Specific Information Research Articles

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

Understanding 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.

Mimicking Behaviors in Separated Domains (Abstract Reprint)

Devising a strategy to make a system mimic behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of LTLf, a formalism commonly used in AI for expressing finite-trace properties. Our model consists of two separated dynamic domains, D_A and D_B, and an LTLf specification that formalizes the notion of mimicking by mapping properties on behaviors (traces) of D_A into properties on behaviors of D_B. The goal is to synthesize a strategy that step-by-step maps every behavior of D_A into a behavior of D_B so that the specification is met. We consider several forms of mapping specifications, ranging from simple ones to full LTLf, and for each, we study synthesis algorithms and computational properties.

The Hautus-Type Inequality for Abstract Fractional Cauchy Problems and Its Observability

In this paper, we investigate the observability of the fractional resolvent family, and we prove two main results: the first result shows a generalization of the Hautus-type test for observable exponentially stable semigroups to the fractional resolvent family and the second result shows the equivalence of the observability and the below boundedness of the linear operator on the wave packet when the generator conforms to a specific form.

International students' perspectives on the genetic counseling application process.

Between 2018 and 2023, one percent of matched applicants to North American genetic counseling graduate programs (GCGPs) have been international applicants (IAs). The COVID-19 pandemic led to changes in the GCGP application processes in 2020, most notably the incorporation of virtual interviews and GRE waivers, which uniquely impacted IAs. Twelve international genetic counseling (GC) students who matriculated into a U.S.-based GCGP in 2021 or 2022 participated in this qualitative study (42% of the total enrolled) to understand their application experience. Cost, location of the program, and rapport during interviews were the most important factors identified by IAs to apply to and rank the GCGPs. Shadowing and volunteer experiences relevant to GC were cited as important for applicants to learn about a genetic counseling career, but many had challenges finding opportunities in their home countries. Unique logistical challenges in taking the GRE, transcript evaluation services, and standardized English proficiency tests were described. Although virtual interviews offered the same experience as domestic applicants, the time difference was a major challenge, requiring IAs to interview through the night, creating additional stressors. Nine of 12 were re-applicants and shared that engaging with GCGPs early in the process was beneficial for improving applications and, at times, requesting waivers for transcript evaluation requirements and considering unique volunteering experiences. Participants suggested GCGPs can address barriers by providing more specific information on their websites as it pertains to IAs, and contact information for the international student office. Improving awareness of the applicants' backgrounds, home country experiences, and time zone differences would provide IAs with a more equitable application experience. Addressing these barriers could help promote diversity, equity, and inclusion allowing for more IAs and the growth of the genetic counseling profession.

Explanation–Question–Response dialogue: An argumentative tool for explainable AI

Advancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it is generated, which is largely obscure to laypeople. A dialectical interaction with such systems may enhance the users’ understanding and build a more robust trust towards AI. Commonly employed as specific formalisms for modelling intra-agent communications, dialogue games prove to be useful tools to rely upon when dealing with user’s explanation needs. The literature already offers some dialectical protocols that expressly handle explanations and their delivery. This paper fully formalises the novel Explanation–Question–Response (EQR) dialogue and its properties, whose main purpose is to provide satisfactory information (i.e., justified according to argumentative semantics) whilst ensuring a simplified protocol, in comparison with other existing approaches, for humans and artificial agents.

Accountability as a service for robotics: Performance assessment of different accountability strategies for autonomous robots

Abstract An essential requirement for increasing human confidence in computer systems is knowing an event’s origin. Therefore, it is necessary to have an efficient method to record such information. It is especially challenging in robotics, where unexpected behaviours can have unpredictable consequences, endangering the interests of people or even their safety. Furthermore, to analyse an incident’s cause or anticipate future behaviours, we must identify the events that cause a specific action. Although it is common to use logging systems for such purposes, issues such as partial recording of events, unhelpful data or the impact on robot performance suggest conceiving new accountability solutions that assist when determining the responsible entities or the provenance of specific information. This paper presents a general-purpose approach to developing an accountability system for autonomous robots. It consists of four main components: a system event logger, a message producer, a distributed event streaming platform and a database. Our proposal is completely decoupled from the monitored system and allows real-time analysis, improving flexibility, besides system protection and transparency. Finally, the need to reduce the impact of the audit process and logging tasks on robot performance has promoted the development of different assessment scenarios to determine the best strategy for providing accounting services.

