High Level Of Abstraction Research Articles

Posing Creative Reduction Tasks

Reduction is a fundamental computer science (CS) notion (Schwill, 1994). In solving reduction tasks one must think at the problem level, in order to recognize suitable correlations between problems. Thinking at the problem level involves recognition of declarative features of problems. This requires a high level of abstraction. Reduction is well apparent in the more advanced stages of CS studies, but it is also relevant at earlier stages. It may serve as an important means for practice and awareness of abstraction. We designed reduction tasks for the earlier tutoring and training of algorithms students. Our designs are illustrated with three creative tasks of different characteristics. Student solutions for each task are presented and discussed. The students demonstrated different levels of abstraction, insight and flexibility in solving the tasks.

The Investment Protection Rules of the EU-Canada Trade Agreement

Abstract This article provides an in-depth analysis of the substantive protection provided to investors against indirect expropriations under the EU-Canada Free Trade Agreement (ceta) and under the Constitution of Finland. More specifically, we analyse these respective spheres of protection in a regulatory context in Finland where Canadian investors operate actively: industrial mining. The purpose of the comparative analysis is to provide tentative answers to three broad research questions: Can investors challenge legitimate public interest measures under ceta’s investment protection rules? Is the protection provided under ceta co-extensive with the protection provided under the constitutions of countries placing high on global rule of law rankings? And are countries upholding the rule of law safe from investor claims under ceta’s reformed investment protection rules? A more general purpose is to bring more depth and nuance into the debates concerning the reform of the investment treaty regime, which often travel at a high level of abstraction.

Abstract 10111: How Do Patients with Heart Failure Perceive Their Symptoms?

Effective symptom perception may prevent unplanned hospitalizations. However, patients with heart failure (HF) often face challenges that interfere with symptom perception. Little is known about how patients perceive their HF symptoms. Aim: To describe how patients with HF perceive symptoms through the processes of monitoring, awareness, and evaluation. Methods: Using a qualitative descriptive design, we conducted semi-structured in-person interviews with a purposeful sample of adults experiencing an unplanned hospitalization for a HF symptom exacerbation. We elicited how patients monitor, become aware of, and evaluate symptoms prior to hospitalization. Data were analyzed using directed qualitative content analysis. We identified symptom monitoring behaviors, types of symptoms experienced, and success or failure in linking the symptoms to HF. Categories/subcategories were developed by grouping codes based on similarities and differences. Themes were conceptualized at a higher level of abstraction by combining categories. Results: A sample of 40 patients (mean age 62 years, 50% male, median HF duration 6 years, 95% HF stage C/D) was enrolled. Patients demonstrated Body listening , which was the use of active and individualized symptom monitoring tactics that involved observing for bodily changes outside one’s usual range. Trajectory of bodily change involved the patterns or characteristics of bodily changes as they became apparent. Three subthemes— sudden and stunning change , gradual change , and fluctuating change emerged as different Trajectories of bodily changes were identified. The same types of bodily changes could develop in different trajectories. Patients simultaneously experienced all or some of the trajectory types of various symptoms. Patients evaluated symptoms through an Exclusionary process , sequentially attributing symptoms to a cause through a cognitive process of excluding possible causes until the most plausible cause remained. Conclusion: Three major themes emerged from an in-depth description of the complex symptom perception processes of monitoring, awareness, and evaluation. Identifying where patients have difficulties in symptom perception will facilitate the development of tailored interventions.

