OpenRaiser/Intern-Atlas huggingface dataset:
Design issues for distributed mobile social networks
Venue: arXiv cs.SI 2014
Year: 2014

Social networks and their applications have become extremely popular during the last years, mostly targeting users via the web. However, it has been recently observed an interest to offer social network services to mobile users. Telecom operators attempt to integrate existing social networks to their systems or develop new ones, in order to offer new services to their subscribers. Subsequently, emphasis is given to the user-context modeling, as well as to the integration of sources that leads to the summarized collection of information anchored to the user; such as its location or its mobile device type, etc. In this paper we discuss the most important factors and challenges encountered during the design of such a system on architectural, technological and tool level.
On the Relation of Interaction Semantics to Continuations and Defunctionalization
Venue: arXiv cs.LO 2014
Year: 2014

In game semantics and related approaches to programming language semantics, programs are modelled by interaction dialogues. Such models have recently been used in the design of new compilation methods, e.g. for hardware synthesis or for programming with sublinear space. This paper relates such semantically motivated non-standard compilation methods to more standard techniques in the compilation of functional programming languages, namely continuation passing and defunctionalization. We first show for the linear λ-calculus that interpretation in a model of computation by interaction can be described as a call-by-name CPS-translation followed by a defunctionalization procedure that takes into account control-flow information. We then establish a relation between these two compilation methods for the simply-typed λ-calculus and end by considering recursion.
Content-Priority based Interest Forwarding in Content Centric Networks
Venue: arXiv cs.NI 2014
Year: 2014

Content Centric Networking (CCN) is a recent advancement in communication networks where the current research is mainly focusing on routing & cache management strategies of CCN. Nonetheless, other perspectives such as network level security and service quality are also of prime importance; areas which have not been covered deeply so far. This paper introduces an interest forwarding mechanism to process the requests of consumers at a CCN router. Interest packets are forwarded with respect to the priorities of addressed content while the priority level settings are done by content publishers during an initialization phase using a collaborative mechanism of exchanging messages to agree to the priority levels of all content according to the content-nature. Interests with higher priority content are recorded in Pending Interest Table (PIT) as well as forwarded to content publishers prior to those with lower priority content. A simulation study is also conducted to show the effectiveness of proposed scheme and we observe that the interests with higher priority content are satisfied earlier than the interests with lower priority content.
Prior Support Knowledge-Aided Sparse Bayesian Learning with Partly Erroneous Support Information
Venue: arXiv cs.IT 2014
Year: 2014

It has been shown both experimentally and theoretically that sparse signal recovery can be significantly improved given that part of the signal's support is known \emph{a priori}. In practice, however, such prior knowledge is usually inaccurate and contains errors. Using such knowledge may result in severe performance degradation or even recovery failure. In this paper, we study the problem of sparse signal recovery when partial but partly erroneous prior knowledge of the signal's support is available. Based on the conventional sparse Bayesian learning framework, we propose a modified two-layer Gaussian-inverse Gamma hierarchical prior model and, moreover, an improved three-layer hierarchical prior model. The modified two-layer model employs an individual parameter $b_i$ for each sparsity-controlling hyperparameter $α_i$, and has the ability to place non-sparsity-encouraging priors to those coefficients that are believed in the support set. The three-layer hierarchical model is built on the modified two-layer prior model, with a prior placed on the parameters $\{b_i\}$ in the third layer. Such a model enables to automatically learn the true support from partly erroneous information through learning the values of the parameters $\{b_i\}$. Variational Bayesian algorithms are developed based on the proposed hierarchical prior models. Numerical results are provided to illustrate the performance of the proposed algorithms.
On Bootstrapping Machine Learning Performance Predictors via Analytical Models
Venue: arXiv cs.PF 2014
Year: 2014

Performance modeling typically relies on two antithetic methodologies: white box models, which exploit knowledge on system's internals and capture its dynamics using analytical approaches, and black box techniques, which infer relations among the input and output variables of a system based on the evidences gathered during an initial training phase. In this paper we investigate a technique, which we name Bootstrapping, which aims at reconciling these two methodologies and at compensating the cons of the one with the pros of the other. We thoroughly analyze the design space of this gray box modeling technique, and identify a number of algorithmic and parametric trade-offs which we evaluate via two realistic case studies, a Key-Value Store and a Total Order Broadcast service.
Stability and Continuity of Centrality Measures in Weighted Graphs
Venue: arXiv cs.SI 2014
Year: 2014

