Exploring the Link between Dominant Logic and Company Performance

To revitalize the discussion on dominant logic our paper aims to establish the forgotten link between dominant logic and firm performance. To do so, the concept is enhanced conceptually and operationalized by developing a framework including firms’ conceptualization of the business (external environment) and of themselves (internal environment) and performance. The framework is applied to a longitudinal study of two consumer electronics firms. The empirical evidence shows that differences in dominant logic lead to different strategic reactions to developments in the industry, and thus result in performance differences.     

Enhanced Topic-based Vector Space Model for semantics-aware spam filtering

Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. More than 85% of received e-mails are spam. Historical approaches to combat these messages including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Currently, many solutions feature machine-learning algorithms trained using statistical representations of the terms that usually appear in the e-mails. Still, these methods are merely syntactic and are unable to account for the underlying semantics of terms within the messages. In this paper, we explore the use of semantics in spam filtering by representing e-mails with a recently introduced Information Retrieval model: the enhanced Topic-based Vector Space Model (eTVSM). This model is capable of representing linguistic phenomena using a semantic ontology. Based upon this representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages.     

Mining disjunctive minimal generators with TitanicOR

Disjunctive minimal generators were proposed by Zhao, Zaki, and Ramakrishnan (2006). They defined disjunctive closed itemsets and disjunctive minimal generators through the disjunctive support function. We prove that the disjunctive support function is compatible with the closure operator presented by Zhao et al. (2006). Such compatibility allows us to adapt the original version of the Titanic algorithm, proposed by Stumme, Taouil, Bastide, Pasquier, and Lakhal (2002) to mine iceberg concept lattices and closed itemsets, to mine disjunctive minimal generators. We present TitanicOR, a new breadth-first algorithm for mining disjunctive minimal generators. We evaluate the performance of our method with both synthetic and real data sets and compare TitanicOR’s performance with the performance of BLOSOM (Zhao et al., 2006), the state of the art method and sole algorithm available prior to TitanicOR for mining disjunctive minimal generators. We show that TitanicOR’s breadth-first approach is up to two orders of magnitude faster than BLOSOM’s depth-first approach.     

Distributed decision support system for airport ground handling management using WSN and MAS

The economic situation in the air transportation industry claims for new business models supported by accurate management processes, which need constant feedback of the real status of the environment. The objective of this work is to achieve an updated/real decision support systems (DSS) to allocate resources in an airport even when disturbances occur by combining artificial intelligent techniques with visibility technologies. This work proposes the combined use of Multi-agent systems (MAS) along with Wireless Sensor Networks (WSN) to provide the required information on the status of the resources and the environment. The MAS is based on a double layer of decision-taking levels and on a Markov reward function whereas the WSN is based on a Zigbee network of Radio Frequency Identification (RFID) readers with active tags as end nodes, which are carried by the physical resources. The proposed distributed DSS has been tested at Ciudad Real Central Airport in Spain.     

Multi-start iterated local search metaheuristic for the multi-mode resource-constrained project scheduling problem

In this paper a multi-start iterated local search (MS-ILS) algorithm is presented as a new and effective approach to solve the multi-mode resource-constrained project scheduling problem (MRCPSP). The MRCPSP is a well-known project scheduling NP-Hard optimization problem, in which there is a trade-off between the duration of each project activity and the amount of resources they require to be completed. The proposed algorithm generates an initial solution, performs a local search to obtain a local optimum, subsequently, for a certain number of iterations, makes a perturbation to that local optimum and performs a new local search on the perturbed solution. This whole process then restarts with a different initial solution for a certain number of restarts. The algorithm was tested on benchmark instances of projects with 30, 50 and 100 activities from well-known libraries. The obtained results were compared to recent benchmark results from the literature. The proposed algorithm outperforms other solution methods found in related literature for the largest tested instances (100 activities), while for smaller instances it shows to be quite competitive, in terms of the average deviation against known lower bounds.     

Model-driven development for early aspects

Currently, non-functional requirements (NFRs) consume a considerable part of the software development effort. The good news is that most of them appear time and again during system development and, luckily, their solutions can be often described as a pattern independently from any specific application or domain. A proof of this are the current application servers and middleware platforms that can provide configurable prebuilt services for managing some of these crosscutting concerns, or aspects. Nevertheless, these reusable pattern solutions presents two shortcomings, among others: (1) they need to be applied manually; and (2) most of these pattern solutions do not use aspect-orientation, and, since NFRs are often crosscutting concerns, this leads to scattered and tangled representations of these concerns. Our approach aims to overcome these limitations by: (1) using model-driven techniques to reduce the development effort associated to systematically apply reusable solutions for satisfying NFRs; and (2) using aspect-orientation to improve the modularization of these crosscutting concerns. Regarding the first contribution, since the portion of a system related to NFRs is usually significant, the reduction on the development effort associated to these NFRs is also significant. Regarding the second contribution, the use aspect-orientation improves maintenance and evolution of the non-functional requirements that are managed as aspects. An additional contribution of our work is to define a mapping and transition from aspectual requirements to aspect-oriented software architectures, which, in turn, contributes to improve the general issue of systematically relating requirements to architecture. Our approach is illustrated by applying it to a Toll Gate case study.     

Model-based energy-aware data movement optimization in the storage I/O stack

The increasing data demands of applications from various domains and the decreasing relative power cost of CPU computation have gradually exposed data movement cost as the prominent factor of energy consumption in computing systems. The traditional organization of the computer system software into a layered stack, while providing a straightforward modularity, poses a significant challenge for the global optimization of data movement in particular and, thus, the energy efficiency in general. Optimizing the energy efficiency of data movement in large-scale systems is a difficult tasks because it depends on a complex interplay of various factors at different system layers. In this work, we address the challenge of optimizing the data movement of the storage I/O stack in a holistic manner. Our approach consists of a model-based system driver that obtains the current I/O power regime and adapts the CPU frequency level according to this information. On the one hand, for simplifying the understanding of the relation between data movement and energy efficiency, this paper proposes novel energy prediction models for data movement based on series of runtime metrics from several I/O stack layers. We provide an in-depth study of the energy consumption in the data path, including the identification and analysis of power and performance regimes that synthesize the energy consumption patterns in a cross-layer approach. On the other hand, we propose and prototype a kernel driver that exploits data movement awareness for improving the current CPU-centric energy management.     

Proxy-based sliding mode control on platform of 3 degree of freedom (3-DOF)

Proxy-based sliding mode control, created by Ryo Kikuuwe and Hideo Fujimoto, combines the advantages of sliding mode control and proportional integral derivative controller. This paper implements this control algorithm on a platform of 3 degrees of freedom (3-DOF) driven by electropneumatic actuators with proportional flow valves. This paper presents an extension of proxy-based sliding mode control. This time it is implemented on a parallel robot. Despite the robot being a coupled system, is reached a perfect decoupled control. The simulations in MSC-ADAMS and experimental results demonstrate good tracking.     

GROUNDING EMOTIONS IN ADAPTIVE SYSTEMS: VOLUME II

ABSTRACT

Ranking fuzzy numbers plays a very important role in decision-making problems. Existing centroid-index ranking methods have some drawbacks. In this article, a new centroid-index ranking method of fuzzy numbers is proposed. The proposed method is using the ideal of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Some numerical examples show that the new method can overcome the drawbacks of the existing methods. Finally, a human selection problem is used to illustrate the efficiency of the proposed fuzzy ranking method.