Multi-dimensional Problem Space Research Articles

Necessity, Entailment, Shared Agonism

This short paper offers a series of responses to Jochem Zwier and Timothy Barker’s comments on my extended paper ‘Taking Exception: Philosophy of Technology as a Multidimensional Problem Space.’ Part one responds to questions concerning the modality of the renewed understanding of the theme of the transcendental that was argued for in my initial paper: I argue for the deep contingency of such a move, against any sense that it is necessary. Part two takes this consideration of modality further, considering the possibilities that a renewal of the theme of the transcendental stands to offer philosophy of technology today. I argue that the contingency of our contemporary sense of the transcendental can be precisely what makes it valuable. Whereas parts one and two turn on incisive questions posed by Zwier, part three closes by reconsidering the claims for a ‘multidimensional problem space’ offered in my initial paper. In response to an acute insight from Barker, I close by arguing that philosophy of technology’s problem space should be explored in terms of a notion of ‘shared agonism’.

The Transcendental and the Agonistic: A Media Philosophy Perspective

This critical response to Dominic Smith’s ‘Taking Exception: Philosophy of Technology as a Multidimensional Problem Space’ begins by outlining the key contributions of his essay, namely his insightful approach to the transcendental, on the one hand, and his introduction of the topological problem space as an image for thought, on the other. The response then suggests ways of furthering this approach by addressing potential reservations about determinism. The response concludes by suggesting a way out of these questions of determinism by thinking the transcendental in concert with the agonistic.

Taking Exception: Philosophy of Technology as a Multidimensional Problem Space

This essay develops three key claims made in my 2018 book, Exceptional Technologies. Part one argues for ‘trivialising the transcendental’, to remove stigmas attached to the word ‘transcendental’ in philosophy in general and philosophy of technology in particular. Part two outlines the concept of ‘exceptional technologies’. These are artefacts and practices that show up as limit cases for our received pictures of what constitutes a ‘technology’ (what I refer to as our ‘pictures of method’) and that force us to reassess the conditions for the possibility of these pictures. I focus on the case of autonomous vehicles here, arguing that Google Street View provides a relatively better picture for approaching philosophical issues at stake than the famous ‘Trolley Problem’. Part three then concludes with a focus on Heidegger’s ‘Question Concerning Technology’ essay. Heidegger asserts that philosophical questioning ‘builds a way’ (1977: 3). I argue that philosophical approaches to technologies might better be considered in terms of a multidimensional problem space.

Toward a Reference Architecture Model for Industrial Edge Computing

In the context of the digital transformation of the industry, whole value chains get connected across various application domains as long as economic, ecological, or social benefits arise to do so. Under the umbrella of the Industrial Internet of Things (IIoT), traditional operational technology (OT) approaches are replaced or at least augmented by information and communication technology (ICT) systems to facilitate this development. To meet industrial requirements, for example, related to privacy, determinism, latency, or autonomy, established cloud computing mechanisms are being moved closer to data sources and actuators. Depending on the context, this distributed cloud computing paradigm is named edge computing or fog computing, and various challenges have been subject to several publications. However, a proper reference model that describes the multi-dimensional problem space, which is being spanned by this paradigm, seems still to be undefined. Such a model should provide orientation, put work in relation, and support the identification of current and future research issues. This article aims to fill this gap with a focus on industrial automation and follows analog models that have been developed for specific domains such as the Smart Grid Architecture Model (SGAM) and the Reference Architecture Model Industrie 4.0 (RAMI4.0). The proposed Reference Architecture Model Edge Computing (RAMEC) identifies 210 views on the edge computing paradigm in the manufacturing domain. Future iterations of this model might be used for the classification of relevant research, standardization, and development activities.

Rendering a multi-dimensional problem space as an unfolding collaborative inquiry process

This study investigates a group working together in an authentic work setting as they collaboratively render a problem space. We employed an existing data set of a group designing for an European automaker. We used a collaborative inquiry paradigm as a lens for investigation. We analysed the dimensions of the problem space the group renders and the collaborative inquiry practices used. The group rendered a multi-dimensional problem space integrating user experience and organizational considerations for realizing user-centred products. We observed collaborative inquiry practices as four modes of evoking ways of knowing and two modes of building coherence. This study offers a new perspective on design as collaborative inquiry – a social process of building coherence to co-construct valid knowledge.

