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Validation and Verification of Safety-Critical Systems in Avionics
Get PDFThis research addresses the issues of safety-critical systems verification and validation. Safety-critical systems such as avionics systems are complex embedded systems. They are composed of several hardware and software components whose integration requires verification and testing in compliance with the Radio Technical Commission for Aeronautics standards and their supplements (RTCA DO-178C). Avionics software requires certification before its deployment into an aircraft system, and testing is mandatory for certification. Until now, the avionics industry has relied on expensive manual testing. The industry is searching for better (quicker and less costly) solutions.
This research investigates formal verification and automatic test case generation approaches to enhance the quality of avionics software systems, ensure their conformity to the standard, and to provide artifacts that support their certification.
The contributions of this thesis are in model-based automatic test case generations approaches that satisfy MC/DC criterion, and bidirectional requirement traceability between low-level requirements (LLRs) and test cases.
In the first contribution, we integrate model-based verification of properties and automatic test case generation in a single framework. The system is modeled as an extended finite state machine model (EFSM) that supports both the verification of properties and automatic test case generation. The EFSM models the control and dataflow aspects of the system. For verification, we model the system and some properties and ensure that properties are correctly propagated to the implementation via mandatory testing. For testing, we extended an existing test case generation approach with MC/DC criterion to satisfy RTCA DO-178C requirements. Both local test cases for each component and global test cases for their integration are generated. The second contribution is a model checking-based approach for automatic test case generation. In the third contribution, we developed an EFSM-based approach that uses constraints solving to handle test case feasibility and addresses bidirectional requirements traceability between LLRs and test cases. Traceability elements are determined at a low-level of granularity, and then identified, linked to their source artifact, created, stored, and retrieved for several purposes. Requirements’ traceability has been extensively studied but not at the proposed low-level of granularity
Evolutionary, ecological, and anthropogenic drivers of phenotypic diversity in ants
Get PDFThe drivers of phenotypic diversity have puzzled humanity for centuries. Functional trait approaches have helped advance the mechanistic understanding of the diversity of life forms. Previous work has shown that evolutionary history and environmental adaptation contribute to the observed diversity of phenotypes. However, most of our understanding comes from plants and studies that often neglect the influence of intraspecific variability. My thesis aims to investigate the drivers of phenotypic diversity across organizational levels using ants as study organisms. In Chapter 2, I examined the influence of evolutionary and environmental heterogeneity on the phenotypic diversity of ant lineages. I found a negative relationship between the diversity of climates occupied by ant genera and their phenotypic integration. This indicates that phenotypic integration may limit ant phenotypic diversification into new climatic zones. For Chapter 3, I examined geographic variation in community-wide patterns of phenotypic diversity, at different organizational levels (i.e., worker, colony, and species), along a 9° latitude gradient in Quebec, Canada. The results suggest that stressful environmental conditions typical of northern ecosystems exert a strong selection pressure on ant morphology causing shifts in optimal trait values of antennae length and eye size. Specifically, I found that shorter antennae and larger eyes possibly represent adaptations to cold, dry, and open habitats. In Chapter 4, I evaluated the impact of coffee plantation management practices on community-wide patterns of ant phenotypic diversity and composition. I found that intensified monocultures harbored phenotypically distinct ant communities. Moreover, community-wide phenotypic composition was more homogeneous in intensified plantations than in nearby forest patches or shade-grown plantations. This indicates that shade-grown strategies in coffee plantations buffer the impoverishment of ant phenotypic diversity following forest conversion, which could help preserve ecosystem services provided by ants. Overall, my thesis suggests that ant phenotypic diversity patterns are limited by phenotypic integration, vary among organizational levels (worker, colony, and species), and are influenced by anthropogenic disturbance across facets (taxonomic, phylogenetic, and functional). These findings have important implications for understanding how phenotypically complex organisms respond to climate change and provide guidance for conservation strategies targeting vulnerable lineages
Journalism and Its Shifting Roles in Online Social Movements: Examining Press Coverage of Femicides in Mexico on Twitter
Get PDFSocial media platforms have opened new doors for feminist movements to publicly address social issues, such as harassment, sexual assault, rape, femicides, and other forms of gender violence. At the same time, news media actors have adopted and adapted the use of social platforms when covering public affairs in general. In this digital journalism research project, I explore how Mexican news media outlets covered issues related to gender violence and femicides during International Women’s Day 2022 on Twitter by analyzing content associated with specific hashtags.
