Editors : Eric Badouel, Paulin Melatagia, and Maurice Tchuenté
The continuous development of ICT facilitates the emergence and rapid proliferation of a wide variety of low-cost processors for the execution of programs in complex embedded applications. In this paper, the study explores the possibility to benefit from this wealth of computing capacity at a reasonable cost to solve concrete problems encountered in implementation of sustainable development processes, particularly in water and energy supply . . . We are focusing autonomous water supply in buildings of several floors using several tanks supplied by several sources of water and pumping energy, based on a multilevel hierarchical priority access to water. The first problem is to propose pumping devices and a switching process between power sources, associated to an architectural structure guaranteeing significant reduction of pumping energy. The second problem is the system controller realization. For this, we have proposed a generic architecture justified by gains in potential energy. We also propose an automatic generation tool of control programs for different microprocessor targets taken from the functional design specification of the system given in a Grafcet form. To put them in evidence, we describe at the end a case study.
We propose two-sources randomness extractors over finite fields and on elliptic curves that can extract from two sources of information without consideration of other assumptions that the starting algorithmic assumptions with a competitive level of security. These functions have several applications. We propose here a description of a version of a Diffie-Hellman key exchange protocol and key extraction.
ABSTRACT. One of social graph's properties is the community structure, that is, subsets where nodes belonging to the same subset have a higher link density between themselves and a low link density with nodes belonging to external subsets. Futhermore, most social network mining algorithms comprise a local exploration of the underlying graph, which consists in referencing nodes in the neighborhood of a particular node. The idea of this paper is to use the community structure during the storage of large graphs that arise in social network mining. The goal is to reduce cache misses and consequently, execution time. After formalizing the problem of social network ordering as a problem of optimal linear arrangement which is known as NP-Complet, we propose NumBaCo, a heuristic based on the community structure. We present for Katz score and Pagerank, simulations that compare classic data structures Bloc and Yale to their corresponding versions that use NumBaCo. Results on a 32 cores NUMA machine using amazon, dblp and web-google datasets show that NumBaCo allows to reduce from 62% to 80% of cache misses and from 15% to 50% of execution time.
We propose a model of growing networks based on cliques formations. A clique is used to illustrate for example co-authorship in co-publication networks, co-occurence of words or collaboration between actors of the same movie. Our model is iterative and at each step, a clique of λη existing vertices and (1 − λ)η new vertices is created and added in the network; η is the mean number of vertices per clique and λ is the proportion of old vertices per clique. The old vertices are selected according to preferential attachment. We show that the degree distribution of the generated networks follows the Power Law of parameter 1 + 1/ λ and thus they are ultra small-world networks with high clustering coefficient and low density. Moreover, the networks generated by the proposed model match with some real co-publication networks such as CARI, EGC and HepTh.