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The commercial potential for voice technology innovations currently being developed by the U.S. Navy is immense.

The purpose of the research is to explore the role of creativity (in business process improvement paradigms) among Japanese organizations and US organizations. Although US and Japanese organizations may use differently terminology, the general idea of process improvement (to add value to products and/or services that exceed customer expectations) is embraced by both. What is not clear is the way in which organizations in the two countries implement change. Eight organizations participated in the study: four organizations from Japan and four from the US. Results of the study revealed that US organizations tend to desire faster change to improve performance. They want to adopt state‐of‐the‐art methodologies and invest heavily in technology to transform their organizations quickly. In contrast, Japanese organizations prefer incremental change as it conforms to their culture and way of life. However, US organizations are beginning to realize that process improvement is greatly enhanced by changing the culture of the work place. Moreover, Japanese organizations are realizing that individual creativity is very important for future competitiveness in the world marketplace.

This paper provides a longitudinal view of one organization’s experiences with IT implementation and Business Process Reengineering since 1990. The organization is EuroProducts; a manufacturer of air freshener and related products located in thecountry, in the West of England, EuroProducts has identified data integration and data standardization as critical to leverage increased performance from its materials requirements process flow. As a result, a new MRP system is being introduced to integrate data input from factory floor workers, management, staff, and IS professionals. The goal is to use the new MRP system as a rallying point to facilitate redesign of material requirements work flows. Aspects of innovation and systems theory are introduced to help the authors organize and identify root causes of the problems EuroProducts has had with its IT implementation and reengineering efforts.

International audience; We study deterministic broadcasting in radio networks in the recently introduced framework of network algorithms with {\em advice}. We concentrate on the problem of trade-offs between the number of bits of information (size of advice) available to nodes and the time in which broadcasting can be accomplished. In particular, we ask what is the minimum number of bits of information that must be available to nodes of the network, in order to broadcast very fast. For networks in which constant time broadcast is possible under complete knowledge of the network we give a tight answer to the above question: $O(n)$ bits of advice are sufficient but $o(n)$ bits are not, in order to achieve constant broadcasting time in all these networks. This is in sharp contrast with geometric radio networks of constant broadcasting time: we show that in these networks a constant number of bits suffices to broadcast in constant time. For arbitrary radio networks we present a broadcasting algorithm whose time is inverse-proportional to the size of advice.

International audience; In the effort to understand the algorithmic limitations of computing by a swarm of robots, the research has focused on the minimal capabilities that allow a problem to be solved. The weakest of the commonly used models is {\sc Asynch} where the autonomous mobile robots, endowed with visibility sensors (but otherwise unable to communicate), operate in Look-Compute-Move cycles performed asynchronously for each robot. The robots are often assumed (or required to be) oblivious: they keep no memory of observations and computations made in previous cycles. We consider the setting when the robots are dispersed in an anonymous and unlabeled graph, and they must perform the very basic task of {\em exploration}: within finite time every node must be visited by at least one robot and the robots must enter a quiescent state. The complexity measure of a solution is the number of robots used to perform the task. We study the case when the graph is an arbitrary tree and establish some unexpected results. We first prove that, in general, exploration cannot be done efficiently. More precisely we prove that there are $n$-node trees where $\Omega(n)$ robots are necessary; this holds even if the maximum degree is $4$. On the other hand, we show that if the maximum degree is $3$, it is possible to explore with only $O(\frac{\log n} {\log\log n})$ robots. The proof of the result is constructive. We also prove that the size of the team used in our solution is asymptotically {\em optimal}: there are trees of degree $3$, whose exploration requires $\Omega(\frac{\log n}{\log\log n})$ robots. Our final result shows that the difficulty in tree exploration comes in fact from the symmetries of the tree. Indeed, we show that, in order to explore trees that do not have any non-trivial automorphisms, 4 robots are always sufficient and often necessary.

International audience; A group of mobile robots (beachcombers) have to search collectively every point of a given domain. At any given moment, each robot can be in {\em walking mode} or in {\em searching mode}. It is assumed that each robot's maximum allowed searching speed is strictly smaller than its maximum allowed walking speed. A point of the domain is searched if at least one of the robots visits it in searching mode. The Beachcombers' Problem consists in developing efficient {\em schedules} (algorithms) for the robots which collectively search all the points of the given domain as fast as possible. We consider searching schedules in the following one-dimensional geometric domains: the cycle of a known circumference $L$, the finite straight line segment of a known length $L$, and the semi-infinite line $[0,+\infty)$.We first consider the {\em online} Beachcombers' Problem (i.e.~the scenario when the robots do not know in advance the length of the segment to be searched), where the robots are initially collocated at the origin of a semi-infinite line. It is sought to design a schedule $A$ with maximum {\em speed} $S$, defined as $S = \inf_{\ell}{\frac{\ell}{t_A(\ell)}}$, where $t_A(\ell)$ denotes the time when the search of the segment $[0,\ell]$ is completed under $A$. We consider a {\em discrete} and a {\em continuous} version of the problem, depending on whether the infimum is takenover $\ell \in \mathbb{N}^*$ or $\ell \geq 1$. We prove that the $\mathtt{LeapFrog}$ algorithm, which was proposed in [Czyzowicz et al., SIROCCO 2014, LNCS 8576, pp. 23--36 (2014)], is in fact optimal in the discrete case. This settles in the affirmative a conjecture from that paper. We also show how to extend this result to the more general continuous online setting.For the {\em offline} version of the Beachcombers' Problem (i.e.~the scenario when the robots know in advance the length of the segment to be searched), we consider the \emph{$t$-source} Beachcombers' Problem (i.e.~all robots start from a fixed number $t \geq 1$ of starting positions) on the cycle and on the finite segment. For the \emph{$t$-source} Beachcombers' Problem on the cycle, we show that the structure of the optimal solutions is identical to the structure of the optimal solutions to the $2t$-source Beachcombers' Problem on a finite segment. In consequence, by using results from [Czyzowicz et al., ALGOSENSORS 2014, LNCS 8847, pp.~3--21 (2014)], we prove that the \emph{1-source} Beachcombers' Problem on the cycle is NP-hard, and we derive approximation algorithms for the problem. For the \emph{$t$-source} variant of the Beachcombers' Problem on the cycle and on the finite segment, we also derive efficient approximation algorithms.One important contribution of our work is that, in all variants of the offline Beachcombers' Problem that we discuss, we allow the robots to \emph{change direction of movement} and search points of the domain on both sides of their respective starting positions. This represents a significant generalization compared to the model considered in~[Czyzowicz et al., ALGOSENSORS 2014, LNCS 8847, pp. 3--21 (2014)], in which each robot had a fixed direction of movement that was specified as part of the solution to the problem. We manage to prove that changes of direction do not help the robots achieve optimality.