Digital-Twin Approach Predicts Fatigue Damage of Marine Risers

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30985, “From Data to Assessment Models, Demonstrated Through a Digital Twin of Marine Risers,” by Ehsan Kharazmi and Zhicheng Wang, Brown University, and Dixia Fan, SPE, Massachusetts Institute of Technology, et al., prepared for the 2021 Offshore Technology Conference, Houston, 16–19 August. The paper has not been peer reviewed. Copyright 2021 Offshore Technology Conference. Reproduced by permission. Assessing fatigue damage in marine risers caused by vortex-induced vibrations (VIV) serves as a comprehensive example of using machine-learning methods to derive assessment models of complex systems. A complete characterization of the response of such complex systems usually is unavailable despite massive experimental data and computation results. These algorithms can use multifidelity data sets from multiple sources. In the complete paper, the authors develop a three-pronged approach to demonstrate how tools in machine learning are used to develop data-driven models that can be used for accurate and efficient fatigue-damage predictions for marine risers subject to VIV. Introduction In this study, machine-learning tools are developed to construct a digital twin of a marine riser. The digital twin uses various sources of training data, including field data, experimental data, computational-fluid-dynamics simulations, extracted databases, semiempirical codes, and existing knowledge of underlying physical models. The authors also show that a well-trained digital twin can use the streaming data from a few field sensors efficiently to provide an accurate reconstruction of motion and to provide fatigue-damage prediction. Several machine-learning algorithms have been developed in the literature to predict the life span of the structure through the changes in parameters. To the best of the authors’ knowledge, most existing methods are developed as black boxes that return parameters by only feeding experimental data and therefore are ignorant of the underlying physics. In the first of three approaches, the authors enhance the capabilities of semiempirical codes by developing efficient databases through active learning. In the second approach, the LSTM-ModNet framework is applied to reconstruct and analyze the entire motion of a riser in deep water from sensor measurements through modal decomposition in space and the sequence-learning capability of recurrent neural networks in time. The formulation described in the paper provides a tool that efficiently combines different types of sensor measurements, such as strain and acceleration. In the third approach, a higher level of abstraction is introduced and the nonlinear operator that maps the inflow current velocity to the root-mean-square function of the riser response is approximated. In particular, the newly developed neural network DeepONet is used as a black box to learn the mapping between the input parameters (the inflow velocity, riser bending stiffness, and tension as a function of water depth) to the output parameters (strain, amplitude, and exciting frequencies as a function of water depth). In these approaches, data from the high-mode VIV test is used to train the networks.

Improved prediction of protein-protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines.

In this paper, for accurate prediction of protein-protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link Siamese neural network (FSNN) with the light gradient boosting machine (LGBM) classifier. The hybrid classifier (FSNN-LGBM) uses the fusion of features derived using pseudo amino acid composition and conjoint triad descriptors. The FSNN extracts the high-level abstraction features from the raw features and LGBM performs the PPI prediction task using these abstraction features. On performing 5-fold cross-validation experiments, the proposed hybrid classifier provides average accuracies of 98.70 and 98.38%, respectively, on the intraspecies PPI data sets of Saccharomyces cerevisiae and Helicobacter pylori. Similarly, the average accuracies for the interspecies PPI data sets of the Human-Bacillus and Human-Yersinia data sets are 98.52 and 97.40%, respectively. Compared with the existing methods, the hybrid classifier achieves higher prediction accuracy on the independent test sets and network data sets. The improved prediction performance obtained by the FSNN-LGBM makes it a flexible and effective PPI prediction model.

Selfish versus Selfish

Abstract Ayn Rand's controversial use of “selfish” and “selfishness” has arguably done as much or more to supply “grist” to her critics and drive people away from her philosophy than to persuade people to adopt it. This article is about her meaning of “selfish” and the common, popular meaning. Succinctly, the former is a high-level abstraction, philosophical, and mainly a way of thinking, whereas the latter is a low-level abstraction, not philosophical, and mainly a way of acting. They also have different contrast terms.

Combining Strategic Learning with Tactical Search in Real-Time Strategy Games

A commonly used technique for managing AI complexity in real-time strategy (RTS) games is to use action and/or state abstractions. High-level abstractions can often lead to good strategic decision making, but tactical decision quality may suffer due to lost details. A competing method is to sample the search space which often leads to good tactical performance in simple scenarios, but poor high-level planning. We propose to use a deep convolutional neural network (CNN) to select among a limited set of abstract action choices, and to utilize the remaining computation time for game tree search to improve low level tactics. The CNN is trained by supervised learning on game states labelled by Puppet Search, a strategic search algorithm that uses action abstractions. The network is then used to select a script — an abstract action -— to produce low level actions for all units. Subsequently, the game tree search algorithm improves the tactical actions of a subset of units using a limited view of the game state only considering units close to opponent units. Experiments in the microRTS game show that the combined algorithm results in higher win-rates than either of its two independent components and other state-of-the-art microRTS agents. To the best of our knowledge, this is the first successful application of a convolutional network to play a full RTS game on standard game maps, as previous work has focused on sub-problems, such as combat, or on very small maps.

