Data gaps in Computer Science

Representative open questions

Showing 30 of 170 — one per source paper, highest-quality first.

• AN ITERATIVE GLMM–XGBOOST ALGORITHM WITH GROUP-AWARE CONDITIONAL PERMUTATION IMPORTANCE FOR EXPLAINING MULTILEVEL ITEM RESPONSE DATA (2026) · doi

  Simulation Study 1 evaluates parameter recovery and prediction accuracy across ICC levels (0.00 to 0.25) and sample sizes, but does not test scenarios with extreme ICC values (>0.50) or highly imbalanced cluster sizes, which are common in educational and longitudinal item response studies.

• William & Mary: Comprehensive AI Governance & Activity Inventory (2026) · doi

  No documentation exists regarding how the AI tool review process (which approved ChatGPT Edu, Microsoft Copilot, Google Gemini, and Google Workspace AI tools) evaluates or tracks the security and accessibility outcomes post-deployment across these enterprise platforms in production use.

• Can deep learning-based segmentation and classification improve the detection of renal cortical abnormalities? (2026) · doi

  The study acknowledges that DMSA scintigraphy underestimates scarring in 30% of kidneys, particularly polar scars which are especially susceptible to misinterpretation. The deep learning-based segmentation and classification models were trained on DMSA images with this inherent underestimation bias, but the paper does not investigate how this label noise affects model performance or propose methods to correct for DMSA underestimation in polar scar detection.

• Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry (2026) · doi

  Class imbalance remains a fundamental constraint for Soot (0.8% support) and biological particles (Bacteria, Snomax, Agar, Hazelnut), where limited training data prevents model development despite their high atmospheric significance. Targeted approaches must be developed to overcome the scarcity of labeled single-particle mass spectra for these underrepresented but scientifically critical aerosol types.

• Quantum Information Framework for Neural Network Generalization: A Comprehensive Experimental Analysis (2026) · doi

  The modular arithmetic dataset generation supports only three operations (x+y, x²+y, x³+xy) with a fixed modulus of 97; systematic investigation of how operation complexity, modulus size, and arithmetic structure affect quantum information metrics during neural network training is absent.

• AI in Cybersecurity: A Systematic Review and Conceptual Audit Model (2026) · doi

  The paper identifies that conventional cybersecurity auditing uses binary compliance checks (control 'implemented' or 'not implemented') but does not quantify how frequently real-world audits fail to detect critical vulnerabilities despite passing binary checks, nor does it provide empirical thresholds for when risk-calibrated auditing should override binary compliance decisions.

• Research on a strongly generalizable fault diagnosis method based on adversarial transfer learning (2026) · doi

  Misclassifications in the HDAL model primarily occurred in Conditions 2, 3, and 12 (Figures 11-12), with Condition 12 showing the highest error rate attributed to data quality issues. However, the paper does not specify what data quality metrics were used to identify this problem or propose techniques to mitigate data quality issues in adversarial transfer learning-based fault diagnosis for nuclear applications.

• Machine-learning-based reconstruction of Ming-dynasty defensive corridors in Yuxian (2026) · doi

  The study identifies that highly suitable defense corridor areas overlap with high-value zones of kernel density and visibility density, but does not quantify the magnitude of spatial overlap, provide statistical correlation coefficients, or test whether kernel density bandwidth selection and visibility raster resolution significantly affect the overlap assessment.

• LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer (2026) · doi

  TransFuzz's bug transfer mechanism relies on API similarity matching to select target APIs for test migration, but the paper lacks analysis of which bug categories transfer successfully across API boundaries versus those that fail (code migration failures). Characterizing the transferability landscape of different silent bug types in deep learning libraries would enable more targeted bug transfer strategies.

• From unstructured text to structured reasoning: a hybrid knowledge graph for Indonesian sentencing analysis (2026) · doi

  The annotation dataset comprised only 200 documents from seven legal professionals; the minimum corpus size and annotator count needed to achieve stable inter-annotator agreement (κ = 0.83) for knowledge graph construction in Indonesian sentencing analysis, and whether smaller specialized subdomains require different dataset sizes, is not investigated.

