OpenRaiser/Intern-Atlas huggingface dataset:
Massive-MIMO Meets HetNet: Interference Coordination Through Spatial Blanking
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we study the downlink performance of a heterogeneous cellular network (HetNet) where both macro and small cells share the same spectrum and hence interfere with each other. We assume that the users are concentrated at certain areas in the cell, i.e., they form hotspots. While some of the hotspots are assumed to have a small cell in their vicinity, the others are directly served by the macrocell. Due to a relatively small area of each hotspot, the users lying in a particular hotspot appear to be almost co-located to the macrocells, which are typically deployed at some elevation. Assuming large number of antennas at the macrocell, we exploit this directionality in the channel vectors to obtain spatial blanking, i.e., concentrating transmission energy only in certain directions while creating transmission opportunities for the small cells lying in the other directions. In addition to this inherent interference suppression, we also develop three low-complexity interference coordination strategies: (i) turn off small cells based on the amount of cross-tier interference they receive or cause to the scheduled macrocell hotspots, (ii) schedule hotspots such that treating interference as noise is approximately optimal for the resulting Gaussian interference channel, and (iii) offload some of the macrocell hotspots to nearby small cells in order to improve throughput fairness across all hotspots. For all these schemes, we study the relative merits and demerits of uniform deployment of small cells vs. deploying more small cells towards the cell center or the cell edge.
Improved Onlooker Bee Phase in Artificial Bee Colony Algorithm
Venue: arXiv cs.NE 2014
Year: 2014

Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.
Tree-based iterated local search for Markov random fields with applications in image analysis
Venue: arXiv cs.AI 2014
Year: 2014

The \emph{maximum a posteriori} (MAP) assignment for general structure Markov random fields (MRFs) is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS) takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhood and does so with limited memory without any requirement on the cost functions. We evaluate the T-ILS in a simulation of Ising model and two real-world problems in computer vision: stereo matching, image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with a significant computational gain.
Symbol interval optimization for molecular communication with drift
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we propose a symbol interval optimization algorithm in molecular communication with drift. Proper symbol intervals are important in practical communication systems since information needs to be sent as fast as possible with low error rates. There is a trade-off, however, between symbol intervals and inter-symbol interference (ISI) from Brownian motion. Thus, we find proper symbol interval values considering the ISI inside two kinds of blood vessels, and also suggest no ISI system for strong drift models. Finally, an isomer-based molecule shift keying (IMoSK) is applied to calculate achievable data transmission rates (achievable rates, hereafter). Normalized achievable rates are also obtained and compared in one-symbol ISI and no ISI systems.
Proving Threshold Saturation for Nonbinary SC-LDPC Codes on the Binary Erasure Channel
Venue: arXiv cs.IT 2014
Year: 2014

We analyze nonbinary spatially-coupled low-density parity-check (SC-LDPC) codes built on the general linear group for transmission over the binary erasure channel. We prove threshold saturation of the belief propagation decoding to the potential threshold, by generalizing the proof technique based on potential functions recently introduced by Yedla et al.. The existence of the potential function is also discussed for a vector sparse system in the general case, and some existence conditions are developed. We finally give density evolution and simulation results for several nonbinary SC-LDPC code ensembles.
Concurrent Dynamic Algebra
Venue: arXiv cs.LO 2014
Year: 2014

We reconstruct Peleg's concurrent dynamic logic in the context of modal Kleene algebras. We explore the algebraic structure of its multirelational semantics and develop an abstract axiomatisation of concurrent dynamic algebras from that basis. In this axiomatisation, sequential composition is not associative. It interacts with concurrent composition through a weak distributivity law. The modal operators of concurrent dynamic algebra are obtained from abstract axioms for domain and antidomain operators; the Kleene star is modelled as a least fixpoint. Algebraic variants of Peleg's axioms are shown to be valid in these algebras and their soundness is proved relative to the multirelational model. Additional results include iteration principles for the Kleene star and a refutation of variants of Segerberg's axiom in the multirelational setting. The most important results have been verified formally with Isabelle/HOL.
Mechanical Proofs of Properties of the Tribonacci Word
Venue: arXiv cs.FL 2014
Year: 2014

