Active Learning from Blackbox to Timed Connectors

Functional programs are well suited for concurrent execution due to the Church-Rosser property. However, for many applications there is still need to explicitly specify process systems instead of relying on compilers. Coordination languages provide one viable solution to the problem of specifying concurrent process systems, since they do not interfere with the properties of the language used for the algorithmic specification of the processes. The coordination language K2 to be proposed in this paper is based on a variant of colored Petri nets. It primarily defines process systems with deterministic behavior, but also allows for controlled forms of non-determinism. Specifications based on Petri nets offer several advantages. An underlying net calculus facilitates formal analysis and verification of basic safety and liveness properties. Graphical representations clearly expose structural dependencies among system components. Our variant of colored Petri nets provides several forms of net abstractions which facilitate the systematic construction of complex systems from small subsystems by composition. Recursive specifications can be used to adapt the structure of a process system to actual problem sizes or to varying input parameters.KeywordsInput StreamOutput StreamLiveness PropertyRecursive ProcessHierarchical ProcessThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Process algebra provides abstract and rigorous means for studying communicating concurrent systems. Coordination languages also provide abstract means for the specifying and programming communication of components. Hence, the two fields seem to have very much in common and the link between these two research areas have been established formally by means of several translations, mainly from coordination languages to process algebras. There have also been proposals of process algebras whose communication policy is inspired by the one underlying coordination languages. The aim of this workshop was to push the state of the art in the study of the connections between process algebra and coordination languages by bringing together experts as well as young researchers from the two fields to communicate their ideas and findings. It includes both contributed and invited papers that have been presented during the one day meeting on Process Algebra and Coordination (PACO 2011) which took place on June 9, 2011 in Reykjavik, Iceland.

The growing adoption of active learning pedagogical methods has shown significant positive effects in higher education as they promote students' engagement and meeting of learning outcomes. Similarly, problem-based learning and media-based learning allow instructors to model educational experiences that prepare students with knowledge and skills necessary for the real world. In this study, the authors wanted to investigate the impact of a group problem-based learning activity using visualization media on students' learning motivation in a mathematics course. In this activity, students were grouped and asked to solve volumes of revolution calculus problems while being either in a traditional classroom or in a classroom equipped with a large-scale visualization display system. The authors assessed the students' self-efficacy, effort regulation, perceived usefulness, task attraction, and user experience after participating in the activity. The authors found a significant difference in students' self-efficacy and task attraction based on the results' analysis. The authors did not find a significant difference in students' effort regulation and perceived usefulness. The students did report a significantly positive experience while using the large-scale visualization display system. The analysis illustrated a medium to large effect size of the results' statistical significance. This study supports future instructors and researchers in designing and developing impactful pedagogical active learning experiences.

Context. The mounting data stream of large time-domain surveys renders the visual inspections of a huge set of transient candidates impractical. Techniques based on deep learning-based are popular solutions for minimizing human intervention in the time domain community. The classification of real and bogus transients is a fundamental component in real-time data processing systems and is critical to enabling rapid follow-up observations. Most existing methods (supervised learning) require sufficiently large training samples with corresponding labels, which involve costly human labeling and are challenging in the early stages of a time-domain survey. One method that can make use of training samples with access to only a limited amount of labels is highly desirable for future large time-domain surveys. These include the forthcoming 2.5-meter Wide-Field Survey Telescope (WFST) six-year survey and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Aims. Deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance. They have been applied to the task of the classification of real and bogus transients. Unlike most existing approaches, which necessitate massive and expensive annotated data, we aim to leverage training samples with only 1000 labels and discover real sources that vary in brightness over time in the early stages of the WFST six-year survey. Methods. We present a novel deep learning method that combines active learning and semi-supervised learning to construct a competitive real-bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlabeled data to enhance the model’s performance and achieve superior results, compared to using only the limited labeled data. Results. Our proposed methodology capitalizes on the potential of active learning and semi-supervised learning. To demonstrate the efficacy of our approach, we constructed three newly compiled datasets from the Zwicky Transient Facility, achieving average accuracies of 98.8, 98.8, and 98.6% across these three datasets. It is important to note that our newly compiled datasets only work in terms of testing our deep learning methodology and there may be a potential bias between our datasets and the complete data stream. Therefore, the observed performance on these datasets cannot be assumed to directly translate to the general alert stream for general transient detection in actual scenarios. The algorithm will be integrated into the WFST pipeline, enabling an efficient and effective classification of transients in the early period of a time-domain survey.

