74,279 research outputs found

    Quantified Constraints in Twenty Seventeen

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    I present a survey of recent advances in the algorithmic and computational complexity theory of non-Boolean Quantified Constraint Satisfaction Problems, incorporating some more modern research directions

    Bureaucratic routines and error management in algorithmic systems

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    This article discusses how an analogy between algorithms and bureaucratic decision-making could help conceptualize error management in algorithmic systems. It argues that a view of algorithms as irreflexive bureaucratic processes is insufficient as an account of errors in complex public sector contexts, where algorithms operate jointly with other organizational work practices. To conceptualize such contexts, the article proposes that algorithms could be viewed as analogous to work routines in bureaucratic organizations. Doing so helps clarify that algorithmic irreflexivity becomes problematic when the coordination of routine work around automation fails. Thus, also the challenges of error management come to concern the wider context of organized work. This argument is illustrated using known examples from the critical literature on algorithms. Finally, drawing on recent studies in routine dynamics, the article formulates empirical research directions on error management in algorithmic systems.This article discusses how an analogy between algorithms and bureaucratic decision-making could help conceptualize error management in algorithmic systems. It argues that a view of algorithms as irreflexive bureaucratic processes is insufficient as an account of errors in complex public sector contexts, where algorithms operate jointly with other organizational work practices. To conceptualize such contexts, the article proposes that algorithms could be viewed as analogous to more traditional work routines in bureaucratic organizations. Doing so helps clarify that algorithmic irreflexivity becomes problematic when the coordination of routine work around automation fails. Thus, also the challenges of error management come to concern the wider context of organized work. This argument is illustrated using known examples from the critical literature on algorithms. Finally, drawing on recent studies in routine dynamics, the article formulates empirical research directions on error management in algorithmic systems.Peer reviewe

    Improving fairness in machine learning systems: What do industry practitioners need?

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    The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

    Cultural Evolution as Distributed Computation

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    The speed and transformative power of human cultural evolution is evident from the change it has wrought on our planet. This chapter proposes a human computation program aimed at (1) distinguishing algorithmic from non-algorithmic components of cultural evolution, (2) computationally modeling the algorithmic components, and amassing human solutions to the non-algorithmic (generally, creative) components, and (3) combining them to develop human-machine hybrids with previously unforeseen computational power that can be used to solve real problems. Drawing on recent insights into the origins of evolutionary processes from biology and complexity theory, human minds are modeled as self-organizing, interacting, autopoietic networks that evolve through a Lamarckian (non-Darwinian) process of communal exchange. Existing computational models as well as directions for future research are discussed.Comment: 13 pages Gabora, L. (2013). Cultural evolution as distributed human computation. In P. Michelucci (Ed.) Handbook of Human Computation. Berlin: Springe

    The General Combinatorial Optimization Problem: Towards Automated Algorithm Design

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    This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimisation Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.e. compositions of algorithmic components, thus represent different generic search algorithms. The objective of GCOP is to find the optimal algorithmic compositions for solving the given optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best algorithms for optimisation problems. Despite recent advances, the evolutionary computation and optimisation research communities are yet to embrace formal standards that underpin automated algorithm design. In this position paper, we establish GCOP as a new standard to define different search algorithms within one unified model. We demonstrate the new GCOP model to standardise various search algorithms as well as selection hyper-heuristics. A taxonomy is defined to distinguish several widely used terminologies in automated algorithm design, namely automated algorithm composition, configuration and selection. We would like to encourage a new line of exciting research directions addressing several challenging research issues including algorithm generality, algorithm reusability, and automated algorithm design

    A survey on metaheuristics for stochastic combinatorial optimization

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    Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this fiel

    A Review on Recent Advances in Video-based Learning Research: Video Features, Interaction, Tools, and Technologies

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    Human learning shifts stronger than ever towards online settings, and especially towards video platforms. There is an abundance of tutorials and lectures covering diverse topics, from fixing a bike to particle physics. While it is advantageous that learning resources are freely available on the Web, the quality of the resources varies a lot. Given the number of available videos, users need algorithmic support in finding helpful and entertaining learning resources. In this paper, we present a review of the recent research literature (2020-2021) on video-based learning. We focus on publications that examine the characteristics of video content, analyze frequently used features and technologies, and, finally, derive conclusions on trends and possible future research directions
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