ATLANTA--(BUSINESS WIRE)--Assurant, Inc. (NYSE: AIZ), a leading global business services company that supports, protects and connects major consumer purchases, today reported results for the third quarter ended September 30, 2023. "Results for the third ...

Assurant Reports Third Quarter 2023 Financial Results

Insurer Assurant reported a surge in third-quarter adjusted profit on Tuesday, helped by the strong performance of its global housing unit and higher investment returns, sending shares up 2.7% in aftermarket trading.

Assurant Q3 profit surges on growth in global housing unit, investment returns

Elizabeth Rivelli is a freelance writer with more than three years of experience writing about insurance. She has extensive knowledge of various insurance lines, including life insurance ...

PPO Insurance: What Is It?

Analysts estimate that Assurant will report an earnings per share (EPS) of $2.46. Assurant bulls will hope to hear the company announce they've not only beaten that estimate, but also to provide positive guidance, or forecasted growth, for the next quarter.

Assurant's Earnings: A Preview

Assurant, Inc. AIZ is slated to report third-quarter 2023 earnings on Oct 31, after the closing bell. AIZ delivered an earnings surprise in each of the last four quarters, the average beat being 24.39%.

What's in Store for Assurant (AIZ) This Earnings Season?

Guidewire (NYSE: GWRE) announced that Assurant's new Ready for Guidewire validated accelerators are now available to ClaimCenter and PolicyCenter users in the Guidewire Marketplace .

Boost Efficiency and Policyholder Satisfaction with Assurant's New Guidewire Marketplace Apps

Guidewire announced Assurant’s new accelerators are now available to ClaimCenter and PolicyCenter users in the Guidewire Marketplace.

Boost Efficiency and Policyholder Satisfaction with Assurant’s New Guidewire Marketplace Apps

Car protection product ‘reinsurance' programs are a helpful revenue stream for dealers, according to Ritch Wheeler, president of Eagle Training Academy and a former longtime Assurant trainer.

Former Assurant trainer explains dealer F&I ‘reinsurance' revenue stream

Summary Assurant Inc. has shown significant improvement in its financial performance, with net income increasing from $52 million to $156 million. The company operates in various specialty niches and has a diverse set of portfolios,

Assurant: Rapid Growth And An Improving Portfolio

In a report released on October 16, Mark Hughes from Truist Financial maintained a Buy rating on Skyward Specialty Insurance Group, Inc. (SKWD

Truist Financial Sticks to Their Buy Rating for Skyward Specialty Insurance Group, Inc. (SKWD)

