pinecone/core-2020-05-10-deduplication huggingface dataset:
Hybrid RANS/LES of plane impinging jets with k-omega based models

Plane impinging jets with nozzle-plate distances H/B=10 at Re=13500 and H/B=9.2 at Re=20000 are simulated with k-omega based hybrid RANS/LES models. Three ways of substitution of the turbulent length scale by the local grid size in the LES mode of the hybrid RANS/LES models are tested. The results show that the hybrid models give much better prediction of wall shear stress and heat transfer rate along the impingement plate than the RANS model. The good performance of the hybrid models is due to their ability to resolve the evolution and break-up of the vortices in the shear layer of the jet, which strongly affects the turbulent flow and convective heat transfer in the stagnation region and the developing wall-jet region
Exact Ramsey Theory: Green-Tao numbers and SAT

We consider the links between Ramsey theory in the integers, based on van der
Waerden's theorem, and (boolean, CNF) SAT solving. We aim at using the problems
from exact Ramsey theory, concerned with computing Ramsey-type numbers, as a
rich source of test problems, where especially methods for solving hard
problems can be developed. In order to control the growth of the problem
instances, we introduce "transversal extensions" as a natural way of
constructing mixed parameter tuples (k_1, ..., k_m) for van-der-Waerden-like
numbers N(k_1, ..., k_m), such that the growth of these numbers is guaranteed
to be linear. Based on Green-Tao's theorem we introduce the "Green-Tao numbers"
grt(k_1, ..., k_m), which in a sense combine the strict structure of van der
Waerden problems with the (pseudo-)randomness of the distribution of prime
numbers. Using standard SAT solvers (look-ahead, conflict-driven, and local
search) we determine the basic values. It turns out that already for this
single form of Ramsey-type problems, when considering the best-performing
solvers a wide variety of solver types is covered. For m > 2 the problems are
non-boolean, and we introduce the "generic translation scheme", which offers an
infinite variety of translations ("encodings") and covers the known methods. In
most cases the special instance called "nested translation" proved to be far
superior.Comment: 25 pages; a shortened version appears in LNCS (Springer), "Theory and
  Applications of Satisfiability Testing - SAT 2010", editors O. Strichman and
  S. Szeider. Revision contains new van-der-Waerden and Green-Tao numbers,
  especially "transversal numbers", corresponding to independence numbers of
  hypergraphs of arithmetic progressions. Some new comments discussing
  behaviour of vdW- and GT-numbers
A metamodel for software requirement patterns

[Context and motivation] Software Requirement Patterns (SRP) are a type of artifact that may be used during requirements elicitation that also impact positively in other activities like documentation and validation. In our experiences, SRP show a great percentage of reuse for the non-functional requirements needed in call-for-tender requirement specifications. [Question/problem] We are facing the need of formulating the accurate definition of SRP for their use in call-for-tender processes to allow reasoning rigorously and know more about their semantics and applicability. [Principal ideas/results] In this paper we present a metamodel for SRP around three main concepts: 1) the structure of SRP themselves; 2) the relationships among them; 3) the classification criteria for grouping them. [Contribution] We provide a rigorous\ud
definition that shows the concepts that are of interest when defining and applying SRP.Peer ReviewedPostprint (published version
Requirements value chains: stakeholder management and requirements engineering in software ecosystems

[Context & motivation] Market-oriented development involves the collaboration of many stakeholders that do not necessarily directly interact with a given development project but still influence its results. These stakeholders are part of the requirements value chain for the concerned software product. [Question/problem] Understanding the structure and functioning of requirements value chains is essential for effective stakeholder management and requirements engineering within the software product’s ecosystem. [Principal ideas/results] The paper explores and exemplifies fundamental concepts that are needed to characterize and reason about requirements value chains. [Contribution] This characterization is used to describe the relevant knowledge landscape and to suggest research avenues for understanding the principles needed for managing requirements-based stakeholder collaboration
HProxy: Client-side detection of SSL stripping attacks

