Satisfiability Checking for Mission-Time LTL

Mission-time LTL (MLTL) is a bounded variant of MTL over naturals designed to generically specify requirements for mission-based system operation common to aircraft, spacecraft, vehicles, and robots. Despite the utility of MLTL as a specification logic, major gaps remain in analyzing MLTL, e.g., for specification debugging or model checking, centering on the absence of any complete MLTL satisfiability checker. We prove that the MLTL satisfiability checking problem is NEXPTIME-complete and that satisfiability checking MLTL0 , the variant of MLTL where all intervals start at 0, is PSPACE-complete. We introduce translations for MLTL-to-LTL, MLTL-to-LTLf , MLTL-to-SMV, and MLTL-to-SMT, creating four options for MLTL satisfiability checking. Our extensive experimental evaluation shows that the MLTL-to-SMT transition with the Z3 SMT solver offers the most scalable performance

Computer Aided Verification

The open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

Computer Aided Verification

The open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

Trace Diagnostics for Signal-based Temporal Properties

Trace checking is a verification technique widely used in Cyber-physical system (CPS) development, to verify whether execution traces satisfy or violate properties expressing system requirements. Often these properties characterize complex signal behaviors and are defined using domain-specific languages, such as SB-TemPsy-DSL, a pattern-based specification language for signal-based temporal properties. Most of the trace-checking tools only yield a Boolean verdict. However, when a property is violated by a trace, engineers usually inspect the trace to understand the cause of the violation; such manual diagnostic is time-consuming and error-prone. Existing approaches that complement trace-checking tools with diagnostic capabilities either produce low-level explanations that are hardly comprehensible by engineers or do not support complex signal-based temporal properties. In this paper, we propose TD-SB-TemPsy, a trace-diagnostic approach for properties expressed using SB-TemPsy-DSL. Given a property and a trace that violates the property, TD-SB-TemPsy determines the root cause of the property violation. TD-SB-TemPsy relies on the concepts of violation cause, which characterizes one of the behaviors of the system that may lead to a property violation, and diagnoses, which are associated with violation causes and provide additional information to help engineers understand the violation cause. As part of TD-SB-TemPsy, we propose a language-agnostic methodology to define violation causes and diagnoses. In our context, its application resulted in a catalog of 34 violation causes, each associated with one diagnosis, tailored to properties expressed in SB-TemPsy-DSL. We assessed the applicability of TD-SB-TemPsy on two datasets, including one based on a complex industrial case study. The results show that TD-SB-TemPsy could finish within a timeout of 1 min for ≈ 83.66% of the trace-property combinations in the industrial dataset, yielding a diagnosis in ≈ 99.84% of these cases; moreover, it also yielded a diagnosis for all the trace-property combinations in the other dataset. These results suggest that our tool is applicable and efficient in most cases

Data balancing approaches in quality, defect, and pattern analysis

The imbalanced ratio of data is one of the most significant challenges in various industrial domains. Consequently, numerous data-balancing approaches have been proposed over the years. However, most of these data-balancing methods come with their own limitations that can potentially impact data-driven decision-making models in critical sectors such as product quality assurance, manufacturing defect identification, and pattern recognition in healthcare diagnostics. This dissertation addresses three research questions related to data-balancing approaches: 1) What are the scopes of data-balancing approaches toward the major and minor samples? 2) What is the effect of traditional Machine Learning (ML) and Synthetic Minority Over-sampling Technique (SMOTE)-based data-balancing on imbalanced data analysis? and 3) How does imbalanced data affect the performance of Deep Learning (DL)-based models? To achieve these objectives, this dissertation thoroughly analyzes existing reference works and identifies their limitations. It has been observed that most existing data-balancing approaches have several limitations, such as creating noise during oversampling, removing important information during undersampling, and being unable to perform well with multidimensional data. Furthermore, it has also been observed that SMOTE-based approaches have been the most widely used data-balancing approaches as they can create synthetic samples that are easy to implement compared to other existing techniques. However, SMOTE also has its limitations, and therefore, it is required to identify whether there is any significant effect of SMOTE-based oversampled approaches on ML-based data-driven models' performance. To do that, the study conducts several hypothesis tests considering several popular ML algorithms with and without hyperparameter settings. Based on the overall hypothesis, it is found that, in many cases based on the reference dataset, there is no significant performance improvement on data-driven ML models once the imbalanced data is balanced using SMOTE approaches. Additionally, the study finds that SMOTE-based synthetic samples often do not follow the Gaussian distribution or do not follow the same distribution of the data as the original dataset. Therefore, the study suggests that Generative Adversarial Network (GAN)-based approaches could be a better alternative to develop more realistic samples and might overcome the limitations of SMOTE-based data-balancing approaches. However, GAN is often difficult to train, and very limited studies demonstrate the promising outcome of GAN-based tabular data balancing as GAN is mainly developed for image data generation. Additionally, GAN is hard to train as it is computationally not efficient. To overcome such limitations, the present study proposes several data-balancing approaches such as GAN-based oversampling (GBO), Support Vector Machine (SVM)-SMOTE-GAN (SSG), and Borderline-SMOTE-GAN (BSGAN). The proposed approaches outperform existing SMOTE-based data-balancing approaches in various highly imbalanced tabular datasets and can produce realistic samples. Additionally, the oversampled data follows the distribution of the original dataset. The dissertation later examines two case scenarios where data-balancing approaches can play crucial roles, specifically in healthcare diagnostics and additive manufacturing. The study considers several Chest radiography (X-ray) and Computed Tomography (CT)-scan image datasets for the healthcare diagnostics scenario to detect patients with COVID-19 symptoms. The study employs six different Transfer Learning (TL) approaches, namely Visual Geometry Group (VGG)16, Residual Network (ResNet)50, ResNet101, Inception-ResNet Version 2 (InceptionResNetV2), Mobile Network version 2 (MobileNetV2), and VGG19. Based on the overall analysis, it has been observed that, except for the ResNet-based model, most of the TL models have been able to detect patients with COVID-19 symptoms with an accuracy of almost 99\%. However, one potential drawback of TL approaches is that the models have been learning from the wrong regions. For example, instead of focusing on the infected lung regions, the TL-based models have been focusing on the non-infected regions. To address this issue, the study has updated the TL-based models to reduce the models' wrong localization. Similarly, the study conducts an additional investigation on an imbalanced dataset containing defect and non-defect images of 3D-printed cylinders. The results show that TL-based models are unable to locate the defect regions, highlighting the challenge of detecting defects using imbalanced data. To address this limitation, the study proposes preprocessing-based approaches, including algorithms such as Region of Interest Net (ROIN), Region of Interest and Histogram Equalizer Net (ROIHEN), and Region of Interest with Histogram Equalization and Details Enhancer Net (ROIHEDEN) to improve the model's performance and accurately identify the defect region. Furthermore, this dissertation employs various model interpretation techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM), to gain insights into the features in numerical, categorical, and image data that characterize the models' predictions. These techniques are used across multiple experiments and significantly contribute to a better understanding the models' decision-making processes. Lastly, the study considers a small mixed dataset containing numerical, categorical, and image data. Such diverse data types are often challenging for developing data-driven ML models. The study proposes a computationally efficient and simple ML model to address these data types by leveraging the Multilayer Perceptron and Convolutional Neural Network (MLP-CNN). The proposed MLP-CNN models demonstrate superior accuracy in identifying COVID-19 patients' patterns compared to existing methods. In conclusion, this research proposes various approaches to tackle significant challenges associated with class imbalance problems, including the sensitivity of ML models to multidimensional imbalanced data, distribution issues arising from data expansion techniques, and the need for model explainability and interpretability. By addressing these issues, this study can potentially mitigate data balancing challenges across various industries, particularly those that involve quality, defect, and pattern analysis, such as healthcare diagnostics, additive manufacturing, and product quality. By providing valuable insights into the models' decision-making process, this research could pave the way for developing more accurate and robust ML models, thereby improving their performance in real-world applications

