pinecone/core-2020-05-10-deduplication huggingface dataset:
An arbiter PUF secured by remote random reconfigurations of an FPGA

We present a practical and highly secure method for the authentication of
chips based on a new concept for implementing strong Physical Unclonable
Function (PUF) on field programmable gate arrays (FPGA). Its qualitatively
novel feature is a remote reconfiguration in which the delay stages of the PUF
are arranged to a random pattern within a subset of the FPGA's gates. Before
the reconfiguration is performed during authentication the PUF simply does not
exist. Hence even if an attacker has the chip under control previously she can
gain no useful information about the PUF. This feature, together with a strict
renunciation of any error correction and challenge selection criteria that
depend on individual properties of the PUF that goes into the field make our
strong PUF construction immune to all machine learning attacks presented in the
literature. More sophisticated attacks on our strong-PUF construction will be
difficult, because they require the attacker to learn or directly measure the
properties of the complete FPGA. A fully functional reference implementation
for a secure "chip biometrics" is presented. We remotely configure ten 64-stage
arbiter PUFs out of 1428 lookup tables within a time of 25 seconds and then
receive one "fingerprint" from each PUF within 1 msec
Towards an accurate solution of wireless network design problems

The optimal design of wireless networks has been widely studied in the
literature and many optimization models have been proposed over the years.
However, most models directly include the signal-to-interference ratios
representing service coverage conditions. This leads to mixed-integer linear
programs with constraint matrices containing tiny coefficients that vary widely
in their order of magnitude. These formulations are known to be challenging
even for state-of-the-art solvers: the standard numerical precision supported
by these solvers is usually not sufficient to reliably guarantee feasible
solutions. Service coverage errors are thus commonly present. Though these
numerical issues are known and become evident even for small-sized instances,
just a very limited number of papers has tried to tackle them, by mainly
investigating alternative non-compact formulations in which the sources of
numerical instabilities are eliminated. In this work, we explore a new approach
by investigating how recent advances in exact solution algorithms for linear
and mixed-integer programs over the rational numbers can be applied to analyze
and tackle the numerical difficulties arising in wireless network design
models.Comment: This is the authors' final version of the paper published in: R.
  Cerulli, S. Fujishige, A.R. Mahjoub (Eds.), Combinatorial Optimization - 4th
  International Symposium, ISCO 2016, LNCS 9849, pp. 135-147, 2016, DOI:
  10.1007/978-3-319-45587-7_12. The final publication is available at Springer
  via http://dx.doi.org/10.1007/978-3-319-45587-7_1
Reducing the clique and chromatic number via edge contractions and vertex deletions.

We consider the following problem: can a certain graph parameter of some given graph G be reduced by at least d, for some integer d, via at most k graph operations from some specified set S, for some given integer k? As graph parameters we take the chromatic number and the clique number. We let the set S consist of either an edge contraction or a vertex deletion. As all these problems are NP-complete for general graphs even if d is fixed, we restrict the input graph G to some special graph class. We continue a line of research that considers these problems for subclasses of perfect graphs, but our main results are full classifications, from a computational complexity point of view, for graph classes characterized by forbidding a single induced connected subgraph H
The Role of Female Sexual-Self Schema in Reactions to Non-Explicit Sexual Advertising Imagery

We explore females’ reactions to a non-explicit, but still sexually themed, advertisement. Specifically, we consider the role of female sexual self-schema (SSS) in the identification of the level of sex present in such an advertisement, and then resultant effects on attitudes and purchase intent. We find that while SSS has no effect on the perceived level of sex present, it does influence resultant dependent variables, particularly for low-SSS females. Informed by our study and extant literature, we also offer areas for further SSS-based advertising research, particularly regarding issues of females’ perceptions of advertisement and brand fit with sexual themes
Modelling avalanches in martensites

Solids subject to continuous changes of temperature or mechanical load often
exhibit discontinuous avalanche-like responses. For instance, avalanche
dynamics have been observed during plastic deformation, fracture, domain
switching in ferroic materials or martensitic transformations. The statistical
analysis of avalanches reveals a very complex scenario with a distinctive lack
of characteristic scales. Much effort has been devoted in the last decades to
understand the origin and ubiquity of scale-free behaviour in solids and many
other systems. This chapter reviews some efforts to understand the
characteristics of avalanches in martensites through mathematical modelling.Comment: Chapter in the book "Avalanches in Functional Materials and
  Geophysics", edited by E. K. H. Salje, A. Saxena, and A. Planes. The final
  publication is available at Springer via
  http://dx.doi.org/10.1007/978-3-319-45612-6_
The complexity of cylindrical algebraic decomposition with respect to
  polynomial degree

