pinecone/core-2020-05-10-deduplication huggingface dataset:
A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds

Most of the clustering methods used in the clustering of chemical structures such as Wards, Group Average, K- means and Jarvis-Patrick, are known as hard or crisp as they partition a dataset into strictly disjoint subsets; and thus are not suitable for the clustering of chemical structures exhibiting more than one activity. Although, fuzzy clustering algorithms such as fuzzy c-means provides an inherent mechanism for the clustering of overlapping structures (objects) but this potential of the fuzzy methods which comes from its fuzzy membership functions have not been utilized effectively. In this work a fuzzy hierarchical algorithm is developed which provides a mechanism not only to benefit from the fuzzy clustering process but also to get advantage of the multiple membership function of the fuzzy clustering. The algorithm divides each and every cluster, if its size is larger than a pre-determined threshold, into two sub clusters based on the membership values of each structure. A structure is assigned to one or both the clusters if its membership value is very high or very similar respectively. The performance of the algorithm is evaluated on two bench mark datasets and a large dataset of compound structures derived from MDL MDDR database. The results of the algorithm show significant improvement in comparison to a similar implementation of the hard c-means algorithm
Tsunami surge in a river: a hydraulic jump in an inhomogeneous channel

A tsunami or storm surge propogating in a river is a very energetic phenomenon and not many human constructions can resist it. We will not consider here the case of the ocean where the bathymetry plays a crucial role in fixing the wave speed but a channel or river where the depth and width vary slowly compared with the wave length. We examined the propagation of a tsunami from the deep sea where non-linearity and dispersion are small to the coastline where those parameters become significant. In particular we showed on the inhomogeneous linear wave equation the importance of bathymetry in the wave amplification
Improving Resource Discovery in the Arigatoni Overlay Network

International audienceArigatoni is a structured multi-layer overlay network providing various services with variable guarantees, and promoting an intermittent participation to the virtual organization where peers can appear, disappear and organize themselves dynamically. Arigatoni mainly concerns with how resources are declared and discovered in the overlay, allowing global computers to make a secure, PKI-based, use of global aggregated computational power, storage, information resources, etc. Arigatoni provides fully decentralized, asynchronous and scalable resource discovery, and provides mechanisms for dealing with dynamic virtual organizations. This paper introduces a non trivial improvement of the original resource discovery protocol by allowing to register and to ask for multiple instances. Simulations show that it is efficient and scalable
A fault tolerance bisimulation proof for consensus

The possibility of partial failure occuring at any stage of computation complicates rigorous formal treatment of distributed algorithms. We propose a methodology for formalising and proving the correctness of distributed algorithms which alleviates this complexity. The methodology uses fault-tolerance bisimulation proof techniques to split the analysis into two phases, that is a failure-free phase and a failure phase, permitting separation of concerns. We design a minimal partial-failure calculus, develop a corresponding bisimulation theory for it and express a consensus algorithm in the calculus. We then use the consensus example and the calculus theory to demonstrate the benefits of our methodology.peer-reviewe
A Calculus for Orchestration of Web Services

We introduce COWS (Calculus for Orchestration of Web Services), a new foundational language for SOC whose design has been influenced by WS-BPEL, the de facto standard language for orchestration of web services. COWS combines in an original way a number of ingredients borrowed from well-known process calculi, e.g. asynchronous communication, polyadic synchronization, pattern matching, protection, delimited receiving and killing activities, while resulting different from any of them. Several examples illustrates COWS peculiarities and show its expressiveness both for modelling imperative and orchestration constructs, e.g. web services, flow graphs, fault and compensation handlers, and for encoding other process and orchestration languages
A Public Health Care Information System Using GIS and GPS: A Case Study of Shiggaon

