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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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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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Scalable Graph Compressed Convolutions

arXiv

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arXiv 2024年

作者： Sun, Junshu Wang, Shuhui Yang, Chenxue Huang, Qingming The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The Agricultural Information Institute China Academy of Agricultural Sciences Beijing100081 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518066 China

Designing effective graph neural networks (GNNs) with message passing has two fundamental challenges, i.e., determining optimal message-passing pathways and designing local aggregators. Previous methods of designing optimal pathways are limited with information loss on the input features. On the other hand, existing local aggregators generally fail to extract multi-scale features and approximate diverse operators under limited parameter scales. In contrast to these methods, Euclidean convolution has been proven as an expressive aggregator, making it a perfect candidate for GNN construction. However, the challenges of generalizing Euclidean convolution to graphs arise from the irregular structure of graphs. To bridge the gap between Euclidean space and graph topology, we propose a differentiable method that applies permutations to calibrate input graphs for Euclidean convolution. The permutations constrain all nodes in a row regardless of their input order and therefore enable the flexible generalization of Euclidean convolution to graphs. Based on the graph calibration, we propose the Compressed Convolution Network (CoCN) for hierarchical graph representation learning. CoCN follows local feature-learning and global parameter-sharing mechanisms of convolution neural networks. The whole model can be trained end-to-end, with compressed convolution applied to learn individual node features and their corresponding structure features. CoCN can further borrow successful practices from Euclidean convolution, including residual connection and inception mechanism. We validate CoCN on both node-level and graph-level benchmarks. CoCN achieves superior performance over competitive GNN baselines. Codes are available at https://***/sunjss/CoCN. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Enhanced Semantic Head for Cascade Instance Segmentation

Enhanced Semantic Head for Cascade Instance Segmentation

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Huang, Xuerong Su, Li Li, Guorong Zhang, Xinfeng Qing, Laiyun Huang, Qingming University of Chinese Academy of Sciences Beijing China Cas Key Lab of Big Data Mining and Knowledge Management Beijing China Institute of Computing Technology Cas Key Lab of Intelligent Information Processing Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recently, cascade instance segmentation inspired by cascade object detection has achieved notable performance. Due to the lack of global information, many methods suffer from incomplete segmentation such as missing edge regions and discontinuities within instances. To solve this problem, we proposed an effective and flexible semantic head to extract enhanced spatial context information. A vision transformer is utilized to generate global context features, and a convolution network is adopted to generate spatial context features. After combining the two modules, we obtain enhanced semantic segmentation features for segmentation. Extensive experiments show that the enhanced semantic head achieves 40.6% and 42.3% mask AP for cascade predictor HTC and DSC, which surpass about 0.9 and 1.4 percentage points respectively. The enhanced semantic head is universal and effective to improve the performance of different cascade predictors. © 2022 IEEE.

关键词： Object detection

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Spatial Representation and Motion Attention Fusion Network for Few-shot Action Recognition

Spatial Representation and Motion Attention Fusion Network f...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Jiabao Wang Baogui Qi National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP) Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recently advancements in few-shot action recognition technology have been notable. However, certain challenges persist. One issue is that it is difficult to effectively model motion information, which results in subpar performance on datasets that emphasize motion itself. Another issue is the neglect of the integration of global background information and local detail information, leading to a lack of attention to appearance information in the models. To address these challenges, we propose Spatial Representation and Motion Attention (SRMA) fusion network. By constructing a dual-branch structure comprising Motion Attention Enrichment Module (MAEM) and Spatial Attention Enrichment Module (SAEM), the network simultaneously captures and models motion information while also focusing on the global and detailed information of the motion space. Furthermore, we integrate the cross-entropy loss from both branches as the loss function for the entire model. Experimental results demonstrate that our method outperforms most other approaches across multiple datasets.

关键词： Training Accuracy Focusing Data processing Spatial databases Robustness Data models

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Joint state-sharing and truncation BCJR for power-efficient coherent optical interconnections utilizing symbol-rate sampling DSP over a hollow-core fiber

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Optics Letters 2025年第10期50卷 3461-3464页

作者： Wang, Chenchen Li, Zhipei Dong, Ze Gao, Ran Song, Junyuan Mai, Tianle Guo, Dong Chang, Huan Pan, Xiaolong Zhu, Lei Xin, Xiangjun School of Information and Electronics Beijing Institute of Technology Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China School of Integrated Circuits Beijing University of Posts and Telecommunications Beijing100876 China

Power-efficient digital signal processing (DSP) architectures are indispensable for enabling the sustainable scalability of optical networks in next-generation data center interconnects (DCIs). This paper introduces a minimalist symbol-rate sampling (SRS) DSP framework for coherent optical systems, seamlessly integrated with a hollow-core fiber (HCF) to enable ultra-low-complexity signal processing. The core of this framework is the joint state-sharing and truncation Bahl–Cocke–Jelinek–Raviv (JSST-BCJR) algorithm, which enhances hardware efficiency by eliminating up to 91% of state-space computations, significantly reducing computational complexity while maintaining negligible performance degradation. Experimental validation confirms the feasibility of the proposed minimalist SRS-DSP framework through the successful transmission of a 100-Gbaud 16QAM signal over 20.6 km of HCF. This work underscores the viability of SRS-DSP in power-constrained DCIs and highlights the synergistic integration of low-complexity DSP and HCF, offering a pathway toward power-efficient, high-throughput optical networks. © 2025 Optica Publishing Group. All rights are reserved.

关键词： Network coding

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Ship flame segmentation by fusing DASPP and PE-SCAM attention 3

Ship flame segmentation by fusing DASPP and PE-SCAM attentio...