Cylindrical algebraic decomposition (CAD) is an important tool for working
with polynomial systems, particularly quantifier elimination. However, it has
complexity doubly exponential in the number of variables. The base algorithm
can be improved by adapting to take advantage of any equational constraints
(ECs): equations logically implied by the input. Intuitively, we expect the
double exponent in the complexity to decrease by one for each EC. In ISSAC 2015
the present authors proved this for the factor in the complexity bound
dependent on the number of polynomials in the input. However, the other term,
that dependent on the degree of the input polynomials, remained unchanged.
  In the present paper the authors investigate how CAD in the presence of ECs
could be further refined using the technology of Groebner Bases to move towards
the intuitive bound for polynomial degree
On the General Analytical Solution of the Kinematic Cosserat Equations

Based on a Lie symmetry analysis, we construct a closed form solution to the
kinematic part of the (partial differential) Cosserat equations describing the
mechanical behavior of elastic rods. The solution depends on two arbitrary
analytical vector functions and is analytical everywhere except a certain
domain of the independent variables in which one of the arbitrary vector
functions satisfies a simple explicitly given algebraic relation. As our main
theoretical result, in addition to the construction of the solution, we proof
its generality. Based on this observation, a hybrid semi-analytical solver for
highly viscous two-way coupled fluid-rod problems is developed which allows for
the interactive high-fidelity simulations of flagellated microswimmers as a
result of a substantial reduction of the numerical stiffness.Comment: 14 pages, 3 figure
Aggregation Operators to Support Collective Reasoning

Moderation poses one of the main Internet challenges. Currently, many Internet platforms and virtual communities deal with it by intensive human labour, some big companies-such as YouTube or Facebook-hire people to do it, others-such as 4chan or fanscup-just ask volunteer users to get in charge of it. But in most cases the policies that they use to decide if some contents should be removed or if a user should be banned are not clear enough to users. And, in any case, typically users are not involved in their definition. Nobel laureate Elinor Ostrom concluded that societies-such as institutions that had to share scarce resources-that involve individuals in the definition of their rules performed better-resources lasted more or did not deplete-than those organisations whose norms where imposed externally. Democracy also relies on this same idea of considering peoples’ opinions. In this vein, we argue that participants in a virtual community will be more prone to behave correctly-and thus the community itself will be “healthier”-if they take part in the decisions about the norms of coexistence that rule the community. With this aim, we investigate a collective decision framework that: (1) structures (relate) arguments issued by different participants; (2) allows agents to express their opinions about arguments; and (3) aggregates opinions to synthesise a collective decision. More precisely, we investigate two aggregation operators that merge discrete and continuous opinions. Finally, we analyse the social choice properties that our discrete aggregator operator satisfies. © Springer International Publishing Switzerland 2016.Work funded by Spanish National project CollectiveWare code TIN2015-66863-C2-1-R (MINECO/FEDER).Peer Reviewe
Axiomatisation of discrete fuzzy integrals with respect to possibility and necessity measures

Necessity (resp. possibility) measures are very simple representations of epistemic uncertainty due to incomplete knowledge. In the present work, a characterization of discrete Choquet integrals with respect to a possibility or a necessity measure is proposed, understood as a criterion for decision under uncertainty. This kind of criterion has the merit of being very simple to define and compute. To get our characterization, it is shown that it is enough to respectively add an optimism or a pessimism axiom to the axioms of the Choquet integral with respect to a general capacity. This additional axiom enforces the maxitivity or the minitivity of the capacity and essentially assumes that the decision-maker preferences only reflect the plausibility ordering between states of nature. The obtained pessimistic (resp. optimistic) criterion is an average of the maximin (resp. maximax) criterion of Wald across cuts of a possibility distribution on the state space. The additional axiom can be also used in the axiomatic approach to Sugeno integral and generalized forms thereof. The possibility of axiomatising of these criteria for decision under uncertainty in the setting of preference relations among acts is also discussed
Authenticated key agreement mediated by a proxy re-encryptor for the Internet of Things

