A Tutorial Introduction to Mosaic Pascal

In this report we describe a Pascal system that has been developed for programming Mosaic multi- computers. The system that we discuss runs on our Sun workstations, and we assume some familiarity with the use thereof. We assume the reader to be also familiar with programming in Pascal, and with message-passing programs. We describe how the Pascal language has been extended to perform message passing. We discuss a few implementation aspects that are relevant only to those users who have a need (or desire) to control some machine-specific aspects. The latter requires some detailed knowledge of the Mosaic system

Weakest Preconditions for Progress

Predicate transformers that map the postcondition and all intermediate conditions of a command to a precondition are introduced. They can be used to specify certain progress properties of sequential programs

Parallel Program Design and Generalized Weakest Preconditions

No abstract available

A Distributed Implementation of a Task Pool

In this paper we present a distributed algorithm to implement a task pool. The algorithm can be used to implement a processor farm, i.e., a collection of processes that consume tasks from the task pool and possibly produce tasks into it. There are no restrictions on which process consumes which task nor on the order in which tasks are processed. The algorithm takes care of the distribution of the tasks over the processes and ensures load balancing. We derive the algorithm by transforming a sequential algorithm into a distributed one. The transformation is guided by the distribution of the data over processes. First we discuss the case of two processes, and then the general case of one or more processes

Runtime evaluation of cognitive systems for non-deterministic multiple output classification problems

Cognitive applications that involve complex decision making such as smart lighting have non-deterministic input-output relationships, i.e., more than one output may be acceptable for a given input. We refer them as non-deterministic multiple output classification (nDMOC) problems, which are particularly difficult for machine learning (ML) algorithms to predict outcomes accurately. Evaluating ML algorithms based on commonly used metrics such as Classification Accuracy (CA) is not appropriate. In a batch setting, Relevance Score (RS) was proposed as a better alternative, which determines how relevant a predicted output is to a given context. We introduce two variants of RS to evaluate ML algorithms in an online setting. Furthermore, we evaluate the algorithms using different metrics for two datasets that have non-deterministic input-output relationships. We show that instance-based learning provides superior RS performance and the RS performance keeps improving with an increase in the number of observed samples, even after the CA performance has converged to its maximum. This is a crucial result as it illustrates that RS is able to capture the performance of ML algorithms in the context of nDMOC problems while CA cannot

Integration of enhanced slot-shifting in uc/os-II

The growing complexity of embedded applications poses new challenges in the application development phase. The time-critical nature of the embedded applications leads to the use of an Real-Time Operating System (RTOS). Typically in a RTOS, applications are divided into a small number of concurrent functional units called tasks. The execution of tasks is accomplished through the scheduler of a RTOS. The job of the scheduler is to pick a task for execution using a scheduling mechanism. The existing scheduling mechanisms handles tasks with only a specific set of constraints such as period and deadline.However, applications may have a set of more complex constraints such as precedence relations between tasks and non-uniform arrival patterns of tasks. Although time-triggered schedulers can solve all these constraints off-line and scheduling decisions are also made off-line, the disadvantage of this approach is that it requires a complete knowledge of tasks and their constraints. Event-triggered schedulers partially require knowledge of tasks and their constraints, but these can handle dynamically arriving tasks with run-time mechanisms and the scheduling decisions are made online. The combination of time-triggered and event-triggered scheduler is suitable for dealing with tasks with complex constraints. Complex task constraints can be resolved during the off-line preparation phase. During run-time, those resources that are unused by the off-line guaranteed tasks can be used for the execution of dynamically arriving tasks

Designing IoT systems: patterns and managerial conflicts

The first step in a system design process is to perform domain analysis. This entails acquiring stakeholder concerns throughout the life cycle of the system. The second step is to design solutions addressing those stakeholder concerns. This entails applying patterns for solving known, recurring problems. For these there are architecture patterns and design patterns for architecture design and detailed design respectively. For Internet of Things (IoT) systems such patterns are hardly defined yet since experience is just evolving. In this paper, we propose our definition of an IoT pattern along with its formal specification, explained by a running example. IoT systems are characterized by the variety of stakeholders involved throughout their life cycle, therefore our pattern specification includes means for identifying possible conflicts between these stakeholders