David Powers

Davoud Mougouei, Aditya Ghose, Hoa Dam et al.

Software products have become an integral part of human lives, and therefore need to account for human values such as privacy, fairness, and equality. Ignoring human values in software development leads to biases and violations of human values: racial biases in recidivism assessment and facial recognition software are well-known examples of such issues. One of the most critical steps in software development is Software Release Planning (SRP), where decisions are made about the presence or absence of the requirements (features) in the software. Such decisions are primarily guided by the economic value of the requirements, ignoring their impacts on a broader range of human values. That may result in ignoring (selecting) requirements that positively (negatively) impact human values, increasing the risk of value breaches in the software. To address this, we have proposed an Integer Programming approach to considering human values in software release planning. In this regard, an Integer Linear Programming (ILP) model has been proposed, that explicitly accounts for human values in finding an "optimal" subset of the requirements. The ILP model exploits the algebraic structure of fuzzy graphs to capture dependencies and conflicts among the values of the requirements.

Nasim Nematzadeh, David MW Powers, Trent Lewis

Over the last decade, a variety of new neurophysiological experiments have led to new insights as to how, when and where retinal processing takes place, and the nature of the retinal representation encoding sent to the cortex for further processing. Based on these neurobiological discoveries, in our previous work, we provided computer simulation evidence to suggest that Geometrical illusions are explained in part, by the interaction of multiscale visual processing performed in the retina. The output of our retinal stage model, named Vis-CRF, is presented here for a sample of natural image and for several types of Tilt Illusion, in which the final tilt percept arises from multiple scale processing of Difference of Gaussians (DoG) and the perceptual interaction of foreground and background elements (Nematzadeh and Powers, 2019; Nematzadeh, 2018; Nematzadeh, Powers and Lewis, 2017; Nematzadeh, Lewis and Powers, 2015).

David M. W. Powers

Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case.

David M. W. Powers

There has been considerable interest in boosting and bagging, including the combination of the adaptive techniques of AdaBoost with the random selection with replacement techniques of Bagging. At the same time there has been a revisiting of the way we evaluate, with chance-corrected measures like Kappa, Informedness, Correlation or ROC AUC being advocated. This leads to the question of whether learning algorithms can do better by optimizing an appropriate chance corrected measure. Indeed, it is possible for a weak learner to optimize Accuracy to the detriment of the more reaslistic chance-corrected measures, and when this happens the booster can give up too early. This phenomenon is known to occur with conventional Accuracy-based AdaBoost, and the MultiBoost algorithm has been developed to overcome such problems using restart techniques based on bagging. This paper thus complements the theoretical work showing the necessity of using chance-corrected measures for evaluation, with empirical work showing how use of a chance-corrected measure can improve boosting. We show that the early surrender problem occurs in MultiBoost too, in multiclass situations, so that chance-corrected AdaBook and Multibook can beat standard Multiboost or AdaBoost, and we further identify which chance-corrected measures to use when.

David M W Powers

Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show chance correction facilitates learning.

Somaiyeh MahmoudZadeh, David MW Powers, Reza Bairam Zadeh

Advances in hardware technology have facilitated more integration of sophisticated software toward augmenting the development of Unmanned Vehicles (UVs) and mitigating constraints for onboard intelligence. As a result, UVs can operate in complex missions where continuous trans-formation in environmental condition calls for a higher level of situational responsiveness and autonomous decision making. This book is a research monograph that aims to provide a comprehensive survey of UVs autonomy and its related properties in internal and external situation awareness to-ward robust mission planning in severe conditions. An advance level of intelligence is essential to minimize the reliance on the human supervisor, which is a main concept of autonomy. A self-controlled system needs a robust mission management strategy to push the boundaries towards autonomous structures, and the UV should be aware of its internal state and capabilities to assess whether current mission goal is achievable or find an alternative solution. In this book, the AUVs will become the major case study thread but other cases/types of vehicle will also be considered. In-deed the research monograph, the review chapters and the new approaches we have developed would be appropriate for use as a reference in upper years or postgraduate degrees for its coverage of literature and algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.

