This book brings together corpus linguistics and pragmatics by extending the emerging corpus analytic framework of local grammar to speech act research, aiming to enrich the toolkit of corpus-based speech act studies. It outlines four directions in which local grammar can be useful for investigating speech acts, namely, a local grammar approach to annotating speech acts, developing local grammars of speech acts, identifying speech act constructions via the lens of local grammars, and applying local grammars into contrastive speech act studies. These directions are illustrated with studies on apology in contemporary spoken British English, which shows that local grammar can be an innovative approach to advance speech act studies and that such research has significant implications and applications. The book would be of interest to researchers and students in corpus linguistics, pragmatics, construction grammar, and (L2) speech act research and teaching.

Many algorithms have recently been studied for scheduling mixed-criticality (MC) tasks. However, most existing MC scheduling algorithms guarantee the timely executions of high-criticality (HC) tasks at the expense of discarding low-criticality (LC) tasks, which can cause serious service interruption for such tasks. In this work, aiming at providing guaranteed services for LC tasks, we study an Elastic Mixed-Criticality (E-MC) task model for dual-criticality systems. Specifically, the model allows each LC task to specify its maximum period (i.e., minimum service level) and a set of early-release points. We propose an Early-Release (ER) mechanism that enables LC tasks be released more frequently and thus improve their service levels at runtime, with both conservative and aggressive approaches to exploiting system slack being considered, which is applied to both EDF and preference-oriented earliest-deadline (POED) schedulers. We formally prove the correctness of the proposed ER-EDF scheduler on guaranteeing the timeliness of all tasks through judicious management of the early releases of LC tasks. The proposed model and schedulers are evaluated through extensive simulations. The results show that, by moderately relaxing the service requirements of LC tasks in MC task sets (i.e., by having LC tasks' maximum periods in the E-MC model be 2 to 3 times of their desired MC periods), most transformed E-MC task sets can be successfully scheduled without sacrificing the timeliness of HC tasks. Moreover, with the proposed ER mechanism, the runtime performance of tasks (e.g., execution frequencies of LC tasks, response times and jitters of HC tasks) can be significantly improved under the ER schedulers when compared to that of the state-of-the-art EDF-VD scheduler.

Automatic speech recognition (ASR) and speaker recognition (SRE) are two important fields of research in speech technology. Over the years, many efforts have been made on improving recognition accuracies on both tasks, and many different technologies have been developed. Given the close relationship between these two tasks, researchers have proposed different ways to introduce techniques developed for these tasks to each other. In the first half of this thesis, I explore ways to improve speaker recognition performance using state-of-the-art speech recognition acoustic models, and then research alternative ways to perform speaker adaptation of deep learning models for ASR using speaker identity vector (i-vector). Experiments from this work shows that ASR and SRE are beneficial to each other and can be used to improve their performance. In the second part of the thesis, I aim to build joint model for speech and speaker recognition. To implement this idea, I first build an open-source experimental framework, TIK, that connects well-known deep learning toolkit Tensorflow and speech recognition toolkit Kaldi. After reproducing state-of-the-art speech and speaker recognition performance using TIK, I then developed a unified model, JointDNN, that is trained jointly for speech and speaker recognition. Experimental results show that the joint model can effectively perform ASR and SRE tasks. In particular, experiments show that the JointDNN model is more effective in speaker recognition than x-vector system, given a limited amount of training data.

We consider a set of real-time periodic tasks where some tasks are preferably executed as soon as possible (ASAP) and others as late as possible (ALAP) while still meeting their deadlines. After introducing the idea of preference-oriented (PO) execution, we formally define the concept of PO-optimality. For fully-loaded systems (with 100% utilization), we first propose a PO-optimal scheduler, namely ASAP-ensured earliest deadline (SEED), by focusing on ASAP tasks where the optimality of tasks’ ALAP preference is achieved implicitly due to the harmonicity of the PO-optimal schedules for such systems. Then, for underutilized systems (with less than 100% utilization), we show the discrepancies between different PO-optimal schedules. By extending SEED, we propose a generalized preference-oriented earliest deadline (POED) scheduler that can obtain a PO-optimal schedule for any schedulable task set. We further evaluate the proposed PO-optimal schedulers through extensive simulations. The results show that, comparing to that of the well-known EDF scheduler, the scheduling overheads of SEED and POED are higher (but still manageable) due to the additional consideration of tasks’ preferences. However, SEED and POED can achieve the preference-oriented execution objectives in a more successful way than EDF.

The Elastic Mixed-Criticality (E-MC) task model and an Early-Release EDF (ER-EDF) scheduling algorithm have been studied to address the service interruption problem for lowcriticality tasks in uniprocessor systems, where the minimum service requirements of low-criticality tasks are guaranteed by their maximum periods. In this paper, focusing on multicore systems, we first investigate and empirically evaluate the schedulability of E-MC tasks under partitioned-EDF (P-EDF) by considering various task-to-core mapping heuristics. Then, to improve the service levels of low-criticality tasks by exploiting slack at runtime, with and without task migrations being considered, we study both global and local early-release schemes. Moreover, considering varied migration overheads between different cores, we propose the multicore-aware and migration constrained globalER schemes. The simulation results show that, compared to the state-of-the-art Global EDF-VD scheduler, P-EDF with various partitioning heuristics can lead to many more schedulable E-MC task sets. Moreover, our proposed global and local ER schemes can significantly improve the execution frequencies (and thus service levels) of low-criticality tasks, while Global EDF-VD may severely affect them by discarding most of their task instances at runtime (especially for systems with more cores). Furthermore, by allowing task migrations, global-ER schemes can dramatically improve low-criticality tasks' service levels for partitionings that do not balance high- and low-criticality tasks among the cores.

To use unicellular microalgae to remove waste nutrients from brewery wastewater while converting them into algal biomass has been explored but high-cost treatment and low-value biomass associated with current technologies have prevented this concept from further attempts. In this study, a filamentous microalga Tribonema aequale was introduced and the alga can grow vigorously in brewery wastewater and algal biomass concentration could be as high as 6.45g L-1 which can be harvested by a cost-effective filtration method. The alga together with autochthonous bacteria removed majority of waste nutrients from brewery wastewater. Specifically, 85.39% total organic carbon (TOC), 79.53% total dissolved nitrogen (TN), 93.38% ammonia (NH3-N) and 71.33% total dissolved phosphate (TP) in brewery wastewater were rapidly removed by co-cultivation of T. aequale and autochthonous bacteria. Treated wastewater met the national wastewater discharge quality, and resulting algal biomass contained large amounts of high-value products chrysolaminarin, palmitoleic acid (PLA) and eicosapentaenoic acid (EPA). It is anticipated that reduced cost of algal harvesting coupled with value-added biomass could make T. aequale as a promising candidate for brewery wastewater treatment and resource utilization.