Predicting the unexpected

Dagstuhl seminar no. 10102 on discrete event logistic systems recognized a network of persistent models to be a “Grand Challenge.” Such on-line model network will offer an infrastructure that facilitates the management of logistic operations. This ambition to create a network of persistent models implies a radical shift for model design activities as the objective is an infrastructure rather than application-specific solutions. In particular, model developers can no longer assume that they know what their model will be used for. It is no longer possible to design for the expected.This paper presents insights in model development and design in the absence of precise knowledge concerning a model's usage. Basically, model developers may solely rely on the presence of the real-world counterpart mirrored by their model and a general idea about the nature of the application (e.g. coordination of logistic operations). When the invariants of their real-world counterpart suffice for models to be valid, these models become reusable and integrate-able. As these models remain valid under a wide range of situations, they become multi-purpose and durable resources rather than single-purpose short-lived components or legacy, which is even worse.Moreover and more specifically, the paper describes how to build models that allow their users to generate predictions in unexpected situations and atypical conditions. Referring to previous work, the paper concisely discusses how these predictions can be generated starting from the models. This prediction-generating technology is currently being transferred into an industrial MES.     

Performance of classification models from a user perspective

This paper proposes a complete framework to assess the overall performance of classification models from a user perspective in terms of accuracy, comprehensibility, and justifiability. A review is provided of accuracy and comprehensibility measures, and a novel metric is introduced that allows one to measure the justifiability of classification models. Furthermore, taxonomy of domain constraints is introduced, and an overview of the existing approaches to impose constraints and include domain knowledge in data mining techniques is presented. Finally, justifiability metric is applied to a credit scoring and customer churn prediction case.     

Resource-efficient routing and scheduling of time-constrained streaming communication on networks-on-chip

Network-on-chip-based multiprocessor systems-on-chip are considered as future embedded systems platforms. One of the steps in mapping an application onto such a parallel platform involves scheduling the communication on the network-on-chip. This paper presents different scheduling strategies that minimize resource usage by exploiting all scheduling freedom offered by networks-on-chip. It also introduces a technique to take the dynamism in applications into account when scheduling the communication of an application on the network-on-chip while minimizing the resource usage. Our experiments show that resource-utilization is improved when compared to existing techniques.     

Misfit versus latency

In this paper we present a framework that combines some ideas of the behavioral modeling approach and the prediction error modeling approach. It is shown that the proposed model selection procedure can be rephrased as an optimization problem that only depends on the model parameters. Experiments illustrate the potential of the so-called misfit versus latency framework.     

Mining frequent conjunctive queries in relational databases through dependency discovery

We present an approach for mining frequent conjunctive in arbitrary relational databases. Our pattern class is the simple, but appealing subclass of simple conjunctive queries. Our algorithm, called Conqueror $^+$ , is capable of detecting previously unknown functional and inclusion dependencies that hold on the database relations as well as on joins of relations. These newly detected dependencies are then used to prune redundant queries. We propose an efficient database-oriented implementation of our algorithm using SQL and provide several promising experimental results.     

Estimating spine shape in lateral sleep positions using silhouette-derived body shape models

This study aims at evaluating spinal alignment during sleep by combining personalized human models with mattress indentation measurements. A generic surface model has been developed that can be personalized based on anthropometric parameters derived from silhouette extraction. Shape assessment of the personalized surface models, performed by comparison with 3-D surface scans of the trunk, showed a mean unsigned distance of 9.77 mm between modeled and scanned surface meshes. The surface model is combined with an inner skeleton model, allowing the model to simulate distinct sleep postures. An automatic fitting algorithm sets the appropriate degrees of freedom to position the model on the measured indentation according to the adopted sleep posture. Validation on lateral sleep positions showed good intraclass correlations (0.73–0.88) between estimated and measured angular spinal deformations, indicating that silhouette-derived body shape models provide a valuable tool for the unobtrusive assessment of spinal alignment during sleep.Relevance to industry: A common drawback of the available techniques to assess spinal deformation on bedding systems is that they interfere with the actual sleep process. The current study presents a novel method based on silhouette-derived body shape models in order to estimate spine shape during sleep unobtrusively.     

Science in Brazil. Part 1: A macro-level comparative study

Summary In the present paper, the evolution of publication activity and citation impact in Brazil is studied for the period 1991-2003. Besides the analysis of trends in publication and citation patterns and of national publication profiles, an attempt is made to find statistical evidences of the relation between international co-authorship and both research profile and citation impact in the Latin American region. Despite similarities and strong co-publication links with the other countries in the region, Brazil has nonetheless a specific research profile, and forms the largest potential in the region.     

Experiences of Autonomy and Control Among Chinese Learners: Vitalizing or Immobilizing?

Various cross-cultural researchers state that autonomy is not valued in Eastern cultures and, hence, is unlikely to predict optimal study functioning and well-being. In contrast, self-determination theory (SDT; R. M. Ryan & E. L. Deci, 2000) maintains that autonomous or volitional study motivation is universally important and should predict better learning and higher well-being, even among Chinese students. Two studies were conducted to shed light on this controversial issue. Findings from both studies indicated that autonomous study motivation positively predicts adaptive learning attitudes, academic success, and personal well-being, whereas controlled motivation was associated with higher drop-out rates, maladaptive learning attitudes, and ill-being. In addition, Study 2 revealed that parental autonomy support versus psychological control is related to more adaptive learning strategies and higher well-being and that these effects were mediated by students' relative autonomy for studying. The importance of defining autonomy as an intraindividual, phenomenological experience versus an interpersonal, culturally bounded value is discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

催化氧化净化黄磷尾气中的磷和硫

利用黄磷尾气生产甲酸、乙酸以及甲醇等高附加值产品，净化预处理成为制药其应用的瓶颈问题。研究了用普通活性炭和自制催化剂KU2催化氧化净化黄磷尾气的方法；讨论了固定床系统中常压下、温度20~140℃，催化剂上磷和硫化氢的吸附特征。KU2和活性炭都能通过催化氧化过程有效脱除黄磷尾气中的P4，PH3和H2S杂质，随着反应温度和氧含量增加可显著提高净化效果，在最优条件下，经催化氧化净化后的总磷含量＜5mg/m3，硫化氢＜5mg/m3，失效后的催化剂易于再生，催化剂经多次再生后催化性能恢复良好；提出了活性炭固定床稳定     

Artificial argument assistants for defeasible argumentation

The present paper discusses experimental argument assistance tools. In contrast with automated reasoning tools, the objective is not to replace reasoning, but to guide the user's production of arguments. Two systems are presented, and based on . The focus is on defeasible argumentation with an eye on the law. Argument assistants for defeasible argumentation naturally correspond to a view of the application of law as dialectical theory construction. The experiments provide insights into the design of argument assistants, and show the pros and cons of different ways of representing argumentative data. The development of the argumentation theories underlying the systems has culminated in the logical system that formalizes the interpretation of prima facie justified assumptions. introduces an innovative use of conditionals expressing support and attack. This allows the expression of warrants for support and attack, making it a transparent and flexible system of defeasible argumentation.