OpenRaiser/Intern-Atlas huggingface dataset:
SocioAware Content Distribution using P2P solutions in Hybrid Networks
Venue: arXiv cs.SI 2014
Year: 2014

The growing online traffic that is bringing the infrastructure to its limits induces an urgent demand for an efficient content delivery model. Capitalizing social networks and using advanced delivery networks potentially can help to solve this problem. However, due to the complex nature of the involved networks such a model is difficult to assess. In this paper we use a simulative approach to analyze how the SatTorrent P2P protocol supported by social networks can improve content delivery by means of reduced download duration and traffic.
On More Bent Functions From Dillon Exponents
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we obtain a new class of $p$-ary binomial bent functions which are determined by Kloosterman sums. The bentness of another three classes of functions is characterized by some exponential sums and some results in \cite{Linian2013} are generalized. Furthermore we obtain, in some special cases, some bent functions are determined by Kloosterman sums.
Automatic Detection of Reuses and Citations in Literary Texts
Venue: arXiv cs.CL 2014
Year: 2014

For more than forty years now, modern theories of literature (Compagnon, 1979) insist on the role of paraphrases, rewritings, citations, reciprocal borrowings and mutual contributions of any kinds. The notions of intertextuality, transtextuality, hypertextuality/hypotextuality, were introduced in the seventies and eighties to approach these phenomena. The careful analysis of these references is of particular interest in evaluating the distance that the creator voluntarily introduces with his/her masters. Phoebus is collaborative project that makes computer scientists from the University Pierre and Marie Curie (LIP6-UPMC) collaborate with the literary teams of Paris-Sorbonne University with the aim to develop efficient tools for literary studies that take advantage of modern computer science techniques. In this context, we have developed a piece of software that automatically detects and explores networks of textual reuses in classical literature. This paper describes the principles on which is based this program, the significant results that have already been obtained and the perspectives for the near future.
Bayesian image segmentations by Potts prior and loopy belief propagation
Venue: arXiv cs.CV 2014
Year: 2014

This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwise interactions of Potts models, and the average vectors and covariant matrices of Gauss distributions in color image modeling. These terms are often referred to as hyperparameters in statistical machine learning theory. In order to determine these hyperparameters, we propose a new scheme for hyperparameter estimation based on conditional maximization of entropy in the Potts prior. The algorithm is given based on loopy belief propagation. In addition, we compare our conditional maximum entropy framework with the conventional maximum likelihood framework, and also clarify how the first order phase transitions in LBP's for Potts models influence our hyperparameter estimation procedures.
Distance Distributions for Real Cellular Networks
Venue: arXiv cs.NI 2014
Year: 2014

This paper presents the general distribution for the distance between a mobile user and any base station (BS). We show that a random variable proportional to the distance squared is Gamma distributed. In the case of the nearest BS, it can be reduced to the well established result of the distance being Rayleigh distributed. We validate our results using a random node simulation and real Vodafone 3G network data, and go on to show how the distribution is tractable by deriving the average aggregate interference power.
Transmit Pulse Shaping for Molecular Communications
Venue: arXiv cs.ET 2014
Year: 2014

This paper presents a method for shaping the transmit pulse of a molecular signal such that the diffusion channel's response is a sharp pulse. The impulse response of a diffusion channel is typically characterised as having an infinitely long transient response. This can cause severe inter-symbol-interference, and reduce the achievable reliable bit rate. We achieve the desired chemical channel response by poisoning the channel with a secondary compound, such that it chemically cancels aspects of the primary information signal. We use two independent methods to show that the chemical concentration of the \emph{information signal} should be $\propto δ(t)$ and that of the \emph{poison signal} should be $\propto t^{-3/2}$.
Interference Allocation Scheduler for Green Multimedia Delivery
Venue: arXiv cs.NI 2014
Year: 2014

One of the key challenges in wireless networking is how to allocate the available radio resources in order to maximise key service delivery parameters such as the aggregate throughput and the multimedia quality of experience (QoE). We propose a novel and effective scheduling policy that allocates resource blocks, such that interference power is shifted towards capacity-saturated users, while improving the throughput of unsaturated users. The highlight of the research is that the proposed scheme can dramatically improve the performance of cells that have a high discrepancy in its signal-to-noise-ratio (SNR) distribution, which is typical in urban areas. The results show that a \emph{free-lunch} solution is possible, whereby for a negligible performance degradation in the saturated users, a large improvement in the non-saturated users can be obtained. However, on average, the number of free-lunch user pairings are low. By relaxing the degradation constraints, the \emph{non-free-lunch} solution can yield a greater multi-user throughput gain. Motivated by surge in mobile multimedia traffic, we further demonstrate that the proposed scheduling may have a profound impact on both energy efficiency and QoE of multimedia service delivery.
Reconsidering unique information: Towards a multivariate information decomposition
Venue: arXiv cs.IT 2014
Year: 2014

