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7th International Conference on Educational Technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming 42

SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shen, Kai Zhang, Dayou Zhu, Zi Zhang, Lei Wang, Fangxin Wang, Dan The Chinese University of HongKong The Future Network of Intelligence Institute Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Peng Cheng Laboratory China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350334142

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%. © 2023 IEEE.

关键词： Quality control

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Iterative Optimal Attention and Local Model for Single Image Rain Streak Removal

arXiv

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arXiv 2025年

作者： Li, Xiangyu Fan, Wanshu Shen, Yue Wang, Cong Wang, Wei Yang, Xin Zhang, Qiang Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Cyber Science and Technology Sun Yat-Sen University Shenzhen China School of Computer Science and Technology Dalian University of Technology Dalian China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University School of Computer Science and Technology Dalian University of Technology Dalian China

High-fidelity imaging is crucial for the successful safety supervision and intelligent deployment of vision-based measurement systems (VBMS). It ensures high-quality imaging in VBMS, which is fundamental for reliable visual measurement and analysis. However, imaging quality can be significantly impaired by adverse weather conditions, particularly rain, leading to blurred images and reduced contrast. Such impairments increase the risk of inaccurate evaluations and misinterpretations in VBMS. To address these limitations, we propose an Expectation Maximization Reconstruction Transformer (EMResformer) for single image rain streak removal. The EMResformer retains the key self-attention values for feature aggregation, enhancing local features to produce superior image reconstruction. Specifically, we propose an Expectation Maximization Block seamlessly integrated into the single image rain streak removal network, enhancing its ability to eliminate superfluous information and restore a cleaner background image. Additionally, to further enhance local information for improved detail rendition, we introduce a Local Model Residual Block, which integrates two local model blocks along with a sequence of convolutions and activation functions. This integration synergistically facilitates the extraction of more pertinent features for enhanced single image rain streak removal. Extensive experiments validate that our proposed EMResformer surpasses current state-of-the-art single image rain streak removal methods on both synthetic and real-world datasets, achieving an improved balance between model complexity and single image deraining performance. Furthermore, we evaluate the effectiveness of our method in VBMS scenarios, demonstrating that high-quality imaging significantly improves the accuracy and reliability of VBMS tasks. Codes are at https://***/ghfkahfk/EMResformer. Copyright © 2025, The Authors. All rights reserved.

关键词： Rain

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Blockchain-Based Efficiently Privacy-Preserving Federated Learning Framework Using Shamir Secret Sharing

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Fu, Xiya Xiong, Ling Li, Fagen Yang, Xingchun Xiong, Naixue School of Computer and Software Engineering Xihua University Chengdu China University of Electronic Science and Technology of China Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Chengdu China College of Intelligence and Computing Tianjin University Tianjin China

Federated learning, an emerging distributed learning paradigm, offers significant advantages and holds promise for addressing trust issues, breaking down data silos, and enabling active data sharing in the realm of consumer electronics. However, traditional federated learning faces critical security and privacy challenges in this domain, including single-point attacks, inference attacks, and poisoning attacks. To address these pressing security concerns, this paper proposes a decentralized federated learning framework focusing on security, reliability, and efficiency, tailored to meet the sustainability demands of consumer electronics. The proposed framework is founded upon masking and secret sharing techniques, establishing an emphatic privacy-preserving federated learning framework that ensures the security of gradient data and robustness against participant dropouts. Additionally, we actively motivate high-quality participants to collaborate by incorporating an incentive mechanism. Building upon the enhancement of existing federated learning approaches reliant on masking techniques, the method outlined in this paper significantly reduces communication overhead while preserving accuracy. Empirical research results comprehensively substantiate the superiority of this approach. Furthermore, compared to prevalent blockchain-based federated learning methods, our approach makes noteworthy strides in accuracy and efficiency. IEEE

关键词： Blockchain

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Fdc: Feature Dropout Consistency for Unsupervised Domain Adaptation Semantic Segmentation

