OpenRaiser/Intern-Atlas huggingface dataset:
Element Distinctness, Frequency Moments, and Sliding Windows
Venue: arXiv cs.CC 2013
Year: 2013

We derive new time-space tradeoff lower bounds and algorithms for exactly computing statistics of input data, including frequency moments, element distinctness, and order statistics, that are simple to calculate for sorted data. We develop a randomized algorithm for the element distinctness problem whose time T and space S satisfy T in O (n^{3/2}/S^{1/2}), smaller than previous lower bounds for comparison-based algorithms, showing that element distinctness is strictly easier than sorting for randomized branching programs. This algorithm is based on a new time and space efficient algorithm for finding all collisions of a function f from a finite set to itself that are reachable by iterating f from a given set of starting points. We further show that our element distinctness algorithm can be extended at only a polylogarithmic factor cost to solve the element distinctness problem over sliding windows, where the task is to take an input of length 2n-1 and produce an output for each window of length n, giving n outputs in total. In contrast, we show a time-space tradeoff lower bound of T in Omega(n^2/S) for randomized branching programs to compute the number of distinct elements over sliding windows. The same lower bound holds for computing the low-order bit of F_0 and computing any frequency moment F_k, k neq 1. This shows that those frequency moments and the decision problem F_0 mod 2 are strictly harder than element distinctness. We complement this lower bound with a T in O(n^2/S) comparison-based deterministic RAM algorithm for exactly computing F_k over sliding windows, nearly matching both our lower bound for the sliding-window version and the comparison-based lower bounds for the single-window version. We further exhibit a quantum algorithm for F_0 over sliding windows with T in O(n^{3/2}/S^{1/2}). Finally, we consider the computations of order statistics over sliding windows.
A Metric-learning based framework for Support Vector Machines and Multiple Kernel Learning
Venue: arXiv cs.LG 2013
Year: 2013

Most metric learning algorithms, as well as Fisher's Discriminant Analysis (FDA), optimize some cost function of different measures of within-and between-class distances. On the other hand, Support Vector Machines(SVMs) and several Multiple Kernel Learning (MKL) algorithms are based on the SVM large margin theory. Recently, SVMs have been analyzed from SVM and metric learning, and to develop new algorithms that build on the strengths of each. Inspired by the metric learning interpretation of SVM, we develop here a new metric-learning based SVM framework in which we incorporate metric learning concepts within SVM. We extend the optimization problem of SVM to include some measure of the within-class distance and along the way we develop a new within-class distance measure which is appropriate for SVM. In addition, we adopt the same approach for MKL and show that it can be also formulated as a Mahalanobis metric learning problem. Our end result is a number of SVM/MKL algorithms that incorporate metric learning concepts. We experiment with them on a set of benchmark datasets and observe important predictive performance improvements.
On Characterising Distributability
Venue: arXiv cs.LO 2013
Year: 2013

We formalise a general concept of distributed systems as sequential components interacting asynchronously. We define a corresponding class of Petri nets, called LSGA nets, and precisely characterise those system specifications which can be implemented as LSGA nets up to branching ST-bisimilarity with explicit divergence.
Robustness analysis of finite precision implementations
Venue: arXiv cs.SE 2013
Year: 2013

A desirable property of control systems is to be robust to inputs, that is small perturbations of the inputs of a system will cause only small perturbations on its outputs. But it is not clear whether this property is maintained at the implementation level, when two close inputs can lead to very different execution paths. The problem becomes particularly crucial when considering finite precision implementations, where any elementary computation can be affected by a small error. In this context, almost every test is potentially unstable, that is, for a given input, the computed (finite precision) path may differ from the ideal (same computation in real numbers) path. Still, state-of-the-art error analyses do not consider this possibility and rely on the stable test hypothesis, that control flows are identical. If there is a discontinuity between the treatments in the two branches, that is the conditional block is not robust to uncertainties, the error bounds can be unsound. We propose here a new abstract-interpretation based error analysis of finite precision implementations, which is sound in presence of unstable tests. It automatically bounds the discontinuity error coming from the difference between the float and real values when there is a path divergence, and introduces a new error term labeled by the test that introduced this potential discontinuity. This gives a tractable error analysis, implemented in our static analyzer FLUCTUAT: we present results on representative extracts of control programs.
Strategic Planning in Air Traffic Control as a Multi-objective Stochastic Optimization Problem
Venue: arXiv cs.AI 2013
Year: 2013

