Logical Framework Research Articles

An Iris Instance for Verifying CompCert C Programs

Iris is a generic separation logic framework that has been instantiated to reason about a wide range of programming languages and language features. Most Iris instances are defined on simple core calculi, but by connecting Iris to new or existing formal semantics for practical languages, we can also use it to reason about real programs. In this paper we develop an Iris instance based on CompCert, the verified C compiler, allowing us to prove correctness of C programs under the same semantics we use to compile and run them. We take inspiration from the Verified Software Toolchain (VST), a prior separation logic for CompCert C, and reimplement the program logic of VST in Iris. Unlike most Iris instances, this involves both a new model of resources for CompCert memories, and a new definition of weakest preconditions/Hoare triples, as the Iris defaults for both of these cannot be applied to CompCert as is. Ultimately, we obtain a complete program logic for CompCert C within Iris, and we reconstruct enough of VST's top-level automation to prove correctness of simple C programs.

Trillium: Higher-Order Concurrent and Distributed Separation Logic for Intensional Refinement

Expressive state-of-the-art separation logics rely on step-indexing to model semantically complex features and to support modular reasoning about imperative higher-order concurrent and distributed programs. Step- indexing comes, however, with an inherent cost: it restricts the adequacy theorem of program logics to a fairly simple class of safety properties. In this paper, we explore if and how intensional refinement is a viable methodology for strengthening higher-order concurrent (and distributed) separation logic to prove non-trivial safety and liveness properties. Specifically, we introduce Trillium, a language-agnostic separation logic framework for showing intensional refinement relations between traces of a program and a model. We instantiate Trillium with a concurrent language and develop Fairis, a concurrent separation logic, that we use to show liveness properties of concurrent programs under fair scheduling assumptions through a fair liveness-preserving refinement of a model. We also instantiate Trillium with a distributed language and obtain an extension of Aneris, a distributed separation logic, which we use to show refinement relations between distributed systems and TLA+ models.

An Axiomatic Basis for Computer Programming on the Relaxed Arm-A Architecture: The AxSL Logic

Very relaxed concurrency memory models, like those of the Arm-A, RISC-V, and IBM Power hardware architectures, underpin much of computing but break a fundamental intuition about programs, namely that syntactic program order and the reads-from relation always both induce order in the execution. Instead, out-of-order execution is allowed except where prevented by certain pairwise dependencies, barriers, or other synchronisation. This means that there is no notion of the 'current' state of the program, making it challenging to design (and prove sound) syntax-directed, modular reasoning methods like Hoare logics, as usable resources cannot implicitly flow from one program point to the next. We present AxSL, a separation logic for the relaxed memory model of Arm-A, that captures the fine-grained reasoning underpinning the low-overhead synchronisation mechanisms used by high-performance systems code. In particular, AxSL allows transferring arbitrary resources using relaxed reads and writes when they induce inter-thread ordering. We mechanise AxSL in the Iris separation logic framework, illustrate it on key examples, and prove it sound with respect to the axiomatic memory model of Arm-A. Our approach is largely generic in the axiomatic model and in the instruction-set semantics, offering a potential way forward for compositional reasoning for other similar models, and for the combination of production concurrency models and full-scale ISAs.

Asynchronous Probabilistic Couplings in Higher-Order Separation Logic

Probabilistic couplings are the foundation for many probabilistic relational program logics and arise when relating random sampling statements across two programs. In relational program logics, this manifests as dedicated coupling rules that, e.g., say we may reason as if two sampling statements return the same value. However, this approach fundamentally requires aligning or "synchronizing" the sampling statements of the two programs which is not always possible. In this paper, we develop Clutch, a higher-order probabilistic relational separation logic that addresses this issue by supporting asynchronous probabilistic couplings. We use Clutch to develop a logical step-indexed logical relation to reason about contextual refinement and equivalence of higher-order programs written in a rich language with a probabilistic choice operator, higher-order local state, and impredicative polymorphism. Finally, we demonstrate our approach on a number of case studies. All the results that appear in the paper have been formalized in the Coq proof assistant using the Coquelicot library and the Iris separation logic framework.

Study on the logic and effectiveness of crisis learning in the promotion policy adjustment: an observation based on the adjustment of COVID-19 prevention policy in China.