Health data maps and Geographic Information Systems (GIS) are significant resources for health planning and health services delivery, particularly at the local level. The ability to visualize the spatial distribution of health status determinants and indicators can be a powerful resource for mobilizing community action to improve the health of residents. Currently, health data maps and other GIS applications tend to be highly technical and specialized, and are therefore of limited use to community members and organizations providing community-based health services. Developing relevant, accessible, and usable GIS and health data maps for communities and local agencies is an important step towards enabling individuals and communities to improve their health and increase their control over it. The final map was prepared by overlaying all the layers generated. The spatial objects were digitized out of LISS and PAN merged data and topomap supplied by the NRSA and Survey of India respectively. Questionnaires were prepared to get the data needed from each hospital and house by field investigation. Finally, a map of Public Health Care Information System was created by interlinking all topographical features with attribute data of the town so as to keep this information for planning and development in days to come
The material side of virtualization

The Environmental Informatics community could recently celebrate its 20th anniversary. The application of Information and Communication Technology (ICT) to problems of environmental research and management has made considerable progress and contributes to sustainable development. Moreover, ICT has the potential to virtualize processes that would otherwise consume considerable amounts of material and energy; virtual meetings, for instance, could avoid 97-98% of the CO2 emissions of physical meetings.The time, space, material and energy needed to provide a unit of ICT service have roughly decreased by a factor of 1000 since the first PC was sold. It seems therefore natural that researchers and industries using ICT in the environmental field ignore the environmental impacts caused by ICT hardware &ndash; they are just negligible compared to the environmental benefits that can be realized with the applications.Paradoxically, it is the progress in ICT hardware efficiency that has made ICT a part of the problem, too. The global mass and energy flows caused throughout the hardware life cycle are increasing due to the wide-spread use of ICT products and their decreasing useful lives. The environ-mental problems caused by the production, use and disposal of ICT hard-ware are solvable in principle; they are not as hard as the discrepancy between - e.g. - growing mobility and CO2 reduction goals. But problems can only be solved if they are not neglected
PyACTS: a high-level framework for fast development of high performance applications

Software reusability has proven to be an effective practice to speed-up the development of complex high-performance scientific and engineering applications. We promote the reuse of high quality software and general purpose libraries through the Advance CompuTational Software (ACTS) Collection. ACTS tools have continued to provide solutions to many of today’s computational problems. In addition, ACTS tools have been successfully ported to a variety of computer platforms; therefore tremendously facilitating the porting of applications that rely on ACTS functionalities. In this contribution we discuss a high-level user interface that provides a faster code prototype and user familiarization with ACTS tools. The high-level user interfaces have been built using Python. Here we focus on Python based interfaces to ScaLAPACK, the PyScaLAPACK component of PyACTS. We briefly introduce their use, functionalities, and benefits. We illustrate a few simple example of their use, as well as exemplar utilization inside large scientific applications. We also comment on existing Python interfaces to other ACTS tools. We present some comparative performance results of PyACTS based versus direct LAPACK and ScaLAPACK code implementations.This research was partially supported by the Spanish Ministry of Science and
Education under grant number TIN2005-09037-C02-02, and by Universidad de
Alicante under grant number VIGROB-020
Coupling from the past in hybrid models for file sharing peer to peer systems

International audienceIn this paper we show how file sharing peer to peer systems can be modeled by hybrid systems with a continuous part corresponding to a fluid limit of files and a discrete part corresponding to customers. Then we show that this hybrid system is amenable to perfect simulations (i.e. simulations providing samples of the system states which distributions have no bias from the asymptotic distribution of the system). An experimental study is carried to show the respective influence that the different parameters (such as time-to-live, rate of requests, connection time) play on the behavior of large peer to peer systems, and also to show the effectiveness of this approach for numerical solutions of stochastic hybrid systems
SVMs for Automatic Speech Recognition: a Survey

Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact.
During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed.
These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research
Design Your Own Research Satellite

In this activity, students gain an understanding of the decision making process that scientists use when designing a satellite. They will discover that satellite designs are constrained by such issues as weight, cost, materials, and feasibility. As they formulate their own satellite design, they will discover the importance of evenly distributing the weight of various instruments and that certain instrument packages must be grouped together. Educational levels: Intermediate elementary, Middle school, Primary elementary
A constrained clustering approach to duplicate detection among relational data

