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Expression Recognition and intelligent Classroom State Mining in Teaching Videos Based on Semi-Supervised Learning Generative Adversarial Network 5

Expression Recognition and Intelligent Classroom State Minin...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Fan, Ruoyu Liang, Meiyu Yin, Mengran Du, Junping Multimedia School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software Beijing China

ISBN: (纸本)9798350393521

We constructed a student classroom teaching video dataset, constructed a student classroom expression dataset through video frame extraction, face detection, face alignment, and facial extraction, and constructed a student classroom behavior dataset through object detection algorithm. Aiming at the characteristics that the annotation information of the students' classroom expression dataset constructed in this paper is limited and the data of some categories are less, an expression recognition method based on semi-supervised learning generative adversarial network (SSLGAN) is proposed. The problem of limited annotation information is solved through semi-supervised learning, and the problem of less data of some categories is solved through data enhancement of generative adversarial network. An intelligent classroom state mining method that integrates student expressions and behaviors has been proposed, achieving intelligent classroom state mining of teaching videos and automatic evaluation of classroom listening status for individual and overall students in different types of classrooms, such as interactive classes, discussion classes, and self-study classes. Compared with CNN, RMN, Temporal Ensembling and Mean Teacher, the proposed SSLGAN has achieved the highest accuracy in the benchmark dataset FER2013, JAFFE, RaFD and self-built student classroom expression dataset. In the case where there are few labels in the self-built student classroom expression dataset, the accuracy of student expression recognition results still reaches 76%, and the proposed intelligent classroom state mining method that integrates student expressions and behaviors for different types of classrooms also has high accuracy on the self-built student classroom teaching video dataset. © 2023 IEEE.

关键词： Students

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Distance-Aware Vector-Field and Vector Screening Strategy for 6D Object Pose Estimation 12th

Distance-Aware Vector-Field and Vector Screening Strategy f...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Wang, Lichun Yang, Chao Xin, Jianjia Yin, Baocai Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783031463075

6D object pose estimation calculates the rotation and translation matrices from the object coordinate system to the camera coordinate system and plays an important role in tasks such as robotic grasping. The voting-based 6D pose estimation method PVNet votes on a set of hypotheses to determine one as the estimation for real keypoint, and uses Perspective-n-Point (PnP) algorithm to calculate 6D pose based on the estimated keypoints. For improving the accuracy of estimated keypoints, the accuracy of hypotheses should be improved firstly. Since each hypothesis is an intersection computed with extended lines of two predicted unit vectors, three factors should be considered for improving its accuracy. The deviation of angle between predicted vector and real vector should be as small as possible. The angular deviation for predicted vectors of pixels farther away from keypoints should be smaller than that of those nearer. Any two approximately parallel or coincident predicted vectors should be prohibited to compute intersection. In light of the three points, this paper predicts vector-field instead of unit vector-field to take into account the distance from pixel to real keypoint, and proposes a distance-aware vector-field prediction loss which requires that the farther pixels from keypoints, the smaller the angular deviation for predicted vectors, and suggests a strategy for preventing approximately parallel or coincident predicted vectors from computing hypothesis. Experiments on LINEMOD and OCC-LINEMOD datasets show that our method achieves 5.9% and 8.4% improvement for the average accuracy of pose estimation in terms of ADD(-S) respectively compared with PVNet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Vectors

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A Visual Affordance Reasoning Network Based on Graph Attention 9

A Visual Affordance Reasoning Network Based on Graph Attenti...

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9th International Conference on Digital Home, ICDH 2022

作者： Xin, Jianjia Wang, Lichun Wang, Shaofan Kong, Dehui Li, Jinghua Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

Visual affordance studies what kind of interaction is possible and whether the interaction is reasonable in the current environment from an image/video. When inferring affordances of objects, semantics and relations of objects in the environment should be considered, and graph is usually used for modeling the environment context for object. Considering the weight of edge in graph describes the amount of contributed information between objects during affordance reasoning, this paper proposes VAR-Net (Visual Affordance Reasoning Network) which models the weights as graph attention coefficients and learns the weights based on objects' semantic and visual features implying their affordances. VAR-Net achieves higher accuracy on COCO-Tasks and ADE-Affordance datasets. Experiments also explain the meaning of edge weights in VAR-Net. For a definite affordance, an object commits it more, the edges linking from it to other objects have larger weights and vice versa, which makes objects' features distinguishable for inferring affordances. © 2022 IEEE.

关键词： computer vision

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Frontal person image generation based on arbitrary-view human images

Frontal person image generation based on arbitrary-view huma...

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作者： Zhang, Yong Zhang, Yuqing Chen, Lufei Yin, Baocai Sun, Yongliang Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Department of Information Science Beijing University of Technology Beijing China China Electronics Technology Group Taiji Co LTD Beijing China

Frontal person images contain the richest detailed features of humans, which can effectively assist in behavioral recognition, virtual dress fitting and other applications. While many remarkable networks are devoted to the person image generation task, most of them need accurate target poses as the network inputs. However, the target pose annotation is difficult and time-consuming. In this work, we proposed a first frontal person image generation network based on the proposed anchor pose set and the generative adversarial network. Specifically, our method first classify a rough frontal pose to the input human image based on the proposed anchor pose set, and regress all key points of the rough frontal pose to estimate an accurate frontal pose. Then, we consider the estimated frontal pose as the target pose, and construct a two-stream generator based on the generative adversarial network to update the person's shape and appearance feature in a crossing way and generate a realistic frontal person image. Experiments on the challenging CMU Panoptic dataset show that our method can generate realistic frontal images from arbitrary-view human images. © 2024 John Wiley & Sons Ltd.