S. MahmoudZadeh, D. M. W Powers, A. Atyabi

This paper describes a reflexive multilayered mission planner with a mounted energy efficient local path planner for Unmanned Underwater Vehicle's (UUV) navigation throughout the complex subsea volume in a time-variant semi-dynamic operation network. The UUV routing protocol in Underwater Wireless Sensor Network (UNSW) is generalized with a homogeneous Dynamic Knapsack-Traveler Salesman Problem emerging with an adaptive path planning mechanism to address UUV's long-duration missions on dynamically changing subsea volume. The framework includes a base layer of global path planning, an inner layer of local path planning and an environmental sub-layer. Such a multilayer integrated structure facilitates the framework to adopt any algorithm with real-time performance. The evolutionary technique known as Differential Evolution algorithm is employed by both base and inner layers to examine the performance of the framework in efficient mission timing and its resilience against the environmental disturbances. Relying on reactive nature of the framework and fast computational performance of the DE algorithm, the simulations show promising results and this new framework guarantees a safe and efficient deployment in a turbulent uncertain marine environment passing through a proper sequence of stations considering various constraint in a complex environment.

Davoud Mougouei, David M W Powers

Software requirements selection aims to find an optimal subset of the requirements with the highest value while respecting the project constraints. But the value of a requirement may depend on the presence or absence of other requirements in the optimal subset. Such Value Dependencies, however, are imprecise and hard to capture. In this paper, we propose a method based on integer programming and fuzzy graphs to account for value dependencies and their imprecision in software requirements selection. The proposed method, referred to as Dependency-Aware Software Requirements Selection (DARS), is comprised of three components: (i) an automated technique for the identification of value dependencies from user preferences, (ii) a modeling technique based on fuzzy graphs that allows for capturing the imprecision of value dependencies, and (iii) an Integer Linear Programming (ILP) model that takes into account user preferences and value dependencies identified from those preferences to reduce the risk of value loss in software projects. Our work is verified by studying a real-world software project. The results show that our proposed method reduces the value loss in software projects and is scalable to large requirement sets.

Davoud Mougouei, David Powers

Software requirement selection aims to find an optimal subset of the requirements with the highest value while respecting the budget. But the value of a requirement may depend on the presence or absence of other requirements in the optimal subset. Existing requirement selection methods, however, do not consider Value Dependencies, thus increasing the risk of value loss. To address this, we have proposed Dependency-Aware Requirement Selection (DARS) method with two main components: (i) a fuzzy-based technique for identifying and modeling value dependencies, and (ii) an Integer Programming model that takes into account value dependencies in software requirement selection. We have further, proposed an alternative optimization model for situations where quantifying value dependencies is hard. The scalability of DARS and its effectiveness in reducing the risk of value loss are demonstrated through exhaustive simulations.

David M W Powers

Storytelling is fundamental to language, including culture, conversation and communication in their broadest senses. It thus emerges as an essential component of intelligent systems, including systems where natural language is not a primary focus or where we do not usually think of a story being involved. In this paper we explore the emergence of storytelling as a requirement in embodied conversational agents, including its role in educational and health interventions, as well as in a general-purpose computer interface for people with disabilities or other constraints that prevent the use of traditional keyboard and speech interfaces. We further present a characterization of storytelling as an inventive fleshing out of detail according to a particular personal perspective, and propose the DREAMT model to focus attention on the different layers that need to be present in a character-driven storytelling system. Most if not all aspects of the DREAMT model have arisen from or been explored in some aspect of our implemented research systems, but currently only at a primitive and relatively unintegrated level. However, this experience leads us to formalize and elaborate the DREAMT model mnemonically as follows: - Description/Dialogue/Definition/Denotation - Realization/Representation/Role - Explanation/Education/Entertainment - Actualization/Activation - Motivation/Modelling - Topicalization/Transformation

Simon R Lang, Martin H Luerssen, David M Powers

In Computer Vision, finding simple features is performed using classifiers called interest point (IP) detectors, which are often utilised to track features as the scene changes. For 2D based classifiers it has been intuitive to measure repeated point reliability using 2D metrics given the difficulty to establish ground truth beyond 2D. The aim is to bridge the gap between 2D classifiers and 3D environments, and improve performance analysis of 2D IP classification on 3D objects. This paper builds on existing work with 3D scanned and artificial models to test conventional 2D feature detectors with the assistance of virtualised 3D scenes. Virtual space depth is leveraged in tests to perform pre-selection of closest repeatable points in both 2D and 3D contexts before repeatability is measured. This more reliable ground truth is used to analyse testing configurations with a singular and 12 model dataset across affine transforms in x, y and z rotation, as well as x,y scaling with 9 well known IP detectors. The virtual scene's ground truth demonstrates that 3D pre-selection eliminates a large portion of false positives that are normally considered repeated in 2D configurations. The results indicate that 3D virtual environments can provide assistance in comparing the performance of conventional detectors when extending their applications to 3D environments, and can result in better classification of features when testing prospective classifiers' performance. A ROC based informedness measure also highlights tradeoffs in 2D/3D performance compared to conventional repeatability measures.

