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IEEE Conference on Global Communications (GLOBECOM)

作者： Xi Wang Xiaotong Zhao Juncheng Wang Qingjiang Shi School of Software Engineering Tongji University Shanghai China Department of Computer Science Hong Kong Baptist University Hong Kong China Shenzhen Research Institute of Big Data Shenzhen China

We consider downlink beamforming design to maximize the weighted sum rate (WSR) in a user-centric cell-free network, where distributed access points (APs) are organized in a cluster to jointly serve each user equipment (UE). This architecture ensures uniform service quality even at the cell edge, but synchronization between APs can be problematic, necessitating the utilization of non-coherent joint transmission (NCJT) that eliminates the need for strict synchronization. Most existing works on beamforming design for NCJT assume that the classic weighted minimum mean square error (WMMSE) approach is not applicable, and design their beamforming algorithms based on the successive convex approximation (SCA) method with high computational complexity. In this work, we for the first time demonstrate the applicability of the WMMSE approach for NCJT in cell-free networks. Based on the unique observations on the structures of the WSR maximization problem for NCJT, we propose an efficient WMMSE based beamforming algorithm. Our proposed algorithm is guaranteed to converge to a stationary point of the WSR maximization problem. Furthermore, our beamforming updates are in closed form with low computational complexity. Simulation results demonstrate substantial performance gain of our proposed algorithm over the current best SCA based alternatives in both computational complexity and convergence time.

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Joint Participation Incentive and Network Pricing Design for Federated Learning

Joint Participation Incentive and Network Pricing Design for...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Ningning Ding Lin Gao Jianwei Huang Department of Electrical and Computer Engineering Northwestern University Evanston IL USA Shenzhen Research Institute of Big Data Shenzhen China School of Electronics and Information Engineering and the Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Shenzhen China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Shenzhen China

Federated learning protects users’ data privacy though sharing users’ local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users’ contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users’ private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server’s cost by up to 24.87% comparing with the state-of-the-art benchmarks.

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Machine Learning-based Alzheimer's Disease Prediction using Personalized Methods 3

Machine Learning-based Alzheimer's Disease Prediction using ...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Lavanya, M. Chandan, Radha Raman Rajasekar, P. Rham, P. Rethik Deivakani, M. Mahesh Kumar, A.S. Master of Computer Applications Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shambhunath Institute of Engineering and Technology Department of Computer Science & Engineering Uttar PradeshJhalwa Prayagraj211015 India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Saveetha Medical College Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Pes College of Engineering Department of Electronics and Communication Engineering Karnataka Mandya571401 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Alzheimer's disease (AD) is a neurological illness which occurs over time. Early detection reduces the likelihood of disease worsening condition. Unfortunately, the present research focuses on defining disease processes in their existing stages rather than forecasting disease progression. The Generative Personalized embedding model (GPE) is a multi-layer perceptron that is proficient of relating prior knowledge to the prediction process by realizing the patient's behavioural data and helps in forecasting the disease progression. This work suggest a prediction process based on GPE. As a result, a GPA network is built with the connectivity layers and activated layers to observe the possible correlation among information and next generation of Alzheimer's disease. Experiments reveal that the outcomes significantly outperformed the majority of previous designs. © 2022 IEEE.

关键词： Machine learning

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DomainAdaptor: A Novel Approach to Test-time Adaptation

arXiv

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arXiv 2023年

作者： Zhang, Jian Qi, Lei Shi, Yinghuan Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science and Engineering Southeast University China

To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that aims to adapt a trained CNN model to unseen domains during the test. To maximumly mine the information in the test data, we propose a unified method called DomainAdaptor for the test-time adaptation, which consists of an AdaMixBN module and a Generalized Entropy Minimization (GEM) loss. Specifically, AdaMixBN addresses the domain shift by adaptively fusing training and test statistics in the normalization layer via a dynamic mixture coefficient and a statistic transformation operation. To further enhance the adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy Minimization loss to better exploit the information in the test data. Extensive experiments show that DomainAdaptor consistently outperforms the state-of-the-art methods on four benchmarks. Furthermore, our method brings more remarkable improvement against existing methods on the few-data unseen domain. The code is available at https://***/koncle/DomainAdaptor. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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A Comparative Study on Multi-modal Fusion for Automated Lung Disease Diagnostics

A Comparative Study on Multi-modal Fusion for Automated Lung...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Sachin Kumar Pradeep Kumar Mallick Olga Vorfolomeyeva Big Data and Machine Learning Lab South Ural State University Chelyabinsk Russia School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India Faculty of Arts and Culture West Kazakhstan State University Uralsk Kazakhstan

High-quality X-rays are now available to diagnose lung diseases with the help of radiologists. However, the diagnostic process is time consuming and depends on specialist availability in medical institutions. Patient information may include chest X-rays of varying quality, medical test results, doctors’ notes and prescriptions, and medication details, among others. In this study, we present a model for classifying pulmonary diseases using multimodal data from patient clinical studies and radiographic images. Various methods were used to generate artificial samples for both images and tabular data on the laboratory study results during data preparation. We also proposed a method for establishing a correspondence between the generated modals. The late fusion architecture of the proposed multimodal model was implemented. We conducted experiments on pulmonary data-set with two modalities. Results shows that an increase in accuracy and other parameters were observed for multimodal data fusion using our model in comparison with image only modality and clinical data only modality. It strengthen the fact that multimodality provides more insight for the fusion model to learn and provide a more precise diagnosis than single modality.

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Container Anomaly Detection System Based on Rule Mining and Matching

Container Anomaly Detection System Based on Rule Mining and ...

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2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022

作者： Yu, Daisong Xie, Yulai Long, Huachao Jin, Minpeng Li, Xuan School of Cyber Science and Engineering Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China Wuhan National Lab for Optoelectronics F311 Wuhan China School of Computer Science Huazhong University of Science and Technology Wuhan China Nsfocus Inc. A9-3/F Optical Valley Software Park Wuhan China

ISBN: (数字)9781665496919

ISBN: (纸本)9781665496919

Compared with virtual machine technology, container technology is more and more popular by virtue of its excellent convenience and reliable security isolation. Containers will encounter various anomalies during operation. For maintenance personnel, a large number of abnormal logs makes the work of anomaly analysis very difficult, so anomaly detection technology is needed. In this paper, a container anomaly detection system based on rule mining and matching is proposed, which collects and simplifies the container's logs generated in the abnormal time period, then delivers them to association rule mining algorithm for rule mining and display rules to the maintainer who makes a decision after a simple analysis. The experimental results show that the rule mining algorithm can process log entries in less time consumption compared with traditional association rule mining algorithm, and container anomaly detection system can effectively help system administrator to locate anomalies. © 2022 IEEE.

关键词： Anomaly detection

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In defense of softmax parametrization for calibrated and consistent learning to defer 23

In defense of softmax parametrization for calibrated and con...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yuzhou Cao Hussein Mozannar Lei Feng Hongxin Wei Bo An School of Computer Science and Engineering Nanyang Technological University Singapore CSAIL and IDSS Massachusetts Institute of Technology Cambridge MA Department of Statistics and Data Science Southern University of Science and Technology China

Enabling machine learning classifiers to defer their decision to a downstream expert when the expert is more accurate will ensure improved safety and performance. This objective can be achieved with the learning-to-defer framework which aims to jointly learn how to classify and how to defer to the expert. In recent studies, it has been theoretically shown that popular estimators for learning to defer parameterized with softmax provide unbounded estimates for the likelihood of deferring which makes them uncalibrated. However, it remains unknown whether this is due to the widely used softmax parameterization and if we can find a softmax-based estimator that is both statistically consistent and possesses a valid probability estimator. In this work, we first show that the cause of the miscalibrated and unbounded estimator in prior literature is due to the symmetric nature of the surrogate losses used and not due to softmax. We then propose a novel statistically consistent asymmetric softmax-based surrogate loss that can produce valid estimates without the issue of unboundedness. We further analyze the non-asymptotic properties of our method and empirically validate its performance and calibration on benchmark datasets.

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ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation

arXiv

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arXiv 2025年

作者： Bian, Qingtian Vinícius de Carvalho, Marcus Li, Tieying Xu, Jiaxing Fang, Hui Ke, Yiping College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Northeastern University China Research Institute for Interdisciplinary Sciences Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it. Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings. Another challenge lies in aligning the domain-specific and cross-domain sequences. Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains. In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). Specifically, leveraging LoRA’s effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation. First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics. Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain. Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths. Experimental results on three datasets demonstrate that our approach outperforms other CDSR counterparts by a large margin. The codes are available in https://***/DiMarzioBian/ABXI. © 2025, CC BY.

关键词： Knowledge transfer

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design

arXiv

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arXiv 2025年

作者： Hu, Yongquan Tang, Jingyu Gong, Xinya Zhou, Zhongyi Zhang, Shuning Elvitigala, Don Samitha Mueller, Florian Hu, Wen Quigley, Aaron J. School of Computer Science and Engineering University of New South Wales Sydney Australia The University of Tokyo Tokyo Japan Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Tsinghua University Beijing China Department of Computer Science and Engineering South University of Science and Technology Shenzhen China CSIRO’s Data61 University of New South Wales Sydney Australia

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware *** Codes 68U35, 68T07 Copyright © 2025, The Authors. All rights reserved.

关键词： Taxonomies

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Stroke Prediction Using Machine Learning

Stroke Prediction Using Machine Learning

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International Conference on Advances in Electronics, computers and Communications (ICAECC)

作者： Abinandhini D M Aman Kumar Gudi Vishnu Teja Mandava Sukesh Divya S Naman Chauhan I R Oviya Kalpana Raja Department of Computer Science and Engineering (Artificial Intelligence) Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

A stroke is a serious medical emergency that happens when bleeding or blood clots cut off the blood flow to a part of the brain. With a mortality rate of 5.5 million per year, it ranks as the second leading cause of death globally. Over 15 million individuals experience a stroke each year, and one person dies from one every four minutes. According to the World Health Organization, stroke is the main cause of death and disability worldwide (WHO). Identifying the many stroke warning signs helps lessen the severity of the stroke. A stroke can be avoided in up to 80% of instances because it is typically the result of a poor lifestyle. As a result, stroke prediction becomes important and should be employed to stop it from causing long-term harm. The current study uses a variety of machine learning models, including Gaussian Naive Bayes, Logistic Regression, Support Vector Machine (SVM), KNN and Random Forest to predict stroke. The paper presents the comparison among all machine learning algorithms. Analysis of results revealed that KNN had the least accuracy of 76.32% and Random Forest had the highest accuracy of 94.81%.

关键词： Support vector machines Accuracy Organizations Stroke (medical condition) Predictive models Vectors Physiology

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