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检索条件"任意字段=2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020"

共 5741 条记录，以下是21-30 订阅

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BLEND & PREDICT: DOMAIN-ADAPTABLE FEW-SHOT LEARNING FOR MICROSCOPY IMAGING 31

BLEND & PREDICT: DOMAIN-ADAPTABLE FEW-SHOT LEARNING FOR MICR...

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2024 international conference on Image processing

作者： Somani, Ayush Gupta, Anshul Sekh, Arif Ahmed Agarwal, Krishna Prasad, Dilip K. UiT Arct Univ Norway Dept Comp Sci Bio AI Lab Tromso Norway Indian Inst Technol ISM Dhanbad Dhanbad Bihar India XIM Univ Bhubaneswar India UiT Arct Univ Norway Dept Phys & Technol Tromso Norway

ISBN: (纸本)9798350349405;9798350349399

Accurate classification of microscopy images is critical for the analysis of biological samples. The availability of large-scale labeled datasets has contributed to recent progress in training large, deep classification models in the medical imaging domain, but methods that cater to a variety of microscopy modalities across a range of biological samples and length scales are scarce. A key reason is that curating labeled data for microscopy images is costly and needs tedious and time-consuming effort of AI and domain experts. We propose a novel few-shot learning technique, specifically "Blend & Predict" that uses small labeled datasets for training and infers unlabeled datasets. We evaluated the performance and generalizability of our approach using three medical image datasets, each with a different microscopy modality and addressing a different biomedical question on different samples. We achieved results comparable to state-of-the-art models like GoogleNet, VGG16, RestNet50 that used large datasets for training.

关键词： Microscopy image classification biomedical application few-shot learning data augmentation

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Naturalistic Music Decoding from EEG data via Latent Diffusion models

Naturalistic Music Decoding from EEG Data via Latent Diffusi...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Postolache, Emilian Polouliakh, Natalia Kitano, Hiroaki Connelly, Akima Rodolà, Emanuele Cosmo, Luca Akama, Taketo DAIS Ca' Foscari University of Venice Venice Italy Sony CSL Tokyo Japan Dept. of TSE Tokyo Institute of Technology Tokyo Japan Dept. of Computer Science Sapienza University of Rome Rome Italy

ISBN: (纸本)9798350368741

In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroencephalogram (EEG) recordings. Unlike simpler music with limited timbres, such as MIDI-generated tunes or monophonic pieces, the focus here is on intricate music featuring a diverse array of instruments, voices, and effects, rich in harmonics and timbre. This study represents an initial foray into achieving general music reconstruction of high-quality using non-invasive EEG data, employing an end-to-end training approach directly on raw data without the need for manual pre-processing and channel selection. We train our models on the public NMED-T dataset and perform quantitative evaluation proposing neural embedding-based metrics. Our work contributes to the ongoing research in neural decoding and brain-computer interfaces, offering insights into the feasibility of using EEG data for complex auditory information reconstruction. © 2025 IEEE.

关键词： diffusion models electroencephalography Generative AI music generation

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2nd international conference on Machine Learning, Image processing, Network Security and data Sciences, MIND 2020

2nd International Conference on Machine Learning, Image Proc...

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2nd international conference on Machine Learning, Image processing, Network Security and data Sciences, MIND 2020

ISBN: (纸本)9789811563171

The proceedings contain 83 papers. The special focus in this conference is on Machine Learning, Image processing, Network Security and data Sciences. The topics include: An Empirical Study to Predict Myocardial Infarction Using K-Means and Hierarchical Clustering;a Robust Technique for End Point Detection Under Practical Environment;an Explainable Machine Learning Approach for Definition Extraction;Steps of Pre-processing for English to Mizo SMT System;efficient Human Feature Recognition Process Using Sclera;optimization of Local Ordering Technique for Nearest Neighbour Circuits;attention-Based English to Mizo Neural Machine Translation;in Depth Analysis of Lung Disease Prediction Using Machine Learning algorithms;improve the Accuracy of Heart Disease Predictions Using Machine Learning and Feature Selection Techniques;Evaluation of Multiplier-Less DCT Transform Using In-Exact Computing;convolutional Neural Network Based Sound Recognition Methods for Detecting Presence of Amateur Drones in Unauthorized Zones;comparison of Different Decision Tree algorithms for Predicting the Heart Disease;dynamic Speech Trajectory Based Parameters for Low Resource Languages;identification and Prediction of Alzheimer Based on Biomarkers Using ‘Machine Learning’;solving Quadratic Assignment Problem Using Crow Search Algorithm in Accelerated Systems;speech Signal Analysis for Language Identification Using Tensors;Effective Removal of Baseline Wander from ECG Signals: A Comparative Study;Face Recognition Based on Human Sketches Using Fuzzy Minimal Structure Oscillation in the SIFT Domain;an Ensemble Model for Predicting Passenger Demand Using Taxi data Set;a Novel Approach to Synthesize Hinglish Text to English Text;legal Amount Recognition in Bank Cheques Using Capsule Networks;comparative Analysis of Neural models for Abstractive Text Summarization.

