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Mary John

Publications and source records attributed to Mary John.

10 recordsLinked to original sources

The Scope Grammar of Natural-Language Rules: Counting and Enumerating Readings modulo Logical Equivalence

A rule written for people fixes its atomic conditions and connectives but leaves open how the connectives are bracketed, and each bracketing is a different logical reading that an autoformalizer must resolve or expose. We study the scope grammar, a deliberately ambiguous context-free grammar whose derivation trees are the readings of a connective skeleton and whose quotient by logical equivalence is what matters downstream. A skeleton with $k$ binary connectives has $C_k$ derivation trees, the $k$-th Catalan number, but far fewer logically distinct readings. For the positive, repetition-free fragment we make the gap exact: through the uniqueness of the read-once normal form we characterize the realizable normal forms by lowest common ancestors and count them with a two-label recurrence over the operator word, verified against exhaustive enumeration up to eleven atoms. The count is invariant under reversal and $\wedge/\vee$ duality; a skeleton is ambiguous if and only if it uses both $\wedge$ and $\vee$, a one-pass test; and the number of classes is at least $2^{\lfloor n/3\rfloor}$. Up to fifteen atoms the alternating pattern is the worst case; its count has a cubic generating function, is the 3-Schröder sequence (OEIS A137842), and grows with base $φ^{5/2}\approx3.330$, below the Catalan base 4. We reduce maximality to an inequality on increments, verified to sixteen atoms, and explain why local exchange arguments cannot prove it. We also treat negation, exceptions, and background axioms, where normal-form uniqueness still decides equivalence but counting is open.

cs.FL↗

An Eco-Friendly Tourism Trip Planner Driven by Z-Number Uncertainty: Evidence from Baku, Dubai, Abu Dhabi, Tashkent, and Istanbul

Deployed tourism trip planners ingest travel times and ratings as exact numbers, although routing services return travel times that vary with the hour of departure and a platform rating may rest on a handful of reviews or on hundreds of thousands. We present an eco-friendly trip planner that represents both quantities as Z-numbers, which pair a fuzzy restriction on a value with a fuzzy reliability on the evidence behind it, and that constructs them directly from routing-service observations at six departure hours and from platform review counts rather than from linguistic elicitation. A hybrid loss combines score, carbon, travel-time, and reliability terms, and an adaptive large neighborhood search returns single-day itineraries through a conversational front end. We compare the planner with a crisp-mean baseline under an identical optimizer in Baku, Dubai, Abu Dhabi, Tashkent, and Istanbul, with fifteen points of interest and thirty seeds per city, and we evaluate the selected itineraries on their trip carbon and their total travel time. Pooled over 150 seed-paired runs, the planner lowers trip carbon from 3.24 to 2.61 kg (19.3%, Wilcoxon p = 0.017; city-clustered bootstrap interval 6.4% to 26.7%) and travel time from 123 to 109 minutes per trip (11.5%, p = 2.8e-10; interval 5.0% to 18.2%). The travel-time reductions survive a Holm correction in Baku, Istanbul, and Abu Dhabi; the carbon reduction is significant in Baku and large but uncertain in the dispersed catalogs of Dubai and Abu Dhabi; and neither metric changes in Tashkent. Review counts are estimates of at least 3,000 for every point of interest, so rating reliability does not vary, and with a single baseline, the results establish feasibility and direction rather than a general advantage. Code, routing caches, frozen routing tables, and per-seed results are openly released.

physics.soc-ph↗

The Plan Language of a Curriculum: A Formal Model and the Complexity of Degree Planning