TikTok as a site of social protest in Iran’s Gen-Z uprising

This paper argues that understanding the power of TikTok’s visual discourse is a crucial part of conceptualising the character, inspiration, and ambition of Iran’s Gen-Z-led uprising, both at home and across the diaspora. TikTok is a social media platform that depends on visuality. As such, it creates its own specific forms of messaging. This paper seeks to apply an innovative methodology of ‘Visual Discourse Tracing’ to the Iranian protests. It uses this carefully devised, process-driven method, to highlight the core ways in which TikTok has amplified the message of the Iranian protests, connecting to the grassroots movement and to the longer history of Iranian women’s struggle for freedom. Visuality and social media have been crucial in shaping the character of these contemporary protests, necessitating proper theorisation when understanding the wider Iranian protest movement.

A compositional simulation framework for Abstract State Machine models of Discrete Event Systems

Modeling complex system requirements often requires specifying system components in separate models, which can be validated and verified in isolation from each other, and then integrating all components’ behavior in order to validate the operation of the whole system. If models are executable, as for state-based formal specifications, engines to orchestrate the simulation of separate component operational models are extremely useful. This paper presents an approach for the co-simulation, according to predefined orchestration schemas, of state-based models of separate components of a Discrete Event System. More precisely, we exploit the Abstract State Machine (ASM) formal method as state-based formalism, and we ( i ) define a set of operators to compose ASMs that communicate with each other through I/O events, and ( ii ) present an engine to execute the compositional simulation of the ASMs as a whole assembly. As proof of concepts, we use a set of model examples of Discrete Event Systems of increasing complexity to show the application of our approach and to evaluate its effectiveness in co-simulating models of real systems.

Camouflaged Object Detection Based on Deep Learning with Attention-Guided Edge Detection and Multi-Scale Context Fusion

In nature, objects that use camouflage have features like colors and textures that closely resemble their background. This creates visual illusions that help them hide and protect themselves from predators. This similarity also makes the task of detecting camouflaged objects very challenging. Methods for camouflaged object detection (COD), which rely on deep neural networks, are increasingly gaining attention. These methods focus on improving model performance and computational efficiency by extracting edge information and using multi-layer feature fusion. Our improvement is based on researching ways to enhance efficiency in the encode–decode process. We have developed a variant model that combines Swin Transformer (Swin-T) and EfficientNet-B7. This model integrates the strengths of both Swin-T and EfficientNet-B7, and it employs an attention-guided tracking module to efficiently extract edge information and identify objects in camouflaged environments. Additionally, we have incorporated dense skip links to enhance the aggregation of deep-level feature information. A boundary-aware attention module has been incorporated into the final layer of the initial shallow information recognition phase. This module utilizes the Fourier transform to quickly relay specific edge information from the initially obtained shallow semantics to subsequent stages, thereby more effectively achieving feature recognition and edge extraction. In the latter phase, which is focused on deep semantic extraction, we employ a dense skip joint attention module to enhance the decoder’s performance and efficiency, ensuring accurate capture of deep-level information, feature recognition, and edge extraction. In the later stage of deep semantic extraction, we use a dense skip joint attention module to improve the decoder’s performance and efficiency in capturing precise deep information. This module efficiently identifies the specifics and edge information of undetected camouflaged objects across channels and spaces. Differing from previous methods, we introduce an adaptive pixel strength loss function for handling key captured information. Our proposed method shows strong competitive performance on three current benchmark datasets (CHAMELEON, CAMO, COD10K). Compared to 26 previously proposed methods using 4 measurement metrics, our approach exhibits favorable competitiveness.

Research Progress on the Composite Methods of Composite Electrolytes for Solid-State Lithium Batteries.

In the current challenging energy storage and conversion landscape, solid-state lithium metal batteries with high energy conversion efficiency, high energy density, and high safety stand out. Due to the limitations of material properties, it is difficult to achieve the ideal requirements of solid electrolytes with a single-phase electrolyte. A composite solid electrolyte is composed of two or more different materials. Composite electrolytes can simultaneously offer the advantages of multiple materials. Through different composite methods, the merits of various materials can be incorporated into the most essential part of the battery in a specific form. Currently, more and more researchers are focusing on composite methods for combining components in composite electrolytes. The ion transport capacity, interface stability, machinability, and safety of electrolytes can be significantly improved by selecting appropriate composite methods. This review summarizes the composite methods used for the components of composite electrolytes, such as filler blending, embedded framework, and multilayer bonding. It also discusses the future development trends of all-solid-state lithium batteries (ASSLBs).