A new human identification protocol and Coppersmith's baby-step giant-step algorithm

We propose a new protocol providing cryptographically secure authentication to unaided humans against passive adversaries. We also propose a new generic passive attack on human identification protocols. The attack is an application of Coppersmith’s baby-step giant-step algorithm on human identification protcols. Under this attack, the achievable security of some of the best candidates for human identification protocols in the literature is further reduced. We show that our protocol preserves similar usability while achieves better security than these protocols. A comprehensive security analysis is provided which suggests parameters guaranteeing desired levels of security.

Hybrid genetic algorithm for elimination of severe stress concentration in railhead ends

This paper presents a computational method for eliminating severe stress concentration at the unsupported railhead ends in rail joints through innovative shape optimization of the contact zone, which is complex due to near field nonlinear contact. With a view to minimizing the computational efforts, hybrid genetic algorithm method coupled with parametric finite element has been developed and compared with the traditional genetic algorithm (GA). The shape of railhead top surface where the wheel contacts nonlinearly was optimized using the hybridized GA method. Comparative study of the optimal result and the search efficiency between the traditional and hybrid GA methods has shown that the hybridized GA provides the optimal shape in fewer computational cycles without losing accuracy. The method will be beneficial to solving complex engineering problems involving contact nonlinearity.

A more efficient and effective heuristic algorithm for the MapReduce placement problem in cloud computing

The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generalization of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics. Also, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement. The comparison results show that the computation using our mapper/reducer placement is much cheaper while still satisfying the computation deadline.

A new grouping genetic algorithm for the MapReduce placement problem in cloud computing

MapReduce is a computation model for processing large data sets in parallel on large clusters of machines, in a reliable, fault-tolerant manner. A MapReduce computation is broken down into a number of map tasks and reduce tasks, which are performed by so called mappers and reducers, respectively. The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NPcomplete. Thus, in this paper we propose a new grouping genetic algorithm for the mappers/reducers placement problem in cloud computing. Compared with the original one, our grouping genetic algorithm uses an innovative coding scheme and also eliminates the inversion operator which is an essential operator in the original grouping genetic algorithm. The new grouping genetic algorithm is evaluated by experiments and the experimental results show that it is much more efficient than four popular algorithms for the problem, including the original grouping genetic algorithm.

Composite SaaS scaling in cloud computing using a hybrid genetic algorithm

A Software-as-a-Service or SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. Components in a composite SaaS may need to be scaled – replicated or deleted, to accommodate the user’s load. It may not be necessary to replicate all components of the SaaS, as some components can be shared by other instances. On the other hand, when the load is low, some of the instances may need to be deleted to avoid resource underutilisation. Thus, it is important to determine which components are to be scaled such that the performance of the SaaS is still maintained. Extensive research on the SaaS resource management in Cloud has not yet addressed the challenges of scaling process for composite SaaS. Therefore, a hybrid genetic algorithm is proposed in which it utilises the problem’s knowledge and explores the best combination of scaling plan for the components. Experimental results demonstrate that the proposed algorithm outperforms existing heuristic-based solutions.

Fluid-structure interaction analysis by coupled FE-SPH model based on a novel searching algorithm

Fluid–Structure Interaction (FSI) problem is significant in science and engineering, which leads to challenges for computational mechanics. The coupled model of Finite Element and Smoothed Particle Hydrodynamics (FE-SPH) is a robust technique for simulation of FSI problems. However, two important steps of neighbor searching and contact searching in the coupled FE-SPH model are extremely time-consuming. Point-In-Box (PIB) searching algorithm has been developed by Swegle to improve the efficiency of searching. However, it has a shortcoming that efficiency of searching can be significantly affected by the distribution of points (nodes in FEM and particles in SPH). In this paper, in order to improve the efficiency of searching, a novel Striped-PIB (S-PIB) searching algorithm is proposed to overcome the shortcoming of PIB algorithm that caused by points distribution, and the two time-consuming steps of neighbor searching and contact searching are integrated into one searching step. The accuracy and efficiency of the newly developed searching algorithm is studied on by efficiency test and FSI problems. It has been found that the newly developed model can significantly improve the computational efficiency and it is believed to be a powerful tool for the FSI analysis.

Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm

Background Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under ‘normal’ conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data. Methods Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series. Results The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors. Conclusions The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.

Weaknesses in the initialisation process of the Common Scrambling Algorithm Stream Cipher

The Common Scrambling Algorithm Stream Cipher (CSASC) is a shift register based stream cipher designed to encrypt digital video broadcast. CSA-SC produces a pseudo-random binary sequence that is used to mask the contents of the transmission. In this paper, we analyse the initialisation process of the CSA-SC keystream generator and demonstrate weaknesses which lead to state convergence, slid pairs and shifted keystreams. As a result, the cipher may be vulnerable to distinguishing attacks, time-memory-data trade-off attacks or slide attacks.

Perturbing event logs to identify cost reduction opportunities: a genetic algorithm-based approach

Organisations are constantly seeking new ways to improve operational efficiencies. This research study investigates a novel way to identify potential efficiency gains in business operations by observing how they are carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how they can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A genetic algorithm-based approach is proposed to explore and assess alternative process execution scenarios, where the objective function is represented by a comprehensive cost structure that captures different process dimensions. Experiments conducted with different variants of the genetic algorithm evaluate the approach's feasibility. The findings demonstrate that a genetic algorithm-based approach is able to make use of cost reduction as a way to identify improved execution scenarios in terms of reduced case durations and increased resource utilisation. The ultimate aim is to utilise cost-related insights gained from such improved scenarios to put forward recommendations for reducing process-related cost within organisations.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Mission path planning of aerial survey tasks using hybrid game evolutionary algorithm

The work presented in this report is aimed to implement a cost-effective offline mission path planner for aerial inspection tasks of large linear infrastructures. Like most real-world optimisation problems, mission path planning involves a number of objectives which ideally should be minimised simultaneously. Understandably, the objectives of a practical optimisation problem are conflicting each other and the minimisation of one of them necessarily implies the impossibility to minimise the other ones. This leads to the need to find a set of optimal solutions for the problem; once such a set of available options is produced, the mission planning problem is reduced to a decision making problem for the mission specialists, who will choose the solution which best fit the requirements of the mission. The goal of this work is then to develop a Multi-Objective optimisation tool able to provide the mission specialists a set of optimal solutions for the inspection task amongst which the final trajectory will be chosen, given the environment data, the mission requirements and the definition of the objectives to minimise. All the possible optimal solutions of a Multi-Objective optimisation problem are said to form the Pareto-optimal front of the problem. For any of the Pareto-optimal solutions, it is impossible to improve one objective without worsening at least another one. Amongst a set of Pareto-optimal solutions, no solution is absolutely better than another and the final choice must be a trade-off of the objectives of the problem. Multi-Objective Evolutionary Algorithms (MOEAs) are recognised to be a convenient method for exploring the Pareto-optimal front of Multi-Objective optimization problems. Their efficiency is due to their parallelism architecture which allows to find several optimal solutions at each time

A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.

A Memetic Algorithm for the location-based continuously operating reference stations placement problem in Network Real-Time Kinematic

Network Real-Time Kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real time, and it is enabled by a network of Continuously Operating Reference Stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a Memetic Algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA.

BOSTER : an efficient algorithm for mining frequent unordered induced subtrees

Extracting frequent subtrees from the tree structured data has important applications in Web mining. In this paper, we introduce a novel canonical form for rooted labelled unordered trees called the balanced-optimal-search canonical form (BOCF) that can handle the isomorphism problem efficiently. Using BOCF, we define a tree structure guided scheme based enumeration approach that systematically enumerates only the valid subtrees. Finally, we present the balanced optimal search tree miner (BOSTER) algorithm based on BOCF and the proposed enumeration approach, for finding frequent induced subtrees from a database of labelled rooted unordered trees. Experiments on the real datasets compare the efficiency of BOSTER over the two state-of-the-art algorithms for mining induced unordered subtrees, HybridTreeMiner and UNI3. The results are encouraging.