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27th RoboCup International Symposium, 2024

作者： Abreu, Miguel Mota, Pedro Azevedo, Tomás Silva, Francisco Reis, Luís Paulo Lau, Nuno Florido, Mário LIACC/LASI/FEUP Artificial Intelligence and Computer Science Lab Faculty of Engineering of the University of Port University of Porto Porto Portugal LIACC/LASI/FCUP Artificial Intelligence and Computer Science Lab Faculty of Sciences of the University of Porto University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

ISBN: (纸本)9783031858581

FC Portugal, a simulated humanoid soccer team from the universities of Porto and Aveiro, achieved the third consecutive victory in the main competition of the 2024 RoboCup 3D Simulation League. The team registered 16 wins, 3 ties, and 2 losses, scoring a total of 105 goals while only conceding 17. FC Portugal also ended first in two secondary events: the Scientific Challenge, which evaluates innovative scientific contributions within the league, and the Fat Proxy Challenge, where teams share a common set of low-level skills and compete for the best high-level strategy. The codebase used in this competition was developed in Python after RoboCup 2021. This paper presents a brief overview of its structure along with the new features developed for this year’s competition. The key developments include a hierarchical learning framework, presented at the Scientific Challenge, and the release of our codebase to the RoboCup community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D modeling

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A Link Simulation Technique for LEO Satellite Constellations in the Electromagnetic Environment 2nd

A Link Simulation Technique for LEO Satellite Constellations...

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2nd International Conference on Network Simulation and Evaluation, NSE 2023

作者： Zhang, Yi Wang, Xiaofeng Shen, Taiqian Zhang, Guoxiu School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9789819745180

Link simulation can provide strong support for the verification and evaluation of new techniques for low earth orbit (LEO) satellite constellations. Aiming at the feature that communication links of LEO satellite constellations are susceptible to electromagnetic environment interference (EEI), we propose a link simulation architecture named LEOLS-Stack. To improve usability, scalability and fidelity, the proposed architecture integrates three key techniques: (1) models construction of natural electromagnetic interference, interstellar electromagnetic interference and artificial electromagnetic interference, which can quickly construct scenarios containing different electromagnetic environments;(2) automatic extraction of interference links, which can automatically divide interference links and calculate link performance data;(3) link simulation combining performance data with interference signals, which can identify the characteristics of interference signals and simulate interference links. Experiments show that constellations in different electromagnetic environments can be quickly built by the interference construction and simulation process. At the same time, the fidelity of link simulation under electromagnetic interference is improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Signal interference

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LegoDroid: flexible Android app decomposition and instant installation

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science China(Information sciences) 2023年第4期66卷 155-173页

作者： Yi LIU Yun MA Xusheng XIAO Tao XIE Xuanzhe LIU School of Computer Science Peking University Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education Institute for Artificial Intelligence Peking University Department of Computer and Data Sciences Case Western Reserve University

Current mobile applications(apps) have become increasingly complicated with increasing features that are represented on the graphical user interface associated with application programming interfaces(APIs) to access backend functionality and data. Meanwhile, apps suffer from the “software bloat” in volume. Some app features may be redundant, with respect to those features(from other apps) that the users already desirably and frequently use. However, the current app release model forces users to download and install a full-size installation package rather than optionally choosing only their desired features. Large-size apps can not only increase the local resource consumption, such as CPU, memory, and energy, but also inevitably compromise the user experience, such as the slow load time in the app. In this article, we first conduct an empirical study to characterize the app feature usage when users interact with Android apps,and surprisingly find that users access only a very small subset of app features. Based on these findings,we design a new approach named Lego Droid, which automatically decomposes an Android app for flexible loading and installation, while preserving the expected functionality with a fast and instant app load. With such a method, a slimmer bundle will be downloaded and host the target APIs inside the original app to satisfy users' requirements. We implement a system for Lego Droid and evaluate it with 1000 real-world Android apps. Compared to the original full-size apps, apps optimized by Lego Droid can substantially improve the load time by reducing the base bundle and feature bundles by 13.06% and 10.93%, respectively,along with the app-package installation size by 44.17%. In addition, we also demonstrate that Lego Droid is quite practical with evolving versions, as it can produce substantial reusable code to alleviate the developers' efforts when releasing new app versions.

