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IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

作者： Jing, Tao Liu, Yue Wang, Xiaoxuan Gao, Qinghe Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

The ultimate goal of Internet of Vehicles (IoV) is to enhance the safety, intelligence, and efficiency of traffic systems. However, dynamic network topology, high speed, and exposed communication links inevitably pose looming threats to vehicle safety. Thus, establishing trust sharing mechanism between traffic entities is pivotal to safeguarding the safety of IoV. This paper proposes a distributed trust sharing provisioning utilizing Reservoir Computing-based path predicting algorithm. In this paper, Echo State Network and Deep Q-Network are novelly combined to realize trust information sharing in Partially Observable Markov Decision Process (POMDP) scenarios. In addition, in DRL state space design, we consider the current trust assessment results of different clusters in front of the vehicle to maximize the protection of vehicle network security. Finally, extensive simulations are conducted to prove that the proposed scheme has excellent convergence speed, verifying the feasibility and effectiveness.

关键词： Training Space vehicles Network topology Vehicle safety Network security Reservoirs prediction algorithms

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Learning Soft Estimator of Keypoint Scale and Orientation with Probabilistic Covariant Loss

Learning Soft Estimator of Keypoint Scale and Orientation wi...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yan, Pei Tan, Yihua Xiong, Shengzhou Tai, Yuan Li, Yansheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Estimating keypoint scale and orientation is crucial to extracting invariant features under significant geometric changes. Recently, the estimators based on self-supervised learning have been designed to adapt to complex imaging conditions. Such learning-based estimators generally predict a single scalar for the keypoint scale or orientation, called hard estimators. However, hard estimators are difficult to handle the local patches containing structures of different objects or multiple edges. In this paper, a Soft Self-Supervised Estimator (S3Esti) is proposed to overcome this problem by learning to predict multiple scales and orientations. S3Esti involves three core factors. First, the estimator is constructed to predict the discrete distributions of scales and orientations. The elements with high confidence will be kept as the final scales and orientations. Second, a probabilistic covariant loss is proposed to improve the consistency of the scale and orientation distributions under different transformations. Third, an optimization algorithm is designed to minimize the loss function, whose convergence is proved in theory. When combined with different keypoint extraction models, S3Esti generally improves over 50% accuracy in image matching tasks under significant viewpoint changes. In the 3D reconstruction task, S3Esti decreases more than 10% reprojection error and improves the number of registered images.

关键词： Point cloud compression Three-dimensional displays Image matching Self-supervised learning Probabilistic logic Feature extraction prediction algorithms

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STeP-UNet: prediction of Moving and Communication Behaviors of Vehicles 94

STeP-UNet: Prediction of Moving and Communication Behaviors ...

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94th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Liang, Daojun Zhang, Haixia Zhou, Xiaotian Li, Dongyang Yuan, Dongfeng Zhang, Minggao Shandong Univ Shandong Key Lab Wireless Commun Technol Qingdao Shandong Peoples R China Shandong Univ Sch Informat Sci & Engn Qingdao Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781665413688

Wireless traffic prediction has drawn increasing research interests as it can provide guidance to the network optimization. With the predicted information, one can preassign the resources on demand and perform network congestion control adaptively. The network efficiency is therefore enhanced. However, the wireless traffic prediction in the context of mobile scenario, such as Internet of Vehicles (IoVs), is still a challenge issue. The mobile nature of devices, which dynamically changes the topology of network, would brings difficulties to the prediction. This paper focuses on the deep learning based wireless traffic prediction in the IoVs scenario. We first propose a novel method to match up the movement- and communication-behavior of users, by merging two independent datasets on the trajectories of vehicles and communication traffic volumes together. Then a novel STeP-UNet is proposed, in which the SpatioTemporal Partial (STeP) Convolutional Neural Network module is embedded to capture cross-domain features of the wireless traffic pattern, and the UNet structure is utilized to realize the skipping connection from front layer to back layer to fuse different resolutions. Experimental results confirms the promising performance of the proposed model, where 4%similar to 8% performance improvement over other benchmark methods can be achieved.