International audienceThe Internet of Things (IoT) is composed of a wide range of heterogeneous network devices that communicate with their users and the surrounding devices. The secure communications between these devices are still essential even with little or no previous knowledge about each other and regardless of their resource capabilities. This particular context requires appropriate security mechanisms which should be wellsuited for the heterogeneous nature of IoT devices, without pre-sharing a secret key for each secure connection. In this work, we first propose a novel symmetric cipher proxy re-encryption scheme. Such a primitive allows a user to delegate her decryption rights to another with the help of a semi-trusted proxy, but without giving this latter any information on the transmitted messages and the user's secret keys. We then propose AKAPR, an Authenticated Key Agreement mediated by a Proxy Re-encryptor for IoT. The mechanism permits any two highly resource-constrained devices to establish a secure communication with no prior trust relationship. AKAPR is built upon our proposed proxy re-encryption scheme. It has been proved by ProVerif to provide mutual authentication for participants while preserving the secrecy of the generated session key. In addition, the scheme benefits from the lightness of our proxy re-encryption algorithm as it requires no expensive cryptographic operations such as pairing or modular exponentiatio
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel
  Machines for Efficient Recognition of Isolated Gestures

In the scope of gestural action recognition, the size of the feature vector
representing movements is in general quite large especially when full body
movements are considered. Furthermore, this feature vector evolves during the
movement performance so that a complete movement is fully represented by a
matrix M of size DxT , whose element M i, j represents the value of feature i
at timestamps j. Many studies have addressed dimensionality reduction
considering only the size of the feature vector lying in R D to reduce both the
variability of gestural sequences expressed in the reduced space, and the
computational complexity of their processing. In return, very few of these
methods have explicitly addressed the dimensionality reduction along the time
axis. Yet this is a major issue when considering the use of elastic distances
which are characterized by a quadratic complexity along the time axis. We
present in this paper an evaluation of straightforward approaches aiming at
reducing the dimensionality of the matrix M for each movement, leading to
consider both the dimensionality reduction of the feature vector as well as its
reduction along the time axis. The dimensionality reduction of the feature
vector is achieved by selecting remarkable joints in the skeleton performing
the movement, basically the extremities of the articulatory chains composing
the skeleton. The temporal dimen-sionality reduction is achieved using either a
regular or adaptive down-sampling that seeks to minimize the reconstruction
error of the movements. Elastic and Euclidean kernels are then compared through
support vector machine learning. Two data sets 1 that are widely referenced in
the domain of human gesture recognition, and quite distinctive in terms of
quality of motion capture, are used for the experimental assessment of the
proposed approaches. On these data sets we experimentally show that it is
feasible, and possibly desirable, to significantly reduce simultaneously the
size of the feature vector and the number of skeleton frames to represent body
movements while maintaining a very good recognition rate. The method proves to
give satisfactory results at a level currently reached by state-of-the-art
methods on these data sets. We experimentally show that the computational
complexity reduction that is obtained makes this approach eligible for
real-time applications
Collaborative Design and Supervision Processes Meta-Model for Rationale Capitalization

Companies act today in a collaborative way, and have to master their product design and supervision processes to remain productive and reactive to the perpetual changes in the industrial context. To achieve this, authors propose a three-layers framework. In the first layer, the design process is modelled. In the second, the traces related to the decisional process are captured. In the third, both the collected traces and the design context model are used to support decision-making. In this paper, authors address the first two issues by proposing a meta-model that allows one to capture the process’ decisional knowledge. The proposal is presented and then illustrated in a case study.FUI GONTRAN
A reverse engineering approach to measure the deformations of a sailing yacht

In this work, a multidisciplinary experience, aimed to study the permanent deformations of the hull of a regatta sailing yacht is described. In particular, a procedure to compare two different surfaces of the hull of a small sailing yacht, designed and manufactured at the University of Palermo, has been developed. The first one represents the original CAD model while the second one has been obtained by means of a reverse engineering approach. The reverse engineering process was performed through an automatic close-range photogrammetry survey, that has allowed to obtain very accurate measures of the hull, and a 3D modelling step by the well-known 3D computer graphics software Rhinoceros. The reverse engineering model was checked through two different procedures implemented by the graphical algorithm editor Grasshopper. The first procedure has allowed to compare the photogrammetric measurements with the rebuilt surface, in order to verify if the reverse engineering process has led to reliable results. The second has been implement to measure the deviations between the original CAD model and the rebuilt surface of the hull. This procedure has given the possibility to highlight any permanent deformation of the hull due to errors during the production phase or to excessive loads during its use. The obtained results have demonstrated that the developed procedure is very efficient and able to give detailed information on the deviation values of the two compared surfaces
Handling Estimation Inaccuracy in Query Optimization

Cost-based Optimizers choose query execution plans using a cost model. The latter relies on the accuracy of estimated statistics. Unfortunately, compile-time estimates often differ significantly from run-time values, leading to a suboptimal plan choices. In this paper, we propose a compile-time strategy, wherein the optimization process is fully aware of the estimation inaccuracy. This is ensured by the use of intervals of estimates rather than single-point estimates of error-prone parameters. These intervals serve to identify plans that provide stable performance in several run-time conditions, so called robust. Our strategy relies on a probabilistic approach to decide which plan to choose to start the execution. Our experiments show that our proposal allows a considerable improvement of the ability of a query optimizer to produce a robust execution plan in case of large estimation errors
Syntactical Similarity Learning by Means of Grammatical Evolution