The modular design maximizes utility by employing standardized rather than customized components in large-scale systems. From a manufacturing view, this approach contributes to green technology by reducing material waste and enhancing component reusability. Additionally, from an industrial view, it offers significant economic advantages by leveraging economies of scale, making it a highly reasonable strategy. Modularization is generally achieved by selecting a representative design that satisfies all required performance from a predefined set of designs. However, achieving effective modularization in mechanical mechanism systems presents additional challenges. First, mechanisms depend on geometric relationships, and varying loads yield different optimal designs. Second, selecting a single design to cover all conditions for modularization inherently results in an over-specified solution. This results in inevitable performance deviations, which become pronounced as the design scale increases. To address these challenges, a modular mechanism design framework based on surrogate-based design optimization is proposed. Surrogate-based optimization is utilized to obtain optimal design solutions for each condition in large-scale engineering systems. The generated optimal designs are partitioned into multiple groups, each unified by a representative design covering all its conditions. Thus, it leads to a multi-objective optimization (MOO) problem that aims to maximize economies of scale while minimizing performance deviations. Unlike conventional approaches, which primarily rely on intuitive design candidates and simple grouping strategies, the proposed framework generates optimal solutions directly, enabling the selection of optimal layouts. Additionally, it analyzes manufacturing cost parameters to support strategy selection for various design scenarios. This approach enhances maintainability and promotes environmentally sustainable manufacturing.

In this paper we introduce a formal model for reasoning about resource sensitive timed component connectors. We extended the constraint automata model, which is used as the semantic model for the exogenous channel-based coordination language Reo, through integrating both resource and time information. This model allows to specify both the interactions that take time to be performed and timeouts. Moreover, the model reflects resource issues, such as bandwidth or allocated memory, that may affect the time needed for interactions when specifying the timed behavior of connectors. The time duration that an interaction takes is represented by a function on the available resources. In addition to the formalism, we also discuss compositional reasoning and present two notions of simulation to relate different connectors from functional and resource-sensitive temporal perspectives respectively.

Software systems contain multiple types of interrelations among components — data, control, and sequencing, among others. We are developing interconnectivity analysis techniques that derive multiple views of the structure of large-scale software systems. These techniques calculate interconnections among components and then recursively group the components into sets according to their degree of interconnection. These techniques are especially suited to large-scale systems (e.g., > 100,000 lines) since numerous types of interconnections can be determined automatically in a tractable manner. Interconnectivity analysis techniques produce visualizations of system structure and can be used to document systems, model their evolution over time, compare system structure, guide regression testing, or localize error-prone structure. In earlier work, one such technique was applied effectively in a feasibility study to characterize the error-prone components in a large-scale system from a production environment. Tools supporting interconnectivity analysis will be integrated into the Amadeus measurement-driven analysis and feedback system.