Nasim Nematzadeh, David M. W. Powers

The Café Wall illusion is one of a class of tilt illusions where lines that are parallel appear to be tilted. We demonstrate that a simple Differences of Gaussian model provides an explanatory mechanism for the illusory tilt perceived in a family of Café Wall illusion generalizes to the dashed versions of Café Wall. Our explanation models the visual mechanisms in low level stages and the lateral inhibition of simple cells that can reveal tilt cues in Geometrical distortion illusions such as Tile illusions particularly Café Wall illusions. For this, we simulate the activations of the retinal/cortical simple cells in responses to these patterns based on a Classical Receptive Field (CRF) model (referred to as Vis-CRF) to explain tilt effects in these illusions. Previously, it was assumed that all these visual experiences of tilt arise from the orientation selectivity properties described for more complex cortical cells. An estimation of an overall tilt angle perceived in these illusions is based on the integration of the local tilts detected by simple cells which is presumed to be a key mechanism utilized by the complex cells to create our final perception of tilt.

Nasim Nematzadeh, David M. W. Powers, Trent Lewis

Illusions are fascinating and immediately catch people's attention and interest, but they are also valuable in terms of giving us insights into human cognition and perception. A good theory of human perception should be able to explain the illusion, and a correct theory will actually give quantifiable results. We investigate here the efficiency of a computational filtering model utilised for modelling the lateral inhibition of retinal ganglion cells and their responses to a range of Geometric Illusions using isotropic Differences of Gaussian filters. This study explores the way in which illusions have been explained and shows how a simple standard model of vision based on classical receptive fields can predict the existence of these illusions as well as the degree of effect. A fundamental contribution of this work is to link bottom-up processes to higher level perception and cognition consistent with Marr's theory of vision and edge map representation.

Nasim Nematzadeh, David M. W. Powers

Geometrical illusions are a subclass of optical illusions in which the geometrical characteristics of patterns such as orientations and angles are distorted and misperceived as the result of low- to high-level retinal/cortical processing. Modelling the detection of tilt in these illusions and their strengths as they are perceived is a challenging task computationally and leads to development of techniques that match with human performance. In this study, we present a predictive and quantitative approach for modeling foveal and peripheral vision in the induced tilt in Café Wall illusion in which parallel mortar lines between shifted rows of black and white tiles appear to converge and diverge. A bioderived filtering model for the responses of retinal/cortical simple cells to the stimulus using Difference of Gaussians is utilized with an analytic processing pipeline introduced in our previous studies to quantify the angle of tilt in the model. Here we have considered visual characteristics of foveal and peripheral vision in the perceived tilt in the pattern to predict different degrees of tilt in different areas of the fovea and periphery as the eye saccades to different parts of the image. The tilt analysis results from several sampling sizes and aspect ratios, modelling variant foveal views are used from our previous investigations on the local tilt, and we specifically investigate in this work, different configurations of the whole pattern modelling variant Gestalt views across multiple scales in order to provide confidence intervals around the predicted tilts. The foveal sample sets are verified and quantified using two different sampling methods. We present here a precise and quantified comparison contrasting local tilt detection in the foveal sets with a global average across all of the Café Wall configurations tested in this work.

