OpenRaiser/Intern-Atlas huggingface dataset:
When is it Biased? Assessing the Representativeness of Twitter's Streaming API
Venue: arXiv cs.SI 2014
Year: 2014

Twitter has captured the interest of the scientific community not only for its massive user base and content, but also for its openness in sharing its data. Twitter shares a free 1% sample of its tweets through the "Streaming API", a service that returns a sample of tweets according to a set of parameters set by the researcher. Recently, research has pointed to evidence of bias in the data returned through the Streaming API, raising concern in the integrity of this data service for use in research scenarios. While these results are important, the methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. In this work we tackle the problem of finding sample bias without the need for "gold standard" Firehose data. Namely, we focus on finding time periods in the Streaming API data where the trend of a hashtag is significantly different from its trend in the true activity on Twitter. We propose a solution that focuses on using an open data source to find bias in the Streaming API. Finally, we assess the utility of the data source in sparse data situations and for users issuing the same query from different regions.
Performance Rescaling of Complex Networks
Venue: arXiv cs.NI 2014
Year: 2014

Recent progress in network topology modeling [1], [2] has shown that it is possible to create smaller-scale replicas of large complex networks, like the Internet, while simultaneously preserving several important topological properties. However, the constructed replicas do not include notions of capacities and latencies, and the fundamental question of whether smaller networks can reproduce the performance of larger networks remains unanswered. We address this question in this letter, and show that it is possible to predict the performance of larger networks from smaller replicas, as long as the right link capacities and propagation delays are assigned to the replica's links. Our procedure is inspired by techniques introduced in [2] and combines a time-downscaling argument from [3]. We show that significant computational savings can be achieved when simulating smaller-scale replicas with TCP and UDP traffic, with simulation times being reduced by up to two orders of magnitude.
Brooks' Vertex-Colouring Theorem in Linear Time
Venue: arXiv cs.DM 2014
Year: 2014

Brooks' Theorem [R. L. Brooks, On Colouring the Nodes of a Network, Proc. Cambridge Philos. Soc.} 37:194-197, 1941] states that every graph $G$ with maximum degree $Δ$, has a vertex-colouring with $Δ$ colours, unless $G$ is a complete graph or an odd cycle, in which case $Δ+1$ colours are required. Lovász [L. Lovász, Three short proofs in graph theory, J. Combin. Theory Ser. 19:269-271, 1975] gives an algorithmic proof of Brooks' Theorem. Unfortunately this proof is missing important details and it is thus unclear whether it leads to a linear time algorithm. In this paper we give a complete description of the proof of Lovász, and we derive a linear time algorithm for determining the vertex-colouring guaranteed by Brooks' Theorem.
Truthful Market-based Trading of Cloud Resources with Reservation Price
Venue: arXiv cs.GT 2014
Year: 2014

With the rapidly growing demand for the cloud services, a need for efficient methods to trade computing resources increases. Commonly used fixed-price model is not always the best approach for trading cloud resources, because of its inflexible and static nature. Dynamic trading systems, which make use of market mechanisms, show promise for more efficient resource allocation and pricing in the cloud. However, most of the existing mechanisms ignore the seller's costs of providing the resources. In order to address it, we propose a single-sided market mechanism for trading virtual machine instances in the cloud, where the cloud provider can express the reservation prices for traded cloud services. We investigate the theoretical properties of the proposed mechanism and prove that it is truthful, i.e. the buyers do not have an incentive to lie about their true valuation of the resources. We perform extensive experiments in order to investigate the impact of the reserve price on the market outcome. Our experiments show that the proposed mechanism yields near optimal allocations and has a low execution time.
Empirically Evaluating Multiagent Learning Algorithms
Venue: arXiv cs.GT 2014
Year: 2014

There exist many algorithms for learning how to play repeated bimatrix games. Most of these algorithms are justified in terms of some sort of theoretical guarantee. On the other hand, little is known about the empirical performance of these algorithms. Most such claims in the literature are based on small experiments, which has hampered understanding as well as the development of new multiagent learning (MAL) algorithms. We have developed a new suite of tools for running multiagent experiments: the MultiAgent Learning Testbed (MALT). These tools are designed to facilitate larger and more comprehensive experiments by removing the need to build one-off experimental code. MALT also provides baseline implementations of many MAL algorithms, hopefully eliminating or reducing differences between algorithm implementations and increasing the reproducibility of results. Using this test suite, we ran an experiment unprecedented in size. We analyzed the results according to a variety of performance metrics including reward, maxmin distance, regret, and several notions of equilibrium convergence. We confirmed several pieces of conventional wisdom, but also discovered some surprising results. For example, we found that single-agent $Q$-learning outperformed many more complicated and more modern MAL algorithms.
Competitive learning of monotone Boolean functions
Venue: arXiv cs.DS 2014
Year: 2014

