pinecone/core-2020-05-10-deduplication huggingface dataset:
Distributed reflectivity solar sails for extended mission applications

The dynamics of solar sails with a variable surface reflectivity distribution are investigated. When changing the reflectivity across the sail film, solar radiation pressure forces and torques can be controlled without changing the attitude of the spacecraft relative to the Sun or using attitude control actuators. The reflectivity can in principle be modified using electro-chromic coatings, which are applied here as examples to counteract gravity-gradient torques in Earth orbit and to enable specific shape profiles of a flexible sail film. This ‘optical reconfiguration’ method introduces an adaptive solar sail as a multi-functional platform for novel mission applications
Faster Chosen-Key Distinguishers on Reduced-Round AES

International audienceIn this paper, we study the AES block cipher in the chosen-key setting. The adversary's goal of this security model is to find triplets (m, m , k) satisfying some properties more efficiently for the AES scheme than generic attacks. It is a restriction of the classical chosen-key model, since as it has been defined originally, differences in the keys are possible. This model is related to the known-key setting, where the adversary receives a key k, and tries to find a pair of messages (m, m) that has some property more efficiently than generic attacks. Both models have been called open-key model in the literature and are interesting for the security of AES-based hash functions. Here, we show that in the chosen-key setting, attacking seven rounds (resp. eight rounds) of AES-128 can be done in time and memory 2 8 (resp. 2 24) while the generic attack would require 2 64 computations as a variant of the birthday paradox can be used to predict the generic complexity. We have checked our results experimentally and we extend them to distinguisers of AES-256
Efficient arithmetic on elliptic curves in characteristic 2

International audienceWe present normal forms for elliptic curves over a field of characteristic 2 analogous to Edwards normal form, and determine bases of addition laws, which provide strikingly simple expressions for the group law. We deduce efficient algorithms for point addition and scalar multiplication on these forms. The resulting algorithms apply to any elliptic curve over a field of characteristic 2 with a 4-torsion point, via an isomorphism with one of the normal forms. We deduce algorithms for duplication in time $2M + 5S + 2m_c$ and for addition of points in time $7M + 2S$, where $M$ is the cost of multiplication, $S$ the cost of squaring , and $m_c$ the cost of multiplication by a constant. By a study of the Kummer curves $\mathcal{K} = E/\{\pm1]\}$, we develop an algorithm for scalar multiplication with point recovery which computes the multiple of a point P with $4M + 4S + 2m_c + m_t$ per bit where $m_t$ is multiplication by a constant that depends on $P$
Faster Gaussian Lattice Sampling Using Lazy Floating-Point Arithmetic

International audienceMany lattice cryptographic primitives require an efficient algorithm to sample lattice points according to some Gaussian distribution. All algorithms known for this task require long-integer arithmetic at some point, which may be problematic in practice. We study how much lattice sampling can be sped up using floating-point arithmetic. First, we show that a direct floating-point implementation of these algorithms does not give any asymptotic speedup: the floating-point precision needs to be greater than the security parameter, leading to an overall complexity $\softO(n^3)$ where $n$ is the lattice dimension. However, we introduce a laziness technique that can significantly speed up these algorithms. Namely, in certain cases such as NTRUsign lattices, laziness can decrease the complexity to $\softO(n^2)$ or even $\softO(n)$. Furthermore, our analysis is practical: for typical parameters, most of the floating-point operations only require the double-precision IEEE standard
Learning a Zonotope and More: Cryptanalysis of NTRUSign Countermeasures

International audienceNTRUsign is the most practical lattice signature scheme. Its basic version was broken by Nguyen and Regev in 2006: one can efficiently recover the secret key from about 400 signatures. However, countermeasures have been proposed to repair the scheme, such as the perturbation used in NTRUsign standardization proposals, and the deformation proposed by Hu et al. at IEEE Trans. Inform. Theory in 2008. These two countermeasures were claimed to prevent the NR attack. Surprisingly, we show that these two claims are incorrect by revisiting the NR gradient-descent attack: the attack is more powerful than previously expected, and actually breaks both countermeasures in practice, e.g. 8,000 signatures suffice to break NTRUsign-251 with one perturbation as submitted to IEEE P1363 in 2003. More precisely, we explain why the Nguyen-Regev algorithm for learning a parallelepiped is heuristically able to learn more complex objects, such as zonotopes and deformed parallelepipeds
Broadcasting XORs: On the Application of Network Coding in Access Point-to-Multipoint Networks

