This paper presents the first worldwide functional prototype omnidirectional multi-rotor aerial vehicle with fixed uni-directional thrusters, with an on-board power source. An optimization algorithm computes the positions and orientations of the propellers in the body frame of the prototype to achieve the omnidirectional capability, while minimizing the platform's weight and the required thrust to hover at any orientation, in addition to other construction requirements. The effect of the aerodynamic interaction between the different propellers is identified experimentally, and the ensuing results are included in the optimization algorithm to avoid such interactions during flight. The prototype's performance is assessed in real experiments demonstrating the decoupling between the forces and moments of the drone, its ability to track concurrently independent positions and orientations, and its ability to hover at a fixed position while rotating.

The growing demand for learning English as a second language has increased interest in automatic approaches for assessing and improving spoken language proficiency. A significant challenge in this field is to provide interpretable scores and informative feedback to learners through individual viewpoints of learners’ proficiency, as opposed to holistic scores. Thus far, holistic scoring remains commonly applied in large-scale commercial tests. As a result, an issue with more detailed evaluation is that human graders are generally trained to provide holistic scores. This paper investigates whether view-specific systems can be trained when only holistic scores are available. To enable this process, view-specific networks are defined where both their inputs and structure are adapted to focus on specific facets of proficiency. It is shown that it is possible to train such systems on holistic scores, such that they provide view-specific scores at evaluation time. View-specific networks are designed in this way for pronunciation, rhythm, text, use of parts of speech and grammatical accuracy. The relationships between the predictions of each system are investigated on the spoken part of the Linguaskill proficiency test. It is shown that the view-specific predictions are complementary in nature and capture different information about proficiency.

The problem of missing data, usually absent in curated and competition-standard datasets, is an unfortunate reality for most machine learning models used in industry applications. Recent work has focused on understanding the nature and the negative effects of such phenomena, while devising solutions for optimal imputation of the missing data, using both discriminative and generative approaches. We propose a novel mechanism based on multi-head attention which can be applied effortlessly in any model and achieves better downstream performance without the introduction of the full dataset in any part of the modeling pipeline. Our method inductively models patterns of missingness in the input data in order to increase the performance of the downstream task. Finally, after evaluating our method against baselines for a number of datasets, we found performance gains that tend to be larger in scenarios of high missingness.

Detecting individual pronunciation errors and diagnosing pronunciation error tendencies in a language learner based on their speech are important components of computer-aided language learning (CALL). The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. This paper presents an approach to these tasks by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate's speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level.

A speaker's rhythm contributes to the intelligibility of their speech and can be characteristic of their language and accent. For non-native learners of a language, the extent to which they match its natural rhythm is an important predictor of their proficiency. As a learner improves, their rhythm is expected to become less similar to their L1 and more to the L2. Metrics based on the variability of the durations of vocalic and consonantal intervals have been shown to be effective at detecting language and accent. In this paper, pairwise variability (PVI, CCI) and variance (varcoV, varcoC) metrics are first used to predict proficiency and L1 of non-native speakers taking an English spoken exam. A deep learning alternative to generalise these features is then presented, in the form of a tunable duration embedding, based on attention over an RNN over durations. The RNN allows relationships beyond pairwise to be captured, while attention allows sensitivity to the different relative importance of durations. The system is trained end-to-end for proficiency and L1 prediction and compared to the baseline. The values of both sets of features for different proficiency levels are then visualised and compared to native speech in the L1 and the L2.

In free speaking tests candidates respond in spontaneous speech to prompts. This form of test allows the spoken language proficiency of a non-native speaker of English to be assessed more fully than read aloud tests. As the candidate's responses are unscripted, transcription by automatic speech recognition (ASR) is essential for automated assessment. ASR will never be 100% accurate so any assessment system must seek to minimise and mitigate ASR errors. This paper considers the impact of ASR errors on the performance of free speaking test auto-marking systems. Firstly rich linguistically related features, based on part-of-speech tags from statistical parse trees, are investigated for assessment. Then, the impact of ASR errors on how well the system can detect whether a learner's answer is relevant to the question asked is evaluated. Finally, the impact that these errors may have on the ability of the system to provide detailed feedback to the learner is analysed. In particular, pronunciation and grammatical errors are considered as these are important in helping a learner to make progress. As feedback resulting from an ASR error would be highly confusing, an approach to mitigate this problem using confidence scores is also analysed.