关键词： Wireless communication Vehicular and wireless technologies Statistical analysis Vehicular ad hoc networks Traffic control Predictive models prediction algorithms

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Multitask Adaptation for Unlabeled Domain Using Multiple Single-Task Domains

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IEEE ACCESS 2024年 12卷 194646-194656页

作者： Kang, Youngwook Jeong, Hawook Shin, Junsup Choi, Jongwon Chung Ang Univ Dept Adv Imaging Seoul 06974 South Korea RideFlux Inc Jeju 63083 South Korea

Semantic segmentation and depth estimation tasks are crucial for autonomous driving systems, but obtaining their labels from real-world datasets is costly. To address the problem, we developed a multitask domain adaptation that uses various labeled datasets with distinct tasks to adapt the multitask model for the unlabeled domain. The proposed framework can handle multiple source domains containing various task labels, which allows us to extend the combinations of acceptable source datasets in contrast to the previous multitask domain adaptation methods. We suggest using the 'TripleMix' approach to obtain the integrated features from the three separate domains, including two labeled domains and one unlabeled domain. In addition, we design a task correlation network that trains multiple tasks through attentional correlation, increasing the synergies between various tasks. To validate the proposed algorithm's state-of-the-art performance based on the interactions of the different domains and tasks, we analyze it using a variety of dataset combinations that consider two virtual domains and one real-world target domain.

关键词： Decoding Semantic segmentation Adaptation models Image reconstruction Correlation Autonomous vehicles Semantics Training Synthetic data prediction algorithms Domain adaptation depth estimation multitask learning semantic segmentation

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Heart Disease prediction using Ensemble ML

Heart Disease Prediction using Ensemble ML

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： Madhvan Bajaj Priyanshu Rawat Chandradeep Bhatt Rahul Chauhan Teekam Singh Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

Worldwide, heart disease is one of the main causes of mortality. Heart disease early identification and prevention can significantly enhance patient outcomes and save healthcare expenditures. This research intends to create a model for forecasting an individual’s probability of developing heart disease based on several risk factors. A sizable patient data collection, comprising demographic information, laboratory test results, and lifestyle characteristics, will be used to train the algorithm. Standard measures like accuracy, sensitivity, and specificity will be used to assess the model’s performance. This project aims to develop a tool that will help medical practitioners recognize high-risk individuals and carry out early therapies to stop the progression of heart disease.

关键词： Heart Text mining Instruments Medical treatment Predictive models Sensitivity and specificity prediction algorithms

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End-user evaluation of an interface for clinical decision support using predictive algorithms 44

End-user evaluation of an interface for clinical decision su...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Kehoe, Iain E. Pepino, Jeremy A. Lee, Jarone Hahn, Jin-Oh Reisner, Andrew T. Massachusetts Gen Hosp Boston MA 02114 USA Univ Maryland Dept Mech Engn College Pk MD 20742 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

There have been decades of interest in advanced computational algorithms with potential for clinical decision support systems (CDSS), yet these have not been widely implemented in clinical practice. One major barrier to dissemination may be a user-friendly interface that integrates into clinical workflows. Complicated or non-intuitive displays may confuse users and may even increase patient management errors. We recently developed a graphical user interface (GUI) intended to integrate a predictive hemodynamic model into the workflow of nurses caring for patients on vasopressors in the intensive care unit (ICU). Here, we evaluated user perceptions of the usability of this system. The software was installed in the room of an ICU patient, running for at least 4 hours with the display hidden. Afterward, we showed nurses a video recording of the session and surveyed their perceptions about the software's potential safety and usefulness. We collected data for nine patients. Overall, nurses expressed reasonable enthusiasm that the software would be useful and without serious safety concerns. However, there was a wide diversity of opinions about what specific aspects of the software would be useful and what aspects were confusing. In several instances, the same elements of the GUI were cited as most useful by some nurses and most confusing by others. Our findings validate that it is possible to develop GUIs for CDSS that are perceived as potentially useful and without substantial risk but also reinforce the diversity of user perceptions about novel CDSS technology.