We implement a decision procedure for answering questions about a class of infinite words that might be called (for lack of a better name) "Tribonacci-automatic". This class includes, for example, the famous Tribonacci word T = 0102010010202 ..., the fixed point of the morphism 0 -> 01, 1 -> 02, 2 -> 0. We use it to reprove some old results about the Tribonacci word from the literature, such as assertions about the occurrences in T of squares, cubes, palindromes, and so forth. We also obtain some new results.
Some Properties of Brzozowski Derivatives of Regular Expressions
Venue: arXiv cs.FL 2014
Year: 2014

Brzozowski derivatives of a regular expression are developed for constructing deterministic automata from the given regular expression in the algebraic way. In this paper,some lemmas of the regular expressions are discussed and the regular languages of the derivatives are illustrated. Also the generalizations of the Brzozowski derivatives are proved as theorems with help of properties and known results.
A Novel Hybrid Algorithm for Permutation Flow Shop Scheduling
Venue: arXiv cs.OH 2014
Year: 2014

In the present scenario the recent engineering and industrial built-up units are facing hodgepodge of problems in a lot of aspects such as machining time, electricity, man power, raw material and customers constraints. The job-shop scheduling is one of the most significant industrial behaviours, particularly in manufacturing planning. This paper proposes the permutation flow shop sequencing problem with the objective of makespan minimization using the new modified proposed method of johnsons algorithm as well as the guptas heuristic algorithm. This paper involves the determination of the order of processing of n jobs in m machines. Although since the problem is known to be np-hard for three or more machines, that produces near optimal solution of the given problem. The proposed method is very simple and easy to understand followed by a numerical illustration is given.
Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications
Venue: arXiv cs.CV 2014
Year: 2014

Automated detection of sclerotic metastases (bone lesions) in Computed Tomography (CT) images has potential to be an important tool in clinical practice and research. State-of-the-art methods show performance of 79% sensitivity or true-positive (TP) rate, at 10 false-positives (FP) per volume. We design a two-tiered coarse-to-fine cascade framework to first operate a highly sensitive candidate generation system at a maximum sensitivity of ~92% but with high FP level (~50 per patient). Regions of interest (ROI) for lesion candidates are generated in this step and function as input for the second tier. In the second tier we generate N 2D views, via scale, random translations, and rotations with respect to each ROI centroid coordinates. These random views are used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. We validate the approach on CT images of 59 patients (49 with sclerotic metastases and 10 normal controls). The proposed method reduces the number of FP/vol. from 4 to 1.2, 7 to 3, and 12 to 9.5 when comparing a sensitivity rates of 60%, 70%, and 80% respectively in testing. The Area-Under-the-Curve (AUC) is 0.834. The results show marked improvement upon previous work.
Linear logic with idempotent exponential modalities: a note
Venue: arXiv cs.LO 2014
Year: 2014

In this note we discuss a variant of linear logic with idempotent exponential modalities. We propose a sequent calculus system and discuss its semantics. We also give a concrete relational model for this calculus.
Modeling languages from graph networks
Venue: arXiv cs.CL 2014
Year: 2014

We model and compute the probability distribution of the letters in random generated words in a language by using the theory of set partitions, Young tableaux and graph theoretical representation methods. This has been of interest for several application areas such as network systems, bioinformatics, internet search, data mining and computacional linguistics.
Novel Realization of Adaptive Sparse Sensing with Sparse Least Mean Fourth Algorithm
Venue: arXiv cs.IT 2014
Year: 2014

Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing (CS) in many applications such as Radar imaging and sparse channel estimation. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter and initial step-size. First, based on the independent assumption, Cramer Rao lower bound (CRLB) is derived as for the performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS.
Affine Combination of Two Adaptive Sparse Filters for Estimating Large Scale MIMO Channels
Venue: arXiv cs.IT 2014
Year: 2014

