174,818 research outputs found
What influences the speed of prototyping? An empirical investigation of twenty software startups
It is essential for startups to quickly experiment business ideas by building
tangible prototypes and collecting user feedback on them. As prototyping is an
inevitable part of learning for early stage software startups, how fast
startups can learn depends on how fast they can prototype. Despite of the
importance, there is a lack of research about prototyping in software startups.
In this study, we aimed at understanding what are factors influencing different
types of prototyping activities. We conducted a multiple case study on twenty
European software startups. The results are two folds, firstly we propose a
prototype-centric learning model in early stage software startups. Secondly, we
identify factors occur as barriers but also facilitators for prototyping in
early stage software startups. The factors are grouped into (1) artifacts, (2)
team competence, (3) collaboration, (4) customer and (5) process dimensions. To
speed up a startups progress at the early stage, it is important to incorporate
the learning objective into a well-defined collaborative approach of
prototypingComment: This is the author's version of the work. Copyright owner's version
can be accessed at doi.org/10.1007/978-3-319-57633-6_2, XP2017, Cologne,
German
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify
clusters based on their associations with subspaces in high dimensional spaces.
SC can be classified into hard subspace clustering (HSC) and soft subspace
clustering (SSC). While HSC algorithms have been extensively studied and well
accepted by the scientific community, SSC algorithms are relatively new but
gaining more attention in recent years due to better adaptability. In the
paper, a comprehensive survey on existing SSC algorithms and the recent
development are presented. The SSC algorithms are classified systematically
into three main categories, namely, conventional SSC (CSSC), independent SSC
(ISSC) and extended SSC (XSSC). The characteristics of these algorithms are
highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
Social norms and human normative psychology
Our primary aim in this paper is to sketch a cognitive evolutionary approach for developing explanations of social change that is anchored on the psychological mechanisms underlying normative cognition and the transmission of social norms. We throw the relevant features of this approach into relief by comparing it with the self-fulfilling social expectations account developed by Bicchieri and colleagues. After describing both accounts, we argue that the two approaches are largely compatible, but that the cognitive evolutionary approach is well- suited to encompass much of the social expectations view, whose focus on a narrow range of norms comes at the expense of the breadth the cognitive evolutionary approach can provide
A Feature-Based Analysis on the Impact of Set of Constraints for e-Constrained Differential Evolution
Different types of evolutionary algorithms have been developed for
constrained continuous optimization. We carry out a feature-based analysis of
evolved constrained continuous optimization instances to understand the
characteristics of constraints that make problems hard for evolutionary
algorithm. In our study, we examine how various sets of constraints can
influence the behaviour of e-Constrained Differential Evolution. Investigating
the evolved instances, we obtain knowledge of what type of constraints and
their features make a problem difficult for the examined algorithm.Comment: 17 Page
Evolutionary game theory: Temporal and spatial effects beyond replicator dynamics
Evolutionary game dynamics is one of the most fruitful frameworks for
studying evolution in different disciplines, from Biology to Economics. Within
this context, the approach of choice for many researchers is the so-called
replicator equation, that describes mathematically the idea that those
individuals performing better have more offspring and thus their frequency in
the population grows. While very many interesting results have been obtained
with this equation in the three decades elapsed since it was first proposed, it
is important to realize the limits of its applicability. One particularly
relevant issue in this respect is that of non-mean-field effects, that may
arise from temporal fluctuations or from spatial correlations, both neglected
in the replicator equation. This review discusses these temporal and spatial
effects focusing on the non-trivial modifications they induce when compared to
the outcome of replicator dynamics. Alongside this question, the hypothesis of
linearity and its relation to the choice of the rule for strategy update is
also analyzed. The discussion is presented in terms of the emergence of
cooperation, as one of the current key problems in Biology and in other
disciplines.Comment: Review, 48 pages, 26 figure
- …