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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Zheng Chen Meiyu Liang Wanying Yu Yongkang Huang Xiaoxiao Wang Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications

ISBN: (数字)9781728189246

ISBN: (纸本)9781728189253

The student's listening status in the classroom is an important indicator to evaluate that if he takes an active participation in the classroom and study seriously. However, the main challenge in the teaching evaluation is that the teacher in class cannot timely, objectively and accurately evaluate each student's state of listening in accordance with the facial expression or behavior of the students. Along with the advance of deep learning algorithms, artificial intelligence technology is more and more widely applied in the field of education. Based on the above challenges, this paper proposes an intelligent teaching evaluation method that integrates student facial expressions and behaviors in teaching videos, designs and implements a deep learning based intelligent teaching evaluation system. We construct the face detection and recognition model based on deep convolutional neural network and triple loss function to realize the detection and recognition of face regions of students. And then the student facial expression recognition model and the student behavior recognition model based on the deep separable convolutional neural network are constructed. Finally, we propose a novel comprehensive teaching evaluation algorithm by fusion of the student facial expression and behavior, aiming at calculating the comprehensive evaluation value and obtain the corresponding evaluation level. Also, we construct the first teaching video database, student facial expression database and student behavior database for intelligent teaching evaluation. In this paper, the evaluation of students fully combines the students' specific facial expressions under certain behaviors in the classroom. Therefore, the final teaching assessment results are more comprehensive and accurate.

关键词： Deep learning Databases Face recognition Education Convolutional neural networks Face detection Videos

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Dual-pathway Attention based Supervised Adversarial Hashing for Cross-modal Retrieval

Dual-pathway Attention based Supervised Adversarial Hashing ...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Xiaoxiao wang Meiyu Liang Xiaowen Cao Junping Du Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications

ISBN: (数字)9781728189246

ISBN: (纸本)9781728189253

Due to the success of deep learning in recent years, cross-modal retrieval has made significant progress. However, there is still a key challenge: how to learn the correlation between different modality data more effectively to improve the retrieval accuracy. Therefore, in this paper we proposed a Dual-pathway Attention based Supervise Adversarial Hashing (DASAH) to obtain a unified cross-modal semantic representation. Based on Dual-pathway attention, that is, learning the attention of image regions (text sequences) to text sequences (image regions), the fine-grained semantic correlation between different modality data is deeply mined, and the adversarial learning is integrated to further improve the learning ability of cross-modal semantic correlation. The model makes full use of dual-pathway attention to guide fine-grained cross modal feature learning, and integrates fine-grained cross modal feature learning and adversarial hashing learning in a unified framework for joint learning and optimization. Extensive empirical studies show that the proposed method outperforms several state-of-the-art methods for cross-modal retrieval.

关键词： Deep learning Correlation Conferences Semantics Big Data Data models Optimization

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Fast Text Classification by Leveraging Class Feature Words

Fast Text Classification by Leveraging Class Feature Words

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2021中国智能自动化大会(CIAC 2021)

作者： Bowen Yu Junping Du Yingxia Shao Ang Li Xin Xu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

Text classification is an important task in the NLP field,which aims to map documents into a set of predefined classes. Supervised machine learning or deep learning models have achieved great success in this field. However, these methods require a large number of high-quality labeled data and high performance machines for adequate training. In many cases, we cannot meet these conditions. In this paper,we propose a text classification algorithm leveraging class feature *** method simulates the human's intuition of document ***, we establish the feature word list of each class, then calculate the similarity between the document and feature word lists of classes and classify the document. The innovation of our method is that it doesn't need external knowledge or a huge pre-trained language model. By establishing a class feature vocabulary and similarity analysis, we achieve the classification of words and documents. In order to verify its generality and robustness, we conduct experiments on 4 mainstream datasets and compare with the supervised text classification algorithms. The experiments show that our algorithm has extremely speed advantage, comparable to the performance of the supervised classification algorithm, and good interpretability.

关键词： Text classification Class feature BoW Class-text similarity

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A Patent Text Classification Method Based on Phrase-Context Fusion Feature

A Patent Text Classification Method Based on Phrase-Context ...

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2021中国智能自动化大会(CIAC 2021)

作者： Yuhui Wang Junping Du Yingxia Shao Ang Li Xin Xu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

The traditional patent classification depends on manual classification,which needs the staff to have a strong professional background and has low efficiency. The accuracy of classification is also greatly affected by the high similarity between the subdivision categories. Therefore, automatic classification of patents is needed to speed up the patent classification and improve the accuracy. In this paper, we present a phrase-context fusion feature(PCFF) method for patent classification. The convolutional neural network(CNN) extracts the phrase feature of the patent text, and the bi-directional long short-term memory(BiLSTM) units produce the contextual features. Furthermore, an attention mechanism is developed to adaptively select the contextual features and combines the high separable parts of the features extracted. PCFF model combines the advantages of the two networks and complements each other through fusing the feature from two networks through fusion attention. Experimental results demonstrate that the PCFF model produces complementary fusion feature and obtains better performance against the state-of-the-art baselines on the patent dataset.