We apply competitive analysis onto the problem of minimizing the number of queries to an oracle to completely reconstruct a given monotone Boolean function. Besides lower and upper bounds on the competitivity we determine optimal deterministic online algorithms for the smallest problem instances.
Group Activity Selection Problem
Venue: arXiv cs.GT 2014
Year: 2014

We consider a setting where one has to organize one or several group activities for a set of agents. Each agent will participate in at most one activity, and her preferences over activities depend on the number of participants in the activity. The goal is to assign agents to activities based on their preferences. We put forward a general model for this setting, which is a natural generalization of anonymous hedonic games. We then focus on a special case of our model, where agents' preferences are binary, i.e., each agent classifies all pairs of the form "(activity, group size)" into ones that are acceptable and ones that are not. We formulate several solution concepts for this scenario, and study them from the computational point of view, providing hardness results for the general case as well as efficient algorithms for settings where agents' preferences satisfy certain natural constraints.
A Formal Proof of Countermeasures against Fault Injection Attacks on CRT-RSA
Venue: arXiv cs.CR 2014
Year: 2014

In this article, we describe a methodology that aims at either breaking or proving the security of CRT-RSA implementations against fault injection attacks. In the specific case-study of the BellCoRe attack, our work bridges a gap between formal proofs and implementation-level attacks. We apply our results to three implementations of CRT-RSA, namely the unprotected one, that of Shamir, and that of Aumüller et al. Our findings are that many attacks are possible on both the unprotected and the Shamir implementations, while the implementation of Aumüller et al. is resistant to all single-fault attacks. It is also resistant to double-fault attacks if we consider the less powerful threat-model of its authors.
Experiments with Three Approaches to Recognizing Lexical Entailment
Venue: arXiv cs.CL 2014
Year: 2014

Inference in natural language often involves recognizing lexical entailment (RLE); that is, identifying whether one word entails another. For example, "buy" entails "own". Two general strategies for RLE have been proposed: One strategy is to manually construct an asymmetric similarity measure for context vectors (directional similarity) and another is to treat RLE as a problem of learning to recognize semantic relations using supervised machine learning techniques (relation classification). In this paper, we experiment with two recent state-of-the-art representatives of the two general strategies. The first approach is an asymmetric similarity measure (an instance of the directional similarity strategy), designed to capture the degree to which the contexts of a word, a, form a subset of the contexts of another word, b. The second approach (an instance of the relation classification strategy) represents a word pair, a:b, with a feature vector that is the concatenation of the context vectors of a and b, and then applies supervised learning to a training set of labeled feature vectors. Additionally, we introduce a third approach that is a new instance of the relation classification strategy. The third approach represents a word pair, a:b, with a feature vector in which the features are the differences in the similarities of a and b to a set of reference words. All three approaches use vector space models (VSMs) of semantics, based on word-context matrices. We perform an extensive evaluation of the three approaches using three different datasets. The proposed new approach (similarity differences) performs significantly better than the other two approaches on some datasets and there is no dataset for which it is significantly worse. Our results suggest it is beneficial to make connections between the research in lexical entailment and the research in semantic relation classification.
Polynomial time recognition of squares of ptolemaic graphs and 3-sun-free split graphs
Venue: arXiv cs.DM 2014
Year: 2014

The square of a graph $G$, denoted $G^2$, is obtained from $G$ by putting an edge between two distinct vertices whenever their distance is two. Then $G$ is called a square root of $G^2$. Deciding whether a given graph has a square root is known to be NP-complete, even if the root is required to be a chordal graph or even a split graph. We present a polynomial time algorithm that decides whether a given graph has a ptolemaic square root. If such a root exists, our algorithm computes one with a minimum number of edges. In the second part of our paper, we give a characterization of the graphs that admit a 3-sun-free split square root. This characterization yields a polynomial time algorithm to decide whether a given graph has such a root, and if so, to compute one.
Popular conjectures imply strong lower bounds for dynamic problems
Venue: arXiv cs.DS 2014
Year: 2014

We consider several well-studied problems in dynamic algorithms and prove that sufficient progress on any of them would imply a breakthrough on one of five major open problems in the theory of algorithms: 1. Is the 3SUM problem on $n$ numbers in $O(n^{2-ε})$ time for some $ε>0$? 2. Can one determine the satisfiability of a CNF formula on $n$ variables in $O((2-ε)^n poly n)$ time for some $ε>0$? 3. Is the All Pairs Shortest Paths problem for graphs on $n$ vertices in $O(n^{3-ε})$ time for some $ε>0$? 4. Is there a linear time algorithm that detects whether a given graph contains a triangle? 5. Is there an $O(n^{3-ε})$ time combinatorial algorithm for $n\times n$ Boolean matrix multiplication? The problems we consider include dynamic versions of bipartite perfect matching, bipartite maximum weight matching, single source reachability, single source shortest paths, strong connectivity, subgraph connectivity, diameter approximation and some nongraph problems such as Pagh's problem defined in a recent paper by Patrascu [STOC 2010].
Dynamical Properties of a Two-gene Network with Hysteresis
Venue: arXiv cs.SY 2014
Year: 2014

A mathematical model for a two-gene regulatory network is derived and several of their properties analyzed. Due to the presence of mixed continuous/discrete dynamics and hysteresis, we employ a hybrid systems model to capture the dynamics of the system. The proposed model incorporates binary hysteresis with different thresholds capturing the interaction between the genes. We analyze properties of the solutions and asymptotic stability of equilibria in the system as a function of its parameters. Our analysis reveals the presence of limit cycles for a certain range of parameters, behavior that is associated with hysteresis. The set of points defining the limit cycle is characterized and its asymptotic stability properties are studied. Furthermore, the stability property of the limit cycle is robust to small perturbations. Numerical simulations are presented to illustrate the results.
An Intuitionisticaly based Description Logic
Venue: arXiv cs.LO 2014
Year: 2014

This article presents iALC, an intuitionistic version of the classical description logic ALC, based on the framework for constructive modal logics presented by Simpson \cite{simpson95} and related to description languages, via hybrid logics, by dePaiva \cite{depaiva2003}. This article correcta and extends the presentation of iALC appearing in \cite{PHR:2010}. It points out the difference between iALC and the intuitionistic hybrid logic presented in \cite{depaiva2003}. Completeness and soundness proofs are provided. A brief discussion on the computacional complexity of iALC provability is taken. It is worth mentioning that iALC is used to formalize legal knowledge \cite{HPR:2010a,HPR:2010ab,Jurix, HPR:2011}, and in fact, was specifically designed to this goal.
The Designing of Online Multiple Intelligence Tools for Lecturers at Polytechnic
Venue: arXiv cs.CY 2014
Year: 2014

This paper addresses the designing of Online Multiple Intelligence (MI) Teaching Tools for Polytechnic lecturers. These teaching tools can assist lecturers to create their own teaching materials without having any knowledge of Information Technology (IT) especially in programming. The theory of MI is used in this paper and this theory postulates that everybody has at least two or more intelligences. Multiple approaches embedded into a series of activities via online teaching tools must be implemented in order to achieve effective teaching and learning in the classroom. The objectives of this paper are to identify the relationship between the students self-perceived MI and their academic achievement in Polytechnic, and design online MI tools for teaching at Polytechnic. This paper also addressed the theoretical framework and MI teaching activities. The instrument used for this study was Ujian Multiple Intelligence (UMI). The results showed Polytechnic students have strength in Interpersonal, Visual-Spatial and Verbal-Linguistic intelligences.
Performance Analysis and Optimization for Interference Alignment over MIMO Interference Channels with Limited Feedback
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we address the problem of interference alignment (IA) over MIMO interference channels with limited channel state information (CSI) feedback based on quantization codebooks. Due to limited feedback and hence imperfect IA, there are residual interferences across different links and different data streams. As a result, the performance of IA is greatly related to the CSI accuracy (namely number of feedback bits) and the number of data streams (namely transmission mode). In order to improve the performance of IA, it makes sense to optimize the system parameters according to the channel conditions. Motivated by this, we first give a quantitative performance analysis for IA under limited feedback, and derive a closed-form expression for the average transmission rate in terms of feedback bits and transmission mode. By maximizing the average transmission rate, we obtain an adaptive feedback allocation scheme, as well as a dynamic mode selection scheme. Furthermore, through asymptotic analysis, we obtain several clear insights on the system performance, and provide some guidelines on the system design. Finally, simulation results validate our theoretical claims, and show that obvious performance gain can be obtained by adjusting feedback bits dynamically or selecting transmission mode adaptively.
Minimum Price in Search Model
Venue: arXiv cs.GT 2014
Year: 2014

This paper investigates the effects of a low bound price. To do so, a popular and empirically proven model (Stahl (89') \cite{Stahl89}) is used. The model is extended to include an exogenously given bound on prices sellers can offer, excluding prices below such bound. The finding are rather surprising - when the bound is set sufficiently high expected price offered (EPO) by sellers drops significantly. The result seem to be robust in the parameters of the model, and driven by the information provided to consumers by such legislation step: when the limitation is set at sufficiently high levels all consumers anticipate the bound price, and searchers reject any price above it. As a result sellers offer the bound price as a pure strategy.
Finding Optimal Strategies of Almost Acyclic Simple Stochatic Games
Venue: arXiv cs.CC 2014
Year: 2014