We investigate network coding (NC) in access point-to-multi-point (PMP) broadcast networks. Characterized by a shared unicast upstream channel and a time-shared broadcast downstream channel, PMP networks are widely deployed in optical and wireless access networks. We develop a queuing-theoretic model of NC at the medium access control (MAC) sublayer and analyze the impact of NC on packet delay. Our analysis is validated through discrete-event simulation and demonstrates significant delay advantages for NC under high loads and localized traffic
WebPut : Efficient web-based data imputation

In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is also proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Experiments based on several real-world data collections demonstrate that WebPut outperforms existing approaches
A framework for distributed managing uncertain data in RFID traceability networks

The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remains many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness.Jiangang Ma, Quan Z. Sheng, Damith Ranasinghe, Jen Min Chuah and Yanbo W
Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches
Towards a user-centric social approach to web services composition, execution, and monitoring

This paper discusses the intertwine of social networks of users and social networks of Web services to compose, execute, and monitor Web services. Each network provides details that permit achieving this intertwine and thus, completing the three operations. A user social-network is used to advise users on the next Web services to select based on their peers’ experiences, whereas a Web service social network is used to advise users on the substitutes to select in case a Web service fails, for example. To make the intertwine of these social networks happen, three components are developed: composer, executor, and monitor. The social composer develops composite Web services considering relations between users and the ones between Web services. The social executor assesses the impact of these relations on these compositeWeb services execution progress. Finally, the social monitor replaces failing Web services to guarantee the execution continuity of these composite Web services. A running example and a prototype illustrate and demonstrate the intertwine of these social networks, respectively.Zakaria Maamar, Noura Faci, Quan Z. Sheng and Lina Ya
A New Genetic Algorithm for Simplified Protein Structure Prediction

In this paper, we present a new genetic algorithm for protein structure prediction problem using face-centred cubic lattice and hydrophobic-polar energy model. Our algorithm uses i) an exhaustive generation approach to diversify the search; ii) a novel hydrophobic core-directed macro move to intensify the search; and iii) a random-walk strategy to recover from stagnation. On a set of standard benchmark proteins, our algorithm significantly outperforms the state-of-the-art algorithms for the same models.Griffith Sciences, School of Information and Communication TechnologyFull Tex
A general representation and approximate inference algorithm for sensing actions

Sensing actions, which allow an agent to increase its knowl- edge about the environment, are problematic for traditional planning languages. In this paper we propose a very general framework for representing both changes to the real world and to the knowledge of an agent, based on a first order linear time calculus. Our framework is more general than most existing approaches, because our semantics explicitly represents, for each point in time, not only the agent’s knowledge about that timepoint, but also about the past and the future. By applying a general approximation method for classical logic to this framework, we obtain an efficient and sound but incomplete reasoning method.status: publishe
Simultaneous interval regression for K-nearest neighbor

International audienceIn some regression problems, it may be more reasonable to predict intervals rather than precise values. We are interested in finding intervals which simultaneously for all input instances x ∈X contain a β proportion of the response values. We name this problem simultaneous interval regression. This is similar to simultaneous tolerance intervals for regression with a high confidence level γ ≈ 1 and several authors have already treated this problem for linear regression. Such intervals could be seen as a form of confidence envelop for the prediction variable given any value of predictor variables in their domain. Tolerance intervals and simultaneous tolerance intervals have not yet been treated for the K-nearest neighbor (KNN) regression method. The goal of this paper is to consider the simultaneous interval regression problem for KNN and this is done without the homoscedasticity assumption. In this scope, we propose a new interval regression method based on KNN which takes advantage of tolerance intervals in order to choose, for each instance, the value of the hyper-parameter K which will be a good trade-off between the precision and the uncertainty due to the limited sample size of the neighborhood around each instance. In the experiment part, our proposed interval construction method is compared with a more conventional interval approximation method on six benchmark regression data sets
Bagging ensemble selection for regression

Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classiﬁcation problems have shown that using random trees as base classiﬁers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by the promising results in classiﬁcation, this paper examines the predictive performance of the BES-OOB strategy for regression problems. Our results show that the BES-OOB strategy outperforms Stochastic Gradient Boosting and Bagging when using regression trees as the base learners. Our results also suggest that the advantage of using a diverse model library becomes clear when the model library size is relatively large. We also present encouraging results indicating that the non negative least squares algorithm is a viable approach for pruning an ensemble of ensembles
Fixed Frame Temporal Pooling

Applications of unsupervised learning techniques to action recognition have proved highly competitive in comparison to supervised and hand-crafted approaches, despite not being designed to handle image processing problems. Many of these techniques are either based on biological models of cognition or have responses that correlate to those observed in biological systems. In this study we apply (for the first time) an adaptation of the latest hierarchical temporal memory (HTM) cortical learning algorithms (CLAs) to the problem of action recognition. These HTM algorithms are both unsupervised and represent one of the most complete high-level syntheses available of the current neuroscientific understanding of the functioning of neocortex. Specifically, we extend the latest HTM work on augmented spatial pooling, to produce a fixed frame temporal pooler (FFTP). This pooler is evaluated on the well-known KTH action recognition data set and in comparison with the best performing unsupervised learning algorithm for bag-of-features classification in the area: independent subspace analysis (ISA). Our results show FFTP comes within 2% of ISA's performance and outperforms other comparable techniques on this data set. We take these results to be promising, given the preliminary nature of the research and that the FFTP algorithm is only a partial implementation of the proposed HTM architecture.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Multiplication of EEG Samples through Replicating, Biasing, and Overlapping

Author version made available in accordance with publisher copyright policy. The final publication is available at link.springer.com.EEG recording is a time consuming operation during which\ud
the subject is expected to stay still for a long time performing tasks. It\ud
is reasonable to expect some \ud
uctuation in the level of focus toward the\ud
performed task during the task period. This study is focused on investi-\ud
gating various approaches for emphasizing regions of interest during the\ud
task period. Dividing the task period into three segments of beginning,\ud
middle and end, is expectable to improve the overall classi cation per-\ud
formance by changing the concentration of the training samples toward\ud
regions in which subject had better concentration toward the performed\ud
tasks. This issue is investigated through the use of techniques such as\ud
i) replication, ii) biasing, and iii) overlapping. A dataset with 4 motor\ud
imagery tasks (BCI Competition III dataset IIIa) is used. The results il-\ud
lustrate the existing variations within the potential of di erent segments\ud
of the task period and the feasibility of techniques that focus the training\ud
samples toward such regions
Heterogeneous mobility in next generation devices: An Android-based case study

http://www.mobimedia.org/2010/index.html  6-8 Sep. 2010, Lisbon, PortugalThe fast growing of mobile Internet users with the ability of using a wide diversity of access technologies such as Wi-Fi, WiMAX and UMTS/LTE, and the increasing proliferation of mobile devices with heterogeneous network interfaces, require versatile mobility mechanisms providing seamless roaming across those access technologies. Mobility agents such as Mobile IP and Fast MIPv6 are common, however, these solutions still have limitations when dealing with multiple link-layer technology. In this context, the emerging standard IEEE 802.21 provides a framework which enables mobile agents and network operators to improve the handover process in heterogeneous networks. This paper debates and presents a case study of heterogeneous mobility on an Android device, using the IEEE 802.21 framework. Resorting to an experimental testbed, including a modified Android user terminal, the obtained results show that the proposed solution is a first step to successfully accomplish seamless mobility of Android-based devices operating on 3G and Wi-Fi networks
Evaluation a of a layered approach to question answering over linked data