To answer this question, I first pursued a thematic analysis of tweets from mainstream news media that used the hashtags #8M, #8Marzo2022, and #DíaInternacionalDeLaMujer, to understand the coverage's content and main narratives more closely. Secondly, I reflected on the findings of this thematic analysis through the lens of Hanitzsch and Vos’ 2018 work on the 18 roles of journalism in political life. In this sense, this study also aims to expand on Hanitzsch and Vos theoretical framework to offer a reviewed approach that is more flexible and applicable to analyzing news coverage on social media in the face of current social movements
Siblings of Young People with Cancer: Medical Knowledge, Well-being and Adjustment
Get PDFChildhood and adolescent cancer is a significant health issue globally, with varying survival rates across countries. While advancements in cancer treatment have improved survival rates, the impact of cancer on the affected child's family, particularly siblings, remains poorly understood. Siblings often experience disruptions in family dynamics, attention disparities, and increased responsibilities due to their brother or sister's illness. Psychological consequences, such as anxiety and depression, have been reported in siblings, yet psychological support for them is limited. The long-term effects of cancer on siblings and their adjustment to non-normative events require further investigation. This study aimed to explore the needs of siblings of young people with cancer in the Quebec context. Thematic analysis of qualitative interviews revealed six primary needs of siblings of young people with cancer: attention and acknowledgment, emotional support, medical knowledge and preparatory information, inclusion, nurturing family relationships, and instrumental support. Addressing these needs through improved family functioning and tailored interventions can better support siblings throughout and after the cancer experience. The findings of this study contribute to the existing literature and provide insights for healthcare professionals, educators, and parents to offer appropriate support to siblings of young people with cancer during the cancer journey
Discourse Analysis of Argumentative Essays of English Learners based on their CEFR Level
Get PDFThis thesis aims to explore the relationship between discourse information and the CEFR-level (Common European Framework of Reference for Languages) in argumentative English learner essays. The study leverages two prominent frameworks: the Rhetorical Structure Theory (RST) and the Penn Discourse TreeBank (PDTB), to analyze essays obtained from The International Corpus Network of Asian Learners (ICNALE) and the Corpus and Repository of Writing (CROW). The research investigates the influence of different discourse relations and connectives on the language proficiency level of the writers, and further explores the potential of using discourse information as additional features for automated CEFR-level determination. The analysis of the collected essays reveals significant findings regarding the utilization of discourse relations by English learners. Notably, the RST relations of EXPLANATION and BACKGROUND are statistically used more often by writers with a CEFR level below fluency. In addition, as the CEFR level increases, the use of the PDTB relation of CONTINGENCY decreases. These results provide empirical evidence of the relationship between discourse relations and language proficiency, highlighting the differential usage patterns among learners at various CEFR levels. To validate these findings computationally, discourse relations and connectives are employed as supplementary features for machine learning models. The experimental results indicate that incorporating discourse information into the automated CEFR-level determination process leads to a mild increase in performance compared to relying solely on lexical and grammatical features. However, it is important to note that the proposed approach does not outperform the use of large language models, such as RoBERTa, which have demonstrated superior performance in various natural language processing tasks.
Nevertheless, this study contributes valuable insights into the relationship between discourse relations and argumentative English learner essays. The findings highlight the potential influence of discourse relations on language proficiency and suggest avenues for further research and development in language assessment methodologies
Extending the Reach of Fault Localization to Assist in Automated Debugging
Get PDFSoftware debugging is one of the most time-consuming tasks in modern software maintenance. To assist developers with debugging, researchers have proposed fault localization techniques. These techniques aim to automate the process of locating faults in software, which can greatly reduce debugging time and assist developers in understanding the faults. Effective fault localization is also crucial for automated program repair techniques, as it helps identify potential faulty locations for patching.
Despite recent efforts to advance fault localization techniques, their effectiveness is still limited. With the increasing complexity of modern software, fault localization may not always provide direct identification of the root causes of faults. Further, there is a lack of studies on their application in modern software development. Most prior studies have evaluated these techniques in traditional software development settings, where only a single snapshot of the system is considered. However, modern software development often involves continuous and fine-grained changes to the system. This dissertation proposes a series of approaches to explore new automated debugging solutions that can enhance software quality assurance and reliability practices, with a specific focus on extending the reach of fault localization in modern software development.