We model an academic curriculum as a generator of a language of feasible study plans: prerequisites are monotone Boolean formulas in conjunctive normal form, degree requirements are credit-threshold covering constraints, and a study plan is a sequence of terms bounded by a per-term credit capacity. Within this model, we settle the complexity of the two natural planning objectives, the number of terms to a degree and the total credit load, and we isolate the structural commitment responsible for each source of hardness. Time to degree is polynomial whenever the per-term capacity is unbounded, for arbitrary disjunctive prerequisites and arbitrary electives, so disjunction never contributes to its hardness, yet it becomes strongly NP-hard as soon as capacity binds, even without any prerequisite. Load is complementary: disjunction and overlapping electives are each strongly NP-hard in isolation and their complexity does not depend on capacity, while load is polynomial on the conjunctive, mandatory fragment. The two objectives therefore have disjoint sources of hardness. We show that the delay-factor component of the standard curricular-complexity metric is a polynomially computable upper bound on time to degree, exact on the conjunctive fragment and loose elsewhere by a quantity we name the disjunctive slack, and we prove that program subsumption is coNP-complete and consensus prerequisite recovery is NP-complete. Instantiating the model on a corpus of twenty-two universities, we find that 88 percent of prerequisite-bearing courses are purely conjunctive and that capacity, not prerequisite logic, is the operative constraint on time to degree. The curriculum corpus is openly available (https://doi.org/10.5281/zenodo.22334674), and the analysis and figure-generation code accompany the paper.

cs.DS↗

Measuring Curriculum Alignment across Topical Coverage, Competency, and Cognitive Depth: A Longitudinal Framework Applied to CS2013 and CS2023

Undergraduate computer science is governed by international curricular guidelines revised about once a decade, yet programs lack a reliable way to measure how completely they cover the current guideline and how coverage shifts when it changes. Existing analyses rely on topic models or manual tagging, seldom report reliability, do not benchmark the matching method, and examine topical overlap at a single point in time. We address these gaps with a staged pipeline that separates candidate generation from confirmation, applied to one accredited Bachelor of Science in Computer Science against Computer Science Curricula 2013 (CS2013) and 2023 (CS2023). Semantic retrieval proposes candidate course-to-knowledge-unit matches, a large language model confirms each against an explicit coverage rule, and an independent expert validates the resulting map. Benchmarking seven retrievers against pooled relevance judgments, we find that no automatic configuration reaches acceptable precision and recall, peaking at an F1 of 0.55 and inflating apparent coverage once tuned for recall, establishing retrieval as a candidate generator, not a measurement. Each map was validated by two independent experts and reconciled to a consensus, with substantial first-pass agreement (Cohen's kappa 0.64 and 0.69); the reported coverage is the lenient end of a sensitivity band whose strict end lies about seven points lower. Coverage of CS2023 is 48.4 percent of knowledge units, 59.4 percent by recommended hours, and about 28 percent of topics, and sixty-nine percent of covered units rest on a single course. The program articulates most competencies it covers yet meets the recommended cognitive depth far less often under CS2023 than under CS2013, a gap that survives a sensitivity analysis of the mapping, while structural gaps stay separable from artifacts of the standard's evolution. The instrument is reusable and released.

cs.AI↗

Matrix-Free Photoacoustic Image Reconstruction via Sensor-Token Self-Attention

Photoacoustic tomography (PAT) combines the optical absorption contrast of biological tissue with the spatial resolution of ultrasound, yet recovering the initial pressure distribution from sparse-view sensor measurements remains an ill-posed inverse problem. Iterative compressive-sensing solvers and unrolled deep networks both retain a dependence on the system matrix at inference, which leaves real-time clinical reconstruction computationally expensive. This paper proposes the Sensor Attention Network (SAN), a Transformer-based architecture that treats the full time series of each sensor as a token and maps raw measurements directly to the reconstructed image without invoking the system matrix at inference. For training and benchmarking, an analytical k-space H-matrix is constructed and validated against the k-Wave pseudo-spectral solver under matched geometry, achieving a mean per-sensor Pearson correlation of 0.919 +/- 0.049, with k-space apodization and Gaussian temporal damping acting synergistically to reduce the energy-normalized mismatch by 49%. Trained with a vessel-weighted loss on 488 augmented samples and evaluated on 46 held-out samples against ISTA, split-Bregman total variation (SBTV), and learned ISTA (LISTA), SAN attains the highest mean SSIM (0.522) and PSNR (22.09 dB) and the lowest NMSE (0.233). Paired t-tests and Wilcoxon signed-rank tests confirm the superiority of SAN over LISTA on PSNR, NMSE, and Pearson correlation at p < 1e-8, and over ISTA and SBTV on all fidelity metrics. By bypassing the H-matrix at inference, SAN reduces reconstruction time by at least an order of magnitude, supporting real-time PAT reconstruction.

