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research on a quantification model of online learning cognitive load based on eye-tracking technology

Multimedia Tools and applications 2025年第18期84卷 18993-19007页

作者： Xue, Yaofeng Zhu, Fangqing Li, Jiaxuan Department of Educational Information Technology and Shanghai Engineering Research Center of Digital Educational Equipment East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China

Online learning is characterized by a high degree of complexity and a wealth of information when compared to traditional classroom learning. This can have an adverse influence on the learning outcomes of online learners. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Eye movements

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Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering

Chain of Condition: Construct, Verify and Solve Conditions f...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lin, Jiuheng Lai, Yuxuan Feng, Yansong Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Department of Computer Science The Open University of China China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China

ISBN: (纸本)9798891761681

Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Top-one Recommendation with Anonymous User Behaviors 39

Top-one Recommendation with Anonymous User Behaviors

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Xiangkui Wu, Jun MoE Key Lab of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Beijing 100044 China MoE Engineering Research Center of Integration and Application of Digital Learning Technology The Open University of China Beijing 100039 China

ISBN: (纸本)157735897X

Top-one recommendation with anonymous user behaviors, also known as session-based recommendation (SBR), faces challenges of top-one ranking and short anonymous sequences. To this end, we propose a novel objective that combines (i) a reciprocal rank loss to directly optimize the benchmark metric of top-one recommendation, with (ii) a listwise contrastive loss to handle short sequences through listwise augmented consistency regularization. Empirical studies demonstrate that optimizing the proposed objective significantly improves the performance of existing SBR baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Smart Contract Vulnerability Detection Model Based on Siamese Network 7th

Smart Contract Vulnerability Detection Model Based on Siames...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Chen, Weijie Guo, Ran Wang, Guopeng Zhang, Lejun Qiu, Jing Su, Shen Liu, Yuan Xu, Guangxia Chen, Huiling College of Information Engineering Yangzhou University Yangzhou225127 China Cyberspace Institute Advanced Technology Guangzhou University Guangzhou510006 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China

ISBN: (纸本)9783031281235

Blockchain is experiencing the transition from the first generation to the second generation, and smart contract is the symbol of the second generation blockchain. Under the background of the explosive growth of the second-generation blockchain platform and applications represented by smart contracts, frequent smart contract vulnerability events seriously threaten the ecological security of the blockchain, reflecting the importance and urgency of smart contract vulnerability detection. In this paper, we proposed a smart contract vulnerability detection method based on a Siamese network. We combined the Siamese network with Long Short-Term Memory (LSTM) Network neural network to complete the task of smart contract vulnerability detection. The Siamese network used in this paper consists of two subnetworks that share the same parameters onto a low dimension and easily separable feature space. Siamese network is now widely used in the field of image similarity and target tracking. In this paper, we improve the Siamese network so that it can be used for smart contract vulnerability detection. By comparing with previous research results, the model has better vulnerability detection performance and a lower false-positive rate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Improving the Generalization Ability in Essay Coherence Evaluation through Monotonic Constraints

arXiv

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arXiv 2023年

作者： Zheng, Chen Zhang, Huan Zhao, Yan Lai, Yuxuan The Open University of China Beijing China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China

Coherence is a crucial aspect of evaluating text readability and can be assessed through two primary factors when evaluating an essay in a scoring scenario. The first factor is logical coherence, characterized by the appropriate use of discourse connectives and the establishment of logical relationships between sentences. The second factor is the appropriateness of punctuation, as inappropriate punctuation can lead to confused sentence structure. To address these concerns, we propose a coherence scoring model consisting of a regression model with two feature extractors: a local coherence discriminative model and a punctuation correction model. We employ gradient-boosting regression trees as the regression model and impose monotonicity constraints on the input features. The results show that our proposed model better generalizes unseen data. The model achieved third place in track 1 of NLPCC 2023 shared task 7. Additionally, we briefly introduce our solution for the remaining tracks, which achieves second place for track 2 and first place for both track 3 and track 4. Copyright © 2023, The Authors. All rights reserved.

关键词： Regression analysis

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An Efficient Detection Model for Smart Contract Reentrancy Vulnerabilities 7th

An Efficient Detection Model for Smart Contract Reentrancy V...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Li, Yuan Guo, Ran Wang, Guopeng Zhang, Lejun Qiu, Jing Su, Shen Liu, Yuan Xu, Guangxia Chen, Huiling College of Information Engineering Yangzhou University Yangzhou225127 China Cyberspace Institute Advanced Technology Guangzhou University Guangzhou510006 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China

ISBN: (纸本)9783031281235

We propose a novel smart contract re-entry vulnerability detection model based on BiGAS. The model combines a BiGRU neural network that introduces an attention mechanism with an SVM. We start from the data features of smart contracts, learn the model layer by layer to achieve feature extraction and vulnerability identification, introduce batch normalization, Dropout processing and use improved model classifiers to improve the vulnerability identification accuracy, model convergence speed and generalization capability of smart contracts. We had conducted numerous experiments, and the experimental results showed that BiGAS Detection Model has a strong vulnerability detection ability. The accuracy of vulnerability detection reached 93.24%, and the F1-score was 93.17%. We compared our approach with advanced automated audit tools and other deep learning-based vulnerability detection methods. The conclusion was that our method is significantly better than the existing advanced methods in detecting smart contract reentrancy vulnerabilities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Vector angle-based and ISDE+ indicator selection for many-objective evolutionary optimization 3

Vector angle-based and ISDE+ indicator selection for many-ob...

