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Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents 31

Development of A Stochastic Traffic Environment with Generat...

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31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Ozturk, Anil Gunel, Mustafa Burak Dal, Melih Yavas, Ugur Ure, Nazim Kemal Faculty of Computer Engineering Istanbul Technical University Turkey Faculty of Aeronautics and Astronautics Aerospace Engineering Istanbul Technical University Turkey Faculty of Computer Engineering Bogazici University Turkey Eatron Technologies Istanbul Turkey Itu Artificial Intelligence and Data Science Research Center Istanbul Technical University Department of Aeronautical Engineering Turkey

Automated lane changing is a critical feature for advanced autonomous driving systems. In recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded successful results in computing lane changing policies that strike a balance between safety, agility and compensating for traffic uncertainty. However, many RL algorithms exhibit simulator bias and policies trained on simple simulators do not generalize well to realistic traffic scenarios. In this work, we develop a data driven traffic simulator by training a generative adverserial network (GAN) on real life trajectory data. The simulator generates randomized trajectories that resembles real life traffic interactions between vehicles, which enables training the RL agent on much richer and realistic scenarios. We demonstrate through simulations that RL agents that are trained on GAN-based traffic simulator has stronger generalization capabilities compared to RL agents trained on simple rule-driven simulators. © 2020 IEEE.

关键词： Reinforcement learning

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A Dependable Hybrid Machine Learning Model for Network Intrusion Detection

arXiv

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arXiv 2022年

作者： Talukder, Md. Alamin Hasan, Khondokar Fida Islam, Md. Manowarul Uddin, Md. Ashraf Akhter, Arnisha Yousuf, Mohammad Abu Alharbi, Fares Moni, Mohammad Ali Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh 2 George Street Brisbane4000 Australia Institute of Information Technology Jahangirnagar University Dhaka Savar Bangladesh Computer Science Department Shaqra University Shaqra15526 Saudi Arabia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today’s networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms in order to find a more efficient algorithm to implement in the pipeline. Furthermore, we chose the most effective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP’99 and CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP’99 and CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues. © 2022, CC BY.

关键词： Balancing

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Boundary-aware dynamic re-weighting for semi-supervised medial image segmentation

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Expert Systems with Applications 2025年 287卷

作者： Jiang, Weili Li, Jiawen Li, Yihao Wei, Xifei Huang, Jianping Quellec, Gwenolé Si, Weixin Ou, Chubin School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu China Norla Institute of Technical Physics Chengdu China United Imaging Healthcare Shanghai China Weizhi Meditech Co. Ltd. Guangdong Foshan China LaTIM UMR 1101 Inserm Brest France Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology China Guangdong General Hospital Guangdong Guangzhou China

Different from traditional semi-supervised learning (SSL), semi-supervised medical image segmentation faces two significant challenges: (1) the imbalanced distribution of labeled data causes models to bias towards majority classes;(2) the distribution discrepancy between labeled and unlabeled samples induces confirmation bias in pseudo-labels. Inspired by clinical practice, where experienced doctors utilize intrinsic features from the interior of target organs to clarify ambiguous boundaries and focus on minority classes, we propose a novel boundary-aware dynamic re-weighting network (BDRN). First, we utilize edge filters to generate visually different but semantically aligned views, compelling two sub-networks to learn informative features from organ interiors and boundaries, respectively. Second, we extract the boundary and interior regions using morphological operators and introduce a shape constraint to enhance feature learning. Additionally, a conflict-adversarial module promotes segmentation consistency between different views. Finally, we propose a dynamic re-weighting strategy based on the effective number to improve attention to imbalanced classes. Experiments demonstrate that our method significantly improves segmentation performance, achieving state-of-the-art results on CT and MR images. Ablation studies further confirm the efficacy of boundary consistency constraints and dynamic re-weighting. The segmentation Dice score for minority organs (e.g., esophagus) on the Synapse dataset is improved by 17.1 %, 46.2 %, and 49.4 % using 10 %, 20 %, and 40 % labeled data, respectively. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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A data Sharing Scheme in NDN Based on MOR Primitive

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International Journal of Network Security 2022年第6期24卷 994-1001页

作者： Shi, Jiaoli Deng, Anyuan Luo, Chao Yao, Shimao He, Kai School of Computer and Big Data Science Jiujiang University Jiangxi Jiujiang China Institute of Information Security Jiujiang University Jiangxi Jiujiang China School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan China Hubei Clothing Information Engineering Technology Research Center Hubei Wuhan China

This work is aimed at a data sharing scheme over NDN (Named data Networking) with multiple demands: loosecouple, fine-grained control, and dynamic authority management. Firstly, a data access control scheme is proposed. Publisher generates data, encrypts the data to ciphertext, binds the ciphertext with an access policy, and publishes the ciphertext on an NDN router. Subscriber can access the part of data authorized when his private key meets the policy bound to the ciphertext, which realizes the loose-couple and fine-grained access control. The scheme can be constructed through Matrix computation, based on the MOR primitive instantiated by LWE. Secondly, a dynamic authority management method is presented. A subscriber’s latest identity public key is generated and stored on edge routers. When the subscriber applies to ciphertext, the edge routers attach the latest identity public key of the subscribers to the ciphertext and send it to the subscriber. This design can drive a subscriber to use his latest private key. © 2022, International Journal of Network Security. All Rights Reserved.