The optimal value computation for turned-based stochastic games with reachability objectives, also known as simple stochastic games, is one of the few problems in $NP \cap coNP$ which are not known to be in $P$. However, there are some cases where these games can be easily solved, as for instance when the underlying graph is acyclic. In this work, we try to extend this tractability to several classes of games that can be thought as "almost" acyclic. We give some fixed-parameter tractable or polynomial algorithms in terms of different parameters such as the number of cycles or the size of the minimal feedback vertex set.
Normalization and sub-formula property for Lambek with product and PCMLL -- Partially Commutative Multiplicative Linear Logic
Venue: arXiv cs.LO 2014
Year: 2014

This paper establishes the normalisation of natural deduction or lambda calculus formulation of Intuitionistic Non Commutative Logic --- which involves both commutative and non commutative connectives. This calculus first introduced by de Groote and as opposed to the classical version by Abrusci and Ruet admits a full entropy which allow order to be relaxed into any suborder. Our result also includes, as a special case, the normalisation of natural deduction the Lambek calculus with product, which is unsurprising but yet unproved. Regarding Intuitionistic Non Commutative Logic with full entropy does not have up to now a proof net syntax, and that for linguistic applications, sequent calculi which are only more or less equivalent to natural deduction, are not convenient because they lack the standard Curry-Howard isomorphism.
Parameterized Complexity Results for Exact Bayesian Network Structure Learning
Venue: arXiv cs.AI 2014
Year: 2014

Bayesian network structure learning is the notoriously difficult problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian network structure learning under graph theoretic restrictions on the (directed) super-structure. The super-structure is an undirected graph that contains as subgraphs the skeletons of solution networks. We introduce the directed super-structure as a natural generalization of its undirected counterpart. Our results apply to several variants of score-based Bayesian network structure learning where the score of a network decomposes into local scores of its nodes. Results: We show that exact Bayesian network structure learning can be carried out in non-uniform polynomial time if the super-structure has bounded treewidth, and in linear time if in addition the super-structure has bounded maximum degree. Furthermore, we show that if the directed super-structure is acyclic, then exact Bayesian network structure learning can be carried out in quadratic time. We complement these positive results with a number of hardness results. We show that both restrictions (treewidth and degree) are essential and cannot be dropped without loosing uniform polynomial time tractability (subject to a complexity-theoretic assumption). Similarly, exact Bayesian network structure learning remains NP-hard for "almost acyclic" directed super-structures. Furthermore, we show that the restrictions remain essential if we do not search for a globally optimal network but aim to improve a given network by means of at most k arc additions, arc deletions, or arc reversals (k-neighborhood local search).
Evaluating Indirect Strategies for Chinese-Spanish Statistical Machine Translation
Venue: arXiv cs.CL 2014
Year: 2014

Although, Chinese and Spanish are two of the most spoken languages in the world, not much research has been done in machine translation for this language pair. This paper focuses on investigating the state-of-the-art of Chinese-to-Spanish statistical machine translation (SMT), which nowadays is one of the most popular approaches to machine translation. For this purpose, we report details of the available parallel corpus which are Basic Traveller Expressions Corpus (BTEC), Holy Bible and United Nations (UN). Additionally, we conduct experimental work with the largest of these three corpora to explore alternative SMT strategies by means of using a pivot language. Three alternatives are considered for pivoting: cascading, pseudo-corpus and triangulation. As pivot language, we use either English, Arabic or French. Results show that, for a phrase-based SMT system, English is the best pivot language between Chinese and Spanish. We propose a system output combination using the pivot strategies which is capable of outperforming the direct translation strategy. The main objective of this work is motivating and involving the research community to work in this important pair of languages given their demographic impact.
Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs
Venue: arXiv cs.AI 2014
Year: 2014

This article presents the state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A* (GMAA*) algorithm, which reduces the problem to a tree of one-shot collaborative Bayesian games (CBGs), we describe several advances that greatly expand the range of Dec-POMDPs that can be solved optimally. First, we introduce lossless incremental clustering of the CBGs solved by GMAA*, which achieves exponential speedups without sacrificing optimality. Second, we introduce incremental expansion of nodes in the GMAA* search tree, which avoids the need to expand all children, the number of which is in the worst case doubly exponential in the nodes depth. This is particularly beneficial when little clustering is possible. In addition, we introduce new hybrid heuristic representations that are more compact and thereby enable the solution of larger Dec-POMDPs. We provide theoretical guarantees that, when a suitable heuristic is used, both incremental clustering and incremental expansion yield algorithms that are both complete and search equivalent. Finally, we present extensive empirical results demonstrating that GMAA*-ICE, an algorithm that synthesizes these advances, can optimally solve Dec-POMDPs of unprecedented size.