The dissertation begins with an empirical study on user-reported logs in bug reports, revealing that re-constructed execution paths from these logs provide valuable debugging hints. To further assist developers in debugging, we propose using static analysis techniques for information-retrieval and path-guided fault localization. By leveraging execution paths from logs in bug reports, we can improve the effectiveness of fault localization techniques. Second, we investigate the characteristics of operational data in continuous integration that can help capture faults early in the testing phase. As there is currently no available continuous integration benchmark that incorporates continuous test execution and failure, we present T-Evos, a dataset that comprises various operational data in continuous integration settings. We propose automated fault localization techniques that integrate change information from continuous integration settings, and demonstrate that leveraging such fine-grained change information can significantly improve their effectiveness. Finally, the dissertation investigates the data cleanness in fault localization by examining developers' knowledge in fault-triggering tests. The study reveals a significant degradation in the performance of fault localization techniques when evaluated on faults without developer knowledge.
Through case studies and experiments, the proposed techniques in this dissertation significantly improve the effectiveness of fault localization and facilitate their adoption in modern software development. Additionally, this dissertation provides valuable insights into new debugging solutions for future research
Low Complexity DPD for Multi-Band Radio over Fiber Transmission Systems
Get PDFThe increasing demand for broadband wireless transmission in the modern internet has led to the proposal and standardization of the fifth-generation (5G) mobile communication system, which offers massive device connectivity, high bit rates, low latency, and cost sustainability. However, maintaining a high transmission rate as well as low latency is difficult to achieve simultaneously, which requires some state-of-art fronthaul transmission techniques. Therefore, radio over fiber (RoF) with different approaches like digital RoF (D-RoF), analog RoF (A-RoF), and delta-sigma modulation based RoF (DSM-RoF) for 5G fronthaul transmission has been introduced. Those RoF techniques may significantly reduce complexity and power consumption at base stations, but the extra electric to optic (E/O), optic to electric (O/E) converters and power amplifiers could introduce extra nonlinearity into the system. Moreover, ultra-broadband or multi-band ultra-broadband signal is introduced in 5G to further increase the transmission rate, which further increases the impact of the nonlinearity. Therefore, broadband linearization techniques are necessary for RoF fronthaul transmission systems due to the fragile of the signal and the inherent nonlinear distortions introduced by RoF link. To reduce the degradation of nonlinearity for RoF link, digital predistortion (DPD) techniques have been extensively researched to address these challenges.
In a multi-band or multi-dimensional RoF system, multi-band DPD is required. Multi-dimensional DPD should be able to suppress the internal distortion within each band/dimension but also inter-distortion between different bands/dimensions. Unfortunately, the dimension higher than 3 causes a high calculation complexity to get the DPD function coefficients. There have been lots of efforts that have been made to obtain less-complexity DPD with better accuracy for multi-band or multidimensional signals. However, very limited DPD techniques have been proposed in simplifying the fundamental linearization function for bands exceeding four. Thus, the multi-band/multidimensional DPD has not been really got in used in commercial products because of the high complexity, high cost and high-power consumption. Thus, a simplified linearization approach for multi-band DPD is still needed.
In this thesis, a new low-complexity multidimensional DPD is introduced. This proposed DPD introduces a simplified DPD function, which evolves from the conventional memory polynomial function. Compared with the conventional multi-dimensional DPD, this proposed approach has lower complexity increased with the increase of signal bands or dimensions, nonlinearity orders, and memory effect depth. For example, the conventional DPD function needs a total of 40040 coefficients for the 6-band signals with a nonlinearity order of 10 and a memory depth of 5. However, this proposed low-complexity DPD function needs 640 coefficients. A substantial reduction in complexity is clearly observed.