As the impact of COVID-19 on normal production and living conditions diminishes, this serious emergency is come to an end. China's policy framework has facilitated positive adjustment over the past 3 years by timely modifying its emergency response to changes in viruses and epidemics. This paper aims to explore the logic of China's policy framework that promoted policy adjustment through crisis learning during COVID-19. By gathering and classifying China's epidemic prevention policies throughout the past 3 years, integrating policy texts, and analyzing key events, this article examines the process of supporting policy adjustment through crisis learning in the policy system during COVID-19. The Chinese government's COVID-19 policy adjustment process can be divided into four stages, namely 'The period of stress response', 'The period of COVID-19 prevention and control', 'The period of regular prevention and control', and 'The period of overall adjustment'. The policy adjustments in each stage demonstrate the logic and effectiveness of crisis learning in the promotion policy adjustment. The study has determined that the motivational logic comprises three crucial elements: security requirements, accountability pressure, and reputation management. The institutional logic encompasses both the organizational and resourceful environments, and the institutional and cultural environment. Additionally, the behavioral logic of policy adaptation aligns with the strategy of crisis learning. Meanwhile, the logical framework of 'crisis learning-policy adjustment' can be verified using the Chinese government's policy adjustment in COVID-19 as an example. Establishing an effective post-crisis learning system is crucial to improving the effectiveness of crisis response. There is a logical link between crisis learning and policy adjustment. The implementation of policy adjustment needs to be based on the results of crisis learning. Government departments are essential for crisis learning and policy adjustment.

Hybrid Architecture Strategies for the Prediction of Acute Pulmonary Embolism from Computed Tomography Images

The timely identification of pulmonary embolism is of utmost importance, as the condition has the potential to be life-threatening if not promptly addressed. The assessment of the severity of a pulmonary embolism (PE) frequently necessitates a time-consuming and potentially life-threatening estimation by a medical practitioner. The primary objective of the study was to investigate the potential utility of an artificial neural network in assisting physicians with the identification and prediction of pulmonary embolism risk in patients. Deep learning algorithms are frequently employed in medical imaging to enhance image analysis due to their ability to automatically learn representations from large datasets, as opposed to relying on pre-programmed instructions . The implementation of automated systems has the potential to decrease the level of physical effort needed and enhance the efficiency of diagnostic procedures for medical professionals. Efficient training and calculation processes are crucial for the proper execution of the implementation. The Tensor Processing Units (TPUs) developed by Google are employed to expedite the process of training., with the computational tasks being executed through Google Colab, a platform offered by Google Cloud TPUs. In order to achieve outcomes comparable to human judgment, deep learning algorithms engage in reasonable assessments of data based on a predetermined logical framework. Diagnosing pulmonary embolism (PE), a potentially lethal yet curable condition, poses challenges in early detection. A distinctive convolutional neural network (CNN) model was developed and examined for the purpose of distinguishing between pulmonary embolism (PE) and computed tomography (CT) pictures. The proposed study yields a precision rate of 91.2%, showcasing an enhancement compared to current convolutional neural network (CNN) architectures that include limited trainable parameters. Furthermore, our model provides interpretability by the utilization of computed tomography (CT) images, specifically in the inferno and bone models. Our proposed deep learning model has the potential to predict the presence of PE and other associated features in current cases.

A logical framework and network form of multi-subject collaborative governance in comprehensive land consolidation from an organizational relationship perspective

A logical framework and network form of multi-subject collaborative governance in comprehensive land consolidation from an organizational relationship perspective

JTB Epistemology and the Gettier Problem in the Framework of Topological Epistemic Logic

JTB Epistemology and the Gettier Problem in the Framework of Topological Epistemic Logic

Perceived Benefits, Social Influence, Self-Efficacy and Digital Consumerism: The Moderating Role of Digital Accountability