This paper presents a formal definition of stability for node centrality measures in weighted graphs. It is shown that the commonly used measures of degree, closeness and eigenvector centrality are stable whereas betweenness centrality is not. An alternative definition of the latter that preserves the same centrality notion while satisfying the stability criteria is introduced. Continuity is presented as a less stringent alternative to stability. Betweenness centrality is shown to be not only unstable but discontinuous. Numerical experiments in synthetic random networks and real-world data show that, in practice, stability and continuity imply different levels of robustness in the presence of noisy data. In particular, the stable betweenness centrality is shown to exhibit resilience against noise that is absent in the discontinuous and unstable standard betweenness centrality, while preserving a similar notion of centrality.
Simulation based Study of TCP Variants in Hybrid Network
Venue: arXiv cs.NI 2014
Year: 2014

Transmission control protocol (TCP) was originally designed for fixed networks to provide the reliability of the data delivery. The improvement of TCP performance was also achieved with different types of networks with introduction of new TCP variants. However, there are still many factors that affect performance of TCP. Mobility is one of the major affects on TCP performance in wireless networks and MANET (Mobile Ad Hoc Network). To determine the best TCP variant from mobility point of view, we simulate some TCP variants in real life scenario. This paper addresses the performance of TCP variants such as TCP-Tahoe, TCP-Reno, TCP-New Reno, TCPVegas,TCP-SACK and TCP-Westwood from mobility point of view.The scenarios presented in this paper are supported by Zone routing Protocol (ZRP) with integration of random waypoint mobility model in MANET area. The scenario shows the speed of walking person to a vehicle and suited particularly for mountainous and deserted areas. On the basis of simulation, we analyze Round trip time (RTT) fairness, End-to-End delay, control overhead, number of broken links during the delivery of data. Finally analyzed parameters help to find out the best TCP variant.
Energy-Spectral Efficiency Trade-off for a Massive SU-MIMO System with Transceiver Power Consumption
Venue: arXiv cs.IT 2014
Year: 2014

We consider a single user (SU) massive MIMO system with multiple antennas at the transmitter (base station) and a single antenna at the user terminal (UT). Taking transceiver power consumption into consideration, for a given spectral efficiency (SE) we maximize the energy efficiency (EE) as a function of the number of base station (BS) antennas $M$, resulting in a closed-form expression for the optimal SE-EE trade-off. It is observed that in contrast to the classical SE-EE trade-off (which considers only the radiated power), with transceiver power consumption taken into account, the EE increases with increasing SE when SE is sufficiently small. Further, for a fixed SE we analyze the impact of varying cell size (i.e., equivalently average channel gain $G_c$) on the optimal EE. We show the interesting result that for sufficiently small $G_c$, the optimal EE decreases as $\mathcal{O}(\sqrt{G_c})$ with decreasing $G_c$. Our analysis also reveals that for sufficiently small SE (or large $G_c$), the EE is insensitive to the power amplifier efficiency.
Private Information Retrieval for Coded Storage
Venue: arXiv cs.IT 2014
Year: 2014

Private information retrieval scheme for coded data storage is considered in this paper. We focus on the case where the size of each data record is large and hence only the download cost (but not the upload cost for transmitting retrieval queries) is of interest. We prove that the tradeoff between storage cost and retrieval/download cost depends on the number of data records in the system. We also propose a fairly general class of linear storage codes and retrieval schemes and derive conditions under which our retrieval schemes are error-free and private. Tradeoffs between the storage cost and retrieval costs are also obtained. Finally, we consider special cases when the underlying storage code is based on an MDS code. Using our proposed method, we show that a randomly generated retrieval scheme is indeed very likely to be private and error-free.
Improvement of PSO algorithm by memory based gradient search - application in inventory management
Venue: arXiv cs.NE 2014
Year: 2014

Advanced inventory management in complex supply chains requires effective and robust nonlinear optimization due to the stochastic nature of supply and demand variations. Application of estimated gradients can boost up the convergence of Particle Swarm Optimization (PSO) algorithm but classical gradient calculation cannot be applied to stochastic and uncertain systems. In these situations Monte-Carlo (MC) simulation can be applied to determine the gradient. We developed a memory based algorithm where instead of generating and evaluating new simulated samples the stored and shared former function evaluations of the particles are sampled to estimate the gradients by local weighted least squares regression. The performance of the resulted regional gradient-based PSO is verified by several benchmark problems and in a complex application example where optimal reorder points of a supply chain are determined.
3D simulation of complex shading affecting PV systems taking benefit from the power of graphics cards developed for the video game industry
Venue: arXiv cs.OH 2014
Year: 2014

