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2021 2nd International Conference on Computer Science and Communication technology, ICCSCT 2021

作者： Tang, Zhen Yin, Zhixiang Yang, Jing Xu, Rujie Wang, Xiyuan Pang, Xue Liu, Congcong School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan232001 China School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai201620 China

Chaotic maps are characterized by randomness, unpredictability and sensitivity to initial values, and are suitable for information encryption. The natural characteristics of DNA molecules have advantages over conventional methods in the process of mass information storage and processing. In this paper, we propose an information encryption scheme based on Logistic map and hybridization chain reaction. Firstly, the plaintext is XOR-operated by chaotic sequences generated by Logistic map, and it is scrambled by hybridization chain reaction, and finally the plaintext is transformed into encrypted text. The analysis of an example shows that the information encryption scheme has good encryption effect. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Cryptography

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Semi-supervised Multi-label Learning with Missing labels via Correlation Information

Semi-supervised Multi-Label Learning with Missing Labels via...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zexian Xie Peipei Li Jinling Jiang Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Knowledge Engineering Research Center Zhejiang Lab Hangzhou Zhejiang China

In multi-label learning, each instance is associated with a set of labels simultaneously. Most existing studies assume that the set of labels for each instance is complete. However, it is generally difficult to obtain all the relevant labels of each instance, and only a partial or even empty set of relevant labels is available, which is called semi-supervised multi-label learning with missing labels. To tackle this problem, we propose a novel framework that considers label correlations and instance correlations to recover the missing labels and utilizes a large amount of unlabeled data simultaneously to improve the classification performance. Specifically, a new supplementary label matrix is firstly obtained by learning the label correlation. Secondly, considering each class label may be decided by some specific characteristics of its own, a label-specific data representation is hence learned for each class label. Thirdly, instance correlations are utilized not only to recover the missing labels, but also to propagate the supervision information from labeled instances to unlabeled ones. In addition, a united objective function is designed to facilitate the above processing and an accelerated proximal gradient method is adopted to solve the optimization problem. Finally, extensive experimental results conducted on several benchmark datasets demonstrate the effectiveness of the proposed method compared to competing ones.

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NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Li, Bowen Song, Yufei Yu, Zhifei Hu, Shengshan Wan, Wei Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs' attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling 38

An Autoencoder-Like Nonnegative Matrix Co-Factorization for ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shenbao Pan, Yinghui Zeng, Yifeng Doshi, Prashant Liu, Guoquan Poh, Kim-Leng Lin, Mingwei College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom Intelligent Thought and Action Lab School of Computing University of Georgia United States Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. © 2024 Neural information processing systems foundation. All rights reserved.

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It Takes Two to Tango: Serverless Workflow Serving via Bilaterally Engaged Resource Adaptation

arXiv

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arXiv 2025年

作者： Wu, Jing Wang, Lin Deng, Quanfeng Yu, Chen Zhang, Dong Yan, Bingheng Liu, Fangming National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology China Paderborn University Germany Inspur Data Co. Ltd. China Peng Cheng Laboratory China

Serverless platforms typically adopt an early-binding approach for function sizing, requiring developers to specify an immutable size for each function within a workflow beforehand. Accounting for potential runtime variability, developers must size functions for worst-case scenarios to ensure service-level objectives (SLOs), resulting in significant resource inefficiency. To address this issue, we propose Janus, a novel resource adaptation framework for serverless platforms. Janus employs a late-binding approach, allowing function sizes to be dynamically adapted based on runtime conditions. The main challenge lies in the information barrier between the developer and the provider: developers lack access to runtime information, while providers lack domain knowledge about the workflow. To bridge this gap, Janus allows developers to provide hints containing rules and options for resource adaptation. Providers then follow these hints to dynamically adjust resource allocation at runtime based on real-time function execution information, ensuring compliance with SLOs. We implement Janus and conduct extensive experiments with real-world serverless workflows. Our results demonstrate that Janus enhances resource efficiency by up to 34.7% compared to the state-of-the-art. © 2025, CC BY-NC-ND.

关键词： Resource allocation

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Flow-Guided Deformable Alignment Network with Self-Supervision for Video Inpainting

Flow-Guided Deformable Alignment Network with Self-Supervisi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhiliang Wu Kang Zhang Changchang Sun Hanyu Xuan Yan Yan School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Illinois Institute of Technology USA School of Big Data and Statistics Anhui University China

Video inpainting aims to utilize plausible contents to fill missing regions in the video. State-of-the-art video inpainting methods typically generate the missing contents of the target frame (current frame) by aggregating the temporal information of reference frames (neighboring frames) aligned using deformable convolution. However, these deformable convolution alignment networks often suffer from offset overflow during training, resulting in unsatisfactory alignment, thereby obtaining compromised inpainting performance. In this paper, we propose a self-supervised Flow-Guided Deformable Alignment (FGDA) network for aligning reference frames at the feature level. FGDA computes the residual of the optical flow as the offsets. This design can effectively reduce the burden of offsets learning, thereby avoiding offset overflow. Furthermore, a gradient-weighted reconstruction loss for supervised completed frame reconstruction is designed, which can use the gradients in all directions of the video frame to emphasize the difficultly reconstructed texture regions, so that detail textures get more attention during training. Experiments show that FGDA-based video inpainting model trained with gradient-weighted reconstruction loss outperforms the state-of-the-art by a significant margin in terms of PSNR and SSIM with relative improvements of 6.2% and 2.1%, respectively.

