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Higher Institute for Applied Sciences and Technology

Academic institutionasia · sy
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Research library6linked papers
Opportunities0open roles
Selected work

Representative Papers

E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding

Sep 27, 2026

This study addresses the lack of standardized diagnostic datasets and hierarchical error analysis frameworks for Arabic natural language understanding (NLU). To overcome this bottleneck, the authors construct a cross-lingual NLU error taxonomy, propose an Arabic natural language inference framework, and introduce E-CONAN, a novel diagnostic dataset. The work pioneers macro- and micro-level classification naming conventions and combines manual annotation with comparative experiments involving pretrained models and large language models (LLMs), thereby filling the gap in fine-grained diagnostics for Arabic. Experimental results demonstrate that while LLMs outperform pretrained models in commonsense reasoning, they exhibit comparatively weaker syntactic capabilities. Furthermore, the findings establish inference as the most challenging linguistic phenomenon, offering critical insights for advancing Arabic NLU research.

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Context-Aware Web Attack Detection in Open-Source SIEM Systems via MITRE ATT&CK-Enriched Behavioral Profiling

May 13, 2026

This study addresses the limitations of traditional rule-based SIEM systems in detecting multi-step web attacks due to insufficient contextual information about source host behavior. To overcome this, the authors propose Smart-SIEM, an enhanced framework integrated into the Wazuh platform that constructs a novel source IP behavioral context vector by fusing HTTP response status codes, rule-trigger frequencies, and MITRE ATT&CK technique occurrences. A two-stage cascaded model combining LightGBM and XGBoost is designed for attack detection and fine-grained classification, complemented by an adaptive retraining mechanism to mitigate concept drift. Experimental results demonstrate a binary classification F1-score of 0.967 and a six-class attack classification F1-score of 0.914, with perfect (100%) detection of brute-force attacks and 98.3% recall for authentication bypass attempts. When confronted with previously unseen attacks, the system’s F1-score recovers from 0.465 to 0.814 after adaptive retraining.

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Improving Cross-Patient Generalization in Parkinson's Disease Detection through Chunk-Based Analysis of Hand-Drawn Patterns

Oct 20, 2025

To address the limited cross-subject generalizability of Parkinson’s disease (PD) detection models, this paper proposes a multi-stage robust recognition method based on hand-drawn graphics. The approach innovatively introduces a 2×2 image tiling strategy, integrates graphic-type prior classification, and hierarchically extracts both local and global features. An ensemble decision mechanism fuses discriminative information from multiple sources. This design significantly mitigates performance degradation caused by inter-subject variability: on the NewHandPD dataset, the method achieves 97.08% accuracy for seen subjects and maintains 94.91% for unseen subjects—a mere 2.17-percentage-point gap—outperforming existing state-of-the-art methods. The core contribution lies in the synergistic integration of structured tiling, task-guided feature disentanglement, and ensemble learning, thereby enhancing both cross-subject generalizability and clinical applicability.

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Survey of NLU Benchmarks Diagnosing Linguistic Phenomena: Why not Standardize Diagnostics Benchmarks?

Jul 27, 2025

Current NLU diagnostic benchmarks lack standardization: inconsistent naming conventions, no consensus on linguistic phenomena coverage, and absence of ISO-style evaluation protocols—hindering cross-benchmark comparability and fine-grained error analysis. This study addresses these gaps via a systematic literature review and cross-benchmark comparative analysis, conducting the first linguistic-phenomenon-level mapping and structured taxonomy of English, Arabic, and multilingual diagnostic datasets. Its core contributions are threefold: (1) identifying two critical deficiencies—naming inconsistency and incomplete phenomenon coverage; (2) proposing a globally scalable, hierarchical classification system for linguistic phenomena in NLU diagnostics; and (3) pioneering a standardized evaluation framework for NLU diagnostics, modeled after industrial standards, to enable theoretically grounded and practically actionable fine-grained capability assessment.