The performance of the proposed DPD is evaluated by both simulation and experiments. An up to 6-band 64-QAM orthogonal frequency division multiplexing (OFDM) signal with each band of 200 MHz in simulations and an up to 5-band 20 MHz 64-QAM OFDM signal in experiments are used. The performance is evaluated in the means of error vector magnitude (EVM) of the received signal. The average improvement of EVM in simulation for 3-band, 4-band, 5-band and 6-band signals is 19.97 dB, 18.65 dB, 16.64 dB and 15.44 dB, respectively. The average improvement of EVM in experiments for 4-band and 5-band signals is 5.67 dB and 8.1 dB, respectively. The above results prove that the proposed DPD can significantly reduce the complexity and provide good linearization
Plastic Trees Tell no Tale: The Non-Subjective Accounts of ‘Nature’ in Philippe Grandrieux’s Sombre and Un Lac
Get PDFThe articulation of audio-visual effects in Philippe Grandrieux’s Sombre (1998) and Un Lac (2009) effectively accounts for the non-anthropogenic world without relying upon its mediation through representation. Through a series of complex formal manipulations of their visual and sonic components, both films shape and inscribe the potential of depicting non-human entities in film beyond paradigms of narration and illustration. Building upon significant works by Gilles Deleuze as well as contemporary scholarship mobilizing some of Deleuze’s concepts, this research approaches two of Grandrieux’s feature films for their unstriated accounts of the non-anthropogenic world. More precisely, this analysis points to the way they frame this world’s qualitative capacity to form territorial arrangements or its potential to convey logics of sensation. Through a close audio-visual analysis of both Sombre and Un Lac, this research endeavor aims to examine epistemologies that account for the depiction of the non-anthropogenic world beyond the Anthropocene. At the same time, it seeks to understand how these same epistemologies account for depiction of relationships in between the anthropogenic world and the non-anthropogenic world in ways that elude their conception in subjectivity
Unsupervised Domain Adaptation for Estimating Occupancy and Recognizing Activities in Smart Buildings
Get PDFActivities Recognition (AR) and Occupancy Estimation (OE) are topics of current interest. AR and OE can develop many smart building applications such as energy management and can help
provide good services for residents. Prior research on AR and OE has typically focused on supervised machine learning methods. For a specific smart building domain, a model is trained using data collected from the current environment (domain). The created model will not generalize well when evaluated in a new related domain due to data distribution differences. Creating a model for
each smart building environment is infeasible due to the lack of labeled data. Indeed, data collection is a tedious and time-consuming task. Unsupervised Domain Adaptation (UDA) is a good solution for the considered case. UDA solves the problem of the lack of labeled data in the target domain by allowing knowledge transfer across domains. In this research, we provide several UDA methods that mitigate the data distribution shift between source and target domains using unlabeled target data for OE and AR with and without direct access to labeled source data. Firstly, we consider
techniques that use only a trained source model instead of a huge amount of labeled source data to make domain adaptation. We adapted and tested several UDA methods such as Source HypOthesis Transfer (SHOT), Higher-Order Moment Matching (HoMM), and Source data Free Domain Adaptation (SFDA) on smart building data. Secondly, we adapt and develop several UDA methods that use labeled source data to estimate the number of occupants and recognize activities. The developed methods that have direct access to the source data are the Virtual Adversarial Domain Adaptation (VADA), Sliced Wasserstein Discrepancy (SWD), and Adaptive Feature Norm (AFN). Finally, we make a comparative analysis between several newly adapted deep UDA methods, applied to the tasks of AR and OE, with and without access to labeled source data
Developing high-capacity composite adsorbents for gold mill processes
Get PDFAddressing the existing problems in the gold mining industry, two types of high-capacity adsorbents have been developed to improve the economics and efficiency of the industrial gold extraction processes, as well as to reduce their environmental impact. In the first sub-project, high-performance lignin-polyethylene composite adsorbents have been developed for extraction of solubilized gold complexes from gold mill leaching solutions that show fast, high selectivity gold capturing from very low concentration of gold leachate solutions. In the second sub-project, high-capacity Mg─Fe layered double hydroxide-graphene oxide (LDH-GO) nanocomposite adsorbents have been developed to remove arsenic as a most toxic and carcinogenic element often present in gold sulfide ores, from gold mill effluent streams, to reduce both the adverse environmental impact of the gold mining activities and the environmental impact of the mining operations.
In our design of both types of composite adsorbents, we employ cost-effective active materials (lignin and magnesium/iron-based LDHs, respectively) of high adsorption capacity towards gold and arsenic species, respectively. Meanwhile, robust support/matrix materials (polyethylene and graphene oxide, respectively) are employed for the effective loading/encapsulation of the active materials. The composition and structure of both classes of composite adsorbents are tuned to achieve optimum adsorption performance. The adsorption properties of the composites have been evaluated towards the adsorption of gold and arsenic, respectively, from simulated waters under different conditions such as pH, contact time and initial concentration in batch process. The results show that the composites are highly effective in removing arsenic and capturing gold. The success of this research is expected to improve the economics and efficiency of the industrial gold extraction processes, and meanwhile make them safer and more environmentally responsible