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A survey on Image semantic segmentation Using Deep learning Techniques

computers, Materials & Continua 2023年第1期74卷 1941-1957页

作者： Jieren Cheng Hua Li Dengbo Li shuai Hua Victor s.sheng School of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security(School of Cryptology) Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China Department of Computer Science Texas Tech University TX 79409USA

Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis,autonomous driving,virtual or augmented reality,*** recent years,due to the remarkable performance of transformer and multilayer perceptron(MLP)in computer vision,which is equivalent to convolutional neural network(CNN),there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning *** survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic ***,the commonly used image segmentation datasets are ***,extensive pioneering works are deeply studied from multiple perspectives(e.g.,network structures,feature fusion methods,attention mechanisms),and are divided into four categories according to different network architectures:CNN-based architectures,transformer-based architectures,MLP-based architectures,and ***,this paper presents some common evaluation metrics and compares the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value on the most widely used ***,possible future research directions and challenges are discussed for the reference of other researchers.

关键词： Deep learning semantic segmentation CNN MLP transformer

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Game accessibility for visually impaired people: a review

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soft Computing 2024年第17-18期28卷 10475-10489页

作者： Agrimi, Emanuele Battaglini, Chiara Bottari, Davide Gnecco, Giorgio Leporini, Barbara IMT School for Advanced Studies Lucca Italy University School for Advanced Studies IUSS Pavia Italy National Research Council Pisa Italy University of Pisa Pisa Italy

Playing games is an important way to promote the integration, inclusion, and socialization of participants. This is especially the case of persons with disabilities, such as visually impaired people. Unfortunately, very few games are accessible to such persons. Hopefully, in many digital games, this accessibility can be enabled in principle by assistive technologies, such as screen readers. The aim of this work consists in reviewing the recent literature on game accessibility for people with visual impairment and discussing benefits, limitations, and possible improvements of currently available accessibility solutions. After providing a definition of visual impairment and describing its relationship with gaming, the work reviews general techniques for designing more accessible games. subsequently, it focuses on specific techniques based on replacing visual stimuli with auditory stimuli (e.g., sonification and sound-source simulation), also presenting some recently proposed sonification-mapping strategies. Then, the application of machine-learning techniques to the development of accessible interfaces for online versions of board games is illustrated by a recent case study. Finally, a discussion and some conclusions are provided, with a particular focus on policy implications of improvements in game accessibility for visually impaired people. © The Author(s) 2024.

关键词： Machine learning

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Detecting coagulation time in cheese making by means of computer vision and machine learning techniques

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computers IN INDUsTRY 2025年 164卷

作者： Loddo, Andrea Di Ruberto, Cecilia Armano, Giuliano Manconi, Andrea Univ Cagliari Dept Math & Comp Sci I-09124 Cagliari Italy CNR Inst Biomed Technol Natl Res Council Via Fratelli Cervi 93 I-20054 Milan Italy

Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental results show the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deep learning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency.

关键词： Image processing computer vision Machine learning Food industry Curd-firming time detection

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superCyberKids: Enhancing Cybersecurity Education in K-12 Through Digital Game-based learning 5th

SuperCyberKids: Enhancing Cybersecurity Education in K-12 Th...

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5th International Conference on Higher Education learning Methodologies and Technologies Online, HELMeTO 2023

作者： Manganello, Flavio Callaghan, Peadar Città, Giuseppe Denaro, Paola Earp, Jeffrey Fante, Chiara Ifenthaler, Dirk Kirna, Catlyn Laszlo, Luca Janka Matteucci, Ilaria Perna, salvatore Plintz, Nicolai Vaccarelli, Anna Gentile, Manuel Institute for Educational Technology National Research Council Genova Italy Institute of Informatics and Telematics National Research Council Pisa Italy School of Digital Technologies Tallinn University Tallinn Estonia School of Governance Law and Society Tallinn University Tallinn Estonia Learning Design and Technology University of Mannheim BW Mannheim Germany Data Science in Higher Education Learning and Teaching Curtin University BentleyWA Australia European School Heads Association Utrecht Netherlands

ISBN: (纸本)9783031673504

This paper presents superCyberKids, a project funded by the European Union that focuses on integrating digital game-based learning into cybersecurity education for children. The project aims to equip educators with effective methodologies and tools for teaching cybersecurity, addressing rising cyber threats and the educational sector’s challenges. superCyberKids utilizes a gamified approach, harmoniously merging digital education with game-based learning, underpinned by fundamental cybersecurity principles. The initiative spans five EU countries, involving a collaboration of academic, cybersecurity and game design experts. The conceptual core of this project is the superCyberKids learning Framework (sCKLF), which is the product of a comprehensive literature review, amalgamation of expert opinions, and analysis of current cybersecurity educational practices. The paper explores the application of two specific digital games, ‘spoofy’ and ‘Nabbovaldo and the Cyber Blackmail’ in the framework, emphasizing their role in fostering critical thinking and cybersecurity skills among young learners in a dynamic and engaging manner. © The Author(s), under exclusive license to springer Nature switzerland AG 2024.