Assurant

assurant

Authors:
(1) Tinghui Ouyang, National Institute of Informatics, Japan (thouyang@nii.ac.jp);
(2) Isao Echizen, National Institute of Informatics, Japan (iechizen@nii.ac.jp);
(3) Yoshiki Seo, Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Japan (y.seo@aist.go.jp). Table of Links Abstract and Introduction Description and Related Work of OOD Detection Methodology Experiments and Results Conclusions, Acknowledgement and References Abstract—Data outside the problem domain poses significant threats to the security of AI-based intelligent systems. Aiming to investigate the data domain and out-of-distribution (OOD) data in AI quality management (AIQM) study, this paper proposes to use deep learning techniques for feature representation and develop a novel statistical measure for OOD detection. First, to extract low-dimensional representative features distinguishing normal and OOD data, the proposed research combines the deep auto-encoder (AE) architecture and neuron activation status for feature engineering. Then, using local conditional probability (LCP) in data reconstruction, a novel and superior statistical measure is developed to calculate the score of OOD detection. Experiments and evaluations are conducted on image benchmark datasets and an industrial dataset. Through comparative analysis with other common statistical measures in OOD detection, the proposed research is validated as feasible and effective in OOD and AIQM studies. Index Terms—out-of-distribution (OOD), AI quality management (AIQM), local conditional probability (LCP), data quality analysis, I. INTRODUCTION In AI quality management (AIQM) [1], a reliable AI system is expected to have the ability to make accurate decisions on both data within the problem domain and unknown examples, e.g., to reject data out of the defined problem domain. A wellknown example is the postcode recognition system, designed to recognize images of handwriting digital numbers of 0-9. However, things often don’t go as planned in the practical operation scenario. An alphabet image is likely fed to the given system for reasons but predicted to a class of 0-9. It is easily understood that the prediction mismatches the realistic expectation because of the absence of a sound problem domain analysis in AIQM. When a similar issue happens in some safety-critical scenarios, like autonomous driving [2], [3], no warning or hand-over of driving control may cause serious accidents when facing unusual scenes. Therefore, for the consideration of safety and security, it is vital to investigate the problem domain and study data quality assurance in AIQM, especially detecting the usual data outside the problem domain. According to the knowledge of general AIs, especially classification or recognition AI systems, their problem domains are usually designed based on closed-world assumptions [4]. Their training and testing data are also assumed to locate in the same problem domain. These problem domains are usually expressed by data distribution in the perspective of data quality analysis, while the unusual examples are regarded as data of out-of-distribution (OOD) [5]. Therefore, the easy solution to warning dangerous scenarios in AIQM is effectively detecting OOD data in advance. It is known that the problem of OOD detection is a typical task in AI quality research and is deeply related to anomaly detection (AD) [6], novelty detection (ND) [7], and open set recognition (OSR) [8]. Its general idea is to use data distribution or related structural characteristics (e.g., distance, density) for separating normal and outlier data, so the straightforward methods are unsupervised learning-based models. There are many OOD detection methods using different types of structural characteristics reported in the literature. For example, the first type uses probability or density, named probabilistic methods [9]. These methods leverage the probability density function of a dataset X under a given model parameter Θ, then determine data points having the smallest likelihood P(X|Θ) as outliers, e.g., the Gaussian Mixture Model (GMM) [10] fitting a number of Gaussian distributions for outlier detection. Probabilistic principal component analysis (PPCA) [11] and least-squares anomaly detection (LSA) [12] are other probabilistic OOD methods. The second type is density-based method. For example, kernel density (KD) estimators [13] approximate the density function of the dataset via kernel function calculation and distinguish outliers by low density in OOD detection. The third type is based on distance, using the idea that outliers are distant from the distribution of normal data. For instance, Mahalanobis distance (MD) is used to detect anomalies under the assumption of Gaussian-shaped data distribution [14]. Moreover, there are also some other metrics used in OOD detection methods, e.g., the local outlier factor (LOF) [15] and kNN [16] leveraging the neighbors’ information, a method using the information-theoretic measure (i.e., Kullback-Leibler (KL) divergence [17]) in OOD investigation. It is generally straightforward to use these statistical measures to detect OOD data directly in the original data space and easily understood to achieve a good performance on simple data. However, facing complex and high-dimensional datasets, e.g., the image data, direct usage of these measures usually fail to effectively detect OOD because of the bad computational scalability and the curse of dimensionality. Therefore, suitable feature engineering is required before applying these statistical measures in the OOD study. Concerning the above issues, this paper proposes a novel OOD method with consideration of both effective feature engineering and a useful statistical measure. The concrete novelties and contributions of the proposed method are summarized as follows: A feature extraction method based on autoencoder (AE) [18] and neuron activation status is proposed. It is commonly known that deep learning (DL) has excellent feature learning ability and wildly succeeds in complex data, e.g., image-based data processing. Meanwhile, as a typical DL algorithm for feature learning, deep AEs attempt to learn low-dimensional salient features to reproduce the original data. Therefore, to preserve image data’s distribution information as much as possible and effectively reduce computation complexity, deep AE is selected as the basic architecture for feature learning in this paper. Then, assuming normal data and outliers may have different neuron behaviors, feature extraction based on deep AE’s neuron activation status is implemented on complex and high-dimensional data to generate data for the OOD study. 