CuSP

Graph analytics systems must analyze graphs with billions of vertices and edges which require several terabytes of storage. Distributed-memory clusters are often used for analyzing such large graphs since the main memory of a single machine is usually restricted to a few hundreds of gigabytes. This requires partitioning the graph among the machines in the cluster. Existing graph analytics systems use a built-in partitioner that incorporates a particular partitioning policy, but the best policy is dependent on the algorithm, input graph, and platform. Therefore, built-in partitioners are not sufficiently flexible. Stand-alone graph partitioners are available, but they too implement only a few policies. CuSP is a fast streaming edge partitioning framework which permits users to specify the desired partitioning policy at a high level of abstraction and quickly generates highquality graph partitions. For example, it can partition wdc12, the largest publicly available web-crawl graph with 4 billion vertices and 129 billion edges, in under 2 minutes for clusters with 128 machines. Our experiments show that it can produce quality partitions 6× faster on average than the state-of-theart stand-alone partitioner in the literature while supporting a wider range of partitioning policies.

A Formal Representation of Conjugate Verbs for Function Modeling

Abstract Modern design problems often require multi-modal, reconfigurable solutions. Function modeling is a common tool used to explore solutions in early mechanical design. Currently, function modeling formalisms minimally support the modeling of multi-modal systems in a formal manner. There is a need in function modeling to capture multi-modal system and analyze the effects of control signals and status signals on their operating modes. This paper presents the concept of functional conjugacy, where two function verbs or functional subgraphs are topological opposites of each other. The paper presents a formal representation of these conjugate verbs that formally captures the transition from one mode of operation to its topological opposite based on the existence of, or the value of, signal flows. Additionally, this paper extends functional conjugacy to functional features, which supports conjugacy-based reasoning at a higher level of abstraction. Through the example of a system-level function model of a geothermal heat pump (GHP)operating in its heating and cooling modes, this paper demonstrates the ability to support modal reasoning on function models using functional conjugacy and illustrates the modeling efficacy of the extended representation.

Rosy: An elegant language to teach the pure reactive nature of robot programming

Robotics is very appealing and is long recognized as a great way to teach programming, while drawing inspiring connections to other branches of engineering and science such as maths, physics or electronics. Although this symbiotic relationship between robotics and programming is perceived as largely beneficial, educational approaches often feel the need to hide the underlying complexity of the robotic system, but as a result fail to transmit the reactive essence of robot programming to the roboticists and programmers of the future. This paper presents Rosy, a novel language for teaching novice programmers through robotics. Its functional style is both familiar with a high-school algebra background and a materialization of the inherent reactive nature of robotic programming. Working at a higher-level of abstraction also teaches valuable design principles of decomposition of robotics software into collections of interacting controllers. Despite its simplicity, Rosy is completely valid Haskell code compatible with the ROS~ecosystem. We make a convincing case for our language by demonstrating how non-trivial applications can be expressed with ease and clarity, exposing its sound functional programming foundations, and developing a web-enabled robot programming environment.

Leveraging network-centric strategic goals in capabilities

Abstract The vision of network-centric operations is to increase operational capabilities through networked collaboration. NATO and its member nations state this vision in strategic documents at a very high level of abstraction. While suitable for giving an overall feel, current documentation renders the steps toward implementing those visions largely unsupported. We outline a method that is based on agile requirements engineering, for converting high-level strategic visions into capabilities whose forms lend themselves to incremental implementation. We illustrate the use of this method in two cases that deal with both operational capabilities and technical capabilities. We also show how the method enables one to prioritise which capabilities to develop first. We conclude that it is necessary to formulate and implement some form of explicit methodology with which to span the gap between strategic visions and an effective implementation of those visions.

Abstract P241: Why Do We Expect Clinicians to Perform the Work of Computers? Developing an Automated Stroke Registry

Background: Stroke registries are established with the primary goal of improving health care quality and performance improvement through increased adherence to evidenced based guideline recommendations. As a Joint Commission certified organization, the Comprehensive Stroke Center uses the American Heart Association’s Get With The Guidelines® (GWTG) stroke patient management tool to collect performance improvement data on patients admitted with a principle diagnosis of stroke. Data abstraction is laborious and time consuming which requires manual entry by multiple clinicians. Therefore, we developed an automated internal stroke database to improve efficiency and augment accuracy. Methods: We collaborated with software engineers and EPIC report writers to create an innovative stroke database with the capability to extract data from an Electronic Health Record (EHR) for upload to GWTG. The clinicians identified data elements for automation in the EHR. Based on final ICD-10 coding the report writer retrieved end-user data, which were organized into tables and sent to the software engineer. These variables were processed to match GWTG specifications and then presented to clinicians in the user interface for validation. Next, a CSV file was generated and uploaded to GWTG, where the remaining data was entered. Results: The case completion time was reduced from 45 minutes to one hour to an average of 25 minutes (median 23 minutes), thus achieving our primary goal. Due to the variance in the number of required data elements based on diagnosis and patient specific variables, we aggregated the data elements, automated versus required, and calculated an automation rate of 54%. Approximately half of the 414 fields required in GWTG have the potential for automation, and we continue to strive toward this goal. Due to the complexity of GWTG questions and the way data is captured in the EHR, some data elements require manual entry. Conclusions: An automated stroke registry has the potential to enhance efficiency and limit the opportunity for human error. By minimizing the clinicians’ data entry tasks to those of higher-level data abstraction, there is more time for performance improvement.