关键词： Decision support systems Predictive models prediction algorithms Software Safety Hemodynamics Cultural differences

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Explainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actions

Explainable Knowledge Graph Embedding: Inference Reconciliat...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Daruna, Angel Das, Devleena Chernova, Sonia Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Learned knowledge graph representations supporting robots contain a wealth of domain knowledge that drives robot behavior. However, there does not exist an inference reconciliation framework that expresses how a knowledge graph representation affects a robot's sequential decision making. We use a pedagogical approach to explain the inferences of a learned, black-box knowledge graph representation, a knowledge graph embedding. Our interpretable model uses a decision tree classifier to locally approximate the predictions of the black-box model and provides natural language explanations interpretable by non-experts. Results from our algorithmic evaluation affirm our model design choices, and the results of our user studies with non-experts support the need for the proposed inference reconciliation framework. Critically, results from our simulated robot evaluation indicate that our explanations enable non-experts to correct erratic robot behaviors due to nonsensical beliefs within the black-box.

关键词： Natural languages Closed box Predictive models Approximation algorithms prediction algorithms Classification algorithms Behavioral sciences

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Set-Valued State Estimation of Linear Discrete-Time Systems with Linear Invariant 8

Set-Valued State Estimation of Linear Discrete-Time Systems ...

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8th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Meslem, Nacim Univ Grenoble Alpes GIPSA Lab Grenoble INP CNRS F-38000 Grenoble France

ISBN: (纸本)9781665496070

This note proposes the use of invariant relationships between the state variables of discrete-time systems to improve the tightness of a class of set-membership state estimators. Thanks to these relationships, contractor algorithms could be designed to discard almost all state vectors that are not compatible with the model-based predicted state enclosure. Moreover, this contribution introduces a less computational complexity algorithm to perform efficiently the prediction stage of the proposed set-valued state estimator.

关键词： Discrete-time systems Linear systems Computational modeling Predictive models prediction algorithms Mathematical models Wrapping

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Money Laundering Fraudulent prediction Using Classifiers

Money Laundering Fraudulent Prediction Using Classifiers

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Agam Ahluwalia Isha Goyal Prafulla Bafna Symbiosis Institute of Computer Studies and Research Symbiosis International University Pune India

Knowing the scale of a problem is an important aspect of understanding it. This helps assess the severity of crime, its impact on the macroeconomic environment, and the effectiveness of countermeasures over time. The same aspect works with money laundering. Money laundering is a technique used by criminals to hide their illicit sources of income. By transferring money, trading, or moving money through complex business chains, money is “removed” from illicit sources and presented as a legitimate business interest. Classification of records is performed to predict bad predictions. Using several metrics such as correctly classified instances, incorrectly classified instances, accuracy, and recall, a comparative analysis of different algorithms is performed to evaluate classifier performance. The ZeroR classification algorithm has an accuracy rating of 40%, OneR has an accuracy rating of 18%, and Part has an accuracy rating of 54 %. inflate the price

关键词： Measurement Subspace constraints prediction algorithms Classification algorithms Fraud Macroeconomics Informatics

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Online Caching with Optimistic Learning

Online Caching with Optimistic Learning

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IFIP Networking Conference (IFIP Networking)

作者： Mhaisen, Naram Iosifidis, George Leith, Douglas Delft Univ Technol Software Technol Grp Delft Netherlands Trinity Coll Dublin Sch Comp Sci & Stat Dublin Ireland

ISBN: (纸本)9783903176485

The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic toolbox for tackling this problem through the lens of optimistic online learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework which is developed further here to include predictions for the file requests, and we design online caching algorithms for bipartite networks with fixed-size caches or elastic leased caches subject to time-average budget constraints. The predictions are provided by a content recommendation system that influences the users viewing activity, and hence can naturally reduce the caching network's uncertainty about future requests. We prove that the proposed optimistic learning caching policies can achieve sub-zero performance loss (regret) for perfect predictions, and maintain the best achievable regret bound O(root T) even for arbitrary-bad predictions. The performance of the proposed algorithms is evaluated with detailed trace-driven numerical tests.

关键词： Uncertainty Social networking (online) Content distribution networks Benchmark testing prediction algorithms Recommender systems Edge computing

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