Search results for "Information System"
showing 10 items of 2404 documents
QSAR Modeling ANTI-HIV-1 Activities by Optimization of Correlation Weights of Local Graph Invariants
2004
Results of using descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase (RT) inhibitors are reported. Presence of different chemical elements in molecular structure of the inhibitors and the presence of Morgan extended connectivity values of zeroth-, first- and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. By Monte Carlo method optimization procedure, values of the CWs which produce as large values as possible of correlation coefficient between the numerical data on the anti-HIV-1 potencies and values of the descriptors on the training s…
A Hardware and Secure Pseudorandom Generator for Constrained Devices
2018
Hardware security for an Internet of Things or cyber physical system drives the need for ubiquitous cryptography to different sensing infrastructures in these fields. In particular, generating strong cryptographic keys on such resource-constrained device depends on a lightweight and cryptographically secure random number generator. In this research work, we have introduced a new hardware chaos-based pseudorandom number generator, which is mainly based on the deletion of an Hamilton cycle within the $N$ -cube (or on the vectorial negation), plus one single permutation. We have rigorously proven the chaotic behavior and cryptographically secure property of the whole proposal: the mid-term eff…
Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems
2020
International audience; Approximate Computing (AxC) is a well-known paradigm able to reduce the computational and power overheads of a multitude of applications, at the cost of a decreased accuracy. Convolutional Neural Networks (CNNs) have proven to be particularly suited for AxC because of their inherent resilience to errors. However, the implementation of AxC techniques may affect the intrinsic resilience of the application to errors induced by Single Events in a harsh environment. This work introduces an experimental study of the impact of neutron irradiation on approximate computing techniques applied on the data representation of a CNN.
Estado de la investigación sobre la colaboración en Entornos Virtuales de Aprendizaje
2019
El aprendizaje colaborativo posee una tradición teórica y práctica amplia en la educación general, como en la educación superior. No obstante, la actividad de coordinación entre personas que buscan aprender no es sencilla de gestionar. Esta complejidad aumenta cuando entre la actividad colaborativa surge un nuevo componente, la tecnología. ¿Qué se ha avanzado sobre el aprendizaje colaborativo mediado tecnológicamente? Este trabajo tiene como objetivo analizar el estado de la investigación sobre aprendizaje colaborativo en Educación Superior en los Entornos Virtuales de Aprendizaje (EVA). Para ello se realiza un análisis bibliométrico de las publicaciones indexadas de la base de datos SCOPUS…
Sequential Mining Classification
2017
Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this technique became useful in many applications: DNA researches, medical diagnosis and prevention, telecommunications, etc. GSP, SPAM, SPADE, PrefixSPan and other advanced algorithms followed. View the evolution of data mining techniques based on sequential data, this paper discusses the multiple …
Overview on Sequential Mining Algorithms and Their Extensions
2018
The main purpose of data mining is to extract hidden, important and nontrivial information from a database. Sequential Pattern Mining is a data mining technique that aims to obtain and analyze frequent subsequences from sequences of events or items with or without time constraint. The importance of a sequence can be measured based on different factors such as the frequency of their occurrence, their length and also their profit. The pattern mining or the discovery of important and unexpected patterns and information was first introduced in 1990 with the well-known Apriori algorithm. Then, and after many studies on frequent pattern mining, a new approach appeared: Sequential Pattern Mining. …
Hop: Histogram of patterns for human action representation
2017
This paper presents a novel method for representing actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. This paper proposes to learn a codebook of frequent sequential patterns by means of an apriori-like algorithm, and to represent an action with a Bag-of-Frequent-Sequential-Patterns approach. Preliminary experiments of the proposed method have been conducted for action classification on skeletal data. The method achieves state-of-the-art accuracy value in cross-subject validation.
Internal Structure and Dynamics of the Decamer D(ATGCAGTCAG) 2 In Li + -H 2 O Solution: A molecular Dynamics Simulation Study
2003
Molecular dynamics simulation of the decamer d(ATGCAGTCAG) 2 in aqueous solution, electroneutralized by Li + ions has been carried out. Emphasis is on the verification of the equilibrium conditions and the related structural and dynamical properties. Applicability of the kinetic part of Boltzmann's H function as a measure of thermodynamic equilibrium is tested. Overall structural stability has been confirmed by different RMSDs. Conformational and helicoidal parameters have been analyzed statistically and dynamically. Dynamical analysis reveals the existence of dynamical sub-states, which typically appear as abrupt changes from a mean level to another in the value of parameter. In statistica…
Arc routing problems: A review of the past, present, and future
2020
[EN] Arc routing problems (ARPs) are defined and introduced. Following a brief history of developments in this area of research, different types of ARPs are described that are currently relevant for study. In addition, particular features of ARPs that are important from a theoretical or practical point of view are discussed. A section on applications describes some of the changes that have occurred from early applications of ARP models to the present day and points the way to emerging topics for study. A final section provides information on libraries and instance repositories for ARPs. The review concludes with some perspectives on future research developments and opportunities for emergin…
A branch-and-cut algorithm for the Profitable Windy Rural Postman Problem
2016
[EN] In this paper we study the profitable windy rural postman problem. This is an arc routing problem with profits defined on a windy graph in which there is a profit associated with some of the edges of the graph, consisting of finding a route maximizing the difference between the total profit collected and the total cost. This problem generalizes the rural postman problem and other well-known arc routing problems and has real-life applications, mainly in snow removal operations. We propose here a formulation for the problem and study its associated polyhedron. Several families of facet-inducing inequalities are described and used in the design of a branch-and-cut procedure. The algorithm…