Nasim Nematzadeh, David M. W. Powers

This paper explores the tilt illusion effect in the Cafe Wall pattern using a classical Gaussian Receptive Field model. In this illusion, the mortar lines are misperceived as diverging or converging rather than horizontal. We examine the capability of a simple bioplausible filtering model to recognize different degrees of tilt effect in the Cafe Wall illusion based on different characteristics of the pattern. Our study employed a Difference of Gaussians model of retinal to cortical ON center and/or OFF center receptive fields. A wide range of parameters of the stimulus, for example mortar thickness, luminance, tiles contrast, phase of the tile displacement, have been studied. Our model constructs an edge map representation at multiple scales that reveals tilt cues and clues involved in the illusory perception of the Cafe Wall pattern. We present here that our model can not only detect the tilt in this pattern, but also can predict the strength of the illusion and quantify the degree of tilt. For the first time quantitative predictions of a model are reported for this stimulus. The results of our simulations are consistent with previous psychophysical findings across the full range of Cafe Wall variations tested. Our results also suggest that the Difference of Gaussians mechanism is the heart of the effects explained by, and the mechanisms proposed for, the Irradiation, Brightness Induction, and Bandpass Filtering models.

Davoud Mougouei, David M. W. Powers, Asghar Moeini

Binary Knapsack Problem (BKP) is to select a subset of an element (item) set with the highest value while keeping the total weight within the capacity of the knapsack. This paper presents an integer programming model for a variation of BKP where the value of each element may depend on selecting or ignoring other elements. Strengths of such Value-Related Dependencies are assumed to be imprecise and hard to specify. To capture this imprecision, we have proposed modeling value-related dependencies using fuzzy graphs and their algebraic structure.

Davoud Mougouei, David M. W. Powers

Considering user preferences is a determining factor in optimizing the value of a software release. This is due to the fact that user preferences for software features specify the values of those features and consequently determine the value of the release. Certain features of a software however, may encourage or discourage users to prefer (select or use) other features. As such, value of a software feature could be positively or negatively influenced by other features. Such influences are known as Value-related Feature (Requirement) Dependencies. Value-related dependencies need to be considered in software release planning as they influence the value of the optimal subset of the features selected by the release planning models. Hence, we have proposed considering value-related feature dependencies in software release planning through mining user preferences for software features. We have demonstrated the validity and practicality of the proposed approach by studying a real world software project.

Tom A. F. Anderson, David M. W. Powers

We report investigations into speaker classification of larger quantities of unlabelled speech data using small sets of manually phonemically annotated speech. The Kohonen speech typewriter is a semi-supervised method comprised of self-organising maps (SOMs) that achieves low phoneme error rates. A SOM is a 2D array of cells that learn vector representations of the data based on neighbourhoods. In this paper, we report a method to evaluate pronunciation using multilevel SOMs with /hVd/ single syllable utterances for the study of vowels, for Australian pronunciation.

Davoud Mougouei, David Powers

It has been widely recognized that uncertainty is an inevitable aspect of diagnosis and treatment of medical disorders. Such uncertainties hence, need to be considered in computerized medical models. The existing medical modeling techniques however, have mainly focused on capturing uncertainty associated with diagnosis of medical disorders while ignoring uncertainty of treatments. To tackle this issue, we have proposed using a fuzzy-based modeling and description technique for capturing uncertainties in treatment plans. We have further contributed a formal framework which allows for goal-oriented modeling and analysis of medical treatments.

Nasim Nematzadeh, David M. W. Powers

The biological characteristics of human visual processing can be investigated through the study of optical illusions and their perception, giving rise to intuitions that may improve computer vision to match human performance. Geometric illusions are a specific subfamily in which orientations and angles are misperceived. This paper reports quantifiable predictions of the degree of tilt for a typical geometric illusion called Café Wall, in which the mortar between the tiles seems to tilt or bow. Our study employs a common bioplausible model of retinal processing and we further develop an analytic processing pipeline to quantify and thus predict the specific angle of tilt. We further study the effect of resolution and feature size in order to predict the different perceived tilts in different areas of the fovea and periphery, where resolution varies as the eye saccades to different parts of the image. In the experiments, several different minimal portions of the pattern, modeling monocular and binocular foveal views, are investigated across multiple scales, in order to quantify tilts with confidence intervals and explore the difference between local and global tilt.

David M. W. Powers

Prof. Robert Berwick's abstract for his forthcoming invited talk at the ACL2016 workshop on Cognitive Aspects of Computational Language Learning revives an ancient debate. Entitled "Why take a chance?", Berwick seems to refer implicitly to Chomsky's critique of the statistical approach of Harris as well as the currently dominant paradigms in CoNLL. Berwick avoids Chomsky's use of "innate" but states that "the debate over the existence of sophisticated mental grammars was settled with Chomsky's Logical Structure of Linguistic Theory (1957/1975)", acknowledging that "this debate has often been revived". This paper agrees with the view that this debate has long since been settled, but with the opposite outcome! Given the embers have not yet died away, and the questions remain fundamental, perhaps it is appropriate to refuel the debate, so I would like to join Bob in throwing fuel on this fire by reviewing the evidence against the Chomskian position!

