Specific Information Research Articles

OCLVerifer: Automated Verfication of OCL Contracts in Requirements Models

Object Constraint Language (OCL) is one lightweight formal specification. Integrated within the Unified Modeling Language (UML) standard, it serves as a cornerstone in requirements modeling, enjoying widespread adoption across various domains. OCL can precisely define the pre- and post-condition of system operations and system invariants. While OCL provides a simple yet expressive syntax, it lacks clarity in mapping Object-Oriented (OO) concepts, such as object states, object links, and object attributes. This ambiguity makes it challenging for OO developers to identify errors in requirements. In this paper, we propose an approach named OCLVerifier, which can automatically detect the requirements errors of OCL, such as conflict, redundancy, and failure error. OCLVerifier first transforms OO contracts and detection patterns into SMT formulas and then proves them by using a SMT solver. Finally, the results are mapped to the original OCL contracts to display detailed error type and location information. To evaluate OCLVerifier, we conducted a comprehensive evaluation of four case studies. Experimental results indicate that OCLVerifier successfully identifies 65.5% of error cases, with each identified case offering accurate error location information. Compared with human experts, OCLVerifier can reduce evaluation time by 80.8% while enhancing repair accuracy by 18%. The results are satisfactory, and the proposed approach can be further extended to the software industry for requirements verification.

A formal specification language and automatic modeling method of asset securitization contract

Asset securitization is an important financial derivative involving complicated asset transfer operations. Therefore, digitizing traditional asset securitization contracts will improve efficiency and facilitate reliability verification. Furthermore, accurate and verifiable requirement description is essential for collaborative development between financial professionals and software engineers. A domain specific language for writing asset securitization contract has been proposed. This solves the problem of difficulty for financial professionals to directly write smart contract by simplifying writing rules. However, due to existing design of the language focused on some simple scenarios, it is insufficient and informal to describe various detailed scenarios. What is more, there are still many reliability issues, such as verifying the correctness of the logical properties of the contract and ensuring the consistency between the contract text and the contract code, within the language in the generation and execution of smart contracts. To overcome the challenges stated above, we extend, simplify and innovate the syntax subset of the domain specific language and name it AS-SC (Asset Securitization – Smart Contract), which can be used by financial professionals to accurately describe requirements. Besides, because formal methods are math-based techniques that describe system properties and can generate programs in a more formal and reliable manner, we propose a semantic consistent code conversion method, named AS2EB, for converting from AS-SC to Event-B, a common and useful formal language. AS2EB method can be used by software engineers to verify requirements. The combination of AS-SC and AS2EB ensures consistency and reliability of the requirements, and reduces the cost of repeated communications and later testing. Taking the credit asset securitization contract as case study, the feasibility and rationality of AS-SC and AS2EB are validated. In addition, by carrying out experiments on three randomly selected real cases in different classic scenarios, we show high-efficiency and reliability of AS2EB method.

A Session Type System for Asynchronous Unreliable Broadcast Communication

Session types are formal specifications of communication protocols, allowing protocol implementations to be verified by typechecking. Up to now, session type disciplines have assumed that the communication medium is reliable, with no loss of messages. However, unreliable broadcast communication is common in a wide class of distributed systems such as ad-hoc and wireless sensor networks. Often such systems have structured communication patterns that should be amenable to analysis by means of session types, but the necessary theory has not previously been developed. We introduce the Unreliable Broadcast Session Calculus, a process calculus with unreliable broadcast communication, and equip it with a session type system that we show is sound. We capture two common operations, broadcast and gather, inhabiting dual session types. Message loss may lead to non-synchronised session endpoints. To further account for unreliability we provide with an autonomous recovery mechanism that does not require acknowledgements from session participants. Our type system ensures soundness, safety, and progress between the synchronised endpoints within a session. We demonstrate the expressiveness of our framework by implementing Paxos, the textbook protocol for reaching consensus in an unreliable, asynchronous network.

Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semisupervised Skeleton-Based Action Recognition.