The information that two random variables $Y$, $Z$ contain about a third random variable $X$ can have aspects of shared information (contained in both $Y$ and $Z$), of complementary information (only available from $(Y,Z)$ together) and of unique information (contained exclusively in either $Y$ or $Z$). Here, we study measures $\widetilde{SI}$ of shared, $\widetilde{UI}$ unique and $\widetilde{CI}$ complementary information introduced by Bertschinger et al., which are motivated from a decision theoretic perspective. We find that in most cases the intuitive rule that more variables contain more information applies, with the exception that $\widetilde{SI}$ and $\widetilde{CI}$ information are not monotone in the target variable $X$. Additionally, we show that it is not possible to extend the bivariate information decomposition into $\widetilde{SI}$, $\widetilde{UI}$ and $\widetilde{CI}$ to a non-negative decomposition on the partial information lattice of Williams and Beer. Nevertheless, the quantities $\widetilde{UI}$, $\widetilde{SI}$ and $\widetilde{CI}$ have a well-defined interpretation, even in the multivariate setting.
Pareto-Path Multi-Task Multiple Kernel Learning
Venue: arXiv cs.LG 2014
Year: 2014

A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing amongst tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO) problem, which considers the concurrent optimization of all task objectives involved in the Multi-Task Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel Support Vector Machine (SVM) MT-MKL framework, that considers an implicitly-defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving better classification performance, when compared to other similar MTL approaches.
Efficient Lock-free Binary Search Trees
Venue: arXiv cs.DC 2014
Year: 2014

In this paper we present a novel algorithm for concurrent lock-free internal binary search trees (BST) and implement a Set abstract data type (ADT) based on that. We show that in the presented lock-free BST algorithm the amortized step complexity of each set operation - {\sc Add}, {\sc Remove} and {\sc Contains} - is $O(H(n) + c)$, where, $H(n)$ is the height of BST with $n$ number of nodes and $c$ is the contention during the execution. Our algorithm adapts to contention measures according to read-write load. If the situation is read-heavy, the operations avoid helping pending concurrent {\sc Remove} operations during traversal, and, adapt to interval contention. However, for write-heavy situations we let an operation help pending {\sc Remove}, even though it is not obstructed, and so adapt to tighter point contention. It uses single-word compare-and-swap (\texttt{CAS}) operations. We show that our algorithm has improved disjoint-access-parallelism compared to similar existing algorithms. We prove that the presented algorithm is linearizable. To the best of our knowledge this is the first algorithm for any concurrent tree data structure in which the modify operations are performed with an additive term of contention measure.
A Continuous Optimization Approach for the Financial Portfolio Selection under Discrete Asset Choice Constraints
Venue: arXiv cs.CE 2014
Year: 2014

In this paper we consider a generalization of the Markowitz's Mean-Variance model under linear transaction costs and cardinality constraints. The cardinality constraints are used to limit the number of assets in the optimal portfolio. The generalized model is formulated as a mixed integer quadratic programming (MIP) problem. The purpose of this paper is to investigate a continuous approach based on difference of convex functions (DC) programming for solving the MIP model. The preliminary comparative results of the proposed approach versus CPLEX are presented.
Network-Oblivious Algorithms
Venue: arXiv cs.DS 2014
Year: 2014

A framework is proposed for the design and analysis of \emph{network-oblivious algorithms}, namely, algorithms that can run unchanged, yet efficiently, on a variety of machines characterized by different degrees of parallelism and communication capabilities. The framework prescribes that a network-oblivious algorithm be specified on a parallel model of computation where the only parameter is the problem's input size, and then evaluated on a model with two parameters, capturing parallelism granularity and communication latency. It is shown that, for a wide class of network-oblivious algorithms, optimality in the latter model implies optimality in the Decomposable BSP model, which is known to effectively describe a wide and significant class of parallel platforms. The proposed framework can be regarded as an attempt to port the notion of obliviousness, well established in the context of cache hierarchies, to the realm of parallel computation. Its effectiveness is illustrated by providing optimal network-oblivious algorithms for a number of key problems. Some limitations of the oblivious approach are also discussed.
Constant Delay and Constant Feedback Moving Window Network Coding for Wireless Multicast: Design and Asymptotic Analysis
Venue: arXiv cs.IT 2014
Year: 2014