cs.AI↗

Measuring Curriculum-Labor Market Alignment at the Scale of a Program Portfolio

A college offering several overlapping computing degrees implicitly assumes that its programs are differentiated in line with how the labor market segments computing work and that, together, they prepare graduates for that market. Testing this is difficult, because the instruments available to curriculum committees, namely advisory boards, tracer studies, and employer surveys, are slow, narrow, and hard to reproduce. We apply one uniform, taxonomy-anchored alignment analysis across all five undergraduate programs of a College of Information Technology, comparing 1,922 course learning outcomes against 103,349 competencies extracted from a unified corpus of 5,186 deduplicated job openings from four boards. Every competency is obtained by a grounded single-language-model procedure that copies it verbatim from the source and verifies it against the source, then assigns it to one of eleven ESCO-aligned domains and a Bloom cognitive level; the curricular supply is read not as a catalog but on a realized-attainment basis that respects the credit-hour and elective constraints under which a student completes a degree. The extraction is validated blind by two independent faculty raters (domain kappa 0.91, Bloom level kappa 0.86) and the ESCO matching against a human-adjudicated gold set (kappa 0.72). Four findings emerge. The content gaps are systemic rather than program-specific, concentrated in systems, software engineering, security, and web development; the shared college core satisfies only about a third of the demanded competencies; the programs are well differentiated in disciplinary content yet homogeneous in where they fall short; and the curriculum is pitched roughly a full Bloom level below the market across the portfolio, most acutely in systems. We discuss the implications for program design, curriculum governance, and the practice of curriculum analytics.

cs.CY↗

An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification

Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness. To address these limitations, this paper proposes a four-stage NLP framework that combines (i) schema-constrained prompting of a two-model frontier-LLM ensemble against a JSON Schema-enforced seven-slot competency formalism, (ii) Sentence-BERT (SBERT) alignment of the extracted records against an eleven-domain ESCO v1.2.1 controlled vocabulary, (iii) a two-tier adjudication protocol that resolves inter-model disagreements, and (iv) a verification mechanism that combines per-slot Cohen's kappa, schema conformance, and document-level completeness audits. The framework is instantiated for a critical application in higher-education quality assurance, namely curriculum-labor market alignment for the ABET-accredited BSc Computer Science program at the United Arab Emirates University. The pipeline extracts 400 competency records from the 85-course 2025-2026 study plan and aligns them, under a five-scope analysis ranging from the computing core to a probability-weighted student trajectory, with 30 job postings (483 requirement clauses) at an SBERT cosine threshold of 0.50. The extractor achieves Cohen's kappa of 0.79 on the skill slot, with 100% schema conformance and 100% document-level completeness. The alignment surfaces interpretable supply-demand gaps of 25.0% in general and transversal skills, 13.8% in algorithms and computational theory, and 12.2% in software engineering and project management, with a near-zero 1.8% gap in artificial intelligence and data science despite 38.6% supply coverage.

cs.AI↗

The Nonverbal Syntax Framework: An Evidence-Based Tiered System for Inferring Learner States from Observable Behavioral Cues

