pinecone/core-2020-05-10-deduplication huggingface dataset:
A fast single server private information retrieval protocol with low communication cost

Existing single server Private Information Retrieval (PIR) protocols are far from practical. To be practical, a single server PIR protocol has to be both communicationally and computationally efficient. In this paper, we present a single server PIR protocol that has low communication cost and is much faster than existing protocols. A major building block of the PIR protocol in this paper is a tree-based compression scheme, which we call folding/unfolding. This compression scheme enables us to lower the communication complexity to O(loglogn). The other major building block is the BGV fully homomorphic encryption scheme. We show how we design the protocol to exploit the internal parallelism of the BGV scheme. This significantly reduces the server side computational overhead and makes our protocol much faster than the existing protocols. Our protocol can be further accelerated by utilising hardware parallelism. We have built a prototype of the protocol. We report on the performance of our protocol based on the prototype and compare it with the current most efficient protocols
Double-authentication-preventing signatures

Digital signatures are often used by trusted authorities to make unique bindings between a subject and a digital object; for example, certificate authorities certify a public key belongs to a domain name, and time-stamping authorities certify that a certain piece of information existed at a certain time.  Traditional digital signature schemes however impose no uniqueness conditions, so a trusted authority could make multiple certifications for the same subject but different objects, be it intentionally, by accident, or following a (legal or illegal) coercion.  We propose the notion of a double-authentication-preventing signature, in which a value to be signed is split into two parts: a subject and a message.  If a signer ever signs two different messages for the same subject, enough information is revealed to allow anyone to compute valid signatures on behalf of the signer.  This double-signature forgeability property discourages signers from misbehaving---a form of self-enforcement---and would give binding authorities like CAs some cryptographic arguments to resist legal coercion.  We give a generic construction using a new type of trapdoor functions with extractability properties, which we show can be instantiated using the group of sign-agnostic quadratic residues modulo a Blum integer
Applying AI for modeling and understanding analogy-based classroom teaching tools and techniques

This paper forms the final part of a short series of related articles[1,2] dedicated to highlighting a fruitful type of application of cognitively-inspired analogy engines in an educational context. It complements the earlier work with an additional fully worked out example by providing a short analysis and a detailed formal model (based on the Heuristic-Driven Theory Projection computational analogy framework) of the Number Highrise, a tool for teaching multiplication-based relations in the range of natural numbers up to 100 to children in their first years of primary school
Acquisition and Reuse of Reasoning Knowledge from Textual Cases for Automated Analysis

Analysis is essential for solving complex problems such as diagnosing a patient, investigating an accident or predicting the outcome of a legal case. It is a non-trivial process even for human experts. To assist experts in this process we propose a CBR-based approach for automated problem analysis. In this approach a new problem is analysed by reusing reasoning knowledge from the analysis of a similar problem. To avoid the laborious process of manual case acquisition, the reasoning knowledge is extracted automatically from text and captured in a graph-based representation, which we dubbed Text Reasoning Graph (TRG), that consists of causal, entailment and paraphrase relations. The reuse procedure involves adaptation of a similar past analysis to a new problem by finding paths in TRG that connect the evidence in the new problem to conclusions of the past analysis. The objective is to generate the best explanation of how the new evidence connects to the conclusion. For evaluation, we built a system for analysing aircraft accidents based on the collection of aviation investigation reports. The evaluation results show that our reuse method increases the precision of the retrieved conclusions.The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11209-1_3
Sentiment and preference guided social recommendation

© Springer International Publishing Switzerland 2014. Social recommender systems harness knowledge from social experiences, expertise and interactions. In this paper we focus on two such knowledge sources: sentiment-rich user generated reviews; and preferences from purchase summary statistics. We formalise the integration of these knowledge sources by mixing a novel aspect-based sentiment ranking with a preference ranking. We demonstrate the utility of our proposed formalism by conducting a comparative analysis on data extracted from Amazon.com. In particular we show that the performance of the proposed aspect based sentiment analysis algorithm is superior to existing aspect extraction algorithms and that combining this with preference knowledge leads to better recommendations.This research has been partially supported by AGAUR Scholarship (2013FI-B 00034) and Project Cognitio TIN2012-38450-C03-03Peer Reviewe
Zero-Knowledge Password Policy Checks and Verifier-Based PAKE

