Differential-linear profiles over finite fields of arbitrary characteristic
研究引入了任意特征有限域上的差分-线性剖面，通过该方法解决了输入差分与输出掩码间依赖关系的测量问题。
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Edge Geography is XNLP-hard for Pathwidth and in XP for Tree-Partition Width
This study investigates the computational complexity of Edge Geography under various graph width parameters, including pathwidth and tree-partition width. Leveraging parameterized complexity theory, graph decomposition techniques, and XNLP-hardness reductions, the work establishes that Edge Geography is XNLP-hard when parameterized by pathwidth and belongs to the XP class with respect to tree-partition width. Furthermore, it presents a fixed-parameter tractable algorithm for the directed variant when parameterized jointly by treewidth and maximum degree. These results resolve an open problem posed by Bodlaender over three decades ago and provide a complete characterization of the precise parameterized complexity landscape of Edge Geography across mainstream width measures.
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ETH-Tight Complexity of Optimal Morse Matching on Bounded-Treewidth Complexes
This work investigates the exact computational complexity of the Optimal Morse Matching (OMM) problem on finite regular CW complexes parameterized by treewidth. For complexes of treewidth $k$, the authors devise an exact algorithm running in time $2^{O(k \log k)} \cdot n$ and, under the Exponential Time Hypothesis (ETH), establish that no algorithm can solve the problem in time $2^{o(k \log k)} \cdot n^{O(1)}$. By integrating techniques from discrete Morse theory, tree decompositions, and parameterized algorithms, this study provides the first tight upper and lower bounds for OMM with respect to treewidth, resolving a long-standing open question regarding its parameterized complexity and establishing the ETH-optimal runtime for this fundamental topological optimization problem.
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An Adaptive Scoring Framework for Attention Assessment in NDD Children via Serious Games
To address the limitation of conventional attention assessments for children with neurodevelopmental disorders (NDDs)—which rely solely on game scores and fail to capture authentic learning processes—this paper proposes a multimodal adaptive assessment framework. The framework integrates three complementary modalities: eye-tracking data (spatial attention), interaction timing sequences (sustained engagement), and in-game behavioral logs (task performance), fused via a dynamically weighted scoring model. It incorporates progressive difficulty adjustment and an adaptive multiplier mechanism to mitigate score inflation for high performers while enhancing sensitivity to improvement among low-performing individuals. Crucially, it establishes, for the first time, an explicit mapping between oculomotor features and educational behaviors. Validation using MAE, RMSE, and bivariate correlation analysis demonstrates significantly improved modeling accuracy of the attention–learning relationship; assessment outputs meet quality thresholds for real-world educational deployment, providing interpretable and actionable quantitative evidence for personalized intervention.
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Privacy Preservation and Identity Tracing Prevention in AI-Driven Eye Tracking for Interactive Learning Environments
To address identity traceability and privacy leakage risks arising from AI-driven eye-tracking in interactive education, this paper proposes a two-stage privacy-preserving framework. In Stage I, virtual identifiers, K-means clustering, and real-time anonymization are integrated to reduce identity re-identification accuracy to 63% while preserving diagnostic utility (classification accuracy: 99.3%). In Stage II, federated learning and fine-grained administrative access control are introduced, eliminating identity re-identification entirely and further improving classification accuracy to 99.40%. The framework strictly complies with GDPR and other regulatory standards. It represents the first approach to simultaneously guarantee high-accuracy neurodevelopmental disorder identification and strong identity unlinkability in educational settings. By enabling trustworthy governance of sensitive biobehavioral data—particularly eye-movement patterns—the framework provides a scalable, privacy-aware methodology for real-world edtech applications.
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Differential-linear profiles over finite fields of arbitrary characteristic
研究引入了任意特征有限域上的差分-线性剖面，通过该方法解决了输入差分与输出掩码间依赖关系的测量问题。
0 citationsRead paper
Edge Geography is XNLP-hard for Pathwidth and in XP for Tree-Partition Width
This study investigates the computational complexity of Edge Geography under various graph width parameters, including pathwidth and tree-partition width. Leveraging parameterized complexity theory, graph decomposition techniques, and XNLP-hardness reductions, the work establishes that Edge Geography is XNLP-hard when parameterized by pathwidth and belongs to the XP class with respect to tree-partition width. Furthermore, it presents a fixed-parameter tractable algorithm for the directed variant when parameterized jointly by treewidth and maximum degree. These results resolve an open problem posed by Bodlaender over three decades ago and provide a complete characterization of the precise parameterized complexity landscape of Edge Geography across mainstream width measures.
0 citationsRead paper
ETH-Tight Complexity of Optimal Morse Matching on Bounded-Treewidth Complexes
This work investigates the exact computational complexity of the Optimal Morse Matching (OMM) problem on finite regular CW complexes parameterized by treewidth. For complexes of treewidth $k$, the authors devise an exact algorithm running in time $2^{O(k \log k)} \cdot n$ and, under the Exponential Time Hypothesis (ETH), establish that no algorithm can solve the problem in time $2^{o(k \log k)} \cdot n^{O(1)}$. By integrating techniques from discrete Morse theory, tree decompositions, and parameterized algorithms, this study provides the first tight upper and lower bounds for OMM with respect to treewidth, resolving a long-standing open question regarding its parameterized complexity and establishing the ETH-optimal runtime for this fundamental topological optimization problem.
0 citationsRead paper
An Adaptive Scoring Framework for Attention Assessment in NDD Children via Serious Games
To address the limitation of conventional attention assessments for children with neurodevelopmental disorders (NDDs)—which rely solely on game scores and fail to capture authentic learning processes—this paper proposes a multimodal adaptive assessment framework. The framework integrates three complementary modalities: eye-tracking data (spatial attention), interaction timing sequences (sustained engagement), and in-game behavioral logs (task performance), fused via a dynamically weighted scoring model. It incorporates progressive difficulty adjustment and an adaptive multiplier mechanism to mitigate score inflation for high performers while enhancing sensitivity to improvement among low-performing individuals. Crucially, it establishes, for the first time, an explicit mapping between oculomotor features and educational behaviors. Validation using MAE, RMSE, and bivariate correlation analysis demonstrates significantly improved modeling accuracy of the attention–learning relationship; assessment outputs meet quality thresholds for real-world educational deployment, providing interpretable and actionable quantitative evidence for personalized intervention.
0 citationsRead paper
Privacy Preservation and Identity Tracing Prevention in AI-Driven Eye Tracking for Interactive Learning Environments
To address identity traceability and privacy leakage risks arising from AI-driven eye-tracking in interactive education, this paper proposes a two-stage privacy-preserving framework. In Stage I, virtual identifiers, K-means clustering, and real-time anonymization are integrated to reduce identity re-identification accuracy to 63% while preserving diagnostic utility (classification accuracy: 99.3%). In Stage II, federated learning and fine-grained administrative access control are introduced, eliminating identity re-identification entirely and further improving classification accuracy to 99.40%. The framework strictly complies with GDPR and other regulatory standards. It represents the first approach to simultaneously guarantee high-accuracy neurodevelopmental disorder identification and strong identity unlinkability in educational settings. By enabling trustworthy governance of sensitive biobehavioral data—particularly eye-movement patterns—the framework provides a scalable, privacy-aware methodology for real-world edtech applications.
0 citationsRead paper