SSRN

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SSRN 2024年

作者： Yao, Chaoyu Fan, Wanshu Wang, Cong Yang, Xin Wei, Xiaopeng Zhou, Dongsheng School of Computer Science and Technology Dalian University of Technology China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University China Department of Computing The Hong Kong Polytechnic University China

In Unsupervised Domain Adaptation Semantic Segmentation (UDASS), while self-training techniques have become one of the most effective methods to date, the absence of target labels makes models susceptible to overfitting. To address this problem, consistency techniques regularize the model by perturbing the target domain and enforcing consistent pixel predictions before and after the perturbation. Consistency techniques are based on the idea that a well-performing model should yield consistent predictions for the target data stream before and after perturbations. In this paper, we introduce the Feature Dropout Consistency (FDC) module, a novel approach that instigates perturbations at the feature level. This is achieved by implementing random feature dropout between the encoder and decoder of the student network. Subsequently, a feature dropout consistency loss is applied to minimize discrepancies in predictions between the perturbed student network and its teacher counterpart. Notably, FDC seamlessly integrates with existing self-training methodologies and, when paired with input perturbation, delves into an even broader perturbation spectrum. Through rigorous experimentation in standard UDA settings—training with synthetic labeled and real unlabeled data—FDC consistently outperforms baseline models, establishing new benchmarks on GTAV →Cityscapes and SYNTHIA →Cityscapes. © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Feature engineering Using File Layout for Malware Detection

arXiv

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arXiv 2023年

作者： Kim, Jeongwoo Cho, Eun-Sun Paik, Joon-Young Department of Computer Science and Engineering Chungnam National University Daejeon Korea Republic of School of Software Tiangong University Tianjin China

Malware detection on binary executables provides a high availability to even binaries which are not disassembled or decompiled. However, a binary-level approach could cause ambiguity problems. In this paper, we propose a new feature engineering technique that use minimal knowledge about the internal layout on a binary. The proposed feature avoids the ambiguity problems by integrating the information about the layout with structural entropy. The experimental results show that our feature improves accuracy and F1-score by 3.3% and 0.07, respectively, on a CNN based malware detector with realistic benign and malicious samples. © 2023, CC BY-NC-ND.

关键词： Malware

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Dynamic Language Refinement for Graceful Degradation in Scene Text Recognition

Dynamic Language Refinement for Graceful Degradation in Scen...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Shaocong Tian Rize Jin Joon-Young Paik Chen Fu School of Software Tiangong University Tianjin China School of Computer Science &Technology Tiangong University Tianjin China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China

Scene text recognition is a mature technology which can get a near-perfect result in regular scenes. However, there are still troubles in recognizing scene texts with image blur, characters missing, and occlusion. Recent end-to-end frameworks have investigated the importance of linguistic knowledge, utilizing either implicit or explicit language models. However, joint training of language and vision models tends to interfere the tunings of each other. In this paper, we present a novel gradient isolation method to train language and vision models separately, yet in an end-to-end architecture. In addition, we propose a dynamic iterative training procedure for graceful refining the language model. Extensive experiments confirm that the proposed method has superiority on regular and irregular scene text images and achieves state-of-the-art results.

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Freshness Classification of Hog Plum Fruit Using Deep Learning 4

Freshness Classification of Hog Plum Fruit Using Deep Learni...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Arunachalaeshwaran, V.R. Mahdi, Hussain Falih Choudhury, Tanupriya Sarkar, Tanmay Bhuyan, Bikram Pratim Informatics Cluster School of Computer Science Uttarakhand Dehradun248007 India University of Diyala Department of Computer and Software Engineering Baqubah Iraq Malda Polytechnic Department of Food Processing West Bengal Malda732102 India