Contrastive learning has been successfully leveraged to learn action representations for addressing the problem of semisupervised skeleton-based action recognition. However, most contrastive learning-based methods only contrast global features mixing spatiotemporal information, which confuses the spatial- and temporal-specific information reflecting different semantic at the frame level and joint level. Thus, we propose a novel spatiotemporal decouple-and-squeeze contrastive learning (SDS-CL) framework to comprehensively learn more abundant representations of skeleton-based actions by jointly contrasting spatial-squeezing features, temporal-squeezing features, and global features. In SDS-CL, we design a new spatiotemporal-decoupling intra-inter attention (SIIA) mechanism to obtain the spatiotemporal-decoupling attentive features for capturing spatiotemporal specific information by calculating spatial- and temporal-decoupling intra-attention maps among joint/motion features, as well as spatial- and temporal-decoupling inter-attention maps between joint and motion features. Moreover, we present a new spatial-squeezing temporal-contrasting loss (STL), a new temporal-squeezing spatial-contrasting loss (TSL), and the global-contrasting loss (GL) to contrast the spatial-squeezing joint and motion features at the frame level, temporal-squeezing joint and motion features at the joint level, as well as global joint and motion features at the skeleton level. Extensive experimental results on four public datasets show that the proposed SDS-CL achieves performance gains compared with other competitive methods.

Diagnostic value of oxidation-reduction potential for male infertility: a systematic review and meta-analysis.

In the last few years, studies have initially confirmed the diagnostic significance of oxidation-reduction potential (ORP) in male infertility patients. In this article, we used meta-analysis to clarify the role of ORP in the diagnosis of male infertility. PubMed, Embase, Web of Science, and Cochrane Library were searched by computer for relevant published literature. Quality assessment of the included literature was performed by Quality Assessment of Diagnostic Accuracy Studies (QUADAS) scale. Heterogeneity analysis of included studies was conducted using Metadisc 1.4 and Stata 12.0, and effective models for quantitative synthesis were selected based on heterogeneity results; the sensitivity and specificity of the synthesis were obtained using the software, and in order to reduce the effects of heterogeneity and thresholds, the information of sensitivity and specificity was integrated. We used the subject receiver operating characteristic (SROC) curve, area under the curve (AUC) and Q* index for comprehensive evaluation. Seven papers were eventually included in the study, and the results showed that ORP had a sensitivity of 0.81 [95% confidence interval (CI): 0.80-0.82] and specificity of 0.66 (95% CI: 0.63-0.69), an AUC of 0.8 and a Q* index of 0.74 for the diagnosis of male infertility. ORP has high sensitivity and specificity for diagnosing male infertility.

Social emergence in cryptocurrency systems and their open, self-organized development

Being supposedly the ground for an exchange system that does not depend on central, top-down regulation, cryptocurrencies increasingly need new algorithmic and policy-driven rules to maintain their trustworthiness and capacity to exhibit empirically supported growth. The present paper offers a conceptual and philosophical discussion on whether and how cryptosystems could be able to generate resilient development in a way that is coherent with a non-reductionist view of positive economics. As proposed, a plausible way to understand them can be achieved considering their complexity and their concrete, local features, which have to be grasped both in terms of formal and material specificity.

A scalable and transparent data pipeline for AI-enabled health data ecosystems.

Transparency and traceability are essential for establishing trustworthy artificial intelligence (AI). The lack of transparency in the data preparation process is a significant obstacle in developing reliable AI systems which can lead to issues related to reproducibility, debugging AI models, bias and fairness, and compliance and regulation. We introduce a formal data preparation pipeline specification to improve upon the manual and error-prone data extraction processes used in AI and data analytics applications, with a focus on traceability. We propose a declarative language to define the extraction of AI-ready datasets from health data adhering to a common data model, particularly those conforming to HL7 Fast Healthcare Interoperability Resources (FHIR). We utilize the FHIR profiling to develop a common data model tailored to an AI use case to enable the explicit declaration of the needed information such as phenotype and AI feature definitions. In our pipeline model, we convert complex, high-dimensional electronic health records data represented with irregular time series sampling to a flat structure by defining a target population, feature groups and final datasets. Our design considers the requirements of various AI use cases from different projects which lead to implementation of many feature types exhibiting intricate temporal relations. We implement a scalable and high-performant feature repository to execute the data preparation pipeline definitions. This software not only ensures reliable, fault-tolerant distributed processing to produce AI-ready datasets and their metadata including many statistics alongside, but also serve as a pluggable component of a decision support application based on a trained AI model during online prediction to automatically prepare feature values of individual entities. We deployed and tested the proposed methodology and the implementation in three different research projects. We present the developed FHIR profiles as a common data model, feature group definitions and feature definitions within a data preparation pipeline while training an AI model for "predicting complications after cardiac surgeries". Through the implementation across various pilot use cases, it has been demonstrated that our framework possesses the necessary breadth and flexibility to define a diverse array of features, each tailored to specific temporal and contextual criteria.

