Search Results (25)

Differential operators often admit multiple algebraically equivalent symbolic formulations, yet those formulations can differ in the organization of their internal structure prior to solution analysis. A reproducible symbolic framework is introduced to compare such formulations at the level of operator expressions. Within a declared symbolic specification consisting of a fixed grammar, an admissible weight class, canonical compression rules, and an admissible family of reformulations, we define four encoding-relative structural descriptors: structural strain $\tau$, structural curvature $\kappa$, compressibility $\sigma$, and the balance ratio $\Gamma =\kappa /\tau$. Structural strain compares an encoding to a designated reference representation, while compressibility measures reduction under canonical symbolic compression. These quantities are deterministic descriptors within the declared encoding class rather than coordinate-free invariants of the underlying operator. The structural length functional underlying these descriptors is developed, canonical compression is formalized, and finite symbolic comparison is distinguished from pathwise symbolic deformation. A robustness theorem shows that, away from the threshold surface $\Gamma =\sigma$, sufficiently small admissible perturbations preserve the induced diagnostic label. A supporting weight-robustness result further shows that qualitative labels persist across a local admissible family of weight choices under corresponding nondegeneracy conditions. The framework serves as a reproducible diagnostic for operator representations alongside Lyapunov, spectral, pseudospectral, and energy-based stability theories. Examples of representative ordinary and partial differential operators illustrate how the descriptors are computed and how they behave under admissible re-expression, while the appendices provide the technical backbone of the paper: formal definitions, reproducibility protocol, extended perturbation arguments, and explicit failure-mode analysis. Additional sensitivity checks regarding encoding, weights, and threshold variation clarify the method’s scope, and explicit failure modes delineate the boundary cases in which the descriptors cease to apply. The main contribution of this study is a formally delimited and reproducible symbolic framework for comparing differential operators under a fixed, declared specification, together with robustness results and worked examples that clarify the method’s scope. Full article

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

With the rapid development of intelligent computing and artificial intelligence, there is an increasing demand for efficient, interpretable, and interactive frameworks for fine-grained text analysis. In the field of argument mining, existing approaches are often constrained by sentence-level processing, limited exploitation of key linguistic markers, and a lack of human–AI collaborative mechanisms, which restrict both recognition accuracy and computational efficiency. To address these challenges, this paper proposes KFF-Transformer, a computing-oriented human–AI collaborative framework for fine-grained argument element identification based on Toulmin’s model. The framework first employs an automatic key marker mining algorithm to expand a seed set of expert-labeled linguistic cues, significantly enhancing coverage and diversity. It then employs a lightweight deep learning architecture that combines BERT for contextual token encoding with a BiLSTM network enhanced by an attention mechanism to perform word-level classification of the six Toulmin elements. This approach leverages enriched key markers as critical features, enhancing both accuracy and interpretability. It should be noted that while our framework leverages BERT—a Transformer-based encoder—for contextual representation, the core sequence labeling module is based on BiLSTM and does not implement a standard Transformer block. Furthermore, a human-in-the-loop interaction mechanism is embedded to support real-time user correction and adaptive system refinement, improving robustness and practical usability. Experiments conducted on a dataset of 180 English argumentative essays demonstrate that KFF-Transformer identifies key markers in 1145 sentences and achieves an accuracy of 72.2% and an F1-score of 66.7%, outperforming a strong baseline by 3.7% and 2.8%, respectively. Moreover, the framework reduces processing time by 18.9% on CPU and achieves near-real-time performance of approximately 3.3 s on GPU. These results validate that KFF-Transformer effectively integrates linguistically grounded reasoning, efficient deep learning, and interactive design, providing a scalable and trustworthy solution for intelligent argument analysis in real-world educational applications. Full article

This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from $\mu =3.9$ (Challenge phase) to $\mu =4.6$ (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education. Full article

Due to its rising carbon footprint, new paradigms for carbon-efficient computing are needed. For distributed computing systems, one option is to shift computing loads in space or time to take advantage of low-carbon electricity, a paradigm known as carbon-aware computing. We present a literature review of carbon-aware scheduling techniques, which shows that most of the literature carried out either spatial or temporal shifting but not both. Of the 28 analyzed studies, 11 considered both spatial and temporal shifting, and only 2 developed a combined optimization algorithm. Additionally, existing approaches typically focus on operational electricity alone. With the growing decarbonization of electricity, however, device production (which involves various industrial processes and cannot be easily decarbonized) is bound to become more relevant and needs to be considered. We thus suggest a novel spatio-temporal scheduling algorithm for cloud and edge computing. Our algorithm performs simultaneous spatio-temporal shifting while taking into consideration both device production and operation. As temporal shifting requires forecasts of future workloads, we also put forward a workload predictor. Although not fully implemented yet, we bring various theoretical arguments in support of our proposed algorithm. Full article

It is well established that women’s representation in scientific and technical fields decreases with societal affluence, but the mechanisms underlying this so-called paradox remain contested. This study leverages distinctive features of the Israeli educational system to identify social psychological and organizational mechanisms driving contextual variability in the gendering of physics and computing subjects. Using in-depth interviews and original surveys, we compare gender gaps in ninth graders’ attitudes and aspirations across two highly segregated yet centrally administered state school sectors: one serving the socioeconomically marginalized Arab Palestinian minority, and one serving the Jewish secular majority. Results reveal curricular affinities, discourses, and course-taking patterns that are differentially gendered across school sectors. While boys and girls in Arab Palestinian schools report more instrumentalist motivations and more positive attitudes toward mathematically intensive fields, students in Jewish schools engage in highly gendered, self-reflexive discourses that support gendered course-taking. Findings support arguments positing gender-specific effects of postmaterialist, individualistic value systems, and suggest that the cultural and organizational processes that generate larger gender gaps in more affluent countries may also play out within countries. Full article

This work investigates the possibility to improve the computational efficiency of a set-based method for the trajectory planning of a car-like vehicle through artificial intelligence. Planning is performed on a graph that represents the operating scenario in which the vehicle moves, and the kinodynamic feasibility of the trajectories is guaranteed through a series of set-based arguments, which involve the solution of semi-definite programming problems. Navigation in the graph is performed through a hybrid A* algorithm whose performance metrics are improved through a properly trained classificator, which can forecast whether a candidate trajectory segment is feasible or not. The proposed solution is validated through numerical simulations, with a focus on the effects of different classificators features and by using two different kinds of artificial intelligence: a support vector machine (SVM) and a long-short term memory (LSTM). Results show up to a 28% reduction in computational effort and the importance of lowering the false negative rate in classification for achieving good planning performance outcomes. Full article

Regarding round-off errors as random is often a necessary simplification to describe their behavior. Assuming, in addition, the symmetry of their distributions, we show that one can, in unstable (ill-conditioned) computer calculations, suppress their effect by statistical averaging. For this, one slightly perturbs the argument of $f\left(x_0\right)$ many times and averages the resulting function values. In this text, we forward arguments to support the assumed properties of round-off errors and critically evaluate the validity of the averaging approach in several numerical experiments. Full article

Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and returns. To overcome these challenges, this work aims to harness learning analytics to address critical areas, such as school dropout rates, the need to foster student collaboration, improving argumentation and writing skills, and the need to enhance computational thinking across all age groups. The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs; this could potentially involve adapting learning processes and organizational structures to meet the needs of students. This work also seeks to evaluate the impact of such analytics tools on education within a multi-dimensional and scalable domain, ranging from individual learners to teachers and principals, and extending to broader governing bodies. The primary objective is articulated through the development of a user-friendly AI-based dashboard for learning. This prototype aims to provide robust support for teachers and principals who are dedicated to enhancing the education they provide within the intricate and multifaceted social domain of the school. Full article

Recent years have seen a growing momentum within UK Higher Education institutions to examine the colonial legacy entanglements of teaching materials and knowledge production, as institutions explore what it means to ‘decolonise the curriculum’. While the movement began in the University of Cape Town, South Africa, in response to a student call for the statue of Cecil Rhodes to be removed, elsewhere this has become a top-down imperative from institutions themselves. In 2014 University College London hosted a panel discussion ‘Why Isn’t My Professor Black’ building on the previous year’s video asking, ‘Why is my curriculum white’. By 2020 the #BlackLivesMatter movement once again illuminated the need to rebalance the power of who decides the ‘facts’ with a call for a transformation of knowledge production. Arts and Humanities curricula have been more easily adapted in response to this call, but the argument for decolonisation of STEM subjects in general and computing in particular have been more difficult to articulate. Moreover, the decolonisation shift has been largely confined to bricks and mortar universities, with little exploration of online and distance learning. This paper reports on an initiative in a British distance learning university to decolonise the computing curriculum, with a focus on students’ perspectives and what barriers might be encountered. A survey of just under 400 undergraduate computing students revealed multiple understandings about decolonisation, and reactions ranging from hostility and resistance to strong support and endorsement. Students identified several challenges to student engagement including structural and practical concerns which should inform the computing education community in taking forward this agenda. Full article

Intrusion Detection Systems (IDS) still prevail as an important line of defense in modern computing environments. Cloud environment characteristics such as resource sharing, extensive connectivity, and agility in deploying new applications pose security risks that are increasingly exploited. New technologies like container platforms require IDS to evolve to effectively detect intrusive activities in these environments, and advancements in this regard are still necessary. In this context, this work proposes a framework for implementing an IDS focused on container platforms using machine learning techniques for anomaly detection in system calls. We contribute with the ability to build a dataset of system calls and share it with the community; the generation of anomaly detection alerts in open-source applications to support the SOC through the analysis of these system calls; the possibility of implementing different machine learning algorithms and approaches to detect anomalies in system calls (such as frequency, sequence, and arguments among other type of data) aiming greater detection efficiency; and the ability to integrate the framework with other tools, improving collaborative security. A five-layer architecture was built using free tools and tested in a corporate environment emulated in the GNS3 software version 2.2.29. In an experiment conducted with a public system call dataset, it was possible to validate the operation and integration of the framework layers, achieving detection results superior to the work that originated the dataset. Full article

Because of China’s new wave of COVID-19 in May 2023, the issue of tackling COVID-19 misinformation remains relevant. Based on Lippmann’s theory of public opinion and agenda setting theory, this article aims to examine the concept of digital pseudo-identification as a type of logical fallacy that refers to supporting journalists’ opinions with ‘false’ arguments that lack factual evidence. To do so, the study applied computer-aided content analysis, as well as rhetorical and critical discourse analyses, to examine 400 articles related to four COVID-19 vaccines (‘Oxford-AstraZeneca’, ‘Pfizer-BioNTech’, ‘Sputnik V’ and ‘Sinovac’) published on the online versions of two major British and American mainstream media sources between August 2020 and December 2021. The results of the study show that journalists of the ‘The New York Times’ and ‘The Guardian’ used similar logical fallacies, including the opinions of pseudo-authorities and references to pseudo-statistics and stereotypes, which contributed to creating distorted representations of the COVID-19 vaccines and propagating online misinformation. The study also reveals political bias in both of the mainstream media sources, with relatively more positive coverage of the European vaccines than non-European vaccines. The findings have important implications for journalism and open up perspectives for further research on the concept of digital pseudo-identification in the humanities and social sciences. Full article

Epistemic growth is a desirable outcome of engaging in argumentation related to sustainable development issues. However, earlier studies have rarely been conducted from the perspective of practical epistemology. This longitudinal study aimed to address this gap and to promote epistemic growth in sustainable development issues via computer-supported argumentation through a practice-based approach, using the Apt-AIR framework. The participants were 96 undergraduate students with various majors. Repeated measures of the frequency and epistemic quality of students’ argumentation comments were taken with respect to six consecutive sustainable development issues to explicate the participants’ epistemic growth. The qualitative data of a specific undergraduate provided procedural evidence confirming a change in the epistemic performance and the epistemic growth curves. The results supported an argumentation-based intervention in education with respect to sustainable development issues and highlighted the possibility that the different aspects of epistemic performance are interrelated. Full article

Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients’ susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals. Full article

Assessment specialists (experts) are sometimes expected to provide two types of information: knowledge of rating domains and the performance of rating objects (called confidence levels). Unfortunately, the results of previous information aggregation studies cannot be properly used to combine the two categories of data covered above. Additionally, a significant range of symmetric/asymmetric events and structures are frequently included in the implementation process or practical use of fuzzy systems. The primary goal of the current study was to use cubic Fermatean fuzzy set features to address such situations. To deal with the ambiguous information of the aggregated arguments, we defined information aggregation operators with confidence degrees. Two of the aggregation operators we initially proposed were the confidence cubic Fermatean fuzzy weighted averaging (CCFFWA) operator and the confidence cubic Fermatean fuzzy weighted geometric (CCFFWG) operator. They were used as a framework to create an MCDM process, which was supported by an example to show how effective and applicable it is. The comparison of computed results was carried out with the help of existing approaches. Full article