Big Data and Cognitive Computing

Sequential resource allocation has long been a central problem in operations research, yet ongoing technological developments, particularly in cloud and high-performance computing and in multi-channel marketing, are giving rise to new structural constraints that classical methods were not designed to handle. Semi-Bandit Feedback (SBF) has emerged as the dominant framework for these modern settings. This paper introduces an alternative that recasts the allocation problem within the Bayesian Optimization (BO) paradigm. All three proposed BO algorithms consistently outperform SBF, with BORAw_SE showing a particularly clear advantage under time-varying budget settings, while CBO achieves comparable rewards under constant budget conditions. The core methodological contribution is a reformulation in which each candidate allocation is represented as a discrete probability distribution over the available options, making the probability simplex the natural search domain. Grounding the search in this space calls for a geometry that respects the structure of distributions: we adopt the optimal transport (Wasserstein) distance, which allows both the Gaussian process surrogate and the acquisition function to be extended as functionals over the simplex. A further practical advantage of the proposed method is its applicability to problem instances where SBF cannot be used without modification. The approach is evaluated on two case studies: the benchmark computing-resource allocation scenario from the original SBF paper, and a budget allocation problem across marketing channels. Full article

Compressed sensing (CS) enables signal reconstruction from fewer measurements when the signal is sparse in a transform domain. However, executing ${\ell }_1$-regularized recovery on MCU-class hardware is challenging due to limited compute resources and the cost of repeated forward and adjoint operator evaluations. This paper presents a band-limited proximal variant of FISTA that enforces known spectral support during thresholding, restricting the effective optimization domain without changing the measurement model. We implement a complete CS reconstruction pipeline on an STM32F407 (Cortex-M4) using CMSIS-DSP FFT/IFFT kernels and evaluate it using ECG waveforms acquired through an AD8232 front end as benchmark signals. With $M=340$ measurements (33% of uniform sampling), the embedded implementation achieves a PRDN of 24.38%, closely matching MATLAB references (CVX: 22.64%, FISTA: 22.39%) under identical hyperparameters. Cycle-accurate profiling shows that FFT/IFFT-based forward/adjoint operators dominate the per-iteration runtime. Under a 60 Hz band-limited setting, the required iterations are reduced from 30 to 16 with an acceptable PRDN, demonstrating a practical trade-off between reconstruction accuracy and computational cost on MCU-class devices. Full article

Traditional acupoint localization methods rely heavily on manual operation, resulting in high subjectivity and limited accuracy. To improve the precision and stability of acupoint detection, this study integrates machine vision technology with in situ projection to achieve automated recognition and real-time visualization of human acupoints. First, an automatic calibration method based on image processing is proposed for back acupoints. Spinal features are extracted from the blue channel, enhanced using adaptive histogram equalization, and processed through region of interest extraction, minimum-threshold binarization, and morphological operations. Key spinal curve points are then fitted using Bézier functions. Canny edge detection is used to extract the human silhouette, locate the acromion, and derive the pixel scale of the “cun” measurement, enabling coordinate computation for 141 back acupoints. In the deep learning component, an improved YOLOv8-Pose model is developed for acupoint localization. Unlike existing methods that use local attention or the original Object Keypoint Similarity (OKS) loss, we introduce two innovations: a non-local attention module for global dependency modeling, and a novel Efficient Object Keypoint Similarity (EOKS) loss function that incorporates geometric constraints—namely, width, height, and center distance—in addition to Euclidean distance. A non-local attention mechanism is incorporated into the backbone to enhance global feature extraction, and the EOKS loss function is designed to improve spatiogeometric regression accuracy. An inference mechanism is further introduced to derive the remaining acupoints from 49 detected keypoints; experiments demonstrate that the improved model achieves 95.0% detection accuracy, outperforming the baseline by 2.62%, with an inference time of 14.5 ms. Finally, an in situ projection platform is constructed, combining camera calibration, four-point proportional scaling, and an OpenCV 4.5.4-based interactive interface. The system supports real-time translation, rotation, and scaling, enabling accurate projection of detected acupoints onto the human body. Full article