关键词： performance software bloat instant installation mobile applications program analysis

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AIMSM - A Mechanism to Optimize Systems with Multiple AI Models: A Case Study in computer Vision for Autonomous Mobile Robots 23rd

AIMSM - A Mechanism to Optimize Systems with Multiple AI M...

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23rd EPIA Conference on artificial intelligence, EPIA 2024

作者： Ferreira, Bruno Georgevich Sousa, Armando Jorge Reis, Luis Paulo Augusto de Sousa, António Rodrigues, Rui Rossetti, Rosaldo Edge Innovation Center Federal University of Alagoas Maceió Brazil FEUP - Faculty of Engineering of the University of Porto Porto Portugal LIACC - Member of LASI LA - Artificial Intelligence and Computer Science Laboratory Porto Portugal INESC TEC - INESC Technology and Science Porto Portugal

ISBN: (纸本)9783031735028

This article proposes the artificial intelligence Models Switching Mechanism (AIMSM), a novel approach to optimize system resource utilization by allowing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn’t have to operate all the time. The AIMSM strategically allows the system to activate and deactivate these models, focusing on system resource optimization. The switching of each AI model can be based on any information, such as context or previous results. In the case study of an autonomous mobile robot performing computer vision tasks, the AIMSM helps the system to achieve a significant increment in performance, with a 50% average increase in frames per second (FPS) rate, for this specific case study, assuming that no erroneous switching occurred. Experimental results have demonstrated that the AIMSM can improve system resource utilization efficiency when properly implemented, optimize overall resource consumption, and enhance system performance. The AIMSM presented itself as a better alternative to permanently loading all the models simultaneously, improving the adaptability and functionality of the systems. It is expected that using the AIMSM will yield a performance improvement that is particularly relevant to systems with multiple AI models of a complex nature, where such models do not need to be all continuously executed or systems that will benefit from lower resource usage. Code is available at https://***/BrunoGeorgevich/AIMSM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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artificial intelligence in computerized adaptive testing to assess the cognitive performance of students: A Systematic Review 6

Artificial intelligence in computerized adaptive testing to ...

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： El Msayer, Mohamed Aoula, Es-Saadia Bouihi, Bouchra University Hassan II Computer Science Artificial Intelligence and Cyber Security Laboratory ENSET of Mohammedia Casablanca Morocco

ISBN: (纸本)9798350350180

computerized adaptive testing is a form of assessment that uses artificial intelligence to provide cognitive diagnoses of learners, in order to situate them in their learning process and offer them personalized support. "Adaptive"refers to the embedded selection algorithm that optimally selects questions based on a continuous estimation of the learner's proficiency at each step of the assessment. computerized adaptive tests (CAT) are computer-based tests that offer individualized, equitable questions based on the learner's abilities. It is designed to correctly measure skills in relation to a latent trait (e.g. comprehension, knowledge, personality characteristics). Item selection, ability estimation and automatic item generation represent the challenges posed by CAT. Traditional approaches use psychometric and statistical models for estimation. However, to improve their performance, many new methods have been developed using artificial intelligence and machine learning techniques. In this article, we present a systematic review of artificial intelligence approaches and techniques used in computerized adaptive testing, highlighting the benefits, challenges and future orientations of this field of study. © 2024 IEEE.

关键词： Contrastive Learning

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Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback 41

Linear Alignment: A Closed-form Solution for Aligning Human ...

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41st International Conference on Machine Learning, ICML 2024

作者： Gao, Songyang Ge, Qiming Shen, Wei Dou, Shihan Ye, Junjie Wang, Xiao Zheng, Rui Zou, Yicheng Chen, Zhi Yan, Hang Zhang, Qi Lin, Dahua Shanghai Artificial Intelligence Laboratory China School of Computer Science Fudan University China ByteDance Inc China