This paper proposes an approach to detect duplicates among relational data. Traditional methods for record linkage or duplicate detection work on a set of records which have no explicit relations with each other. These records can be formatted into a single database table for processing. However, there are situations that records from different sources can not be flattened into one table and records within one source have certain (semantic) relations between them. The duplicate detection issue of these relational data records/instances can be dealt with by formatting them into several tables and applying traditional methods to each table. However, as the relations among the original data records are ignored, this approach generates poor or inconsistent results. This paper analyzes the characteristics of relational data and proposes a particular clustering approach to perform duplicate detection. This approach incorporates constraint rules derived from the characteristics of relational data and therefore yields better and more consistent results, which are revealed by our experiments
A fast algorithm for finding correlation clusters in noise data

[Abstract]: Noise significantly affects cluster quality. Conventional clustering methods hardly detect clusters in a data set containing a large amount of noise. Projected clustering sheds light on identifying correlation clusters in such a data set.
In order to exclude noise points which are usually scattered in a subspace, data points are projected to form dense areas in the subspace that are regarded as correlation
clusters. However, we found that the existing methods for the projected clustering did not work very well with noise data, since they employ randomly
generated seeds (micro clusters) to trade-off the clustering quality. In this paper, we propose a divisive method for the projected clustering that does not rely on
random seeds. The proposed algorithm is capable of producing higher quality correlation clusters from noise data in a more efficient way than an agglomeration
projected algorithm. We experimentally show that our algorithm captures correlation clusters in noise data better than a well-known projected clustering method
Genetic algorithms with elitism-based immigrants for changing optimization problems

Copyright @ Springer-Verlag Berlin Heidelberg 2007.Addressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into genetic algorithms to enhance their performance in dynamic environments. One major approach is to maintain the diversity of the population, e.g., via random immigrants. This paper proposes an elitism-based immigrants scheme for genetic algorithms in dynamic environments. In the scheme, the elite from previous generation is used as the base to create immigrants via mutation to replace the worst individuals in the current population. This way, the introduced immigrants are more adapted to the changing environment. This paper also proposes a hybrid scheme that combines the elitism-based immigrants scheme with traditional random immigrants scheme to deal with significant changes. The experimental results show that the proposed elitism-based and hybrid immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments
Triggered memory-based swarm optimization in dynamic environments

This is a post-print version of this article - Copyright @ 2007 Springer-VerlagIn recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a triggered memory generator. Experimental study over a benchmark dynamic problem shows that the triggered memory-based particle swarm optimization algorithm has stronger robustness and adaptability than traditional particle swarm optimization algorithms, both with and without traditional memory scheme, for dynamic optimization problems
Air-blown rivulet flow of a perfectly wetting fluid on an inclined substrate

Thin-film flows occur in a variety of physical contexts including, for example, industry, biology and nature, and have been the subject of considerable theoretical research. (See, for example, the review by Oron, Davis and Bankoff [4].) In particular, there are several practically important situations in which an external airflow has a significant effect on the behaviour of a film of fluid, and consequently there has been considerable theoretical and numerical work done to try to understand better the various flows that can occur. (See, for example, the studies by King and Tuck [2] and Villegas-Díaz, Power and Riley [6].) The flow of a rivulet on a planar substrate subject to a shear stress at its free surface has been investigated by several authors, notably Myers, Liang and Wetton [3], Saber and El-Genk [5], and Wilson and Duffy [9]. All of these works concern a non-perfectly wetting fluid; the flow of a rivulet of a perfectly wetting fluid in the absence of a shear stress at its free surface has been treated by Alekseenko, Geshev and Kuibin [1], and by Wilson and Duffy [7,8]. In the present short paper we use the lubrication approximation to obtain a complete description of the steady unidirectional flow of a thin rivulet of a perfectly wetting fluid on an inclined substrate subject to a prescribed uniform longitudinal shear stress at its free surface
A Mathematical Morphology Approach to Cell Shape Analysis

This contribution aims to apply mathematical morphology operators to quantify the shape of round-objects which present irregularities from an ideal circular pattern. More specifically we illustrate, on the one hand, the application of morphological granulometries for size/shape multi-scale description and on the other hand, the radial/angular decompo- sitions using skeletons in polar-logarithmic representation. We discuss also the aspects related to the properties of invariance of these tools, which is important to describe cell shapes acquired under different magnifications, orientations, etc
Algebraic semantics of service component modules.