A major challenge of wireless multicast is to be able to support a large number of users while simultaneously maintaining low delay and low feedback overhead. In this paper, we develop a joint coding and feedback scheme named Moving Window Network Coding with Anonymous Feedback (MWNC-AF) that successfully addresses this challenge. In particular, we show that our scheme simultaneously achieves both a constant decoding delay and a constant feedback overhead, irrespective of the number of receivers $n$, without sacrificing either throughput or reliability. We explicitly characterize the asymptotic decay rate of the tail of the delay distribution, and prove that transmitting a fixed amount of information bits into the MWNC-AF encoder buffer in each time-slot (called "constant data injection process") achieves the fastest decay rate, thus showing how to obtain delay optimality in a large deviation sense. We then investigate the average decoding delay of MWNC-AF, and show that when the traffic load approaches the capacity, the average decoding delay under the constant injection process is at most one half of that under a Bernoulli injection process. In addition, we prove that the per-packet encoding and decoding complexity of MWNC-AF both scale as $O(\log n)$, with the number of receivers $n$. Our simulations further underscore the performance of our scheme through comparisons with other schemes and show that the delay, encoding and decoding complexity are low even for a large number of receivers, demonstrating the efficiency, scalability, and ease of implementability of MWNC-AF.
A 2D based Partition Strategy for Solving Ranking under Team Context (RTP)
Venue: arXiv cs.DB 2014
Year: 2014

In this paper, we propose a 2D based partition method for solving the problem of Ranking under Team Context(RTC) on datasets without a priori. We first map the data into 2D space using its minimum and maximum value among all dimensions. Then we construct window queries with consideration of current team context. Besides, during the query mapping procedure, we can pre-prune some tuples which are not top ranked ones. This pre-classified step will defer processing those tuples and can save cost while providing solutions for the problem. Experiments show that our algorithm performs well especially on large datasets with correctness.
Targeting HIV-related Medication Side Effects and Sentiment Using Twitter Data
Venue: arXiv cs.SI 2014
Year: 2014

We present a descriptive analysis of Twitter data. Our study focuses on extracting the main side effects associated with HIV treatments. The crux of our work was the identification of personal tweets referring to HIV. We summarize our results in an infographic aimed at the general public. In addition, we present a measure of user sentiment based on hand-rated tweets.
Avoiding Undesired Choices Using Intelligent Adaptive Systems
Venue: arXiv cs.AI 2014
Year: 2014

We propose a number of heuristics that can be used for identifying when intransitive choice behaviour is likely to occur in choice situations. We also suggest two methods for avoiding undesired choice behaviour, namely transparent communication and adaptive choice-set generation. We believe that these two ways can contribute to the avoidance of decision biases in choice situations that may often be regretted.
On Backdoors To Tractable Constraint Languages
Venue: arXiv cs.AI 2014
Year: 2014

In the context of CSPs, a strong backdoor is a subset of variables such that every complete assignment yields a residual instance guaranteed to have a specified property. If the property allows efficient solving, then a small strong backdoor provides a reasonable decomposition of the original instance into easy instances. An important challenge is the design of algorithms that can find quickly a small strong backdoor if one exists. We present a systematic study of the parameterized complexity of backdoor detection when the target property is a restricted type of constraint language defined by means of a family of polymorphisms. In particular, we show that under the weak assumption that the polymorphisms are idempotent, the problem is unlikely to be FPT when the parameter is either r (the constraint arity) or k (the size of the backdoor) unless P = NP or FPT = W[2]. When the parameter is k+r, however, we are able to identify large classes of languages for which the problem of finding a small backdoor is FPT.
Information Security Management of Web Portals Based on Joomla CMS
Venue: arXiv cs.CR 2014
Year: 2014

Information is the key asset of all organizations and can exist in many forms. It can be printed or written on paper, stored electronically, transmitted by mail or by electronic means, shown in films, or spoken in conversation. In today's competitive business environment, such information is constantly under threat from many sources, which can be internal, external, accidental, or malicious. Joomla is a very popular Content Management System (CMS) used for web page maintenance. This highly versatile software has found itself in both large corporate web portals, and simple web pages such as blogs. Such popularity increases its vulnerability to potential attacks and therefore needs an appropriate security management. ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Commission) created the series of standards aimed at providing a model for establishing, implementing, operating, monitoring, reviewing, maintaining and improving an Information Security Management System (ISMS). This paper shows how principles set in ISO/IEC 27000 series of standards can be used to improve security of Joomla based web portals.
Group size effect on cooperation in social dilemmas
Venue: arXiv cs.GT 2014
Year: 2014