2) A novel statistical measure for OOD detection is proposed based on local conditional probability (LCP) and data reconstruction. Considering the advantages of different types of OOD methods, e.g., the kernel-function-based density having advantages at addressing data without parametric probability density distribution (PDF) function, the effectiveness of LOF and kNN on using neighbor information, this paper proposed a new metric combining these two ideas. The new metric considers calculating probability via neighbors’ kernel distance, using the local conditional probability to reconstruct data, and describing OOD data via reconstruction error. It can achieve comparative superiority over conventional statistical measures in OOD detection. Details are presented in Section 3. 3) The proposed method performs better than conventional OOD detection methods. This paper selects four conventional statistical measures for OOD study, such as KD, LOF, MD, and kNN. Then, experiments based on image benchmark datasets and an industrial dataset are implemented. Results illustrate the proposed method’s effectiveness and superiority on OOD data description detection. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tinghui Ouyang, National Institute of Informatics, Japan (thouyang@nii.ac.jp); (2) Isao Echizen, National Institute of Informatics, Japan (iechizen@nii.ac.jp); (3) Yoshiki Seo, Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Japan (y.seo@aist.go.jp). Authors: Authors: (1) Tinghui Ouyang, National Institute of Informatics, Japan (thouyang@nii.ac.jp); (2) Isao Echizen, National Institute of Informatics, Japan (iechizen@nii.ac.jp); (3) Yoshiki Seo, Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Japan (y.seo@aist.go.jp). Table of Links Abstract and Introduction Abstract and Introduction Description and Related Work of OOD Detection Description and Related Work of OOD Detection Methodology Methodology Experiments and Results Experiments and Results Conclusions, Acknowledgement and References Conclusions, Acknowledgement and References Abstract—Data outside the problem domain poses significant threats to the security of AI-based intelligent systems. Aiming to investigate the data domain and out-of-distribution (OOD) data in AI quality management (AIQM) study, this paper proposes to use deep learning techniques for feature representation and develop a novel statistical measure for OOD detection. First, to extract low-dimensional representative features distinguishing normal and OOD data, the proposed research combines the deep auto-encoder (AE) architecture and neuron activation status for feature engineering. Then, using local conditional probability (LCP) in data reconstruction, a novel and superior statistical measure is developed to calculate the score of OOD detection. Experiments and evaluations are conducted on image benchmark datasets and an industrial dataset. Through comparative analysis with other common statistical measures in OOD detection, the proposed research is validated as feasible and effective in OOD and AIQM studies. Abstract —Data outside the problem domain poses significant threats to the security of AI-based intelligent systems. Aiming to investigate the data domain and out-of-distribution (OOD) data in AI quality management (AIQM) study, this paper proposes to use deep learning techniques for feature representation and develop a novel statistical measure for OOD detection. First, to extract low-dimensional representative features distinguishing normal and OOD data, the proposed research combines the deep auto-encoder (AE) architecture and neuron activation status for feature engineering. Then, using local conditional probability (LCP) in data reconstruction, a novel and superior statistical measure is developed to calculate the score of OOD detection. Experiments and evaluations are conducted on image benchmark datasets and an industrial dataset. Through comparative analysis with other common statistical measures in OOD detection, the proposed research is validated as feasible and effective in OOD and AIQM studies. Abstract Index Terms—out-of-distribution (OOD), AI quality management (AIQM), local conditional probability (LCP), data quality analysis, Index Terms —out-of-distribution (OOD), AI quality management (AIQM), local conditional probability (LCP), data quality analysis, Index Terms I. INTRODUCTION In AI quality management (AIQM) [1], a reliable AI system is expected to have the ability to make accurate decisions on both data within the problem domain and unknown examples, e.g., to reject data out of the defined problem domain. A wellknown example is the postcode recognition system, designed to recognize images of handwriting digital numbers of 0-9. However, things often don’t go as planned in the practical operation scenario. An alphabet image is likely fed to the given system for reasons but predicted to a class of 0-9. It is easily understood that the prediction mismatches the realistic expectation because of the absence of a sound problem domain analysis in AIQM. When a similar issue happens in some safety-critical scenarios, like autonomous driving [2], [3], no warning or hand-over of driving control may cause serious accidents when facing unusual scenes. Therefore, for the consideration of safety and security, it is vital to investigate the problem domain and study data quality assurance in AIQM, especially detecting the usual data outside the problem domain. According to the knowledge of