Artificial intelligence (AI) is increasingly applied to healthcare decision-making; however, many persistent patient safety risks arise from sociotechnical conditions such as communication breakdowns, coordination failures, and organisational culture rather than diagnostic or decision error alone. While simulation can engage these dimensions of care, AI-supported simulation remains limited by heterogeneity and a lack of explicit conceptual structure. This study presents a narrative and conceptual review of the healthcare simulation and AI literature to identify structural barriers to coherent AI reasoning about simulation. Drawing on this synthesis, we introduce Transformative Simulation (TfS) as an intentional framework that can be formalised as an ontology for AI-supported simulation focused on cultural and systems-level change. TfS structures simulation through explicit Simulation-Based Intentions, an aligned design–delivery–data–debrief process, and foundational considerations of purpose, perspective, power, preparation, and possibility. Framed in this way, TfS enables AI systems to interpret simulation artefacts in relation to declared intent, sociotechnical context, and ethical boundaries. We further describe an Intentionality–Simulation–Intelligence triad and a continuous learning loop that align human values, simulation structure, and AI reasoning. The findings of this review suggest that an important challenge in applying AI to healthcare simulation may be ontological as well as technical, and that explicit representation of intention and context is necessary to support coherent, context-sensitive, and system-aligned AI reasoning in healthcare. Full article

Words occurring in similar contexts have been observed to have similar meanings. A natural and established method within computational linguistics implements this observation by representing words as vectors with dimensions determined by words that are witnessed in fixed positions in relation to the target word. We generalize this context vector approach to part-of-speech (POS) sequences appropriate to word sequences. As with words, the context of a POS tag (considering the POS tags occurring before and after any target tag) reflects its syntactic constraints and may approximate the "meaning'' of the target tag, from a distributional perspective. We use the 111-million-word British National Corpus (BNC) and the sequence of POS labels lifted from those texts to calculate POS context vectors. We observed significant agreement between the clusters of POS context vectors and the supercategories of corresponding POS tags, and examined potential categorization of the POS categories that emerged from the vector clusters. We also found that though vector measures partially align with the predictions of generativist linguistic theories, the approach suggests a more complex relation between syntactic categories. We conclude that a mutual-information-based approach better approximates the distributional "meaning'' of syntactic categories than the conditional probability distribution of POS symbols. Full article

This paper extends a previous classification study by examining clustering methods on the same synthetic datasets and comparing their behavior with the previously obtained classification results. This study investigates the performance of selected time series clustering methods under controlled changes in noise level and class complexity. Six clustering methods representing distance-based, feature-based, and deep learning approaches were evaluated on 82 balanced synthetic datasets. The datasets contained from two to six classes, different levels of additive Gaussian noise, 200 time series per dataset, and 1000 observations per time series. The analysis focused on clustering quality, comparative behavior with classification models, and computational cost in terms of training time and peak memory usage. Clustering quality was assessed mainly using Adjusted Rand Index and V-measure, while accuracy after Hungarian label matching was used as an auxiliary measure for comparison with classification models. The results show that distance-based methods, and particularly TimeSeriesKMedoids, achieved the most robust and consistent clustering performance across the considered settings. Clustering quality decreased with both the number of classes and the noise level, but the effect of noise was clearly stronger. Feature-based and deep learning-based clustering methods were generally more sensitive to noise, while deep models were also associated with substantially higher computational cost. In terms of memory usage, classical clustering methods remained below 50 MiB, whereas deep learning-based clustering methods required substantially more memory. This study further shows that accuracy computed after Hungarian label matching may provide an overly optimistic view of clustering quality. Accuracy after Hungarian label matching is reported only as an auxiliary metric, while the main interpretation of clustering quality is based on structure-sensitive measures such as Adjusted Rand Index and V-measure. Overall, the findings highlight the importance of robust distance-based approaches and of using structure-sensitive evaluation measures when analyzing time series clustering. Full article