In today's world wide web hundreds of thousands of companies use SSL to protect their customers' transactions from potential eavesdroppers. Recently,a new attack against the common usage of SSL surfaced, SSL stripping. The 
attack is based on the fact that users almost never request secure pages explicitly but rather rely on the servers, to redirect them to the appropriate secure version of a particular website. An attacker, after becoming man-in-the-middle can suppress such messages and provide the user with "stripped" versions of the requested website forcing him to communicate over an insecure channel. In this paper, we analyze the ways that SSL stripping can be used by attackers and present a countermeasure against
such attacks. We leverage the browser's history to create a security profile for each visited website. 
Each profile contains information about the exact use of SSL at each website and all future connections to that 
site are validated against it.  We show that SSL stripping attacks can be prevented with acceptable overhead and without support from web servers or trusted third parties.status: publishe
Taming computational complexity: efficient and parallel SimRank optimizations on undirected graphs

SimRank has been considered as one of the promising link-based ranking algorithms to evaluate similarities of web documents in many modern search engines. In this paper, we investigate the optimization problem of SimRank similarity computation on undirected web graphs. We ﬁrst present a novel algorithm to estimate the SimRank between vertices in O(n3+ Kn2) time, where n is the number of vertices, and K is the number of iterations. In comparison, the most efﬁcient implementation of SimRank algorithm in [1] takes O(K n3 ) time in the worst case. To efﬁciently handle large-scale computations, we also propose a parallel implementation of the SimRank algorithm on multiple processors. The experimental evaluations on both synthetic and real-life data sets demonstrate the better computational time and parallel efﬁciency of our proposed techniques
Associative classifier for uncertain data

Associative classifiers are relatively easy for people to understand and often outperform decision tree learners on many classification problems. Existing associative classifiers only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. And uncertainty may render many conventional classifiers inapplicable to uncertain classification tasks. In this paper, based on U-Apriori algorothm and CBA algorithm, we propose an associative classifier for uncertain data, uCBA (uncertain Classification Based on Associative), which can classify both certain and uncertain data. The algorithm redefines the support, confidence, rule pruning and classification strategy of CBA. Experimental results on 21 datasets from UCI Repository demonstrate that the proposed algorithm yields good performance and has satisfactory performance even on highly uncertain data
On the suitability of combining feature selection and resampling to manage data complexity

The effectiveness of a learning task depends on data com- plexity (class overlap, class imbalance, irrelevant features, etc.). When more than one complexity factor appears, two or more preprocessing techniques should be applied. Nevertheless, no much effort has been de- voted to investigate the importance of the order in which they can be used. This paper focuses on the joint use of feature reduction and bal- ancing techniques, and studies which could be the application order that leads to the best classification results. This analysis was made on a spe- cific problem whose aim was to identify the melodic track given a MIDI file. Several experiments were performed from different imbalanced 38- dimensional training sets with many more accompaniment tracks than melodic tracks, and where features were aggregated without any correla- tion study. Results showed that the most effective combination was the ordered use of resampling and feature reduction techniques
Improving local search for the fuzzy job shop using a lower bound

Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2009 (13th. 2009. Sevilla, España)We consider the fuzzy job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan. A recent local search method from the literature has proved to be very competitive when used in combination with a genetic algorithm, but at the expense of a high computational cost. Our aim is to improve its efficiency with an alternative rescheduling algorithm and a makespan lower bound to prune non-improving neighbours. The experimental results illustrate the success of our proposals in reducing both CPU time and number of evaluated neighbour
Detecting Change via Competence Model

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine âwhenâ  and âhowâ  the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change
Use of ePassport for Identity Management in Network-Based Citizen-Life Processes

Digital identity management (IdM) for citizen life processes requires trusted relationship among the service providers and users. Current IdM systems tend to lack the trust component in particular for online transactions. We propose the use of ePassport as a globally interoperable trust token to bridge the gap between offline and online environments. The paper analyses trust attributes of the ePassport and recognizes the extensions required to its deployment in an online IdM for high-value transactions. An architecture is proposed for a network-centric IdM system to support three categories of life processes: eGovernment services, high value private services, and eCommerce. The solution is compatible with privacy enhancing technologies while at the same time creating trusted digital identities and offering users convenience.JRC.DG.G.6-Security technology assessmen
A Logical Product Approach to Zonotope Intersection

