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Dual-State Personalized Knowledge Tracing with Emotional Incorporation

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arXiv 2024年

作者： Wang, Shanshan Yuan, Fangzheng Wang, keyang Yang, Xun Zhang, Xingyi Wang, Meng Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230601 China Zhejiang Dahua Technology Co. Ltd China Department of Electronic Engineering and Information Science School of information Science and Technology University of Science and Technology of China Hefei230026 China Key Laboratory of intelligent computing and Signal Processing Ministry of Education school of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model's ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: Firstly, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Secondly, we design an Emotional State Tracing Module to monitor students' personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students' response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learningbased Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

Direct Participant Recruitment Strategy in Sparse Mobile Crowdsensing

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Jisuanji Xuebao/Chinese Journal of Computers 2022年第7期45卷 1539-1556页

作者： Tu, Chun-Yu Yu, Zhi-Yong Han, Lei Zhu, Wei-Ping Huang, Fang-Wan Guo, Wen-Zhong Wang, Le-Ye College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Key Lab of High Confidence Software Technologies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Sparse Mobile Crowdsensing (Sparse MCS) selects a small part of sub-areas for data collection and infers the data of other sub-areas from the collected data. Compared with Mobile Crowdsensing (MCS) that does not use data inference methods, Sparse MCS saves sensing costs while ensuring the quality of global data. However, the existing research works on Sparse MCS only focus on selecting a small part of sub-areas with higher value. It does not consider whether the recruited participants can collect the data of the required sub-areas, and also ignores the value of other data collected by the participants. In order to solve the limitations of traditional methods in sub-areas selection, this paper starts from the perspective of participants and concentrates on the contribution of the data collected by each participant to the entire collection task. All the data contributions collected by each participant will become the basis for decision-making for the participant's choice. And correspondingly, a new idea to deal with the problem of participant selection under Sparse MCS is proposed. In view of the fact that each person's daily movement trajectory is basically stable, and the data collected by different people on their respective trajectories have different values, this paper uses this regularity and difference to study how to directly recruit participants who can collect high-value data. Furthermore, the participant selection problem considered in this paper is not limited to the data collection in the next cycle, but directly recruits some participants to continue the data collection task in the next multiple cycles. The participant selection problem that spans multiple cycles can be modeled as a dynamic decision-making problem. Since heuristic strategies may fall into a local optimal solution, this paper uses reinforcement learning to solve the participant selection problem: We use the participant selection system as an agent of reinforcement learning, and design the

关键词： Compressed sensing

M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection

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arXiv 2021年

作者： Wang, Junke Wu, Zuxuan Ouyang, Wenhao Han, Xintong Chen, Jingjing Lim, Ser-Nam Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Huya Inc Meta AI

The widespread dissemination of Deepfakes demands effective approaches that can detect perceptually convincing forged images. In this paper, we aim to capture the subtle manipulation artifacts at different scales using transformer models. In particular, we introduce a Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels. M2TR further learns to detect forgery artifacts in the frequency domain to complement RGB information through a carefully designed cross modality fusion block. In addition, to stimulate Deepfake detection research, we introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods. We conduct extensive experiments to verify the effectiveness of the proposed method, which outperforms state-of-the-art Deepfake detection methods by clear margins. Copyright © 2021, The Authors. All rights reserved.

关键词： Frequency domain analysis

Consensus Disturbance Rejection of Nonlinear Multi-Agent Systems Over Cooperation-Competition Networks

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Consensus Disturbance Rejection of Nonlinear Multi-Agent Sys...

Chinese Automation Congress (CAC)

作者： Qiang Wang Wangli He Dayu Tan Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665426480

This study addresses the consensus problem for nonlinear multi-agent systems (MASs) under the exogenous disturbances over cooperation-competition networks. Firstly, for judging the disturbances produced by the exogenous environment, a disturbance observer-based approach is developed and incorporated into the controller. Secondly, two novel nonlinear control protocols depended on state feedback and output feedback control are constructed to guarantee consensus for MASs over the signed directed graph, respectively. Thirdly, theoretical guarantees on the effectiveness of the proposed controller in steering the system to a bipartite leader-following consensus with exogenous disturbances are derived. Finally, numerical simulations inspired by a real-world physical MAS are provided to illustrate the effectiveness of the proposed controllers.