Purpose: The study finds out the impact of perceived benefits, social influence, and self-efficacy on digital consumerism and how digital accountability moderates the conceptual relationship. Organizational capabilities, data privacy and protection, legal, and ethical marketing practices have been conceptualized as digital accountability with fact-based literature review. It is increasingly important for managers and consumer researchers to understand the trend and advocate digital accountability for enhancing digital consumerism in developing economies such as Bangladesh. Methods: Relevant literature on the relationships using a simple extension of the Technology Acceptance Model (TAM) with key variables and subsequent digital consumerism would be the focal points of this study. For developing economies perceived digital accountability has been conceptualized as a moderator. The study is secondary with available literature review and data from the Bangladesh Telecommunication and Regulatory Commissions (BTRC). Findings: The proficiency of organizational capabilities, the role of regulatory authorities, data privacy, legal protection, unethical business practices, and subsequent digital vulnerability will continue to be a big challenge in this 21st century. There is an urgency for a reconceptualization of consumerism so that consumer protections can be improved and a better-functioning digital market can be developed. This paper focuses on the literature gap in key variables and digital accountability to safeguard data privacy and information transparency, improve organizational capability, legal protection of consumers, and ethical business practices into consideration while enhancing digital consumerism. The logical conceptual framework provides the foundation that will further guide this study and future empirical findings. Originality and Value: In the present scenario majority of the population in Bangladesh use computers, mobile devices or smartphones for their digital marketing needs. There is no previous research in digital consumerism in the context of developing nations. Therefore, it is expected to contribute new findings. It will help researchers and policymakers for future policy implications by addressing digital accountability from all dimensions to enhance better functioning digital consumerism. Limitations: The study is based on secondary information and an extensive literature review. The paper only highlights the current state of affairs and draws conclusions based on fact findings and literature gaps.

PowerSyn: A Logic Synthesis Framework With Early Power Optimization

PowerSyn: A Logic Synthesis Framework With Early Power Optimization

Wittgenstein, Carnap, & Copernicus

My point of departure is a passage in which Coffa claims: “Wittgenstein’s and Carnap’s insights on the a priori belong in the same family as Kant’s... What we witness circa 1930 is a Copernican turn that, like Kant’s, bears the closest connection to the a priori; but its topic is meaning rather than experience” [Coffa, 1991, p. 263]. I draw out Kantian resonances in Wittgenstein’s and Carnap’s work on logic, grammar, and theoretical frameworks. In the end, Coffa’s remark comes out as significantly illuminating for a variety of questions, issues, and dynamic historical trends.

Goal setting for family firm owners: An agency-stewardship perspective

Goal setting for family firms is a vital activity since the performance of businesses is measured against their goals. Using prior research on goal-setting in the context of family firms, this narrative literature review paper aims to bring forth, the importance of goal-setting specific to family business owners who straddle family and business. A review of family-business literature indicates that using the three-circle model comprising family, business, and ownership as the base theory, goal setting in family firms has been examined from the perspective of whether they are economic or non-economic and whether they are family-oriented or business-oriented. Both these approaches to family business goal setting are based on the ideology that the unique feature of family businesses is that they aim to satisfy two groups of stakeholders — family members and business members. While family and business perspectives are captured in these two approaches, goal setting from an owner’s perspective has been largely ignored. Rooted in the concept of corporate responsibility, the agency-stewardship framework is emerging as an alternative perspective for family businesses and hence is proposed as a useful dominant logic framework for decisions on goal setting. This conceptual paper based on a narrative literature review aims to present the agency-stewardship framework as a relevant and important lens in the two most important decision contexts of family businesses, i.e., governance and succession. The paper exemplifies how the agency-stewardship continuum can be used for goal setting to nurture next-generation family business leaders and manage family business operations through goal setting in governance policies and succession processes.

Synergizing Aptitude, Interest, and Intelligence: A Systematic Approach to Career Selection Decision-making in Higher Education

Abstract— The process of making informed career decisions during higher education holds great significance, both for individuals and society at large. Careful evaluation of factors such as aptitude, interests, and intelligence is essential when selecting a suitable career path. The impact of this choice is far-reaching, influencing professional paths and contributing to personal contentment and societal advancement. This study delves into the pivotal roles played by aptitude, interest, and intellect in shaping students' career choices, particularly within the realm of higher education. The primary objective of this paper is to examine existing research on the process of career decision-making within the context of higher education. By establishing connections among aptitude, interest, and intelligence, the study aims to offer insights into an effective framework for supporting career decision-making approaches, thereby guiding students toward careers that align with their aspirations and yielding successful outcomes.To achieve this goal, a fuzzy-based technique is used to aid students in making well-informed career selections subsequent to completing their higher education. The system incorporates interest, intellect, and aptitude as input variables, encompassing a comprehensive array of eleven inputs, including nine specific aptitude dimensions. The ultimate output variable encompasses the chosen career option. The synthesis of scores from diverse aptitude, interest, and intelligence assessments serves as the basis for determining the most suitable career path. Employing the Mamdani technique within a fuzzy logic framework facilitates a thorough analysis of these inputs, offering pragmatic career alternatives congruent with individual skill sets. In order to gauge the system's efficacy, a comparative analysis is conducted between the proposed fuzzy-based approach and the traditional manual methodology employed by career counselors. The system's effectiveness is tested by comparing its results with those of the counselor. A high degree of positive correlation (0.988) is observed between the two results, indicating the system's good efficiency. The results of this correlation reveal that the fuzzy-based approach demonstrates a high degree of similarity to the outcomes generated by human counselors, thus validating its effectiveness as a valuable tool for aiding students in their career decision-making process. Keywords — Career selection, Decision making, Aptitude, Interest, Intelligence, fuzzy

