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

Multimedia Tools and applications 2024年第18期84卷 1-15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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HIET:Hybrid Information Enhancement Transformer Network for Single-Photon Image Reconstruction

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Journal of Beijing Institute of technology 2025年第1期34卷 1-17页

作者： Yiming Liu Xuri Yao Tao Zhang Yifei Sun Ying Fu School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Center for Quantum Technology Research and Key Laboratory of Advanced Optoelec-tronic Quantum Architecture and Measurements(MOE) School of PhysicsBeijing Institute of TechnologyBeijing 100081China School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Uniview Technology Hangzhou 310056China

Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal ***,these advantages also make them highly susceptible to ***,single-photon cameras face severe quantization as low as 1 bit/*** factors make it a daunting task to recover high-quality scene information from noisy single-photon *** current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm *** this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel ***,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution ***,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon *** results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.

关键词： single-photon images hybrid information enhancement structual feature enhancement data simulation pipeline

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Atomistic simulation of{0001}<1210>crack propagation with Nb precipitates inα-Zr

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Rare Metals 2023年第5期42卷 1663-1669页

作者： Hua Ji Jian Yin Guo Wei Wen-Sheng Lai Bai-Xin Liu Jian-Bo Liu Suzhou Nuclear Power Research Institute Suzhou215004China School of Physics and Technology Center for Ion Beam Applicationand Hubei Nuclear Solid Physics Key LaboratoryWuhan UniversityWuhan430072China Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing100084China

The fracture characteristics of Zr alloys significantly impact the performance of fuel cladding tubes in pressurized water reactors of nuclear power *** this study,molecular dynamics(MD)simulations were performed to investigate the{0001}(1210)crack propagation inα-*** influence of temperature and Nb precipitates on the crack propagation was investigated.A total of 469tensile simulations were *** innovative approach to study the interaction between crack propagation and second-phase particles in the crystals was adopted,which could effectively capture the interaction of secondphase particles with dislocations and twins generated during crack *** the simulation,{1126}<1121>and{1012}<1011>twins were observed during crack propagation with Nb precipitates in the *** simulation results revealed that the Nb precipitates further resisted crack propagation,especially those with a radius of approximately 2 *** addition,the Nb precipitates in the extension direction of the{1026}<1121>twin exhibited higher resistance to crack propagation than those at other locations.

关键词： Zirconium alloys Crack propagation Twinning Nb precipitates Molecular dynamic(MD)simulation

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Archer: Adaptive Memory Compression with Page-Association-Rule Awareness for High-Speed Response of Mobile Devices 23

Archer: Adaptive Memory Compression with Page-Association-Ru...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

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FedCo: Self-Supervised Learning in Federated Learning with Momentum Contrast 24

FedCo: Self-Supervised Learning in Federated Learning with M...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wei, Shuai Cao, Guitao Dai, Cheng Dai, Shengxin Guo, Bing College of Computer Science Sichuan University Chengdu China MoE Engineering Research Center of SW/HW Co-design Technology and Application MoE East China Normal University Shanghai China MoE Engineering Research Center of SW/HW Co-design Technology and Application MoE East China Normal University Chengdu China Sichuan University Chengdu China

ISBN: (纸本)9798350319934

Federated learning(FL) enables multiple partici-pants to build a common, robust machine learning model without sharing data, which is a key technique to address data privacy, security, access rights, and access to heterogeneous data. However, existing FL algorithms still fall short of expectations in unsupervised learning and Non-iid data, which are two major challenges that limit the applicability and accuracy. For example, the lack of data labels or skewed feature distributions leads to loss of representativeness of the model, at the same time, tagging client data will increase a lot of costs in real application scenarios, therefore, supervised federated learning greatly limits the applicability of FL. In this paper, we propose a new unsupervised FL algorithm FedCO. In particular, at the parameter aggregation phase, we apply the momentum update queue to improve the training performance, enhance the model accuracy, and lower the labeling costs. At the local client training phase, the common queue that could be regarded as a big dictionary solve the data heterogeneity. The experiment on the benchmark data set shows that our method can be compared to other supervised and semi-supervised federated learning models, which proves the effectiveness of FedCo. © 2022 IEEE.

关键词： Unsupervised learning

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Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning 44

Brief Industry Paper: Towards Efficient Task Scheduling for ...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Yang, Yanxing Zhang, Nan Yan, Dengke Wei, Xian Zhou, Junlong Liu, Hong Chen, Mingsong MoE Engineering Research Center of SW/HW Co-Design Technology and Application East China Normal University China School of Computer Science and Engineering Nanjing University of Science and Technology China Shanghai Uni-Sentry Intelligent Technology Co. Ltd. China

ISBN: (纸本)9798350328578

As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design automation tools, it is difficult to quickly figure out an optimal task scheduling scheme for an AUTOSAR-based system. To address this problem, we introduce a novel task scheduling method that can parallelly search for an optimal solution with the help of our proposed pruning strategy. Experimental results on a real-world AUTOSAR-based autonomous driving system demonstrate that our approach can achieve much better task scheduling solutions than the ones obtained manually and significantly reduce the overall task scheduling time. © 2023 IEEE.