关键词： Training Optical losses Convolution Acoustics Speech processing Optical flow

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Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education 25

Exploring Heterogeneity and Uncertainty for Graph-based Cogn...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and data Mining V.1

作者： Pengyang Shao Yonghui Yang Chen Gao Lei Chen Kun Zhang Chenyi Zhuang Le Wu Yong Li Meng Wang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei Anhui China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group HangZhou Zhejiang China

ISBN: (纸本)9798400712456

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence ***, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD.

关键词： cognitive diagnosis

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Deep Stereo Video Inpainting

Deep Stereo Video Inpainting

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiliang Wu Changchang Sun Hanyu Xuan Yan Yan School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Illinois Institute of Technology USA School of Big Data and Statistics Anhui University China

Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously. Compared with the single video inpainting that has achieved promising results using deep convolutional neural networks, inpainting the missing regions of stereo video has not been thoroughly explored. In essence, apart from the spatial and temporal consistency that single video inpainting needs to achieve, another key challenge for stereo video inpainting is to maintain the stereo consistency between left and right views and hence alleviate the 3D fatigue for viewers. In this paper, we propose a novel deep stereo video inpainting network named SVINet, which is the first attempt for stereo video inpainting task utilizing deep convolutional neural networks. SVINet first utilizes a self-supervised flow-guided deformable temporal alignment module to align the features on the left and right view branches, respectively. Then, the aligned features are fed into a shared adaptive feature aggregation module to generate missing contents of their respective branches. Finally, the parallax attention module (PAM) that uses the cross-view information to consider the significant stereo correlation is introduced to fuse the completed features of left and right views. Furthermore, we develop a stereo consistency loss to regularize the trained parameters, so that our model is able to yield high-quality stereo video inpainting results with better stereo consistency. Experimental results demonstrate that our SVINet outperforms state-of-the-art single video inpainting models.

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Revealing the significant role of band structure asymmetry on thermoelectric bipolar conduction

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Physical Review B 2025年第4期111卷 045203-045203页

作者： Jiamin Qiu Shizhen Zhi Peng Zhao Jian Wang Xiaojing Ma Sheng Ye Chenhao Lin Xuanhe Zhang Zuoxu Wu School of Materials Science and Engineering and Institute of Materials Genome and Big Data Harbin Institute of Technology Shenzhen 518055 People's Republic of China School of Science and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen 518055 China

Bipolar conduction, which is widely observed in various materials, plays a deleterious role in the thermoelectric properties. Traditionally, the single-band model is often applied to understand the transport properties of thermoelectric materials. However, it ignores the contribution from minority carriers and is incapable of quantifying the bipolar conduction. Herein, a general and feasible approach for calculating all the temperature-dependent transport properties based on the two-band model has been developed. In addition to the reduced electron Fermi energy and reduced band-gap energy, band structure asymmetry has been identified to play a significant role in bipolar conduction. Importantly, the effect of band structure asymmetry on bipolar conduction has been highlighted by using the Mg3Bi2−xSbx alloy as a typical example. Our results demonstrate that the band structure asymmetry is of great significance to the materials' thermoelectric performance.

关键词： Energy gap

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Research on Noise Reduction Methods for Wheel Force Sensor Signals

Research on Noise Reduction Methods for Wheel Force Sensor S...

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Sensors and Information technology (ICSI), International Conference on

作者： Junjie Yu Huibin Cao Qiang Li Yuxiang Sun Hu Xu School of Electronic and Information Engineering Anhui Jianzhu University Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Big Data and Internetline Shenzhen Technology University Shenzhen China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

The wheel force data acquisition system for six-component force measurements often encounters noise signals during the data acquisition process. If these noises are not removed, they can severely impact the accuracy of vehicle performance analysis. This paper proposes a noise reduction method (IWOA-VMD-IWT) based on an improved whale optimization algorithm combined with variational modal decomposition and improved wavelet thresholding. By using IWOA to optimize the VMD parameters, the sensor signal is decomposed into intrinsic mode functions (IMFs). These IMFs are then classified into noise-dominant and signal-dominant components based on similarity results. The noise-dominant components undergo the improved wavelet thresholding (IWT) process, and the noise-reduced signal is obtained through final reconstruction. The experimental results indicate that the SNR exceeds 14dB and the RMSE is below 0.17. This represents an improvement of over 8dB in SNR and a reduction of more than 0.14 in RMSE compared to traditional wavelet threshold noise reduction methods. These findings demonstrate the feasibility of the proposed method in vehicle motion analysis.

关键词： Accuracy Noise reduction data acquisition Wheels Whale optimization algorithms Space exploration Performance analysis Force sensors Root mean square Signal to noise ratio

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