Choosing the Sweet Life with DOLCE

An ontology in philosophy is a description of what exists; an ontology in information science is the formal specification of a domain for information organization. A top-level ontology is an ontology of high-level abstractions that is meant to provide a standard for other ontologies to make them interoperable. Top-level ontologies commit to certain philosophical assumptions in the worlds they model, such as naive scientific realism, or the choice to model only those worlds which “actually” exist. This paper uses a comparison of Basic Formal Ontology (BFO) and Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) to investigate whether naive scientific realism is useful as a philosophical stance in ontological modelling. It investigates the specific philosophical and technical features of DOLCE which make it unique. BFO and DOLCE are compared, as well as a miniature literature review of comparisons between other top-level ontologies. Then it describes the history of DOLCE and BFO’s development in the context of the collaboration between Barry Smith and Nicola Guarino. Finally, it examines the application of DOLCE in various domains, such as “sweetening” WordNet.

Online identification of chemical constituents in Mongolian medicine Zhachong-13 pills by UHPLC-Q-exactive Orbitrap MS

Zhachong-13 pills (ZC-13), as a traditional prescription of Mongolian medicine, are often used in the clinical practice of Mongolian hospitals for the treatment of stroke and rheumatic arthritis. In this experiment, UHPLC-Q-Exactive Orbitrap MS was used to explore the chemical composition of ZC-13. The results showed that 315 compounds were identified or inferred, including 56 alkaloids, 77 2-(2-phenylethyl)chromones, 61 flavonoids, 31 tannins, 8 coumarins, 16 lignans, 21 terpenoids, 5 amino acids, 19 organic acids, and 21 other components. In addition, the pharmacological activities related to anti-cerebral ischemia of these components were summarized. This result laid a foundation for further study on the pharmacodynamic material basis of ZC-13 and provided a scientific basis for the formulation of ZC-13 quality specifications.

WIKANG BAHAGHARI: ANALYSIS OF THE CONTEMPORARY VISAYAN GAY LINGO

This research aims to identify Visayan gay-lingo's contemporary-Bekimong Bisaya. This study examines the unique gay language used in Southern Leyte, which includes the analysis of its meaning, importance, etymology, and usage in daily conversations. According to this study, the Visayan gay-lingo or bekimong Bisaya is a sociolinguistic, gender-based language medium that is been utilized by gay men in their interaction with other people. There are words spoken backward, words with added sounds, added letters, and words with different spellings. Some words originated from different celebrities and personalities with different, fresh meanings. The Visayan gay lingo (bekimong Bisaya), similar to the gay lingos spoken in the different regions of the Philippines, has no formal structure, specification, conventions, or subscriptions to grammatical rules. It is a dynamic, creative, and free-flowing language that shaped the gay perspective. KEYWORDS – bekimon, Bisaya, Gay Lingo, gender-based language medium, sosyolek, Visayan

A decentralized model for usage and information flow control in distributed systems

Data usage control enables data owners to enforce policies for their data, by defining authorizations, but also obligations, which are actions to be performed before, during or after being granted access such as accepting web cookies, and conditions bearing on the system and environment attributes, e.g., the time. Usage control is often coupled with information flow control to monitor how data are propagated. While usage control is well established and modeled in centralized systems, the literature has only partially addressed usage control for distributed systems, for instance by distributing the usage control system components. However, when it comes to assigning policy to certain data, it is always enforced by a central authority. This paper proposes an extended usage control model to integrate decentralized information flow control (DIFC), which enables users to decide collectively which policy to apply to their common data. Functions to handle connection status aspects are also considered, for dynamic Internet of Things (IoT) or peer-to-peer networks where parts of the distributed network can be disconnected. Architectural aspects and formal definitions to enable decentralized policies for shared data are proposed as a novelty, resulting from the integration of DIFC. We used the TLA+ formal specification language on the proposed model and its attached model checker TLC to detect potential issues. We detected potential deadlocks due to the new connection functions as well as temporal ordering issues then suggested mitigations accordingly. A privacy analysis is provided using a car-sharing scenario to highlight the benefits of usage control.

Bioinformatic Resources for Plant Genomic Research

Abstract: Genome assembly and annotation are crucial steps in plant genomics research as they provide valuable insights into plant genetic makeup, gene regulation, evolutionary history, and biological processes. In the emergence of high-throughput sequencing technologies, a plethora of genome assembly tools have been developed to meet the diverse needs of plant genome researchers. Choosing the most suitable tool to suit a specific research need can be daunting due to the complex and varied nature of plant genomes and reads from the sequencers. To assist informed decision-making in selecting the appropriate genome assembly and annotation tool(s), this review offers an extensive overview of the most widely used genome and transcriptome assembly tools. The review covers the specific information on each tool in tabular data, and the data types it can process. In addition, the review delves into transcriptome assembly tools, plant resource databases, and repositories (12 for Arabidopsis, 9 for Rice, 5 for Tomato, and 8 general use resources), which are vital for gene expression profiling and functional annotation and ontology tools that facilitate data integration and analysis.

