Active learning of interface programs

Computer systems today are no longer monolithic programs; instead they usually comprise multiple interacting programs. With the continuous growth of these systems and with their integration into systems of systems, interoperability becomes a fundamental issue. Integration of systems is more complex and occurs more frequently than ever before. One solution to this problem could be the automated model-based synthesis of mediators at runtime. However, this approach has strong prerequisites. It requires the existence of adequate models of the systems to be connected. Many systems encountered in practice, on the other hand, do not come with models. In such cases models have to be constructed ex post (at runtime). Furthermore, adequate models must capture control as well as data aspects of a system. In most protocols, for instance, data parameters (e.g., session identifiers or sequence numbers) can influence system behavior. Models of such systems can be thought of as interface programs: Rather than covering only the control behavior, they describe explicitly which data values are relevant to the communication and have to be remembered and reused. This thesis addresses the problem of inferring interface programs of systems at runtime using active automata learning techniques. Active automata learning uses a test-based and counterexample-driven approach to inferring models of black-box systems. The method has originally been introduced for finite automata (the popular L* algorithm). Extending active learning to interface programs requires research in three directions: First, the efficiency of active learning algorithms has to be optimized to scale when dealing with data parameters. Second, techniques are needed for finding counterexamples driving the learning process in practice. Third, active learning has to be extended to richer models than Mealy machines or DFAs, capable of expressing interface programs. The work presented in this thesis improves the state of the art in all three directions. More concretely, the contributions of this thesis are the following: first, an efficient active learning algorithm for DFAs and Mealy machines that combines the ideas of several known active learning algorithms in a non-trivial way; second, a framework for finding counterexamples in black-box scenarios, leveraging the incremental and monotonic evolution of hypothetical models characteristic of active automata learning; third, and most importantly, the technically involved extension of the partition/refinement-based approach of active learning to interface programs. The impact of extending active learning to interface programs becomes apparent already for small systems. We inferred a simple data structure (a nested stack of overall capacity 16) as an interface program in no more than 20 seconds, using less than 45,000 tests and only 9 counterexamples. The corresponding Mealy machine model, on the other hand, would have more than 10 to the power of 9 states already in the case of a very small finite data domain of size 4 and require significantly more than 10 to the power of 9 tests when being inferred using the classic L* algorithm

Penguatan Keterampilan Guru dalam Pemanfaatan GeoGebra sebagai Media Pembelajaran Program Linear

To support students facing rapid technological development, teachers must provide real experience in the learning process. Linear programming materials that require visualization in their understanding can be taught by integrating GeoGebra software. This community service aims to strengthen teacher skills in using GeoGebra for linear programming learning media. Community service is carried out through training and mentoring with an active learning participant approach. The activities are carried out through three core stages: teacher training in mastering the GeoGebra interface, assistance in visualizing linear programming problems, and aid in solving linear programs. Although most of the participants stated that GeoGebra could help learn linear programming in the classroom, its implementation needs to consider various aspects, such as the preparation of learning tools and equipment, learning time, and student readiness

Blended Learning in Anesthesia Education: Current State and Future Model

Purpose of review: Educators in anesthesia residency programs across the country are facing a number of challenges as they attempt to integrate blended learning techniques in their curriculum. Compared with the rest of higher education, which has made advances to varying degrees in the adoption of online learning anesthesiology education has been sporadic in the active integration of blended learning. The purpose of this review is to discuss the challenges in anesthesiology education and relevance of the Universal Design for Learning framework in addressing them. Recent findings: There is a wide chasm between student demand for online education and the availability of trained faculty to teach. The design of the learning interface is important and will significantly affect the learning experience for the student. Summary: This review examines recent literature pertaining to this field, both in the realm of higher education in general and medical education in particular, and proposes the application of a comprehensive learning model that is new to anesthesiology education and relevant to its goals of promoting self-directed learning

Novis: A notional machine implementation for teaching introductory programming

Comprehension of programming and programs is known to be a difficult task for many beginning students, with many computing courses showing significant drop out and failure rates. In this paper, we present a notional machine imple- mentation, Novis, to help with understanding of program- ming and its dynamics for beginning learners. The notional machine offers an abstraction of the physical machine de- signed for comprehension and learning purposes. Novis pro- vides a real-time visualisation of this notional machine, and is integrated into BlueJ

Deuce: A Lightweight User Interface for Structured Editing

We present a structure-aware code editor, called Deuce, that is equipped with direct manipulation capabilities for invoking automated program transformations. Compared to traditional refactoring environments, Deuce employs a direct manipulation interface that is tightly integrated within a text-based editing workflow. In particular, Deuce draws (i) clickable widgets atop the source code that allow the user to structurally select the unstructured text for subexpressions and other relevant features, and (ii) a lightweight, interactive menu of potential transformations based on the current selections. We implement and evaluate our design with mostly standard transformations in the context of a small functional programming language. A controlled user study with 21 participants demonstrates that structural selection is preferred to a more traditional text-selection interface and may be faster overall once users gain experience with the tool. These results accord with Deuce's aim to provide human-friendly structural interactions on top of familiar text-based editing.Comment: ICSE 2018 Paper + Supplementary Appendice

Flexible learning systems : an insight into personalised learning systems

Web services are defined as accessible software programs ex- posed through an Internet interface description which enhances client to server requests and are not only easily invoked and consumed but they provide interoperability for applications through Service-Oriented Architectures. The Semantic Web, Web services and Web technologies, have so far been mostly utilised in business models and processes throughout industry. This research paper proposes to show how these emergent technologies are also being exploited for E-learning environments. Such a service applies in fact not only to businesses and the work-place but also to academic settings. The ability to make a provision for flexible, personalised and adaptable services is heavily dependent on Web technologies which need to be moulded into rich, dynamic and active environments based on individual user needs and requirements. The paper aims to highlight ongoing projects in this area offering a brief description of their findings and achievements as well as identify future trends in the areas of flexible learning systems.peer-reviewe

Load flow studies on stand alone microgrid system in Ranau, Sabah

This paper presents the power flow or load flow analysis of Ranau microgrid, a standalone microgrid in the district of Ranau,West Coast Division of Sabah. Power flow for IEEE 9 bus also performed and analyzed. Power flow is define as an important tool involving numerical analysis applied to power system. Power flow uses simplified notation such as one line diagram and per-unit system focusing on voltages, voltage angles, real power and reactive power. To achieved that purpose, this research is done by analyzing the power flow analysis and calculation of all the elements in the microgrid such as generators, buses, loads, transformers, transmission lines using the Power Factory DIGSilent 14 software to calculate the power flow. After the analysis and calculations, the results were analysed and compared

Optimization-based interactive segmentation interface for multiregion problems.

Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality