Specific Information Research Articles

Ritual networks and the structure of moral communities in Classic Maya society

Ritual plays an important integrative function in the creation, maintenance, and transformation of human society. The shared experience of ritual establishes strong bonds between individuals that defines their membership in certain social groups. However, rituals are not timeless traditions, nor do they simply restore social equilibrium. Rather, rituals are active and ongoing social processes that unite and divide across multiple social categories. This paper applies archaeological network methods to analyze the multiscalar structure of ritual traditions across Classic Maya (ca. 300–900 CE) society using hieroglyphic inscriptions from dated and provenienced monuments cataloged in the Maya Hieroglyphic Database (Looper and Macri 1991–2024). For the Classic Maya, public ritual and performance were highly charged political events where meaning and power could be negotiated, creating opportunities for identity formation and community integration. Such contexts helped establish strong moral bonds in Classic Maya society. However, we know relatively little about the specific forms and content of these ritual practices. In this study we construct ritual similarity networks from hieroglyphic inscriptions to analyze the structure and organization of these moral communities as well as the ritual relations that bound them together.

Validating multiple variants of an automotive light system with Alloy 6

AbstractThis paper reports on the development and validation of a formal model for an automotive adaptive exterior lights system (ELS) with multiple variants in 6, which is the most recent version of the lightweight formal specification language that supports mutable relations and temporal logic. We explore different strategies to address variability, one in pure and another through an annotative language extension. We then show how and its 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 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

AbstractModern 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 Abstract 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.

A maintainable and iterative development approach of critical systems with FoCaLiZe

The life-cycle development of critical systems follows mainly a V-cycle process overlapped with proofs and/or verification techniques. At the first development stage, a critical system must be, precisely, and as completely as possible described. Thus, it is recommended to use a graphical modeling language standard such as the Unified Modeling Language (UML) and the Object Constraint Language (OCL), to which both customers and developers could contribute. Then, the graphical design with its formal specification (requirements) is mapped into a complete formal development environment, using an MDE (Model-driven engineering) approach. The formal environment has to assess the development until the generation of a secure code. In this context, we propose a life-cycle development approach that combines UML/OCL and the FoCaLiZe formal environment for the secure development of critical systems. The proposed approach ensures essential development constraints such as strict boundaries and traceability between development phases. It also ensures a secure maintenance phase using partial transformation techniques from UML/OCL to FoCaLiZe. The latter enhances the transformation from UML/OCL into FoCaLiZe to support incremental development and assist developers in correcting modeling errors. The proposed approach is mostly dedicated to secure and maintainable lifecycle development processes of critical systems. The formal environment assesses the development process until the generation of a secure code. To illustrate the proposed approach, we present the stages of development of a constrained system controlling military objects. The development of this system uses the iterative, incremental, and maintainability stages as described by the development approach, until the generation of secure code.

Extracting TLA+ Specifications Out of a Program for a BEAM Virtual Machine

Formal specifications are mathematical descriptions of the desired system functionality. Since they are usually written separately from the software itself, it is important to ensure that the software implements what the specification requires. A common approach to achieve this is to have a specification detailed enough to generate source code but those are rarely written due to expertise required. If code is not generated, then currently there is no straightforward way to reliably show that implementation conforms to initial formal specification. This research attempts to define a way to extract formal TLA+ specification by translating Elixir source code and generating detailed specification to give the system developer the ability to show that it refines the initial one.

