Enhancing mirror adaptive random testing through dynamic partitioning

Context: Adaptive random testing (ART), originally proposed as an enhancement of random testing, is often criticized for the high computation overhead of many ART algorithms. Mirror ART (MART) is a novel approach that can be generally applied to improve the efficiency of various ART algorithms based on the combination of ''divide-and-conquer'' and ''heuristic'' strategies. Objective: The computation overhead of the existing MART methods is actually on the same order of magnitude as that of the original ART algorithms. In this paper, we aim to further decrease the order of computation overhead for MART. Method: We conjecture that the mirroring scheme in MART should be dynamic instead of static to deliver a higher efficiency. We thus propose a new approach, namely dynamic mirror ART (DMART), which incrementally partitions the input domain and adopts new mirror functions. Results: Our simulations demonstrate that the new DMART approach delivers comparable failure-detection effectiveness as the original MART and ART algorithms while having much lower computation overhead. The experimental studies further show that the new approach also delivers a better and more reliable performance on programs with failure-unrelated parameters. Conclusion: In general, DMART is much more cost-effective than MART. Since its mirroring scheme is independent of concrete ART algorithms, DMART can be generally applied to improve the cost-effectiveness of various ART algorithms

A survey on adaptive random testing

Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work

KD-ART: Should we intensify or diversify tests to kill mutants?

CONTEXT: Adaptive Random Testing (ART) spreads test cases evenly over the input domain. Yet once a fault is found, decisions must be made to diversify or intensify subsequent inputs. Diversification employs a wide range of tests to increase the chances of finding new faults. Intensification selects test inputs similar to those previously shown to be successful. OBJECTIVE: Explore the trade-off between diversification and intensification to kill mutants. METHOD: We augment Adaptive Random Testing (ART) to estimate the Kernel Density (KD–ART) of input values found to kill mutants. KD–ART was first proposed at the 10th International Workshop on Mutation Analysis. We now extend this work to handle real world non numeric applications. Specifically we incorporate a technique to support programs with input parameters that have composite data types (such as arrays and structs). RESULTS: Intensification is the most effective strategy for the numerical programs (it achieves 8.5% higher mutation score than ART). By contrast, diversification seems more effective for programs with composite inputs. KD–ART kills mutants 15.4 times faster than ART. CONCLUSION: Intensify tests for numerical types, but diversify them for composite types

A study of the application of adaptive optics (AO) in optical coherence tomography (OCT) and confocal microscopy for the purpose of high resolution imaging

A problem is presented when imaging the eye in that optical aberrations are introduced by tissues of the anterior eye such as the cornea and lens. Adaptive optics (AO) and scanning laser ophthalmoscopy (SLO) have been combined to detect and compensate for these aberrations through the use of one or more correcting devices. Di erent corrector options exist, such as a liquid crystal lens or a deformable mirror (DM), such as that used in this thesis. This study seeks to use the ability of the DM to add focus/defocus aberrations to the closed loop AO system. This procedure could allow for dynamic focus control during generation of B-scan images using spectral domain optical coherence tomography (SD-OCT), where typically this is only possible using slower time domain techniques. The confocal gate scanning is controlled using the focus altering aberrations created by changing the shape of the deformable mirror. Using the novel master-slave interferometry method, multiple live en-face images can be acquired simultaneously. In this thesis, application of this method to an AO system is presented whereby en-face images may be acquired at multiple depths simultaneously. As an extension to this research, an OCT despeckle method is demonstrated. Further to this work is the investigation of the role in AO for optimisation of optical systems without the requirement for direct aberration measurement. Towards this end, genetic algorithms (GA) may be employed to control the DM in an iterative process to improve the coupling of light into fibre

Measuring Political Bias in British Media: Using Recurrent Neural Networks for Long Form Textual Analysis

In this thesis we aim to explore methods of determining political bias in the traditional British print media. It can be shown that much of the British public perceive there to be explicit political biases in many of the UK's most popular media outlets. It is also known that people are inherently prone to political influence from their sources of news. Due to this reason, there is motivation to seek a means to formalise political bias in British media outlets. In our study, we took the 2016 UK referendum of EU membership as the source to identify a political bias. We sought to find a means in which to determine on a sentence level, whether a newspaper identified with a pro-leave or pro-remain philosophy. For this, we used the newspapers explicit endorsements for a certain referendum outcome that are provided by the newspapers themselves as a ground truth. Recurrent neural networks have been shown to be useful when working over data of varying sizes, such as encoded textual data. Recurrent neural networks have also been used to perform classification tasks over short form textual data in the scope of determining political bias. However, little work has been done on processing more long-form textual data for classification tasks within a political bias domain. Here, we sought to determine if recurrent neural networks would be a viable approach for solving this problem, compared to more traditional and simple approaches, such as a Naive Bayes model. In our study, we were able to determine that recurrent neural networks are successfully able to determine a political bias in British media outlets. Our models also indicated slight biases in supposedly unbiased outlets, such as the BBC. The generated models were also able to transfer their learnings into new domains, such as determining EU membership political bias in more recent news articles. However, due to a lack of input data, and ground truths applied in a broad manner, traditional methods such as the Naive Bayes were able to achieve similar results to the recurrent neural networks, with much less compute power required

A Comparison of Personality Traits of Female Athletes with a High Incidence of Injury to Those with a Low Incidence of Injury