MLTL Benchmark Generation via Formula Progression

Safe cyber-physical system operation requires runtime verification (RV), yet the burgeoning collection of RV technologies remain comparatively untested due to a dearth of benchmarks with oracles enabling objectively evaluating their performance. Mission-time LTL (MLTL) adds integer temporal bounds to LTL to intuitively describe missions of such systems. An MLTL benchmark for runtime verification is a 3-tuple consisting of (1) an MLTL specification phi; (2) a set of finite input streams representing propositional system variables (call this computation pi) over the alphabet of phi; (3) an oracle stream of pairs where verdict v is the result (true or false) for time t of evaluating whether pi(t) vertical bar= phi (computation pi at time t satisfies formula phi). We introduce an algorithm for reliably generating MLTL benchmarks via formula progression. We prove its correctness, demonstrate it executes efficiently, and show how to use it to generate a variety of useful patterns for the evaluation and comparative analysis of RV tools.This is a post-peer-review, pre-copyedit version of a proceeding published as Li, J., Rozier, K.Y. (2018). MLTL Benchmark Generation via Formula Progression. In: Colombo, C., Leucker, M. (eds) Runtime Verification. RV 2018. Lecture Notes in Computer Science, vol 11237. Springer, Cham. The final authenticated version is available online at DOI: 10.1007/978-3-030-03769-7_25. Copyright 2018 Springer Nature Switzerland AG. Posted with permission

Third International Symposium on Space Mission Operations and Ground Data Systems, part 1

Under the theme of 'Opportunities in Ground Data Systems for High Efficiency Operations of Space Missions,' the SpaceOps '94 symposium included presentations of more than 150 technical papers spanning five topic areas: Mission Management, Operations, Data Management, System Development, and Systems Engineering. The papers focus on improvements in the efficiency, effectiveness, productivity, and quality of data acquisition, ground systems, and mission operations. New technology, techniques, methods, and human systems are discussed. Accomplishments are also reported in the application of information systems to improve data retrieval, reporting, and archiving; the management of human factors; the use of telescience and teleoperations; and the design and implementation of logistics support for mission operations

New Right Conservatism and the Scottish leisure profession: a critical analysis 1979-97

The nature of the leisure profession and the leisure professional has been recharacterised by a series of government policies first implemented by the Conservative government during the period 1979-97. Whilst the re-characterisation has been acknowledged by leisure professional bodies and also in an emerging body of literature, no systematic analysis of this process has been undertaken in the Scottish context. This thesis addresses this through an ideological analysis of New Right Conservatism and the impact of New Right policies in Scotland and on the Scottish Leisure profession. Scottish political and cultural traditions together with the notion of credentialism provide original dimensions to this critical analysis. Using a multimethodological research approach, this thesis examines the link between New Right government policies and the Scottish leisure profession. It establishes whether or not the process of professionalisation is a coherent one that will underpin a collective legitimacy for the Scottish leisure profession. It is concluded that the New Right undermined the professionalisation of leisure management in Scotland. Leisure management has been restructured and generalised and the resulting professional anticollectivism within the industry has left the standing of the profession in doubt. This original theoretically and empirically informed study of the leisure profession in Scotland makes a small contribution to the growing body of work on professionalism and professionalisation