We define and study a new abstract domain which is a fine-grained combination
of zonotopes with polyhedric domains such as the interval, octagon, linear
templates or polyhedron domain. While abstract transfer functions are still
rather inexpensive and accurate even for interpreting non-linear computations,
we are able to also interpret tests (i.e. intersections) efficiently. This
fixes a known drawback of zonotopic methods, as used for reachability analysis
for hybrid sys- tems as well as for invariant generation in abstract
interpretation: intersection of zonotopes are not always zonotopes, and there
is not even a best zonotopic over-approximation of the intersection. We
describe some examples and an im- plementation of our method in the APRON
library, and discuss some further in- teresting combinations of zonotopes with
non-linear or non-convex domains such as quadratic templates and maxplus
polyhedra
Lean manufacturing in public services: prospects for value creation

The purpose of this paper is to investigate the utilization\ud
of lean manufacturing systems in public service operations for poten-\ud
tial added value. A case study of lean manufacturing implementation\ud
at a UK city council was carried out using in-depth interviews with\ud
key personnel coupled with documents collection. The Organizational\ud
Commitment Questionnaire (OCQ) was administered among front-line\ud
employees. Results show that lean manufacturing systems could create\ud
signi cant added value to the business and employees. A strong relation-\ud
ship was demonstrated between the lean manufacturing implementation\ud
and the a ective commitment level of employees. This paper is one of a\ud
few studies that demonstrate the applicability of manufacturing systems\ud
in other settings and that they can generate significant added value for\ud
the service department and its employees
Towards eco-efficient lean production systems

Lean Production has proved itself a worthwhile production strategy in many
distinct industries across all regions of the planet by achieving higher levels of
production efficiency. Several authors identified that Lean inadvertently has
had significant environmental gains. Such achievements are considered of
special relevance in a global and highly competitive economy which is
progressively both tied-up and driven by an environmental agenda. The main
goal of the present study is to enlighten the contribution of Lean for achieving
a better environmental performance of production systems and identify this as
an emergent business model for supporting eco-efficiency
Modeling Tutoring Knowledge

This is a preliminary version of the chapter, the final one can be accessed at http://www.springerlink.com/content/978-3-642-14362-5#section=784256&page=1&locus=29This chapter introduces the topic "modeling tutoring knowledge" in ITS research. Starting with its origin and with a characterization of tutoring, it proposes a general definition of tutoring, and a description of tutoring functions, variables, and interactions. The Interaction Hypothesis is presented and discussed, followed by the development of the tutorial component of ITSs, and their evaluation. New challenges are described, such as integrating the emotional states of the learner. Perspectives of opening the Tutoring Model and of equipping it with social intelligence are also presented
Anatomical landmark based registration of contrast enhanced T1-weighted MR images

In many problems involving multiple image analysis, an im- age registration step is required. One such problem appears in brain tumor imaging, where baseline and follow-up image volumes from a tu- mor patient are often to-be compared. Nature of the registration for a change detection problem in brain tumor growth analysis is usually rigid or affine. Contrast enhanced T1-weighted MR images are widely used in clinical practice for monitoring brain tumors. Over this modality, con- tours of the active tumor cells and whole tumor borders and margins are visually enhanced. In this study, a new technique to register serial contrast enhanced T1 weighted MR images is presented. The proposed fully-automated method is based on five anatomical landmarks: eye balls, nose, confluence of sagittal sinus, and apex of superior sagittal sinus. Af- ter extraction of anatomical landmarks from fixed and moving volumes, an affine transformation is estimated by minimizing the sum of squared distances between the landmark coordinates. Final result is refined with a surface registration, which is based on head masks confined to the sur- face of the scalp, as well as to a plane constructed from three of the extracted features. The overall registration is not intensity based, and it depends only on the invariant structures. Validation studies using both synthetically transformed MRI data, and real MRI scans, which included several markers over the head of the patient were performed. In addition, comparison studies against manual landmarks marked by a radiologist, as well as against the results obtained from a typical mutual information based method were carried out to demonstrate the effectiveness of the proposed method
Current capabilities, requirements and a proposed strategy for interdependency analysis in the UK

The UK government recently commissioned a research study to identify the state-of-the-art in Critical Infrastructure modelling and analysis, and the government/industry requirements for such tools and services. This study (Cetifs) concluded with a strategy aiming to bridge the gaps between the capabilities and requirements, which would establish interdependency analysis as a commercially viable service in the near future. This paper presents the findings of this study that was carried out by CSR, City University London, Adelard LLP, a safety/security consultancy and Cranfield University, defense academy of the UK
Aircraft engine health monitoring using Self-Organizing Maps