Response Item Network (ResIN): A network-based approach to explore attitude systems

Belief network analysis (BNA) refers to a class of methods designed to detect and outline structural organizations of complex attitude systems. BNA can be used to analyze attitude-structures of abstract concepts such as ideologies, worldviews, and norm systems that inform how people perceive and navigate the world. The present manuscript presents a formal specification of the Response-Item Network (or ResIN), a new methodological approach that advances BNA in at least two important ways. First, ResIN allows for the detection of attitude asymmetries between different groups, improving the applicability and validity of BNA in research contexts that focus on intergroup differences and/or relationships. Second, ResIN’s networks include a spatial component that is directly connected to item response theory (IRT). This allows for access to latent space information in which each attitude (i.e. each response option across items in a survey) is positioned in relation to the core dimension(s) of group structure, revealing non-linearities and allowing for a more contextual and holistic interpretation of the attitudes network. To validate the effectiveness of ResIN, we develop a mathematical model and apply ResIN to both simulated and real data. Furthermore, we compare these results to existing methods of BNA and IRT. When used to analyze partisan belief-networks in the US-American political context, ResIN was able to reliably distinguish Democrat and Republican attitudes, even in highly asymmetrical attitude systems. These results demonstrate the utility of ResIN as a powerful tool for the analysis of complex attitude systems and contribute to the advancement of BNA.

Knowledge-based system and expectation-maximization to discovering causes of imperfect labels in vehicular networks clustering

This paper focuses on the development of a knowledge-based system for automatically diagnosing issues in Vehicular ad hoc networks (VANETs). VANETs enable communication between vehicles and infrastructure, enhancing road safety and efficiency through timely information exchange. The proposed system aims to efficiently maintain and ensure the continuity of network service by leveraging innovative pattern recognition methods tailored to VANETs. The automatic diagnosis problem in VANETs involves estimating the operating class of network components based on sensor observations. This entails associating sensor measurements with specific operating modes. By implementing condition-based preventive maintenance procedures, potential component failures can be detected early, mitigating network disruptions. Various approaches, such as expert systems, fault trees, network state models, and statistical learning through pattern recognition, can be employed to address this problem. This paper primarily focuses on the statistical learning approach, where a classification or regression function is learned from a set of examples to assign operation modes to new measurements. It discusses relevant metrics and preprocessing techniques to simplify the decision-making process. The diagnostic system's results are determined based on the formulation of the classification or regression problem. The learning base is constructed, and an appropriate classification method is selected to develop and validate the automatic diagnosis system. While non-parametric models like support vector machines are commonly used, this article emphasizes the significance of considering assumptions and leveraging additional information to enhance performance. It proposes a more specific formalization of the problem, integrating the unique characteristics of VANETs. The contributions of this article revolve around the theory of belief functions, a generative approach, and the utilization of parametric models defined using graphical models. Experimental studies conducted on artificial datasets have demonstrated the benefits of the semi-supervised approach within the context of VANET networks.

A Co-Training Framework for Heterogeneous Heuristic Domain Adaptation.

The purpose of this article is to address unsupervised domain adaptation (UDA) where a labeled source domain and an unlabeled target domain are given. Recent advanced UDA methods attempt to remove domain-specific properties by separating domain-specific information from domain-invariant representations, which heavily rely on the designed neural network structures. Meanwhile, they do not consider class discriminate representations when learning domain-invariant representations. To this end, this article proposes a co-training framework for heterogeneous heuristic domain adaptation (CO-HHDA) to address the above issues. First, a heterogeneous heuristic network is introduced to model domain-specific characters. It allows structures of heuristic network to be different between domains to avoid underfitting or overfitting. Specially, we initialize a small structure that is shared between domains and increase a subnetwork for the domain which preserves rich specific information. Second, we propose a co-training scheme to train two classifiers, a source classifier and a target classifier, to enhance class discriminate representations. The two classifiers are designed based on domain-invariant representations, where the source classifier learns from the labeled source data, and the target classifier is trained from the generated target pseudolabeled data. The two classifiers teach each other in the training process with high-quality pseudolabeled data. Meanwhile, an adaptive threshold is presented to select reliable pseudolabels in each classifier. Empirical results on three commonly used benchmark datasets demonstrate that the proposed CO-HHDA outperforms the state-of-the-art domain adaptation methods.