A new binary hybrid particle swarm optimization with wavelet mutation

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm, where individual elements, termed as particles, move around a multi-dimensional problem space at different directions (i.e., trajectories) and speeds (i.e., velocities) to find the best solution for non-deterministic polynomial time. The movement for a particle is determined by its previous best result and the previous best result of the entire population. In one of the current PSO variants called Hybrid Particle Swarm Optimisation (HPSOWM), where a mutation process based on wavelet theory was added to the original PSO to prevent premature conclusion of the best solution. This hybridisation with PSO has improved both solution stability and quality over the original algorithm as well as many other hybrid PSO algorithms. However, this solution is not practicable and very limited to work on a continuous problem space. As such, in this research, we propose a discrete binary HPSOWM which operates on binary-based problem space, and termed as “Binary Hybrid Particle Swarm Optimization with Wavelet Mutation” (BHPSOWM). In this work, the movement mechanism of particles as well as the mutation process are modified and transformed to work with binary elements. We conduct a series of experiments to compare the performance of the binary versions of three algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (BPSO) against our proposed algorithm - BHPSOWM. The experiment results show that our proposed model delivers better performance in terms of the mean cost value, standard deviation and the convergence rate under the same settings.

EM-KDE: A locality-aware job scheduling policy with distributed semantic caches

In modern query processing systems, the caching facilities are distributed and scale with the number of servers. To maximize the overall system throughput, the distributed system should balance the query loads among servers and also leverage cached results. In particular, leveraging distributed cached data is becoming more important as many systems are being built by connecting many small heterogeneous machines rather than relying on a few high-performance workstations. Although many query scheduling policies exist such as round-robin and load-monitoring, they are not sophisticated enough to both balance the load and leverage cached results. In this paper, we propose distributed query scheduling policies that take into account the dynamic contents of distributed caching infrastructure and employ statistical prediction methods into query scheduling policy.We employ the kernel density estimation derived from recent queries and the well-known exponential moving average (EMA) in order to predict the query distribution in a multi-dimensional problem space that dynamically changes. Based on the estimated query distribution, the front-end scheduler assigns incoming queries so that query workloads are balanced and cached results are reused. Our experiments show that the proposed query scheduling policy outperforms existing policies in terms of both load balancing and cache hit ratio.

Opposition-based differential evolution for IIR system identification problem

Nature inspired optimization algorithms have made substantial step towards solving of various engineering and scientific real-life problems. Success achieved for those evolutionary optimization techniques are due to simplicity and flexibility of algorithm structures. In this paper, optimal set of filter coefficients are searched by the evolutionary optimization technique called Opposition-based Differential Evolution (ODE) for solving infinite impulse response (IIR) system identification problem. Opposition-based numbering concept is embedded into the primary foundation of Differential Evolution (DE) technique metaphorically to enhance the convergence speed and the performance for finding the optimal solution. The population is generated with the evaluation of a solution and its opposite solution by fitness function for choosing potent solutions for each iteration cycle. With this competent population, faster convergence speed and better solution quality are achieved. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters for mutation, crossover and selection adopted in the basic DE technique. When tested against standard benchmark examples, for same order and reduced order models, the simulation results establish the ODE as a competent candidate to others in terms of accuracy and convergence speed.

A new design method using opposition-based BAT algorithm for IIR system identification problem

BAT algorithm (BA) is a meta-heuristic algorithm, based on the echolocation behaviour of bats. In this paper, optimal set of filter coefficients is searched by the modified optimisation methodology called opposition-based BAT algorithm (OBA) for infinite impulse response (IIR) system identification problem. Opposition based numbering concept is embedded into the primary foundation of BA metaphorically to enhance the convergence speed and performance for finding better near-global optimal solution. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters of OBA technique. When tested against standard benchmark examples, for same and reduced order models, the simulation results establish the OBA as a more competent candidate to other evolutionary algorithms as real coded genetic algorithm (RGA), differential evolution (DE) and particle swarm optimisation (PSO) in terms of accuracy and convergence speed.