With the objective of handling the airspace sector congestion subject to continuously growing air traffic, we suggest to create a collaborative working plan during the strategic phase of air traffic control. The plan obtained via a new decision support tool presented in this article consists in a schedule for controllers, which specifies time of overflight on the different waypoints of the flight plans. In order to do it, we believe that the decision-support tool shall model directly the uncertainty at a trajectory level in order to propagate the uncertainty to the sector level. Then, the probability of congestion for any sector in the airspace can be computed. Since air traffic regulations and sector congestion are antagonist, we designed and implemented a multi-objective optimization algorithm for determining the best trade-off between these two criteria. The solution comes up as a set of alternatives for the multi-sector planner where the severity of the congestion cost is adjustable. In this paper, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was used to solve an artificial benchmark problem involving 24 aircraft and 11 sectors, and is able to provide a good approximation of the Pareto front.
Using Self-Organizing Maps for Sentiment Analysis
Venue: arXiv cs.IR 2013
Year: 2013

Web 2.0 services have enabled people to express their opinions, experience and feelings in the form of user-generated content. Sentiment analysis or opinion mining involves identifying, classifying and aggregating opinions as per their positive or negative polarity. This paper investigates the efficacy of different implementations of Self-Organizing Maps (SOM) for sentiment based visualization and classification of online reviews. Specifically, this paper implements the SOM algorithm for both supervised and unsupervised learning from text documents. The unsupervised SOM algorithm is implemented for sentiment based visualization and classification tasks. For supervised sentiment analysis, a competitive learning algorithm known as Learning Vector Quantization is used. Both algorithms are also compared with their respective multi-pass implementations where a quick rough ordering pass is followed by a fine tuning pass. The experimental results on the online movie review data set show that SOMs are well suited for sentiment based classification and sentiment polarity visualization.
Utilizing Noise Addition for Data Privacy, an Overview
Venue: arXiv cs.CR 2013
Year: 2013

The internet is increasingly becoming a standard for both the production and consumption of data while at the same time cyber-crime involving the theft of private data is growing. Therefore in efforts to securely transact in data, privacy and security concerns must be taken into account to ensure that the confidentiality of individuals and entities involved is not compromised, and that the data published is compliant to privacy laws. In this paper, we take a look at noise addition as one of the data privacy providing techniques. Our endeavor in this overview is to give a foundational perspective on noise addition data privacy techniques, provide statistical consideration for noise addition techniques and look at the current state of the art in the field, while outlining future areas of research.
Inadmissible Class of Boolean Functions under Stuck-at Faults
Venue: arXiv cs.OH 2013
Year: 2013

Many underlying structural and functional factors that determine the fault behavior of a combinational network, are not yet fully understood. In this paper, we show that there exists a large class of Boolean functions, called root functions, which can never appear as faulty response in irredundant two-level circuits even when any arbitrary multiple stuck-at faults are injected. Conversely, we show that any other Boolean function can appear as a faulty response from an irredundant realization of some root function under certain stuck-at faults. We characterize this new class of functions and show that for n variables, their number is exactly equal to the number of independent dominating sets (Harary and Livingston, Appl. Math. Lett., 1993) in a Boolean n-cube. We report some bounds and enumerate the total number of root functions up to 6 variables. Finally, we point out several open problems and possible applications of root functions in logic design and testing.
A probabilistic and deterministic modular algorithm for computing Groebner basis over $\Q$
Venue: arXiv cs.SC 2013
Year: 2013

Modular algorithm are widely used in computer algebra systems (CAS), for example to compute efficiently the gcd of multivariate polynomials. It is known to work to compute Groebner basis over $\Q$, but it does not seem to be popular among CAS implementers. In this paper, I will show how to check a candidate Groebner basis (obtained by reconstruction of several Groebner basis modulo distinct prime numbers) with a given error probability, that may be 0 if a certified Groebner basis is desired. This algorithm is now the default algorithm used by the Giac/Xcas computer algebra system with competitive timings, thanks to a trick that can accelerate computing Groebner basis modulo a prime once the computation has been done modulo another prime.
Facebook Applications' Installation and Removal: A Temporal Analysis
Venue: arXiv cs.SI 2013
Year: 2013

Facebook applications are one of the reasons for Facebook attractiveness. Unfortunately, numerous users are not aware of the fact that many malicious Facebook applications exist. To educate users, to raise users' awareness and to improve Facebook users' security and privacy, we developed a Firefox add-on that alerts users to the number of installed applications on their Facebook profiles. In this study, we present the temporal analysis of the Facebook applications' installation and removal dataset collected by our add-on. This dataset consists of information from 2,945 users, collected during a period of over a year. We used linear regression to analyze our dataset and discovered the linear connection between the average percentage change of newly installed Facebook applications and the number of days passed since the user initially installed our add-on. Additionally, we found out that users who used our Firefox add-on become more aware of their security and privacy installing on average fewer new applications. Finally, we discovered that on average 86.4% of Facebook users install an additional application every 4.2 days.
Algorithms for Computing Topological Invariants in 2D and 3D Digital Spaces
Venue: arXiv cs.CG 2013
Year: 2013