This is the author's final draft of the paper published as Lecture Notes in Computer Science, 2007, volume 4409, pp. 37-55.  The original publication is available at www.springerlink.com.  Doi: 10.1007/978-3-540-71998-4We present a notion of module acquired from developing an algebraic framework for service-oriented modelling. More specifically, we give an account of the notion of module that supports the composition model of the SENSORIA Reference Modelling Language (SRML). The proposed notion is independent of the logic in which properties are expressed and components are programmed. Modules in SRML are inspired in concepts proposed for Service Component Architecture (SCA) and Web Services, as well the modules that have been proposed for Algebraic Specifications, namely by H. Ehrig and F. Orejas, among others; they include interfaces for required (imported) and provided (exported) services, as well as a number of components (body) whose orchestrations ensure how given behavioural properties of the provided services are guaranteed assuming that the requested services satisfy required properties
Development of processing chains for rapid mapping with satellite data

In case of a disaster on a large scale the International Charter of Space and Major Disasters provides satellite data from different sensors. Since the year 2000 more than 140 Charter Calls have proven the great value of this\ud
fast disaster response instrument but have also shown different handling problems causing time delay and downgrading the value of rapid mapping products. This paper proposes a framework for a rapid mapping processing\ud
chain based on the experiences gathered during the 2005 flood crisis in Switzerland. It focuses on the pre-processing of satellite data, the valueadding step and the visualization of the results. Critical elements are\ud
hereby the availability of essential datasets, the processing time, the information extraction and the usability of the products for the end-users. International\ud
programs containing rapid mapping elements as PREVIEW or\ud
GMES RESPOND and RISK-EOS need further research on the abovementioned topics to improve the usability and significance of their products and to improve the benefit of satellite data for disaster management support
Optimistic Non-repudiation Protocol Analysis

The original publication is available at www.springerlink.com ; ISBN 978-3-540-72353-0 (Pring) 0302-9743 (Online) 1611-3349International audienceNon-repudiation protocols with session labels have a number of vulnerabilities. Recently Cederquist, Corin and Dashti have proposed an optimistic non-repudiation protocol that avoids altogether the use of session labels. We have specified and analysed this protocol using an extended version of the AVISPA Tool and one important fault has been discovered. We describe the protocol, the analysis method, show two attack traces that exploit the fault and propose a correction to the protocol
Resolution sensitivity of a compound terrain derivative as computed from LiDAR-based elevation data

New technologies such as Light Detection And Ranging (LiDAR) provide high resolution digital elevation data. These data offer new possibilities in the field of terrain modelling and analysis. However, not very much is known about the effects when these data are used to compute broadly applied terrain derivatives. In this paper the sensitivity of the Topographic Wetness Index (TWI) and its two constituting components gradient and Specific Catchment Area (SCA) regarding the resolution of LiDAR-based elevation data is examined. For coarser resolutions a shift in the TWI distribution to higher values is noted. TWI distributions at different resolutions differ significantly from each other. These findings have an impact on aspatial and spatial modelling based on the TWI
Enhanced genetic algorithm-based fuzzy multiobjective\ud
strategy to multiproduct batch plant design

The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work proposes an alternative treatment of the imprecision (demands) by using fuzzy concepts. In this study, we introduce a new approach to the design problem based on a multiobjective genetic algorithm, taking into account simultaneously maximization of the net present value NPV ~ and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage. Besides, a hybrid selection method Pareto rank-tournament was proposed and showed a better performance than the classical Goldberg’s wheel, systematically leading to a higher number of non-dominated solutions