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The Journal of Machine Learning Research 2022年第1期23卷 2414-2468页

作者： Zijun Gao Trevor Hastie Department of Statistics Stanford University Stanford CA Department of Statistics and Department of Biomedical Data Science Stanford University Stanford CA

Conditional density estimation is a fundamental problem in statistics, with scientific and practical applications in biology, economics, finance and environmental studies, to name a few. In this paper, we propose a conditional density estimator based on gradient boosting and Lindsey's method (LinCDE). LinCDE admits flexible modeling of the density family and can capture distributional characteristics like modality and shape. In particular, when suitably parametrized, LinCDE will produce smooth and non-negative density estimates. Furthermore, like boosted regression trees, LinCDE does automatic feature selection. We demonstrate LinCDE's efficacy through extensive simulations and three real data examples.

关键词： conditional density estimation gradient boosting Lindsey's method

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Malicious Traffic Classification in WSN using Deep Learning Approaches 2

Malicious Traffic Classification in WSN using Deep Learning ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Gupta, Chhote Lal Prasad Rajassekharan, Dinesh Sharma, Dilip Kumar Elangovan, Mohanraj Myilsamy, Varatharaj Upreti, Kamal Bansal Institute of Engineering and Technology Dept. of Computer Science and Engineering Lucknow India Peninsula College Faculty of Computer Science Peninsula Malaysia Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruchengode637215 India V.S.B College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350369960

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. © 2023 IEEE.

关键词： data routing deep learning LSTM malicious transmission neural network Traffic data transmission WSN

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Aspect category and sentiment polarity detection using a permutation language-based transformer model

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Procedia computer science 2025年 258卷 4179-4189页

作者： Ashok Kumar Jayaraman Tina Ester Trueman Gayathri Ananthakrishnan Abirami Murugappan Erik Cambria Manikandan G Satanik Mitra HCLTech Engineering and R&D Services Chennai Tamil Nadu India Department of Computer Science University of the People California USA School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. Recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result.

关键词： Aspect-based sentiment analysis permutation language model deep learning transformers multiclass classification

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Virtual staining for pathology:Challenges,limitations and perspectives

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Intelligent Oncology 2025年第2期1卷 105-119页

作者： Weiping Lin Yihuang Hu Runchen Zhu Baoshun Wang Liansheng Wang Department of Computer Science School of InformaticsXiamen UniversityXiamen Fujian 361005China National Institute for Data Science in Health and Medicine Xiamen UniversityXiamen Fujian 361005China

In pathological examinations,tissue must first be stained to meet specific diagnostic requirements,a meticulous process demanding significant time and expertise from *** advancements in deep learning,this staining process can now be achieved through computational methods known as virtual *** technique replicates the visual effects of traditional histological staining in pathological imaging,enhancing efficiency and reducing *** research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic *** previous reviews that broadly cover the clinical applications of virtual staining,this paper focuses on the technical methodologies,encompassing current models,datasets,and evaluation *** highlights the unique challenges of virtual staining compared to traditional image translation,discusses limitations in existing work,and explores future *** a macro perspective,we avoid overly intricate technical details to make the content accessible to clinical ***,we provide a brief introduction to the purpose of virtual staining from a medical standpoint,which may inspire algorithm-focused *** paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians,fostering the integration of technical solutions and medical expertise in the development of virtual staining *** collaboration seeks to create more efficient,generalized,and versatile virtual staining models for a wide range of clinical applications.

关键词： Virtual staining Pathological image generation Models and datasets Model evaluation

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Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing

arXiv

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arXiv 2024年

作者： Zeng, Xianli Cheng, Guang Dobriban, Edgar Department of Statistics and Data Science University of Pennsylvania United States University of California Los Angeles United States

Machine learning algorithms may have disparate impacts on protected groups. To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairness constraints. We introduce the notion of linear disparity measures, which are linear functions of a probabilistic classifier;and bilinear disparity measures, which are also linear in the group-wise regression functions. We show that several popular disparity measures-the deviations from demographic parity, equality of opportunity, and predictive equality-are bilinear. We find the form of Bayes-optimal fair classifiers under a single linear disparity measure, by uncovering a connection with the Neyman-Pearson lemma. For bilinear disparity measures, Bayesoptimal fair classifiers become group-wise thresholding rules. Our approach can also handle multiple fairness constraints (such as equalized odds), and the common scenario when the protected attribute cannot be used at the prediction phase. Leveraging our theoretical results, we design methods that learn fair Bayes-optimal classifiers under bilinear disparity constraints. Our methods cover three popular approaches to fairness-aware classification, via pre-processing (Fair Up- and Down-Sampling), in-processing (Fair Cost-Sensitive Classification) and post-processing (a Fair Plug-In Rule). Our methods control disparity directly while achieving near-optimal fairness-accuracy tradeoffs. We show empirically that our methods compare favorably to existing algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Enhancing Breast Cancer Diagnosis with Vision Transformers: A Novel Approach using MaskGuided Attention for Ultrasound Image Classification

Enhancing Breast Cancer Diagnosis with Vision Transformers: ...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Deena Sivakumar Anamika Kumari Divya Dharshini K Samiksha S Hemachandran M Department of Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Chennai Department of Computer Science (AI & Data Analytics) Sri Ramachandra Institute of Higher Education and Research Chennai