Based on previous results of digital topology, this paper focuses on algorithms of topological invariants of objects in 2D and 3D Digital Spaces. We specifically interest in solving hole counting of 2D objects and genus of closed surface in 3D. We first prove a new formula for hole counting in 2D. The number of of holes is $h=1 + (|C_4|-|C_2|)/4$ where $C_4$ and $C_2$ are sets of inward and outward corner points, respectively. This paper mainly deals with algorithm design and implementation of practical computation of topological invariants in digital space. The algorithms relating to data structures, and pathological case detection and original data modification are main issues. This paper designed fast algorithms for topological invariants such as connected components, hole counting in 2D and boundary surface genus for 3D. For 2D images, we designed a linear time algorithm to solve hole counting problem. In 3D, we designed also O(n) time algorithm to get genus of the closed surface. These two algorithms are both in $O(\log n)$ space complexity.
Base Station Activation and Linear Transceiver Design for Optimal Resource Management in Heterogeneous Networks
Venue: arXiv cs.IT 2013
Year: 2013

In a densely deployed heterogeneous network (HetNet), the number of pico/micro base stations (BS) can be comparable with the number of the users. To reduce the operational overhead of the HetNet, proper identification of the set of serving BSs becomes an important design issue. In this work, we show that by jointly optimizing the transceivers and determining the active set of BSs, high system resource utilization can be achieved with only a small number of BSs. In particular, we provide formulations and efficient algorithms for such joint optimization problem, under the following two common design criteria: i) minimization of the total power consumption at the BSs, and ii) maximization of the system spectrum efficiency. In both cases, we introduce a nonsmooth regularizer to facilitate the activation of the most appropriate BSs. We illustrate the efficiency and the efficacy of the proposed algorithms via extensive numerical simulations.
Analysis of Blockage Effects on Urban Cellular Networks
Venue: arXiv cs.IT 2013
Year: 2013

Large-scale blockages like buildings affect the performance of urban cellular networks, especially at higher frequencies. Unfortunately, such blockage effects are either neglected or characterized by oversimplified models in the analysis of cellular networks. Leveraging concepts from random shape theory, this paper proposes a mathematical framework to model random blockages and analyze their impact on cellular network performance. Random buildings are modeled as a process of rectangles with random sizes and orientations whose centers form a Poisson point process on the plane. The distribution of the number of blockages in a link is proven to be Poisson random variable with parameter dependent on the length of the link. A path loss model that incorporates the blockage effects is proposed, which matches experimental trends observed in prior work. The model is applied to analyze the performance of cellular networks in urban areas with the presence of buildings, in terms of connectivity, coverage probability, and average rate. Analytic results show while buildings may block the desired signal, they may still have a positive impact on network performance since they can block significantly more interference.
Optimal Distributed Controller Design with Communication Delays: Application to Vehicle Formations
Venue: arXiv cs.SY 2013
Year: 2013

This paper develops a controller synthesis algorithm for distributed LQG control problems under output feedback. We consider a system consisting of three interconnected linear subsystems with a delayed information sharing structure. While the state-feedback case of this problem has previously been solved, the extension to output-feedback is nontrivial, as the classical separation principle fails. To find the optimal solution, the controller is decomposed into two independent components. One is delayed centralized LQR, and the other is the sum of correction terms based on additional local information. Explicit discrete-time equations are derived whose solutions are the gains of the optimal controller.
An Energy Efficient Decoding Scheme for Wireless Body Area Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

One of the major challenges in Wireless Body Area Networks (WBANs) is to prolong the lifetime of network. Traditional research work focuses on minimizing transmit power; however, in the case of short range communication the consumption power in decoding is significantly larger than transmit power. This paper investigates the minimization of total power consumption by reducing the decoding power consumption. For achieving a desired Bit Error Rate (BER), we introduce some fundamental results on the basis of iterative message-passing algorithms for Low Density Parity Check Code (LDPC). To reduce energy dissipation in decoder, LDPC based coded communications between sensors are considered. Moreover, we evaluate the performance of LDPC at different code rates and introduce Adaptive Iterative Decoding (AID) by exploiting threshold on the number of iterations for a certain BER (0.0004). In iterative LDPC decoding, the total energy consumption of network is reduced by 20 to 25%.
Modeling and Evaluating Performance of Routing Operations in Reactive Routing Protocols
Venue: arXiv cs.NI 2013
Year: 2013