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TRANSFORMER MODEL WITH MULTI-TYPE CLASSIFICATION DECISIONS FOR INTRUSION ATTACK DETECTION OF TRACK TRAFFIC AND VEHICLE 49

TRANSFORMER MODEL WITH MULTI-TYPE CLASSIFICATION DECISIONS F...

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guan, Quanlong Zhang, Tian Qin, Yu Zhou, Yuyu Zhu, Yangguang Zhong, Yuansheng Huang, Xiujie Duan, Zhifei Li, Zhefu Liu, Changjiang Wu, Xiaofeng Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai Peoples R China Guangdong Testing Inst Product Qual Supervis Guangdong Peoples R China Guangzhou Polytech Sports Guangzhou Peoples R China Guangzhou XPeng Motors Technol Co Ltd Guangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Security vulnerabilities, illustrated by the menace of track traffic or vehicle hacking, present a substantial risk to the Controller Area Network (CAN) bus, enabling unauthorized remote access and intrusion. Nevertheless, existing vehicle intrusion detection models encounter challenges in capturing temporal aspects, compromising the preservation of temporal attributes in the data. Addressing this predicament, we propose a novel Vehicle Intrusion Detection System (PTIDS) model based on Principal Component Analysis (PCA) and Transformer architecture, equipped with multi-type classification decisions. This model utilizes the PCA algorithm to preprocess the data and employs a multi-head self-attention mechanism to simulate the continuous input of the real-world environment in vehicle or track traffic data. Experimental results demonstrate that the PTIDS model outperforms traditional neural networks regarding accuracy, precision, recall, and F1-score for vehicle intrusion detection, confirming the importance of temporal variables in intrusion detection. However, owing to the high accuracy and low discrimination of the PTIDS model on the Car Hacking dataset, we migrated the model to the M-CAN and B-CAN intrusion datasets and conducted ablation experiments. The results reveal that the model achieved an accuracy of 90.14% on the M-CAN dataset, surpassing other models by 54.5%, demonstrating robust generalization ability.

关键词： Vehicle intrusion detection Temporal attributes Transformer Multi-head Self-attention Track traffic

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An Efficient Scheduling Algorithm for Multi-mode Tasks on Near-data processing SSDs 1

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23rd international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Li, Guo Chen, Xianzhang Liu, Duo Li, Jiali Tan, Yujuan Ren, Ao Chongqing Univ Chongqing 400044 Peoples R China

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

Near-data processing (NDP) architectures have been proposed to alleviate the large overhead of data movement between the host and the Computational Storage Device (CSD) by offloading tasks to the CSD. In NDP architectures, each task can run in multiple modes according to the resource it takes for computing, such as the CPU of the host, the accelerator or the processor of the CSD. However, existing task scheduling algorithms on NDP architectures are unaware of the multi-mode tasks, leading to increased completion time of tasks and low resource utilization. In this paper, we propose a Multi-Mode Task Scheduling (MMTS) algorithm to optimize the completion time of the multi-mode tasks in NDP architectures. MMTS employs a greedy strategy to fully use the computing resources in the host and the CSD and align the completion time of the tasks by picking the proper modes. Our experimental results show that MMTS achieves 20.6% performance improvement on average over the state-of-the-art task scheduling algorithm on NDP-based system.

关键词： Near data processing Computational storage Multi-mode task scheduling

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An efficient 3D point cloud classification approach via persistent homology 3

An efficient 3D point cloud classification approach via pers...

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3rd international conference on algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhou, Xin Pan, Yu Zhang, Lei Sun, Huafei School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314019 China

ISBN: (纸本)9781510680098

Point cloud is a critically important geometric data structure, and researchers have increasingly focused on and achieved promising results in terms of point cloud processing since PointNet's pioneering work. However, most previous methods only represent the shape of point clouds through coordinates or normal vectors, neglecting the intrinsic geometric and topological properties of this data structure. In this paper, we present an effective point cloud analysis approach which is using topological information. By employing a simplified version of the PointNet++(SSG version), we conduct benchmark experiments on the ModelNet40 dataset to evaluate TPA's performance in the classification task. Our improved method can still directly process point clouds, as the topological invariants ensure the permutation invariance of the input points. Simulation results show that the topological approach based on persistent homology can effectively provide topological structural features and improve the accuracy of the models. © 2024 SPIE.

