scholarly journals Underestimated Cost of Targeted Attacks on Complex Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiao-Long Ren ◽  
Niels Gleinig ◽  
Dijana Tolić ◽  
Nino Antulov-Fantulin
Keyword(s):  
Complex Networks ◽  
State Of The Art ◽  
Normalized Cut ◽  
Targeted Attacks ◽  
Edge Removal ◽  
Realistic Assumption ◽  
Sparse Networks ◽  
Definition Of ◽  
The Cost ◽  
Node Removal

The robustness of complex networks under targeted attacks is deeply connected to the resilience of complex systems, which is defined as the ability to make appropriate response to the attack. In this paper, we study robustness of complex networks under a realistic assumption that the cost of removing a node is not constant but rather proportional to the degree of a node or equivalently to the number of removed links a removal action produces. We have investigated the state-of-the-art targeted node removing algorithms and demonstrate that they become very inefficient when the cost of the attack is taken into consideration. For the case when it is possible to attack or remove links, we propose a simple and efficient edge removal strategy named Hierarchical Power Iterative Normalized cut (HPI-Ncut). The results on real and artificial networks show that the HPI-Ncut algorithm outperforms all the node removal and link removal attack algorithms when the same definition of cost is taken into consideration. In addition, we show that, on sparse networks, the complexity of this hierarchical power iteration edge removal algorithm is only On log2+ε⁡n.

Optimization of sensor placement for structural health monitoring: a review

2019 ◽  
Vol 18 (3) ◽  
pp. 963-988 ◽  
Author(s):  
Wieslaw Ostachowicz ◽  
Rohan Soman ◽  
Pawel Malinowski
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
State Of The Art ◽  
Optimization Algorithms ◽  
Sensor Placement ◽  
Problem Definition ◽  
Structural Health ◽  
Definition Of ◽  
Major Argument ◽  
The Cost

The deployment cost of the structural health monitoring (SHM) system is the major argument against the more widespread use of the structural health monitoring techniques. Optimization of sensor placement offers an opportunity to reduce the cost of the SHM system without compromising on the quality of the monitoring approach. Several studies in the area of optimization of sensor placement for SHM applications have been undertaken but the approach has been rather application specific. This article is an attempt to present an unbiased state of the art of the work carried out in the area. The article is targeted towards researchers working in the field of structural health monitoring and optimization of sensor placement as well as practising engineers. This article reviews the work in the area of optimization of sensor placement. It first presents the definition of the optimization problem and then describes each step of the optimization. The current state of the art is then classified based on the techniques for which the optimization of sensor placement has been optimized. The article covers vibration-based monitoring, strain monitoring and elastic wave-based monitoring, as in the eyes of the authors these three techniques are most commonly used and accepted in the SHM community. The article later discusses the different optimization algorithms that have been applied in the literature. The article highlights the different pitfalls of the optimization algorithms and the countermeasures different researchers have proposed to overcome the known shortcomings. In the later section, the multi-objective optimization or the problem definition, keeping in mind the structural as well as executional demands, is discussed. A section has also been developed to showcase the use of optimization of sensor placement techniques’ data fusion–based systems.

scholarly journals Costs and Benefits of Autonomous Shipping—A Literature Review

2021 ◽  
Vol 11 (10) ◽  
pp. 4553
Author(s):  
Ewelina Ziajka-Poznańska ◽  
Jakub Montewka
Keyword(s):  
Literature Review ◽  
Business Models ◽  
Financial Models ◽  
State Of The Art ◽  
Costs And Benefits ◽  
Capital Costs ◽  
Research Activities ◽  
Benefit Estimation ◽  
Autonomous Ship ◽  
The Cost

The development of autonomous ship technology is currently in focus worldwide and the literature on this topic is growing. However, an in-depth cost and benefit estimation of such endeavours is in its infancy. With this systematic literature review, we present the state-of-the-art system regarding costs and benefits of the operation of prospective autonomous merchant ships with an objective for identifying contemporary research activities concerning an estimation of operating, voyage, and capital costs in prospective, autonomous shipping and vessel platooning. Additionally, the paper outlines research gaps and the need for more detailed business models for operating autonomous ships. Results reveal that valid financial models of autonomous shipping are lacking and there is significant uncertainty affecting the cost estimates, rendering only a reliable evaluation of specific case studies. The findings of this paper may be found relevant not only by academia, but also organisations considering to undertake a challenge of implementing Maritime Autonomous Surface Ships in their operations.