Somaiyeh Mahmoud. Zadeh, David M. W Powers, Karl Sammut

Today AUVs operation still remains restricted to very particular tasks with low real autonomy due to battery restrictions. Efficient motion planning and mission scheduling are principle requirement toward advance autonomy and facilitate the vehicle to handle long-range operations. A single vehicle cannot carry out all tasks in a large scale terrain; hence, it needs a certain degree of autonomy in performing robust decision making and awareness of the mission/environment to trade-off between tasks to be completed, managing the available time, and ensuring safe deployment at all stages of the mission. In this respect, this research introduces a modular control architecture including higher/lower level planners, in which the higher level module is responsible for increasing mission productivity by assigning prioritized tasks while guiding the vehicle toward its final destination in a terrain covered by several waypoints; and the lower level is responsible for vehicle's safe deployment in a smaller scale encountering time-varying ocean current and different uncertain static/moving obstacles similar to actual ocean environment. Synchronization between higher and lower level modules is efficiently configured to manage the mission time and to guarantee on-time termination of the mission. The performance and accuracy of two higher and lower level modules are tested and validated using ant colony and firefly optimization algorithm, respectively. After all, the overall performance of the architecture is investigated in 10 different mission scenarios. The analyze of the captured results from different simulated missions confirm the efficiency and inherent robustness of the introduced architecture in efficient time management, safe deployment, and providing beneficial operation by proper prioritizing the tasks in accordance with mission time.

Somaiyeh Mahmoud. Zadeh, Karl Sammut, David M. W Powers et al.

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation.

Somaiyeh Mahmoud. Zadeh, David M. W Powers, Karl Sammut et al.

This paper presents a hybrid route-path planning model for an Autonomous Underwater Vehicle's task assignment and management while the AUV is operating through the variable littoral waters. Several prioritized tasks distributed in a large scale terrain is defined first; then, considering the limitations over the mission time, vehicle's battery, uncertainty and variability of the underlying operating field, appropriate mission timing and energy management is undertaken. The proposed objective is fulfilled by incorporating a route-planner that is in charge of prioritizing the list of available tasks according to available battery and a path-planer that acts in a smaller scale to provide vehicle's safe deployment against environmental sudden changes. The synchronous process of the task assign-route and path planning is simulated using a specific composition of Differential Evolution and Firefly Optimization (DEFO) Algorithms. The simulation results indicate that the proposed hybrid model offers efficient performance in terms of completion of maximum number of assigned tasks while perfectly expending the minimum energy, provided by using the favorable current flow, and controlling the associated mission time. The Monte-Carlo test is also performed for further analysis. The corresponding results show the significant robustness of the model against uncertainties of the operating field and variations of mission conditions.

Somaiyeh Mahmoud Zadeh, Amir Mehdi Yazdani, Karl Sammut et al.

In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a nonlinear optimal control problem (NOCP) and then numerical solutions are provided. A penalty function method is utilized to combine the boundary conditions, vehicular and environmental constraints with the performance index that is final rendezvous time.Four evolutionary based path planning methods namely particle swarm optimization (PSO), biogeography-based optimization (BBO), differential evolution (DE) and Firefly algorithm (FA) are employed to establish a reactive planner module and provide a numerical solution for the proposed NOCP. The objective is to synthesize and analysis the performance and capability of the mentioned methods for guiding an AUV from loitering point toward the rendezvous place through a comprehensive simulation study.The proposed planner module entails a heuristic for refining the path considering situational awareness of underlying environment, encompassing static and dynamic obstacles overwhelmed in spatiotemporal current vectors.This leads to accommodate the unforeseen changes in the operating field like emergence of unpredicted obstacles or variability of current vector filed and turbulent regions. The simulation results demonstrate the inherent robustness and significant efficiency of the proposed planner in enhancement of the vehicle's autonomy in terms of using current force, coping undesired current disturbance for the desired rendezvous purpose. Advantages and shortcoming of all utilized methods are also presented based on the obtained results.

Somaiyeh Mahmoud Zadeh, David MW Powers, Karl Sammut et al.

