, this type of strategy implies having high quality control knowledge to guide the quest for the optimal solution. Unfortunately, this control knowledge is rarely perfect. Moreover, in artificial systems-as in humans-self-evaluation of one's own knowledge is often difficult. Yet, this self-evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to propose an automated approach to evaluate the quality of control knowledge in artificial systems based on a specific trial and error strategy, namely the informed tree search strategy. Our revision approach consists in analysing the system's execution logs, and in using the belief theory to evaluate the global quality of the knowledge. We present a real-world industrial application in the form of an experiment using this approach in the domain of cartographic generalisation. Thus far, the results of using our approach have been encouraging.
Instantaneous Relaying: Optimal Strategies and Interference Neutralization
Venue: arXiv cs.IT 2012
Year: 2012

In a multi-user wireless network equipped with multiple relay nodes, some relays are more intelligent than other relay nodes. The intelligent relays are able to gather channel state information, perform linear processing and forward signals whereas the dumb relays is only able to serve as amplifiers. As the dumb relays are oblivious to the source and destination nodes, the wireless network can be modeled as a relay network with *smart instantaneous relay* only: the signals of source-destination arrive at the same time as source-relay-destination. Recently, instantaneous relaying is shown to improve the degrees-of-freedom of the network as compared to classical cut-set bound. In this paper, we study an achievable rate region and its boundary of the instantaneous interference relay channel in the scenario of (a) uninformed non-cooperative source-destination nodes (source and destination nodes are not aware of the existence of the relay and are non-cooperative) and (b) informed and cooperative source-destination nodes. Further, we examine the performance of interference neutralization: a relay strategy which is able to cancel interference signals at each destination node in the air. We observe that interference neutralization, although promise to achieve desired degrees-of-freedom, may not be feasible if relay has limited power. Simulation results show that the optimal relay strategies improve the achievable rate region and provide better user-fairness in both uninformed non-cooperative and informed cooperative scenarios.
Deterministic Automata for the (F,G)-fragment of LTL
Venue: arXiv cs.LO 2012
Year: 2012

When dealing with linear temporal logic properties in the setting of e.g. games or probabilistic systems, one often needs to express them as deterministic omega-automata. In order to translate LTL to deterministic omega-automata, the traditional approach first translates the formula to a non-deterministic Büchi automaton. Then a determinization procedure such as of Safra is performed yielding a deterministic ω-automaton. We present a direct translation of the (F,G)-fragment of LTL into deterministic ω-automata with no determinization procedure involved. Since our approach is tailored to LTL, we often avoid the typically unnecessarily large blowup caused by general determinization algorithms. We investigate the complexity of this translation and provide experimental results and compare them to the traditional method.
Analysis and Design of Irregular Graphs for Node-Based Verification-Based Recovery Algorithms in Compressed Sensing
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we present a probabilistic analysis of iterative node-based verification-based (NB-VB) recovery algorithms over irregular graphs in the context of compressed sensing. Verification-based algorithms are particularly interesting due to their low complexity (linear in the signal dimension $n$). The analysis predicts the average fraction of unverified signal elements at each iteration $\ell$ where the average is taken over the ensembles of input signals and sensing matrices. The analysis is asymptotic ($n \rightarrow \infty$) and is similar in nature to the well-known density evolution technique commonly used to analyze iterative decoding algorithms. Compared to the existing technique for the analysis of NB-VB algorithms, which is based on numerically solving a large system of coupled differential equations, the proposed method is much simpler and more accurate. This allows us to design irregular sensing graphs for such recovery algorithms. The designed irregular graphs outperform the corresponding regular graphs substantially. For example, for the same recovery complexity per iteration, we design irregular graphs that can recover up to about 40% more non-zero signal elements compared to the regular graphs. Simulation results are also provided which demonstrate that the proposed asymptotic analysis matches the performance of recovery algorithms for large but finite values of $n$.
An Algebraic Framework for Concatenated Linear Block Codes in Side Information Based Problems
Venue: arXiv cs.IT 2012
Year: 2012

This work provides an algebraic framework for source coding with decoder side information and its dual problem, channel coding with encoder side information, showing that nested concatenated codes can achieve the corresponding rate-distortion and capacity-noise bounds. We show that code concatenation preserves the nested properties of codes and that only one of the concatenated codes needs to be nested, which opens up a wide range of possible new code combinations for these side information based problems. In particular, the practically important binary version of these problems can be addressed by concatenating binary inner and non-binary outer linear codes. By observing that list decoding with folded Reed- Solomon codes is asymptotically optimal for encoding IID q-ary sources and that in concatenation with inner binary codes it can asymptotically achieve the rate-distortion bound for a Bernoulli symmetric source, we illustrate our findings with a new algebraic construction which comprises concatenated nested cyclic codes and binary linear block codes.
WILI - Web Interface for people with Lowvision Issues
Venue: arXiv cs.HC 2012
Year: 2012