Large scale multiple-input multiple-output (MIMO) system is considered one of promising technologies for realizing next-generation wireless communication system (5G) to increasing the degrees of freedom in space and enhancing the link reliability while considerably reducing the transmit power. However, large scale MIMO system design also poses a big challenge to traditional one-dimensional channel estimation techniques due to high complexity and curse of dimensionality problems which are caused by long delay spread as well as large number antenna. Since large scale MIMO channels often exhibit sparse or/and cluster-sparse structure, in this paper, we propose a simple affine combination of adaptive sparse channel estimation method for reducing complexity and exploiting channel sparsity in the large scale MIMO system. First, problem formulation and standard affine combination of adaptive least mean square (LMS) algorithm are introduced. Then we proposed an effective affine combination method with two sparse LMS filters and designed an approximate optimum affine combiner according to stochastic gradient search method as well. Later, to validate the proposed algorithm for estimating large scale MIMO channel, computer simulations are provided to confirm effectiveness of the proposed algorithm which can achieve better estimation performance than the conventional one as well as traditional method.
A framework for contextual information retrieval from the WWW
Venue: arXiv cs.IR 2014
Year: 2014

Search engines are the most commonly used type of tool for finding relevant information on the Internet. However, today's search engines are far from perfect. Typical search queries are short, often one or two words, and can be ambiguous therefore returning inappropriate results. Contextual information retrieval (CIR) is a critical technique for these search engines to facilitate queries and return relevant information. Despite its importance, little progress has been made in CIR due to the difficulty of capturing and representing contextual information about users. Numerous contextual information retrieval approaches exist today, but to the best of our knowledge none of them offer a similar service to the one proposed in this paper. This paper proposes an alternative framework for contextual information retrieval from the WWW. The framework aims to improve query results (or make search results more relevant) by constructing a contextual profile based on a user's behaviour, their preferences, and a shared knowledge base, and using this information in the search engine framework to find and return relevant information.
Computing the $2$-blocks of directed graphs
Venue: arXiv cs.DS 2014
Year: 2014

Let $G$ be a directed graph. A \textit{$2$-directed block} in $G$ is a maximal vertex set $C^{2d}\subseteq V$ with $|C^{2d}|\geq 2$ such that for each pair of distinct vertices $x,y \in C^{2d}$, there exist two vertex-disjoint paths from $x$ to $y$ and two vertex-disjoint paths from $y$ to $x$ in $G$. In contrast to the $2$-vertex-connected components of $G$, the subgraphs induced by the $2$-directed blocks may consist of few or no edges. In this paper we present two algorithms for computing the $2$-directed blocks of $G$ in $O(\min\lbrace m,(t_{sap}+t_{sb})n\rbrace n)$ time, where $t_{sap}$ is the number of the strong articulation points of $G$ and $t_{sb}$ is the number of the strong bridges of $G$. Furthermore, we study two related concepts: the $2$-strong blocks and the $2$-edge blocks of $G$. We give two algorithms for computing the $2$-strong blocks of $G$ in $O( \min \lbrace m,t_{sap} n\rbrace n)$ time and we show that the $2$-edge blocks of $G$ can be computed in $O(\min \lbrace m, t_{sb} n \rbrace n)$ time. In this paper we also study some optimization problems related to the strong articulation points and the $2$-blocks of a directed graph. Given a strongly connected graph $G=(V,E)$, find a minimum cardinality set $E^{*}\subseteq E$ such that $G^{*}=(V,E^{*})$ is strongly connected and the strong articulation points of $G$ coincide with the strong articulation points of $G^{*}$. This problem is called minimum strongly connected spanning subgraph with the same strong articulation points. We show that there is a linear time $17/3$ approximation algorithm for this NP-hard problem. We also consider the problem of finding a minimum strongly connected spanning subgraph with the same $2$-blocks in a strongly connected graph $G$. We present approximation algorithms for three versions of this problem, depending on the type of $2$-blocks.
Existence of Comfortable Team in some Special Social Networks
Venue: arXiv cs.SI 2014
Year: 2014

Comfortability is one of the important attributes (characteristics) for a successful team work in any organization. It is necessary to find a comfortable and successful team in any given social network. We have introduced "comfortability" as a new SNA index. Comfortable team exists only in some social networks. In this paper, we analyze the existence of comfortable team in product graphs, such as strong product and Lexicographic product of two given graphs.
FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation
Venue: arXiv cs.CV 2014
Year: 2014