关键词： State feedback Protocols Automation Directed graphs Numerical simulation Disturbance observers Output feedback

Design of MEMS Step Impedance Microstrip Filter in Ku Band

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Design of MEMS Step Impedance Microstrip Filter in Ku Band

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Tianyu Bai Guohui Yang Kuang Zhang Yingsong Li Tao Jiang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin China

According to the miniaturization characteristics of step impedance resonators, a step impedance bandpass filter with a center frequency of 16.55 GHz and a bandwidth of 500MHz is designed. The effects of parameter changes such as substrate resistivity and the shape of metal via etching on the performance of the filter are studied. Finally, the filter is fabricated and its performance is tested under MEMS technology.

关键词： Band-pass filters Micromechanical devices Microstrip filters Shape Resonator filters Metals Conductivity

The minority matters: a diversity-promoting collaborative metric learning algorithm 22

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The minority matters: a diversity-promoting collaborative me...

Proceedings of the 36th International Conference on Neural Information processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

关键词：

Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024, CC BY.

关键词： Semantics

Hybrid iterative optimized phase retrieval technology for defect detection in optical components

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Journal of Optics 2025年第6期27卷 065607-065607页

作者： Hong Cheng Zijing Hu Jinyu Fang Chuan Shen Key Laboratory of intelligent Computing & Signal Processing Anhui University Hefei Anhui Province 230601 People’s Republic of China Anhui Shengri Intelligent Tech Co. Ltd Hefei Anhui Province 230088 People’s Republic of China

The preparation process of optical components will inevitably produce damage, with the surface undergoing scratches; the sizes of these scratches will determine the loss of quality of the instrument imaging, which can result in large errors in experiments. Since most of the information about the shapes and depths of the scratches is characterized by the phase information, acquisition of this phase information is crucial in order to detect the depth of the scratches. The classical light scattering method can only detect information about the intensity of the scratches through existing imaging devices, and cannot directly obtain the phase information for the scratches. Although the angular spectrum iteration algorithm can obtain the phase information, it has a limitation in that it is easily affected by the initial random phase. In this paper, a hybrid iterative optimized phase retrieval algorithm is proposed that combines the intensity transfer equation with the angular spectrum iterative algorithm, an approach that can improve the accuracy of the depth detection of scratches while ensuring a good convergence speed. The initial phase results from a scratched sample are first obtained by solving the intensity transport equation, and the optimized phase results and the corresponding three-dimensional distribution of the scratches are then presented using an optimization iteration module, based on amplitude optimization weighting and phase gradient descent. Finally, the required scratch depth information is obtained from the phase modulation characteristics of the scratches. The feasibility and accuracy of the method proposed in this paper are confirmed by simulation experiments and optical experiments, respectively; the root mean square error is found to be reduced from 13.14% to 4.31% and the structural similarity is improved from 84.01% to 94.46% in the scratch reconstruction experiments.

关键词： scratch detection angular spectrum iteration hybrid iteration transport of intensity equation optimized weighting gradient descent

Quasi-cyclic perfect codes in Doob graphs and special partitions of Galois rings

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arXiv 2023年

作者： Shi, Minjia Li, Xiaoxiao Krotov, Denis S. Özbudak, Ferruh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China School of Mathematical Sciences Anhui University Hefei230601 China Sobolev Institute of Mathematics pr. Akademika Koptyuga 4 Novosibirsk630090 Russia Faculty of Engineering and Natural Sciences Sabancı University Middle East Technical University 34956 Istanbul Ankara06800 Turkey

The Galois ring GR(4∆) is the residue ring Z4[x]/(h(x)), where h(x) is a basic primitive polynomial of degree ∆ over Z4. For any odd ∆ larger than 1, we construct a partition of GR(4∆)\{0} into 6-subsets of type {a, b, −a−b, −a, −b, a+b} and 3-subsets of type {c, −c, 2c} such that the partition is invariant under the multiplication by a nonzero element of the Teichmuller set in GR(4∆) and, if ∆ is not a multiple of 3, under the action of the automorphism group of GR(4∆). As a corollary, this implies the existence of quasi-cyclic additive 1-perfect codes of index (2∆ − 1) in D((2∆ − 1)(2∆ − 2)/6, 2∆ − 1) where D(m, n) is the Doob metric scheme on Z2m+n .MSC Codes 94B99 Copyright © 2023, The Authors. All rights reserved.

关键词： Algebra