Evaluation of recommender systems: A new approach

It is difficult to deny that comparison between recommender systems requires a common way for evaluating them. Nevertheless, at present, they have been evaluated in many, often incompatible, ways. We affirm this problem is mainly due to the lack of a common framework for recommender systems, a framework general enough so that we may include the whole range of recommender systems to date, but specific enough so that we can obtain solid results. In this paper, we propose such a framework, attempting to extract the essential features of recommender systems. In this framework, the most essential feature is the objective of the recommender system. What is more, in this paper, recommender systems are viewed as applications with the following essential objective. Recommender systems must: (i) choose which (of the items) should be shown to the user, (ii) decide when and how the recommendations must be shown. Next, we will show that a new metric emerges naturally from this framework. Finally, we will conclude by comparing the properties of this new metric with the traditional ones. Among other things, we will show that we may evaluate the whole range of recommender systems with this single metric.     

ManufAg: a multi-agent-system framework for production control of complex manufacturing systems

In this paper, we present a framework for the implementation of multi-agent-systems for production control of complex manufacturing systems. We present the results of a requirement analysis for production control systems for complex manufacturing systems; then we describe the framework design criteria. Our framework supports the inclusion of distributed hierarchical decision-making schemes into the production control. Furthermore, in order to increase the coordination abilities of multi-agent-systems, we follow the decision-making and staff agent architecture suggested in the PROSA reference architecture. We indicate the usage of the framework for designing and implementing an agent-based production control system for semiconductor manufacturing processes in a case study.     

Unifying Agent Systems

Whilst there has been an explosion of interest multi-agent systems, there are still many problems that may have a potentially deleterious impact on the progress of the area. These problems have arisen primarily through the lack of a common structure and language for understanding multi-agent systems, and with which to organise and pursue research in this area. In response to this, previous work has been concerned with developing a computational formal framework for agency and autonomy which, we argue, provides an environment in which to develop, evaluate, and compare systems and theories of multi-agent systems. In this paper we go some way towards justifying these claims by reviewing the framework and showing what we can achieve within it by developing models of agent dimensions, categorising key inter-agent relationships and by applying it to evaluate existing multi-agent systems in a coherent computational model. We outline the benefits of specifying each of the systems within the framework and consider how it allows us to unify different systems and approaches in general.     

Interaction and Outcomes in Collaborative Storytelling Systems: a Framework,a Field Study,and a Model

In the last decades, advances in interactive information technologies have facilitated collaborative fiction writing, which has become widespread and large-scale. This paper proposes a framework to analyze collaborative storytelling systems, made of a set of parameters divided into six conceptual areas. Four of them relate to the systems and two (process and output) to the results of the collaboration. Through this framework we can study more precisely these different factors of the systems, their interplay, and how they impact the creators’ performance. We also present a controlled extended-duration field study on collaborative storytelling, and we use this framework to comparatively analyze these observations and other relevant experiences in the field of co-creation of shared narrative spaces. As a result, we propose a human-information interaction model for collaborative narrative systems, intended to better support co-creation and address the barriers of this kind of systems turning them into new opportunities for collaboration.     

Complementarity modeling of hybrid systems

A complementarity framework is described for the modeling of certain classes of mixed continuous/discrete dynamical systems. The use of such a framework is well known for mechanical systems with inequality constraints, but we give a more general formulation which also applies, for instance, to switching control systems. The main theoretical results in the paper are concerned with uniqueness of smooth continuations; the solution of this problem requires the construction of a map from the continuous state to the discrete state. A crucial technical tool is the so-called linear complementarity problem from mathematical programming, and we introduce various generalizations of this problem     

A multi-agent simulation framework on small Hadoop cluster

In this paper, we explore the benefits and possibilities about the implementation of multi-agents simulation framework on a Hadoop cloud. Scalability, fault-tolerance and failure-recovery have always been a challenge for a distributed systems application developer. The highly efficient fault tolerant nature of Hadoop, flexibility to include more systems on the fly, efficient load balancing and the platform-independent Java are useful features for development of any distributed simulation. In this paper, we propose a framework for agent simulation environment built on Hadoop cloud. Specifically, we show how agents are represented, how agents do their computation and communication, and how agents are mapped to datanodes. Further, we demonstrate that even if some of the systems fail in the distributed setup, Hadoop automatically rebalances the work load on remaining systems and the simulation continues. We present some performance results on this environment for a few example scenarios.     

