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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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CASE STUDY OF A PORTUGUESE MUNICIPALITY IN A POST-CYBERATTACK CONTEXT 18

CASE STUDY OF A PORTUGUESE MUNICIPALITY IN A POST-CYBERATTAC...

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18th International Conferences IADIS Information Systems 2025 and 23rd International Conference on e-Society 2025

作者： Leite, Patrícia Antunes, Margarida Ferreira, Isabel Carvalho, Sandro Teixeira, Paulo Polythecnic University of Cavado and Ave Barcelos Portugal LIACC - Artificial Intelligence and Computer Science Lab Porto Portugal 2Ai School of Technology IPCA Barcelos Portugal

ISBN: (纸本)9789898704658

Given the importance of the continuous study of cybersecurity methods in the digital context in Portugal, to combat cyber incidents and safeguard fundamental rights, there is an urgent need for the State to develop a document of best practices to guide public services in their protection and recovery processes when targeted by cyber incidents. A technological incident response plan is suggested for the studied Municipal Council, with the objective of facilitating and streamlining the post-cyberattack recovery process, based on the identified need during the analysis conducted after the incident. © 2025 Proceedings of the International Conferences IADIS Information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Cyber attacks

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

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Neural Computing and Applications 2025年 1-36页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering 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

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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science China(Information sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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Systematic Selection of a Suitable Data Imputation Technique Based on Data Characteristics

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SN computer science 2025年第5期6卷 1-19页

作者： Sebastian, Anu Maria Peter, David Artificial Intelligence Lab Department of Computer Science Cochin University of Science and Technology Kerala Cochin India Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

Analyzing incomplete data is one of the prime concerns in data analysis. Discarding the missing records or values might result in inaccurate analysis outcomes or loss of helpful information, especially when the size of the data is small. A preferable alternative is to substitute the missing values using imputation such that the substituted values are very close to the actual missing values and this is a challenging task. In spite of the existence of many imputation algorithms, there is no universal imputation algorithm that can yield the best values for imputing all types of datasets. This is mainly because of the dependence of the imputation algorithm on the inherent properties of the data. These properties include type of data distribution, data size, dimensionality, presence of outliers, data dependency among the attributes, and so on. In the literature, there exists no straightforward method for determining a suitable imputation algorithm based on the data characteristics. The existing practice is to conduct exhaustive experimentation using the available imputation techniques with every dataset and this requires a lot of time and effort. Moreover, the current approaches for checking the suitability of imputations cannot be done when the ground truth data is not available. In this paper, we propose a new method for the systematic selection of a suitable imputation algorithm based on the inherent properties of the dataset which eliminates the need for exhaustive experimentation. Our method determines the imputation technique which consistently gives lower errors while imputing datasets with specific properties. Also, our method is particularly useful when the real-world data do not have the ground truth for missing data to check the imputation performance and suitability. Once the suitability of a DI technique is established based on the data properties, this selection will remain valid for another dataset with similar properties. Thus, our method can save time an

关键词： Data analysis Data distribution Data imputation Data preprocessing Imputation performance Machine learning Missing value handling

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Improving prompt tuning-based software vulnerability assessment by fusing source code and vulnerability description

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Automated Software Engineering 2025年第2期32卷 1-29页

作者： Wang, Jiyu Chen, Xiang Pei, Wenlong Yang, Shaoyu School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong China State Key Lab. for Novel Software Technology Nanjing University Jiangsu Nanjing China

To effectively allocate resources for vulnerability remediation, it is crucial to prioritize vulnerability fixes based on vulnerability severity. With the increasingnumber of vulnerabilities in recent years, there is an urgent need for automated methods for software vulnerability assessment (SVA). Most of the previous SVA studies mainly rely on traditional machine learning methods. Recently, fine-tuning pre-trained language models has emerged as an intuitive method for improving performance. However, there is a gap between pre-training and fine-tuning, and their performance heavily depends on the dataset’s quality of the downstream task. Therefore, we propose a prompt tuning-based method PT-SVA. Different from the fine-tuning paradigm, the prompt-tuning paradigm involves adding prompts to make the training process similar to pre-training, thereby better adapting to downstream tasks. Moreover, previous research aimed to automatically predict severity by only analyzing either the vulnerability descriptions or the source code of the vulnerability. Therefore, we further consider both types of vulnerability information for designing hybrid prompts (i.e., a combination of hard and soft prompts). To evaluate PT-SVA, we construct the SVA dataset based on the CVSS V3 standard, while previous SVA studies only consider the CVSS V2 standard. Experimental results show that PT-SVA outperforms ten state-of-the-art SVA baselines, such as by 13.7% to 42.1% in terms of MCC. Finally, our ablation experiments confirm the effectiveness of PT-SVA’s design, specifically in replacing fine-tuning with prompt tuning, incorporating both types of vulnerability information, and adopting hybrid prompts. Our promising results indicate that prompt tuning-based SVA is a promising direction and needs more follow-up studies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Bi-modal information Hybrid prompt Pre-trained language model Prompt tuning Software vulnerability assessment

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Innovation Lessons from CEF Projects’ Management

Innovation Lessons from CEF Projects’ Management

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International Conference on Marketing and Technologies, ICMarkTech 2023

作者： Carreira, Rui Campolargo, Margarida Gemein, Olaf-Gerd Mulquin, Michael N2i-Maia Polytechnic Institute LIACC-Artificial Intelligence and Computer Science Laboratory Porto Portugal University of Maia Maia Portugal VeroCity Kiel Germany Brussels Belgium

ISBN: (纸本)9789819736973

Multinational CEF research projects are mostly focused on technical innovations;however, they should also innovate from the project management practices’ perspective. In particular, the ODALA project has already improved technical data management in cities by developing Mobility Data Toolkit Components and an Interoperability framework for Environmental Sensing. Therefore, this paper promotes and describes a concrete mixed-methods approach (i.e., qualitative and quantitative) in which CEF project partners were involved to understand the intricate details about such projects in order to originate generic good practices which should be made available to similar projects. That way multinational projects’ management may be facilitated by promoting cross-learning and also avoiding the most common pitfalls addressed by the respective partners. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Project management

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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