检索结果-内蒙古大学图书馆

2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Zhang, Ruiyao Yoshida, Naohisa Dohi, Osamu Peng, Boyuan Liu, Xinkai Huang, Jie Zhu, Xin Graduate School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Department of Molecular Gastroenterology and Hepatology Graduate School of Medical Science Kyoto Prefectural University of Medicine Kyoto Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan

ISBN: (纸本)9798350350548

Colonoscopy is the gold standard for colorectal lesion detection, with its accuracy directly influencing patient prognosis. The accurate detection of Annotation Boxes in computer-Aided Detection (CADe) systems is important for improving their performance in colonoscopy procedures. This research introduces a novel algorithm that combines active learning with a modified YOLOv8 algorithm, named ARBS-YOLOv8 (Active learning with RepVGG Block and SIoU loss function base on YOLOv8), for the detection of Annotation Boxes generated by colonoscopy CADe systems. We compared standard YOLOv8, YOLOv6, YOLOX algorithms and their active learning variants using colonoscopy video data from 31 patients. The active learning method involves analyzing false positives generated by the initially trained model on an active learning dataset. These false detection samples are then reintroduced into the training set as negative examples. This active learning method led to substantial performance improvements across all algorithms, with particularly notable increase in precision. Notably, the application of active learning to YOLOv6 resulted in a significant increase in Precision by 23.28%, reaching 95.79%. Furthermore, when active learning was integrated with YOLOv8, it achieved an impressive F1 Score of 99.70% on the test set. Compared to the standard YOLOv8, ARBS-YOLOv8 demonstrated 0.88% increase in F1 Score while maintaining high Sensitivity. This improvement suggests that ARBS-YOLOv8 is more effective at extracting features from the data and increase the F1 score in detecting Annotation Boxes. © 2024 IEEE.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Implicit Reasoning in Transformers is Reasoning through Shortcuts

arXiv

引用

arXiv 2025年

作者： Lin, Tianhe Xie, Jian Yuan, Siyu Yang, Deqing School of Data Science Fudan University China School of Computer Science Fudan University China

Test-time compute is emerging as a new paradigm for enhancing language models’ complex multi-step reasoning capabilities, as demonstrated by the success of OpenAI’s o1 and o3, as well as DeepSeek’s R1. Compared to explicit reasoning in test-time compute, implicit reasoning is more inference-efficient, requiring fewer generated tokens. However, why does the advanced reasoning capability fail to emerge in the implicit reasoning style? In this work, we train GPT-2 from scratch on a curated multi-step mathematical reasoning dataset and conduct analytical experiments to investigate how language models perform implicit reasoning in multi-step tasks. Our findings reveal: 1) Language models can perform step-by-step reasoning and achieve high accuracy in both in-domain and out-of-domain tests via implicit reasoning. However, this capability only emerges when trained on fixed-pattern data. 2) Conversely, implicit reasoning abilities emerging from training on unfixed-pattern data tend to overfit a specific pattern and fail to generalize further. Notably, this limitation is also observed in state-of-the-art large language models. These findings suggest that language models acquire implicit reasoning through shortcut learning, enabling strong performance on tasks with similar patterns while lacking generalization. Resources are available on the GitHub. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

引用

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

TSDDISCOVER: Discovering data Dependency for Time Series data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

引用

40th IEEE International Conference on data Engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Mixed Bus Fleet Charging Schedule Problem with Time-of-Use for Real-world data-sets 32

Multi-Objective Mixed Bus Fleet Charging Schedule Problem wi...

引用

32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Jarvis, Padraigh Climent, Laura Arbelaez, Alejandro Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland Universitat Politècnica de València Valencia Spain

As the effects of climate change are increasingly felt worldwide, the transition to Electric Buses (EB) presents an opportunity to decarbonize the transportation sector. Several issues exist that hinder the adoption of a green bus fleet. Such as the increased cost, reduced travel distances, and required recharge times, which may negatively impact service quality. This work proposes a Mixed Integer Programming model to solve a multi-objective mixed fleet charging schedule problem. The mixed fleet considers EBs and Internal Combustion Engine Buses (ICEBs) and minimizes daily costs, such as fuel price, the Social Cost of Carbon (SCC) produced by the bus fleet, and the Value of Time (VoT) of public transport users. Non-linear charging is considered as well as alternative approaches along with Time of Use (TOU) constraints for electricity price and SCC. Empirical evaluation shows that significant savings can be made, with reductions of over 20000 in fuel costs, and reductions of over 100 tCO2eq per day. Consideration of VoT minimizes negative customer impact, limiting late arrivals to an average of 8.78 seconds per EB. The inclusion of non-linear charging makes minimal positive impact compared to limiting the total capacity of the battery, and while the inclusion of TOU constraints correlates to more savings, the amount saved is minute. © 2024 Copyright for this paper by its authors.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

An Advanced Intrusion Detection Solution for Networks based on Honeypot Servers 6