scholarly journals A review on the properties, reinforcing effects, and commercialization of nanomaterials for cement-based materials

2020 ◽  
Vol 9 (1) ◽  
pp. 303-322 ◽  
Author(s):  
Zhifang Zhao ◽  
Tianqi Qi ◽  
Wei Zhou ◽  
David Hui ◽  
Cong Xiao ◽  
...  
Keyword(s):  
Cement Hydration ◽  
State Of The Art ◽  
Research Progress ◽  
Market Survey ◽  
Future Perspectives ◽  
Nano Silica ◽  
Physical Processes ◽  
Existing Problems ◽  
Cement Based Materials ◽  
The Cost

AbstractThe behavior of cement-based materials is manipulated by chemical and physical processes at the nanolevel. Therefore, the application of nanomaterials in civil engineering to develop nano-modified cement-based materials is a promising research. In recent decades, a large number of researchers have tried to improve the properties of cement-based materials by employing various nanomaterials and to characterize the mechanism of nano-strengthening. In this study, the state of the art progress of nano-modified cement-based materials is systematically reviewed and summarized. First, this study reviews the basic properties and dispersion methods of nanomaterials commonly used in cement-based materials, including carbon nanotubes, carbon nanofibers, graphene, graphene oxide, nano-silica, nano-calcium carbonate, nano-calcium silicate hydrate, etc. Then the research progress on nano-engineered cementitious composites is reviewed from the view of accelerating cement hydration, reinforcing mechanical properties, and improving durability. In addition, the market and applications of nanomaterials for cement-based materials are briefly discussed, and the cost is creatively summarized through market survey. Finally, this study also summarizes the existing problems in current research and provides future perspectives accordingly.

Constructing Images of Deviance: A Look at State Intervention into the Problem of Wife-Battery

1983 ◽  
Vol 31 (1_suppl) ◽  
pp. 60-76
Author(s):  
Patricia A. Morgan
Keyword(s):  
Social Problem ◽  
State Intervention ◽  
The United States ◽  
The State ◽  
Structural Position ◽  
Wife Battering ◽  
The Social ◽  
Class Context ◽  
Definition Of ◽  
The Cost

Patricia Morgan's paper describes what happens when the state intervenes in the social problem of wife-battering. Her analysis refers to the United States, but there are clear implications for other countries, including Britain. The author argues that the state, through its social problem apparatus, manages the image of the problem by a process of bureaucratization, professionalization and individualization. This serves to narrow the definition of the problem, and to depoliticize it by removing it from its class context and viewing it in terms of individual pathology rather than structure. Thus refuges were initially run by small feminist collectives which had a dual objective of providing a service and promoting among the women an understanding of their structural position in society. The need for funds forced the groups to turn to the state for financial aid. This was given, but at the cost to the refuges of losing their political aims. Many refuges became larger, much more service-orientated and more diversified in providing therapy for the batterers and dealing with other problems such as alcoholism and drug abuse. This transformed not only the refuges but also the image of the problem of wife-battering.

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

2021 ◽  
pp. 088541222199424
Author(s):  
Mauro Francini ◽  
Lucia Chieffallo ◽  
Annunziata Palermo ◽  
Maria Francesca Viapiana
Keyword(s):  
Cluster Analysis ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Performance Indicators ◽  
State Of The Art ◽  
Measurement Model ◽  
Broader Impacts ◽  
Research Goal ◽  
Smart Mobility ◽  
Definition Of

This work aims to reorganize theoretical and empirical research on smart mobility through the systematic literature review approach. The research goal is to reach an extended and shared definition of smart mobility using the cluster analysis. The article provides a summary of the state of the art that can have broader impacts in determining new angles for approaching research. In particular, the results will be a reference for future quantitative developments for the authors who are working on the construction of a territorial measurement model of the smartness degree, helping them in identifying performance indicators consistent with the definition proposed.

scholarly journals Stochastic methods defeat regular RSA exponentiation algorithms with combined blinding methods

