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The Journal of machine learning Research 2024年第1期25卷 2308-2358页

作者： Ville Hyvönen Elias Jääsaari Teemu Roos Department of Computer Science Aalto University Machine Learning Department Carnegie Mellon University Department of Computer Science University of Helsinki

To learn partition-based index structures for approximate nearest neighbor (ANN) search, both supervised and unsupervised machine learning algorithms have been used. Existing supervised algorithms select all the points that belong to the same partition element as the query point as nearest neighbor candidates. Consequently, they formulate the learning task as finding a partition in which the nearest neighbors of a query point belong to the same partition element with it as often as possible. In contrast, we formulate the candidate set selection in ANN search directly as a multilabel classification problem where the labels correspond to the nearest neighbors of the query point. In the proposed framework, partition-based index structures are interpreted as partitioning classifiers for solving this classification problem. Empirical results suggest that, when combined with any partitioning strategy, the natural classifier based on the proposed framework leads to a strictly improved performance compared to the earlier candidate set selection methods. We also prove a sufficient condition for the consistency of a partitioning classifier for ANN search, and illustrate the result by verifying this condition for chronological k-d trees and (both dense and sparse) random projection trees.

关键词： approximate nearest neighbor search multilabel classification partitioning models statistical learning theory

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Low-rank optimal transport through factor relaxation with latent coupling 24

Low-rank optimal transport through factor relaxation with la...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Peter Halmos Xinhao Liu Julian Gold Benjamin J. Raphael Department of Computer Science Princeton University Center for Statistics and Machine Learning Princeton University

ISBN: (纸本)9798331314385

Optimal transport (OT) is a general framework for finding a minimum-cost transport plan, or coupling, between probability distributions, and has many applications in machine learning. A key challenge in applying OT to massive datasets is the quadratic scaling of the coupling matrix with the size of the dataset. Forrow et al. (2019) introduced a factored coupling for the k-Wasserstein barycenter problem, which Scetbon et al. (2021) adapted to solve the primal low-rank OT problem. We derive an alternative parameterization of the low-rank problem based on the latent coupling (LC) factorization previously introduced by Lin et al. (2021) generalizing Forrow et al. (2019). The LC factorization has multiple advantages for low-rank OT including decoupling the problem into three OT problems and greater flexibility and interpretability. We leverage these advantages to derive a new algorithm Factor Relaxation with Latent Coupling (FRLC), which uses coordinate mirror descent to compute the LC factorization. FRLC handles multiple OT objectives (Wasserstein, Gromov-Wasserstein, Fused Gromov-Wasserstein), and marginal constraints (balanced, unbalanced, and semi-relaxed) with linear space complexity. We provide theoretical results on FRLC, and demonstrate superior performance on diverse applications - including graph clustering and spatial transcriptomics - while demonstrating its interpretability.

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An improved particle swarm optimization algorithm for task scheduling in cloud computing

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4313-4327页

作者： Pirozmand, Poria Jalalinejad, Hoda Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Li, Yingqiu Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding071002 China Department of Mathematics and Computer Science Bandar Abbas Branch Islamic Azad University Bandar Abbas Iran Department of Computer Science University of Regina Regina Canada Tehran Iran

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cloud computing

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Do Not Trust a Model because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based learning 2023

Do Not Trust a Model because It is Confident: Uncovering and...

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13th International Conference on learning Analytics and Knowledge: Towards Trustworthy learning Analytics, LAK 2023

作者： Galici, Roberta Kaser, Tanja Fenu, Gianni Marras, Mirko Department of Mathematics and Computer Science University of Cagliari Cagliari Italy Machine Learning for Education Laboratory EPFL Lausanne Switzerland

ISBN: (纸本)9781450398657

Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is one of the main factors undermining users' trust, since model predictions could for instance lead an instructor to not intervene on a student in need. In this paper, we unveil the need of detecting and characterizing unknown unknowns in student success prediction in order to better understand when models may fail. Unknown unknowns include the students for which the model is highly confident in its predictions, but is actually wrong. Therefore, we cannot solely rely on the model's confidence when evaluating the predictions quality. We first introduce a framework for the identification and characterization of unknown unknowns. We then assess its informativeness on log data collected from flipped courses and online courses using quantitative analyses and interviews with instructors. Our results show that unknown unknowns are a critical issue in this domain and that our framework can be applied to support their detection. The source code is available at https://***/epfl-ml4ed/unknown-unknowns. © 2023 ACM.

关键词： Students

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Detecting Hate Speech Utilizing Deep Convolutional Network and Transformer Models

Detecting Hate Speech Utilizing Deep Convolutional Network a...

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2023 IEEE International Conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Mittal, Utkarsh Stanford University Manager of Machine Learning and Automation Gap Inc. Student Department of Computer Science United States

ISBN: (纸本)9798350305111

Online social networks exhibit a significant prevalence of hate speeches, which poses a potential threat to the society and fosters targeted animosity towards specific communities and authorities. Although online platforms are available to automate few mechanisms of hate speeches but classification w.r.t. to different domains and their accuracy are the big issues, and are challenging the researchers, media, and the academic world. The present study addresses the identifying of Hate Speeches through the comparative analysis of the classification efficacy and model intricacy of four distinct Deep Neural Network models;namely CNN (baseline), bidirectional LSTM with attention, pretrained BERT, and fine-tuned RoBERTa transformer models, and utilizing a ternary classification system (hate, offensive, non-hate). The performance of the subject under consideration was assessed through the application of Accuracy, F1-score, and Matthew's correlation coefficient (MCC) metrics on the test set. © 2023 IEEE.

关键词： Social networking (online)

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Feature Extraction and Similarity-Based Movie Recommendation Using Bag-of-Words Model

Feature Extraction and Similarity-Based Movie Recommendation...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Raj, Swati Dutta, Jhalak Bandopadhyay, Saikat Prasad, Rajesh Department of Computer Science and Engineering Christ University Bengaluru India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

ISBN: (纸本)9789819613472

Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recommendations, offering a viable alternative to collaborative filtering methods. Unlike collaborative filtering, which relies on user ratings and demographic data, the proposed system operates without such prerequisites, making it particularly advantageous in scenarios with limited user engagement. By leveraging content attributes such as title, genre, cast, and crew, the system identifies similarities between movies and recommends those akin to a user’s preferences. Through innovative preprocessing techniques and feature extraction methodologies, the system transforms textual descriptions into numerical vectors, enabling efficient recommendation generation. The proposed model contributes to addressing critical challenges inherent in collaborative filtering, including the cold start problem, data sparsity, and popularity bias. Furthermore, it underscores the broad applicability of recommender systems across various domains and emphasizes the continuous evolution of interface designs and algorithms within the research community. Overall, the proposed content-based recommender system offers a promising solution for delivering personalized movie recommendations while advancing the state of the art in recommendation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bag-of-words Cosine similarity Filtering system Recommender system TF-IDF

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Evaluating Text Summarization Techniques and Factual Consistency with Language Models

Evaluating Text Summarization Techniques and Factual Consist...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Islam, Md Moinul Muhammad, Usman Oussalah, Mourad University of Oulu Center for Machine Vision and Signal Analysis Finland Aalto University Department of Computer Science Finland

ISBN: (纸本)9798350362480

Standard evaluation of automated text summarization (ATS) methods relies on manually crafted golden summaries. With the advances in Large Language Models (LLMs), it is legitimate to question whether these models can now potentially complement or replace human-crafted summaries. This study examines the effectiveness of several language models (LMs) in specifically addressing the issue of preserving factual consistency. By conducting a thorough assessment of various conventional and state-of-the-art performance metrics, such as ROUGE, BLEU, BERTScore, FActScore, and LongDocFACTScore across diverse datasets, our findings highlight the important relationship between linguistic eloquence and factual accuracy. The findings suggest that whereas LLMs, such as GPT and LLaMA, demonstrate considerable competence in producing concise and contextually-aware summaries, there remain difficulties in ensuring factual accuracy, particularly in domain-specific situations. Moreover, this work enhances the existing knowledge on summarization dynamics and highlights the need of developing more reliable and tailored evaluation techniques that minimize the probability of factual errors in text generated by ATS. In particular, the findings advance the current domain by providing a rigorous assessment of the balance between linguistic fluency and factual correct- ness, highlighting the limitations of current ATS frameworks and metrics to enhance the factual reliability of LM-generated summaries. © 2024 IEEE.

