pinecone/core-2020-05-10-deduplication huggingface dataset:
Lattice-Based Group Signatures with Logarithmic Signature Size

International audienceGroup signatures are cryptographic primitives where users can anonymously sign messages in the name of a population they belong to. Gordon {\em et al.} (Asiacrypt 2010) suggested the first realization of group signatures based on lattice assumptions in the random oracle model. A significant drawback of their scheme is its linear signature size in the cardinality $N$ of the group. A recent extension proposed by Camenisch {\em et al.} (SCN 2012) suffers from the same overhead. In this paper, we describe the first lattice-based group signature schemes where the signature and public key sizes are essentially logarithmic in~$N$ (for any fixed security level). Our basic construction only satisfies a relaxed definition of anonymity (just like the Gordon {\it et al.} system) but readily extends into a fully anonymous group signature ({\it i.e.}, that resists adversaries equipped with a signature opening oracle). We prove the security of our schemes in the random oracle model under the SIS and LWE assumptions
Advances in Nonnegative Matrix Decomposition with Application to Cluster Analysis

Nonnegative Matrix Factorization (NMF) has found a wide variety of applications in machine learning and data mining. NMF seeks to approximate a nonnegative data matrix by a product of several low-rank factorizing matrices, some of which are constrained to be nonnegative. Such additive nature often results in parts-based representation of the data, which is a desired property especially for cluster analysis. 

This thesis presents advances in NMF with application in cluster analysis. It reviews a class of higher-order NMF methods called Quadratic Nonnegative Matrix Factorization (QNMF). QNMF differs from most existing NMF methods in that some of its factorizing matrices occur twice in the approximation. The thesis also reviews a structural matrix decomposition method based on Data-Cluster-Data (DCD) random walk. DCD goes beyond matrix factorization and has a solid probabilistic interpretation by forming the approximation with cluster assigning probabilities only. Besides, the Kullback-Leibler divergence adopted by DCD is advantageous in handling sparse similarities for cluster analysis. 

Multiplicative update algorithms have been commonly used for optimizing NMF objectives, since they naturally maintain the nonnegativity constraint of the factorizing matrix and require no user-specified parameters. In this work, an adaptive multiplicative update algorithm is proposed to increase the convergence speed of QNMF objectives. 

Initialization conditions play a key role in cluster analysis. In this thesis, a comprehensive initialization strategy is proposed to improve the clustering performance by combining a set of base clustering methods. The proposed method can better accommodate clustering methods that need a careful initialization such as the DCD. 

The proposed methods have been tested on various real-world datasets, such as text documents, face images, protein, etc. In particular, the proposed approach has been applied to the cluster analysis of emotional data
EM Training of Hidden Markov Models for Shape Recognition Using Cyclic Strings

Shape descriptions and the corresponding matching techniques must be robust to noise and invariant to transformations for their use in recognition tasks. Most transformations are relatively easy to handle when contours are represented by strings. However, starting point invariance is difficult to achieve. One interesting possibility is the use of cyclic strings, which are strings with no starting and final points. Here we present the use of Hidden Markov Models for modelling cyclic strings and their training using Expectation Maximization. Experimental results show that our proposal outperforms other methods in the literature
Maximum-Margin Framework for Training Data Synchronization in Large-Scale Hierarchical Classification

International audienceIn the context of supervised learning, the training data for large-scale hierarchical classification consist of (i) a set of input-output pairs, and (ii) a hierarchy structure defining parent-child relation among class labels. It is often the case that the hierarchy structure given a-priori is not optimal for achieving high classification accuracy. This is especially true for web-taxonomies such as Yahoo! directory which consist of tens of thousand of classes. Furthermore, an important goal of hierarchy design is to render better navigability and browsing. In this work, we propose a maximum-margin framework for automatically adapting the given hierarchy by using the set of input-output pairs to yield a new hierarchy. The proposed method is not only theoretically justified but also provides a more principled approach for hierarchy flattening techniques proposed earlier, which are ad-hoc and empirical in nature. The empirical results on publicly available large-scale datasets demonstrate that classification with new hierarchy leads to better or comparable generalization performance than the hierarchy flattening techniques
Defining sustainability requirements for design-build (DB) contractor selection in public sector projects