Multiobjective Decision Processes under Uncertainty: Applications, Problem Formulations, and Solution Strategies

We consider the decision-making problems that firms face when operating in a changing and uncertain environment. Problems of this type arise in many important decision contexts in various industries and pose challenges for both practitioners and researchers. This paper is a contribution to the development of a general framework for formulating and solving this class of problems through an investigation of current applications and solution methodologies. We consider a general class of problems: multiobjective decision processes under uncertainty, with concentration on its application areas, problem formulations, and solution strategies. A morphology classifies the relevant literature by projecting the problems reported onto a multidimensional problem space. A problem of coordinated capacity planning and inventory control serves as an example of this problem class to illustrate the issues related to formulations and solutions throughout the paper. We develop an approximation architecture that decomposes th...

Why Has Critique Run out of Steam? From Matters of Fact to Matters of Concern

Wars. Somanywars.Wars outside andwars inside.Culturalwars, science wars, and wars against terrorism.Wars against poverty andwars against the poor. Wars against ignorance and wars out of ignorance. My question is simple: Should we be at war, too, we, the scholars, the intellectuals? Is it really our duty to add fresh ruins to fields of ruins? Is it really the task of the humanities to add deconstruction to destruction? More iconoclasm to iconoclasm?What has become of the critical spirit? Has it run out of steam? Quite simply, my worry is that it might not be aiming at the right target. To remain in the metaphorical atmosphere of the time, military experts constantly revise their strategic doctrines, their contingency plans, the size, direction, and technology of their projectiles, their smart bombs, theirmissiles; I wonder why we, we alone, would be saved from those sorts of revisions. It does not seem to me that we have been as quick, in academia, to prepare ourselves for new threats, new dangers, new tasks, new targets. Are wenot like thosemechanical toys that endlesslymake the samegesturewhen everything else has changed around them? Would it not be rather terrible if we were still training young kids—yes, young recruits, young cadets—for wars that are no longer possible, fighting enemies long gone, conquering territories that no longer exist, leaving them ill-equipped in the face of threats we had not anticipated, for whichwe are so thoroughlyunprepared? Generals have always been accused of being on the ready one war late— especially French generals, especially these days. Would it be so surprising,

Evidence of second‐order factor structure in a diagnostic problem space: Implications for medical education

Classification tasks form an integral part of diagnosing a patient's medical problem. As experience is gained, knowledge of a medical problem space becomes represented by various disease classes. This knowledge and classification task ability for a medical problem space has been previously represented in an artificial intelligence setting using a knowledge‐based inference tool simulation. In this study, chest pain was defined as a specific medical problem space, and the various associated disease classes were modeled to define it. Results indicated a second‐order factor structure that related these various disease classes to chest pain. Findings suggest that chest pain is a multidimensional problem space. This has implications for how medical education and differential diagnostic problem‐solving instruction should be taught.

Knowledge Acquisition for Image Understanding Research

Use of knowledge has facilitated complex problem solving in many areas of research. However, in the image understanding area, we do not have any systematic treatment and codification of knowledge that is useful in image perception. Further, we do not even have adequate tools for acquiring the necessary knowledge base. In this paper we present an experimental paradigm for knowledge acquisition, discuss an analysis technique, and illustrate the different types of knowledge that seem to be useful in image understanding research. Three major respects of knowledge are presented: primitive feature extraction operators, rewriting rules, and flow of control. A limited number of feature extraction operators were repeatedly used by the subjects to specify location, size, shape, quantity, color, texture, and patterns , of various components found in scenes. Six types of rewriting rules were identified; assertions, negative assertions, context-free, conditional, generative, and analytical inferences . Flow of control exhibited characteristics of an hypothesize and test paradigm capable of using imprecise, conflicting hypotheses in cooperation with others in a multidimensional problem space.