The illusory nature of industrial buildings` renovation (on the example of the Tervahovi silo in Oulu, Finland)

The article highlights approaches to the renovation of industrial buildings of some European silos in the context of assessing the need and their success. Most strategies are based on adaptive reuse, often focusing on preservation for preservation's sake or renovation as a trend approach in modern architecture. To understand it as a typological object, the emergence, evolution, planning structure, constructive materials, etc. are revealed. The process of the appearance of the silo (elevator) in the USA and its appearance in Europe is analyzed; the main types are defined depending on the configuration, constituent elements, and function: flat, full, vertical, battery-powered; the reasons why architects are interested in this type of industrial buildings and their significance for the urban planning landscape have been established. Due to its specificity and unusual form, construction and function, the building of the former Tervahovi grain silo in Oulu (Finland), which was part of a powerful industrial district, was chosen as an example. The analysis of the renovation project of the mentioned building, which was carried out in 2014 and was part of the reconstruction of the entire city quarter with the aim of returning active social life and new use among residents, was performed. As a result of the example`s study, the main features of the design strategy were determined – an illusory renovation, where the preservation of the authentic appearance remains minimal, actually creating an external screen behind which a completely new building is created. This approach distorts the concept of revitalization, the principles of sustainability and declared reductions in carbon emissions and the use of natural resources. It was also concluded that not all renovation projects of industrial heritage are reasonable and necessary, they should be analyzed in detail, where, in addition to the architectural component, there should be an assessment of efficiency, further use by the community, urban planning value, etc.

A Parallel Optimization Method for Robustness Verification of Deep Neural Networks

Deep neural networks (DNNs) have gained considerable attention for their expressive capabilities, but unfortunately they have serious robustness risks. Formal verification is an important technique to ensure network reliability. However, current verification techniques are unsatisfactory in time performance, which hinders the practical applications. To address this issue, we propose an efficient optimization method based on parallel acceleration with more computing resources. The method involves the speedup configuration of a partition-based verification aligned with the structures and robustness formal specifications of DNNs. A parallel verification framework is designed specifically for neural network verification systems, which integrates various auxiliary modules and accommodates diverse verification modes. The efficient parallel scheduling of verification queries within the framework enhances resource utilization and enables the system to process a substantial volume of verification tasks. We conduct extensive experiments on multiple commonly used verification benchmarks to demonstrate the rationality and effectiveness of the proposed method. The results show that higher efficiency is achieved after parallel optimization integration.

A Review of Abstraction Methods Toward Verifying Neural Networks

Neural networks as a machine learning technique are increasingly deployed in various domains. Despite their performance and their continuous improvement, the deployment of neural networks in safety-critical systems, in particular for autonomous mobility, remains restricted. This is mainly due to the lack of (formal) specifications and verification methods and tools that allow for having sufficient confidence in the behavior of the neural-network-based functions. Recent years have seen neural network verification getting more attention; many verification methods were proposed, yet the practical applicability of these methods to real-world neural network models remains limited. The main challenge of neural network verification methods is related to the computational complexity and the large size of neural networks pertaining to complex functions. As a consequence, applying abstraction methods for neural network verification purposes is seen as a promising mean to cope with such issues. The aim of abstraction is to build an abstract model by omitting some irrelevant details or some details that are not highly impacting w.r.t some considered features. Thus, the verification process is made faster and easier while preserving, to some extent, the relevant behavior regarding the properties to be examined on the original model. In this article, we review both the abstraction techniques for activation functions and model size reduction approaches, with a particular focus on the latter. The review primarily discusses the application of abstraction techniques on feed-forward neural networks and explores the potential for applying abstraction to other types of neural networks. Throughout the article, we present the main idea of each approach and then discuss its respective advantages and limitations in detail. Finally, we provide some insights and guidelines to improve the discussed methods.