We present a question answering system architecture which processes natural language questions in a pipeline consisting of five steps: i) question parsing and query template generation, ii) lookup in an inverted index, iii) string similarity computation, iv) lookup in a lexical database in order to find synonyms, and v) semantic similarity computation. These steps are ordered with respect to their computational effort, following the idea of layered processing: questions are passed on along the pipeline only if they cannot be answered on the basis of earlier processing steps, thereby invoking computationally expensive operations only for complex queries that require them. In this paper we present an evaluation of the system on the dataset provided by the 2nd Open Challenge on Question Answering over Linked Data (QALD-2). The main, novel contribution is a systematic empirical investigation of the impact of the single processing components on the overall performance of question answering over linked data.
Provenance for SPARQL queries

Determining trust of data available in the Semantic Web is fundamental for
applications and users, in particular for linked open data obtained from SPARQL
endpoints. There exist several proposals in the literature to annotate SPARQL
query results with values from abstract models, adapting the seminal works on
provenance for annotated relational databases. We provide an approach capable
of providing provenance information for a large and significant fragment of
SPARQL 1.1, including for the first time the major non-monotonic constructs
under multiset semantics. The approach is based on the translation of SPARQL
into relational queries over annotated relations with values of the most
general m-semiring, and in this way also refuting a claim in the literature
that the OPTIONAL construct of SPARQL cannot be captured appropriately with the
known abstract models.Comment: 22 pages, extended version of the ISWC 2012 paper including proof
Privacy compliance verification in cryptographic protocols

To provide privacy protection, cryptographic primitives are frequently applied to communication protocols in an open environment (e.g. the Internet). We call these protocols privacy enhancing protocols (PEPs) which constitute a class of cryptographic protocols. Proof of the security properties, in terms of the privacy compliance, of PEPs is desirable before they can be deployed. However, the traditional provable security approach, though well-established for proving the security of\ud
cryptographic primitives, is not applicable to PEPs. We apply the formal language of Coloured Petri Nets (CPNs) to construct an executable specification of a representative PEP, namely the Private Information Escrow Bound to Multiple Conditions Protocol (PIEMCP). Formal semantics of the CPN specification allow us to reason about various privacy properties of PIEMCP using state space analysis techniques. This investigation provides insights into the modelling and analysis of PEPs in general, and demonstrates the benefit of applying a CPN-based formal approach to the privacy compliance verification of PEPs
Inference of Necessary Field Conditions with Abstract Interpretation

We present a new static analysis to infer necessary field conditions for object-oriented programs. A necessary field condition is a property that should hold on the fields of a given object, for otherwise there exists a calling context leading to a failure due to bad object state. Our analysis also infers the provenance of the necessary condition, so that if a necessary field condition is violated then an explanation containing the sequence of method calls leading to a failing assertion can be produced.
When the analysis is restricted to readonly fields, i.e., fields that can only be set in the initialization phase of an object, it infers object invariants. We provide empirical evidence on the usefulness of necessary field conditions by integrating the analysis into cccheck, our static analyzer for .NET. Robust inference of readonly object field invariants was the #1 request from cccheck users
The ISQoS Grid Broker for Temporal and Budget Guarantees

We introduce our Grid broker that uses SLAs in job submission with the aim of ensuring jobs are computed on time and on budget. We demonstrate our broker's ability to perform negotiation and to select preferentially higher priority jobs, in a tender market and discuss the architecture that makes this possible. We additionally show the effects of rescheduling and how careful consideration is required in order to avoid price instability. We therefore make recommendations upon how to maintain this stability, given rescheduling
Lexical Recount between Factor Analysis and Kohonen Map: Mathematical Vocabulary of Arithmetic in the Vernacular Language of the Late Middle Ages

International audienceIn this paper we present a combination of factorial projections and of SOM algorithm applied to a text mining problem. The corpus consists of 8 medieval texts which were used to teach arithmetic techniques to merchants. Classical Factorial Component Analysis (FCA) gives nice representations of the selected words in association with the texts, but the quality of the representation is poor in the center of the graphs and it is not easy to look for the successive projections to conclude. So using the nice properties of Kohonen maps, we can highlight the words which seems to play a special role in the vocabulary since they are associated with very different words from a map to another. Finally we show that combination of both representations is a powerful help to text analysis