An Autonomous Underwater Vehicle (AUV) needs to acquire a certain degree of autonomy for any particular underwater mission to fulfill the mission objectives successfully and ensure its safety in all stages of the mission in a large scale operating filed. In this paper, a novel combinatorial conflict-free-task assignment strategy consisting an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method is established upon the heuristic search potency of the Particle Swarm Optimisation (PSO) algorithm to address the discrete nature of routing-task assignment approach and the complexity of NP-hard path planning problem. The proposed hybrid method is highly efficient for having a reactive guidance framework that guarantees successful completion of missions specifically in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of vehicle's autonomy by relying on its reactive nature and capability of providing fast feasible solutions.

S. Mahmoud Zadeh, D. Powers, K. Sammut et al.

This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.

Somaiyeh Mahmoud Zadeh, David M. W. Powers, AmirMehdi Yazdani

Promoting the levels of autonomy facilitates the vehicle in performing long-range operations with minimum supervision. The capability of Autonomous Underwater Vehicles (AUVs) to fulfill the mission objectives is directly influenced by route planning and task assignment system performance. The system fives the error of "Bad character(s) in field Abstract" for no reason. Please refer to manuscript for the full abstract

S. Mahmoud Zadeh, D. M. W. Powers, A. Yazdani et al.

The AUV three-dimension path planning in complex turbulent underwater environment is investigated in this research, in which static current map data and uncertain static-moving time variant obstacles are taken into account. Robustness of AUVs path planning to this strong variability is known as a complex NP-hard problem and is considered a critical issue to ensure vehicles safe deployment. Efficient evolutionary techniques have substantial potential of handling NP hard complexity of path planning problem as more powerful and fast algorithms among other approaches for mentioned problem. For the purpose of this research Differential Evolution (DE) technique is conducted to solve the AUV path planning problem in a realistic underwater environment. The path planners designed in this paper are capable of extracting feasible areas of a real map to determine the allowed spaces for deployment, where coastal area, islands, static/dynamic obstacles and ocean current is taken into account and provides the efficient path with a small computation time. The results obtained from analyze of experimental demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles path planning capability in coping undesired current, using useful current flow, and avoid colliding collision boundaries in a real-time manner. The proposed approach is also flexible and strictly respects to vehicle's kinematic constraints resisting current instabilities.

David M. W. Powers

Evaluation often aims to reduce the correctness or error characteristics of a system down to a single number, but that always involves trade-offs. Another way of dealing with this is to quote two numbers, such as Recall and Precision, or Sensitivity and Specificity. But it can also be useful to see more than this, and a graphical approach can explore sensitivity to cost, prevalence, bias, noise, parameters and hyper-parameters. Moreover, most techniques are implicitly based on two balanced classes, and our ability to visualize graphically is intrinsically two dimensional, but we often want to visualize in a multiclass context. We review the dichotomous approaches relating to Precision, Recall, and ROC as well as the related LIFT chart, exploring how they handle unbalanced and multiclass data, and deriving new probabilistic and information theoretic variants of LIFT that help deal with the issues associated with the handling of multiple and unbalanced classes.

David M. W. Powers

Over the last decade there has been increasing concern about the biases embodied in traditional evaluation methods for Natural Language Processing/Learning, particularly methods borrowed from Information Retrieval. Without knowledge of the Bias and Prevalence of the contingency being tested, or equivalently the expectation due to chance, the simple conditional probabilities Recall, Precision and Accuracy are not meaningful as evaluation measures, either individually or in combinations such as F-factor. The existence of bias in NLP measures leads to the 'improvement' of systems by increasing their bias, such as the practice of improving tagging and parsing scores by using most common value (e.g. water is always a Noun) rather than the attempting to discover the correct one. The measures Cohen Kappa and Powers Informedness are discussed as unbiased alternative to Recall and related to the psychologically significant measure DeltaP. In this paper we will analyze both biased and unbiased measures theoretically, characterizing the precise relationship between all these measures as well as evaluating the evaluation measures themselves empirically using a Monte Carlo simulation.

David M. W. Powers

The F-measure or F-score is one of the most commonly used single number measures in Information Retrieval, Natural Language Processing and Machine Learning, but it is based on a mistake, and the flawed assumptions render it unsuitable for use in most contexts! Fortunately, there are better alternatives.