Shading reduces the power output of a photovoltaic (PV) system. The design engineering of PV systems requires modeling and evaluating shading losses. Some PV systems are affected by complex shading scenes whose resulting PV energy losses are very difficult to evaluate with current modeling tools. Several specialized PV design and simulation software include the possibility to evaluate shading losses. They generally possess a Graphical User Interface (GUI) through which the user can draw a 3D shading scene, and then evaluate its corresponding PV energy losses. The complexity of the objects that these tools can handle is relatively limited. We have created a software solution, 3DPV, which allows evaluating the energy losses induced by complex 3D scenes on PV generators. The 3D objects can be imported from specialized 3D modeling software or from a 3D object library. The shadows cast by this 3D scene on the PV generator are then directly evaluated from the Graphics Processing Unit (GPU). Thanks to the recent development of GPUs for the video game industry, the shadows can be evaluated with a very high spatial resolution that reaches well beyond the PV cell level, in very short calculation times. A PV simulation model then translates the geometrical shading into PV energy output losses. 3DPV has been implemented using WebGL, which allows it to run directly from a Web browser, without requiring any local installation from the user. This also allows taken full benefits from the information already available from Internet, such as the 3D object libraries. This contribution describes, step by step, the method that allows 3DPV to evaluate the PV energy losses caused by complex shading. We then illustrate the results of this methodology to several application cases that are encountered in the world of PV systems design.
OHMF: A Query Based Optimal Healthcare Medication Framework
Venue: arXiv cs.CY 2014
Year: 2014

Today cloud computing infrastructure is largely being deployed in healthcare to access various healthcare services easily over the Internet on an as needed basis. The main advantage of healthcare cloud is that it can be used as a tool for patients, medical professionals and insurance providers, to query and coordinate among medical departments, organizations and other healthcare related hubs. Although healthcare cloud services can enable better medication process with high responsiveness, but the privacy and other requirements of the patients need to be ensured in the process. Patients medical data may be required by the medical professionals, hospitals, diagnostic centers for analysis and diagnosis. However, data privacy and service quality cannot be compromised. In other words, there may exist various service providers corresponding to a specific healthcare service. The main challenge is to find the appropriate providers that comply best with patients requirement. In this paper, we propose a query based optimal medication framework to support the patients healthcare service accessibility comprehensively with considerable response time. The framework accepts related healthcare queries in natural language through a comprehensive user-interface and then processes the input query through a first order logic based evaluation engine and finds all possible services satisfying the requirements. First order logic is used for modeling of user requirements and queries. The query evaluation engine is built using zChaff, a Boolean logic satisfiability solver. The efficacy and usability of the framework is evaluated with initial case studies on synthetic and real life healthcare cloud.
System Approximations and Generalized Measurements in Modern Sampling Theory
Venue: arXiv cs.IT 2014
Year: 2014

This paper studies several aspects of signal reconstruction of sampled data in spaces of bandlimited functions. In the first part, signal spaces are characterized in which the classical sampling series uniformly converge, and we investigate whether adaptive recovery algorithms can yield uniform convergence in spaces where non-adaptive sampling series does not. In particular, it is shown that the investigation of adaptive signal recovery algorithms needs completely new analytic tools since the methods used for non-adaptive reconstruction procedures, which are based on the celebrated Banach-Steinhaus theorem, are not applicable in the adaptive case. The second part analyzes the approximation of the output of stable linear time-invariant (LTI) systems based on samples of the input signal, and where the input is assumed to belong to the Paley-Wiener space of bandlimited functions with absolute integrable Fourier transform. If the samples are acquired by point evaluations of the input signal f, then there exist stable LTI systems H such that the approximation process does not converge to the desired output H(f) even if the oversampling factor is arbitrarily large. If one allows generalized measurements of the input signal, then the output of every stable LTI system can be uniformly approximated in terms of generalized measurements of the input signal. The last section studies the situation where only the amplitudes of the signal samples are known. It is shown that one can find specific measurement functionals such that signal recovery of bandlimited signals from amplitude measurement is possible, with an overall sampling rate of four times the Nyquist rate.
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
Venue: arXiv cs.CL 2014
Year: 2014

We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement.
Distributed Detection in Tree Networks: Byzantines and Mitigation Techniques
Venue: arXiv cs.CR 2014
Year: 2014

In this paper, the problem of distributed detection in tree networks in the presence of Byzantines is considered. Closed form expressions for optimal attacking strategies that minimize the miss detection error exponent at the fusion center (FC) are obtained. We also look at the problem from the network designer's (FC's) perspective. We study the problem of designing optimal distributed detection parameters in a tree network in the presence of Byzantines. Next, we model the strategic interaction between the FC and the attacker as a Leader-Follower (Stackelberg) game. This formulation provides a methodology for predicting attacker and defender (FC) equilibrium strategies, which can be used to implement the optimal detector. Finally, a reputation based scheme to identify Byzantines is proposed and its performance is analytically evaluated. We also provide some numerical examples to gain insights into the solution.
Uncovering the Perfect Place: Optimising Workflow Engine Deployment in the Cloud
Venue: arXiv cs.DC 2014
Year: 2014