Fulfilling OCaml Modules with Transparency

ML modules come as an additional layer on top of a core language to offer large-scale notions of composition and abstraction. They largely contributed to the success of OCaml and SML. While modules are easy to write for common cases, their advanced use may become tricky. Additionally, despite a long line of works, their meta-theory remains difficult to comprehend, with involved soundness proofs. In fact, the module layer of OCaml does not currently have a formal specification and its implementation has some surprising behaviors. Building on previous translations from ML modules to Fω, we propose a type system, called Mω, that covers a large subset of OCaml modules, including both applicative and generative functors, and extended with transparent ascription. This system produces signatures in an OCaml-like syntax extended with Fω quantifiers. We provide a reverse translation from Mω signatures to path-based source signatures along with a characterization of signature avoidance cases, making Mω signatures well suited to serve as a new internal representation for a typechecker. The soundness of the type system is shown by elaboration in Fω. We improve over previous encodings of sealing within applicative functors, by the introduction of transparent existential types, a weaker form of existential types that can be lifted out of universal and arrow types. This shines a new light on the form of abstraction provided by applicative functors and brings their treatment much closer to those of generative functors.

A technical infrastructure for primary education data that contributes to data standardization

AbstractThere is a significant amount of data available about students and their learning activities in many educational systems today. However, these datasets are frequently spread across several different digital services, making it challenging to use them strategically. In addition, there are no established standards for collecting, processing, analyzing, and presenting such data. As a result, school leaders, teachers, and students do not capitalize on the possibility of making decisions based on data. This is a serious barrier to the improvement of work in schools, teacher and student progress, and the development of effective Educational Technology (EdTech) products and services. Data standards can be used as a protocol on how different IT systems communicate with each other. When working with data from different public and private institutions simultaneously (e.g., different municipalities and EdTech companies), having a trustworthy data pipeline for retrieving the data and storing it in a secure warehouse is critical. In this study, we propose a technical solution containing a data pipeline by employing a secure warehouse—the Swedish University Computer Network (SUNET), which is an interface for information exchange between operational processes in schools. We conducted a user study in collaboration with four municipalities and four EdTech companies based in Sweden. Our proposal involves introducing a data standard to facilitate the integration of educational data from diverse resources in our SUNET drive. To accomplish this, we created customized scripts for each stakeholder, tailored to their specific data formats, with the aim of merging the students’ data. The results of the first four steps show that our solution works. Once the results of the next three steps are in, we will contemplate scaling up our technical solution nationwide. With the implementation of the suggested data standard and the utilization of the proposed technical solution, diverse stakeholders can benefit from improved management, transportation, analysis, and visualization of educational data.

Exploring API behaviours through generated examples

AbstractUnderstanding the behaviour of a system’s API can be hard. Giving users access to relevant examples of how an API behaves has been shown to make this easier for them. In addition, such examples can be used to verify expected behaviour or identify unwanted behaviours. Methods for automatically generating examples have existed for a long time. However, state-of-the-art methods rely on either white-box information, such as source code, or on formal specifications of the system behaviour. But what if you do not have access to either? This may be the case, for example, when interacting with a third-party API. In this paper, we present an approach to automatically generate relevant examples of behaviours of an API, without requiring either source code or a formal specification of behaviour. Evaluation on an industry-grade REST API shows that our method can produce small and relevant examples that can help engineers to understand the system under exploration.

First-Order Temporal Logic on Finite Traces: Semantic Properties, Decidable Fragments, and Applications

Formalisms based on temporal logics interpreted over finite strict linear orders, known in the literature as finite traces , have been used for temporal specification in automated planning, process modelling, (runtime) verification and synthesis of programs, as well as in knowledge representation and reasoning. In this article, we focus on first-order temporal logic on finite traces . We first investigate preservation of equivalences and satisfiability of formulas between finite and infinite traces, by providing a set of semantic and syntactic conditions to guarantee when the distinction between reasoning in the two cases can be blurred. Moreover, we show that the satisfiability problem on finite traces for several decidable fragments of first-order temporal logic is ExpSpace -complete, as in the infinite trace case, while it decreases to NExpTime when finite traces bounded in the number of instants are considered. This leads also to new complexity results for temporal description logics over finite traces. Finally, we investigate applications to planning and verification, in particular by establishing connections with the notions of insensitivity to infiniteness and safety from the literature.