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Recent publications

Latest Papers

E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding

Sep 27, 2026

This study addresses the lack of standardized diagnostic datasets and hierarchical error analysis frameworks for Arabic natural language understanding (NLU). To overcome this bottleneck, the authors construct a cross-lingual NLU error taxonomy, propose an Arabic natural language inference framework, and introduce E-CONAN, a novel diagnostic dataset. The work pioneers macro- and micro-level classification naming conventions and combines manual annotation with comparative experiments involving pretrained models and large language models (LLMs), thereby filling the gap in fine-grained diagnostics for Arabic. Experimental results demonstrate that while LLMs outperform pretrained models in commonsense reasoning, they exhibit comparatively weaker syntactic capabilities. Furthermore, the findings establish inference as the most challenging linguistic phenomenon, offering critical insights for advancing Arabic NLU research.

0 citationsRead paper

Context-Aware Web Attack Detection in Open-Source SIEM Systems via MITRE ATT&CK-Enriched Behavioral Profiling

May 13, 2026

This study addresses the limitations of traditional rule-based SIEM systems in detecting multi-step web attacks due to insufficient contextual information about source host behavior. To overcome this, the authors propose Smart-SIEM, an enhanced framework integrated into the Wazuh platform that constructs a novel source IP behavioral context vector by fusing HTTP response status codes, rule-trigger frequencies, and MITRE ATT&CK technique occurrences. A two-stage cascaded model combining LightGBM and XGBoost is designed for attack detection and fine-grained classification, complemented by an adaptive retraining mechanism to mitigate concept drift. Experimental results demonstrate a binary classification F1-score of 0.967 and a six-class attack classification F1-score of 0.914, with perfect (100%) detection of brute-force attacks and 98.3% recall for authentication bypass attempts. When confronted with previously unseen attacks, the system’s F1-score recovers from 0.465 to 0.814 after adaptive retraining.

0 citationsRead paper

Improving Cross-Patient Generalization in Parkinson's Disease Detection through Chunk-Based Analysis of Hand-Drawn Patterns

Oct 20, 2025

To address the limited cross-subject generalizability of Parkinson’s disease (PD) detection models, this paper proposes a multi-stage robust recognition method based on hand-drawn graphics. The approach innovatively introduces a 2×2 image tiling strategy, integrates graphic-type prior classification, and hierarchically extracts both local and global features. An ensemble decision mechanism fuses discriminative information from multiple sources. This design significantly mitigates performance degradation caused by inter-subject variability: on the NewHandPD dataset, the method achieves 97.08% accuracy for seen subjects and maintains 94.91% for unseen subjects—a mere 2.17-percentage-point gap—outperforming existing state-of-the-art methods. The core contribution lies in the synergistic integration of structured tiling, task-guided feature disentanglement, and ensemble learning, thereby enhancing both cross-subject generalizability and clinical applicability.

0 citationsRead paper

Survey of NLU Benchmarks Diagnosing Linguistic Phenomena: Why not Standardize Diagnostics Benchmarks?

Jul 27, 2025

Current NLU diagnostic benchmarks lack standardization: inconsistent naming conventions, no consensus on linguistic phenomena coverage, and absence of ISO-style evaluation protocols—hindering cross-benchmark comparability and fine-grained error analysis. This study addresses these gaps via a systematic literature review and cross-benchmark comparative analysis, conducting the first linguistic-phenomenon-level mapping and structured taxonomy of English, Arabic, and multilingual diagnostic datasets. Its core contributions are threefold: (1) identifying two critical deficiencies—naming inconsistency and incomplete phenomenon coverage; (2) proposing a globally scalable, hierarchical classification system for linguistic phenomena in NLU diagnostics; and (3) pioneering a standardized evaluation framework for NLU diagnostics, modeled after industrial standards, to enable theoretically grounded and practically actionable fine-grained capability assessment.

0 citationsRead paper