Forecasting cryptocurrency prices remains difficult because market dynamics are highly volatile, non-stationary, and regime-dependent. This study investigates whether combining a spiking-inspired recurrent architecture with the Grey Wolf Optimizer (GWO) can improve one-step-ahead Bitcoin forecasting within a controlled model family. We compare four configurations, LSTM, SLSTM, GWO-LSTM, and GWO-SLSTM, on 4039 daily BTC–USD closing prices from 17 September 2014 to 9 October 2025 using Min–Max normalization, strict chronological splitting, windowed regime-based robustness analysis across three distinct market regimes, and repeated-run testing. The proposed SLSTM replaces the conventional hidden-state recurrence with leaky integrate-and-fire-inspired synaptic, membrane, and adaptive-threshold dynamics, functioning as a spiking-inspired recurrent model with thresholded event gating (reset = `none’, learnable threshold). On the primary hold-out split, GWO-SLSTM achieved a test RMSE of 1840.97 and a test MAPE of 1.76%, compared with 2217.24 and 2.46% for GWO-LSTM, 3501.48 and 3.86% for SLSTM, and 4030.10 and 4.40% for LSTM. Both GWO-optimized models exhibited substantial improvements over their non-optimized counterparts, while the SLSTM baseline also outperformed the plain LSTM, indicating gains from both spiking-inspired recurrence and evolutionary hyperparameter optimization. Both optimized models exhibited near-zero bias (PBIAS 0.11% for GWO-LSTM and 0.36% for GWO-SLSTM). Within the present implementation, GWO-SLSTM also trained faster than GWO-LSTM (39.71 s vs. 137.28 s), although this runtime difference should be interpreted as setup-specific because the model families were implemented in different frameworks and stopped after different numbers of epochs. Overall, within the expanded univariate BTC–USD setting, the results support GWO-SLSTM as a strong within-family candidate for one-step-ahead forecasting under non-stationary conditions. Full article

The increasing complexity and volume of technical documentation, including requirements specifications, patents, and engineering reports, create significant challenges for manual analysis and knowledge extraction. This paper includes a systematic review of methods for semantic content analysis of technical documents, with a particular focus on Natural Language Processing (NLP) techniques and Transformer-based models. The study formalizes the task of structured information extraction and provides a mathematical description of Named Entity Recognition (NER) as a core subtask. A practical case study demonstrates an end-to-end NER pipeline for Russian-language technical requirements, leveraging ruRoberta-large via spaCy-transformers. The results highlight both the potential and limitations of current approaches, emphasizing the critical role of annotation consistency and document format normalization. This work contributes to the development of intelligent systems for engineering documentation analysis and outlines key directions for future research. Full article

Although many researchers use pre-trained models to better solve downstream tasks, further exploration of more effective pre-training methods remains necessary, especially for multi-modal pre-training where high-quality training data is more difficult to obtain. This work aims to improve the knowledge-learning performance in multi-modal pre-training. Some researchers focus on injecting entity knowledge into language pre-trained models based on masked entity model (MEM) training, which masks entities randomly and lets the model recover. These methods cannot guarantee good performance due to the lack of consideration of which entities are more valuable for learning. Moreover, in multi-modal training data, some entities may be unrelated to visual content. In this work, for the vision-language pre-trained model, we propose a Masked Entity Model pre-training method based on Active learning (ActiveMEM). It is designed to actively mask important and informative entities—those that are both informative and uncertain—for the model to recover, thereby encouraging it to extract more valuable knowledge from the data. The proposed method is evaluated using three pre-training datasets and four downstream datasets, and the experimental results demonstrate the effectiveness of our method. Full article

As the environmental sustainability of blockchain technology becomes a focal point of public and academic debate, understanding how technically engaged communities frame this issue is increasingly important. This study examines 3000 long-form comments from a highly active sustainability-focused Bitcointalk thread to analyze sentiment patterns, recurring arguments, and the linguistic cues associated with community responses to environmental criticism. Using Natural Language Processing (NLP) methods, we apply Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis to classify the discourse, n-gram extraction to identify dominant thematic expressions, and a Random Forest model combined with SHapley Additive exPlanations (SHAP) to interpret the lexical features most strongly associated with sentiment polarity. The results show a strongly positive and internally consistent discourse structure: 87.63% of comments are classified as positive, while negative and neutral comments are comparatively rare. The dominant themes emphasize energy consumption as a necessary trade-off for network security, while external criticism is frequently reframed or rejected. Explanatory modeling further indicates that negative sentiment is primarily driven by terms associated with climate risk, damage, and reputational concerns when users respond to criticism. Rather than claiming to capture the cryptocurrency ecosystem as a whole, this study presents a localized case study of one Bitcointalk mega-thread and describes it as a highly homogeneous narrative space shaped by recurrent rebuttal and rhetorical reinforcement. The findings offer a focused contribution to understanding how insider communities construct sustainability narratives around blockchain energy use, while also highlighting the need for broader comparative and network-structural research in future work. Full article

Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a research gap in unified semantic transformation, purpose-oriented judgment, bounded imperfection handling, and semantic self-protection. To address this gap, this paper proposes a DIKWP+BUG semantic–cognitive reference architecture for artificial-consciousness-oriented computing, without claiming definitive artificial consciousness. The architecture represents cognition through the Data–Information–Knowledge–Wisdom–Purpose (DIKWP) model and uses BUG theory to model bounded approximation, incomplete evidence, and confidence miscalibration in cross-dimensional reasoning. The model is mapped to an Artificial Consciousness Processing Unit (ACPU) reference substrate, an Artificial Consciousness Operating System (ACOS), a DIKWP semantic communication subsystem, and a concept–semantic fused security subsystem. The components are implemented through runtime emulation and evaluated in smart-city governance, autonomous-driving, and medical-triage simulations. Compared with selected baselines, the prototype increased cognitive throughput from $4.5\mathrm{k}$ to $7.8\mathrm{k}$ logged events, reduced perception–action latency from $340\mathrm{ms}$ to $120\mathrm{ms}$, reduced CPU utilization from $95\%$ to $68\%$, lowered smart-city congestion duration by $30\%$, improved emergency response time by approximately $40\%$, achieved 0 collisions versus approximately $2/10$ baseline IoV runs, and improved medical-triage accuracy from $85\%$ to $92\%$. These online-runtime results provide initial feasibility evidence under controlled simulation conditions; they do not include offline model-preparation costs and therefore should not be interpreted as end-to-end lifecycle speedups. Matched-compute ablation, statistical benchmarking, hardware prototyping, and real-world validation remain future work. Full article

Most existing dialogue understanding methods rely on flat classification paradigms, failing to capture hierarchical semantic structures and cross-level dependencies. To address this limitation, we reformulate dialogue understanding as a hierarchical semantic path inference problem, where prediction is performed over a constrained path space rather than independent label spaces. We propose HiCoPro, a graph-conditioned structured inference framework for modeling multi-level dialogue semantics. The framework consists of the following: (i) a Graph-Conditioned Label Space (GCLS) that encodes hierarchical dependencies into label embeddings via graph propagation; (ii) a compatibility-based logit fusion mechanism that jointly scores semantic relevance and structural consistency; and (iii) a constraint-aware decoding strategy that enforces hard parent–child dependencies during inference. By integrating semantic representations with graph-conditioned label structures via a bilinear compatibility function and learnable logit-level fusion, the model jointly captures semantic relevance and structural consistency. To support this task, we construct PrefDial, a general-domain hierarchical dialogue dataset with systematic three-level annotations, serving as a benchmark for structured dialogue understanding. Experimental results demonstrate that HiCoPro achieves superior Macro F1, Exact Match, and Hierarchical Consistency on PrefDial, while remaining competitive on multiple public benchmarks. Further analysis highlights the effectiveness of graph-conditioned modeling in balancing semantic discrimination, hierarchical consistency, and robustness. Full article

Accurate obesity risk classification is often hindered by the asymmetric and heteroscedastic nature of health data, where traditional mean-based machine learning models fail to capture critical distribution tails. This study addresses this gap by proposing a robust Expectile Random Forest (ERF) model, a novel ensemble architecture that integrates an expectile learning framework via the Asymmetric Least Squares (ALS) loss function for seven-level (multilevel) classification. Utilizing a dataset of 2111 empirical records, the sensitivity analysis identifies $\tau =0.7$ as the optimal configuration, achieving an overall Accuracy of 94.6 ± 0.7% and a Macro F1-Score of 94.5 ± 0.7%. This performance represents a significant quantitative improvement over state-of-the-art benchmarks, outperforming XGBoost by 1.8% and standard Random Forest by 3.9%. Feature importance analysis identifies body weight, age, and sedentary factors as primary predictors, while the ERF model demonstrates exceptional ordinal consistency and robustness against clinical outliers. These findings provide a superior methodological framework for developing precise medical decision support systems, shifting the paradigm from central-tendency predictions to tail-sensitive health risk mapping. Full article