关键词： Adversarial machine learning

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Machine learning for the Evaluation of the Nephrops norvegicus Population 10th

Machine Learning for the Evaluation of the Nephrops norv...

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10th International Conference on Machine learning, Optimization, and Data science, LOD 2024

作者： Reggiannini, Marco Martinelli, Michela Papini, Oscar Zacchetti, Lorenzo Domenichetti, Filippo Pieri, Gabriele National Research Council of Italy Via G. Moruzzi 1 Pisa56124 Italy National Research Council of Italy Largo Fiera della Pesca 2 Ancona60125 Italy Alma Mater Studiorum – University of Bologna Piazza di Porta S. Donato 1 Bologna40126 Italy

ISBN: (纸本)9783031824838

This paper introduces computer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves human operators visually assessing videos, which is prone to errors and subjectivity. Automated machine learning systems show promise in identifying and counting burrows, potentially standardising recognition and reducing operator errors. However, challenges exist in implementing computer vision techniques. An automated system aims to process video streams, detect seabed openings, extract visual features, and classify N. norvegicus burrows, significantly advancing the automation of underwater video reading. The primary processing presented in the paper lies in a boosting algorithm capable of extending the original annotated ground truth and assessing the improved performance of the extended data set with respect to the original one. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Deep learning

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A Machine learning Algorithm to Convert Geostationary satellite LsT to Air Temperature Using In situ Measurements: A Case study in Rome and High-Resolution spatio-Temporal UHI Analysis

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REMOTE sENsING 2025年第3期17卷

作者： Cecilia, Andrea Casasanta, Giampietro Petenko, Igor Argentini, stefania Italian Natl Res Council CNR Inst Atmospher Sci & Climate ISAC I-00133 Rome Italy

Air temperature (Ta) measurements are crucial for characterizing phenomena like the urban heat island (UHI), which can create critical conditions in cities during summer. This study aims to develop a machine learning-based model, namely gradient boosting, to estimate Ta from geostationary satellite LsT data and to apply these estimates to investigate UHI dynamics. Using Rome, Italy, as a case study, the model was trained with Ta data from 15 weather stations, taking multi-temporal LsT values (instantaneous and lagged up to 4 h) and additional predictors. The model achieved an overall RMsE of 0.9 degrees C. The resulting Ta fields, with a 3 km spatial and hourly temporal resolution, enabled a detailed analysis of UHI intensity and dynamics during the summers of 2019-2020, significantly enhancing the spatial and temporal detail compared to previous studies based solely on in situ data. The results also revealed a slightly higher nocturnal UHI intensity than previously reported, attributed to the inclusion of rural areas with near-zero imperviousness, thanks to the complete mapping of Ta across the domain now accessible.

关键词： urban heat island machine learning gradient boosting land surface temperature geostationary satellite air temperature citizen science

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Real-Time Implementation of Quadrotor UAV Control system based on a Deep Reinforcement learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha souham Meshoul Fouad Khenfri Raouf Fareh Hadil shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines simulated Annealing(sA)with a reinforcement learning algorithm,specifically simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(sA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and sliding Mode Control(sMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed sA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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Real-world validation of safe reinforcement learning,model predictive control and decision tree-based home energy management systems

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Energy and AI 2024年第4期18卷 470-488页

作者： Julian Ruddick Glenn Ceusters Gilles Van Kriekinge Evgenii Genov Cedric De Cauwer Thierry Coosemans Maarten Messagie Electric Vehicle and Energy Research Group(EVERGI) MobilityLogistics and Automotive Technology Research Centre(MOBI)Department of Electrical Engineering and Energy TechnologyVrije Universiteit BrusselPleinlaan 2Brussels1050Belgium ABB N.V. Culliganlaan 3B 101Diegem1831Belgium AI lab Vrije Universiteit BrusselPleinlaan 2Brussels1050Belgium