关键词： data structures

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Beyond large language models: rediscovering the role of classical statistics in modern data science

Beyond large language models: rediscovering the role of clas...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： Gutierrez, Inmaculada Gomez, Daniel Castro, Javier Bimber, Bruce Labarre, Julien Univ Complutense Madrid Fac Stat Studies Madrid Spain Univ Calif Santa Barbara Ctr Informat Technol & Soc Santa Barbara CA 93106 USA

ISBN: (纸本)9798350319552;9798350319545

This study explores the synergy between large language models and classical statistics in contemporary data science. In the field of large language models, we find there is no one-size-fits-all model which satisfies the needs of other scientists. There are differences in the soft results which may be a limitation on their application. To analyze these differences and lack of robustness, we propose a robust methodology that integrates classical statistical experimental design principles with the these advanced models, aiming to identify statistically significant differences among their outcomes. In particular, an experimental design is presented in which the main factors, levels, treatments and interactions that influence the predictions made by different models of complex natural language processing are identified. The main aim of this research is to better understand the influence of some controlled factors that are used in complex natural language processing models by applying classical statistical techniques, providing a comprehensive perspective on the relative effectiveness of different zero-shot classification models. It aims to offer practitioners insights into when and where certain models may be more or less sensitive, facilitating informed decision-making in applying these advanced language models. Additionally, computational results obtained from a pilot dataset are presented. These results illustrate the entire process of the proposed methodology, highlighting the importance of considering statistical evidence when making decisions.

关键词： Large Language Model Transformers Explainable Artificial Intelligence Experiment Design Statistics

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FGT-SAMK-NN: Impact of The "Right to Be Forgotten" Using a Lazy Algorithm in data Stream Learning 23

FGT-SAMK-NN: Impact of The "Right to Be Forgotten" Using a L...

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16th international conference on Theory and Practice of Electronic Governance (ICEGOV)

作者： Peniche, Eduardo Bernardini, Flavia Miranda, Leandro Viterbo, Jose Fluminense Fed Univ Inst Comp Niteroi RJ Brazil

ISBN: (纸本)9798400707421

"Right to Be Forgotten" is guaranteed by new international regulations on personal management data. This means that individuals can request the erasure of their data from third-party tools and services. However, this poses a challenge for machine learning estimators, who will need to forget parts of their knowledge. This paper examines the impact of learning and forgetting policies in data Stream Learning. Storing data or retraining learning models from scratch in data stream mining is usually not feasible due to the large volume of instances. Therefore, more efficient solutions are necessary to deal with the dynamic nature of online machine learning. To address this issue, we implemented FGT-SAMK-NN, an incremental version of one of the most knowledgeable algorithms in data Stream lazy algorithms: The SAMK-NN classifier. FGT-SAMK-NN can erase its past data, and we investigate the impact of data forgetting on predictive performance. Our proposal is compared to the original SAMK-NN algorithm using four non-stationary stream datasets. Our results demonstrate that evaluation metrics did not undergo significant changes, which may support the idea that has a good architecture for adaptations of the proposed nature. However, it was also noted that the processing time is very high for cases involving more forgettings, which may indicate that the high complexity of the model creates conflicts if the pattern of data streams, where the algorithm is used, involves a high forgetfulness rate.

关键词： Lazy Learning data Stream Learning Right to Be Forgotten SamK-NN data Stream

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Proceedings of the 13th international conference on Educational data Mining, EDM 2020

Proceedings of the 13th International Conference on Educatio...

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13th international conference on Educational data Mining, EDM 2020

ISBN: (纸本)9781733673617

The proceedings contain 123 papers. The topics discussed include: when is deep learning the best approach to knowledge tracing?;analyzing student strategies in blended courses using clickstream data;increasing enrollment by optimizing scholarship allocations using machine learning and genetic algorithms;predicting engagement in video lectures;the ebb and flow of student engagement: measuring motivation through temporal pattern of self-regulation;can we take advantage of time-interval pattern mining to model students activity?;automatic subject-based contextualization of programming assignment lists;enhancing student competency models for game-based learning with a hybrid stealth assessment framework;harbingers of collaboration? the role of early-class behaviors in predicting collaborative problem solving;evaluating sources of course information and models of representation on a variety of institutional prediction tasks;and pick the moment: identifying critical pedagogical decisions using long-short term rewards.

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Multivariate data Acquisition Strategy by Employing Signal processing on Graph in Edge Computing Based Industrial Internet of Things 7

Multivariate Data Acquisition Strategy by Employing Signal P...

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7th international conference on Advanced algorithms and Control Engineering, ICAACE 2024

作者： Yang, Lishan Zhang, Yining Bai, Chenglin Xu, Hengying Wang, Guizhen Wang, Xuezhen School Of Physics Science And Information Engineering Liaocheng University Liaocheng China School Of Mathematical Sciences Liaocheng University Liaocheng China

ISBN: (纸本)9798350361445

Multivariate data acquisition strategy is a crucial issue in edge computing based Industrial Internet of Things (EC-IIoT), which should be carefully designed to improve energy efficiency and prolong network lifetime of EC-IIoT. Sampling on graph provides a promising opportunity to solve resource limited sensor selection problem via forming the appropriate sampling set and reconstructing original graph signal. In this paper, we translate the multivariate data acquisition strategy to the graph sampling set forming problem, which could exploit the adjacent correlation of the vertices in the spatial and variable domain. The simulation result demonstrates the effectiveness of the proposed data acquisition strategy. © 2024 IEEE.

关键词： Internet of things

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