关键词： Access control

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Scaling Neural-Network-Based Molecular Dynamics with Long-Range Electrostatic Interactions to 51 Nanoseconds per Day

arXiv

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arXiv 2025年

作者： Li, Jianxiong Zhang, Beining Li, Mingzhen Hu, Siyu Zeng, Jinzhe Liu, Lijun Yuan, Guojun Wang, Zhan Tan, Guangming Jia, Weile SKLP Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence and Data Science University of Science and Technology of China Suzhou Big Data & AI Research and Engineering Center Hefei China Department of Mechanical Engineering Graduate School of Engineering The University of Osaka Suita Japan

Neural network-based molecular dynamics (NNMD) simulations incorporating long-range electrostatic interactions have significantly extended the applicability to heterogeneous and ionic systems, enabling effective modeling critical physical phenomena such as protein folding and dipolar surface and maintaining ab initio accuracy. However, neural network inference and long-range force computation remain the major bottlenecks, severely limiting simulation speed. In this paper, we target DPLR, a state-of-the-art NNMD package that supports long-range electrostatics, and propose a set of comprehensive optimizations to enhance computational efficiency. We introduce (1) a hardware-offloaded FFT method to reduce the communication overhead;(2) an overlapping strategy that hides long-range force computations using a single core per node, and (3) a ring-based load balancing method that enables atom-level task evenly redistribution with minimal communication overhead. Experimental results on the Fugaku supercomputer show that our work achieves a 37x performance improvement, reaching a maximum simulation speed of 51 ns/*** Codes 82M37 Copyright © 2025, The Authors. All rights reserved.

关键词： Supercomputers

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Analysis Pre and Post COVID-19 Pandemic Rorschach Test data of Using EM Algorithms and GMM Models

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data ...

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2022 Scholar's Yearly Symposium of technology, engineering and Mathematics, SYSTEM 2022

作者： Ponzi, Valerio Russo, Samuele Wajda, Agata Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy Institute of Energy and Fuel Processing Technology Zabrze41-803 Poland Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Principal component analysis

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PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

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IEEE transactions on neural networks and learning systems 2021年第10期32卷 4278-4290页

作者： Ke Gu Hongyan Liu Zhifang Xia Junfei Qiao Weisi Lin Daniel Thalmann Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing University of Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore EPFL CH Lausanne Switzerland

This article devises a photograph-based monitoring model to estimate the real-time PM concentrations, overcoming currently popular electrochemical sensor-based PM monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

关键词： Feature extraction Monitoring Atmospheric modeling COVID-19 Atmospheric measurements Transforms Temperature measurement

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： artificial intelligence Big data data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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Few-shot learning of initial coulombic efficiency using adaptive combination kernel deep Gaussian process regression

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Journal of Energy Storage 2025年 128卷

作者： Hai Guo Hongcheng Zhang Xiaofeng Lv Xiaoxu Liu Tianyi Ji School of Data Science and Artificial Intelligence Wenzhou University of Technology 325035 Wenzhou China College of Computer Science and Engineering Dalian Minzu University Dalian 116600 China School of Material Science and Engineering Shaanxi University of Science and Technology Xi'an 710021 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China

The initial coulombic efficiency (ICE) is critical for minimizing active ion loss and enhancing battery stability. However, the complex interplay between the multiscale structure of electrode materials and manufacturing processes obscures the underlying optimization mechanisms, hindering the development of high-performance batteries. Herein, a machine learning method based on adaptive combinatorial kernel deep Gaussian process (AKC-DGPR) is proposed on 110 sets of experimentally collected datasets, including the preparation process, structural parameters, and ICEs of hard carbon. A few-shot learning framework is developed especially for laboratory environments where data are collected manually. The kernel functions are first collected and organized to construct the kernel function list. The global optimal solution is gradually approached through iteration, and the SHapley Additive exPlanations (SHAP) method is used for model interpretation. The results show that the R 2 of the model is 0.74, and the RMSE is 0.13. The proposed model has high accuracy and reliability in predicting ICE, and the SHAP method explains the effect of input features on ICE and makes the model's decision-making process transparent. An inverse design idea is further proposed based on this model, which provides solid support for few-shot data-driven SIB material design and performance optimization.

关键词： Deep Gaussian process Few-shot learning Initial coulombic efficiency SHAP analysis Sodium-ion battery

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Privileged graph distillation for cold start recommendation

arXiv

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arXiv 2021年

作者： Wang, Shuai Zhang, Kun Wu, Le Ma, Haiping Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui University China

The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction records. In these recommendation systems, warm users (items) have privileged collaborative signals of interaction records compared to cold start users (items), and these Collaborative Filtering (CF) signals are shown to have competing performance for recommendation. Many researchers proposed to learn the correlation between collaborative signal embedding space and the attribute embedding space to improve the cold start recommendation, in which user and item categorical attributes are available in many online platforms. However, the cold start recommendation is still limited by two embedding spaces modeling and simple assumptions of space transformation. As user-item interaction behaviors and user (item) attributes naturally form a heterogeneous graph structure, in this paper, we propose a privileged graph distillation model (PGD). The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links. The student model is composed of an entity-attribute graph without CF links. Specifically, the teacher model can learn better embeddings of each entity by injecting complex higher-order relationships from the constructed heterogeneous graph. The student model can learn the distilled output with privileged CF embeddings from the teacher embeddings. Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item. Finally, extensive experimental results on the real-world datasets clearly show the effectiveness of our proposed model on different types of cold start problems, with average 6.6%, 5.6%, and 17.1% improvement over state-of-the-art baselines on three datasets, respectively. © 2021, CC BY.

关键词： Embeddings

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