Characterising Modal Formulas with Examples

We study the existence of finite characterisations for modal formulas. A finite characterisation of a modal formula φ is a finite collection of positive and negative examples that distinguishes φ from every other, non-equivalent modal formula, where an example is a finite pointed Kripke structure. This definition can be restricted to specific frame classes and to fragments of the modal language: a modal fragment ℒ admits finite characterisations with respect to a frame class ℱ if every formula φ ∈ ℒ has a finite characterisation with respect to ℒ consisting of examples that are based on frames in ℱ. Finite characterisations are useful for illustration, interactive specification and debugging of formal specifications, and their existence is a precondition for exact learnability with membership queries. We show that the full modal language admits finite characterisations with respect to a frame class ℱ only when the modal logic of ℱ is locally tabular. We then study which modal fragments, freely generated by some set of connectives, admit finite characterisations. Our main result is that the positive modal language without the truth-constants ⊤ and ⊥ admits finite characterisations w.r.t. the class of all frames. This result is essentially optimal: finite characterisability fails when the language is extended with the truth constant ⊤ or ⊥ or with all but very limited forms of negation.

Interactive Formal Specification for Mathematical Problems of Engineers

Interactive Formal Specification for Mathematical Problems of Engineers

Classification-driven intelligent system for automated evaluation of higher education exam paper quality

AbstractEvaluating the quality of university exam papers is crucial for universities seeking institutional and program accreditation. Currently, exam papers are assessed manually, a process that can be tedious, lengthy, and in some cases, inconsistent. This is often due to the focus on assessing only the formal specifications of exam papers. This study develops an intelligent system for the automatic evaluation of university exam papers in terms of form and content, ensuring adherence to quality standards. The system is composed of two subsystems: the first evaluates compliance with formal specifications, and the second analyzes the content. The content analysis involves automatically categorizing exam questions based on Bloom's cognitive levels (BCLs) and determining the representation ratio of these levels in the exam paper. This subsystem comprises four main modules: 1) question collection, 2) text pre-processing using natural language processing (NLP) methods, 3) feature engineering using the CountVectorizer method to convert questions into feature vectors, and 4) a classification module based on the Logistic Regression (LR) algorithm to categorize exam questions into categories like knowledge, comprehension, application, analysis, synthesis, and evaluation. Experimental results indicate that the system achieves an average accuracy of 98.5%.

Phenomenologies of loneliness: alone without you, alone without an us

Loneliness is not simply characterised by a lack of other people in one’s environment or lifeworld, but by a lack of specific forms of relationship, connection or belonging. In this paper I connect the conversation about different types of loneliness with debates happening in the philosophy of intersubjectivity, specifically the phenomenology of intersubjectivity. Philosophers of intersubjectivity tend to characterise experiences of direct encounter as having a second-personal ‘I-thou’ quality. By contrast, experiences of belonging tend to be characterised by first-person plural ‘we’ structures. In this paper I will compare and contrast some of the different phenomenological qualities that come with the absence or poverty of these two types of intersubjective structure respectively. These different types of loneliness are characterised by different phenomenological properties, but also by (the absence of) specific interpersonal and social structures. I argue that the absence of these different interpersonal structures respectively gives rise to loneliness as the experience of being unseen and loneliness as the experience of not-being-at-home. This is significant for loneliness studies, as understanding and combating loneliness is consequently likely to take variegated rather than homogeneous forms. This is particularly significant for a psychosocial approach to loneliness which seeks to understand, hold together and integrate both the structural features of loneliness (in this case the relevant interpersonal and social structures) and the lived experience of individuals and groups (in this case the phenomenology of loneliness).

Comparing "Meaningful Use" of Health Information Technology in Pennsylvania: Electronic Prescription Rates of Metropolitan and Rural Counties.