ISBN: (纸本)9781665468350

Automatic freshness classification of fruits and veg-etables is an active area of research in the modern era. In order to maintain and preserve the grade quality of the fruits, the need to speed up processing is in a great urge. The fruit Hog Plum is noted to have high demand in Indian subcontinents. In this work we have utilized deep learning based techniques for performing freshness classification on hog Plum fruit. In this work, four convolutional neural network based models viz. ResNet18, MobileNetV2, MobileNetV3-Small, MobileNetV3-Large is pre-trained on the ImageNet dataset which were fine-tuned to perform binary classification on Hog Plum dataset cre-ated by us. The dataset was augmented for training purpose and hyperparameter tuning was performed using grid search and k-fold cross validation. The results of the model are then evaluated and compared in terms of several metrics like accuracy, precision, recall, Fl-Score, ROC-AUC Score and balanced accuracy score. Our results demonstrate that deep learning based techniques are effective for performing freshness classification of Hog Plum fruit. The models trained by us are lightweight in nature, hence are suitable to run on low-end and mobile devices increasing the applicability and adaptability of our proposed method. © 2022 IEEE.

关键词： Fruits

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Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction

arXiv

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arXiv 2024年

作者： Liao, Weibin Zhu, Yinghao Zhang, Zhongji Wang, Yuhang Wang, Zixiang Chu, Xu Wang, Yasha Ma, Liantao Peking University School of Computer Science Beijing China Ministry of Education Key Laboratory of High Confidence Software Technologies Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China Peking University School of Software and Microelectronics Beijing China Peking University School of Computer Science Center on Frontiers of Computing Studies Beijing China Tianjin China

Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks (DNNs) to directly model the patient’s health status. Existing DNNs training protocols, including Impute-then-Regress Procedure and Jointly Optimizing of Impute-n-Regress Procedure, require the additional imputation models to reconstruction missing values. However, Impute-then-Regress Procedure introduces the risk of injecting imputed, non-real data into downstream clinical prediction tasks, resulting in power loss, biased estimation, and poorly performing models, while Jointly Optimizing of Impute-n-Regress Procedure is also difficult to generalize due to the complex optimization space and demanding data requirements. Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all state-of-the-arts EHR analysis models on four real-world datasets across two clinical prediction tasks. Further experimental analysis indicates that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, as a plug-and-play protocol, PAI can be easily integrated into any existing or even imperceptible future EHR analysis models. The code of this work is deployed publicly available at https://***/MrBlankness/PAI to help the research community reproduce the results and assist the EHR analysis tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Efficient Resource Allocation Using Service Function Chain in Agricultural Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Yao, Jiamin Qiao, Sibo Xie, Yu Kumari, Saru Lin, Qiao Khan, Fazlullah Shanghai Maritime University College of Information Engineering Shanghai201306 China Tiangong University School of Software Tianjin300387 China Chaudhary Charan Singh University Department of Mathematics Meerut India University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China

As the level of intelligence in agricultural consumer electronics continues to advance, data-driven devices are often faced with challenges such as resource shortages, high real-time requirements, and complex data processing. In particular, the diversity and variability of massive agricultural consumer electronics in the agricultural Internet of Things (IoT) complicate the resource allocation process. Traditional centralized cloud computing architectures often struggle to meet these demands. To address these issues, this paper proposes a resource collaborative allocation method for agricultural consumer electronics based on Service Function Chain (SFC). First, we formalize the resource requirements of various agricultural consumer electronics applications based on SFC in a resource mapping model. Subsequently, we develop a multidimensional resource balancing model with computing resources, storage resources, and bandwidth resources requirements of agricultural consumer electronics. We also devise a fine-grained end-to-end latency model to comprehensively consider the effective utilization of multidimensional resources and latency guarantees for agricultural consumer electronics. Finally, we employ a deep reinforcement learning algorithm with experience replay to optimize the resource allocation strategy in real-time, thereby enhancing the agricultural consumer electronics system's responsiveness and resource utilization efficiency. Experimental results demonstrate that our proposed strategy outperforms existing methods in terms of improving resource utilization and reducing latency. © 1975-2011 IEEE.

关键词： Chains

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