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Attention Markets of Blockchain-Based Decentralized Autonomous Organizations

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1370-1380页

作者： Juanjuan Li Rui Qin Sangtian Guan Wenwen Ding Fei Lin Fei-Yue Wang State Key Laboratory of Multimodal Artificial intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’***,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is ***,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as *** equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention ***,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is ***,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different *** study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

关键词： Attention decentralized autonomous organizations Harberger tax Stackelberg game.

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Transfer Learning in Motor Imagery Brain Computer Interface: A Review

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Journal of Shanghai Jiaotong university(Science) 2024年第1期29卷 37-59页

作者：李明爱许冬芹 faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training *** recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the *** paper surveys the development of transfer learning and reviews the transfer learning approaches in *** addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer ***,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,*** the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.

关键词： transfer learning brain-computer interface(BCI) electroencephalogram machine learning

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Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

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Computers, Materials & Continua 2024年第4期79卷 651-673页

作者： Ahsan Wajahat Jingsha He Nafei Zhu Tariq Mahmood Tanzila Saba Amjad Rehman Khan Faten S.A.lamri faculty of Information Technology Beijing University of TechnologyBeijing100124China Artificial intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy *** escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital *** methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false *** meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in *** proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data *** Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction *** study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert *** proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,*** empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both *** app

关键词： Android malware detection machine learning SVC K-Nearest Neighbors(KNN) RF

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Hydrodynamic function and spring constant calibration of FluidFM micropipette cantilevers

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Microsystems & Nanoengineering 2024年第1期10卷 277-286页

作者： Attila Bonyár Ágoston G.Nagy Hans Gunstheimer Gotthold Fläschner Robert Horvath Department of Electronics Technology Faculty of Electrical Engineering and InformaticsBudapest University of Technology and EconomicsBudapestHungary Nanobiosensorics Laboratory Institute of Technical Physics and Materials ScienceCentre for Energy ResearchHUN-RENBudapestHungary Nanosurf AG Gräubernstrasse 12CH-4410 LiestalSwitzerland

Fluidic force microscopy(FluidFM)fuses the force sensitivity of atomic force microscopy with the manipulation capabilities of microfluidics by using microfabricated cantilevers with embedded fluidic *** innovation initiated new research and development directions in biology,biophysics,and material *** acquire reliable and reproducible data,the calibration of the force sensor is ***,the hollow FluidFM cantilevers contain a row of parallel pillars inside a rectangular *** precise spring constant calibration of the internally structured cantilever is far from trivial,and existing methods generally assume simplifications that are not applicable to these special types of *** addition,the Sader method,which is currently implemented by the FluidFM community,relies on the precise measurement of the quality factor,which renders the calibration of the spring constant sensitive to *** this study,the hydrodynamic function of these special types of hollow cantilevers was experimentally determined with different *** on the hydrodynamic function,a novel spring constant calibration method was adapted,which relied only on the two resonance frequencies of the cantilever,measured in air and in a *** on these results,our proposed method can be successfully used for the reliable,noise-free calibration of hollow FluidFM cantilevers.

关键词： spring cantilever function

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Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

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Computers, Materials & Continua 2024年第1期78卷 1171-1187页

作者： Mohamed Abdel-Basset Hossam Hawash Mohamed Abouhawwash S.S.Askar Alshaimaa A.Tantawy faculty of Computers and informatics Zagazig UniversityZagazig44519Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT *** study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 *** paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT ***,an efficient U-shaped transformer network is integrated for lung image ***,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input ***,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer *** evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical *** Conformer Network achieves 97.40% of detection accuracy under cross-validation *** model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our *** findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

关键词： Deep learning COVID-19 multi-modal medical image fusion diagnostic image fusion

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The engineering of circular causality for specialization and design of complex systems:cad2CAS and casCAD2

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Frontiers of Information Technology & Electronic Engineering 2024年第2期25卷 323-332页