The Health Information Technology for Economic and Clinical Health Act incentivized the adoption of electronic health records (EHRs). Health systems looked to leverage technology to assist in serving populations in health professional shortage areas. Qualitative research points to EHR usability as a source of health inequities in rural settings, making the challenges of EHR usage a subject of interest. Pennsylvania offers a model for investigating rural health infrastructure with it having the third largest rural population in the United States. This study analyzed the adoption of Electronic Prescribing in the 67 Pennsylvania (PA) counties. Physician adoption and usage data for PA and the United States were compared using a t-test to establish a basis for comparison. PA counties were categorized using the United States Department of Agriculture (USDA)'s Rural-Urban Commuting Areas (RUCAs) system. Surescript use percentages were plotted against the RUCA scores of each PA county to create a polynomial regression model. PA office-based physicians, on average, utilize e-prescription tools at the same rate as the national average with 59% of practices utilizing Surescripts as of 2013. There was no significant correlation between Surescript usage and the rural/urban classification of counties in Pennsylvania (R-squared value of 0.06). Pennsylvania was able to adopt health information technology (HIT) infrastructure at the same rate as the national average. Rural and metropolitan definitions do not correlate to meaningful use of HIT, thus usability of HIT cannot be tied to health outcomes. Future studies looking at specific forms of HIT and their ability to decrease the burden of administrative work for clinicians.

Does Knowledge of Results Affect Motor Skill Learning and Adaptation in Interception-Like Tasks?

Knowledge of results (KR), particularly its informational role, has often been regarded as redundant for learning interception-like tasks, such as coincidence–anticipation timing tasks. However, it is possible that the KR’s guiding effect might be detrimental to motor adaptation, instead of only redundant, leading to a dependency on KR and steering the sensorimotor system away from relevant information of the task. In this study, we aimed to investigate KR’s effect on learning a coincidence–anticipation timing tasks and on the adaptation to unpredictable perturbations. Two groups of participants practiced a coincidence–anticipation timing tasks with or without KR on 1 day and underwent testing the next day for learning (Retention test) and for adaptation to unpredictable perturbations (Exposure phase). Both groups exhibited similar learning results but failed to adapt to the perturbations, contradicting the assumption of negative guidance effects of KR and the positive effects of relying solely on intrinsic information (no KR). These findings suggest that motor adaptation may require specific information during the acquisition process, highlighting for more systematic analyses to understand this phenomenon better. Such insights could have practical implications in contexts like sports and rehabilitation, by providing learners with appropriate information for acquiring adaptive internal representations of tasks.

Locally Abstract, Globally Concrete Semantics of Concurrent Programming Languages

Formal, mathematically rigorous programming language semantics are the essential prerequisite for the design of logics and calculi that permit automated reasoning about concurrent programs. We propose a novel modular semantics designed to align smoothly with program logics used in deductive verification and formal specification of concurrent programs. Our semantics separates local evaluation of expressions and statements performed in an abstract, symbolic environment from their composition into global computations, at which point they are concretised. This makes incremental addition of new language concepts possible, without the need to revise the framework. The basis is a generalisation of the notion of a program trace as a sequence of evolving states that we enrich with event descriptors and trailing continuation markers. This allows to postpone scheduling constraints from the level of local evaluation to the global composition stage, where well-formedness predicates over the event structure declaratively characterise a wide range of concurrency models. We also illustrate how a sound program logic and calculus can be defined for this semantics.

Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods

In this work, we attempt to bring together the origins, main results, and recent advances on discrete-event simulation of continuous-time systems. Starting from the early approaches that aimed to represent continuous-time dynamics within the discrete-event system specification (DEVS) formalism framework, the work shows how these ideas gave place to the formalization of the quantized state system (QSS) family of numerical integration algorithms. Then, we describe the QSS algorithms, their properties, their extensions, and the main practical software tools implementing them. We also present a selection of simulation examples illustrating the main features and advantages through comparisons with state-of-the-art continuous-time simulation solvers.