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3rd International Conference on Neural Networks, information and Communication Engineering, NNICE 2023

作者： Zheng, Xuecong Chen, Li Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Wuhan Institute of Marine Electric Propulsion Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China

ISBN: (纸本)9798350335972

To solve the problem that the flame area of small targets on ships can not be easily segmented and the flame boundary is obviously affected by other background such as smoke, this experiment improves DeepLabV3+ algorithm from the following three aspects. Firstly, Dense Atrous Spatial Pyramid Pooling (DASPP) was improved to enhance the extraction and utilization of flame boundary and small target flame features. Secondly, in order to avoid the influence of background such as smoke around the flame, the high-level flame features and shallow flame features obtained by DASPP were combined with the improved Attention Mechanism (PE-SCAM) to obtain more context information in spatial and channel dimensions, so as to enhance the processing effect of the model on the flame and its boundaries. Finally, the traditional cross loss function is optimized to reduce the influence caused by the imbalance of positive and negative samples between the flame and the background. Experimental results show that the MIou of the proposed algorithm on the ship flame data set reaches 88.43%, which can realize the accurate segmentation of small target flames and flame boundaries on the basis of ensuring high real-time performance. © 2023 IEEE.

关键词： Ships

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SNIDA: Unlocking Few-Shot Object Detection with Non-Linear Semantic Decoupling Augmentation

SNIDA: Unlocking Few-Shot Object Detection with Non-Linear S...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yanjie Wang Xu Zou Luxin Yan Sheng Zhong Jiahuan Zhou Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology China Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Once only a few-shot annotated samples are available, the performance of learning-based object detection would be heavily dropped. Many few-shot object detection (FSOD) methods have been proposed to tackle this issue by adopting image-level augmentations in linear manners. Nevertheless, those handcrafted enhancements often suf-fer from limited diversity and lack of semantic awareness, resulting in unsatisfactory performance. To this end, we propose a Semantic-guided Nonlinear Instance-level Data Augmentation method (SNIDA) for FSOD by decoupling the foreground and background to increase their diversities respectively. We design a semantic awareness enhancement strategy to separate objects from backgrounds. Concretely, masks of instances are extracted by an unsupervised semantic segmentation module. Then the diversity of samples would be improved by fusing instances into different backgrounds. Considering the shortcomings of augmenting images in a limited transformation space of existing traditional data augmentation methods, we introduce an object reconstruction enhancement module. The aim of this module is to generate sufficient diversity and nonlinear training data at the instance level through a semantic-guided masked autoencoder. In this way, the potential of data can be fully exploited in various object detection scenarios. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that the proposed method outperforms base-lines by a large margin and achieves new state-of-the-art results under different shot settings.

关键词： Computer vision Fuses Semantic segmentation Computational modeling Semantics Training data Object detection

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Secure Communication Against Active UAV Eavesdropper: A Fingerprint-Localization and Channel Tracking Approach

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IEEE Transactions on Communications 2025年

作者： Wang, Xinyao Zheng, Zhong Fei, Zesong Wu, Qingqing Beijing Institute of Technology School of Information and Electronics Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China

Unmanned aerial vehicle (UAV) can be threatening to the information security of wireless communications. By launching the pilot spoofing attack (PSA), a UAV, operating as the active aerial-eavesdropper (A-Eve), is able to intercept the confidential messages sent over the air. On one hand, it is difficult to distinguish the channel state information (CSI) of the ground users (GUs) and the CSI of A-Eve in the contaminated pilots. On the other hand, due to the high-mobility of A-Eve, the CSI of A-Eve is rapidly changing, making the design of secure transmissions challenging. To address these issues, we first propose a location-based minimum mean square error (MMSE) channel estimation algorithm to separate the CSI of GUs and the CSI of A-Eve, where the location of A-Eve is obtained by designing a cooperative localization neural network (CLNet), leveraging its angular-domain channel fingerprint (CF) of A-Eve. Furthermore, we propose an artificial noise (AN) injected MMSE precoding scheme to maximize the worst-case secrecy rate of the multi-user communications, where the power allocation between signal and AN is optimized via a long short-term memory (LSTM)-based secure predictive beamforming neural network (SPBNet). Numerical results verify the secrecy performance gain of the proposed scheme achieved by utilizing the localization ability via the CLNet and the channel tracking ability via the SPBNet, compared to the canonical nullspace AN injection scheme without prior knowledge of A-Eve’s location. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A New Method for Evaluating Core Developers In Open Source Software 13

A New Method for Evaluating Core Developers In Open Source S...

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13th IEEE International Conference on Software Engineering and Service Science, ICSESS 2022

作者： Song, Yulin Wang, Tao Shen, Yang Chang, Junsheng National University of Defense Technology National Laboratory for Parallel and Distributed Processing Hunan Province Changsha China National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Hunan Province Changsha China

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The high-quality development of Open Source Software(OSS) benefits from the contributions of developers. An important research question is how to truly assess the extent of contribution of all developers in open source software and effectively identify the core developers to avoid their loss. In this paper, we use the undirected weighted network model to build a Developer Collaboration Network(DCN) based on developers and their contributions to the GitHub community's open source software Rails, analyse the characteristics of developer partnerships through the Developer Collaboration Network, and propose a Developer Personal Impact Factor(DPIF) to evaluate the importance of developer, select the top 10 core developers in Rails, focus on their participation in the open source software process, and predicting the changes of their contributions. The experimental results show that core developers with extensive partnerships are more likely to associate with developers with fewer partnerships. The average degree of developer contribution does not increase with more developers. Developer partnerships are characterized by close localization and loose overall relationships. The Developer Personal Impact Factor identifies the core developers in open source software well. The Developer Personal Impact Factor gives a ranking in which the core team members rank in the top 1.17% on average. © 2022 IEEE.

关键词： Open source software

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