• Enhancing Breast Cancer Diagnosis through Machine Learning: A Robust Approach for Early Detection (2026) · doi

  The current breast cancer diagnosis system relies on clinical and demographic tabular data. Integration of multi-modal data sources including mammographic images, histopathological features, and genetic data to improve prediction performance has not been implemented or evaluated.

• GaussianSeal: Rooting Adaptive Watermarks for 3D Gaussian Generation Model (2026) · doi

  While the paper compares GaussianSeal against GaussianMarker and post-generation methods like 3DGS+HiDDeN on specific datasets (Chair, Lego, Hotdog, Mic), comprehensive evaluation across diverse 3D object categories with varying geometric complexity, texture density, and scale properties is not provided. Generalization of watermark capacity and robustness to complex real-world 3D scenes remains unexplored.

• Scour depth prediction using machine learning and explainable AI: assessment of bridge vulnerability (2026) · doi

  The study compared Gradient Boosting, XGBoost, CatBoost, Random Forest, and ANN-based models on a single scour dataset; cross-validation across multiple independent bridge scour datasets with different geological, hydrodynamic, and pier geometry characteristics is needed to confirm model generalizability for practical bridge vulnerability assessment.

• A Motion-Based Compression and Tracking System for Video Camera Trap-Based Insect Behaviour Studies (2026) · doi

  The proposed motion-based compression system was evaluated on four datasets (Ratnayake et al. 2020, Voort der van Driessche et al., Navid et al., and Nest Monitoring), but systematic evaluation across camera trap deployments with varying environmental conditions (wind patterns, illumination changes, vegetation density) and different insect taxa beyond Honey bees, Syrphidae, Lepidoptera, and Vespidae has not been conducted. Dataset diversity limitations prevent generalization of compression efficiency and behavioral detection accuracy claims.

• A general framework for Gaussian Splatting-based human-centric volumetric videos (2026) · doi

  Feedforward generative networks for 3D Gaussian Splatting that directly predict per-frame dynamic Gaussian attributes (position, rotation, scale, color) from multi-view video remain underdeveloped, with current attempts still struggling to match optimization-based quality due to unresolved challenges in constructing large-scale high-quality dynamic datasets and improving network generalization ability.

• UNLOCKING SECURE SEARCH: KEY-AGGREGATE TECHNIQUES FOR ENCRYPTED CLOUD DATA RETRIEVAL (2026) · doi

  The experimental evaluation lacks testing on realistic large-scale cloud datasets with millions of documents and dynamic insertion/deletion operations; scalability of the key-aggregate mechanism and optimized indexing to production-scale encrypted cloud environments remains unvalidated.

• Criminal Face Sketch Recognition and Construction (2026) · doi

  The drag-and-drop sketch construction interface is described qualitatively as requiring no artistic expertise, but no user study data, error rates for untrained users, or comparison of sketch quality produced by forensic artists versus novice users is provided to validate usability claims.

• Smart Prediction of Weather-Induced Flight Delays Applying Deep Learning (2026) · doi

  The feature set is limited to meteorological variables (visibility, rain, wind speed) and does not incorporate air traffic control data or historical delay trends. Enrichment with ATC congestion metrics, runway utilization, and cumulative delay history would require evaluation on extended datasets to improve deep learning model performance.

• ENHANCING IOT SECURITY USING LIGHTWEIGHT BLOCKCHAIN FOR DATA INTEGRITY AND TRACEABILITY (2026) · doi

  The edge node validation process and its latency characteristics were not empirically characterized across different edge computing hardware configurations (e.g., ARM processors, Raspberry Pi variants, industrial gateways). The paper lacks specification of which edge devices and processing architectures were used in the experimental evaluation.

• Deep Learning Based Fish Species and Freshness Detection Using Convolutional Neural Networks (2026) · doi

  The dataset composition and size are not specified in the paper; expanding the training dataset to include multiple fish species beyond those currently represented, with images captured under diverse environmental conditions (temperature variations, humidity levels, different lighting), is needed to improve the generalization capability of the deep learning model.