Recent advancements in machine learning based energy management approaches,specifically reinforcement learning with a safety layer(OptLayerPolicy)and a metaheuristic algorithm generating a decision tree control policy(TreeC),have shown ***,their effectiveness has only been demonstrated in computer *** paper presents the real-world validation of these methods,comparing them against model predictive control and simple rule-based control *** experiments were conducted on the electrical installation of four reproductions of residential houses,each with its own battery,photovoltaic,and dynamic load system emulating a non-controllable electrical load and a controllable electric vehicle *** results show that the simple rules,TreeC,and model predictive control-based methods achieved similar costs,with a difference of only 0.6%.The reinforcement learning based method,still in its training phase,obtained a cost 25.5%higher to the other *** simulations show that the costs can be further reduced by using a more representative training dataset for TreeC and addressing errors in the model predictive control implementation caused by its reliance on accurate data from various *** OptLayerPolicy safety layer allows safe online training of a reinforcement learning agent in the real world,given an accurate constraint function *** proposed safety layer method remains error-prone;nonetheless,it has been found beneficial for all investigated *** TreeC method,which does require building a realistic simulation for training,exhibits the safest operational performance,exceeding the grid limit by only 27.1 Wh compared to 593.9 Wh for reinforcement learning.

关键词： Energy management system Machine learning Reinforcement learning Decision tree Model predictive control Hardware-in-the-loop Implementation Experimental

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Exploring the vulnerability of black-box adversarial attack on prompt-based learning in language models

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Neural Computing and Applications 2025年第3期37卷 1457-1473页

作者： Tan, Zihao Chen, Qingliang Zhu, Wenbin Huang, Yongjian Liang, Chen Department of Computer Science Jinan University Guangdong Guangzhou510632 China Guangzhou Xuanyuan Research Institute Co. Ltd. Guangdong Guangzhou510006 China Department of Computing The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Prompt-based learning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings. However, the trustworthiness of PLMs is of paramount significance and potential vulnerabilities have been shown in prompt-based templates that could mislead the predictions of language models, causing serious security concerns. In this paper, we will shed light on some vulnerabilities of PLMs, by proposing a prompt-based adversarial attack on manual templates in black-box scenarios. First of all, we design character-level and word-level heuristic approaches to break manual templates separately. Then we present a greedy algorithm for the attack based on the above heuristic destructive approaches and further combine it with negative words. Finally, we evaluate our approach with the classification tasks on three variants of BERT series models and eight datasets. And comprehensive experimental results justify the effectiveness of our approach in terms of attack success rate and attack speed. On average, it achieves an attack success rate of close to 90% and a query time of around 3000, which is significantly better than the compared baseline methods. Further experimental studies indicate that our proposed method also displays good capabilities in scenarios with varying shot counts, template lengths and query counts, exhibiting good generalizability. © The Author(s), under exclusive licence to springer-Verlag London Ltd., part of springer Nature 2024.

关键词： Adversarial machine learning

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learning Dual-Layer User Representation for Enhanced Item Recommendation

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computers, Materials & Continua 2024年第7期80卷 949-971页

作者： Fuxi Zhu Jin Xie Mohammed Alshahrani Applied Research Center of Artificial Intelligence Wuhan CollegeWuhan430212China College of Computer Science South-Central MINZU UniversityWuhan430074China Unmanned.Company Riyadh11564Saudi Arabia

User representation learning is crucial for capturing different user preferences,but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data,and thus cannot be measured ***-based data models can learn user representations by mining latent semantics,which is beneficial to enhancing the semantic function of user ***,these technologies only extract common features in historical records and cannot represent changes in user ***,sequential feature can express the user’s interests and intentions that change time by *** the sequential recommendation results based on the user representation of the item lack the interpretability of preference *** address these issues,we propose in this paper a novel model with Dual-Layer User Representation,named DLUR,where the user’s intention is learned based on two different layer ***,the latent semantic layer adds an interactive layer based on Transformer to extract keywords and key sentences in the text and serve as a basis for *** sequence layer uses the Transformer model to encode the user’s preference intention to clarify changes in the user’s ***,this dual-layer user mode is more comprehensive than a single text mode or sequence mode and can effectually improve the performance of *** extensive experiments on five benchmark datasets demonstrate DLUR’s performance over state-of-the-art recommendation *** addition,DLUR’s ability to explain recommendation results is also demonstrated through some specific cases.

关键词： User representation latent semantic sequential feature interpretability

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