Reactive routing protocols are gaining popularity due to their event driven nature day by day. In this vary paper, reactive routing is studied precisely. Route request, route reply and route maintenance phases are modeled with respect to control overhead. Control overhead varies with respect to change in various parameters. Our model calculates these variations as well. Besides modeling, we chose three most favored reactive routing protocols as Ad-Hoc on Demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Dynamic MANET on Demand (DYMO) for our experiments. We simulated these protocols using ns-2 for a detailed comparison and performance analysis with respect to mobility and scalability issues keeping metrics of throughput, route delay and control over head. Their performances and comparisons are extensively presented in last part of our work.
Specifying Robustness
Venue: arXiv cs.LO 2013
Year: 2013

This paper proposes a new logic RoCTL* to model robustness in concurrent systems. RoCTL* extends CTL* with the addition of Obligatory and Robustly operators, which quantify over failure-free paths and paths with one more failure respectively. We present a number of examples of problems to which RoCTL* can be applied. The core result of this paper is to show that RoCTL* is expressively equivalent to CTL* but is non-elementarily more succinct. We present a translation from RoCTL* into CTL* that preserves truth but may result in non-elementary growth in the length of the translated formula as each nested Robustly operator may result in an extra exponential blowup. However, we show that this translation is optimal in the sense that any equivalence preserving translation will require an extra exponential growth per nested Robustly. We also compare RoCTL* to Quantified CTL* (QCTL*) and hybrid logics.
GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images
Venue: arXiv cs.CV 2013
Year: 2013

Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cell nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cell nuclei is very time consuming, which remains a bottleneck in large scale biological experiments. In this work we present a tool for automated segmentation of cell nuclei from 3D fluorescent microscopic data. Our tool is based on state-of-the-art image processing and machine learning techniques and supports a friendly graphical user interface (GUI). We show that our tool is as accurate as manual annotation but greatly reduces the time for the registration.
Robust Heterogeneous Network to Support Multitasking
Venue: arXiv cs.NI 2013
Year: 2013

Due to emerging technology, efficient multitasking approach is highly demanded. But it is hard to accomplish in heterogeneous wireless networks, where diverse networks have dissimilar geometric features in service and traffic models. Multitasking loss examination based on Markov chain becomes inflexible in these networks owing to rigorous computations is obligatory. This paper emphases on the performance of heterogeneous wireless networks based on multitasking. A method based on multitasking of the interrelated traffic is used to attain an approximate performance in heterogeneous wireless networks with congested traffic. The accuracy of the robust heterogeneous network with multitasking is verified by using ns2 simulations.
A novel approach for nose tip detection using smoothing by weighted median filtering applied to 3D face images in variant poses
Venue: arXiv cs.CV 2013
Year: 2013

This paper is based on an application of smoothing of 3D face images followed by feature detection i.e. detecting the nose tip. The present method uses a weighted mesh median filtering technique for smoothing. In this present smoothing technique we have built the neighborhood surrounding a particular point in 3D face and replaced that with the weighted value of the surrounding points in 3D face image. After applying the smoothing technique to the 3D face images our experimental results show that we have obtained considerable improvement as compared to the algorithm without smoothing. We have used here the maximum intensity algorithm for detecting the nose-tip and this method correctly detects the nose-tip in case of any pose i.e. along X, Y, and Z axes. The present technique gave us worked successfully on 535 out of 542 3D face images as compared to the method without smoothing which worked only on 521 3D face images out of 542 face images. Thus we have obtained a 98.70% performance rate over 96.12% performance rate of the algorithm without smoothing. All the experiments have been performed on the FRAV3D database.
New Approximability Results for the Robust k-Median Problem
Venue: arXiv cs.DS 2013
Year: 2013

We consider a robust variant of the classical $k$-median problem, introduced by Anthony et al. \cite{AnthonyGGN10}. In the \emph{Robust $k$-Median problem}, we are given an $n$-vertex metric space $(V,d)$ and $m$ client sets $\set{S_i \subseteq V}_{i=1}^m$. The objective is to open a set $F \subseteq V$ of $k$ facilities such that the worst case connection cost over all client sets is minimized; in other words, minimize $\max_{i} \sum_{v \in S_i} d(F,v)$. Anthony et al.\ showed an $O(\log m)$ approximation algorithm for any metric and APX-hardness even in the case of uniform metric. In this paper, we show that their algorithm is nearly tight by providing $Ω(\log m/ \log \log m)$ approximation hardness, unless ${\sf NP} \subseteq \bigcap_{δ>0} {\sf DTIME}(2^{n^δ})$. This hardness result holds even for uniform and line metrics. To our knowledge, this is one of the rare cases in which a problem on a line metric is hard to approximate to within logarithmic factor. We complement the hardness result by an experimental evaluation of different heuristics that shows that very simple heuristics achieve good approximations for realistic classes of instances.