作者： Xiaolong LIANG Rui QIN Juanjuan LI Fei-Yue WANG faculty of Innovation Engineering Macao University of Science and TechnologyMacao999078China State Key Laboratory of Multimodal Artificial intelligence Systems Chinese Academy of SciencesBeijing100190China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190China

The emergence of decentralized autonomous organizations and operations(DAOs),equipped with innovative mechanisms,enables new possibilities for transforming traditional social collaborative *** are the underlying drive... 详细信息

关键词： complexity. circular equipped

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A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

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Computers, Materials & Continua 2024年第6期79卷 4149-4167页

作者： Yan Xiang Xuedong Zhao Junjun Guo Zhiliang Shi Enbang Chen Xiaobo Zhang faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China Yunnan Key Laboratory of Artificial intelligence Kunming University of Science and TechnologyKunming650500China Kunming Enersun Technology Co. Ltd.Kunming650217China

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information *** previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous ***,a unified CNER is often needed in real-world *** studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified ***,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved *** this study,we enhance the character-pair grid representation by incorporating both local and global ***,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation *** devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character *** approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition *** conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and *** results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

关键词： Chinese named entity recognition character-pair relation classification grid tagging U-shaped segmentation network

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Detecting Malicious Uniform Resource Locators Using an Applied intelligence Framework

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Computers, Materials & Continua 2024年第6期79卷 3827-3853页

作者： Simona-Vasilica Oprea Adela Bara Department of Economic informatics and Cybernetics Bucharest University of Economic StudiesBucharest010572Romania

The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)*** this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators(URLs).Three categories of features,both ML and Deep Learning(DL)algorithms and a ranking schema are included in the proposed *** apply frequency and prediction-based embeddings,such as hash vectorizer,Term Frequency-Inverse Dense Frequency(TF-IDF)and predictors,word to vector-word2vec(continuous bag of words,skip-gram)from Google,to extract features from ***,we apply more state-of-the-art methods to create vectorized features,such as ***,feature engineering that is specific to URL structure is deployed to detect scams and other *** framework assessment,four ranking indicators are weighted:computational time and performance as accuracy,F1 score and type error *** the computational time,we propose a new metric-Feature Building Time(FBT)as the cutting-edge feature builders(like doc2vec or GloVe)require more *** applying the proposed assessment step,the skip-gram algorithm of word2vec surpasses other feature builders in ***,eXtreme Gradient Boost(XGB)outperforms other *** this setup,we attain an accuracy of 99.5%and an F1 score of 0.99.

关键词： Detecting malicious URL classifiers text to feature deep learning ranking algorithms feature building time

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Position-aware pushing and grasping synergy with deep reinforcement learning in clutter

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CAAI Transactions on intelligence Technology 2024年第3期9卷 738-755页

作者： Min Zhao Guoyu Zuo Shuangyue Yu Daoxiong Gong Zihao Wang Ouattara Sie Intelligent Robotics Laboratory Faculty of Information TechnologyBeijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational intelligence and Intelligent Systems BeijingChina Laboratory of Biomechatronics and Intelligent Robotics(BIRO) Department of Mechanical and Aerospace EngineeringNorth Carolina State UniversityRaleighNorth CarolinaUSA

The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in *** effectively perform grasping and pushing manipu-lations,robots need to perceive the position information of objects,including the co-ordinates and spatial relationship between objects(e.g.,proximity,adjacency).The authors propose an end-to-end position-aware deep Q-learning framework to achieve efficient collaborative pushing and grasping in ***,a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high-quality affordance maps of operating positions with features of pushing and grasping *** addition,the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial re-lationships between objects in cluttered *** further enhance the perception capacity of position information of the objects,the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function.A series of experiments are carried out in simulation and real-world which indicate that the method improves sample efficiency,task completion rate,grasping success rate and action efficiency compared to state-of-the-art end-to-end *** that the authors’system can be robustly applied to real-world use and extended to novel *** material is available at https://***/NhG\_k5v3NnM}{https://***/NhG\_k5v3NnM.

关键词： deep learning deep neural networks intelligent robots

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