Application of Partial-Order Methods to Reactive Systems with Event Memorization

International audienceWe are concerned in this paper with the verification of reactive systems with event memorization. The reactive systems are specified with an asynchronous reactive language Electre the main feature of which is the capability of memorizing occurrences of events in order to process them later. This memory capability is quite interesting for specifying reactive systems but leads to a verification model with a dramatically large number of states (due to the stored occurrences of events). In this paper, we show that partial-order methods can be applied successfuly for verification purposes on our model of reactive programs with event memorization. The main points of our work are two-fold: (1) we show that the independance relation which is a key point for applying partial-order methods can be extracted automatically from an \sf Electre program; (2) the partial-order technique turns out to be very efficient and may lead to a drastic reduction in the number of states of the model as demonstrated by a real-life industrial case study

RDF Querying

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Learning from Major Accidents: a Machine Learning Approach

A B S T R A C T Learning from past mistakes is crucial to prevent the reoccurrence of accidents involving dangerous sub-stances. Nevertheless, historical accident data are rarely used by the industry, and their full potential is largely unexpressed. In this setting, this study set out to take advantage of improvements in data sci-ence and Machine Learning to exploit accident data and build a predictive model for severity prediction. The proposed method makes use of classification algorithms to map the features of an accident to the corresponding severity category (i.e., the number of people that are killed and injured). Data extracted from existing databases is used to train the model. The method has been applied to a case study, where three classification models - i.e., Wide, Deep Neural Network, and Wide&Deep - have been trained and evaluated on the Major Hazard Incident Data Service database (MHIDAS). The results indicate that the Wide&Deep model offers the best performance.(c) 2022 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/

An input centric paradigm for program dynamic optimizations and lifetime evolvement

Accurately predicting program behaviors (e.g., memory locality, method calling frequency) is fundamental for program optimizations and runtime adaptations. Despite decades of remarkable progress, prior studies have not systematically exploited the use of program inputs, a deciding factor of program behaviors, to help in program dynamic optimizations. Triggered by the strong and predictive correlations between program inputs and program behaviors that recent studies have uncovered, the dissertation work aims to bring program inputs into the focus of program behavior analysis and program dynamic optimization, cultivating a new paradigm named input-centric program behavior analysis and dynamic optimization.;The new optimization paradigm consists of three components, forming a three-layer pyramid. at the base is program input characterization, a component for resolving the complexity in program raw inputs and extracting important features. In the middle is input-behavior modeling, a component for recognizing and modeling the correlations between characterized input features and program behaviors. These two components constitute input-centric program behavior analysis, which (ideally) is able to predict the large-scope behaviors of a program\u27s execution as soon as the execution starts. The top layer is input-centric adaptation, which capitalizes on the novel opportunities created by the first two components to facilitate proactive adaptation for program optimizations.;This dissertation aims to develop this paradigm in two stages. In the first stage, we concentrate on exploring the implications of program inputs for program behaviors and dynamic optimization. We construct the basic input-centric optimization framework based on of line training to realize the basic functionalities of the three major components of the paradigm. For the second stage, we focus on making the paradigm practical by addressing multi-facet issues in handling input complexities, transparent training data collection, predictive model evolvement across production runs. The techniques proposed in this stage together cultivate a lifelong continuous optimization scheme with cross-input adaptivity.;Fundamentally the new optimization paradigm provides a brand new solution for program dynamic optimization. The techniques proposed in the dissertation together resolve the adaptivity-proactivity dilemma that has been limiting the effectiveness of existing optimization techniques. its benefits are demonstrated through proactive dynamic optimizations in Jikes RVM and version selection using IBM XL C Compiler, yielding significant performance improvement on a set of Java and C/C++ programs. It may open new opportunities for a broad range of runtime optimizations and adaptations. The evaluation results on both Java and C/C++ applications demonstrate the new paradigm is promising in advancing the current state of program optimizations

The Impact of Career and Technical Education Programs on At-Risk Secondary Students

As the number of youth at risk for educational failure has increased, so has the debate over the appropriate nature of career and technical education (CTE) programs for such youth. The purpose of this study was to gain an understanding about the manner in which CTE programs within vocational schools affected secondary students at risk for educational failure. The educational theories of Pestalozzi, Dewey, and Rousseau served as the conceptual framework for this study by supporting the development of students\u27 intellectual, social, and emotional growth through hands-on activities rather than traditional rote learning. Data for this case study were collected through interviews and observations from 9 purposefully selected students enrolled in vocational school CTE programs. Qualitative strategies of memoing and coding supported interpretative data analysis for this case study. The participants revealed that their CTE programs had a positive impact on their lives. Findings that emerged from this study centered on job security, hands-on learning, and personal growth. These findings provide important empirical evidence of the utility of CTE programs for at-risk students. This evidence contributes to positive social change by illuminating an alternative education setting that enables at-risk students to attain and maintain academic success. This evidence also holds promise for positive social change by guiding the efforts of education stakeholders in determining the appropriate educational placement for at-risk students, placements that will promote a sense of belonging rather than alienation

The impact of the quota-based system on social stratification among the students at the Indian Institute of Technology, Madras, India

This study attempted to identify the impact of the recent expansion in the quota system among Quota-Based Students (QBS) and General Category Students (GCS) at the Indian Institute of Technology (Madras) (IIT(M)). The study emphasized various institutional behaviors that affected the students and their educational endeavors and examined the impact of institutional practices on the students. This study examined some of the socialization practices of QBS and GCS based on Van Maanen and Schein’s (1979) work and revealed that hostels (residence halls) played a significant role especially among QBS educational endeavors at IIT(M). Van Maanen and Schein’s (1979) investiture socialization perspective was extensively evident among various student groups that were allowed by the institution to continue with their previous espoused values within the institution. Right from the admission criteria which is based on caste, numerous signs of social stratification reinforced the class hierarchy along with the existence of various aspects of deficit thinking among QBS at IIT(M). Existence of social stratification within the institution also portrayed traces of stigma consciousness among QBS at the IIT(M). Based on the findings of this research, the co-existence of QBS and GCS along with the emergence of social and academic streams was clearly evident at IIT(M). These two streams shared various similarities with Tinto’s (1988) formal and informal dimensions model. How these groups of students managed to survive in harmony without divesting their Schein’s artifacts, espoused values and underlying assumptions is one of the major outcomes of this study. The results of this study have recommended various strategies to develop programs that will improve the institutional experience and help gain better educational equity among QBS and GCS at IIT(M), India