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5th International Conference on Algorithms, Computing and Artificial Intelligence, ACai 2022

作者： Li, Shiyang Xia, Guoen Zhang, Xianquan School of Computer Science and Engineering Guangxi Normal University China Guangxi Big Data Analysis of Taxation Research Center of Engineering Guangxi University of Finance and Economics China

ISBN: (纸本)9781450398343

In order to improve the hit ratio of lost customers in telecom industry, a combination prediction model of customer churn based on one-dimensional convolutional neural network(1DCNN) and gradient boosting decision tree(GBDT) is proposed. Firstly, customer data is fed into 1DCNN model, which uses one-dimensional convolution to automatically extract customer features and then predicts customer churn through full connection layer. If the prediction result of 1DCNN model is churn, the result is directly output. If the prediction result is non-churn, the customer data will be re-introduced into GBDT model for second forecast, and the new prediction result will be output. Experiments on two publicly available telecom customer data set show that the proposed combined model significantly improves the recall rate and F1 score of customer churn prediction. © 2022 ACM.

关键词： Decision trees

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion

arXiv

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arXiv 2024年

作者： Liu, Zhaochen Li, Zhixuan Jiang, Tingting National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China School of Computer Science and Engineering Nanyang Technological University Singapore National Biomedical Imaging Center Peking University China

Perceiving the complete shape of occluded objects is essential for human and machine intelligence. While the amodal segmentation task is to predict the complete mask of partially occluded objects, it is time-consuming and labor-intensive to annotate the pixel-level ground truth amodal masks. Box-level supervised amodal segmentation addresses this challenge by relying solely on ground truth bounding boxes and instance classes as supervision, thereby alleviating the need for exhaustive pixel-level annotations. Nevertheless, current box-level methodologies encounter limitations in generating low-resolution masks and imprecise boundaries, failing to meet the demands of practical real-world applications. We present a novel solution to tackle this problem by introducing a directed expansion approach from visible masks to corresponding amodal masks. Our approach involves a hybrid end-to-end network based on the overlapping region - the area where different instances intersect. Diverse segmentation strategies are applied for overlapping regions and non-overlapping regions according to distinct characteristics. To guide the expansion of visible masks, we introduce an elaborately-designed connectivity loss for overlapping regions, which leverages correlations with visible masks and facilitates accurate amodal segmentation. Experiments are conducted on several challenging datasets and the results show that our proposed method can outperform existing state-ofthe-art methods with large margins. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Deep CNN based brain tumor detection in intelligent systems

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International Journal of Intelligent Networks 2024年第1期5卷 30-37页

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taiwan Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Symbiosis International University Pune India School of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates Department of Business Administration Asia University Taiwan Taiwan Dehradun India

The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications. © 2024 The Authors

关键词： Deep learning

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Learning Latent Structures in Network Games via data-Dependent Gated-Prior Graph Variational Autoencoders 41

Learning Latent Structures in Network Games via Data-Depende...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Xue Li, Muchen Leng, Yan Liao, Renjie Center for Applied Statistics School of Statistics Renmin University of China Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China Department of Computer Science University of British Columbia VancouverBC Canada McCombs School of Business The University of Texas at Austin AustinTX United States Vector Institute for AI Canada Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada

In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning model, called data-dependent gated-prior graph variational autoencoder (GPGVAE), that infers the underlying latent interaction type (strategic complement vs. substitute) among individuals and the latent network structure based on their observed actions. Specially, we propose a spectral graph neural network (GNN) based encoder to predict the interaction type and a data-dependent gated prior that models network structures conditioned on the interaction type. We further propose a Transformer based mixture of Bernoulli encoder of network structures and a GNN based decoder of game actions. We systematically study the Monte Carlo gradient estimation methods and effectively train our model in a stage-wise fashion. Extensive experiments across various synthetic and real-world network games demonstrate that our model achieves state-of-the-art performances in inferring network structures and well captures interaction types. Copyright 2024 by the author(s)

关键词： Monte Carlo methods

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Advanced Persistent Threats (APT): evolution, anatomy, attribution and countermeasures

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9355-9381页

作者： Sharma, Amit Gupta, Brij B. Singh, Awadhesh Kumar Saraswat, V.K. Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India International Center for AI and Cyber Security Research and Innovations Department of Computer Science and Information Engineering Asia University Taichung Taiwan NITI Aayog New Delhi110001 India Lebanese American University Beirut Lebanon School of Computing Skyline University College Sharjah United Arab Emirates Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India Birkbeck University of London London United Kingdom

In today’s cyber warfare realm, every stakeholder in cyberspace is becoming more potent by developing advanced cyber weapons. They have equipped with the most advanced malware and maintain a hidden attribution. The precocious cyber weapons, targeted and motivated with some specific intention are called as Advanced Persistent Threats (APT). Developing defense mechanisms and performing attribution analysis of such advanced attacks are extremely difficult due to the intricate design of attack vector and sophisticated malware employed with high stealth and evasive techniques. These attacks also include advanced zero-day and negative-day exploits and payloads. This paper provides a comprehensive survey on the evolution of advanced malware design paradigms, APT attack vector and its anatomy, APT attack Tactics, Techniques, and Procedures (TTP) and specific case studies on open-ended APT attacks. The survey covers a detailed discussion on APT attack phases and comparative study on threat life-cycle specification by various organizations. This work also addresses the APT attack attribution and countermeasures against these attacks from classical signature and heuristic based detection to modern machine learning and genetics based detection mechanisms along with sophisticated zero-day and negative day malware countermeasure by various techniques like monitoring of network traffic and DNS logs, moving target based defense, and attack graph based defenses. Furthermore, the survey addresses various research scopes in the domain of APT cyber-attacks. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Life cycle

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Wangdong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER. © 2025 IEEE.