The integration of domain-specific conversational assistants into administrative and payroll information systems has the potential to improve operational efficiency and decision support in regulated enterprise environments. However, empirical evidence regarding their behavior, perceived usefulness, and operational performance in real-world administrative workflows remains limited. This paper introduces the design and empirical evaluation of a document-grounded conversational assistant for payroll management, implemented using a Retrieval-Augmented Generation (RAG) architecture enhanced with an evidence-grounded response validation layer designed to constrain responses to retrieved payroll documentation, enforce source traceability, and reduce unsupported content generation in administrative workflows. The primary evaluation dataset consisted of 200 controlled interactions generated by 20 experienced payroll administrators who completed predefined payroll-related tasks. In addition, an auxiliary dataset of approximately 800 supplementary interactions, produced during iterative expert testing and operational system validation activities, contributed to the broader operational interaction corpus used for exploratory robustness analysis and deployment-oriented system evaluation. The collected interaction logs were analyzed as an anonymized operational interaction corpus combining controlled participant sessions and supplementary expert-assisted testing activities. Consequently, the reported statistical findings should be interpreted primarily as an anonymized operational interaction dataset rather than strictly controlled inferential evidence. Quantitative assessment included response latency, user-perceived accuracy and satisfaction, and automatic evaluation metrics (BLEU, ROUGE-L, and semantic similarity), complemented by qualitative feedback collected through structured user sessions. The results indicate moderate levels of user-perceived accuracy and satisfaction. Semantic similarity demonstrates strong correlations with human evaluations of response quality, showing stronger associations than traditional n-gram metrics, while response latency shows only weak association with satisfaction within acceptable operational thresholds. The findings provide preliminary evidence that evidence-grounded validation mechanisms may support response traceability and reduction in unsupported content generation in administrative environments. This research contributes a domain-specific RAG-based conversational assistant incorporating evidence-grounded response validation mechanisms, together with an exploratory operational evaluation framework for assessing conversational AI behavior in compliance-sensitive enterprise environments. Full article

Recent advances in multi-modal large language models have enabled systems to jointly process text, images, and audio. However, these developments have primarily benefited high-resource languages, leaving many low-resource communities underserved. In response, we introduce Qolda-AVL, a compact five-billion-parameter audio–vision–language model tailored for Kazakh. Qolda-AVL extends our previous Qolda vision–language model by adding a dedicated audio perception branch while maintaining strong visual and linguistic performance. Built on the Qwen3-VL-Thinking backbone, we incorporate Audio DeepStack, which transfers features from three intermediate Whisper encoder layers into the first three layers of the language model using dedicated projections and residual connections. The model is trained through a four-stage pipeline: adapting the Whisper encoder and language model to Kazakh, aligning the new audio branch to the language backbone, and jointly fine-tuning all modules on chain-of-thought reasoning tasks across audio, image, and text. All audio, vision, and language capabilities are evaluated using the model’s native reasoning mode, and a chain-of-thought trace is generated before each final answer during the performance assessment. To facilitate further research, we open-source the model along with the adapted Kazakh versions of four audio benchmarks, covering spoken attribute reasoning, spoken mathematical question answering, and audio captioning with question answering. Full article

Foreign-exchange decisions rest on hierarchically organized evidence whose latent structure is inadequately captured by Euclidean representations. Reinforcement-learning agents trained on flat embeddings inherit stability guarantees that do not transfer to the manifold supporting the latent state. We address both limitations through a hybrid architecture in which a schema-constrained structured chain-of-thought is embedded into a Poincaré ball, transported to a qubit register via angle encoding, and processed by an L-layer hardware-efficient variational ansatz on a state-vector backend. The circuit exposes two read-outs to the policy, namely, a scalar Pauli-Z observable and a projected quantum kernel inducing a fidelity-based similarity over magnet-price attractors, the latter identified via kernel-weighted recurrence density and finite-time Lyapunov statistics. The Lipschitz constraint on the action-value function is lifted from the hyperbolic geodesic distance to a joint metric on ${\mathbb{B}}_{\kappa }^n\times \mathbb{P}\left(\mathcal{H}\right)$. A stability theorem yields an explicit bound depending on the read-out operator norm, on the depth–width product of the ansatz, and on the curvature–Hilbert balance. The pipeline is evaluated on nine major FX crosses over a 2015–2025 out-of-sample window, with rolling-origin walk-forward retraining and broker-published transaction costs. The system attains $2.55\%$ pair-averaged non-compounded monthly P&L and $8.83\%$ maximum drawdown, with Sharpe $1.78$, Calmar $3.43$, and Probabilistic Sharpe Ratio exceeding $0.95$ on every cross. The gain remains significant under a deflated-Sharpe-ratio test with $N_{\mathrm{trials}}=42$ correction. Block-wise ablations exhibit strictly monotone degradation: removing the projected kernel costs $-4.15$ p.p. on annualized P&L, the joint Lipschitz penalty $-6.42$ p.p., the attractor module $-7.64$ p.p., and the hyperbolic embedding $-8.40$ p.p. The quantum block thereby instantiates a structurally non-classical, geometry-aware regularizer identifiable through ablation rather than asymptotically advantageous. Full article

High-frequency monitoring of manufacturing agility and Environmental, Social, and Governance (ESG) responsiveness is increasingly required in data-rich operations, yet many practical indices remain low-frequency, weakly decomposable, or difficult to interpret in weekly control settings. This study presents a single-enterprise methodological demonstration of a weekly fuzzy monitoring model with dual benchmarks for explainable operational control. The empirical panel covers 156 weeks from 2023 to 2025 across three plants, five product families, and 27 KPIs grouped into Operational Agility, Sustainable Responsiveness, and Socio-Market Adaptability. KPI and benchmark weights were elicited through a two-round Delphi process with 24 experts. The model combines fixed target-centered B1 compliance thresholds with percentile-calibrated B2 thresholds for direction-adjusted week-to-week adaptation. In the calibrated specification, the overall index mean is 0.618 with a range of 0.489 to 0.741, while the mean B1 and B2 values are 0.619 and 0.617. Matched-level validation at the plant–product–week level (N = 2340) shows a positive association with EBIT (Pearson r = 0.222, 95% CI [0.200, 0.245]), and time-safe calibration checks preserve the substantive interpretation of the index. The results support the model as an explainable, human-in-the-loop instrument for within-case weekly monitoring and diagnosis rather than as a broadly validated predictive model. Full article

Discharge notes contain complex clinical language that patients often struggle to understand. We investigate the impact of automatic highlighting and multi-step prompting on discharge note simplification using in-context learning with large language models. Highlighting is performed using a domain-specific Cardiology Interface Terminology. We used 28 MIMIC-III discharge notes paired with physician-authored summaries and evaluated four configurations (U1, U2, H1, H2): one-step and two-step simplification applied to either highlighted or unhighlighted notes. In one-step simplification (U1 or H1), the model generated a structured, patient-friendly summary directly from the note. In two-step simplification (U2 or H2), the model first generated a structured summary and then simplified it to a 6th-grade reading level. Manual evaluation showed that completeness improved from U1 to H2 (U1 < H1 < U2 < H2), with H2 achieving the highest completeness (92.45%) and the fewest errors (1) compared to U1 (79.05%, 5 errors). Improvements were statistically significant (p < 0.001), except between H1 and U1. Readability improves across all methods (e.g., FKGL reduced from 11 to 7.7 in U2). LLM-based evaluation using both ChatGPT and Gemini shows strong agreement (ρ = 0.88) and favors H2. This pilot study shows combining highlighting with two-step prompting yields more patient-comprehensible summaries. Full article

Upstream oil and gas operations generate large volumes of multivariate data from seismic surveys, well logs, production sensor networks, and reservoir simulation models. Advances in machine learning, artificial intelligence, and other Industry 4.0 technologies are increasingly enabling data-driven applications across exploration, reservoir characterization, drilling optimization, and production forecasting. However, publicly available upstream datasets vary substantially in data modality, labeling strategy, machine learning compatibility, and benchmark maturity. To date, no standardized framework or taxonomy exists to guide dataset selection, benchmark design, or cross-study comparison. This study addresses that gap by proposing a structured, machine learning-centric taxonomy that organizes upstream datasets according to properties that are directly relevant to machine learning requirements. The proposed taxonomy provides a shared reference framework to support consistent dataset description, informed selection, and reproducible benchmarking in upstream machine learning research and applications. Full article