关键词： Real time control

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Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty

arXiv

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arXiv 2024年

作者： Li, Haoxuan Ouyang, Yuanxin Liu, Zhuang Rong, Wenge Xiong, Zhang Engineering Research Center of Advanced Computer Application Technology Ministry of Education School of Computer Science and Engineering Beihang University Beijing100191 China

Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space, lacking the ability to capture uncertainty. While probabilistic embedding is proposed to intergrate uncertainty, they suffer from several limitations when introduced to graph-based recommender systems. Graph convolutional network framework would confuse the semantic of uncertainty in the nodes, and similarity measured by Kullback–Leibler (KL) divergence suffers from degradation problem and demands an exponential number of samples. To address these challenges, we propose a novel approach, called the Wasserstein dependent Graph ATtention network (W-GAT), for collaborative filtering with uncertainty. We utilize graph attention network and Wasserstein distance to learn Gaussian embedding for each user and item. Additionally, our method incorporates Wasserstein-dependent mutual information further to increase the similarity between positive pairs. Experimental results on three benchmark datasets show the superiority of W-GAT compared to several representative baselines. Extensive experimental analysis validates the effectiveness of W-GAT in capturing uncertainty by modeling the range of user preferences and categories associated with items. Copyright © 2024, The Authors. All rights reserved.

关键词： Collaborative filtering

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Multi-view Transformer-Based Network for Prerequisite Learning in Concept Graphs 23rd

Multi-view Transformer-Based Network for Prerequisite Learn...

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23rd International Semantic Web Conference, ISWC 2024

作者： Wang, Zhichun Shao, Yifeng Peng, Boci Li, Bangui Li, Yun Wang, Qianren Li, Nijun School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing China School of Intelligence Science and Technology Peking University Beijing China School of Computer Science and Engineering Beihang University Beijing China Advanced Cognitive AI Lab Shanghai Huawei Technologies Shanghai China

ISBN: (纸本)9783031778438

Prerequisite learning is the task of identifying prerequisite relations among concepts, which is important for many AI-based educational applications. Previous approaches explore different kinds of learning resources to obtain useful features for predicting prerequisites. Early approaches use handcrafted features while recent approaches use neural networks to encode information of concepts. To further improve the results of prerequisite learning, we build concept graphs to include comprehensive information about concepts from open data and learning resources, and propose a Multi-view Transformer-based Network (MTN) to encode multi-view features of concepts in the graph. Multi-view features are fused to make accurate predictions of concept prerequisites. We evaluate our approach on four public datasets and compare it with recently published approaches. The results show that our approach can achieve state-of-the-art results in the task of prerequisite learning. The source code and data are available at https://***/kg-bnu/MTN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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DCNet: Weakly Supervised Saliency Guided Dual Coding Network for Visual Sentiment Recognition 26

DCNet: Weakly Supervised Saliency Guided Dual Coding Network...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Zhang, Xinyue Xiang, Jing Zhang, Hanxiu Wu, Chunwei Wang, Hailing Cao, Guitao School of Computer Science and Technology East China Normal University Shanghai China Shanghai Institute of Ai for Education East China Normal University Shanghai China MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781643684369

Visual sentiment recognition is a challenging task with scientific significance in probing vision-processing mechanisms. Recent approaches mainly focused on using overall images or precise annotations to learn emotional representations, yet neglected to capture abstract semantics from regional information, or led to a heavy annotation burden. In this paper, we propose an end-to-end weakly supervised framework, called Dual Coding Network (DCNet), which models a dual coding process for both shallow features and high-level regional information. On the one hand, with the help of the fine-grained module (FG), visual features (e.g. texture features) are utilized to enhance the learning of distinguished representation. On the other hand, the DCNet innovatively leverages saliency information to imitate the neural decoding of perceived visual sentiment contents in human brain activity. Specifically, the saliency information guides the generation of sentiment-specific pseudo affective maps (SAMG), which serve as weak annotations. Then the DCNet couples fine-grained features with pseudo affective maps, and obtains semantic vectors for final sentiment prediction. Extensive experiments show that the proposed DCNet outperforms the state-of-the-art performance on five benchmark datasets. © 2023 The Authors.

关键词： Network coding

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Sparrow: Flexible Memory Deduplication in Android Systems with Similar-Page Awareness

Sparrow: Flexible Memory Deduplication in Android Systems wi...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Guangyu Wei Changlong Li Rui Xu Qingfeng Zhuge Edwin H.-M. Sha School of Computer Science and Technology East China Normal University MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application Department of Computer Science City University of Hong Kong

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Mobile devices have become ubiquitous in daily life. In contrast to traditional servers, mobile devices suffer from limited memory resources, leading to a significant degradation in the user experience. This paper demonstrates that the primary cause of memory consumption lies in anonymous pages associated with application heaps. Existing schemes are ineffective in deduplicating these pages due to the limited occurrence of the same anonymous pages. This paper presents Sparrow, a similar-page aware deduplication solution for mobile systems. Sparrow shows that memory pages still have the potential to deduplicate, even though the same pages are rare. An interesting observation inspires this, that is, a high number of pages having the partially-same contents. We have implemented Sparrow on real-life smartphones. Experimental results indicate that 30.45% more space can be saved with Sparrow.

关键词： Degradation Memory management User experience Mobile applications Servers Smart phones

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