Special major 1, 3 satisfiability logic in discrete Hopfield neural networks

<abstract> <p>Currently, the discrete Hopfield neural network deals with challenges related to searching space and limited memory capacity. To address this issue, we propose integrating logical rules into the neural network to regulate neuron connections. This approach requires adopting a specific logic framework that ensures the network consistently reaches the lowest global energy state. In this context, a novel logic called major 1,3 satisfiability was introduced. This logic places a higher emphasis on third-order clauses compared to first-order clauses. The proposed logic is trained by the exhaustive search algorithm, aiming to minimize the cost function toward zero. To evaluate the proposed model effectiveness, we compare the model's learning and retrieval errors with those of the existing non-systematic logical structure, which primarily relies on first-order clauses. The similarity index measures the similarity benchmark neuron state with the existing and proposed model through extensive simulation studies. Certainly, the major random 1,3 satisfiability model exhibited a more extensive solution space when the ratio of third-order clauses exceeds 0.7% compared to first-order clauses. As we compared the experimental results with other state-of-the-art models, it became evident that the proposed model achieved significant results in capturing the overall neuron state. These findings emphasize the notable enhancements in the performance and capabilities of the discrete Hopfield neural network.</p> </abstract>

Research on the relationship between ESG disclosure quality and stock liquidity of Chinese listed companies

<abstract> <p>In this article, we aim to verify the relationship between ESG disclosure quality and stock liquidity of listed companies and to provide a detailed analysis of its mechanisms. Based on the theories of information asymmetry, signal transmission, reputation, and stakeholder, we summarize and analyze the theoretical and logical framework of how ESG information disclosure can impact stock liquidity. Following the fixed effect (considering individual, year, and industry), panel model was applied to empirically test the relationship between ESG disclosure score and stock liquidity with data ranging from 2012 to 2021. The research findings indicate that improving the quality of ESG disclosure by companies can significantly enhance the level of stock liquidity. Furthermore, we analyze and verify through mechanism tests that ESG disclosure can influence stock liquidity by increasing analyst attention and media coverage (information effect) and enhancing reputation (reputation effect). From a theoretical perspective, the paper enriches the research related to the economic impact of ESG information disclosure and factors affecting stock liquidity. Also, we validate theories connected to information and reputation. From a practical perspective, the research has specific reference value for policymakers, enterprise managers, and investors.</p> </abstract>

Research on Digital Media Content Production and Dissemination Mode Based on Artificial Intelligence

Abstract In order to study the effect of digital media content production and dissemination supported by artificial intelligence technology, we aim to improve the dissemination and influence of digital media in short video platforms. This paper takes digital media as the research object and launches research on digital media content production and dissemination mode. Based on the research method of inquiry description, the core process of digital media intelligent transformation was deeply analyzed, and the overall logical framework of digital media intelligent transformation was designed. The digital media content production flow model is built by combining data integration and aggregation mining from a digital media content perspective. A graph-convolution matrix decomposition recommendation model for digital media broadcasting was developed by analyzing the performance of algorithmic recommendation in digital media applications. The results are presented: in terms of the order of influence degree size, the degree of influence on the number of likes is greater for political news (Beta = 0.2026, p < 0.01) than for military science and technology (Beta = 0.1637, p < 0.01) than for social news (Beta = 0.1225, p < 0.05), i.e.It indicates that the content theme is shifting from traditional social news to political news, which makes it easier to increase the heat of content dissemination. This study helps us think about the impact of media content production and dissemination strategies in the age of mobile Internet and provides new inspiration for communication theory research.