关键词： Emotion analysis Group emotion recognition Multi-Scale context learning Noise suppression

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Computational Modeling of Multilevel Organizational Learning: From Conceptual to Computational Mechanisms 1

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2nd International Conference on Information Technology, InCITe 2022

作者： Canbaloğlu, Gülay Treur, Jan Wiewiora, Anna Department of Computer Engineering Koç University Istanbul Turkey Social AI Group Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands Center for Safety in Healthcare Delft University of Technology Delft Netherlands School of Management QUT Business School Queensland University of Technology Brisbane Australia

ISBN: (数字)9789811973468

ISBN: (纸本)9789811973451

This paper addresses formalization and computational modeling of multilevel organizational learning, which is one of the major challenges for the area of organizational learning. It is discussed how various conceptual mechanisms in multilevel organizational learning as identified in the literature, can be formalized by computational mechanisms which provide mathematical formalizations that enable computer simulation. The formalizations have been expressed using a self-modeling network modeling approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Learning systems

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Multi-Tree Genetic Programming for Learning Color and Multi-Scale Features in Image Classification

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Fan, Qinglan Bi, Ying Xue, Bing Zhang, Mengjie School of Computer Science and Technology Shandong University of Finance and Economics Jinan China School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

data-efficient image classification, which focuses on achieving accurate classification performance with limited labeled data, has garnered significant attention. Genetic programming (GP) has achieved impressive progress in image classification, particularly in scenarios involving small amounts of labeled data. GP research typically focuses on designing tree-based model representations to learn useful image features for classification. However, most GP methods are proposed for gray-scale images and ignore the color features. Furthermore, the existing GP methods typically learn features on a single scale/resolution, restricting potential accuracy enhancements. To address these issues, this paper proposes a new multi-tree GP In single-tree GP (or simply GP), each individual consists of a single tree. In contrast, in multi-tree GP, each individual comprises multiple trees. representation for image feature learning and classification. In each individual, three trees are included to extract discriminative features from the red, green, and blue channels of the image. With the new image resizing layer in the tree representation, the proposed approach can achieve multi-scale feature extraction, i.e., flexibly learning fine-grained details and coarse-grained structures in the image, improving the classification performance. In addition, since a limitation of GP is premature convergence due to a decline in population diversity, this paper develops a hybrid parent selection method consisting of tournament and lexicase selection to increase population diversity, find the best individual, and improve classification accuracy. The experiments on six image classification datasets indicate that the proposed approach outperforms state-of-the-art neural network-based and GP-based methods in almost all comparisons. Further analyses demonstrate the effectiveness of each component and the potentially high interpretability of the proposed approach. IEEE

关键词： Genetic programming

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An Unsupervised Network Architecture Search Method for Solving Partial Differential Equations

arXiv

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arXiv 2025年

作者： Li, Qing Chen, Jingrun School of Artificial Intelligence and Data Science University of Science and Technology of China China School of Mathematical Sciences Suzhou Institute for Advanced Research University of Science and Technology of China China Suzhou Big Data & AI Research and Engineering Center China

Solving partial differential equations (PDEs) has been indispensable in scientific and engineering applications. Recently, deep learning methods have been widely used to solve high-dimensional problems. The interplay between deep learning and numerical PDEs has resulted in a variety of new methods, one of which is the physics-informed neural network (PINN). Typically, a deep learning method has three main components: a neural network, a loss function, and an optimizer. While the construction of the loss function is rooted in the definition of solution space, how to choose a optimal neural network is somewhat ad hoc, leaving much room for improvement. In the framework of PINN, we propose an unsupervised network architecture search method for solving PDEs, termed PINN-DARTS, which applies the differentiable architecture search (DARTS) to find the optimal network architecture structure in a given set of neural networks. In this set, the number of layers and the number of neurons in each layer can change. In the searching phase, both network and architecture parameters are updated simultaneously, so the running time is close to that of PINN with a pre-determined network structure. Unlike available works, our approach is unsupervised and purely based on the PDE residual without any prior usage of solutions. PINN-DARTS outputs the optimal network structure as well as the associated numerical solution. The performance of PINN-DARTS is verified on several benchmark PDEs, including elliptic, parabolic, wave, and Burgers’ equations. Compared to traditional architecture search methods, PINN-DARTS achieves significantly higher architectural accuracy. Another interesting observation is that both the solution complexity and the PDE type have a prominent impact on the optimal network architecture. Our study suggests that architectures with uneven widths from layer to layer may have superior performance across different solution complexities and different PDE types. Copyright © 202

关键词： Wave equations

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