Upgrading arc median shortest path problem for an urban transportation network

In this paper, we propose an algorithm for an upgrading arc median shortest path problem for a transportation network. The problem is to identify a set of nondominated paths that minimizes both upgrading cost and overall travel time of the entire network. These two objectives are realistic for transportation network problems, but of a conflicting and noncompensatory nature. In addition, unlike upgrading cost which is the sum of the arc costs on the path, overall travel time of the entire network cannot be expressed as a sum of arc travel times on the path. The proposed solution approach to the problem is based on heuristic labeling and exhaustive search techniques, in criteria space and solution space, respectively. The first approach labels each node in terms of upgrading cost, and deletes cyclic and infeasible paths in criteria space. The latter calculates the overall travel time of the entire network for each feasible path, deletes dominated paths on the basis of the objective vector and identifies a set of Pareto optimal paths in the solution space. The computational study, using two small-scale transportation networks, has demonstrated that the algorithm proposed herein is able to efficiently identify a set of nondominated median shortest paths, based on two conflicting and noncompensatory objectives.

An algorithm to median shortest path problem (MSPP) in the design of urban transportation networks

This paper proposes an efficient solution algorithm for realistic multi-objective median shortest path problems in the design of urban transportation networks. The proposed problem formulation and solution algorithm to median shortest path problem is based on three realistic objectives via route cost or investment cost, overall travel time of the entire network and total toll revenue. The proposed solution approach to the problem is based on the heuristic labeling and exhaustive search technique in criteria space and solution space of the algorithm respectively. The first labels each node in terms of route cost and deletes cyclic and infeasible paths in criteria space imposing cyclic break and route cost constraint respectively. The latter deletes dominated paths in terms of objectives vector in solution space in order to identify a set of Pareto optimal paths. The approach, thus, proposes a non-inferior solution set of Pareto optimal paths based on non-dominated objective vector and leaves the ultimate decision to decision-makers for purpose specific final decision during applications. A numerical experiment is conducted to test the proposed algorithm using artificial transportation network. Sensitivity analyses have shown that the proposed algorithm is advantageous and efficient over existing algorithms to find a set of Pareto optimal paths to median shortest paths problems.

Combining extension matrix and integer programming for optimal concept learning

This paper proposes two integer programming models and their GA-based solutions for optimal concept learning. The models are built to obtain the optimal concept description in the form of propositional logic formulas from examples based on completeness, consistency and simplicity. The simplicity of the propositional rules is selected as the objective function of the integer programming models, and the completeness and consistency of the concept are used as the constraints. Considering the real-world problems that certain level of noise is contained in data set, the constraints in model 11 are slacked by adding slack-variables. To solve the integer programming models, genetic algorithm is employed to search the global solution space. We call our approach IP-AE. Its effectiveness is verified by comparing the experimental results with other well- known concept learning algorithms: AQ15 and C4.5.

Fuzzy ARTMAP and a hybrid chaos genetic algorithm for medical pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN), which combines the Fuzzy ARTMAP (FAM) neural network and a hybrid Chaos Genetic Algorithm (CGA), is proposed for undertaking pattern classification tasks. The hybrid CGA is a modified version of the hybrid real-coded genetic algorithms that includes a Chaotic Mapping Operator (CMO) in its search and adaptation process. It is used to evolve the connection weights in FAM, and the resulting EANN is known as FAM-hybrid CGA. The CMO in the hybrid CGA is used to generate a group of chromosomes that incorporates the characteristics of chaos. The chromosomes are then adapted with an arbitrary small amount of variation in every generation. As the evolution procedure proceeds, chromosomes with considerable differences are produced. Such chromosomes, which are located at different regions of interest in the solution space, are able to provide good solutions to undertake search and adaption problems. The effectiveness of the proposed FAM-hybrid CGA model is first evaluated using benchmark medical data sets from the UCI machine learning repository. Its applicability to medical decision support is then demonstrated using a real database of patient records with suspected Acute Coronary Syndrome. The results indicate that FAM-hybrid CGA is able to outperform its neural network counterpart (i.e., FAM), and it can be employed as a useful pattern classification tool for tackling medical decision support tasks.