An Advanced Intrusion Detection Solution for Networks based ...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Veena, K. Meena, K. Mercy Theresa, M. Hemalatha, C. Rajalakshmi, D. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600119 India Department of Computer Science and Engineering GITAM School of Technology GITAM Bangalore561203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Computer Science and Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (纸本)9798350398496

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds. © 2023 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Indoor Navigation Using Ultra Wide Band

Indoor Navigation Using Ultra Wide Band

引用

2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Divya, B. Veeramakali, T. Revathi, S. Harisharajan, R.R. Adhirooban, D. Ebishdon, G.V. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India School of Computing Srm Institute of Science and Technology Dept of Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350394580

Indoor positioning is a significant and intriguing topic in navigation systems that present numerous use cases for investigation. Researchers are exploring technologies capable of providing accurate locations in indoor spaces where GNSS positioning systems are ineffective. In the numerous solutions available for indoor location services, Ultra-wideband (UWB) technology was chosen for its ability to provide high-precision positioning information for multiple points in real-time. This paper presents a UWB indoor positioning system that utilizes time of flight (TOF) algorithm. At times, time difference of arrival (TDOA) algorithms compute range measurements, this facilitates the trans-receiver (TAG) plane position algorithm's location determination. UWB technology was chosen because it is the latest technology available on Android 12 and further also offers high accuracy, resistance to interference and minimal signal reflection from walls. UltraWide Band(UWB) localization encounters various obstacles, such as multipath distortions, additional signal delays, time discrepancies in clocks, signal interference, and the computational resources necessary to determine the user's location. © 2023 IEEE.

关键词： Indoor Positioning System(IPS) Time of Flight(TOF) Ultra Wide Band(UWB)

来源：评论

学校读者我要写书评

暂无评论

BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV 13

BMA for the BMA: Boosting Mobility Analysis for the Bangkok ...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kujareanpaisal, Poonnaphop Mayhasap, Rujira Tea-Makorn, Pin Pin Jindahra, Pavitra Starita, Stefano Cooharojananone, Nagul Atchariyachanvanich, Kanokwan Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Sasin School of Management Data Analytics Center Bangkok Thailand School of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798350373332

The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Research on Multi-Label Classification of Chinese Text Based on Word-Label Probability and DistilBERT 2

Research on Multi-Label Classification of Chinese Text Based...

引用

2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Zhao, Hong Tian, Weiquan An, Ding Mi, Shan School of Computer Science and Communication Lanzhou University of Technology Lanzhou China Cccc Big Data Technology Co. Ltd. Lanzhou China

ISBN: (纸本)9798350360240

Multi-label text classification is a fundamental task in the field of natural language processing. Currently, there are issues in the Chinese multi-label text classification tasks, such as insufficient extraction of text label features and a lack of learning about the correlations between labels. To address these challenges, we proposed a Multi-label Text Classification Model based on DistilBERT and Word-Label Probabilities (DBWL). The model consists of a metric space module that integrates word-label probabilities and a deep neural network module based on DistilBERT. Firstly, the trained Labeled Latent Dirichlet Allocation (Labeled-LDA) model is utilized to obtain the probabilities of words associated with labels, which are then used to calculate the text mapping vectors and map them into the same metric space as the labels. Similarity measurement is employed to extract features between text-label pairs and label-label pairs. Then, in the deep neural network module, DistilBERT is employed to jointly embed texts and labels, capturing rich semantic information about text labels, followed by feature extraction using TextCNN. Finally, the features extracted from both modules are weighted and fused for label prediction. Experimental results on two benchmark datasets demonstrate that the proposed model outperforms current mainstream multi-label classification models in terms of key performance metrics. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Estimation of Facial Emotion Based on Landmark Points by Applying Artificial Intelligence and Machine Learning 7

Estimation of Facial Emotion Based on Landmark Points by App...

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Osman Abdalraheem, Mohammed Hassan Hossain, Mohammad Alamgir Ahmed Hamdan, Alfadil Kechadi, M. Tahar Limker, Suresh Jazan University Department of Information Technology Saudi Arabia Jazan University Department of Computer Science Saudi Arabia Sudan University of Science and Technology Department of Computer Science Sudan School of Computer Science University College Dublin Dublin Ireland Aissms Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350304268

In the science of emotion processing, it is hard to figure out how to predict an emotion from naturalistic facial expressions in a multidimensional space. While many studies and works have been done to develop methods for making accurate predictions, relatively little has been done to put the uniqueness of the human face to use in reading and responding to people's expressions of emotion. This work proposes an emotional image retrieval strategy for multimodal emotion detection. This strategy is based on the fact that face features are always there and that the human face has regional redundancy that is independent of face features. Our method uses landmark points to get rid of unnecessary information about facial expressions and focus on the most attractive ones in each frame. It's possible that by making use of the landmarks, we'll be able to predict facial emotions more precisely. This is especially helpful for long-term emotion prediction since it allows us to recover time and space representations by introducing an emotional face. The preliminary results based on a benchmark dataset show that the proposed method exceeds its competitors in terms of prediction accuracy. © 2023 IEEE.

关键词： emotion prediction facial expression landmark point LBP method

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：