OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Zipf's law: Modeling the structure of human language
Venue: arXiv cs.CL 2009
Year: 2009

Human language, the most powerful communication system in history, is closely associated with cognition. Written text is one of the fundamental manifestations of language, and the study of its universal regularities can give clues about how our brains process information and how we, as a society, organize and share it. Still, only classical patterns such as Zipf's law have been explored in depth. In contrast, other basic properties like the existence of bursts of rare words in specific documents, the topical organization of collections, or the sublinear growth of vocabulary size with the length of a document, have only been studied one by one and mainly applying heuristic methodologies rather than basic principles and general mechanisms. As a consequence, there is a lack of understanding of linguistic processes as complex emergent phenomena. Beyond Zipf's law for word frequencies, here we focus on Heaps' law, burstiness, and the topicality of document collections, which encode correlations within and across documents absent in random null models. We introduce and validate a generative model that explains the simultaneous emergence of all these patterns from simple rules. As a result, we find a connection between the bursty nature of rare words and the topical organization of texts and identify dynamic word ranking and memory across documents as key mechanisms explaining the non trivial organization of written text. Our research can have broad implications and practical applications in computer science, cognitive science, and linguistics.
A Simple Extraction Procedure for Bibliographical Author Field
Venue: arXiv cs.DL 2009
Year: 2009

A procedure for bibliographic author metadata extraction from scholarly texts is presented. The author segments are identified based on capitalization and line break patterns. Two main author layout templates, which can retrieve from a varied set of title pages, are provided. Additionally, several disambiguating rules are described.
Towards a Theory of Requirements Elicitation: Acceptability Condition for the Relative Validity of Requirements
Venue: arXiv cs.SE 2009
Year: 2009

A requirements engineering artifact is valid relative to the stakeholders of the system-to-be if they agree on the content of that artifact. Checking relative validity involves a discussion between the stakeholders and the requirements engineer. This paper proposes (i) a language for the representation of information exchanged in a discussion about the relative validity of an artifact; (ii) the acceptability condition, which, when it verifies in a discussion captured in the proposed language, signals that the relative validity holds for the discussed artifact and for the participants in the discussion; and (iii) reasoning procedures to automatically check the acceptability condition in a discussions captured by the proposed language.
New Confidence Measures for Statistical Machine Translation
Venue: arXiv cs.CL 2009
Year: 2009

A confidence measure is able to estimate the reliability of an hypothesis provided by a machine translation system. The problem of confidence measure can be seen as a process of testing : we want to decide whether the most probable sequence of words provided by the machine translation system is correct or not. In the following we describe several original word-level confidence measures for machine translation, based on mutual information, n-gram language model and lexical features language model. We evaluate how well they perform individually or together, and show that using a combination of confidence measures based on mutual information yields a classification error rate as low as 25.1% with an F-measure of 0.708.
On finding a particular class of combinatorial identities
Venue: arXiv cs.DM 2009
Year: 2009

In this paper, a class of combinatorial identities is proved. A method is used which is based on the following rule: counting elements of a given set in two ways and making equal the obtained results. This rule is known as "counting in two ways". The principle of inclusion and exclusion is used for obtaining a class of (0,1)-matrices.
A Model for Managing Collections of Patterns
Venue: arXiv cs.AI 2009
Year: 2009

Data mining algorithms are now able to efficiently deal with huge amount of data. Various kinds of patterns may be discovered and may have some great impact on the general development of knowledge. In many domains, end users may want to have their data mined by data mining tools in order to extract patterns that could impact their business. Nevertheless, those users are often overwhelmed by the large quantity of patterns extracted in such a situation. Moreover, some privacy issues, or some commercial one may lead the users not to be able to mine the data by themselves. Thus, the users may not have the possibility to perform many experiments integrating various constraints in order to focus on specific patterns they would like to extract. Post processing of patterns may be an answer to that drawback. Thus, in this paper we present a framework that could allow end users to manage collections of patterns. We propose to use an efficient data structure on which some algebraic operators may be used in order to retrieve or access patterns in pattern bases.
The Complexity of Datalog on Linear Orders
Venue: arXiv cs.LO 2009
Year: 2009

We study the program complexity of datalog on both finite and infinite linear orders. Our main result states that on all linear orders with at least two elements, the nonemptiness problem for datalog is EXPTIME-complete. While containment of the nonemptiness problem in EXPTIME is known for finite linear orders and actually for arbitrary finite structures, it is not obvious for infinite linear orders. It sharply contrasts the situation on other infinite structures; for example, the datalog nonemptiness problem on an infinite successor structure is undecidable. We extend our upper bound results to infinite linear orders with constants. As an application, we show that the datalog nonemptiness problem on Allen's interval algebra is EXPTIME-complete.
Directed paths on a tree: coloring, multicut and kernel
Venue: arXiv cs.DM 2009
Year: 2009