International audienceAircraft engines are designed to be used during several tens of years. Ensuring a proper operation of engines over their lifetime is therefore an important and difficult task. The maintenance can be improved if efficients procedures for the understanding of data flows produced by sensors for monitoring purposes are implemented. This paper details such a procedure aiming at visualizing in a meaningful way successive data measured on aircraft engines. The core of the procedure is based on Self-Organizing Maps (SOM) which are used to visualize the evolution of the data measured on the engines. Rough measurements can not be directly used as inputs, because they are influenced by external conditions. A preprocessing procedure is set up to extract meaningful information and remove uninteresting variations due to change of environmental conditions. The proposed procedure contains three main modules to tackle these difficulties: environmental conditions normalization (ECN), change detection and adaptive signal modeling (CD) and finally visualization with Self-Organizing Maps (SOM). The architecture of the procedure and of modules are described in details in this paper and results on real data are also supplied
Spam email filtering using network-level properties

Spam is serious problem that affects email users (e.g. phishing attacks, viruses and time spent reading unwanted messages). We propose a novel spam email filtering approach based on network-level attributes (e.g. the IP sender geographic coordinates) that are more persistent in time when compared to message content. This approach was tested using two classifiers, Naive Bayes (NB) and Support Vector Machines (SVM), and compared against bag-of-words models and eight blacklists. Several experiments were held with recent collected legitimate (ham) and non legitimate (spam) messages, in order to simulate distinct user profiles from two countries (USA and Portugal). Overall, the network-level based SVM model achieved the best discriminatory performance. Moreover, preliminary results suggests that such method is more robust to phishing attacks.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA/64541/200
Data mining with neural networks and support vector machines using the R/rminer tool

We present rminer, our open source library for the R tool that facilitates the use of data mining (DM) algorithms, such as neural Networks (NNs) and support vector machines (SVMs), in classification and regression tasks. Tutorial examples with real-world problems (i.e. satellite image analysis and prediction of car prices) were used to demonstrate the rminer capabilities and NN/SVM advantages. Additional experiments were also held to test the rminer predictive capabilities, revealing competitive performances.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA/64541/200
Re-mining positive and negative association mining results

Positive and negative association mining are well-known and extensively studied data mining techniques to analyze market basket data. Efficient algorithms exist to find both types of association, separately or simultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the pricing and time information has not been incorporated into market basket analysis so far, and additional attributes have been handled using quantitative association mining. In this paper, a new approach is proposed to incorporate price, time and domain related attributes into data mining by re-mining the association mining results. The underlying factors behind positive and negative relationships, as indicated by the association rules, are characterized and described through the second data mining stage re-mining. The applicability of the methodology is demonstrated by analyzing data coming from apparel retailing industry, where\ud
price markdown is an essential tool for promoting sales and generating increased revenue
Building Specialized Multilingual Lexical Graphs Using Community Resources

International audienceWe are describing methods for compiling domain-dedicated multilingual terminological data from various resources. We focus on collecting data from online community users as a main source, therefore, our approach depends on acquiring contributions from volunteers (explicit approach), and it depends on analyzing users' behaviors to extract interesting patterns and facts (implicit approach). As a generic repository that can handle the collected multilingual terminological data, we are describing the concept of dedicated Multilingual Preterminological Graphs MPGs, and some automatic approaches for constructing them by analyzing the behavior of online community users. A Multilingual Preterminological Graph is a special lexical resource that contains massive amount of terms related to a special domain. We call it preterminological, because it is a raw material that can be used to build a standardized terminological repository. Building such a graph is difficult using traditional approaches, as it needs huge efforts by domain specialists and terminologists. In our approach, we build such a graph by analyzing the access log files of the website of the community, and by finding the important terms that have been used to search in that website, and their association with each other. We aim at making this graph as a seed repository so multilingual volunteers can contribute. We are experimenting this approach with the Digital Silk Road Project. We have used its access log files since its beginning in 2003, and obtained an initial graph of around 116000 terms. As an application, we used this graph to obtain a preterminological multilingual database that is serving a CLIR system for the DSR project