Zero-Knowledge Password Policy Checks (ZKPPC), introduced in this work, enable blind registration of client passwords at remote servers, i.e., client passwords are never transmitted to the servers. This eliminates the need for trusting servers to securely process and store client passwords. A ZKPPC protocol, executed as part of the registration procedure, allows clients to further prove compliance of chosen passwords with respect to password policies defined by the servers. The main benefit of ZKPPC-based password registration is that it guarantees that registered passwords never appear in clear on the server side. At the end of the registration phase the server only receives and stores some verification information that can later be used for authentication in a suitable Verifier-based Password Authenticated Key Exchange (VPAKE) protocol. We give general and concrete constructions of ZKPPC protocols and suitable VPAKE protocols for ASCII-based passwords and policies that are commonly used on the web. To this end we introduce a reversible mapping of ASCII characters to integers that can be used to preserve the structure of the password string and a new randomized password hashing scheme for ASCII-based passwords
LeakWatch: Estimating Information Leakage from Java Programs

International audiencePrograms that process secret data may inadvertently reveal information about those secrets in their publicly-observable output. This paper presents LeakWatch, a quantitative information leakage analysis tool for the Java programming language; it is based on a flexible "point-to-point" information leakage model, where secret and publicly-observable data may occur at any time during a program's execution. LeakWatch repeatedly executes a Java program containing both secret and publicly-observable data and uses robust statistical techniques to provide estimates, with confidence intervals, for min-entropy leakage (using a new theoretical result presented in this paper) and mutual information. We demonstrate how LeakWatch can be used to estimate the size of information leaks in a range of real-world Java programs
Bitcoin Transaction Malleability and MtGox

In Bitcoin, transaction malleability describes the fact that the signatures
that prove the ownership of bitcoins being transferred in a transaction do not
provide any integrity guarantee for the signatures themselves. This allows an
attacker to mount a malleability attack in which it intercepts, modifies, and
rebroadcasts a transaction, causing the transaction issuer to believe that the
original transaction was not confirmed. In February 2014 MtGox, once the
largest Bitcoin exchange, closed and filed for bankruptcy claiming that
attackers used malleability attacks to drain its accounts. In this work we use
traces of the Bitcoin network for over a year preceding the filing to show
that, while the problem is real, there was no widespread use of malleability
attacks before the closure of MtGox
Up, down, turn around: assisted wayfinding involving level changes

Both maps and verbal descriptions have been shown to be an effective wayfinding assistance. However, most studies investigating these aids have been performed in two-dimensional spaces that ignore level changes. It seems less clear that both types of assistance work equally well in settings that involve going up some stairs or taking an elevator. In this paper, we present a study that had participants follow a route in a multi-level setting involving several level changes while being assisted by either a textual description or a sketch map. Results indicate that both types of assistance are effective and that the few differences in performance that we discovered can be attributed to differences in the employed wayfinding strategies rather than differences in the assistance types. Our findings have implications for the design of (mobile) assistance services that aim at using graphical instructions for guiding users seamlessly through indoor and outdoor environments
Boundaries and Prototypes in Categorizing Direction

Projective terms such as left, right, front, back are conceptually interesting due to their flexibility of contextual usage and their central relevance to human spatial cognition. Their default acceptability areas are well known, with prototypical axes representing their most central usage and decreasing acceptability away from the axes. Previous research has shown these axes to be boundaries in certain non-linguistic tasks, indicating an inverse relationship between linguistic and non-linguistic direction concepts under specific circumstances. Given this striking mismatch, our study asks how such inverse non-linguistic concepts are represented in language, as well as how people describe their categorization. Our findings highlight two distinct grouping strategies reminiscent of theories of human categorization: prototype based or boundary based. These lead to different linguistic as well as non-linguistic patterns
Simple, efficient, sound and complete combinator parsing for all context-free grammars, using an oracle

timestamp: Tue, 09 Sep 2014 10:57:11 +0200 biburl: http://dblp.uni-trier.de/rec/bib/conf/sle/Ridge14 bibsource: dblp computer science bibliography, http://dblp.orgParsers for context-free grammars can be implemented directly and naturally in a functional style known as “combinator parsing”, using recursion following the structure of the grammar rules. Traditionally parser combinators have struggled to handle all features of context-free grammars, such as left recursion.\ud
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Previous work introduced novel parser combinators that could be used to parse all context-free grammars. A parser generator built using these combinators was proved both sound and complete in the HOL4 theorem prover. Unfortunately the performance was not as good as other parsing methods such as Earley parsing.\ud
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In this paper, we build on this previous work, and combine it in novel ways with existing parsing techniques such as Earley parsing. The result is a sound-and-complete combinator parsing library that can handle all context-free grammars, and has good performance.Peer-reviewedPost-printSoftware Language Engineering - 7th International Conference, SLE 2014, Västerås, Sweden, September 15-16, 201
Evaluating the usability of a visual feature modeling notation