general AIs, especially classification or recognition AI systems, their problem domains are usually designed based on closed-world assumptions [4]. Their training and testing data are also assumed to locate in the same problem domain. These problem domains are usually expressed by data distribution in the perspective of data quality analysis, while the unusual examples are regarded as data of out-of-distribution (OOD) [5]. Therefore, the easy solution to warning dangerous scenarios in AIQM is effectively detecting OOD data in advance. It is known that the problem of OOD detection is a typical task in AI quality research and is deeply related to anomaly detection (AD) [6], novelty detection (ND) [7], and open set recognition (OSR) [8]. Its general idea is to use data distribution or related structural characteristics (e.g., distance, density) for separating normal and outlier data, so the straightforward methods are unsupervised learning-based models. There are many OOD detection methods using different types of structural characteristics reported in the literature. For example, the first type uses probability or density, named probabilistic methods [9]. These methods leverage the probability density function of a dataset X under a given model parameter Θ, then determine data points having the smallest likelihood P(X|Θ) as outliers, e.g., the Gaussian Mixture Model (GMM) [10] fitting a number of Gaussian distributions for outlier detection. Probabilistic principal component analysis (PPCA) [11] and least-squares anomaly detection (LSA) [12] are other probabilistic OOD methods. The second type is density-based method. For example, kernel density (KD) estimators [13] approximate the density function of the dataset via kernel function calculation and distinguish outliers by low density in OOD detection. The third type is based on distance, using the idea that outliers are distant from the distribution of normal data. For instance, Mahalanobis distance (MD) is used to detect anomalies under the assumption of Gaussian-shaped data distribution [14]. Moreover, there are also some other metrics used in OOD detection methods, e.g., the local outlier factor (LOF) [15] and k NN [16] leveraging the neighbors’ information, a method using the information-theoretic measure (i.e., Kullback-Leibler (KL) divergence [17]) in OOD investigation. k It is generally straightforward to use these statistical measures to detect OOD data directly in the original data space and easily understood to achieve a good performance on simple data. However, facing complex and high-dimensional datasets, e.g., the image data, direct usage of these measures usually fail to effectively detect OOD because of the bad computational scalability and the curse of dimensionality. Therefore, suitable feature engineering is required before applying these statistical measures in the OOD study. Concerning the above issues, this paper proposes a novel OOD method with consideration of both effective feature engineering and a useful statistical measure. The concrete novelties and contributions of the proposed method are summarized as follows: A feature extraction method based on autoencoder (AE) [18] and neuron activation status is proposed. It is commonly known that deep learning (DL) has excellent feature learning ability and wildly succeeds in complex data, e.g., image-based data processing. Meanwhile, as a typical DL algorithm for feature learning, deep AEs attempt to learn low-dimensional salient features to reproduce the original data. Therefore, to preserve image data’s distribution information as much as possible and effectively reduce computation complexity, deep AE is selected as the basic architecture for feature learning in this paper. Then, assuming normal data and outliers may have different neuron behaviors, feature extraction based on deep AE’s neuron activation status is implemented on complex and high-dimensional data to generate data for the OOD study. A feature extraction method based on autoencoder (AE) [18] and neuron activation status is proposed. It is commonly known that deep learning (DL) has excellent feature learning ability and wildly succeeds in complex data, e.g., image-based data processing. Meanwhile, as a typical DL algorithm for feature learning, deep AEs attempt to learn low-dimensional salient features to reproduce the original data. Therefore, to preserve image data’s distribution information as much as possible and effectively reduce computation complexity, deep AE is selected as the basic architecture for feature learning in this paper. Then, assuming normal data and outliers may have different neuron behaviors, feature extraction based on deep AE’s neuron activation status is implemented on complex and high-dimensional data to generate data for the OOD study. 2) A novel statistical measure for OOD detection is proposed based on local conditional probability (LCP) and data reconstruction. Considering the advantages of different types of OOD methods, e.g., the kernel-function-based density having advantages at addressing data without parametric probability density distribution (PDF) function, the effectiveness of LOF and k NN on using neighbor information, this paper proposed a new metric combining these two ideas. The new metric considers calculating probability via neighbors’ kernel distance, using the local conditional probability to reconstruct data, and describing OOD data via reconstruction error. It can achieve comparative superiority over conventional statistical measures in OOD detection. Details are presented in Section 3. k 3) The proposed method performs better than conventional OOD detection methods. This paper selects four conventional statistical measures for OOD study, such as KD, LOF, MD, and k NN. Then, experiments based on image benchmark datasets and an industrial dataset are implemented. Results illustrate the proposed method’s effectiveness and superiority on OOD data description detection. k This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Quality Assurance of a GPT-Based Sentiment Analysis System