The design-build (DB) delivery system is an effective means of delivering a green construction project and selecting an appropriate contractor is critical to project success. Moreover, the delivery of green buildings requires specific design, construction and operation and maintenance considerations not generally encountered in the procurement of conventional buildings. Specifying clear sustainability requirements to potential contractors is particularly important in achieving sustainable project goals. However, many client/owners either do not explicitly specify sustainability requirements or do so in a prescriptive manner during the project procurement process. This paper investigates the current state-of-the-art procurement process used in specifying the sustainability requirements of the public sector in the USA construction market by means of a robust content analysis of 40 design-build requests for proposals (RFPs). The results of the content analysis indicate that the sustainability requirement is one of the most important dimensions in the best-value evaluation of DB contractors. Client/owners predominantly specify the LEED certification levels (e.g. LEED Certified, Silver, Gold, and Platinum) for a particular facility, and include the sustainability requirements as selection criteria (with specific importance weightings) for contractor evolution. Additionally, larger size projects tend to allocate higher importance weightings to sustainability requirements.This study provides public DB client/owners with a number of practical implications for selecting appropriate design-builders for sustainable DB projects
Incorporating PGMs into a BDI Architecture

In this paper, we present a hybrid BDI-PGM framework, in which PGMs (Probabilistic Graphical Models) are incorporated into a BDI (belief-desire-intention) architecture. This work is motivated by the need to address the scalability and noisy sensing issues in SCADA (Supervisory Control And Data Acquisition) systems. Our approach uses the incorporated PGMs to model the uncertainty reasoning and decision making processes of agents situated in a stochastic environment. In particular, we use Bayesian networks to reason about an agent's beliefs about the environment based on its sensory observations, and select optimal plans according to the utilities of actions defined in influence diagrams. This approach takes the advantage of the scalability of the BDI architecture and the uncertainty reasoning capability of PGMs. We present a prototype of the proposed approach using a transit scenario to validate its effectiveness. © 2013 Springer-Verlag.Peer Reviewe
No Free Lunch versus Occam’s Razor in supervised learning

The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the case for a universal bias allowing an algorithm to succeed in all interesting problem domains. Additionally, we give a new algorithm for off-line classification, inspired by Solomonoff induction, with good performance on all structured (compressible) problems under reasonable assumptions. This includes a proof of the efficacy of the well-known heuristic of randomly selecting training data in the hope of reducing the misclassification rate
(Non-)Equivalence of universal priors

Ray Solomonoff invented the notion of universal induction featuring an aptly termed “universal” prior probability function over all possible computable environments [9]. The essential property of this prior was its ability to dominate all other such priors. Later, Levin introduced another construction — a mixture of all possible priors or “universal mixture”[12]. These priors are well known to be equivalent up to multiplicative constants. Here, we seek to clarify further the relationships between these three characterisations of a universal prior (Solomonoff’s, universal mixtures, and universally dominant priors). We see that the the constructions of Solomonoff and Levin define an identical class of priors, while the class of universally dominant priors is strictly larger. We provide some characterisation of the discrepancy
R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection

short paperInternational audienceAn indicator-based evolutionary multiobjective optimization algorithm (EMOA) is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered continuous multiobjective optimization problems. Furthermore, decision makers' preferences can be included by adjusting the weight vector distributions of the indicator which results in a focused search behavior
Priority-Based Human Resource Allocation in Business Processes

In Business Process Management Systems, human resource management typically covers two steps: resource assignment at design time and resource allocation at run time. Although concepts like rolebased assignment often yield several potential performers for an activity, there is a lack of mechanisms for prioritizing them, e.g., according to their skills or current workload. in this paper, we address this research gap. More specifically, we introduce an approach to define resource preferences grounded on a validated, generic user preference model initially developed for semantic web services. Furthermore, we show an implementation of the approach demonstrating its feasibility. Keywords: preference modeling, preference resolution, priority-based allocation, priority ranking, RAL, resource allocation, SOUP
PPINOT Tool Suite:  a Performance Management Solution for Process-Oriented Organisations

A key aspect in any process-oriented organisation is the measurement of process performance for the achievement of its strategic and operational goals. Process Performance Indicators (PPIs) are a key asset to carry out this evaluation, and, therefore, the management of these PPIs throughout the whole BP lifecycle is crucial. in this demo we present PPINOT Tool Suite, a set of tools aimed at facilitating and automating the PPI management. The support includes their definition using either a graphical or a template-based textual notation, their automated analysis at design-time, and their automated computation based on the instrumentation of a Business Process Management System
!Agree Studio:  a Platform to Edit and Validate Ws-Agreement Documents

The widespread use of SLA-regulated Cloud services, in which the violation of SLA terms may imply a penalty for the parties, have increased the importance and complexity of systems supporting the SLA lifecycle. Although these systems can be very different from each other, ranging from service monitoring platforms to auto-scaling solutions according to SLAs, they all share the need of having machine-processable and semantically valid SLAs. in this paper we present iAgree studio, the first application, up to our knowledge, that is able to edit and semantically validate agreement documents that are compliant with the WS–Agreement specification by checking properties such as its consistency, and the compliance between templates and agreement offers. in addition, it reports explanations when documents are not valid. Moreover, it allows users to combine the validation and explanation operations by means of a scenarios develope
Algorithms to measure diversity and clustering in social networks through dot product graphs.