In the present paper, we study algorithmic questions for the arc-intersection graph of directed paths on a tree. Such graphs are known to be perfect (proved by Monma and Wei in 1986). We present faster algorithms than all previously known algorithms for solving the minimum coloring and the minimum clique cover problems. They both run in $O(np)$ time, where $n$ is the number of vertices of the tree and $p$ the number of paths. Another result is a polynomial algorithm computing a kernel in the intersection graph, when its edges are oriented in a clique-acyclic way. Indeed, such a kernel exists for any perfect graph by a theorem of Boros and Gurvich. Such algorithms computing kernels are known only for few classes of perfect graphs.
Discovering general partial orders in event streams
Venue: arXiv cs.AI 2009
Year: 2009

Frequent episode discovery is a popular framework for pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Efficient (and separate) algorithms exist for episode discovery when the associated partial order is total (serial episode) and trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with general partial orders. These algorithms can be easily specialized to discover serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that there is an inherent combinatorial explosion in frequent partial order mining and most importantly, frequency alone is not a sufficient measure of interestingness. We propose a new interestingness measure for general partial order episodes and a discovery method based on this measure, for filtering out uninteresting partial orders. Simulations demonstrate the effectiveness of our algorithms.
Nonclairvoyant Speed Scaling for Flow and Energy
Venue: arXiv cs.DS 2009
Year: 2009

We study online nonclairvoyant speed scaling to minimize total flow time plus energy. We first consider the traditional model where the power function is P (s) = s\^\propto. We give a nonclairvoyant algorithm that is shown to be O(\propto\^3)-competitive. We then show an Ω(\propto\^(1/3-ε)) lower bound on the competitive ratio of any nonclairvoyant algorithm. We also show that there are power functions for which no nonclairvoyant algorithm can be O(1)-competitive.
Lower Bounds for Multi-Pass Processing of Multiple Data Streams
Venue: arXiv cs.DS 2009
Year: 2009

This paper gives a brief overview of computation models for data stream processing, and it introduces a new model for multi-pass processing of multiple streams, the so-called mp2s-automata. Two algorithms for solving the set disjointness problem wi th these automata are presented. The main technical contribution of this paper is the proof of a lower bound on the size of memory and the number of heads that are required for solvin g the set disjointness problem with mp2s-automata.
Package upgrades in FOSS distributions: details and challenges
Venue: arXiv cs.SE 2009
Year: 2009

The upgrade problems faced by Free and Open Source Software distributions have characteristics not easily found elsewhere. We describe the structure of packages and their role in the upgrade process. We show that state of the art package managers have shortcomings inhibiting their ability to cope with frequent upgrade failures. We survey current countermeasures to such failures, argue that they are not satisfactory, and sketch alternative solutions.
Perfect Matchings in Õ(n^{1.5}) Time in Regular Bipartite Graphs
Venue: arXiv cs.DS 2009
Year: 2009

We consider the well-studied problem of finding a perfect matching in $d$-regular bipartite graphs with $2n$ vertices and $m = nd$ edges. While the best-known algorithm for general bipartite graphs (due to Hopcroft and Karp) takes $O(m \sqrt{n})$ time, in regular bipartite graphs, a perfect matching is known to be computable in $O(m)$ time. Very recently, the $O(m)$ bound was improved to $O(\min\{m, \frac{n^{2.5}\ln n}{d}\})$ expected time, an expression that is bounded by $\tilde{O}(n^{1.75})$. In this paper, we further improve this result by giving an $O(\min\{m, \frac{n^2\ln^3 n}{d}\})$ expected time algorithm for finding a perfect matching in regular bipartite graphs; as a function of $n$ alone, the algorithm takes expected time $O((n\ln n)^{1.5})$. To obtain this result, we design and analyze a two-stage sampling scheme that reduces the problem of finding a perfect matching in a regular bipartite graph to the same problem on a subsampled bipartite graph with $O(n\ln n)$ edges that has a perfect matching with high probability. The matching is then recovered using the Hopcroft-Karp algorithm. While the standard analysis of Hopcroft-Karp gives us an $\tilde{O}(n^{1.5})$ running time, we present a tighter analysis for our special case that results in the stronger $\tilde{O}(\min\{m, \frac{n^2}{d} \})$ time mentioned earlier. Our proof of correctness of this sampling scheme uses a new correspondence theorem between cuts and Hall's theorem ``witnesses'' for a perfect matching in a bipartite graph that we prove. We believe this theorem may be of independent interest; as another example application, we show that a perfect matching in the support of an $n \times n$ doubly stochastic matrix with $m$ non-zero entries can be found in expected time $\tilde{O}(m + n^{1.5})$.
Even Faster Exact Bandwidth
Venue: arXiv cs.CC 2009
Year: 2009

We deal with exact algorithms for Bandwidth, a long studied NP-hard problem. For a long time nothing better than the trivial O*(n!) exhaustive search was known. In 2000, Feige an Kilian came up with a O*(10^n)-time algorithm. Recently we presented algorithm that runs in O*(5^n) time and O*(2^n) space.. In this paper we present a major modification to our algorithm which makes it run in O(4.83^n) time with the cost of O*(4^n) space complexity. This modification allowed us to perform Measure & Conquer analysis for the time complexity which was not used for such types of problems before.
Back analysis of microplane model parameters using soft computing methods
Venue: arXiv cs.NE 2009
Year: 2009

A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed.
Counting Distinctions: On the Conceptual Foundations of Shannon's Information Theory
Venue: arXiv cs.IT 2009
Year: 2009

Categorical logic has shown that modern logic is essentially the logic of subsets (or "subobjects"). Partitions are dual to subsets so there is a dual logic of partitions where a "distinction" [an ordered pair of distinct elements (u,u') from the universe U ] is dual to an "element". An element being in a subset is analogous to a partition p on U making a distinction, i.e., if u and u' were in different blocks of p. Subset logic leads to finite probability theory by taking the (Laplacian) probability as the normalized size of each subset-event of a finite universe. The analogous step in the logic of partitions is to assign to a partition the number of distinctions made by a partition normalized by the total number of ordered pairs |UxU| from the finite universe. That yields a notion of "logical entropy" for partitions and a "logical information theory." The logical theory directly counts the (normalized) number of distinctions in a partition while Shannon's theory gives the average number of binary partitions needed to make those same distinctions. Thus the logical theory is seen as providing a conceptual underpinning for Shannon's theory based on the logical notion of "distinctions." (forthcoming in Synthese)
Correlation Robust Stochastic Optimization
Venue: arXiv cs.DS 2009
Year: 2009

We consider a robust model proposed by Scarf, 1958, for stochastic optimization when only the marginal probabilities of (binary) random variables are given, and the correlation between the random variables is unknown. In the robust model, the objective is to minimize expected cost against worst possible joint distribution with those marginals. We introduce the concept of correlation gap to compare this model to the stochastic optimization model that ignores correlations and minimizes expected cost under independent Bernoulli distribution. We identify a class of functions, using concepts of summable cost sharing schemes from game theory, for which the correlation gap is well-bounded and the robust model can be approximated closely by the independent distribution model. As a result, we derive efficient approximation factors for many popular cost functions, like submodular functions, facility location, and Steiner tree. As a byproduct, our analysis also yields some new results in the areas of social welfare maximization and existence of Walrasian equilibria, which may be of independent interest.
Matrix Graph Grammars with Application Conditions
Venue: arXiv cs.DM 2009
Year: 2009

In the Matrix approach to graph transformation we represent simple digraphs and rules with Boolean matrices and vectors, and the rewriting is expressed using Boolean operators only. In previous works, we developed analysis techniques enabling the study of the applicability of rule sequences, their independence, state reachability and the minimal graph able to fire a sequence. In the present paper we improve our framework in two ways. First, we make explicit (in the form of a Boolean matrix) some negative implicit information in rules. This matrix (called nihilation matrix) contains the elements that, if present, forbid the application of the rule (i.e. potential dangling edges, or newly added edges, which cannot be already present in the simple digraph). Second, we introduce a novel notion of application condition, which combines graph diagrams together with monadic second order logic. This allows for more flexibility and expressivity than previous approaches, as well as more concise conditions in certain cases. We demonstrate that these application conditions can be embedded into rules (i.e. in the left hand side and the nihilation matrix), and show that the applicability of a rule with arbitrary application conditions is equivalent to the applicability of a sequence of plain rules without application conditions. Therefore, the analysis of the former is equivalent to the analysis of the latter, showing that in our framework no additional results are needed for the study of application conditions. Moreover, all analysis techniques of [21, 22] for the study of sequences can be applied to application conditions.
NNRU, a noncommutative analogue of NTRU
Venue: arXiv cs.CR 2009
Year: 2009

NTRU public key cryptosystem is well studied lattice-based Cryptosystem along with Ajtai-Dwork and GGH systems. Underlying NTRU is a hard mathematical problem of finding short vectors in a certain lattice. (Shamir 1997) presented a lattice-based attack by which he could find the original secret key or alternate key. Shamir concluded if one designs a variant of NTRU where the calculations involved during encryption and decryption are non-commutative then the system will be secure against Lattice based attack.This paper presents a new cryptosystem with above property and we have proved that it is completely secure against Lattice based attack. It operates in the non-commutative ring M=M_k Z[X]/(X^n - I_{k*k}, where M is a matrix ring of k*k matrices of polynomials in R={Z}[X]/(X^n-1). Moreover We have got speed improvement by a factor of O(k^{1.624) over NTRU for the same bit of information.
Topological Centrality and Its Applications
Venue: arXiv cs.IR 2009
Year: 2009

