ppxscal/embedding_network huggingface dataset:
The Grid as a Single Entity: Towards a Behavior Model of the Whole Grid
Authors: Jesús Montes, Alberto Sánchez, Julio J. Valdés, María S. Pérez, Pilar Herrero
Venue: Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems: (2008)

Grids emerged in the last decade as large distributed environments where the greatest challenges of the scientific community could be faced. These challenges, commonly known as grand challenge applications (GCAs), are characterized as problems that, given their size and/or complexity, can not be solved by means of conventional computing techniques. The grid scalability, flexibility and massive computation power make it an ideal environment to face these GCAs.In order to provide the expected service and achieve the required performance for these applications, grids must have powerful management mechanisms that efficiently deal with the natural variability and heterogeneity of the environment. Since the birth of this technology, this has been one of the key aspects of its development and, in many ways, one of the most problematic. Conventional grid management mechanisms try to improve performance based on the individual analysis of every component on the system. Then they intend to adjust the configuration or predict the behavior of each independent element. This approach may seem reasonable considering the large scale and complexity of the grid, but could possibly fail to achieve optimal performance, because in most cases it lacks the capability to understand the effects that different elements have on each other when they work together. From a more theoretical point of view, if we consider "the grid" as an individual entity (with its computational power, storage capacity and so on), it seems logical to analyze it as such, instead of composed of a huge set of individual resources. This could be similar as how computers are regarded as individual entities, even though they are made of several electronic components of different nature, or how clusters are most times considered as single machines, when in fact they are composed by many computers.In this paper we present a set of techniques oriented to build a global behavioral model of a large scale distributed environment, such as a grid. This kind of model would provide a deep understanding of the system and a better reference for management and optimization. A case study is also presented, in order to provide a validation of this approach.
Multimodal system evaluation using modality efficiency and synergy metrics
Authors: Manolis Perakakis, Alexandros Potamianos
Venue: Proceedings of the 10th international conference on Multimodal interfaces (2008)

In this paper, we propose two new objective metrics, relative modality efficiency and multimodal synergy, that can provide valuable information and identify usability problems during the evaluation of multimodal systems. Relative modality efficiency (when compared with modality usage) can identify suboptimal use of modalities due to poor interface design or information asymmetries. Multimodal synergy measures the added value from efficiently combining multiple input modalities, and can be used as a single measure of the quality of modality fusion and fission in a multimodal system. The proposed metrics are used to evaluate two multimodal systems that combine pen/speech and mouse/keyboard modalities respectively. The results provide much insight into multimodal interface usability issues, and demonstrate how multimodal systems should adapt to maximize modalities synergy resulting in efficient, natural, and intelligent multimodal interfaces.
Oppositional target domain estimation using grid-based simulation
Authors: Maryam Shokri, Hamid R. Tizhoosh, Mohamed S. Kamel
Venue: Applied Soft Computing (2009)

In this paper we address the problem of estimating the target domain for search and navigation problems. We propose oppositional target domain estimation by modeling the search and navigation environment as a grid. Typically real-world applications exhibit an environment that is extremely large, dramatically diminishing the usability of intelligent agents for search and navigation. The reduction of the size of environment, hence, can help to increase the efficiency and applicability of the agents. We address this issue by modeling the environment as a grid and estimating the target domain inside the environment. The target domain is a reduced space which includes the target. The proposed technique is specifically concerned with reducing the environment using the concept of opposition. Experimental results show significant reduction of the environment size resulting in a shorter search time.