An Adaptive Application Framework with Customizable Quality Metrics

2022 ◽  
Vol 27 (2) ◽  
pp. 1-33
Author(s):  
Liu Liu ◽  
Sibren Isaacman ◽  
Ulrich Kremer

Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget. An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time.

2020 ◽  
Vol 10 (8) ◽  
pp. 2675
Author(s):  
Weiwen Zhang ◽  
Jianqi Liu ◽  
Lianglun Cheng ◽  
Ricardo Shirota Filho ◽  
Fei Gao

With an increasing number of aircraft systems, a fully manual developmental approach is impractical for finding optimal hardware and software mapping from overwhelming configurations for Distributed Integrated Modular Avionics (DIMA) systems. The automation of finding such optimized mapping should be available and thoroughly understood. This paper is an investigation on the foundations of optimal hardware and software mapping for DIMA. We begin by reviewing the DIMA system architecture. Following that, we present the problem statement of hardware and software mapping and its ensuring mathematical optimization models. A set of primary architectural quality metrics (e.g., reliability and scalability) and aircraft constraints (e.g., segregation and resource constraints) are identified, which can be used to compose an objective function or compare and trade alternatives. Based on the quality metrics and aircraft constraints, we synthesize an encompassing formulation by means of multi-objective optimization. Various optimization approaches for hardware and software mapping are then reviewed and compared. Case studies of DIMA optimization are presented for avionics systems, in which running time is reported for different optimization problems with different objectives and constraints. In addition, we present and discuss open issues and future trends, from which future developments may draw upon.


2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>


1986 ◽  
Vol 25 (2) ◽  
pp. 175-192
Author(s):  
Shahrukh Rafi Khan ◽  
Naushin Mahmood ◽  
Rehana Siddiqui

Planning documents for the Seventies emphasized the importance of primary education and the curtailment of the mushrooming growth at the higher level. Our review suggests that this policy has had only partial success in implementation. Viewed in the context of educational planning theory and the evidence available for Pakistan, the policy is found to be sound. While the benefits of a correct distribution of investment within the educational sector are self-evident, resource constraints have been leading to an overall underinvestment in the educational sector. We show that Pakistan's public sector education is highly subsidized and so to supplement the limited resources devoted to it, we recommend, as a possible solution, a selective application of user charges.


2020 ◽  
Author(s):  
Madeleine Pownall

Currently under review at Psychology Teaching Review. Over recent years, Psychology has become increasingly concerned with reproducibility and replicability of research findings (Munafò et al., 2017). One method of ensuring that research is hypothesis driven, as opposed to data driven, is the process of publicly pre-registering a study’s hypotheses, data analysis plan, and procedure prior to data collection (Nosek, Ebersole, DeHaven, &amp; Mellor, 2018). This paper discusses the potential benefits of introducing pre-registration to the undergraduate dissertation. The utility of pre-registration as a pedagogic practice within dissertation supervision is also critically appraised, with reference to open science literature. Here, it is proposed that encouraging pre-registration of undergraduate dissertation work may alleviate some pedagogic challenges, such as statistics anxiety, questionable research practices, and research clarity and structure. Perceived barriers, such as time and resource constraints, are also discussed.


2016 ◽  
Vol 6 (7) ◽  
Author(s):  
Nancy E. Dunlap ◽  
◽  
David J. Ballard ◽  
Robert A. Cherry ◽  
Wm. Claiborne Dunagan ◽  
...  

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