Evaluating the effectiveness of class-level LLM-generated test suites in Python
研究通过不同提示策略和模型选择评估LLM生成的Python类级测试套件的有效性，使用执行结果、覆盖率、变异测试得分和结构质量指标进行综合评价。
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smartcor: Intelligent Correlation Method Selection for Mixed Variable Types
This study addresses the common misuse of Pearson correlation for mixed variable types—such as binary, ordinal, and nominal—which often introduces bias in traditional correlation analyses. To resolve this, the authors introduce smartcor (for R) and pysmartcor (for Python), the first toolkits to systematically support all ten possible combinations of variable types. These packages employ automatic variable-type detection and a rule-based engine to intelligently select the optimal correlation or association method from a repertoire of fourteen, while also providing interpretable justifications for each choice. Monte Carlo simulations demonstrate substantially improved selection accuracy, and real-world case studies reveal meaningful discrepancies between type-aware analyses and naive Pearson correlations, thereby enhancing the reliability of statistical inference.
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QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation
This work addresses the bias in relationship prediction caused by the long-tailed predicate distribution in scene graph generation by proposing a hybrid quantum-classical architecture. Specifically, the conventional predicate classification head of a causal feature enhancement network is replaced with a quantum predicate head trained via weighted cross-entropy. The approach demonstrates, for the first time on Visual Genome 150, the effectiveness of quantum circuits for modeling scene graph relationships: amplitude embedding combined with strongly entangling layers constructs 4- or 8-qubit circuits that compress 4096-dimensional features into 16 dimensions using only 96–384 trainable parameters. Experimental results show that the 4-qubit model achieves an mR@100 of 57.25%, substantially outperforming the classical CFEN (41.1%), while the 8-qubit variant maintains strong long-tail performance (55.38%) with a 256-fold improvement in parameter efficiency.
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Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts
This study addresses authorship identification in historical Arabic manuscripts by proposing a convolutional neural network integrated with an attention mechanism. The model performs closed-set author classification under two evaluation protocols: line-level and page-independent settings. Notably, it innovatively treats rare manuscript lines containing contributions from two authors as composite categories. Leveraging a substantially expanded and manually verified version of the Muharaf dataset, this work establishes the first baseline results for both protocols. Extensive experiments employing backbone architectures such as DenseNet201 and diverse training strategies—validated through fourteen ablation configurations—achieve a Top-1 accuracy of 99.05% (F1: 97.44%) under the line-level protocol and 78.61% (F1: 66.55%) under the page-independent protocol, demonstrating significant performance gains in manuscript authorship identification.
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Noise Steering for Controlled Text Generation: Improving Diversity and Reading-Level Fidelity in Arabic Educational Story Generation
This work proposes a training-free, noise-guided approach to enhance narrative diversity in Arabic early-grade reading assessment stories while preserving strict educational constraints—such as vocabulary control, readability level, and narrative structure. The method injects calibrated Gaussian perturbations into internal representations of Transformer models, specifically targeting the residual stream and attention entropy. Evaluation across five Arabic-specific models (7–9B parameters) demonstrates that residual stream noise significantly boosts narrative diversity with minimal impact on text quality or constraint adherence, while attention entropy noise injection (AENI) effectively stabilizes logical coherence and recovers textual fluency. Unlike conventional high-temperature sampling, which often induces readability drift and quality degradation, the proposed technique successfully balances diversity with pedagogical fidelity.
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Evaluating the effectiveness of class-level LLM-generated test suites in Python
研究通过不同提示策略和模型选择评估LLM生成的Python类级测试套件的有效性，使用执行结果、覆盖率、变异测试得分和结构质量指标进行综合评价。
0 citationsRead paper
smartcor: Intelligent Correlation Method Selection for Mixed Variable Types
This study addresses the common misuse of Pearson correlation for mixed variable types—such as binary, ordinal, and nominal—which often introduces bias in traditional correlation analyses. To resolve this, the authors introduce smartcor (for R) and pysmartcor (for Python), the first toolkits to systematically support all ten possible combinations of variable types. These packages employ automatic variable-type detection and a rule-based engine to intelligently select the optimal correlation or association method from a repertoire of fourteen, while also providing interpretable justifications for each choice. Monte Carlo simulations demonstrate substantially improved selection accuracy, and real-world case studies reveal meaningful discrepancies between type-aware analyses and naive Pearson correlations, thereby enhancing the reliability of statistical inference.
0 citationsRead paper
QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation
This work addresses the bias in relationship prediction caused by the long-tailed predicate distribution in scene graph generation by proposing a hybrid quantum-classical architecture. Specifically, the conventional predicate classification head of a causal feature enhancement network is replaced with a quantum predicate head trained via weighted cross-entropy. The approach demonstrates, for the first time on Visual Genome 150, the effectiveness of quantum circuits for modeling scene graph relationships: amplitude embedding combined with strongly entangling layers constructs 4- or 8-qubit circuits that compress 4096-dimensional features into 16 dimensions using only 96–384 trainable parameters. Experimental results show that the 4-qubit model achieves an mR@100 of 57.25%, substantially outperforming the classical CFEN (41.1%), while the 8-qubit variant maintains strong long-tail performance (55.38%) with a 256-fold improvement in parameter efficiency.
0 citationsRead paper
Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts
This study addresses authorship identification in historical Arabic manuscripts by proposing a convolutional neural network integrated with an attention mechanism. The model performs closed-set author classification under two evaluation protocols: line-level and page-independent settings. Notably, it innovatively treats rare manuscript lines containing contributions from two authors as composite categories. Leveraging a substantially expanded and manually verified version of the Muharaf dataset, this work establishes the first baseline results for both protocols. Extensive experiments employing backbone architectures such as DenseNet201 and diverse training strategies—validated through fourteen ablation configurations—achieve a Top-1 accuracy of 99.05% (F1: 97.44%) under the line-level protocol and 78.61% (F1: 66.55%) under the page-independent protocol, demonstrating significant performance gains in manuscript authorship identification.
0 citationsRead paper
Noise Steering for Controlled Text Generation: Improving Diversity and Reading-Level Fidelity in Arabic Educational Story Generation
This work proposes a training-free, noise-guided approach to enhance narrative diversity in Arabic early-grade reading assessment stories while preserving strict educational constraints—such as vocabulary control, readability level, and narrative structure. The method injects calibrated Gaussian perturbations into internal representations of Transformer models, specifically targeting the residual stream and attention entropy. Evaluation across five Arabic-specific models (7–9B parameters) demonstrates that residual stream noise significantly boosts narrative diversity with minimal impact on text quality or constraint adherence, while attention entropy noise injection (AENI) effectively stabilizes logical coherence and recovers textual fluency. Unlike conventional high-temperature sampling, which often induces readability drift and quality degradation, the proposed technique successfully balances diversity with pedagogical fidelity.
0 citationsRead paper