International audienceFeature modeling is a popular Software Product Line Engineering (SPLE) technique used to describe variability in a product family. A usable feature modeling tool environment should enable SPLE practitioners to produce good quality models, in particular, models that effectively communicate modeled information. FAMILIAR is a text-based environment for manipulating and composing Feature Models (FMs). In this paper we present extensions we made to FAMILIAR to enhance its usability. The extensions include a visualization of FMs, or more precisely , a feature diagram rendering mechanism that supports the use of a combination of text and graphics to describe FMs, their configurations, and the results of FM analyses. We also present the results of a preliminary evaluation of the environment's usability. The evaluation involves comparing the use of the extended environment with the previous text-based console-driven version. The preliminary experiment provides some evidence that use of the new environment results in increased cognitive effectiveness of novice users and improved quality of new FMs
Weak and strong electronic correlations in Fe superconductors

In this chapter the strength of electronic correlations in the normal phase
of Fe-superconductors is discussed. It will be shown that the agreement between
a wealth of experiments and DFT+DMFT or similar approaches supports a scenario
in which strongly-correlated and weakly-correlated electrons coexist in the
conduction bands of these materials. I will then reverse-engineer the realistic
calculations and justify this scenario in terms of simpler behaviors easily
interpreted through model results. All pieces come together to show that Hund's
coupling, besides being responsible for the electronic correlations even in
absence of a strong Coulomb repulsion is also the origin of a subtle emergent
behavior: orbital decoupling. Indeed Hund's exchange decouples the charge
excitations in the different Iron orbitals involved in the conduction bands
thus causing an independent tuning of the degree of electronic correlation in
each one of them. The latter becomes sensitive almost only to the offset of the
orbital population from half-filling, where a Mott insulating state is
invariably realized at these interaction strengths. Depending on the difference
in orbital population a different 'Mottness' affects each orbital, and thus
reflects in the conduction bands and in the Fermi surfaces depending on the
orbital content.Comment: Book Chapte
An introduction to sampling via measure transport

We present the fundamentals of a measure transport approach to sampling. The
idea is to construct a deterministic coupling---i.e., a transport map---between
a complex "target" probability measure of interest and a simpler reference
measure. Given a transport map, one can generate arbitrarily many independent
and unweighted samples from the target simply by pushing forward reference
samples through the map. We consider two different and complementary scenarios:
first, when only evaluations of the unnormalized target density are available,
and second, when the target distribution is known only through a finite
collection of samples. We show that in both settings the desired transports can
be characterized as the solutions of variational problems. We then address
practical issues associated with the optimization--based construction of
transports: choosing finite-dimensional parameterizations of the map, enforcing
monotonicity, quantifying the error of approximate transports, and refining
approximate transports by enriching the corresponding approximation spaces.
Approximate transports can also be used to "Gaussianize" complex distributions
and thus precondition conventional asymptotically exact sampling schemes. We
place the measure transport approach in broader context, describing connections
with other optimization--based samplers, with inference and density estimation
schemes using optimal transport, and with alternative transformation--based
approaches to simulation. We also sketch current work aimed at the construction
of transport maps in high dimensions, exploiting essential features of the
target distribution (e.g., conditional independence, low-rank structure). The
approaches and algorithms presented here have direct applications to Bayesian
computation and to broader problems of stochastic simulation.Comment: To appear in Handbook of Uncertainty Quantification; R. Ghanem, D.
  Higdon, and H. Owhadi, editors; Springer, 201
Towards Machine Wald

The past century has seen a steady increase in the need of estimating and
predicting complex systems and making (possibly critical) decisions with
limited information. Although computers have made possible the numerical
evaluation of sophisticated statistical models, these models are still designed
\emph{by humans} because there is currently no known recipe or algorithm for
dividing the design of a statistical model into a sequence of arithmetic
operations. Indeed enabling computers to \emph{think} as \emph{humans} have the
ability to do when faced with uncertainty is challenging in several major ways:
(1) Finding optimal statistical models remains to be formulated as a well posed
problem when information on the system of interest is incomplete and comes in
the form of a complex combination of sample data, partial knowledge of
constitutive relations and a limited description of the distribution of input
random variables. (2) The space of admissible scenarios along with the space of
relevant information, assumptions, and/or beliefs, tend to be infinite
dimensional, whereas calculus on a computer is necessarily discrete and finite.
With this purpose, this paper explores the foundations of a rigorous framework
for the scientific computation of optimal statistical estimators/models and
reviews their connections with Decision Theory, Machine Learning, Bayesian
Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty
Quantification and Information Based Complexity.Comment: 37 page
Metamodel-based sensitivity analysis: Polynomial chaos expansions and
  Gaussian processes