A framework for decision support systems evaluation

In this paper, we present a framework for the Decision Support Systems evaluation problem. Using the Gorry-Scott Morton's framework for information systems, we develop several evaluation methods that structure the evaluation process. The framework determines the best methods of evaluation that are suitable to the characteristics of the Decision Support System concerned. Finally, we use the framework to evaluate two widely used Decision Support Systems.     

Comparison of component frameworks for real-time embedded systems

The use of components significantly helps in development of real-time embedded systems. There have been a number of component frameworks developed for this purpose, and some of them have already became well established in this area. Even though these frameworks share the general idea of component-based development, they significantly differ in the range of supported features and maturity. This makes it relatively difficult to select the right component framework and thus poses a significant obstacle in adoption of the component-based development approach for developing real-time embedded systems. To provide guidance in choosing a component framework, or at least relevant concepts when building a custom framework, we present a survey, which illustrates distinguishing features and provides comparison of selected modern component-based frameworks for real-time embedded systems. Compared to other existing surveys, this survey focuses specifically on criteria connected with real-time and embedded systems. Further, to be practically relevant, we restrict the survey only to the frameworks that support the full development life cycle (i.e. from design till execution support). In this context, the survey illustrates the complexity of development in each framework by giving specification and code samples.     

GENERAL SYSTEMS THEORY AND ALGORITHM THEORY

This paper briefly considers tome relationships of general systems theory to algorithm theory. A general systems theory type of approach provides a framework for algorithm design. We explicate this framework, and then we demonstrate new insights into algorithm design which follow from the framework. Of particular importance in algorithm theory is the proper introduction of the concept of design principle and its relation to algorithm model.     

Countering security information overload through alert and packet visualization

This article presents a framework for designing network security visualization systems as well as results from the end-to-end design and implementation of two highly interactive systems. In this article, we provide multiple contributions: we present the results of our survey of security professionals, the design framework, and lessons learned from the design of our systems as well as an evaluation of their effectiveness. Our results indicate that both systems effectively present significantly more information when compared to traditional textual approaches. We believe that the interactive, graphical techniques that we present will have broad applications in other domains seeking to deal with information overload.     

Weighted Distances and Digital Disks on the Khalimsky Grid

Generalized linear image processing systems have been developed from physical image formation models, human visual perception models, and mathematical models. Although there have been many papers on the extension, parameterization, and symmetrization of some of these systems, what is lacking is a unified framework such that the development and study of such systems can be performed based on a common ground. In this paper, we propose a conceptual image sensor which models how the light energy is converted into the sensor data. In the proposed sensor model, the input energy is regarded as a random variable and the conversion is through the cumulative distribution function. Based on the sensor model, we suggest a statistical framework by which new systems can be derived, and existing and seemingly unrelated systems can be studied from a unified perspective. The proposed statistical framework not only provides a principled way to symmetrizing systems through the even extension of the probability distribution function (PDF) and a natural way for the parameterization of systems through parameters of PDF. In this paper, we demonstrate new applications of the statistical framework through a numerical approximation of the lower incomplete gamma function, through the enhancement of the dynamic range and manipulation of the sharpness of images by using the scalar multiplication operation of the parametric system, through an application of a new system in fusion of multi-exposure images, and through an application of the three new systems for the correction of incorrect exposure.     

Energy-based control for hybrid port-controlled Hamiltonian systems

In this paper we develop an energy-based hybrid control framework for hybrid port-controlled Hamiltonian systems. In particular, we obtain constructive sufficient conditions for hybrid feedback stabilization that provide a shaped energy function for the closed-loop system, while preserving a hybrid Hamiltonian structure at the closed-loop level. Furthermore, an inverse optimal hybrid feedback control framework is developed that characterizes a class of globally stabilizing energy-based controllers that guarantee hybrid sector and gain margins to multiplicative input uncertainty of hybrid Hamiltonian systems.     

