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10th IEEE Smart World Congress, SWC 2024

作者： Fan, Guangyu Guo, Jiajia Cheng, Songlin Yang, Dingyu Shanghai Dianji University School of Electronic Information Shanghai China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798331520861

Using Internet, artificial intelligence and other technologies, online learning such as MOOCs provides us with new learning platforms and methods. In the paper, a transformer-based model for online learning prediction in MOOCs using deep learning approaches, namely ONLPSformer, is proposed. The model transforming the learner's data to a sequential form and extracts feature information from students' learning behavior using transformer-based mechanism model with their behavioral changes in online learning. After extracting feature information, the previous learning behavior data are combined to predict the students' learning behavior and their learning effect. To verify the performances of the model, many experiments are conducted using the MOOCCubeX dataset. The experimental results show that the model achieved better effectiveness in learning prediction, compared with the traditional methods. The values of RMSE and MAE of the proposed method are 0.159 and 0.102, respectively, which are the smallest among the three models. © 2024 IEEE.

关键词： Contrastive Learning

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International Standardization of blockchain and Distributed Ledger Technology:Overlaps,Gaps and Challenges

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1491-1523页

作者： Xiangjuan Jia Jing Xu Mengwei Han Qing Zhang Lu Zhang Xiaofeng Chen Department of Standardization and Research Project Management Hangzhou Qulian Technology Co.Ltd.Hangzhou310000China State Key Laboratory of Blockchain and Data Security Zhejiang UniversityHangzhou310000China

As one of the most concerned digital technologies in recent years,blockchain and distributed ledger technology are an important driving force for a new round of technological *** is currently in the process of accelerating its evolution and maturity,and has gradually integrated with other digital *** has been applied in many industries,providing decentralized solutions for various industries,realizing innovative storage models,and building a new trust *** blockchain technology is officially incorporated into China’s new information infrastructure category,the application fields of blockchain have expanded rapidly,gradually extending from the financial field and government affairs to other fields in the real *** the same time,with the continuous development of the globalized economy,blockchain technology will also have a profound impact on international technological and economic ***,for the healthy and orderly development and real implementation of the blockchain industry,standardize the application of blockchain,effectively break through the cognitive and technical barriers between different countries,industries and systems on a global scale,prevent application risks,the development of the global blockchain industry needs standardization basis,which is particularly important and urgent.A sound standard system is an important key to the successful development of technology,and formulating the right standard at the right time for technology development helps ensure the ease of use and interoperability of the *** the perspective of international standardization,this article first introduces the general situation of ISO,ITU-T,IEEE,W3C and other international standardization organizations,and sorts out the status quo of the blockchain standardization working groups of mainstream international standardization *** blockchain-related standards under development have been analyzed for the charac

关键词： ISO ITU-T IEEE blockchain distributed ledger technology international standard standardization status overlaps gaps challenges

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A Student Emotions Recognition Based Online Texts with Large Language Models for Learning Prediction

A Student Emotions Recognition Based Online Texts with Large...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fan, Guangyu Zhang, Shikun Cheng, Songlin Yang, Dingyu Shanghai Dianji University School of Electronic Information Shanghai China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798350386226

with the rapid development of artificial intelligence (AI) and Large Language Models (LLM) technologies, it offers innovative approaches to model and analyze to predict student performance with educational behavior and learning data. But the challenges of data diversity, technical complex, and lack of semantic comprehension ability that limited the use of AI-based tools for learning performance prediction. In the paper, student emotions recognition based online learning data especially forum texts using LLMs is proposed, and an improved online learning performance prediction with online learning data, including student emotions using Signed Graph Neural Networks (SGNN) and LLMs, namely ER-SGNN-LLM, is constructed. In the model, the keywords are extracted from both forum texts and answers generated by students using LLM, and the keywords extracted from student's forum texts are used for student emotions recognition, the keywords extracted from student's answers, as well as the student emotions are used for student learning prediction. The relationships between each keyword extracted from texts and knowledge points of the course are encoded with SGNN. A graphic contrastive learning model is used to handle the noise in the dataset caused by students' subjective reasons. The combination of GNN and LLM is used to student emotions recognition and learning prediction. To verify the performances of the model, many experiments are conducted using the public dataset. The results demonstrate that, the model achieved better effectiveness in learning prediction, compared with other models. The values of F1 score of the proposed model is improved 0.032 compared that of the exist SGNN-LLM model. © 2024 IEEE.