Several research efforts have shown that a similarity function synthesized from examples may capture an application-specific similarity criterion in a way that fits the application needs more effectively than a generic distance definition. In this work, we propose a similarity learning algorithm tailored to problems of syntax-based entity extraction from unstructured text streams. The algorithm takes in input pairs of strings along with an indication of whether they adhere or not adhere to the same syntactic pattern. Our approach is based on Grammatical Evolution and explores systematically a similarity definition space including all functions that may be expressed with a specialized, simple language that we have defined for this purpose. We assessed our proposal on patterns representative of practical applications. The results suggest that the proposed approach is indeed feasible and that the learned similarity function is more effective than the Levenshtein distance and the Jaccard similarity index
Variable interaction in multi-objective optimization problems

14th International Conference on Parallel Problem Solving from Nature – PPSN XIV, 2016-09-17, 2016-09-21, Edinburgh, UK, pp. 399 - 409This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.The final publication is available at link.springer.comVariable interaction is an important aspect of a problem, which reflects its structure, and has implications on the design of efficient optimization algorithms. Although variable interaction has been widely studied in the global optimization community, it has rarely been explored in the multi-objective optimization literature. In this paper, we empirically and analytically study the variable interaction structures of some popular multi-objective benchmark problems. Our study uncovers nontrivial variable interaction structures for the ZDT and DTLZ benchmark problems which were thought to be either separable or non-separable
An Evolutionary Hyper-Heuristic for the Software Project Scheduling Problem

Software project scheduling plays an important role in reducing the cost and duration of software projects. It is an NP-hard combinatorial optimization problem that has been addressed based on single and multi-objective algorithms. However, such algorithms have always used fixed genetic operators, and it is unclear which operators would be more appropriate across the search process. In this paper, we propose an evolutionary hyper-heuristic to solve the software project scheduling problem. Our novelties include the following: (1) this is the first work to adopt an evolutionary hyper-heuristic for the software project scheduling problem; (2) this is the first work for adaptive selection of both crossover and mutation operators; (3) we design different credit assignment methods for mutation and crossover; and (4) we use a sliding multi-armed bandit strategy to adaptively choose both crossover and mutation operators. The experimental results show that the proposed algorithm can solve the software project scheduling problem effectively.This paper was partly supported by the National Natural Science Foundation of China under Grant (Grants. 51305024 and 61329302) and EPSRC (Grant No. EP/J017515/1). Xin Yao was supported by a Royal Society Wolfson Research Merit Award.Peer-reviewedPost-printInternational Conference on Parallel Problem Solving from Nature, Edinburgh, UK, September 17-21, 2016
Towards many-objective optimisation with hyper-heuristics: Identifying good heuristics with indicators

PPSN 2016: 14th International Conference on Parallel Problem Solving from Nature, 17-21 September 2016, Edinburgh, ScotlandThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.The use of hyper-heuristics is increasing in the multi-objective optimisation domain, and the next logical advance in such methods is to use them in the solution of many-objective problems. Such problems comprise four or more objectives and are known to present a significant challenge to standard dominance-based evolutionary algorithms. We in- corporate three comparison operators as alternatives to dominance and investigate their potential to optimise many-objective problems with a hyper-heuristic from the literature. We discover that the best results are obtained using either the favour relation or hypervolume, but conclude that changing the comparison operator alone will not allow for the generation of estimated Pareto fronts that are both close to and fully cover the true Pareto front.This work was funded under EPSRC grant EP/K000519/1
Data classification using carbon-nanotubes and evolutionary algorithms.

The potential of Evolution in Materio (EiM) for machine learning problems is explored here. This technique makes use of evolutionary algorithms (EAs) to influence the processing abilities of an un-configured physically rich medium, via exploitation of its physical properties. The EiM results reported are obtained using particle swarm optimisation (PSO) and differential evolution (DE) to exploit the complex voltage/current relationship of a mixture of single walled carbon nanotubes (SWCNTs) and liquid crystals (LCs). The computational problem considered is simple binary data classification. Results presented are consistent and reproducible. The evolutionary process based on EAs has the capacity to evolve the material to a state where data classification can be performed. Finally, it appears that through the use of smooth signal inputs, PSO produces classifiers out of the SWCNT/LC substrate which generalise better than those evolved with DE