When orchestrating highly distributed and data-intensive Web service workflows the geographical placement of the orchestration engine can greatly affect the overall performance of a workflow. We present CloudForecast: a Web service framework and analysis tool which, given a workflow specification, computes the optimal Amazon EC2 Cloud region to automatically deploy the orchestration engine and execute the workflow. We use geographical distance of the workflow, network latency and HTTP round-trip time between Amazon Cloud regions and the workflow nodes to find a ranking of Cloud regions. This overall ranking predicts where the workflow orchestration engine should be deployed in order to reduce overall execution time. Our experimental results show that our proposed optimisation strategy, depending on the particular workflow, can speed up execution time on average by 82.25% compared to local execution.
Optimization Modulo Theories with Linear Rational Costs
Venue: arXiv cs.LO 2014
Year: 2014

In the contexts of automated reasoning (AR) and formal verification (FV), important decision problems are effectively encoded into Satisfiability Modulo Theories (SMT). In the last decade efficient SMT solvers have been developed for several theories of practical interest (e.g., linear arithmetic, arrays, bit-vectors). Surprisingly, little work has been done to extend SMT to deal with optimization problems; in particular, we are not aware of any previous work on SMT solvers able to produce solutions which minimize cost functions over arithmetical variables. This is unfortunate, since some problems of interest require this functionality. In the work described in this paper we start filling this gap. We present and discuss two general procedures for leveraging SMT to handle the minimization of linear rational cost functions, combining SMT with standard minimization techniques. We have implemented the procedures within the MathSAT SMT solver. Due to the absence of competitors in the AR, FV and SMT domains, we have experimentally evaluated our implementation against state-of-the-art tools for the domain of linear generalized disjunctive programming (LGDP), which is closest in spirit to our domain, on sets of problems which have been previously proposed as benchmarks for the latter tools. The results show that our tool is very competitive with, and often outperforms, these tools on these problems, clearly demonstrating the potential of the approach.
Determinization of fuzzy automata by means of the degrees of language inclusion
Venue: arXiv cs.FL 2014
Year: 2014

Determinization of fuzzy finite automata is understood here as a procedure of their conversion into equivalent crisp-deterministic fuzzy automata, which can be viewed as being deterministic with possibly infinitely many states, but with fuzzy sets of terminal states. Particularly significant determinization methods are those that provide a minimal crisp-deterministic fuzzy automaton equivalent to the original fuzzy finite automaton, called canonization methods. One canonization method for fuzzy finite automata, the Brzozowski type determinization, has been developed recently by Jančić and Ćirić in [10]. Here we provide another canonization method for a fuzzy finite automaton $\cal A=(A,σ, δ,τ)$ over a complete residuated lattice $\cal L$, based on the degrees of inclusion of the right fuzzy languages associated with states of $\cal A$ into the left derivatives of the fuzzy language recognized by $\cal A$. The proposed procedure terminates in a finite number of steps whenever the membership values taken by $δ$, $σ$ and $τ$ generate a finite subsemiring of the semiring reduct of $\cal L$. This procedure is generally faster than the Brzozowski type determinization, and if the basic operations in the residuated lattice $\cal L$ can be performed in constant time, it has the same computational time as all other determinization procedures provided in [8], [11], [12].
Motion Estimation via Robust Decomposition with Constrained Rank
Venue: arXiv cs.CV 2014
Year: 2014

In this work, we address the problem of outlier detection for robust motion estimation by using modern sparse-low-rank decompositions, i.e., Robust PCA-like methods, to impose global rank constraints. Robust decompositions have shown to be good at splitting a corrupted matrix into an uncorrupted low-rank matrix and a sparse matrix, containing outliers. However, this process only works when matrices have relatively low rank with respect to their ambient space, a property not met in motion estimation problems. As a solution, we propose to exploit the partial information present in the decomposition to decide which matches are outliers. We provide evidences showing that even when it is not possible to recover an uncorrupted low-rank matrix, the resulting information can be exploited for outlier detection. To this end we propose the Robust Decomposition with Constrained Rank (RD-CR), a proximal gradient based method that enforces the rank constraints inherent to motion estimation. We also present a general framework to perform robust estimation for stereo Visual Odometry, based on our RD-CR and a simple but effective compressed optimization method that achieves high performance. Our evaluation on synthetic data and on the KITTI dataset demonstrates the applicability of our approach in complex scenarios and it yields state-of-the-art performance.