• Cleansera: A Context-Aware, Algorithm-Centric Data Cleaning System with RAG-Enhanced Intelligence (2026) · doi

  The paper states future work will evaluate Cleansera's performance and empirical efficacy across industry datasets, but does not specify which industry types (healthcare, finance, e-commerce, manufacturing), dataset sizes, data quality profiles, or cleaning rule complexities will be tested. Comparative performance evaluation against existing data cleaning systems on standardized benchmarks is not outlined.

• Artificial Intelligence and Multi-Omics for Anticancer Drug Development and Repurposing (2026) · doi

  Data heterogeneity across clinical datasets (imaging, genomic profiles, morphological features) lacks standardized integration protocols for multi-omics drug discovery pipelines. The disparity between high-volume data generation and capacity to normalize and integrate these diverse modalities for AI-driven drug repurposing remains unresolved, particularly for proprietary clinical response datasets.

• Unified URL and QR Based Phishing Detection Framework (2026) · doi

  The paper states datasets were balanced to minimize bias, but does not specify the class distribution (ratio of legitimate to phishing URLs/QR codes), sampling strategy, or whether the balanced dataset reflects real-world phishing prevalence, which affects practical false positive rate generalization.

• REGİONUN TURİZM CƏLBEDİCİLİYİNİN ARTIRILMASINDA BEYNƏLXALQ TƏCRÜBƏNİN TƏTBİQİ VƏ İNNOVATİV YANAŞMALAR (2026) · doi

  The paper advocates for big data analytics and artificial intelligence to forecast tourist flows and optimize resource allocation in regional tourism systems, but does not specify data requirements, model training datasets, algorithmic approaches, or how prediction accuracy should be validated in Azerbaijan's context where historical tourism data from reconstructed regions is sparse or unavailable.

• Microscopic Image-Based TB Detection Using an Enhanced Bi-LSTM Model Optimized by Firefly Algorithm (2026) · doi

  The proposed Bi-LSTM + Firefly Algorithm model was evaluated on only 4,200 microscopic TB images from sputum smear samples. Validation across larger, multi-center datasets with diverse staining protocols, microscope types, and geographic populations is needed to establish generalization for deployment in low-resource clinical environments.

• Real-Time Bank Transaction Fraud Detection Using Kafka and Machine Learning (2026) · doi

  The system currently processes game log data; integration with multi-source data including customer device information, geolocation data, and social network analysis for contextual fraud detection has been proposed but not experimentally validated for detecting subtle anomalies undetectable from transaction data alone.

• A Comparative Analysis of Machine Learning and Deep Learning Approaches to Enhanced Fake News Detection (2026) · doi

  The study mentions the need for larger and more diverse datasets for fake news detection but does not specify dataset characteristics such as multilingual coverage, temporal distribution of fake news trends, or domain-specific fake news categories (e.g., political, health, financial) that would improve robustness of machine learning and deep learning models.

• An AI Powered Passive Reconnaissance Kit To Identifies Potential Vulnerabilities By Correlating Open Source Intelligence (OSINT) (2026) · doi

  The passive reconnaissance kit dynamically generates its dataset at runtime from observed network and OS metadata, but the paper provides no analysis of dataset completeness, temporal consistency, or false negatives when services are intentionally hidden or obscured. Testing against evasion techniques (stealth port configurations, service fingerprint obfuscation) is not documented.

• A Machine Learning-Based Sentiment Analysis Framework for Twitter Tweets Using Natural Language Processing (2026) · doi

  The sentiment analysis system is currently trained and validated only on English-language Twitter tweets; the paper identifies extending multilingual support to other languages as a concrete gap, requiring adaptation of the NLP preprocessing pipeline and VADER sentiment analyzer for non-English social media discourse in mental health contexts.

• Maternal Health Risk Prediction using Machine Learning (2026) · doi

  The paper mentions evaluating large amounts of data and developing a hybrid algorithm or new algorithm for maternal health risk prediction, but does not specify the dataset size, data sources, or geographic populations to be included. The current evaluation lacks clarity on whether the model will be validated across different maternal health datasets, demographic groups, or healthcare systems.