Global sensitivity analysis is now established as a powerful approach for
determining the key random input parameters that drive the uncertainty of model
output predictions. Yet the classical computation of the so-called Sobol'
indices is based on Monte Carlo simulation, which is not affordable when
computationally expensive models are used, as it is the case in most
applications in engineering and applied sciences. In this respect metamodels
such as polynomial chaos expansions (PCE) and Gaussian processes (GP) have
received tremendous attention in the last few years, as they allow one to
replace the original, taxing model by a surrogate which is built from an
experimental design of limited size. Then the surrogate can be used to compute
the sensitivity indices in negligible time. In this chapter an introduction to
each technique is given, with an emphasis on their strengths and limitations in
the context of global sensitivity analysis. In particular, Sobol' (resp. total
Sobol') indices can be computed analytically from the PCE coefficients. In
contrast, confidence intervals on sensitivity indices can be derived
straightforwardly from the properties of GPs. The performance of the two
techniques is finally compared on three well-known analytical benchmarks
(Ishigami, G-Sobol and Morris functions) as well as on a realistic engineering
application (deflection of a truss structure).Comment: Handbook of Uncertainty Quantification, Ghanem, R., Higdon, D.,
  Owhadi, H. (Eds.), 201
SciRecSys: A Recommendation System for Scientific Publication by
  Discovering Keyword Relationships

In this work, we propose a new approach for discovering various relationships
among keywords over the scientific publications based on a Markov Chain model.
It is an important problem since keywords are the basic elements for
representing abstract objects such as documents, user profiles, topics and many
things else. Our model is very effective since it combines four important
factors in scientific publications: content, publicity, impact and randomness.
Particularly, a recommendation system (called SciRecSys) has been presented to
support users to efficiently find out relevant articles
Logarithmic asymptotics of the densities of SPDEs driven by spatially
  correlated noise

We consider the family of stochastic partial differential equations indexed
by a parameter $\eps\in(0,1]$, \begin{equation*} Lu^{\eps}(t,x) =
\eps\sigma(u^\eps(t,x))\dot{F}(t,x)+b(u^\eps(t,x)), \end{equation*}
$(t,x)\in(0,T]\times\Rd$ with suitable initial conditions. In this equation,
$L$ is a second-order partial differential operator with constant coefficients,
$\sigma$ and $b$ are smooth functions and $\dot{F}$ is a Gaussian noise, white
in time and with a stationary correlation in space. Let $p^\eps_{t,x}$ denote
the density of the law of $u^\eps(t,x)$ at a fixed point
$(t,x)\in(0,T]\times\Rd$. We study the existence of $\lim_{\eps\downarrow 0}
\eps^2\log p^\eps_{t,x}(y)$ for a fixed $y\in\R$. The results apply to a class
of stochastic wave equations with $d\in\{1,2,3\}$ and to a class of stochastic
heat equations with $d\ge1$.Comment: 39 pages. Will be published in the book " Stochastic Analysis and
  Applications 2014. A volume in honour of Terry Lyons". Springer Verla
Cutting edges at random in large recursive trees

We comment on old and new results related to the destruction of a random
recursive tree (RRT), in which its edges are cut one after the other in a
uniform random order. In particular, we study the number of steps needed to
isolate or disconnect certain distinguished vertices when the size of the tree
tends to infinity. New probabilistic explanations are given in terms of the
so-called cut-tree and the tree of component sizes, which both encode different
aspects of the destruction process. Finally, we establish the connection to
Bernoulli bond percolation on large RRT's and present recent results on the
cluster sizes in the supercritical regime.Comment: 29 pages, 3 figure
Towards a Better Understanding of the Local Attractor in Particle Swarm
  Optimization: Speed and Solution Quality

Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic
for solving continuous optimization problems. Although this technique is widely
used, the understanding of the mechanisms that make swarms so successful is
still limited. We present the first substantial experimental investigation of
the influence of the local attractor on the quality of exploration and
exploitation. We compare in detail classical PSO with the social-only variant
where local attractors are ignored. To measure the exploration capabilities, we
determine how frequently both variants return results in the neighborhood of
the global optimum. We measure the quality of exploitation by considering only
function values from runs that reached a search point sufficiently close to the
global optimum and then comparing in how many digits such values still deviate
from the global minimum value. It turns out that the local attractor
significantly improves the exploration, but sometimes reduces the quality of
the exploitation. As a compromise, we propose and evaluate a hybrid PSO which
switches off its local attractors at a certain point in time. The effects
mentioned can also be observed by measuring the potential of the swarm
Learning Sentiment from Students' Feedback for Real-Time Interventions in Classrooms

Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded