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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Yu Li, Ruoyu Wu, Nengwu Li, Qing Lin, Xinhan Hu, Yang Li, Tao Jiang, Yong Shanghai Artificial Intelligence Laboratory China Peng Cheng Laboratory China College of Computer Science and Software Engineering Shenzhen University China Tsinghua University China Tsinghua Shenzhen International Graduate School China Hunan University of Science and Technology China

In high-stakes sectors such as network security, IoT security, accurately distinguishing between normal and anomalous data is critical due to the significant implications for operational success and safety in decision-making. The complexity is exacerbated by the presence of unlabeled data and the opaque nature of black-box anomaly detection models, which obscure the rationale behind their predictions. In this paper, we present a novel method to interpret the decision-making processes of these models, which are essential for detecting malicious activities without labeled attack data. We put forward the Segmentation Clustering Decision Tree (SCD-Tree), designed to dissect and understand the structure of normal data distributions. The SCD-Tree integrates predictions from the anomaly detection model into its splitting criteria, enhancing the clustering process with the model's insights into anomalies. To further refine these segments, the Gaussian Boundary Delineation (GBD) algorithm is employed to define boundaries within each segmented distribution, effectively delineating normal from anomalous data points. At this point, this approach addresses the curse of dimensionality by segmenting high-dimensional data and ensures resilience to data variability and perturbations through flexible boundary fitting. We transform the intricate operations of anomaly detection into an interpretable rule's format, constructing a comprehensive set of rules for understanding. Our method's evaluation on diverse datasets and models demonstrates superior explanation accuracy, fidelity, and robustness over existing method, proving its efficacy in environments where interpretability is paramount. © 2024 Neural information processing systems foundation. All rights reserved.

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Exploring Deep Learning and Grad-CAM for Speech-Based Detection of Mild Traumatic Brain Injury

Exploring Deep Learning and Grad-CAM for Speech-Based Detect...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rojas, Fredy Madanian, Samaneh Templeton, John Michael Poellabauer, Christian Schneider, Sandra L. Department of Computer Science and Software Engineering Auckland New Zealand University of South Florida Department of Computer Science and Engineering TampaFL United States Florida International University School of Computing and Information Sciences MiamiFL United States Saint Mary's College Department of Communicative Sciences & Disorders Notre DameIN United States

ISBN: (纸本)9798350362480

Mild traumatic brain injury (mTBI) is challenging to diagnose due to its subtle and transient symptoms, making noninvasive diagnostic tools crucial for early detection. This study explores the use of a custom ResNet deep learning model combined with the Grad-CAM interpretability technique for mTBI detection via speech analysis. Speech data were transformed into Mel-spectrograms and fed into the model for binary classification between concussed and control individuals. The Grad-CAM method provided insights into which frequency regions of the Mel-spectrogram were most important for the model's predictions, with higher-frequency regions identified as significant for the model in detecting mTBI. Using Monte Carlo Cross-Validation (MCCV), we evaluated 50 different subject train-test split configurations to gain insights into the model's performance stability and variability. This analysis can assess the model's ability to learn consistent patterns within the dataset and suggest potential generalization tendencies. The variability observed in the performance metrics distribution underscores the importance of robust evaluation methods, particularly when working with small datasets. The combination of deep learning, robust evaluation technique and interpretability in this study contributes to the development of clinically viable, speech-based tools for mTBI detection, with potential applications in sports and healthcare settings. © 2024 IEEE.

关键词： Speech analysis

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Dynamic Language Refinement for Graceful Degradation in Scene Text Recognition 6

Dynamic Language Refinement for Graceful Degradation in Scen...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tian, Shaocong Jin, Rize Paik, Joon-Young Fu, Chen Tiangong University School of Software Tianjin China Tiangong University School of Computer Science &technology Tianjin China Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China

ISBN: (纸本)9798350325485

Scene text recognition is a mature technology which can get a near-perfect result in regular scenes. However, there are still troubles in recognizing scene texts with image blur, characters missing, and occlusion. Recent end-To-end frameworks have investigated the importance of linguistic knowledge, utilizing either implicit or explicit language models. However, joint training of language and vision models tends to interfere the tunings of each other. In this paper, we present a novel gradient isolation method to train language and vision models separately, yet in an end-To-end architecture. In addition, we propose a dynamic iterative training procedure for graceful refining the language model. Extensive experiments confirm that the proposed method has superiority on regular and irregular scene text images and achieves state-of-The-Art results. © 2023 IEEE.

关键词： Iterative methods

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Client Segmentation and Customization in E-Commerce: Applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features 13

Evolutionary Multiobjective Feature Selection Assisted by Un...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Duan, Xuan Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets. © 2024 IEEE.

关键词： Feature Selection

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Data Mining Management System Optimization using Swarm Intelligence 5

Data Mining Management System Optimization using Swarm Intel...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Hasan Al Mashhadani, Asraa Ahmed Inan, Timur Ahmed, Ali Saadoon Altinbas University Department of Information Technologies Istanbul Turkey Altinbas University Department of Software Engineering Istanbul Turkey AlMaarif University College Department of Computer Science AlAnbar Iraq

ISBN: (纸本)9798350337525

Because of a phenomenon known as the 'curs e of dimensionality,' standard machine learning algorithms have difficulty dealing with high-dimensional data. There are more possible examples in the data space as the number of dimensions increases;however, as the number of dimensions increases, the amount of data that can be accessed decreases. There are a greater number of potential instances in the data space when there are more dimensions. The amount of data required by machine learning algorithms to address problems with such a high dimension increases exponentially with the number of problem-related characteristics. In this paper, we examine the suggested algorithms' methods for selecting features and their relationship to the data representation. © 2023 IEEE.

关键词： Swarm intelligence

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第4期3卷 526-540页

作者： Sun, Yanan Yen, Gary G. Xue, Bing Zhang, Mengjie Lv, Jiancheng Sichuan University College of Computer Science Chengdu610065 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74078 United States Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand

The superiority of convolutional neural networks (CNNs) largely relies on their architectures that are usually manually crafted with extensive human expertise. Unfortunately, such kind of domain knowledge is not necessarily owned by every interested user. Data mining on existing CNNs can discover useful patterns and fundamental comments from their architectures, providing researchers with strong prior knowledge to design effective CNN architectures when they have no expertise in CNNs. There are various state-of-the-art data mining algorithms at hand, while there is only rare work on mining CNN architectures. One of the main reasons is the gap between CNN architectures and data mining algorithms. Specifically, the current CNN architecture descriptions cannot be exactly vectorized to feed to a data mining algorithm. In this article, we propose a unified approach, named ArcText, to describing CNN architectures based on text. Particularly, four different units and an ordering method have been elaborately designed in ArcText, to uniquely describe the same CNN architecture with sufficient information. Also, the resulted description can be exactly converted back to the corresponding CNN architecture. ArcText bridges the gap between CNN architectures and data mining researchers, and has the potential to be utilized to wider scenarios. © 2020 IEEE.

关键词： Data mining

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Saving The Elf: Immersive VR Experience Utilizing Multiple Relaxing Approaches

Saving The Elf: Immersive VR Experience Utilizing Multiple R...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Nie, Cheng Ding, Ding Wu, Jinhao Yang, Xu Ou, Ronghuang Du, Wei College of Software Engineering Southeast University Nanjing China Southeast University Computer Experimental Teaching Center Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence Southeast University Nanjing China School of Public Health Southeast University Nanjing China

ISBN: (纸本)9781665410205

The global prevalence of anxiety has seen a significant increase in the past few years. Anxiety, if left untreated, can lead to various detrimental effects, such as sleep disturbances, excessive fear, and even suicidal tendencies. Traditional face-to-face therapies have been employed to address this issue;however, their effectiveness is limited by time and space constraints, particularly during the COVID-19 pandemic. As a result, computer-based systems have emerged as a potential solution. However, most existing relaxation systems rely on a single method and have unnatural interactions, leading to a lack of variety and engagement. To overcome these limitations, this study proposes a virtual reality (VR) relaxation system that combines various relaxation techniques, a captivating storyline and natural interactions to enhance the user experience. To assess the effectiveness of the proposed system, a pilot study was conducted with 36 participants. The study utilized a between-subjects design, dividing the participants into three groups: a control group, a progressive muscular relaxation group, and a virtual reality relaxation group. The findings revealed that the virtual reality relaxation system significantly reduced anxiety levels among the participants. Importantly, this effect was observed to persist even after a period of time and was found to be more effective than using only one relaxation method in the long term. Moreover, the performance of our system is close to that of commercial systems. © 2024 IEEE.

关键词： Virtual environments

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Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

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computers, Materials & Continua 2022年第10期73卷 165-180页

作者： Wenbin Bi Fang Yu Ning Cao Russell Higgs School of Computer and Software Dalian Neusoft University of InformationDalian116023China School of Information Engineering Qingdao Bin Hai UniversityQingdaoChina School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China School of Mathematics and Statistics University College DublinDublinIreland

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite *** order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of ***,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are ***,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the ***,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of *** shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud *** with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

关键词： Internet of Vehicles mobile cloud computing resource load predicting multi distributed resource computing scheduling chaos analysis algorithm improved artificial bee colony function

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