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Deep learning models, notably convolutional neural networks (CNNs), demonstrate great promise in medical image processing. Nonetheless, CNNs frequently encounter challenges in capture holistic context and long- range dependencies in high-resolution images, which are crucial for accurate medical diagnostics. A new state-of-the-art technique called Vision Transformers (ViTs) uses self-attention mechanisms for gathering global correlations between image patches, thereby addressing some of the inherent limitations of CNNs. This study investigates the application of ViTs for diagnosing breast cancer from ultrasound images of tumor. The findings suggest that ViTs not only achieve competitive accuracy in identifying breast cancer lesions but also offer superior interpretability through attention mechanism, which can aid clinicians in understanding model decisions. This paper highlights the potential of ViTs as a powerful tool in medical imaging, particularly for breast cancer diagnosis, and outlines future research directions for enhancing clinical applicability.

关键词： Deep learning computer vision Accuracy Attention mechanisms Ultrasonic imaging Transformers Breast cancer Convolutional neural networks Medical diagnosis Biomedical imaging

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Inducibility of rainbow graphs

arXiv

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arXiv 2024年

作者： Cairncross, Emily Mizgerd, Clayton Mubayi, Dhruv Department of Mathematics Statistics and Computer Science University of Illinois ChicagoIL60607 United States

Fix k ≥ 11 and a rainbow k-clique R. We prove that the inducibility of R is k!/(kk − k). An extremal construction is a balanced recursive blow-up of R. This answers a question posed by Huang, that is a generalization of an old problem of Erdős and Sós. It remains open to determine the minimum k for which our result is true. More generally, we prove that there is an absolute constant C > 0 such that every k-vertex connected rainbow graph with minimum degree at least C log k has inducibility k!/(kk − k). © 2024, CC BY.

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Evaluating Individual Contributions in Teams for Project-Based Learning in data science

Evaluating Individual Contributions in Teams for Project-Bas...

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Frontiers in Education (FIE) Conference

作者： Arko Barman Su Chen Genevera I. Allen Data to Knowledge Lab Rice University Houston TX USA Department of Statistics and Data Science The University of Texas at Austin Austin TX USA Department of Statistics Columbia University New York NY USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

In this Innovative-Practice Full Paper, we present the design and implementation of a novel strategy for the assessment of the individual contributions of students working in teams for an interdisciplinary project-based learning (PBL) course. Team-based and project-based learning have been the cornerstones of experiential learning in recent years. In such courses, the assessment of students is often performed solely at a team level, resulting in student discontent, concerns about fairness, and social loafing. The application of a single method for evaluating individual contributions has its shortcomings, prompting us to incorporate a combination of evidence-based approaches. Thus, to mitigate these problems, we developed an assessment strategy that involves three components - self and peer evaluation (SPA), instructor's evaluation of individual contribution, and class participation. Anonymized SPA was carried out at three different checkpoints over the semester for both formative and summative assessment. The individual contribution of the students is also evaluated by the instructor and/or other mentors based on their interactions with all the students in the team over the entire semester. Class participation is another component of individual contribution incorporated in our scheme, where instructors evaluate the participation of individual students in classroom activities, presentations, and meetings. We collected feedback about the perception of students towards our assessment policies for individual contribution. We observed overall satisfaction and positive attitudes toward our scheme. We noted positive student perceptions of fairness in grading through our scheme and reduced chances of social loafing. Further, in the feedback, the students noted the effectiveness of using SPA as an evaluation tool, the usefulness of instructor's evaluation, and the role of class participation in creating a more engaging classroom and a more enriching experience. We also empha

关键词： data science

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design 25

Vision-Based Multimodal Interfaces: A Survey and Taxonomy fo...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

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

ISBN: (纸本)9798400713941

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 systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Human computer interaction

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5G-Enabled V2X Communication with Cloud-Powered XGBoost Algorithm for Electric Transportation 10

5G-Enabled V2X Communication with Cloud-Powered XGBoost Algo...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Gnana Jeslin, J. Sujatha, M. Aghalya, S. Jehan, C. Ishwarya, M.V. Vedasundara Vinayagam, P. Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multitech Dr.Rangarajan Dr.Sagunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The opportunity for electric transportation system optimization has never been greater with the combination of cloud-based machine learning algorithms and 5G-enabled Vehicle-to-Everything (V2X) connectivity. To improve the effectiveness, reliability, and longevity of electric vehicle (EV) networks, it provides a new architecture combining V2X communication enabled by 5G and an XGBoost algorithm driven by the cloud. Vehicles may exchange data in real time and make decisions with the help of infrastructure and the cloud via the seamless communication made possible by 5G networks' high-speed, low-latency connection. The XGBoost algorithm, hosted on cloud servers, can use this data to forecast several metrics, including traffic flow, energy usage, and the best way to charge. Cloud computing allows more sophisticated analysis and prediction models by moving the processing load from individual cars to large, powerful servers. It improves transportation efficiency, decreases congestion, minimizes energy usage, and optimizes charging infrastructure via simulation and real-world experiments. © 2024 IEEE.

关键词： Machine learning

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