The way a non-native speaker pronounces the phones of a language is an important predictor of their proficiency. In grading spontaneous speech, the pairwise distances between generative statistical models trained on each phone have been shown to be powerful features. This paper presents a deep learning alternative to model-based phone distances in the form of a tunable Siamese network feature extractor to extract distance metrics directly from the audio frame sequence. Features are extracted at the phone instance level and combined to phone-level representations using an attention mechanism. Pair-wise distances between phone features are then projected through a feed-forward layer to predict score. The extraction stage is initialised on either a binary phone instance-pair classification task, or to mimic the model-based features, then the whole system is fine-tuned end-to-end, optimising the learning of the distance metric to the score prediction task. This method is therefore more adaptable and more sensitive to phone instance level phenomena. Its performance is compared against

Copyright © 2017 ISCA. Automatic systems for practice and exams are essential to support the growing worldwide demand for learning English as an additional language. Assessment of spontaneous spoken English is, however, currently limited in scope due to the difficulty of achieving sufficient automatic speech recognition (ASR) accuracy. "Off-the-shelf" English ASR systems cannot model the exceptionally wide variety of accents, pronunications and recording conditions found in non-native learner data. Limited training data for different first languages (L1s), across all proficiency levels, often with (at most) crowd-sourced transcriptions, limits the performance of ASR systems trained on non-native English learner speech. This paper investigates whether the effect of one source of error in the system, lexical modelling, can be mitigated by using graphemic lexicons in place of phonetic lexicons based on native speaker pronunications. Graphemicbased English ASR is typically worse than phonetic-based due to the irregularity of English spelling-to-pronunciation but here lower word error rates are consistently observed with the graphemic ASR. The effect of using graphemes on automatic assessment is assessed on different grader feature sets: audio and fluency derived features, including some phonetic level features; and phone/grapheme distance features which capture a measure of pronunciation ability.

High-end parallel and multicore processors rely on compilers to perform the necessary optimizations and exploit concurrency in order to achieve higher performance. However, the source code for high-performance computers is extremely complex to analyze and optimize. In particular, program analysis techniques often do not take into account complex expressions during the data dependence analysis phase. Most data dependence tests are only able to analyze linear expressions, even though nonlinear expressions occur very often in practice. Therefore, considerable amounts of potential parallelism remain unexploited. In this paper, we propose new data dependence analysis techniques to handle such complex instances of the dependence problem and increase program parallelization. Our method is based on a set of polynomial-time techniques that can prove or disprove dependences in source codes with nonlinear and symbolic expressions, complex loop bounds, arrays with coupled subscripts, and if-statement constraints. In addition, our algorithm can produce accurate and complete direction vector information, enabling the compiler to apply further transformations. To validate our method, we performed an experimental evaluation and comparison against the I-Test, the Omega test, and the Range test in the Perfect and SPEC benchmarks. The experimental results indicate that our dependence analysis tool is accurate, efficient, and more effective in program parallelization than the other dependence tests. The improved parallelization results into higher speedups and better program execution performance in several benchmarks.

There exist several scheduling schemes for parallelizing loops without dependences for shared and distributed memory systems. However, efficiently parallelizing loops with dependences is a more complicated task. This becomes even more difficult when the loops are executed on a distributed memory cluster where communication and synchronization can be a bottleneck. The problem lies in the processor idle time which occurs during the beginning and final stages of the execution. In this paper we propose a new scheduling scheme that minimizes the processor idle time and thus it enhances load balancing and performance. The new scheme is applied to two-dimensional iteration spaces with dependences. The proposed scheduling scheme follows a tiled wavefront pattern in which the tile size gradually decreases in all dimensions. We have tested the proposed scheme on a dedicated and homogeneous cluster of workstations and we verified that it significantly improves execution times over scheduling using traditional tiling.