关键词： Linguistics

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DROPEX: Disaster Rescue Operations and Probing using EXpert drones 8

DROPEX: Disaster Rescue Operations and Probing using EXpert ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kannan, Kausthub Awati, Aditya N. Rao, Smruthi S. Malagi, Vindhya P. Dayananda Sagar College of Engineering Department of Computer Science And Design Bangalore India Dayananda Sagar College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331505462

Disasters, both natural and man-made, pose significant risks to human life and infrastructure, necessitating swift and efficient search and rescue (SAR) operations. Traditional SAR methods often struggle to access hazardous areas, resulting in delayed responses and increased risk to rescuers. These methods are unable to run automated simultaneous rescues which lead the operations to face challenges such as difficulty in quickly assessing damage, locating survivors, and delivering aid. This paper address the problem by proposing a autonomous swarm of drones framework which improves the response time as well as increases accessibility zone of the rescue operation. The proposed framework DROPEX, is an autonomous UAV (Unmanned Aerial Vehicle) which employs a dual-dome architecture with surveillance drones to detect individuals in distress and payload drones to deliver aid. This framework ensures rapid deployment, accurate navigation, and efficient data transmission in disaster-stricken areas while minimizing the need for manual intervention. The drones are able to recognise the victims in need using object detection models such as YOLO and Detection Transformer (DETR) with thermal vision. By using Long Range Wide Area Network (LoRaWAN) and object detection models, the drones are able to reduce the response time and increase accessibility. The main focus is on creating a robust, scalable, and economical system to enhance the speed, efficiency, and effectiveness of disaster rescue operations. © 2024 IEEE.

关键词： Drones

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RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service Robots Using Foundation Models for Perception and Planning

RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service...

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27th RoboCup International Symposium, 2024

作者： Memmesheimer, Raphael Nogga, Jan Pätzold, Bastian Kruzhkov, Evgenii Bultmann, Simon Schreiber, Michael Bode, Jonas Karacora, Bertan Park, Juhui Savinykh, Alena Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn Bonn Germany

ISBN: (纸本)9783031858581

We present the approaches and contributions of the winning team NimbRo@Home at the RoboCup@Home 2024 competition in the Open Platform League held in Eindhoven, NL. Further, we describe our hardware setup and give an overview of the results for the task stages and the final demonstration. For this year’s competition, we put a special emphasis on open-vocabulary object segmentation and grasping approaches that overcome the labeling overhead of supervised vision approaches, commonly used in RoboCup@Home. We successfully demonstrated that we can segment and grasp non-labeled objects by text descriptions. Further, we extensively employed Large Language Model (LLMs) for natural language understanding and task planning. Throughout the competition, our approaches showed robustness and generalization capabilities. A video of our performance can be found online (https://***/videos/RoboCup_2024). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robot programming

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ClinicCare Manager: An Efficient Electronic Health Record (EHR) and Healthcare Management System Using Spring Boot and React 5

ClinicCare Manager: An Efficient Electronic Health Record (E...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bindu, S. Ashok, K. Krishnamurthy, Keerthana Chethan, C. Jatinkrishna, Gajula Srinivas Gagan, V. New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518097

Healthcare institutions often face challenges in managing patient data, optimizing clinic workflows, and ensuring timely reporting. Smaller clinics, in particular, may lack the resources to streamline services for both patients and healthcare providers. This work introduces a platform that automates patient data analysis and report generation to address these needs. Built using React, Spring Boot, and PostgreSQL, the application provides efficient, end-to-end service with robust data handling and fast processing. Performance metrics show efficient resource utilization, with CPU usage rarely exceeding 3 % and heap memory remaining well within the 2048 MB limit. Compared to other health care applications, this platform offers greater scalability and responsiveness. Additionally, the framework supports secure authentication and role-based access, making it a reliable and scalable solution that meets the expanding demands of health care environments. © 2024 IEEE.

关键词： Electronic health record

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