Social networks are often analyzed through a graph model of the network. The dot product model assumes that two individuals are connected in the social network if their attributes or opinions are similar. In the model, a d-dimensional vector a v represents the extent to which individual v has each of a set of d attributes or opinions. Then two individuals u and v are assumed to be friends, that is, they are connected in the graph model, if and only if a u · a v  ≥ t, for some fixed, positive threshold t. The resulting graph is called a d-dot product graph..\ud
We consider two measures for diversity and clustering in social networks by using a d-dot product graph model for the network. Diversity is measured through the size of the largest independent set of the graph, and clustering is measured through the size of the largest clique. We obtain a tight result for the diversity problem, namely that it is polynomial-time solvable for d = 2, but NP-complete for d ≥ 3. We show that the clustering problem is polynomial-time solvable for d = 2. To our knowledge, these results are also the first on the computational complexity of combinatorial optimization problems on dot product graphs.\ud
We also consider the situation when two individuals are connected if their preferences are not opposite. This leads to a variant of the standard dot product graph model by taking the threshold t to be zero. We prove in this case that the diversity problem is polynomial-time solvable for any fixed d
Terrain visibility with multiple viewpoints

We study the problem of visibility in polyhedral terrains in the presence of multiple viewpoints. We consider three fundamental visibility structures: the visibility map, the colored visibility map, and the Voronoi visibility map. We study the complexity of each structure for both 1.5D and 2.5D terrains, and provide efficient algorithms to construct them. Our algorithm for the visibility map in 2.5D terrains improves on the only existing algorithm in this setting.Peer ReviewedPostprint (author’s final draft
A Probabilistic Analysis of Kademlia Networks

Kademlia is currently the most widely used searching algorithm in P2P
(peer-to-peer) networks. This work studies an essential question about Kademlia
from a mathematical perspective: how long does it take to locate a node in the
network? To answer it, we introduce a random graph K and study how many steps
are needed to locate a given vertex in K using Kademlia's algorithm, which we
call the routing time. Two slightly different versions of K are studied. In the
first one, vertices of K are labelled with fixed IDs. In the second one,
vertices are assumed to have randomly selected IDs. In both cases, we show that
the routing time is about c*log(n), where n is the number of nodes in the
network and c is an explicitly described constant.Comment: ISAAC 201
Colouring of graphs with Ramsey-type forbidden subgraphs.

A colouring of a graph G = (V;E) is a mapping c : V !\ud
f1; 2; : : :g such that c(u) 6= c(v) if uv 2 E; if jc(V )j  k then c is a\ud
k-colouring. The Colouring problem is that of testing whether a given\ud
graph has a k-colouring for some given integer k. If a graph contains no\ud
induced subgraph isomorphic to any graph in some family H, then it\ud
is called H-free. The complexity of Colouring for H-free graphs with\ud
jHj = 1 has been completely classied. When jHj = 2, the classication\ud
is still wide open, although many partial results are known. We\ud
continue this line of research and forbid induced subgraphs fH1;H2g,\ud
where we allow H1 to have a single edge and H2 to have a single nonedge.\ud
Instead of showing only polynomial-time solvability, we prove that\ud
Colouring on such graphs is xed-parameter tractable when parameterized\ud
by jH1j + jH2j. As a byproduct, we obtain the same result both\ud
for the problem of determining a maximum independent set and for the\ud
problem of determining a maximum clique
DVFS Aware CPU Credit Enforcement in a Virtualized System

Nowadays, virtualization is present in almost all computing infrastructures. Thanks to VM migration and server consolidation, virtualization helps reducing power consumption in distributed environments. On another side, Dynamic Voltage and Frequency Scaling (DVFS) allows servers to dynamically modify the processor frequency (according to the CPU load) in order to achieve less energy consumption. We observed that these two techniques have several incompatibilities. For instance, if two virtual machines VM1 and VM2 are running on the same physical host (with their respective allocated credits), VM1 being overloaded and VM2 being underloaded, the host may be globally underloaded leading to a reduction of the processor frequency, which would penalize VM1 even if VM1’s owner booked a given CPU capacity. In this paper, we analyze the compatibility of available VM schedulers with DVFS management in virtualized environments, we identify key issues and finally propose a DVFS aware VM scheduler which addresses these issues. We implemented and evaluated our prototype in the Xen virtualized environment
User-Centered Social Information Retrieval Model Exploiting Annotations and Social Relationships