A tutorial on event-based optimization—a new optimization framework

In many practical systems, the control or decision making is triggered by certain events. The performance optimization of such systems is generally different from the traditional optimization approaches, such as Markov decision processes or dynamic programming. The goal of this tutorial is to introduce, in an intuitive manner, a new optimization framework called event-based optimization. This framework has a wide applicability to aforementioned systems. With performance potential as building blocks, we develop two intuitive optimization algorithms to solve the event-based optimization problem. The optimization algorithms are proposed based on an intuitive principle, and theoretical justifications are given with a performance sensitivity based approach. Finally, we provide a few practical examples to demonstrate the effectiveness of the event-based optimization framework. We hope this framework may provide a new perspective to the optimization of the performance of event-triggered dynamic systems.     

Characteristics of IT artifacts: a systems thinking‐based framework for delineating and theorizing IT artifacts

The information technology artifact (ITA) has been suggested as the core of information systems (IS) research, and the research community has been encouraged to deeply engage with the ITA. Various studies highlight, however, that the ITA continues to receive only limited attention and thus, little foundation exists for IS researchers to delineate and theorize about the ITAs studied. In this paper, we develop a framework that can be utilized as a language for articulating and theorizing the ITA in IS research. Our framework builds on the multi‐faceted theoretical paradigm of systems thinking from which we derive several concepts and appropriate them to the context at hand, resulting in a seven‐dimensional framework of characteristics for ITAs. In a literature survey of research on enterprise systems and enterprise resource planning systems in top IS journals, we show how ITA characteristics are currently included to present details of the relevant ITA instance and identify theoretical relationships between ITA characteristics and outcomes. We conclude the study by demonstrating the use of the ITA framework for delineating and theorizing the ITA in IS research.     

A taxonomy of argumentation models used for knowledge representation

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.     

Robust nonlinear feedback control for uncertain linear systems with nonquadratic performance criteria

In this paper we develop a unified framework to address the problem of optimal nonlinear robust control for linear uncertain systems. Specifically, we transform a given robust control problem into an optimal control problem by properly modifying the cost functional to account for the system uncertainty. As a consequence, the resulting solution to the modified optimal control problem guarantees robust stability and performance for a class of nonlinear uncertain systems. The overall framework generalizes the classical Hamilton–Jacobi–Bellman conditions to address the design of robust nonlinear optimal controllers for uncertain linear systems. © 1998 Elsevier Science B.V.     

Robust nonlinear feedback control for uncertain linear systems with nonquadratic performance criteria

In this paper we develop a unified framework to address the problem of optimal nonlinear robust control for linear uncertain systems. Specifically, we transform a given robust control problem into an optimal control problem by properly modifying the cost functional to account for the system uncertainty. As a consequence, the resulting solution to the modified optimal control problem guarantees robust stability and performance for a class of nonlinear uncertain systems. The overall framework generalizes the classical Hamilton–Jacobi–Bellman conditions to address the design of robust nonlinear optimal controllers for uncertain linear systems. © 1998 Elsevier Science B.V.     

Approximate Simulation Relations for Hybrid Systems

Approximate simulation relations have recently been introduced as a powerful tool for the approximation of discrete and continuous systems. In this paper, we extend this abstraction framework to hybrid systems. Using the notion of simulation functions, we develop a characterization of approximate simulation relations which can be used for hybrid systems approximation. For several classes of hybrid systems, this characterization leads to effective algorithms for the computation of approximate simulation relations. An application in the context of reachability analysis is shown.     

IHE标准下的数字化医院集成及实现机制研究

现阶段我国医院管理信息系统（HIS）正处在综合集成阶段,文章根据医院信息化建设的实际情况,提出了一种现阶段数字化医院系统集成框架,该框架是在医院IHE集成标准模型与方法范畴下,进行建模与细化,是IHE的子集。同时对该框架下的数据中心、集成平台、应用实现的实现机制进行了研究。采用HL7 V3开发框架下参考信息模型RIM对医院相应角色进行建模与集成,最后在“军卫一号HIS”基础上进行了集成医生工作站的功能实现。