Evolutionary optimization: a big data perspective

Stochastic search techniques such as evolutionary algorithms (EA) are known to be better explorer of search space as compared to conventional techniques including deterministic methods. However, in the era of big data like most other search methods and learning algorithms, suitability of evolutionary algorithms is naturally questioned. Big data pose new computational challenges including very high dimensionality and sparseness of data. Evolutionary algorithms' superior exploration skills should make them promising candidates for handling optimization problems involving big data. High dimensional problems introduce added complexity to the search space. However, EAs need to be enhanced to ensure that majority of the potential winner solutions gets the chance to survive and mature. In this paper we present an evolutionary algorithm with enhanced ability to deal with the problems of high dimensionality and sparseness of data. In addition to an informed exploration of the solution space, this technique balances exploration and exploitation using a hierarchical multi-population approach. The proposed model uses informed genetic operators to introduce diversity by expanding the scope of search process at the expense of redundant less promising members of the population. Next phase of the algorithm attempts to deal with the problem of high dimensionality by ensuring broader and more exhaustive search and preventing premature death of potential solutions. To achieve this, in addition to the above exploration controlling mechanism, a multi-tier hierarchical architecture is employed, where, in separate layers, the less fit isolated individuals evolve in dynamic sub-populations that coexist alongside the original or main population. Evaluation of the proposed technique on well known benchmark problems ascertains its superior performance. The algorithm has also been successfully applied to a real world problem of financial portfolio management. Although the proposed method cannot be considered big data-ready, it is certainly a move in the right direction.

Establishing Competitive Domination Cycles for Peer-to-peer Game Combat

Games require balance to be fair and enjoyable. In two player combat settings balance can be achieved by ensuring that both units are equally capable. The possibility of alliances changes the nature of balance when additional players are introduced. One approach to achieving balance is to use a domination loop between agents in a rock, scissors, paper style approach. This paper investigates whether such loops exist within the existing rules of game combat. Search processes within the attribute space of the game units are used to identify loops within an existing game architecture. A dominance metric is used to identify cycles where the victory is achieved by a clear threshold. Cycles with up to 5 players are demonstrated, although larger cycles require more effort to find. Use of models of game play and attribute space search are recommended as a mechanism for balancing games of this format.

Dynamic on-the-fly minimum cost benchmarking for storing generated scientific datasets in the cloud

Massive computation power and storage capacity of cloud computing systems enable users to either store large generated scientific datasets in the cloud or delete and then regenerate them whenever reused. Due to the pay-as-you-go model, the more datasets we store, the more storage cost we need to pay, alternatively, we can delete some generated datasets to save the storage cost but more computation cost is incurred for regeneration whenever the datasets are reused. Hence, there should exist a trade-off between computation and storage in the cloud, where different storage strategies lead to different total costs. The minimum cost, which reflects the best trade-off, is an important benchmark for evaluating the cost-effectiveness of different storage strategies. However, the current benchmarking approach is neither efficient nor practical to be applied on the fly at runtime. In this paper, we propose a novel Partitioned Solution Space based approach with efficient algorithms for dynamic yet practical on-the-fly minimum cost benchmarking of storing generated datasets in the cloud. In this approach, we pre-calculate all the possible minimum cost storage strategies and save them in different partitioned solution spaces. The minimum cost storage strategy represents the minimum cost benchmark, and whenever the datasets storage cost changes at runtime in the cloud (e.g. new datasets are generated and/or existing datasets' usage frequencies are changed), our algorithms can efficiently retrieve the current minimum cost storage strategy from the partitioned solution space and update the benchmark. By dynamically keeping the benchmark updated, our approach can be practically utilised on the fly at runtime in the cloud, based on which the minimum cost benchmark can be either proactively reported or instantly responded upon request. Case studies and experimental results based on Amazon cloud show the efficiency, scalability and practicality of our approach.

The mythical properties of police body-worn cameras: A solution in the search of a problem

Over the past few years, Australian police agencies have begun to enthusiastically introduce body-worn cameras on police personnel. These devices are now either implemented or under trial across the country. There is also an emergent ‘surveillance consensus’ (Hempel and Töpfer 2009) concerning their use amongst Australian police. While more detailed empirical examination of information flows that shape this surveillance consensus is warranted, this contribution to the debate seeks to draw from policing scholarship to critically explore the intersections between the rationalizations for body-worn cameras and the broader policing scholarship. More directly, body-worn cameras cannot be understood in narrow instrumental terms, but must be located within the broader literature on governing police and the law and order politics that surrounds many contemporary police and criminal justice reforms (Cox 2015; Gregg and Wilson 2015). I begin with a summary of the introduction of body-worn cameras in Australia. The article then identifies five problems body-worn cameras purportedly address and provides a brief case summary indicating how current ‘privacy protections’ fail to establish real limits to the collection, use, and dissemination of images from body-worn cameras.