The success of AI assistants based on Language Models (LLMs) hinges on Reinforcement Learning from Human Feedback (RLHF) to comprehend and align with user intentions. However, traditional alignment algorithms, such as PPO, are hampered by complex annotation and training requirements. This reliance limits the applicability of RLHF and hinders the development of professional assistants tailored to diverse human preferences. In this work, we introduce Linear Alignment, a novel algorithm that aligns language models with human preferences in one single inference step, eliminating the reliance on data annotation and model training. Linear alignment incorporates a new parameterization for policy optimization under divergence constraints, which enables the extraction of optimal policy in a closed-form manner and facilitates the direct estimation of the aligned response. Extensive experiments on both general and personalized preference datasets demonstrate that linear alignment significantly enhances the performance and efficiency of LLM alignment across diverse scenarios. Our code and dataset is published on https://***/Wizardcoast/Linear_***. Copyright 2024 by the author(s)

关键词： Human form models

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LE2Fusion: A Novel Local Edge Enhancement Module for Infrared and Visible Image Fusion 12th

LE2Fusion: A Novel Local Edge Enhancement Module for Infrar...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Xiao, Yongbiao Li, Hui Cheng, Chunyang Song, Xiaoning International Joint Laboratory on Artificial Intelligence of Jiangsu Province School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

ISBN: (纸本)9783031463044

Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this end, we propose a fusion network based on the local edge enhancement, named LE2Fusion. Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image. For feature extraction, a multi-scale residual attention (MRA) module is applied to extract rich features. Then, with LE2, a set of enhancement weights are generated which are utilized in feature fusion strategy and used to guide the image reconstruction. To better preserve the local detail information and structure information, the pixel intensity loss function based on the local region is also presented. The experiments demonstrate that the proposed method exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image fusion

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Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion

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Plant Phenomics 2023年第4期5卷 825-835页

作者： Yanan Li Yuling Tang Yifei Liu Dingrun Zheng School of Computer Science and Engineering School of Artificial IntelligenceWuhan Institute of TechnologyWuhan 430205China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of TechnologyWuhan 430073China

Automated counting of grape berries has become one of the most important tasks in grape yield ***,dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep *** collection of data required for model training is also a tedious and expensive *** address these issues and cost-effectively count grape berries,a semi-supervised counting of grape berries in the field based on density mutual exclusion(CDMENet)is *** algorithm uses VGG16 as the backbone to extract image *** tasks based on density mutual exclusion are *** tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled *** addition,a density difference loss is *** feature representation is enhanced by amplifying the difference of features between different density *** experimental results on the field grape berry dataset show that CDMENet achieves less counting *** with the state of the arts,coefficient of determination(R^(2))is improved by 6.10%,and mean absolute error and root mean square error are reduced by 49.36%and 54.08%,*** code is available at.

关键词： Grape mutual backbone

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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Selective Depth Attention Networks for Adaptive Multiscale Feature Representation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第10期5卷 5064-5074页

作者： Guo, Qingbei Wu, Xiao-Jun Xu, Tianyang Si, Tongzhen Hu, Cong Tian, Jinglan University of Jinan Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan250022 China Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China

Existing multiscale methods lead to a risk of just increasing the receptive field sizes while neglecting small receptive fields. Thus, it is a challenging problem to effectively construct adaptive neural networks for recognizing various spatial-scale objects. To tackle this issue, we first introduce a new attention dimension, i.e., depth, in addition to existing attentions such as channel-attention, spatial-attention, branch-attention, and self-attention. We present a novel selective depth attention network to treat multiscale objects symmetrically in various vision tasks. Specifically, the blocks within each stage of neural networks, including convolutional neural networks (CNNs), e.g., ResNet, SENet, and Res2Net, and vision transformers (ViTs), e.g., PVTv2, output the hierarchical feature maps with the same resolution but different receptive field sizes. Based on this structural property, we design a depthwise building module, namely an selective depth attention (SDA) module, including a trunk branch and a SE-like attention branch. The block outputs of the trunk branch are fused to guide their depth attention allocation through the attention branch globally. According to the proposed attention mechanism, we dynamically select different depth features, which contributes to adaptively adjusting the receptive field sizes for the variable-sized input objects. Moreover, our method is orthogonal to multiscale networks and attention networks, so-called SDA-xNet. Extensive experiments demonstrate that the proposed SDA method significantly improves the original performance as a lightweight and efficient plug-in on numerous computer vision tasks, e.g., image classification, object detection, and instance segmentation. © 2020 IEEE.

关键词： Deep neural networks

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