7,774 research outputs found
Average Drift Analysis and Population Scalability
This paper aims to study how the population size affects the computation time
of evolutionary algorithms in a rigorous way. The computation time of an
evolutionary algorithm can be measured by either the expected number of
generations (hitting time) or the expected number of fitness evaluations
(running time) to find an optimal solution. Population scalability is the ratio
of the expected hitting time between a benchmark algorithm and an algorithm
using a larger population size. Average drift analysis is presented for
comparing the expected hitting time of two algorithms and estimating lower and
upper bounds on population scalability. Several intuitive beliefs are
rigorously analysed. It is prove that (1) using a population sometimes
increases rather than decreases the expected hitting time; (2) using a
population cannot shorten the expected running time of any elitist evolutionary
algorithm on unimodal functions in terms of the time-fitness landscape, but
this is not true in terms of the distance-based fitness landscape; (3) using a
population cannot always reduce the expected running time on fully-deceptive
functions, which depends on the benchmark algorithm using elitist selection or
random selection
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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes
The impact of agent density on scalability in collective systems : noise-induced versus majority-based bistability
In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making
Detecting Irregular Patterns in IoT Streaming Data for Fall Detection
Detecting patterns in real time streaming data has been an interesting and
challenging data analytics problem. With the proliferation of a variety of
sensor devices, real-time analytics of data from the Internet of Things (IoT)
to learn regular and irregular patterns has become an important machine
learning problem to enable predictive analytics for automated notification and
decision support. In this work, we address the problem of learning an irregular
human activity pattern, fall, from streaming IoT data from wearable sensors. We
present a deep neural network model for detecting fall based on accelerometer
data giving 98.75 percent accuracy using an online physical activity monitoring
dataset called "MobiAct", which was published by Vavoulas et al. The initial
model was developed using IBM Watson studio and then later transferred and
deployed on IBM Cloud with the streaming analytics service supported by IBM
Streams for monitoring real-time IoT data. We also present the systems
architecture of the real-time fall detection framework that we intend to use
with mbientlabs wearable health monitoring sensors for real time patient
monitoring at retirement homes or rehabilitation clinics.Comment: 7 page
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
Existing Android malware detection approaches use a variety of features such
as security sensitive APIs, system calls, control-flow structures and
information flows in conjunction with Machine Learning classifiers to achieve
accurate detection. Each of these feature sets provides a unique semantic
perspective (or view) of apps' behaviours with inherent strengths and
limitations. Meaning, some views are more amenable to detect certain attacks
but may not be suitable to characterise several other attacks. Most of the
existing malware detection approaches use only one (or a selected few) of the
aforementioned feature sets which prevent them from detecting a vast majority
of attacks. Addressing this limitation, we propose MKLDroid, a unified
framework that systematically integrates multiple views of apps for performing
comprehensive malware detection and malicious code localisation. The rationale
is that, while a malware app can disguise itself in some views, disguising in
every view while maintaining malicious intent will be much harder.
MKLDroid uses a graph kernel to capture structural and contextual information
from apps' dependency graphs and identify malice code patterns in each view.
Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted
combination of the views which yields the best detection accuracy. Besides
multi-view learning, MKLDroid's unique and salient trait is its ability to
locate fine-grained malice code portions in dependency graphs (e.g.,
methods/classes). Through our large-scale experiments on several datasets
(incl. wild apps), we demonstrate that MKLDroid outperforms three
state-of-the-art techniques consistently, in terms of accuracy while
maintaining comparable efficiency. In our malicious code localisation
experiments on a dataset of repackaged malware, MKLDroid was able to identify
all the malice classes with 94% average recall
Algorithm for Mesoscopic Advection-Diffusion
In this paper, an algorithm is presented to calculate the transition rates
between adjacent mesoscopic subvolumes in the presence of flow and diffusion.
These rates can be integrated in stochastic simulations of reaction-diffusion
systems that follow a mesoscopic approach, i.e., that partition the environment
into homogeneous subvolumes and apply the spatial stochastic simulation
algorithm (spatial SSA). The rates are derived by integrating Fick's second law
over a single subvolume in one dimension (1D), and are also shown to apply in
three dimensions (3D). The proposed algorithm corrects the derived rates to
ensure that they are physically meaningful and it is implemented in the AcCoRD
simulator (Actor-based Communication via Reaction-Diffusion). Simulations using
the proposed method are compared with a naive mesoscopic approach, microscopic
simulations that track every molecule, and analytical results that are exact in
1D and an approximation in 3D. By choosing subvolumes that are sufficiently
small, such that the Peclet number associated with a subvolume is sufficiently
less than 2, the accuracy of the proposed method is comparable with the
microscopic method, thus enabling the simulation of
advection-reaction-diffusion systems with the spatial SSA.Comment: 12 pages, 9 figures. Submitted to IEEE Transactions on NanoBioscienc
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