Cultural issues in online learning - is blended learning a possible solution?

The emergence of cross-cultural classrooms has been steadily increasing in Australian tertiary institutions, due to the growing population of international students enrolling to complete their degrees. This increase in international students has signified a change in the student demographics, thus recognizing the differences in students’ learning styles, and indicating that a more flexible approach is needed for learner content delivery. Research has suggested that students from different cultures have varying compatibility with different learning environments. With tertiary institutions now expanding towards the online forum for delivery of units, these compatibilities of students are even more evident. Hence, in order to ensure that all students are able to participate in this domain, preparations are needed to accommodate all cultural types. Therefore with the emphasis on creating flexible learning environments for all students the blended learning approach has been suggested as a solution.

Photocatalytic degradation of dye effluent by titanium dioxide pillar pellets in aqueous solution

Photocatalytic oxidation (PCO) process is an effective way to deal with organic pollutants in wastewater which could be difficult to be degraded by conventional biological treatment methods. Normally the TiO2 powder in nanometre size range was directly used as photocatalyst for dye degradation in wastewater. However the titanium dioxide powder was arduous to be recovered from the solution after treatment. In this application, a new form of TiO2 (i.e. pillar pellets ranging from 2.5 to 5.3 mm long and with a diameter of 3.7 mm) was used and investigated for photocatalytic degradation of textile dye effluent. A test system was built with a flat plate reactor (FPR) and UV light source (blacklight and solar simulator as light source respectively) for investigating the effectiveness of the new form of TiO2. It was found that the photocatalytic process under this configuration could efficiently remove colours from textile dyeing effluent. Comparing with the TiO2 powder, the pellet was very easy to recovered from the treated solution and can be reused in multiple times without the significant change on the photocatalytic property. The results also showed that to achieve the same photocatalytic performance, the FPR area by pellets was about 91% smaller than required by TiO2 powder. At least TiO2 pellet could be used as an alternative form of photocatalyst in applications for textile effluent treatment process, also other wastewater treatment processes.

Further pruning for efficient association rule discovery

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS AR. We demonstrate that application of OPUS AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

Solution to robotic landmine detection through use of path planning and motor control

Low cost robotic detectors are a promising new approach to combat the disturbing landmine crisis. In this paper a low-cost robotic solution is proposed, we present several control techniques used to improve the precision of the robotic motion. A P and PD controller is applied, and it is concluded that a cascaded control system provides a more stable and accurate response. Two search patterns for landmine detection are considered, rectangular and spiral, these are used to analyse the effectiveness of the control system.

Process analysis of the solution strategies used for problems contained in the Minnesota Paper Board Form Board test

This thesis reviews progress toward an understanding of the processes involved in the solution of spatial problems. Previous work employing factor analysis and information processing analysis is reviewed and the emphasis on variations in speed and accuracy as the major contributers to individual differences is noted. It is argued that the strategy used by individuals is a preferable explanatory concept for identifying the cognitive substratum necessary for problem solving. Using the protocols obtained from subjects solving The Minnesota Paper Form Board (Revised), a test commonly regarded as measuring skill in spatial visualization, a number of different strategies are isolated. Assumptions as to the task variants which undergird these strategies are made and tested experimentally. The results suggest that task variants such as the size of the stimulus and the shape of the pieces interact with subject variables to produce the operating strategy. Skill in problem solving is revealed in the ability to structure the array, to hold a structured image and to reduce the number of answers requiring intensive processing. The interaction between task and subject variables results in appropriate or inappropriate strategies which in turn affect speed and accuracy. Results suggest that strategy formation and usage are the keys to explaining individual differences and an heuristic model is presented to explain the performance of individual subjects on the problems involved in the Minnesota Paper Form Board. The model can be used to predict performance on other tests; and as an aid to teaching subjects experiencing difficulties. The model presented incorporates strategy variation and is consequently mores complex than previously suggested models. It is argued that such complexity is necessary to explain the nature of a subject's performance and is also necessary to perform diagnostic evaluation. Certain structural -features of the Minnesota Paper Form Board are questioned and suggestions for improvement included. The essential explanatory function of the strategy in use makes the prevalent group administration approach suspect in the prediction of future performance in spatial or vocational activity.