关键词： Patent classification Fusion feature Attention CNN LSTM

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SIPR: Side-Information Pointwise Ranking Model for Scientific Research Project Query

SIPR: Side-Information Pointwise Ranking Model for Scientifi...

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2021中国智能自动化大会(CIAC 2021)

作者： Benzhi Wang Feifei Kou Juping Du Mingying Xu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

Learning to rank has been applied to many web searches, but it cannot be directly applied to retrieval scientific research projects. In scientific research project query, people are not only concerned about the name of the project, but also the digital information and sideinformation, such as duration, achievements, funding amount of this project. Howerver, the existing learning to rank methods ignore the side-information of scientific research projects. Therefore, we propose a Side-information Pointwise Ranking model(SIPR) for scientific research project query based on deep language model, click model and learning to rank. First, we use the deep language model to extract the semantic information of text, and design a relevance calculation model to extract features of side-information, then we merge the above two features. After that we use the click model to eliminate position bias, and get a ranking score through pointwise DNN. Finally, we can get the query results ordered by these scores. Experiments on real scientific research projects dataset demonstrate that our model can achieve better performance.

关键词： Learning to rank Semantic matching Relevance matching Deep language model Machine learning

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Spatio-temporal Attention Network for Student Action Recognition in Classroom Teaching Videos

Research Square

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Research Square 2021年

作者： Huang, Yongkang Liang, Meiyu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China

Inspired by the wide application of transformer in computer vision and its excellent ability in temporal feature learning. This paper proposes a novel and efficient spatio-temporal residual attention network for student action recognition in classroom teaching video. It first fuses 2D spatial convolution and 1D temporal convolution to study spatio-temporal feature, then combines the powerful Reformer to better study the deeper spatio-temporal characteristics with visual significance of student classroom action. Based on the spatio-temporal residual attention network, a single person action recognition model in classroom teaching video is proposed. Considering that there are often multiple students in the classroom video scene, on the basis of single person action recognition, combined with object detection and tracking technology, the association of temporal and spatial characteristics of the same student targets is established, so as to realize the multi-student action recognition in classroom video scene. The experimental results on classroom teaching video dataset and public video dataset show that the proposed model achieves higher action recognition performance than the existing excellent models and methods. © 2021, CC BY.

关键词： Students

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Federal Learning Based COVID-19 Fake News Detection With Deep Self-Attention Network

Federal Learning Based COVID-19 Fake News Detection With Dee...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Suyu Ouyang Junping Du Benzhi Wang Bowen Yu Yuhui Wang Meiyu Liang Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665441506

As social media becomes more and more popular, fake news spreads rapidly which is more likely to cause serious consequences, especially during the COVID-19 pandemic. On the premise of meeting data privacy and security requirements, federated learning uses multi-party heterogeneous data to further promote machine learning. This paper proposes a federal learning based COVID-19 fake news detection model with deep self-attention network (FL_FNDM). We construct a deep self-attention network for fake news detection, which combines self-attention-based pretrained model BERT and deep convolutional neural network to detect fake news. Moreover, the fake news detection model is learned under the framework of horizontal federated learning, aiming at protecting users’ data security and privacy. The experimental results demonstrate that the proposed model can improve the performance of fake news detection on the COVID-19 dataset, which can achieve almost the same effect of sharing data without leaking user data.

关键词： COVID-19 Data privacy Social networking (online) Bit error rate Feature extraction Collaborative work Data models

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Federated Learning Method Based on Knowledge Distillation and Deep Gradient Compression

Federated Learning Method Based on Knowledge Distillation an...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Haiyan Cui Junping Du Yang Jiang Yue Wang Runyu Yu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665441506

Federated learning is a new type of multi-agency collaborative training model paradigm, which is widely used in many fields, among which communication overhead is a key issue. In order to reduce the amount of data transmitted in the communication process, we propose a federated learning algorithm based on knowledge distillation and deep gradient compression (Fed-KDDGC-SGD). First, we use local data on the client to train the teacher network, and then use the soft labels generated by the teacher network to train the student network and upload the gradient to the central server during the training process. In order to further reduce the communication bandwidth occupied by sending the gradient, the deep gradient compression algorithm is used to compress the gradient vector, and only the gradient value of the top R% of the absolute value is sent. The experimental results show that the improved federated learning algorithm effectively reduces the communication overhead and has certain practical significance.

关键词： Training Solid modeling Conferences Computational modeling Collaboration Collaborative work Data models

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FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensitive Information Leakage in Federated Learning-based Android Malware Classifier

FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensiti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning Information leakage Malware Security Risk management

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HF-Mid: A Hybrid Framework of Network Intrusion Detection for Multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

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