2021 ◽  
Vol 15 (1) ◽  
pp. 408-433
Author(s):  
Margaux Dugardin ◽  
Werner Schindler ◽  
Sylvain Guilley
Keyword(s):  
State Of The Art ◽  
Stochastic Methods ◽  
Rsa Algorithm ◽  
Montgomery Ladder ◽  
Channel Information ◽  
Probability Mass ◽  
The Cost ◽  
Noisy Measurements ◽  
Mass Functions ◽  
Larger Sample

Abstract Extra-reductions occurring in Montgomery multiplications disclose side-channel information which can be exploited even in stringent contexts. In this article, we derive stochastic attacks to defeat Rivest-Shamir-Adleman (RSA) with Montgomery ladder regular exponentiation coupled with base blinding. Namely, we leverage on precharacterized multivariate probability mass functions of extra-reductions between pairs of (multiplication, square) in one iteration of the RSA algorithm and that of the next one(s) to build a maximum likelihood distinguisher. The efficiency of our attack (in terms of required traces) is more than double compared to the state-of-the-art. In addition to this result, we also apply our method to the case of regular exponentiation, base blinding, and modulus blinding. Quite surprisingly, modulus blinding does not make our attack impossible, and so even for large sizes of the modulus randomizing element. At the cost of larger sample sizes our attacks tolerate noisy measurements. Fortunately, effective countermeasures exist.

scholarly journals A trade-off between classical and quantum circuit size for an attack against CSIDH

2020 ◽  
Vol 15 (1) ◽  
pp. 4-17
Author(s):  
Jean-François Biasse ◽  
Xavier Bonnetain ◽  
Benjamin Pring ◽  
André Schrottenloher ◽  
William Youmans
Keyword(s):  
Endomorphism Ring ◽  
State Of The Art ◽  
Quantum Circuit ◽  
List Type ◽  
Hard Problem ◽  
Trade Off ◽  
Classical State ◽  
Security Parameter ◽  
The Cost ◽  
Quadratic Order

AbstractWe propose a heuristic algorithm to solve the underlying hard problem of the CSIDH cryptosystem (and other isogeny-based cryptosystems using elliptic curves with endomorphism ring isomorphic to an imaginary quadratic order 𝒪). Let Δ = Disc(𝒪) (in CSIDH, Δ = −4p for p the security parameter). Let 0 < α < 1/2, our algorithm requires:A classical circuit of size $2^{\tilde{O}\left(\log(|\Delta|)^{1-\alpha}\right)}.$A quantum circuit of size $2^{\tilde{O}\left(\log(|\Delta|)^{\alpha}\right)}.$Polynomial classical and quantum memory.Essentially, we propose to reduce the size of the quantum circuit below the state-of-the-art complexity $2^{\tilde{O}\left(\log(|\Delta|)^{1/2}\right)}$ at the cost of increasing the classical circuit-size required. The required classical circuit remains subexponential, which is a superpolynomial improvement over the classical state-of-the-art exponential solutions to these problems. Our method requires polynomial memory, both classical and quantum.

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

Computing Robust Principal Components by A* Search

2018 ◽  
Vol 27 (07) ◽  
pp. 1860013 ◽  
Author(s):  
Swair Shah ◽  
Baokun He ◽  
Crystal Maung ◽  
Haim Schweitzer
Keyword(s):  
State Of The Art ◽  
Principal Component ◽  
Low Rank ◽  
A Algorithm ◽  
Running Time ◽  
Current State ◽  
Dimensionality Reduction Technique ◽  
Related Variant ◽  
Low Rank Representation ◽  
The Cost

Principal Component Analysis (PCA) is a classical dimensionality reduction technique that computes a low rank representation of the data. Recent studies have shown how to compute this low rank representation from most of the data, excluding a small amount of outlier data. We show how to convert this problem into graph search, and describe an algorithm that solves this problem optimally by applying a variant of the A* algorithm to search for the outliers. The results obtained by our algorithm are optimal in terms of accuracy, and are shown to be more accurate than results obtained by the current state-of-the- art algorithms which are shown not to be optimal. This comes at the cost of running time, which is typically slower than the current state of the art. We also describe a related variant of the A* algorithm that runs much faster than the optimal variant and produces a solution that is guaranteed to be near the optimal. This variant is shown experimentally to be more accurate than the current state-of-the-art and has a comparable running time.

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