Understanding learners' cognitive and affective states underpins adaptive educational systems and effective teaching. Although research links nonverbal cues to internal states, no framework calibrates them to evidence. We present the Nonverbal Syntax Framework, drawn from a systematic review of 908 studies and 17,043 cue-state mappings (Turaev et al., 2026). The framework addresses three challenges: terminological fragmentation (behaviors described inconsistently), evidence heterogeneity (single observations to replicated findings), and state ambiguity (similar patterns indicating multiple states). Normalization consolidated 5,537 state labels into 2,010 canonical states (63.7%) and 11,521 cues into 6,434 normalized cues (44.2%) across nine behavioral channels. Dual-evidence assessment separately evaluates Component Evidence (coverage of cues and states) and Relationship Evidence (independent studies per cue-state link). 52% of "Very High" relationships rest on one paper, so separation enables calibrated rather than overconfident inference from preliminary findings. The framework's four levels comprise a Cue Vocabulary of 6,434 indicators classified as observable/instrumental; State Clusters linking 2,010 states to indicative cues; State Profiles with multimodal behavioral signatures and actionable specifications; and Discriminative Analysis distinguishing 1,215 confusable state pairs. We identify 480 actionable R1-R4 relationships (three or more independent papers), the replicated core of six decades of research, covering 35.5% of mappings across 47 key learning states and 111 distinct indicators. The remaining 91.5% (9,653 single-paper findings) form exploratory hypotheses for replication. The framework gives researchers an empirical foundation for identifying gaps, practitioners evidence-based tools for state inference, and technologists validated features for multimodal detection.

cs.AI↗

Data-Driven Framework Development for Public Space Quality Assessment

Public space quality assessment lacks systematic methodologies that integrate factors across diverse spatial typologies while maintaining context-specific relevance. Current approaches remain fragmented within disciplinary boundaries, limiting comprehensive evaluation and comparative analysis across different space types. This study develops a systematic, data-driven framework for assessing public space quality through the algorithmic integration of empirical research findings. Using a 7-phase methodology, we transform 1,207 quality factors extracted from 157 peer-reviewed studies into a validated hierarchical taxonomy spanning six public space typologies: urban spaces, open spaces, green spaces, parks and waterfronts, streets and squares, and public facilities. The methodology combines semantic analysis, cross-typology distribution analysis, and domain knowledge integration to address terminological variations and functional relationships across space types. The resulting framework organizes 1,029 unique quality factors across 14 main categories and 66 subcategories, identifying 278 universal factors applicable across all space types, 397 space-specific factors unique to particular typologies, and 124 cross-cutting factors serving multiple functions. Framework validation demonstrates systematic consistency in factor organization and theoretical alignment with established research on public spaces. This research provides a systematic methodology for transforming empirical public space research into practical assessment frameworks, supporting evidence-based policy development, design quality evaluation, and comparative analysis across diverse urban contexts.

cs.CY↗

Multidimensional Assessment of Public Space Quality: A Comprehensive Framework Across Urban Space Typologies

This study presents a comprehensive framework for evaluating the quality of public spaces across various urban typologies. Through a systematic review of 159 research studies, we identify universal quality factors that transcend spatial types as well as specialized factors unique to specific public environments. Our findings establish accessibility (73.6%), safety/security (58.4%), and comfort (52.8%) as foundational requirements across all public space types, while revealing distinct quality priorities for different typologies: open spaces emphasize comfort (70%), parks prioritize activities (60%), green spaces focus on aesthetics and natural elements (70% and 60%), and public facilities uniquely emphasize indoor environment quality (41.7%). The research reveals a hierarchical relationship between factors, where accessibility enables other qualities, safety serves as a prerequisite for utilization, and comfort determines engagement quality. We identify critical limitations in current assessment approaches, including artificial intelligence studies focused on easily quantifiable factors, domain-specific research confined within disciplinary boundaries, and overreliance on subjective perceptions without objective measures. This research provides a foundation for integrated approaches to public space assessment that acknowledge the complexity of public urban environments while addressing both universal human needs and context-specific requirements. The findings support urban planners, designers, and policymakers in developing balanced assessment methodologies that ensure both comparability across spaces and sensitivity to local conditions, ultimately contributing to the creation of high-quality public spaces that enhance urban life and community wellbeing.

physics.soc-ph↗