Recent development of network structure analysis shows that it plays an important role in characterizing complex system of many branches of sciences. Different from previous network centrality measures, this paper proposes the notion of topological centrality (TC) reflecting the topological positions of nodes and edges in general networks, and proposes an approach to calculating the topological centrality. The proposed topological centrality is then used to discover communities and build the backbone network. Experiments and applications on research network show the significance of the proposed approach.
A formally verified compiler back-end
Venue: arXiv cs.LO 2009
Year: 2009

This article describes the development and formal verification (proof of semantic preservation) of a compiler back-end from Cminor (a simple imperative intermediate language) to PowerPC assembly code, using the Coq proof assistant both for programming the compiler and for proving its correctness. Such a verified compiler is useful in the context of formal methods applied to the certification of critical software: the verification of the compiler guarantees that the safety properties proved on the source code hold for the executable compiled code as well.
XML Representation of Constraint Networks: Format XCSP 2.1
Venue: arXiv cs.AI 2009
Year: 2009

We propose a new extended format to represent constraint networks using XML. This format allows us to represent constraints defined either in extension or in intension. It also allows us to reference global constraints. Any instance of the problems CSP (Constraint Satisfaction Problem), QCSP (Quantified CSP) and WCSP (Weighted CSP) can be represented using this format.
Finding Community Structure Based on Subgraph Similarity
Venue: arXiv cs.NI 2009
Year: 2009

Community identification is a long-standing challenge in the modern network science, especially for very large scale networks containing millions of nodes. In this paper, we propose a new metric to quantify the structural similarity between subgraphs, based on which an algorithm for community identification is designed. Extensive empirical results on several real networks from disparate fields has demonstrated that the present algorithm can provide the same level of reliability, measure by modularity, while takes much shorter time than the well-known fast algorithm proposed by Clauset, Newman and Moore (CNM). We further propose a hybrid algorithm that can simultaneously enhance modularity and save computational time compared with the CNM algorithm.
Nested Lattice Codes for Gaussian Relay Networks with Interference
Venue: arXiv cs.IT 2009
Year: 2009

In this paper, a class of relay networks is considered. We assume that, at a node, outgoing channels to its neighbors are orthogonal, while incoming signals from neighbors can interfere with each other. We are interested in the multicast capacity of these networks. As a subclass, we first focus on Gaussian relay networks with interference and find an achievable rate using a lattice coding scheme. It is shown that there is a constant gap between our achievable rate and the information theoretic cut-set bound. This is similar to the recent result by Avestimehr, Diggavi, and Tse, who showed such an approximate characterization of the capacity of general Gaussian relay networks. However, our achievability uses a structured code instead of a random one. Using the same idea used in the Gaussian case, we also consider linear finite-field symmetric networks with interference and characterize the capacity using a linear coding scheme.
Object Classification by means of Multi-Feature Concept Learning in a Multi Expert-Agent System
Venue: arXiv cs.MA 2009
Year: 2009

Classification of some objects in classes of concepts is an essential and even breathtaking task in many applications. A solution is discussed here based on Multi-Agent systems. A kernel of some expert agents in several classes is to consult a central agent decide among the classification problem of a certain object. This kernel is moderated with the center agent, trying to manage the querying agents for any decision problem by means of a data-header like feature set. Agents have cooperation among concepts related to the classes of this classification decision-making; and may affect on each others' results on a certain query object in a multi-agent learning approach. This leads to an online feature learning via the consulting trend. The performance is discussed to be much better in comparison to some other prior trends while system's message passing overload is decreased to less agents and the expertism helps the performance and operability of system win the comparison.
Adaptive Decoding of LDPC Codes with Binary Messages
Venue: arXiv cs.IT 2009
Year: 2009

A novel adaptive binary decoding algorithm for LDPC codes is proposed, which reduces the decoding complexity while having a comparable or even better performance than corresponding non-adaptive alternatives. In each iteration the variable node decoders use the binary check node decoders multiple times; each single use is referred to as a sub-iteration. To process the sequences of binary messages in each iteration, the variable node decoders employ pre-computed look-up tables. These look-up tables as well as the number of sub-iterations per iteration are dynamically adapted during the decoding process based on the decoder state, represented by the mutual information between the current messages and the syndrome bits. The look-up tables and the number of sub-iterations per iteration are determined and optimized using density evolution. The performance and the complexity of the proposed adaptive decoding algorithm is exemplified by simulations.