International audienceSocial Information Retrieval (SIR) has extended the classical information retrieval models and systems to take into account social information of the user within his social networks. We assume that a SIR system can exploit the informational social context (ISC) of the user in order to refine his retrieval, since different users may express different information needs as the same query. Hence, we present a SIR model that takes into account the user's social data, such as his annotations and his social relationships through social networks. We propose to integrate the user's ISC into the documents indexing process, allowing the SIR system to personalize the list of documents returned to the user. Our approach has shown interesting results on a test collection built from the social collaborative bookmarking network Delicious
Classification of mexican paper currency denomination by extracting their discriminative colors

In this paper we describe a machine vision approach to recognize the denomination classes of the Mexican paper currency by extracting their color features. A banknote’s color is characterized by summing all the color vectors of the image’s pixels to obtain a resultant vector, the banknote’s denomination is classified by knowing the orientation of the resulting vector within the RGB space. In order to obtain a more precise characterization of paper currency, the less discriminative colors of each denomination are eliminated from the images; the color selection is applied in the RGB and HSV spaces, separately. Experimental results with the current Mexican banknotes are presented.Proyecto PROMEP 103.5/13/653
Dalal's Revision without Hamming Distance

A well known strategy for belief revision is the use of an operator which takes as input a belief base and formula and outputs a new consistent revised belief base. Many operators require additional information such as epistemic entrenchment relations, system of spheres, faithful orderings, subformulae relation, etc. However, in many applications this extra information does not exist and all beliefs have to be equally considered. Other operators that can do without background information are dependent on the syntax. Among the few operators that possess both kinds of independence: of extra information and of the syntax, Dalal’s operator is the most outstanding. Dalal’s revision moves from the models of the base to the models of the input formula which are closest in terms of Hamming distance. A drawback of Dalal’s approach is that it fails when faced with inconsistent belief bases. This paper proposes a new method for computing Dalal’s revision that avoids the computation of belief bases models. We propose a new distance between formulae based on distances between terms of formulae in DNF and a revision operator based on these distances. The proposed operator produces Dalal’s equivalent results when the belief base and new input are both consistent. Moreover, this new operator is able to handle inconsistent belief bases. We also analyze several properties of the new operator. While the input belief base and formula need a compilation to DNF, the operator meets desirable properties making the approach suitable for implementation
Benchmarking: A methodology for ensuring the relative quality of recommendation systems in software engineering

This chapter describes the concepts involved in the process of benchmarking of recommendation systems. Benchmarking of recommendation systems is used to ensure the quality of a research system or production system in comparison to other systems, whether algorithmically, infrastructurally, or according to any sought-after quality. Specifically, the chapter presents evaluation of recommendation systems according to recommendation accuracy, technical constraints, and business values in the context of a multi-dimensional benchmarking and evaluation model encompassing any number of qualities into a final comparable metric. The focus is put on quality measures related to recommendation accuracy, technical factors, and business values. The chapter first introduces concepts related to evaluation and benchmarking of recommendation systems, continues with an overview of the current state of the art, then presents the multi-dimensional approach in detail. The chapter concludes with a brief discussion of the introduced concepts and a summary
The nuclear energy density functional formalism

The present document focuses on the theoretical foundations of the nuclear
energy density functional (EDF) method. As such, it does not aim at reviewing
the status of the field, at covering all possible ramifications of the approach
or at presenting recent achievements and applications. The objective is to
provide a modern account of the nuclear EDF formalism that is at variance with
traditional presentations that rely, at one point or another, on a {\it
Hamiltonian-based} picture. The latter is not general enough to encompass what
the nuclear EDF method represents as of today. Specifically, the traditional
Hamiltonian-based picture does not allow one to grasp the difficulties
associated with the fact that currently available parametrizations of the
energy kernel $E[g',g]$ at play in the method do not derive from a genuine
Hamilton operator, would the latter be effective. The method is formulated from
the outset through the most general multi-reference, i.e. beyond mean-field,
implementation such that the single-reference, i.e. "mean-field", derives as a
particular case. As such, a key point of the presentation provided here is to
demonstrate that the multi-reference EDF method can indeed be formulated in a
{\it mathematically} meaningful fashion even if $E[g',g]$ does {\it not} derive
from a genuine Hamilton operator. In particular, the restoration of symmetries
can be entirely formulated without making {\it any} reference to a projected
state, i.e. within a genuine EDF framework. However, and as is illustrated in
the present document, a mathematically meaningful formulation does not
guarantee that the formalism is sound from a {\it physical} standpoint. The
price at which the latter can be enforced as well in the future is eventually
alluded to.Comment: 64 pages, 8 figures, submitted to Euroschool Lecture Notes in Physics
  Vol.IV, Christoph Scheidenberger and Marek Pfutzner editor