Design and Simulation of IoT Systems Using the Cisco Packet Tracer

Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants; steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SOx emissions, converter head temperature, NOx emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.

Model-Driven Simulation of Elastic OCCI Cloud Resources

Abstract Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.

LabVIEW

LabVIEW™ is unusual among programming languages in that we did not intend to create a new language but rather to develop a tool for non-programmer scientists and engineers to assist them in automating their test and measurement systems. Prior experience creating software for controlling instruments led us to the perspective that the software ought to be modeled as a hierarchy of ”virtual instruments”. The lowest level virtual instruments were simply reflections of the individual physical instruments they controlled. Higher level virtual instruments combined lower level ones to deliver more complex measurements. A frequency response virtual instrument could be implemented using a voltmeter and a sine-wave generator inside a loop that stepped through a frequency range. This was mostly an abstract concept at the time because it was hard to imagine how an existing language or tool could provide the rich yet intuitive experience of using a real instrument. Inspired by the first Macintosh computer, we quickly realized the graphical user interface would be a natural way to interact with a virtual instrument, but it also sparked our imaginations about using graphics for creating software at a higher level of abstraction. The February 1982 issue of IEEE Computer was devoted to data-flow models of computation, and it convinced us that graphical data-flow diagrams needed to be part of the solution. The major difficulty we saw, however, was the need to use cycles in the data-flow diagram to represent loops. Cycles increased complexity and made diagrams hard to understand and even harder to create. This concern led to a major innovation in creating LabVIEW: merging structured programming concepts with data-flow. We represented control-flow structures as boxes in a data-flow diagram. We knew how to reason about loops, so we could introduce them as first class elements of the graphical representation rather than being constructed from lower-level elements. A box could encapsulate the semantics of the iterative behavior; it could clearly separate the body of the loop (the diagram inside the box) from the code before and after the loop (the diagram outside the box); and, its boundary could hold iteration state information. Those fundamental concepts of ”graphical”, ”structured” and ”data-flow” enabled us to propose a software product. We staffed up a small skunkworks team to implement it. We called it LabVIEW. It was to be an engineer’s tool for automating measurement systems. At first, we were reluctant to admit that we had created a graphical programming language. When we finally did, we nicknamed it G, for Graphical language, so we could talk about the language as distinct from the integrated development environment (IDE), LabVIEW. In practice, almost everyone refers to both the language and the IDE as LabVIEW. Without intending to do so, we created a programming language radically different from those that came before, pioneering techniques of graphically creating and viewing code, eliminating manual memory management without adding garbage collection overhead, and anticipating the massively parallel systems of the modern era. LabVIEW continues to evolve and thrive after more than 30 years.

Exploring the latent variables which support SMEs to become learning organizations

Abstract The purpose of this paper is to explore the latent variables which support small and medium-sized enterprises (SMEs) in becoming learning organizations. These latent variables represent abstract ideas that cannot be measured directly in practice, and they need to be explored by using different sets of other variables or indicators defined specifically for certain research of the business environment. The concept of “the learning organization” has been introduced into knowledge management literature about 30 years ago, but due to its complexity and high level of abstractness, its implementation in real life or organizations produced insufficient results for developing a well-defined model and a convincing managerial approach. However, the attractiveness of the learning organization is so appealing that researchers continue to investigate the complex mechanisms and processes supporting it. Our research is such an effort in exploring the latent variables for the small and medium-sized enterprises from the North-Easter region of Romania in their drive of becoming learning organizations. Our research is based on a comprehensive literature search and a quantitative approach supported by a survey and statistical processing of the collected data. We processed 950 valid questionnaires obtained from 147 small and medium-sized enterprises from the North-Eastern region of Romania. Statistical processing was done by using specialized software. We perform a Factor Analysis with IBM SPSS v.19, and then a Confirmatory Factor Analysis with AMOS v.18. Finally, we obtained six latent variables that support the transformation of SMEs into learning organizations.