The purpose of this study was to determine if there is a significant difference in the personality traits of collegiate female athletes with a high incidence of injury when compared to female athletes with a low incidence of injury. Fourteen female intercollegiate athletes completed a personal information questionnaire and Cattell\u27s Sixteen Personality Factor Questionnaire during a one hour session of testing. The mean raw scores of the two groups were analyzed to determine if they differed statistically. The mean raw scores were then converted to mean sten scores under the recommendations of Cattell. A visual depiction of the mean sten scores was also completed to identify any possible trends. The results of this study indicate that there is a significant difference between those athletes with a high and low incidence of injury on the primary personality factor B. Factor B indicates an individuals reasoning abilities. The findings of this study indicate that athletes with a high incidence of injury are more concrete thinkers, while those athletes with a low incidence of injury tend to be more abstract thinkers. However, this finding may not be as profound because the mean sten scores of both groups fall within the average range of the population. A visual inspection of the data also seems to indicate with more subjects there may have been a significant difference in the personality traits of warmth, dominance, and independence. It was also found that there seems to be a relationship between the number of high school injuries and collegiate injuries. After review of the personal information questionnaires, it was found that all but one individual that was classified as having a high incidence of injury in college would also have been classified similarly in high school. The findings of this study indicate that the use of a personality inventory may be helpful to some degree in determining the incidence of injury in collegiate female athletes. This may help coaches, and athletic trainers to better help such athletes whether it be in prevention or rehabilitation. This study also seems to indicate that more studies should be done in this area. However, future studies should address a wider variety of issues associated with injury such as exposures, and type of equipment available to the athletes

Role of Water Flow Regime in the Swimming Behaviour and Escape Performance of a Schooling Fish

Animals are exposed to variable and rapidly changing environmental flow conditions, such as wind in terrestrial habitats and currents in aquatic systems. For fishes, previous work suggests that individuals exhibit flow-induced changes in aerobic swimming performance. Yet, no one has examined whether similar plasticity is found in fast-start escape responses, which are modulated by anaerobic swimming performance, sensory stimuli and neural control. In this study, we used fish from wild schools of the tropical damselfish Chromis viridis from shallow reefs surrounding Lizard Island in the Great Barrier Reef, Australia. The flow regime at each site was measured to ascertain differences in mean water flow speed and its temporal variability. Swimming and escape behaviour in fish schools were video-recorded in a laminar-flow swim tunnel. Though each school\u27s swimming behaviour (i.e. alignment and cohesion) was not associated with local flow conditions, traits linked with fast-start performance (particularly turning rate and the distance travelled with the response) were significantly greater in individuals from high-flow habitats. This stronger performance may occur due to a number of mechanisms, such as an in situ training effect or greater selection pressure for faster performance phenotypes in areas with high flow speed

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

The raw depth image captured by indoor depth sensors usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and the limited distance range. The incomplete depth map with missing values burdens many downstream vision tasks, and a rising number of depth completion methods have been proposed to alleviate this issue. While most existing methods can generate accurate dense depth maps from sparse and uniformly sampled depth maps, they are not suitable for complementing large contiguous regions of missing depth values, which is common and critical in images captured in indoor environments. To overcome these challenges, we design a novel two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure, by adhering to the Manhattan world assumption and utilizing normal maps from RGB-D information as guidance, to regress the local dense depth values from the raw depth map. In the other branch, we propose an RGB-depth fusion CycleGAN to transfer the RGB image to the fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate the features across the two branches, and we append a confidence fusion head to fuse the two outputs of the branches for the final depth map. Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method clearly improves the depth completion performance, especially in a more realistic setting of indoor environments, with the help of our proposed pseudo depth maps in training.Comment: Haowen Wang and Zhengping Che are with equal contributions. Under review. An earlier version has been accepted by CVPR 2022 (arXiv:2203.10856

Can morphotaxa be assessed with photographs? Estimating the accuracy of two-dimensional cranial geometric morphometrics for the study of threatened populations of African monkeys

The classification of most mammalian orders and families is under debate and the number of species is likely greater than currently recognized. Improving taxonomic knowledge is crucial, as biodiversity is in rapid decline. Morphology is a source of taxonomic knowledge, and geometric morphometrics applied to two dimensional (2D) photographs of anatomical structures is commonly employed for quantifying differences within and among lineages. Photographs are informative, easy to obtain, and low cost. 2D analyses, however, introduce a large source of measurement error when applied to crania and other highly three dimensional (3D) structures. To explore the potential of 2D analyses for assessing taxonomic diversity, we use patas monkeys (Erythrocebus), a genus of large, semi-terrestrial, African guenons, as a case study. By applying a range of tests to compare ventral views of adult crania measured both in 2D and 3D, we show that, despite inaccuracies accounting for up to one-fourth of individual shape differences, results in 2D almost perfectly mirror those in 3D. This apparent paradox might be explained by the small strength of covariation in the component of shape variance related to measurement error. A rigorous standardization of photographic settings and the choice of almost coplanar landmarks are likely to further improve the correspondence of 2D to 3D shapes. 2D geometric morphometrics is, thus, appropriate for taxonomic comparisons of patas ventral crania. Although it is too early to generalize, our results corroborate similar findings from previous research in mammals, and suggest that 2D shape analyses are an effective heuristic tool for morphological investigation of small differences

Subpixel real-time jitter detection algorithm and implementation for polarimetric and helioseismic imager

The polarimetric and helioseismic imager instrument for the Solar Orbiter mission from the European Space Agency requires a high stability while capturing images, specially for the polarimetric ones. For this reason, an image stabilization system has been included in the instrument. It uses global motion estimation techniques to estimate the jitter in real time with subpixel resolution. Due to instrument requirements, the algorithm has to be implemented in a Xilinx Virtex-4QV field programmable gate array. The algorithm includes a 2-D paraboloid interpolation algorithm based on 2-D bisection. We describe the algorithm implementation and the tests that have been made to verify its performance. The jitter estimation has a mean error of 125  pixel of the correlation tracking camera. The paraboloid interpolation algorithm provides also better results in terms of resources and time required for the calculation (at least a 20% improvement in both cases) than those based on direct calculation