Recently there has been much emphasis on inclusion of technology to improve student learning in engineering and this has proven to be very effective in a variety of engineering courses. The objective of this paper is to supplement technology-based education by teaching fundamental concepts using emptyhanded demonstrations. This instructive technique is defined as one that is accomplished by using whatever is typically available in or near traditional classrooms and by simulating concepts with students and/or faculty as system components. The defining feature of this strategy is that it does not require special equipment or software to clarify fundamental engineering concepts. Empty-handed demonstrations are dynamic, illustrative and interactive, and increase course effectiveness. Forms of active learning that engage students beyond lecture have been shown to have lasting value in students' memory (Bransford et al., 2000; Felder & Brent, 2003). Our ambition is to generate a conference session to demonstrate, share, compile, and catalog some of these no-tech teaching tools. Our secondary objective is to stimulate engineering educators to think “inside box” of classroom and realize that, with some ingenuity, world around us is replete with modeling and teaching opportunities. This paper provides several examples of empty-handed demos from a variety of engineering disciplines describing goals, materials and set up, implementation strategies, and inspiration and lessons for each demonstration. Introduction Much of classic learning model is about acquisition of knowledge and its subsequent application. Effective education provides a means for students to absorb and comprehend relevant information so that they can retain and apply that knowledge. The undisputed fundamental components of engineering education are those that are described in textbooks and those that we, as professors, introduce in our lectures and labs. Sometimes having students read books and listen to lectures is not enough and thus greater depth and challenge must be provided in their education. Depth of understanding has often been provided by encouraging students to become personally involved in activities that simulate or mirror circumstances under discussion. This teaching strategy is similar to techniques of interactive engagement (IE) referred to by Hake (1998). Hake maintains that the use of IE strategies can increase course effectiveness beyond that obtained with traditional methods. Strategies of active engagement and active learning, while beneficial in their instructive qualities, can be prohibitive unless they efficiently and effectively carried out, with minimal cost to engineering program. It is preferable to keep them simple in content to avoid educational messages being lost in complexity of set up. An ideal example of this is human histogram created in an engineering statistics course at University of Oklahoma to illustrate birthday distributions and related probability measures (Rhoads, 2004). The students physically line up in categories to form and observe birthday frequency distribution. They use their own birth date data to calculate relevant probability measures and confirm established equations related to these statistics. In this paper, we consider situations -like statistics example abovethat have been found to be problematic for student comprehension and provide examples of activities that have been successfully applied to illustrate course topics. We shown how everyday examples in life and ordinary materials in our immediate surroundings can be used to illustrate broad engineering principles and inspire engineering ingenuity. They are described as “empty-handed demos” because each exercise described requires no or minimal materials to conduct and none requires technology. Furthermore, professor remains virtually empty-handed throughout presentations, primarily driving and supervising activity and allowing students’ participation, observations, and experiences to catalyze learning process.

Coordination languages, as Reo, have emerged for the specification and implementation of interaction protocols among concurrent entities. In this paper, we propose a framework for generating partially-distributed, partially-centralized implementations of Reo connectors to improve 1) build-time compilation and 2) run-time throughput and parallelism. Our framework relies on the definition of a new formal product operator on constraint automata (Reo's formal semantics), which enables the formally correct distribution of disjoint parts of a coordination scheme over different machines according to several possible motivations (e.g., performance, privacy, QoS constraints, resource availability, network topology). First, we describe the design and a proof-of-concept implementation of our framework. Then, in a case study, we show and explain how a generated connector implementation can be executed in the Cloud and supports Big Data coordination.

Compositional model checking with divergence preserving branching bisimilarity is lively

Background: The digital environment possesses a strong influence on the learning environment and enhances the development and refinement of both teachers' and learners’ preexisting skills. Essentially, teaching and learning with ICT encourages learner-centered approach which is associated with more learner involvement and active learning. Method: The qualitative interpretive approach was used. A multi-case study design was used. Purposeful sampling and random sampling techniques were used to select the participants. Data were collected using in-depth semi-structured interviews with fifteen teachers. Result: The findings revealed that teachers lacked ICT skills. Also, the lack of ICT infrastructure and internet access made it difficult for teachers to help learners use ICT and inhibited active learning. Thus, training could enable teachers to acquire ICT skills to improve the teachinglearning process. Conclusion: Therefore, making ICT an integral component of the rural primary school education system is a serious issue for policy-making and planning to enhance learner participation, collaboration, and communication processes during the teaching-learning process. Furthermore, mechanisms, such as ICT training, provision of laptops, and improved internet access could provide opportunities for learners to utilize technology during the teaching-learning process in rural primary schooling.