Development of aggressive driving conditions for light-duty fuel cell vehicles in Beijing

Abstract As the main technology route of new energy vehicles, the reliability and durability of fuel cell vehicles have gradually become the focus of researchers. Based on the actual road driving data of fuel cell light-duty vehicles in Beijing, this paper screened out the aggressive driving segments of different speed intervals through the RPA95% quantile curve and constructed a library of aggressive driving conditions of fuel cell vehicles, based on this library, we developed the aggressive driving conditions of fuel cell vehicles that meet the actual driving scenarios of users. The driving conditions have a deviation of no more than 8% from the corresponding library in the three key indexes of average speed, average acceleration, and average deceleration. In addition, this paper also compared the differences between the constructed aggressive driving condition and the CLTC-P condition, which verifies the necessity of the aggressive driving condition in the durability test of fuel cell vehicles. So, this condition can be used as the basic condition for the subsequent durability test of fuel cell vehicles. It will help enterprises shorten the test cycle and reduce the test cost. It can also support the formulation of the durability test specification for the subsequent fuel cell vehicles. At the same time, it can also support the formulation of subsequent durability test specifications for fuel cell vehicles.

Reflexive Event-B: Semantics and Correctness the EB4EB Framework

The Event-B method enables correct by construction modeling of systems. It relies on set theory and first-order logic, to describe a series of refined system models expressed as a set of events modifying state variables. Invariants and theorems are introduced to express system properties submitted to the proof system associated with Event-B. While Event-B has proven its efficiency for the proof of this type of property, it does not offer powerful means allowing the explicit description of properties other than safety and specific forms of reachability. Checking other properties such as deadlock-freeness, liveness, or event scheduling requires ad hoc modeling techniques and external tools such as model checkers or other proof systems. This article presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EB4EB</i> , a new modeling framework offering the capability to introduce formally defined Event-B extensions, in particular new proof obligations corresponding to new properties. It is based on metamodeling techniques. It includes a theory (a metatheory) modeling Event-B and offers means for explicit manipulation of Event-B features and an extension mechanism to explicitly formalize and prove other properties. This reflexive framework relies on a trace-based semantics of Event-B and introduces a set of Event-B theories defining data types, operators, well-defined conditions, theorems, and proof rules to define Event-B constructs and their semantics. Deep and shallow instantiation mechanisms are set up to instantiate the obtained metatheory. The EB4EB framework and its instantiation mechanisms are developed in Event-B using the Rodin platform ensuring correctness and internal consistency of the defined theories. Lamport's clock example, instantiating EB4EB in both shallow and deep mechanisms, is used to evaluate the proposed approach.

Fast Incomplete Multi-View Clustering With View-Independent Anchors.

Multi-view clustering (MVC) methods aim to exploit consistent and complementary information among each view and achieve encouraging performance improvement than single-view counterparts. In practical applications, it is common to obtain instances with partially available information, raising researches of incomplete multi-view clustering (IMC) issues. Recently, several fast IMC methods have been proposed to process the large-scale partial data. Though with considerable acceleration, these methods seek view-shared anchors and ignore specific information among single views. To tackle the above issue, we propose a fast IMC with view-independent anchors (FIMVC-VIA) method in this article. Specifically, we learn individual anchors based on the diversity of distribution among each incomplete view and construct a unified anchor graph following the principle of consistent clustering structure. By constructing an anchor graph instead of pairwise full graph, the time and space complexities of our proposed FIMVC-VIA are proven to be linearly related to the number of samples, which can efficiently solve the large-scale task. The experiment performed on benchmarks with different missing rate illustrates the improvement in complexity and effectiveness of our method compared with other IMC methods. Our code is publicly available at https://github.com/Tracesource/ FIMVC-VIA.

Validating multiple variants of an automotive light system with Alloy 6

This paper reports on the development and validation of a formal model for an automotive adaptive exterior lights system (ELS) with multiple variants in Alloy 6, which is the most recent version of the Alloy lightweight formal specification language that supports mutable relations and temporal logic. We explore different strategies to address variability, one in pure Alloy and another through an annotative language extension. We then show how Alloy and its Analyzer can be used to validate systems of this nature, namely by checking that the reference scenarios are admissible, and to automatically verify whether the established requirements hold. A prototype was developed to translate the provided validation sequences into Alloy and back to further automate the validation process. The resulting ELS model was validated against the provided validation sequences and verified for most of requirements for all variants.

A journey with ASMETA from requirements to code: application to an automotive system with adaptive features

Modern automotive systems with adaptive control features require rigorous analysis to guarantee correct operation. We report our experience in modeling the automotive case study from the ABZ2020 conference using the ASMETA toolset, based on the State Machine formal method. We adopted a seamless system engineering method: from an incremental formal specification of high-level requirements to increasingly refined ASMETA models, to the C++ code generation from the model. Along this process, different validation and verification activities were performed. We explored modeling styles and idioms to face the modeling complexity and ensure that the ASMETA models can best capture and reflect specific behavioral patterns. Through this realistic automotive case study, we evaluated the applicability and usability of our formal modeling approach.