Pioneering Security: A Hybrid Logic Framework for the Future of IoT Protection

In a complicated situation, the Internet of Things simplifies the process of connecting a wide range of things. Because of its openness and lack of human control, the Internet of Things is open to assaults like denial of service (DoS) and man-in-the-middle attacks. Furthermore, any device that can connect to the internet may be hacked. These attacks put the network connections and the actual equipment at danger. IoT security and privacy will so be compromised. Due to its limited power, bandwidth, and storage, the Internet of Things requires a security solution that does not overload it. This study aims to preserve consumers' trust in the Internet of Things (IoT) by safeguarding their data against unauthorised access and maintaining the privacy of their personal information. With the ultimate objective of presenting a unique hybrid and optimised lightweight logical security architecture to ensure data privacy and integrity while making effective use of available resources, all research are carried out with this purpose in mind. The Hybrid Lightweight Security Framework (HLSF), which this study suggests, offers secrecy and integrity in addition to authentication. The structure consists of three distinct steps. The first step is registration, the second is authentication, and the third is transit security, which protects data while it is being transported. Compared to other current frameworks, the results reveal that HLSF performs better in terms of precision, recall, and accuracy when applied to an IoT situation.

A Study of the Impact of Smart Contract Technology on the Understanding and Application of Contract Law from the Perspective of the Legal Profession

Abstract This paper constructs the logical framework of the smart contract model based on the sale and purchase contract transaction, integrating the legal framework and the smart contract. The smart contract model merges blockchain technology’s decentralized features with smart contracts to create a decentralized service mode for sale and purchase transactions. The classification of transaction attributes optimizes and adjusts the matching degree calculation method to meet the needs of both buyers and sellers. The matching degree determines the total satisfaction of both buyers and sellers. The simulation experiment model is designed to analyze the matching results, and the experimental results indicate that the overall utility value range is between 0.1 and 0.5, resulting in a higher level of satisfaction for both parties involved. Applying the model to actual purchase and sale transaction contracts, the expected profit of transaction users after using smart contracts is 26,300 yuan higher than that without using smart contracts. The accuracy and confidence scores of experienced users are 0,8886 and 4.0493, respectively, when evaluating their perceptions of the use of smart contracts. Smart contracts can help participants identify sales and purchase problems and provide innovative perspectives on the application of contract law.

ИССЛЕДОВАНИЯ ПО МОДЕРНИЗАЦИИ УПРАВЛЕНИЯ ОБРАЗОВАНИЕМ

The humanistic concept is based on human nature and is the starting point of education management. A humanistic concept should be incorporated into the educational management process to provide a logical framework to promote the modernization of education management. However, the idea of “human orientation” is not fully reflected in the process of implementing modern education management. Taking effective measures to truly integrate the humanistic concept into the educational management process is an urgent problem that needs to be solved in order to implement the modernization of the education system, which will be discussed in this article.

The Impact of Withholding Cost-Effective Early Treatments, such as Vitamin D, on COVID-19: An Analysis Using an Innovative Logical ParadigmThe Impact of Withholding Cost-Effective Early Treatments, such as Vitamin D, on COVID-19: An Analysis Using an Inno

Purpose: While numerous widely accepted nutraceuticals lack randomized clinical trial (RCT) validation, regulatory bodies prioritize RCTs as the primary evidence for testing hypotheses for drug approvals. Despite challenges in authorizing generic therapies for SARS-CoV-2, regulatory bodies promptly granted Emergency Use Authorization for patented agents. This study evaluated whether the clinical trial data yielded adequate evidence to justify the approval of generic compounds, like vitamin D, as adjunct therapy to combat SARS-CoV-2. Methods: We employed an ancient logic system, seamlessly integrated with modern scientific principles and artificial intelligence principles, to analyze empirical data from 7 papers published in 2020. Subsequently, we compared the results with over 200 scientific papers (including over 100 treatment studies) referenced in a large (big)database. This study aimed to determine if there was substantial evidence in 2020 to support the approval of generic agents such as vitamin D (and ivermectin) for treating COVID-19. Results: The drug approval process undervalues well-designed observational studies, placing them in a subordinate position to RCTs when assessing effectiveness, even for nutrients. Our utilization of Catuskoti, an innovative logical method, highlights its potential as a catalyst for scientific progress, including big data analysis and integrating artificial intelligence into nutrient and pharmaceutical approval processes. Conclusions: Analyses conducted within this logical framework affirmed a robust inverse correlation between vitamin D levels and positive clinical outcomes in COVID-19 cases. Emphasizing the broader adoption of Catuskoti logic, particularly in analyzing big data and Machine Learning paradigms, becomes crucial in drug approvals. This approach aims to mitigate harm to individuals in future pandemics by promptly providing a more comprehensive understanding of the relationships between variables.