Though World Wide Web is the single largest source of information, it is ill-equipped to serve the people with vision related problems. With the prolific increase in the interest to make the web accessible to all sections of the society, solving this accessibility problem becomes mandatory. This paper presents a technique for making web pages accessible for people with low vision issues. A model for making web pages accessible, WILI (Web Interface for people with Low-vision Issues) has been proposed. The approach followed in this work is to automatically replace the existing display style of a web page with a new skin following the guidelines given by Clear Print Booklet provided by Royal National Institute of Blind. "Single Click Solution" is one of the primary advantages provided by WILI. A prototype using the WILI model is implemented and various experiments are conducted. The results of experiments conducted on WILI indicate 82% effective conversion rate.
Non-Hamiltonian Holes in Grid Graphs
Venue: arXiv cs.DM 2012
Year: 2012

In this paper we extend general grid graphs to the grid graphs consist of polygons tiling on a plane, named polygonal grid graphs. With a cycle basis satisfied polygons tiling, we study the cyclic structure of Hamilton graphs. A Hamilton cycle can be expressed as a symmetric difference of a subset of cycles in the basis. From the combinatorial relations of vertices in the subset of cycles in the basis, we deduce the formula of inside faces in Grinberg theorem, called Grinberg equation, and derive a kind of cycles whose existence make a polygonal grid graph non-Hamiltonian, called non-Hamiltonian holes, and then we characterize the existence condition of non-Hamiltonian holes and obtain the necessary and sufficient condition of a polygonal grid graph to be Hamiltonian. The result in this paper provides a new starting point for developing a polynomial-time algorithm for Hamilton problem in general grid graphs.
A Tunable Mechanism for Identifying Trusted Nodes in Large Scale Distributed Networks
Venue: arXiv cs.SI 2012
Year: 2012

In this paper, we propose a simple randomized protocol for identifying trusted nodes based on personalized trust in large scale distributed networks. The problem of identifying trusted nodes, based on personalized trust, in a large network setting stems from the huge computation and message overhead involved in exhaustively calculating and propagating the trust estimates by the remote nodes. However, in any practical scenario, nodes generally communicate with a small subset of nodes and thus exhaustively estimating the trust of all the nodes can lead to huge resource consumption. In contrast, our mechanism can be tuned to locate a desired subset of trusted nodes, based on the allowable overhead, with respect to a particular user. The mechanism is based on a simple exchange of random walk messages and nodes counting the number of times they are being hit by random walkers of nodes in their neighborhood. Simulation results to analyze the effectiveness of the algorithm show that using the proposed algorithm, nodes identify the top trusted nodes in the network with a very high probability by exploring only around 45% of the total nodes, and in turn generates nearly 90% less overhead as compared to an exhaustive trust estimation mechanism, named TrustWebRank. Finally, we provide a measure of the global trustworthiness of a node; simulation results indicate that the measures generated using our mechanism differ by only around 0.6% as compared to TrustWebRank.
Implementing Turing Machines in Dynamic Field Architectures
Venue: arXiv cs.FL 2012
Year: 2012

Cognitive computation such as e.g. language processing, is conventionally regarded as Turing computation, and Turing machines can be uniquely implemented as nonlinear dynamical systems using generalized shifts and subsequent Gödel encoding of the symbolic repertoire. The resulting nonlinear dynamical automata (NDA) are piecewise affine-linear maps acting on the unit square that is partitioned into rectangular domains. Iterating a single point, i.e. a microstate, by the dynamics yields a trajectory of, in principle, infinitely many points scattered through phase space. Therefore, the NDAs microstate dynamics does not necessarily terminate in contrast to its counterpart, the symbolic dynamics obtained from the rectangular partition. In order to regain the proper symbolic interpretation, one has to prepare ensembles of randomly distributed microstates with rectangular supports. Only the resulting macrostate evolution corresponds then to the original Turing machine computation. However, the introduction of random initial conditions into a deterministic dynamics is not really satisfactory. As a possible solution for this problem we suggest a change of perspective. Instead of looking at point dynamics in phase space, we consider functional dynamics of probability distributions functions (p.d.f.s) over phase space. This is generally described by a Frobenius-Perron integral transformation that can be regarded as a neural field equation over the unit square as feature space of a dynamic field theory (DFT). Solving the Frobenius-Perron equation, yields that uniform p.d.f.s with rectangular support are mapped onto uniform p.d.f.s with rectangular support, again. Thus, the symbolically meaningful NDA macrostate dynamics becomes represented by iterated function dynamics in DFT; hence we call the resulting representation dynamic field automata.