The use of computer simulations has become a viable alternative for real-life controlled experiments. Due to the computational cost of these high fidelity simulations, surrogate models are often employed as an approximation for the original simulator. Because of their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, optimization, visualization and sensitivity analysis. Additionally, multiple surrogate models can be chained together to model large scale systems where the direct use of the original expensive simulators would be too cumbersome. Many surrogate model types, such as neural networks, support vector machines and rational models have been proposed, and many more techniques have been developed to minimize the number of expensive simulations required to train a sufficiently accurate surrogate model. In this chapter, we present a fully automated and integrated global surrogate modeling methodology for regression modeling and active learning that readily enables the adoption of advanced global surrogate modeling methods by application scientists. The work brings together insights from distributed systems, artificial intelligence, and modeling & simulation, and has applications in a very wide range of fields. The merits of this approach are illustrated with several examples, and several surrogate model types.

Coordination languages simplify design and development of concurrent systems. Particularly, exogenous coordination languages, like BIP and Reo, enable system designers to express the interactions among components in a system explicitly. In this paper we establish a formal relation between BI(P) (i.e., BIP without the priority layer) and Reo, by defining transformations between their semantic models. We show that these transformations preserve all properties expressible in a common semantics. This formal relation comprises the basis for a solid comparison and consolidation of the fundamental coordination concepts behind these two languages. Moreover, this basis offers translations that enable users of either language to benefit from the toolchains of the other.

This paper discusses some intuitions about models of concurrency. Several examples of concurrent systems are presented to illustrate different kinds of concurrent computational structures. I argue that supporting large-scale concurrent systems requires building on some surprisingly simple programming primitives. However, some models of concurrency cannot support these primitives. I describe the actor model which incorporates these primitives and illustrate its potential use in supporting different programming paradigms. Developments in concurrent computers are briefly discussed; specifically, I argue that because of their scalability, multicomputers represent the most promising development in computer architecture. Multicomputers provide a powerful realization of the actor model. Finally, I sketch the relation between different programming paradigms and actors and argue for the advantages of multiparadigm programming systems obtained by embedding different programming languages in an actor base.

Initialization methods are needed for geophysical components of Earth system prediction models. These methods are needed from medium-range to decadal predictions and also for short-range Earth system forecasts in support of safety (e.g., severe weather), economic (e.g., energy), and other applications. Strongly coupled land–atmosphere data assimilation (SCDA), producing balanced initial conditions across the land–atmosphere components, has not yet been introduced to operational numerical weather prediction (NWP) systems. Most NWP systems have evolved separate data assimilation (DA) procedures for the atmosphere versus land/snow system components. This separated method has been classified as a weakly coupled DA system (WCDA). In the NOAA operational short-range weather models, a moderately coupled land–snow–atmosphere assimilation method (MCLDA) has been implemented, a step forward from WCDA toward SCDA. The atmosphere and land (including snow) variables are both updated within the DA using the same set of observations (aircraft, radiosonde, satellite radiances, surface, etc.). Using this assimilation method, land surface state variables have cycled continuously for 6 years since 2015 for the 3-km NOAA HRRR model and with CONUS cycling since 1997. Month-long experiments were conducted with and without MCLDA for both winter and summer seasons using the 13-km Rapid Refresh model with atmosphere (50 levels), soil (9 levels), and snow (up to 2 layers if present) on the same horizontal grid. Improvements were evident for 2-m temperature for all times of day out to 6–12 h for both seasons but stronger in winter. Better temperature forecasts were also shown in the 1000–900-hPa layer corresponding roughly to the boundary layer. Significance Statement Accuracy of weather models depends on accurate initial conditions for soil temperature and moisture as well as for the atmosphere itself. This paper describes a moderately coupled data assimilation method that modifies soil conditions based on forecast error corrections indicated by atmospheric observations. This method has been tested for a month-long period in summer and winter and shown to consistently improve short-range forecasts of 2-m temperature and moisture. This coupled data assimilation method is used already in NOAA operational short-range models to improve its prediction skill for clouds, convective storms, and general weather conditions.