Pangolin

There is growing interest in graph pattern mining (GPM) problems such as motif counting. GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. However, existing systems may take hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability. We present Pangolin , an efficient and flexible in-memory GPM framework targeting shared-memory CPUs and GPUs. Pangolin is the first GPM system that provides high-level abstractions for GPU processing. It provides a simple programming interface based on the extend-reduce-filter model, which allows users to specify application specific knowledge for search space pruning and isomorphism test elimination. We describe novel optimizations that exploit locality, reduce memory consumption, and mitigate the overheads of dynamic memory allocation and synchronization. Evaluation on a 28-core CPU demonstrates that Pangolin outperforms existing GPM frameworks Arabesque, RStream, and Fractal by 49×, 88×, and 80× on average, respectively. Acceleration on a V100 GPU further improves performance of Pangolin by 15× on average. Compared to state-of-the-art hand-optimized GPM applications, Pangolin provides competitive performance with less programming effort.

QCOR

Quantum computing (QC) is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing programming models for QC were designed with fault-tolerant hardware in mind, envisioning stand-alone applications. However, the susceptibility of near-term quantum computers to noise limits their stand-alone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their classical counterparts in a heterogeneous fashion. This modus operandi calls out for a programming model and a high-level programming language that natively and seamlessly supports heterogeneous quantum-classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR , which enables single-source quantum-classical programming. Programs written using the QCOR library–based language extensions can be compiled to produce functional hybrid binary executables. After defining QCOR’s programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed-forward QC. QCOR approaches quantum-classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high-performance computing. The high level of abstraction in the language extension is intended to accelerate the adoption of QC by researchers familiar with classical high-performance computing.

Book review

Computing was in a state of crisis. As the price of the hardware continued to fall, and the power of processors continued to rise, the cost of programming the devices was becoming a major part of the overall cost. Small wonder. In 1954, although there were only a few dozen computers in existence, their programming was carried by a small army of experts coding at worst in binary code and at best in some form of assembler. Enter John Backus. This somewhat unorthodox but brilliant mathematician had been hired off the street by IBM in 1949 to work on its Selective Sequence Electronic Calculator (SSEC) computer. As a result of his experience there, he realised that the only way out of this impasse was to enable programmers to code more efficiently using a higher level of abstraction. But that would not, itself, be sufficient. It would be essential that the final binary code produced by any automatic coding system be as efficient as that being produced by the laborious hand coding.

Remarks on Certain Affinities and Differences Between Aesthetic and Scientific Practices

In 1969 Werner Heisenberg presented a paper at a symposium initiated by the Karajan Foundation in Salzburg. The theme of this symposium was the ‘significance of modern scientific knowledge—in medicine, physiology, and physics—for art, music, pedagogy and aesthetic practice.’ Heisenberg’s paper was titled “The Tendency to Abstraction in Modern Art and Science.”1. Heisenberg dissembled, preferring to avoid an approach from a technical point of view, in favour of a consideration of principle, or of a “philosophy of culture”, in order to ask whether certain tendencies in modern art, at times strange or incomprehensible, might have some parallel in the form of similar phenomena in modern science. Heisenberg was not concerned with specific forms or techniques of contemporary aesthetic or scientific practice, but with what he described as their “overall shape”. It is an interesting position, not because it afforded Heisenberg a necessarily new or privileged insight, but because unlike most discussions of the relations between art and science it did not proceed in a hegemonic manner wherein one discipline annexes and establishes sovereignty over another. In Heisenberg’s query scientific procedures did not circumscribe or annex art (as mere illustration, exemplar or ornament) and aesthetic practices did not circumscribe and annex scientific data (as argument, justification, evidence or authority). Neither was he overly concerned with an equanimity or symmetry in the relationships of these various disciplines; he was interested in certain affinities, the possibility of common grounds, in science and art as they are practised. . . .the step towards greater generality is always itself a step into abstraction—or more precisely, into the next highest level of abstraction; for the most general unites the wealth of diverse individual things or processes under a unitary point of view, which means at the same time that it disregards other features considered to be unimportant. In other words, it abstracts from them.2 It is in this context that I will situate my remarks on certain affinities and differences between scientific and aesthetic practices, by considering the possibility of their common ground in terms of abstraction, technics, and capture, (i.e., what it is that is purported to be captured, secured or preserved, in order to be represented).