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3rd International Conference on Electronic Information engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Liu, Peng Chen, Hanning Liu, Keyi Wang, Guopeng Song, Lizhe Xia, Yelin Jia, Kun School of Computer Science and Technology Tiangong University Tianjin300387 China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin300457 China School of Mechanical and Electrical Engineering Xinjiang Institute of Engineering Xinjiang Wulumuqi830023 China The Open University of China Beijing100039 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Jingwei Textile Machinery Beijing00176 China

ISBN: (纸本)9781510680449

Many-objective optimization problems are challenging for the majority of extant multi-objective evolutionary algorithms to solve because they are unable to strike a balance between diversity and convergence in the high-dimensional objective space. Therefore, for many-objective evolutionary optimization, this paper proposes a vector angle-based and ISDE+ indicator selection strategies. Using these two strategies, the worst individual are repeatedly removed from the population through environmental selection. More specially, the first one is aimed at finding any two individuals that have the smallest vector angle, which indicates that they are looking in the same research direction. The latter considers the individuals with poor convergence and diversity, which are eliminated according to the value of ISDE+ indicator one by one. Experimental results revel that the proposed algorithm obtains competitive advantages on various benchmark problems with up to 10 objectives, when the proposed algorithm is compared with several state-of-the-art many-objective algorithms. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Vectors

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Joint Visual-Textual Reasoning and Visible-Infrared Modality Alignment for Person Re-Identification

Joint Visual-Textual Reasoning and Visible-Infrared Modality...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Na Jiang Yuxuan Qiu Wei Song Jiawei Liu Zhiping Shi Liyang Wang Information Engineering College Capital Normal University Beijing China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China Department of Automation University of Science and Technology of China Hefei China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Visible-infrared person re-identification (VI-ReID) is a cross-modality fine-grained classification task. Existing approaches for VI-ReID mainly explore modality-invariant features for person retrieval. However, modality-invariant features pay more attention to global contexts, due to the lack of texture information in infrared images. This leads to a person with similar silhouette often being misidentified. Targeting this problem, this paper innovatively introduces natural language specification to learn global-local contexts for VI-ReID. Specifically, our framework jointly optimizes visible-infrared alignment (VIA) and visual-textual reasoning (VTR). VIA achieves cross-modal between RGB and IR. It can explicitly utilize designed modality-guided alignment and relationship-reinforced fusion to explore the potential of local cues in global features. VTR proposes the pooling selection and dual-level reasoning mechanisms to force the image encoder to pay attention to significant regions based on textual descriptions. Extensive experimental results on the popular SYSU-MM01 and RegDB datasets show that the proposed method significantly outperforms state-of-the-art approaches.

关键词： Natural languages Force Market research Cognition Video recording Identification of persons

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Direct monocular vision algorithm based on deep constraints of point and line features fusion

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Optics Letters 2025年第12期50卷 3804-3807页

作者： Keqiang Bai Xiuhong Li Yalan Zhu Feiyan Wang Southwest University of Science and Technology Mianyang 621010 China 8761571@*** Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education 100039 China Chinese Academy of Agriculture Mechanization Sciences Group Co. Ltd. Beijing 100101 China ***@***

The development of technology and the rapid increase of computing power have enabled the wide application of simultaneous localization and mapping (SLAM) in smart devices. Nevertheless, visual odometry based on the direct method exhibits inaccurate pose estimation in structured environments, because it ignores diverse line segment information, constraints of associated points, and estimated position information. Objective: This study aimed to address the issue of inaccurate pose estimation in structured environments for direct method-based visual odometry by proposing a direct monocular vision algorithm based on deep constraints of point and line features (DMVA-PLF), with the goal of improving pose estimation accuracy. Methods: The algorithm integrated line features from the environment into visual odometry through colinear and deep constraints, combined with historical pose information, to more effectively utilize image features and optimize the pose estimation process. Achievements: Experimental results demonstrated that the DMVA-PLF significantly improved pose estimation accuracy by efficiently leveraging image features, outperforming traditional methods in structured environments.

关键词： Cameras Crosstalk Detectors Image quality Segmentation Transforms

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Instruct Large Language Models to Generate Scientific Literature Survey Step by Step

arXiv

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arXiv 2024年

作者： Lai, Yuxuan Wu, Yupeng Wang, Yidan Hu, Wenpeng Zheng, Chen The Open University of China Beijing China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China FloatMiracle Beijing China PLA Academy of Military Science China

Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses substantial challenges for generative models. In this paper, we design a series of prompts to systematically leverage large language models (LLMs), enabling the creation of comprehensive literature surveys through a step-by-step approach. Specifically, we design prompts to guide LLMs to sequentially generate the title, abstract, hierarchical headings, and the main content of the literature survey. We argue that this design enables the generation of the headings from a high-level perspective. During the content generation process, this design effectively harnesses relevant information while minimizing costs by restricting the length of both input and output content in LLM queries. Our implementation with Qwen-long achieved third place in the NLPCC 2024 Scientific Literature Survey Generation evaluation task, with an overall score only 0.03% lower than the second-place team. Additionally, our soft heading recall is 95.84%, the second best among the submissions. Thanks to the efficient prompt design and the low cost of the Qwen-long API, our method reduces the expense for generating each literature survey to 0.1 RMB, enhancing the practical value of our method. Copyright © 2024, The Authors. All rights reserved.

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