Optimizing compilers rely upon program analysis techniques to detect data dependences between program statements. Data dependence information captures the essential ordering constraints of the statements in a program that need to be preserved in order to produce valid optimized and parallel code. Data dependence testing is very important for automatic parallelization, vectorization, and any other code transformation. In this paper, we examine the impact of data dependence analysis in practice. A number of data dependence tests have been proposed in the literature. In each test, there are different trade offs between accuracy and efficiency. We present an experimental evaluation of several data dependence tests, including the Banerjee test, the I-Test, and the Omega test. We compare these tests in terms of data dependence accuracy, compilation efficiency, effectiveness in parallelization, and program execution performance. We analyze the reasons why a data dependence test can be inexact and we explain how the examined tests handle such cases. We run various experiments using the Perfect Club Benchmarks and the scientific library Lapack. We present the measured accuracy of each test and the reasons for any approximation. We compare these tests in term's of efficiency and we analyze the trade offs between accuracy and efficiency. We also determine the impact of each data dependence test on the total compilation time. Finally, we measure the number of loops parallelized by each test and we compare the execution performance of each benchmark on a multiprocessor. Our results indicate that the Omega test is more accurate, but also very inefficient in the cases where the other two tests are inaccurate. In general, the cost of the Omega test is high and uses a significant percentage of the total compilation time. Furthermore, the difference in accuracy of the Omega test over the Banerjee test and the l-Test does not improve parallelization and program execution performance.

Optimizing compilers rely upon program analysis techniques to detect data dependences between program statements. Data dependence information captures the essential ordering constraints of the statements in a program that need to be preserved in order to produce valid optimized and parallel code. Data dependence testing is very important for automatic parallelization, vectorization and any other code transformation. In this paper we examine the impact of data dependence analysis in practice. A number of data dependence tests have been proposed in the literature. In each test there are different tradeoffs between accuracy and efficiency. We present an experimental evaluation of several data dependence tests, including the Banerjee test, the I-Test and the Omega test. We compare these tests in terms of data dependence accuracy, compilation efficiency, effectiveness in parallelization and program execution performance. We analyze the reasons why a data dependence test can be inexact and we explain how the examined tests handle such cases. We run various experiments using the Perfect Club Benchmarks and the scientific library Lapack. We present the measured accuracy of each test and the reasons for any approximation. We compare these tests in terms of efficiency and we analyze the tradeoffs between accuracy and efficiency. We also determine the impact of each data dependence test on the total compilation time. Finally, we measure the number of loops parallelized by each test and we compare the execution performance of each benchmark on a multiprocessor. Our results indicate that the Omega test is more accurate, but also very inefficient in the cases where the other two tests are inaccurate. In general the cost of the Omega test is high and a significant percentage of the total compilation time. Furthermore, the difference in accuracy of the Omega test over the Banerjee test and the I-Test does not improve parallelization and program execution performance.

Parallelizing compilers rely on data dependence information in order to produce valid parallel code. Polynomial data dependence analysis techniques, such as the Banerjee test and the I-Test, can efficiently compute data dependence information for simple instances of the data dependence problem. In more complicated cases such as triangular or trapezoidal loop regions with direction vector constraints these tests, including the triangular Banerjee test, ignore or simplify many of the constraints and thus introduce further approximations. The I-Test and the Omega test are two data dependence tests that can provide exact data dependence information. In addition the Omega test can accurately handle complex loop regions but at a higher computation cost. We extend the ideas behind the I-Test to handle such complex regions which are frequently found in actual source code. In particular, we provide a polynomial-time algorithm, the VI-Test that can detect data dependences in loops with triangular bounds and symbolic variables subject to any direction vector. We also perform an extensive experimental evaluation of the various dependence tests, including the I-Test, the VI-Test and the Omega test. We run several experiments using the Perfect Club Benchmarks and the scientific libraries Eispack, Linpack and Lapack. We present accuracy results, reasons for inconclusive answers, and comparative efficiency metrics.