Authors:
(1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu);
(2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com);
(3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Related Work Project Prerequisites Project Implementation Insight and Reflections Conclusions, Acknowledgement and References ABSTRACT Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where I’m a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests that meaningfully contribute to open-source scientific computing projects developed in connection with our lab. We describe the concepts covered, tools used, and software tests written in this early introduction to software engineering while maintaining shared emphases on education and the deployment of our work. KEYWORDS Automated Testing, Continuous Integration, GitHub Actions, High School Summer Research, Software Engineering, Version Control, Website Debugging 1 INTRODUCTION During the summer of 2023, two high school students volunteered to assist in one of Wayne State University’s computer science research labs, which focuses on high-performance computing and software engineering for scientific applications. The students aspire to study computer science as their major when they begin their undergraduate studies in 2024. Prior to volunteering in our lab, they had successfully completed AP Computer Science courses where they learned Java and Python but had no prior experience with software engineering concepts such as version control, continuous integration, and unit testing. Our goals were to develop a summer project that would be appropriate for the students’ current experience and education, provide a learning opportunity to gain new skills relevant to assisting in a laboratory environment, and to provide an opportunity to produce work that meaningfully contributes to the scientific computing applications developed in connection with our lab. Two scientific computing applications developed in connection with our lab include JETSCAPE [1] and GOMC [2]. JETSCAPE is a multi-institutional collaboration that develops event generators to simulate ultra-relativistic heavy-ion collisions. GOMC is an application to simulate molecular systems using the Metropolis Monte Carlo Algorithm [3]. Both projects are opensource and maintain their respective repositories on GitHub. They also use GitHub Pages to maintain and deploy their project websites. To meet our project goals, we decided to emphasize automated software testing. JETSCAPE [1] and GOMC [2] have previously developed and deployed some tests, but there was an interest among the collaborators to further expand upon the tests in place. Since the student volunteers had not yet studied software engineering and were not yet familiar with concepts such as continuous integration and unit testing, attaining these skills while working on this project would provide a unique educational value. Since the students had already completed AP Computer Science and were already comfortable with the Java and Python languages, the students could apply the programming skills they already learned to develop these automated tests. Although having studied Java and Python, the students had no prior experience with Linux or with working at the command line. They also had no previous experience working with Git or any version control platform. This project would therefore have to begin with preliminary introductions to the Linux shell environment and version control. Since continuous integration would also be a new concept, a demonstration of GitHub Actions and writing YAML files would conclude our preliminary introduction. The main substantive tasks of the project would be the implementation of three automated tests. The first two tests, an HTML validator, and a link checker, would be deployed on the JETSCAPE [1] and GOMC [2] websites. The implementation of the third test would require discussing the concept of unit testing and then deploying a series of unit tests to ensure the correctness of JETSCAPE’s XML formatted input. The tools used throughout the project include Visual Studio Code, the Ubuntu Distribution installed on WSL and accessed through Windows Terminal, version control with Git and GitHub, automation with GitHub Actions, static hosting with GitHub Pages, Python scripting to customize and enhance the functionality of GitHub Marketplace Actions, and Python’s UNITTEST framework. In the following sections, we provide a detailed description of the project and the concepts covered. We then reflect upon the successes and challenges we faced and report this experience so that other students at a similar level in their computer science education might find comparable opportunities to study these software engineering concepts earlier than they otherwise might while also attaining practical lab experience to supplement their traditional high school computer science curriculum. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Authors: Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Project Prerequisites Project Prerequisites Project Implementation Project Implementation Insight and Reflections Insight and Reflections Conclusions, Acknowledgement and References Conclusions, Acknowledgement and References ABSTRACT Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where I’m a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests that meaningfully contribute to open-source scientific computing projects developed in connection with our lab. We describe the concepts covered, tools used, and software tests written in this early introduction to software engineering while maintaining shared emphases on education and the deployment of our work. KEYWORDS Automated Testing, Continuous Integration, GitHub Actions, High School Summer Research, Software Engineering, Version Control, Website Debugging 1 INTRODUCTION During the summer of 2023, two high school students volunteered to assist in one of Wayne State University’s computer science research labs, which focuses on high-performance computing and software engineering for scientific applications. The students aspire to study computer science as their major when they begin their undergraduate studies in 2024. Prior to volunteering in our lab, they had successfully completed AP Computer Science courses where they learned Java and Python but had no prior experience with software engineering concepts such as version control, continuous integration, and unit testing. Our goals were to develop a summer project that would be appropriate for the students’ current experience and education, provide a learning opportunity to gain new skills relevant to assisting in a laboratory environment, and to provide an opportunity to produce work that meaningfully contributes to the scientific computing applications developed in connection with our lab. Two scientific computing applications developed in connection with our lab include JETSCAPE [1] and GOMC [2]. JETSCAPE is a multi-institutional collaboration that develops event generators to simulate ultra-relativistic heavy-ion collisions. GOMC is an application to simulate molecular systems using the Metropolis Monte Carlo Algorithm [3]. Both projects are opensource and maintain their respective repositories on GitHub. They also use GitHub Pages to maintain and deploy their project websites. To meet our project goals, we decided to emphasize automated software testing. JETSCAPE [1] and GOMC [2] have previously developed and deployed some tests, but there was an interest among the collaborators to further expand upon the tests in place. Since the student volunteers had not yet studied software engineering and were not yet familiar with concepts such as continuous integration and unit testing, attaining these skills while working on this project would provide a unique educational value. Since the students had already completed AP Computer Science and were already comfortable with the Java and Python languages, the students could apply the programming skills they already learned to develop these automated tests. Although having studied Java and Python, the students had no prior experience with Linux or with working at the command line. They also had no previous experience working with Git or any version control platform. This project would therefore have to begin with preliminary introductions to the Linux shell environment and version control. Since continuous integration would also be a new concept, a demonstration of GitHub Actions and writing YAML files would conclude our preliminary introduction. The main substantive tasks of the project would be the implementation of three automated tests. The first two tests, an HTML validator, and a link checker, would be deployed on the JETSCAPE [1] and GOMC [2] websites. The implementation of the third test would require discussing the concept of unit testing and then deploying a series of unit tests to ensure the correctness of JETSCAPE’s XML formatted input. The tools used throughout the project include Visual Studio Code, the Ubuntu Distribution installed on WSL and accessed through Windows Terminal, version control with Git and GitHub, automation with GitHub Actions, static hosting with GitHub Pages, Python scripting to customize and enhance the functionality of GitHub Marketplace Actions, and Python’s UNITTEST framework. In the following sections, we provide a detailed description of the project and the concepts covered. We then reflect upon the successes and challenges we faced and report this experience so that other students at a similar level in their computer science education might find comparable opportunities to study these software engineering concepts earlier than they otherwise might while also attaining practical lab experience to supplement their traditional high school computer science curriculum. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Introducing High School Students to Version Control, Continuous Integration, and Quality Assurance