One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in significantly faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrarily length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.
On the Range of Equilibria Utilities of a Repeated Epidemic Dissemination Game with a Mediator
Venue: arXiv cs.DC 2014
Year: 2014

We consider eager-push epidemic dissemination in a complete graph. Time is divided into synchronous stages. In each stage, a source disseminates $ν$ events. Each event is sent by the source, and forwarded by each node upon its first reception, to $f$ nodes selected uniformly at random, where $f$ is the fanout. We use Game Theory to study the range of $f$ for which equilibria strategies exist, assuming that players are either rational or obedient to the protocol, and that they do not collude. We model interactions as an infinitely repeated game. We devise a monitoring mechanism that extends the repeated game with communication rounds used for exchanging monitoring information, and define strategies for this extended game. We assume the existence of a trusted mediator, that players are computationally bounded such that they cannot break the cryptographic primitives used in our mechanism, and that symmetric ciphering is cheap. Under these assumptions, we show that, if the size of the stream is sufficiently large and players attribute enough value to future utilities, then the defined strategies are Sequential Equilibria of the extended game for any value of $f$. Moreover, the utility provided to each player is arbitrarily close to that provided in the original game. This shows that we can persuade rational nodes to follow a dissemination protocol that uses any fanout, while arbitrarily minimising the relative overhead of monitoring.
A robust and adaptable method for face detection based on Color Probabilistic Estimation Technique
Venue: arXiv cs.CV 2014
Year: 2014

Human face perception is currently an active research area in the computer vision community. Skin detection is one of the most important and primary stages for this purpose. So far, many approaches are proposed to done this case. Near all of these methods have tried to find best match intensity distribution with skin pixels based on popular color spaces such as RGB, HSI or YCBCR. Results show that these methods cannot provide an accurate approach for every kind of skin. In this paper, an approach is proposed to solve this problem using a color probabilistic estimation technique. This approach is including two stages. In the first one, the skin intensity distribution is estimated using some train photos of pure skin, and at the second stage, the skin pixels are detected using Gaussian model and optimal threshold tuning. Then from the skin region facial features have been extracted to get the face from the skin region. In the results section, the proposed approach is applied on FEI database and the accuracy rate reached 99.25%. The proposed approach can be used for all kinds of skin using train stage which is the main advantage among the other advantages, such as Low noise sensitivity and low computational complexity.
Novel and Automatic Parking Inventory System Based on Pattern Recognition and Directional Chain Code
Venue: arXiv cs.CV 2014
Year: 2014

The objective of this paper is to design an efficient vehicle license plate recognition System and to implement it for automatic parking inventory system. The system detects the vehicle first and then captures the image of the front view of the vehicle. Vehicle license plate is localized and characters are segmented. For finding the place of plate, a novel and real time method is expressed. A new and robust technique based on directional chain code is used for character recognition. The resulting vehicle number is then compared with the available database of all the vehicles so as to come up with information about the vehicle type and to charge entrance cost accordingly. The system is then allowed to open parking barrier for the vehicle and generate entrance cost receipt. The vehicle information (such as entrance time, date, and cost amount) is also stored in the database to maintain the record. The hardware and software integrated system is implemented and a working prototype model is developed. Under the available database, the average accuracy of locating vehicle license plate obtained 100%. Using 70% samples of character for training, we tested our scheme on whole samples and obtained 100% correct recognition rate. Further we tested our character recognition stage on Persian vehicle data set and we achieved 99% correct recognition.
Securing Cloud from Cloud Drain
Venue: arXiv cs.CR 2014
Year: 2014

Today, in the world of communication, connected systems is growing at a rapid pace. To accommodate this growth the need for computational power and storage is also increasing at a similar rate. Companies are investing a large amount of resources in buying, maintaining and ensuring availability of the system to their customers. To mitigate these issues, cloud computing is playing a major role.The underlying concept of cloud computing dates back to the 50's but the term entering into widespread usage can be traced to 2006 when Amazon.com announced the Elastic Compute Cloud.In this paper, we will discuss about cloud security approaches. We have used the term Cloud-Drain to define data leakage in case of security compromise.