关键词： Contrastive Learning

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An Emotion Recognition with Online Learning data Using Deep Learning Approaches

An Emotion Recognition with Online Learning Data Using Deep ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fan, Guangyu Guo, Jiajia Cheng, Songlin Yang, Dingyu Shanghai Dianji University School of Electronic Information Shanghai China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798350386226

Emotion recognition helps teachers to obtain students' emotional states and their learning progress in their study. Many deep learning-based technologies, such as convolutional neural networks (CNN) and large language models (LLM) models are used for emotion recognition and prediction with learning data of students. An emotion recognition and prediction for online learning students using deep learning approaches is proposed, namely EPLLM. The method transforming the students' data, such as forum texts, questions, and answers, to a sequential form and extracts feature information from students' data using deep learning models with their behavioral changes in online learning. The student emotions are recognized and predicted based online learning data with combination of the deep learning models. To verify the performances of the model, many experiments are conducted using the public dataset. The results verify that, the model achieved better effectiveness in emotion prediction, compared with existing models. The values of F1 score of the proposed model is improved 0.051 compared that of the exist SGNN-LLM model. © 2024 IEEE.

关键词： Contrastive Learning

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SmartLite: A DBMS-based Serving System for DNN Inference in Resource-constrained Environments 50th

SmartLite: A DBMS-based Serving System for DNN Inference in ...

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Lin, Qiuru Wu, Sai Zhao, Junbo Dai, Jian Shi, Meng Chen, Gang Li, Feifei The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang University China Alibaba Group China

Many IoT applications require the use of multiple deep neural networks (DNNs) to perform various tasks on low-cost edge devices with limited computation resources. However, existing DNN model serving platforms, such as TensorFlow Serving and TorchServe, are resource-intensive and require high-performance GPUs that are often not available on low-cost edge devices. In this paper, we propose SmartLite, a lightweight DBMS that addresses these challenges by storing the parameters and structural information of neural networks as database tables and implementing neural network operators inside the DBMS engine. SmartLite quantizes model parameters as binarized values, applies neural pruning techniques to compress the models, and transforms tensor manipulations into value lookup operations of the DBMS to reduce computation overhead. Experimental results show that SmartLite requires 98% less memory while achieving about a 134% performance speedup compared to TorchServe. Our proposed solution addresses the challenges of running multiple DNN models on low-cost edge devices and provides a significant contribution to the field of IoT applications. © 2023, VLDB Endowment. All rights reserved.

关键词： Deep neural networks

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Fast Query Answering by Labeling Index on Uncertain Graphs 40

Fast Query Answering by Labeling Index on Uncertain Graphs

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wang, Zeyu Shi, Qihao Chen, Jiawei Wang, Can Song, Mingli Wang, Xinyu Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Hangzhou City University Hangzhou China Bangsun Technology Hangzhou China

ISBN: (纸本)9798350317152

Given the ubiquity of Uncertain Graphs (UGs), the field of UG mining has garnered increasing attention. Among various mining tasks, query processing stands out as the most fundamental and crucial. Current methods for query answering on UGs primarily rely on Monte-Carlo sampling and heuristic approaches. However, these techniques either struggle with a significant efficiency-accuracy trade-off or lack generalization over different graphs and queries. To circumvent these limitations, this work proposes a novel index-based method for query answering on UGs. We construct a labeling index framework, which can answer queries by pre-computed and stored operators. To the best of our knowledge, this is the first index frame-work that can deal with reliability, expected reliable distance and distance-constrained reliability queries, providing lower or upper bounded query answer results. By transferring the time consuming sampling process into the offline index operator computation, the query answering only needs to traverse a limited number of operators, which accelerates the response time of query answering with several orders of magnitude. We further utilize the vertex cover and its h-hop extension to prune the index structure, thereby reducing the space complexity. Experimental results on five real-world datasets demonstrate that the proposed index framework is both effective and efficient. © 2024 IEEE.

关键词： Reliability

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Biometric Authentication Based on Enhanced Remote Photoplethysmography Signal Morphology 18

Biometric Authentication Based on Enhanced Remote Photopleth...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Sun, Zhaodong Li, Xiaobai Komulainen, Jukka Zhao, Guoying Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China University of Oulu Center for Machine Vision and Signal Analysis Oulu Finland