Social dilemmas are central to human society. Depletion of natural resources, climate protection, security of energy supply, and workplace collaborations are all examples of social dilemmas. Since cooperative behaviour in a social dilemma is individually costly, Nash equilibrium predicts that humans should not cooperate. Yet experimental studies show that people do cooperate even in anonymous one-shot interactions. In spite of the large number of participants in many modern social dilemmas, little is known about the effect of group size on cooperation. Does larger group size favour or prevent cooperation? We address this problem both experimentally and theoretically. Experimentally, we find that there is no general answer: it depends on the strategic situation. Specifically, we find that larger groups are more cooperative in the Public Goods game, but less cooperative in the N-person Prisoner's dilemma. Theoretically, we show that this behaviour is not consistent with either the Fehr & Schmidt model or (a one-parameter version of) the Charness & Rabin model, but it is consistent with the cooperative equilibrium model introduced by the second author.
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
Venue: arXiv cs.CV 2014
Year: 2014

Face verification remains a challenging problem in very complex conditions with large variations such as pose, illumination, expression, and occlusions. This problem is exacerbated when we rely unrealistically on a single training data source, which is often insufficient to cover the intrinsically complex face variations. This paper proposes a principled multi-task learning approach based on Discriminative Gaussian Process Latent Variable Model, named GaussianFace, to enrich the diversity of training data. In comparison to existing methods, our model exploits additional data from multiple source-domains to improve the generalization performance of face verification in an unknown target-domain. Importantly, our model can adapt automatically to complex data distributions, and therefore can well capture complex face variations inherent in multiple sources. Extensive experiments demonstrate the effectiveness of the proposed model in learning from diverse data sources and generalize to unseen domain. Specifically, the accuracy of our algorithm achieves an impressive accuracy rate of 98.52% on the well-known and challenging Labeled Faces in the Wild (LFW) benchmark. For the first time, the human-level performance in face verification (97.53%) on LFW is surpassed.
Boltzmann samplers for random generation of lambda terms
Venue: arXiv cs.DS 2014
Year: 2014

Randomly generating structured objects is important in testing and optimizing functional programs, whereas generating random $'l$-terms is more specifically needed for testing and optimizing compilers. For that a tool called QuickCheck has been proposed, but in this tool the control of the random generation is left to the programmer. Ten years ago, a method called Boltzmann samplers has been proposed to generate combinatorial structures. In this paper, we show how Boltzmann samplers can be developed to generate lambda-terms, but also other data structures like trees. These samplers rely on a critical value which parameters the main random selector and which is exhibited here with explanations on how it is computed. Haskell programs are proposed to show how samplers are actually implemented.
Scalable Matting: A Sub-linear Approach
Venue: arXiv cs.CV 2014
Year: 2014

Natural image matting, which separates foreground from background, is a very important intermediate step in recent computer vision algorithms. However, it is severely underconstrained and difficult to solve. State-of-the-art approaches include matting by graph Laplacian, which significantly improves the underconstrained nature by reducing the solution space. However, matting by graph Laplacian is still very difficult to solve and gets much harder as the image size grows: current iterative methods slow down as $\mathcal{O}\left(n^2 \right)$ in the resolution $n$. This creates uncomfortable practical limits on the resolution of images that we can matte. Current literature mitigates the problem, but they all remain super-linear in complexity. We expose properties of the problem that remain heretofore unexploited, demonstrating that an optimization technique originally intended to solve PDEs can be adapted to take advantage of this knowledge to solve the matting problem, not heuristically, but exactly and with sub-linear complexity. This makes ours the most efficient matting solver currently known by a very wide margin and allows matting finally to be practical and scalable in the future as consumer photos exceed many dozens of megapixels, and also relieves matting from being a bottleneck for vision algorithms that depend on it.
A Game Theoretic Approach to Minimize the Completion Time of Network Coded Cooperative Data Exchange
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we introduce a game theoretic framework for studying the problem of minimizing the completion time of instantly decodable network coding (IDNC) for cooperative data exchange (CDE) in decentralized wireless network. In this configuration, clients cooperate with each other to recover the erased packets without a central controller. Game theory is employed herein as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. We model the session by self-interested players in a non-cooperative potential game. The utility function is designed such that increasing individual payoff results in a collective behavior achieving both a desirable system performance in a shared network environment and the Pareto optimal solution. Through extensive simulations, our approach is compared to the best performance that could be found in the conventional point-to-multipoint (PMP) recovery process. Numerical results show that our formulation largely outperforms the conventional PMP scheme in most practical situations and achieves a lower delay.