关键词： Generative adversarial networks

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User story clustering in agile development:a framework and an empirical study

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Frontiers of computer Science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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An Axiom System of Probabilistic Mu-Calculus

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Tsinghua Science and technology 2022年第2期27卷 372-385页

作者： Wanwei Liu Junnan Xu David N.Jansen Andrea Turrini Lijun Zhang College of Computer Science National University of Defense TechnologyChangsha 410073China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100039China Institute of Intelligent Software Guangzhou 511458China

Mu-calculus(a.k.a.μTL)is built up from modal/dynamic logic via adding the least fixpoint operatorμ.This type of logic has attracted increasing attention since Kozen’s seminal work.PμTL is a succinct probabilistic extension of the standardμTL obtained by making the modal operators *** of this logic,such as expressiveness and satisfiability decision,have been studied *** consider another important problem:the axiomatization of that *** extending the approaches of Kozen and Walukiewicz,we present an axiom system for Pμ*** addition,we show that the axiom system is complete for aconjunctive formulas.

关键词： PμTL axiom system aconjunctive formula tableau approach

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LAMD$^{2}$2: Enabling Economical and Green Travel for Diversified Mobility on Demand Systems

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IEEE Transactions on Mobile Computing 2024年第8期23卷 8525-8540页

作者： Lige Ding Dong Zhao Zhaofeng Wang Huadong Ma Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China

The diversified mobility on demand (MoD) systems integrate both traditional fuel vehicles and green transportation tools (e.g. shared bicycles and shared e-bikes), which can not only reduce the fleet size of traditional fuel vehicles but also address the demand for short-distance travel and alleviate environmental pollution. However, despite having a variety of travel tools, the existing MoD systems neglect the guidance on passengers according to their preferences and travel characteristics and thus lead to the failure of effective cooperation among multiple travel modes and additional waste of resources. This inspired us to design a novel order allocation mechanism for diversified MoD systems. Specifically, we construct a heterogeneous order graph based on the order sets, transform the minimum fleet problem into the minimum trajectory coverage problem on the heterogeneous order graph and propose a learning-based order allocation method LAMD $^{2}$ containing three modules. i) The online breadth-first order search framework fully considers the characteristics of different travel modes and the interaction of multiple vehicles, and then leverages the competitive mechanism to well handle the heterogeneity of travel modes and improve the overall efficiency. ii) The multi-semantic travel mode selection module analyzes users’ preferences for diversified travel modes based on multi-semantic historical travel data and then determines the service mode based on the similarity of order spatiotemporal characteristics. iii) The Reinforcement Learning (RL)-based order evaluation module evaluates the long-term benefits of expanding existing For-Hire Vehicle (FHV) trajectories with different orders and updates the behavioral strategies through interactive feedback with the environment. We implement and evaluate the proposed method with a real-world trajectory dataset, demonstrating that LAMD $^{2}$ outperforms all the baselines and reduces the fleet size and energy consumption by

关键词： Resource management Spatiotemporal phenomena Trajectory Green products Vehicle dynamics Real-time systems Fuels

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Optimizing Bloom Filter Settings for Multi-keyword Search in Kademlia-Like DHT Peer-to-Peer Networks

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Chinese Journal of Electronics 2025年第2期23卷 388-393页

作者： Wenming Ma Yujie Zhang Xiangwu Meng School of Computer Science Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing China

This paper has proposed a novel method to reduce the multi-keyword query traffic in Kademlia-like Peer-to-peer (P2P) networks by optimizing the Bloom filter settings. We build some models to estimate the communication cost, the union set size, and the loss rate of performing union and intersection operations. We implement a Kademlia-like system and generate a group of datasets. We use one part of the datasets to obtain the functions how to compute the optimal parameters and use another part of datasets to verify our method. Each query can determine the optimal settings of Bloom filter with no extra configuration. Our simulation experimental results show that with optimal Bloom filters settings, we can greatly reduce the communication cost under an acceptable loss rate.

关键词： Costs Computational modeling Estimation Peer-to-peer computing

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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CSBNC-PAL: Consistency Semi-supervised Brain Network Classification Framework with Prototypical-Adversarial Learning

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Ji, Junzhong Liu, Gan Wang, Xingyu Beijing University of Technology Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology College of Computer Science Beijing Artificial Intelligence Institute Beijing100124 China

In recent years, semi-supervised learning (SSL) for functional brain network (FBN) classification has gained considerable attention due to its potential to leverage large amounts of unlabeled data from multisite. However, existing SSL methods often struggle to address the distributional differences across different sites, which limits their ability to extract discriminative features from the unlabeled data, thus hindering classification performance. To overcome this challenge, we propose a novel consistency semi-supervised FBN classification framework with prototypical-adversarial learning, termed CSBNC-PAL. Specifically, we first design a contrastive consistency module (CCM) that utilizes contrastive learning to more effectively exploit unlabeled data and learn preliminary feature representations. Then, we introduce a prototype alignment module (PAM) that computes site-aware prototypes through weighted feature clustering to guide inter-site feature alignment, and achieve inter-site equilibrium feature representations. Finally, we develop an adversarial alignment module (AAM) that employs site-discriminative adversarial training based on a gradient reversal layer to guide intra-site feature alignment, and learn site-invariant features. The three modules above are optimized collectively in an end-to-end manner, ensuring effective learning from both labeled and unlabeled data while alleviating the distribution differences of multisite data. Experiments on the ABIDE I, ABIDE II, and ADHD-200 datasets demonstrate that the CSBNC-PAL outperforms many state-of-the-art SSL methods in FBN classification. © 2013 IEEE.

关键词： Semi-supervised learning

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