ISBN: (纸本)9798350364132

Remote photoplethysmography (rPPG) is a non-contact method for measuring cardiac signals from facial videos, offering a convenient alternative to contact photoplethysmography (cPPG) obtained from contact sensors. Recent studies have shown that each individual possesses a unique cPPG signal morphology that can be utilized as a biometric identifier, which has inspired us to utilize the morphology of rPPG signals extracted from facial videos for person authentication. Since the facial appearance and rPPG are mixed in the facial videos, we first de-identify facial videos to remove facial appearance while preserving the rPPG information, which protects facial privacy and guarantees that only rPPG is used for authentication. The de-identified videos are fed into an rPPG model to get the rPPG signal morphology for authentication. In the first training stage, unsupervised rPPG training is performed to get coarse rPPG signals. In the second training stage, an rPPG-cPPG hybrid training is performed by incorporating external cPPG datasets to achieve rPPG biometric authentication and enhance rPPG signal morphology. Our approach needs only de-identified facial videos with subject IDs to train rPPG authentication models. The experimental results demonstrate that rPPG signal morphology hidden in facial videos can be used for biometric authentication. The code is available at https://***/zhaodongsun/rppg-biometrics. © 2024 IEEE.

关键词： Photoplethysmography

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dTEE: A Declarative Approach to Secure IoT Applications Using TrustZone 23

dTEE: A Declarative Approach to Secure IoT Applications Usin...

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23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Sun, Tong Li, Borui Teng, Yixiao Gao, Yi Dong, Wei Zhejiang University The State Key Laboratory of Blockchain and Data Security College of Computer Science China Southeast University School of Computer Science and Engineering China

ISBN: (纸本)9798350362015

Internet of Things (IoT) applications have recently been widely used in safety-critical scenarios. To prevent sensitive information leaks, IoT device vendors provide hardware-assisted protections, called Trusted Execution Environments (TEEs), like ARM Trust-Zone. Programming a TEE-based application requires separate code for two components, significantly slowing down the development process. Existing solutions tackle this issue by automatic code partition while not successfully applying it in two complicated scenarios: adding trusted logic and interactions with secure *** propose dTEE, a declarative approach to secure IoT applications based on TrustZone. dTEE proposes a rapid approach that enables developers to declare tiered-sensitive variables and functions of existing applications. Besides, dTEE automatically transforms device drivers into trusted ones. We evaluate dTEE on four real-world IoT applications and seven micro-benchmarks. Results show that dTEE achieves high expressiveness for supporting 50% more applications than existing approaches and reduces 90% of the lines of code against handcrafted development. © 2024 IEEE.

关键词： Internet of things

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Unified Video and Image Representation for Boosted Video Face Forgery Detection 27

Unified Video and Image Representation for Boosted Video Fac...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Liu, Haotian Pan, Chenhui Liu, Yang Zhao, Guoying Li, Xiaobai Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9781643685489

Face forgery detection is crucial in preserving the security and integrity of facial data amidst the rapid developments in face manipulation techniques and deep generative models. Existing methods for video face forgery detection typically assume that all frames in a forged video are manipulated, while identifying partially forged videos with only a subset of altered frames is still a challenge to be solved. To address this issue, we propose a novel framework, i.e., the UVIF, that utilizes additional annotated images to provide fine-grained supervision for detecting partial forgeries in videos. The UVIF integrates a unified encoder and a multi-task learning paradigm to model both facial videos and images for boosted video face forgery detection. A 2D backbone with temporal fusion modules is employed for the unified encoder. A pseudo labeling process is also designed for facial video frames to bridge the representation of individual video frames and static images. Extensive experiments on benchmark datasets demonstrate the effectiveness of our framework, outperforming state-of-the-art methods in detecting partially forged videos while introducing no additional computational overhead. Our code is available at https://***/haotianll/UVIF. © 2024 The Authors.

关键词： Face recognition

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NLCTables: A dataset for Marrying Natural Language Conditions with Table Discovery

arXiv

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arXiv 2025年

作者： Cui, Lingxi Li, Huan Chen, Ke Shou, Lidan Chen, Gang The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

With the growing abundance of repositories containing tabular data, discovering relevant tables for in-depth analysis remains a challenging task. Existing table discovery methods primarily retrieve desired tables based on a query table or several vague keywords, leaving users to manually filter large result sets. To address this limitation, we propose a new task: NL-conditional table discovery (nlcTD), where users combine a query table with natural language (NL) requirements to refine search results. To advance research in this area, we present nlcTables, a comprehensive benchmark dataset comprising 627 diverse queries spanning NL-only, union, join, and fuzzy conditions, 22,080 candidate tables, and 21,200 relevance annotations. Our evaluation of six state-of-the-art table discovery methods on nlcTables reveals substantial performance gaps, highlighting the need for advanced techniques to tackle this challenging nlcTD scenario. The dataset, construction framework, and baseline implementations are publicly available at https://***/SuDIS-ZJU/nlcTables to foster future *** Codes 68P20 Copyright © 2025, The Authors. All rights reserved.

关键词： Query languages

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