Survey and Analysis of Smart Contract Quality Assurance: Conclusions, Acknowledgement and References

Survey and Analysis of Smart Contract Quality Assurance: Evaluation

Survey and Analysis of Smart Contract Quality Assurance: Defense Methodologies

Survey and Analysis of Smart Contract Quality Assurance: Attacks on Smart Contracts

Survey and Analysis of Smart Contract Quality Assurance: Vulnerability in Smart Contracts

Survey and Analysis of Smart Contract Quality Assurance: Overview of Smart Contracts

Survey and Analysis of Smart Contract Quality Assurance: Abstract and Introduction

Author:
(1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Table of Links Abstract and Introduction
Dancing Links
The Rust Programming Language
RAC: Hardware/Software Co-Assurance at Scale
Rust and RAR
Dancing Links in Rust
Related Work
Conclusion, Acknowledgments, and References 4 RAC: Hardware/Software Co-Assurance at Scale In order to begin to realize hardware/software co-assurance at scale, we have conducted several experiments employing a state-of-the-art toolchain, due to Russinoff and O’Leary, and originally designed for use in floating-point hardware verification [27], to determine its suitability for the creation of safetycritical/security-critical applications in various domains. Note that this toolchain has already demonstrated the capability to scale to industrial designs in the floating-point hardware design and verification domain, as it has been used in design verifications for CPU products at both Intel and Arm. Algorithmic C [21] is a High-Level Synthesis (HLS) language, and is supported by hardware/software co-design environments from Mentor Graphics, e.g., Catapult [22]. Algorithmic C defines C++ header files that enable compilation to both hardware and software platforms, including support for the peculiar bit widths employed, for example, in floating-point hardware design. The Russinoff-O’Leary Restricted Algorithmic C (RAC) toolchain, depicted in Fig. 2, translates a subset of Algorithmic C source to the Common Lisp subset supported by the ACL2 theorem prover, as augmented by Russinoff’s Register Transfer Logic (RTL) books. The ACL2 Translator component of Fig. 2 provides a case study in the bridging of Formal Modeling and Real-World Development concerns, as summarized in Table 1. The ACL2 translator converts imperative RAC code to functional ACL2 code. Loops are translated into tail-recursive functions, with automatic generation of measure functions to guarantee admission into the logic of ACL2 (RAC subsetting rules ensure that loop measures can be automatically determined). Structs and arrays are converted into functional ACL2 records. The combination of modular arithmetic and bit-vector operations of typical RAC source code is faithfully translated to functions supported by Russinoff’s RTL books. ACL2 is able to reason about non-linear arithmetic functions, so the usual concern about formal reasoning about non-linear arithmetic functions does not apply. Finally, the RTL books are quite capable of reasoning about a combination of arithmetic and bit-vector operations, which is a very difficult feat for most automated solvers. Recently, we have investigated the synthesis of Field-Programmable Gate Array (FPGA) hardware directly from high-level architecture models, in collaboration with colleagues at Kansas State University. The goal of this work is to enable the generation of high-assurance hardware and/or software from highlevel architectural specifications expressed in the Architecture Analysis and Design Language (AADL) [9], with proofs of correctness in ACL2. This paper is available on arxiv under CC 4.0 license. Author: (1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Author: Author: (1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Table of Links Abstract and Introduction Dancing Links The Rust Programming Language RAC: Hardware/Software Co-Assurance at Scale Rust and RAR Dancing Links in Rust Related Work Conclusion, Acknowledgments, and References Abstract and Introduction Abstract and Introduction Dancing Links Dancing Links The Rust Programming Language The Rust Programming Language RAC: Hardware/Software Co-Assurance at Scale RAC: Hardware/Software Co-Assurance at Scale Rust and RAR Rust and RAR Dancing Links in Rust Dancing Links in Rust Related Work Related Work Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References 4 RAC: Hardware/Software Co-Assurance at Scale In order to begin to realize hardware/software co-assurance at scale, we have conducted several experiments employing a state-of-the-art toolchain, due to Russinoff and O’Leary, and originally designed for use in floating-point hardware verification [27], to determine its suitability for the creation of safetycritical/security-critical applications in various domains. Note that this toolchain has already demonstrated the capability to scale to industrial designs in the floating-point hardware design and verification domain, as it has been used in design verifications for CPU products at both Intel and Arm. Algorithmic C [21] is a High-Level Synthesis (HLS) language, and is supported by hardware/software co-design environments from Mentor Graphics, e.g., Catapult [22]. Algorithmic C defines C++ header files that enable compilation to both hardware and software platforms, including support for the peculiar bit widths employed, for example, in floating-point hardware design. The Russinoff-O’Leary Restricted Algorithmic C (RAC) toolchain, depicted in Fig. 2, translates a subset of Algorithmic C source to the Common Lisp subset supported by the ACL2 theorem prover, as augmented by Russinoff’s Register Transfer Logic (RTL) books. The ACL2 Translator component of Fig. 2 provides a case study in the bridging of Formal Modeling and Real-World Development concerns, as summarized in Table 1. The ACL2 translator converts imperative RAC code to functional ACL2 code. Loops are translated into tail-recursive functions, with automatic generation of measure functions to guarantee admission into the logic of ACL2 (RAC subsetting rules ensure that loop measures can be automatically determined). Structs and arrays are converted into functional ACL2 records. The combination of modular arithmetic and bit-vector operations of typical RAC source code is faithfully translated to functions supported by Russinoff’s RTL books. ACL2 is able to reason about non-linear arithmetic functions, so the usual concern about formal reasoning about non-linear arithmetic functions does not apply. Finally, the RTL books are quite capable of reasoning about a combination of arithmetic and bit-vector operations, which is a very difficult feat for most automated solvers. Recently, we have investigated the synthesis of Field-Programmable Gate Array (FPGA) hardware directly from high-level architecture models, in collaboration with colleagues at Kansas State University. The goal of this work is to enable the generation of high-assurance hardware and/or software from highlevel architectural specifications expressed in the Architecture Analysis and Design Language (AADL) [9], with proofs of correctness in ACL2. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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