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

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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IMGTP: A Unified Framework for Improving and Measuring the Generalizability of Trajectory Prediction Models

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Luyao Zhou, Zikang Wang, Jianping Li, Yung-Hui Jan, Nien-Yi Lin, Yi-Rong Lin, Yen-Cheng National Engineering Research Center of Educational Big Data and the Faculty of Artificial Intelligence in Education Central China Normal University China Department of Computer Science City University of Hong Kong and City University of Hong Kong Shenzhen Research Institute China AI Research Center of Hon Hai Research Institute China

Accurately predicting nearby agents' future trajectories is fundamental for ensuring the safety and efficiency of autonomous driving. However, existing learning-based trajectory prediction models struggle with poor generalization to out-of-distribution (OOD) scenes, potentially leading to failed predictions in real-world applications. To tackle this challenge, we introduce IMGTP, a unified framework for systematically evaluating, improving, and measuring the generalization performance of trajectory prediction models in OOD scenes. IMGTP consists of three primary components: the Argoverse-Shift dataset, the FrenÃ©t+ strategy, and an approach for OOD measurement. First, the Argoverse-Shift is a cross-domain dataset that highlights the significant differences in data distributions across domains, serving as a testing platform for models' generalization ability. Second, the FrenÃ©t+ strategy leverages map centerlines and the FrenÃ©t Frame to enhance the generalizability of baseline models by mitigating the domain gap resulting from road geometry variations. Third, we provide a comprehensive evaluation methodology for OOD measurement, quantifying the range of data that can be effectively handled by prediction models. This measurement approach employs the accuracy rate of the correctly predicted samples and their corresponding mean distance as well as standard deviation to the training set center as indicators of the model's generalization ability. The mean distance also serves as a valid criterion for OOD detection and uncertainty quantification. Experimental results demonstrate that our IMGTP framework enhances the generalization capability of mainstream trajectory prediction models by substantially improving their prediction performance in unseen domains. This advancement contributes to developing more robust and safer prediction models for autonomous driving in the real world. The code can be found at https://***/XIAOYEJIAYOU/Frenet-Strategy. IEEE

关键词： Autonomous vehicles

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Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm 24

Towards the Identifiability and Explainability for Personali...

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33rd ACM Web Conference, WWW 2024

作者： Li, Jiatong Liu, Qi Wang, Fei Liu, Jiayu Huang, Zhenya Yao, Fangzhou Zhu, Linbo Su, Yu University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Hefei Normal University Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

ISBN: (纸本)9798400701719

Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners' cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning services. Existing cognitive diagnosis models (CDMs) follow theproficiency-response paradigm that views learner traits and question parameters as trainable embeddings and learns them through learner performance prediction. However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novelresponse-proficiency-response paradigm inspired by encoder-decoder models. Specifically, we first devise the diagnostic module of ID-CDF, which leverages inductive learning to eliminate randomness in optimization to guarantee identifiability and captures the monotonicity between overall response data distribution and cognitive states to prevent explainability overfitting. Next, we propose a flexible predictive module for ID-CDF to ensure diagnosis preciseness. We further present an implementation of ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on four real-world datasets with different characteristics demonstrate that ID-CDF can effectively address the problems without loss of diagnosis preciseness. Our code is available at https://***/CSLiJT/ID-CDF. © 2024 ACM.

关键词： Learning systems

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research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big data

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Computers, Materials & Continua 2024年第10期81卷 1409-1432页

作者： Hongjun Zhang Zeyu Zhang Yilong Ruan Hao Ye Peng Li Desheng Shi School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China Ministry of Science and Technology Innovation China Communications Services Corporation LimitedBeijing100071China School of Artificial Intelligence The University of ManchesterManchesterM139PLUK Ministry of Technology Innovation China Telecom Artificial Intelligence Technology(Beijing)Corporation LimitedBeijing100032China Ministry of Science and Technology Innovation Zhongbo Information Technology Research Institute Corporation LimitedNanjing210012China Jiangsu Postal Big Data Technology and Application Engineering Research Center Nanjing University of Posts and TelecommunicationsNanjing210003China

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering *** address this issue,this paper introduces a new method named Big data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering ***,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing ***,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary *** new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in ***,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic *** experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most ***,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering *** not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through *** method shows great potential in real-world applications where dynamic data growth is common,and it is of significant imp

关键词： Tensor incremental update distributed clustering processing big data

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Big data and AI in Financial Risk Assessment: The New Scorecard Approach 4

Big Data and AI in Financial Risk Assessment: The New Scorec...

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4th IEEE International Conference on Information Technology, Big data and artificial intelligence, ICIBA 2024

作者： Li, Hualin Chen, Kai Yang, Suyi Zhang, Dequan Zhou, Chenchen Li, Kaidong Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center China Shu Yi Xin Credit Management Co Ltd China Chongqing Institute of Engineering School of Big Data and Artificial Intelligence China

ISBN: (纸本)9798350363586

This paper presents a framework for assessing financial risk across industries by integrating rule-based modeling, the Analytic Hierarchy Process (AHP), and artificial intelligence (AI) techniques. The framework utilizes diverse data sources, including financial, tax, and judicial records, as well as financial reports from listed companies, to improve enterprise credit *** process begins with data preparation, which involves collecting and preprocessing data to ensure its quality and consistency. A rule-based preprocessing algorithm is developed to standardize the data for scoring. In the second phase, scoring is done using rule-based modeling informed by expert advice and AHP, which prioritizes and weights factors impacting industrial risk. This generates a customized scorecard for each industry. The third phase focuses on training AI models on the dataset. These models automatically learn to assign weights to indicators and generate industry scores by identifying complex patterns between the data and industry performance. The framework concludes with cross-validation to confirm the accuracy and reliability of the results. This approach provides insights into industry-wide scoring, supports continuous monitoring of industry performance, and enhances financial risk management decision-making. The proposed framework offers a robust and reliable tool for assessing financial risk across various industries. © 2024 IEEE.

关键词： Decentralized finance

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Iterative Active-Inactive Obstacle Classification for Time-Optimal Collision Avoidance

arXiv

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arXiv 2024年

作者： Kaymaz, Mehmetcan Ure, Nazim Kemal The Data & Decision Lab. ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Turkey

Time-optimal obstacle avoidance is a prevalent problem encountered in various fields, including robotics and autonomous vehicles, where the task involves determining a path for a moving vehicle to reach its goal while navigating around obstacles within its environment. This problem becomes increasingly challenging as the number of obstacles in the environment rises. We propose an iterative active-inactive obstacle approach, which involves identifying a subset of the obstacles as"active", that considers solely the effect of the"active" obstacles on the path of the moving vehicle. The remaining obstacles are considered"inactive" and are not considered in the path planning process. The obstacles are classified as’active’ on the basis of previous findings derived from prior iterations. This approach allows for a more efficient calculation of the optimal path by reducing the number of obstacles that need to be considered. The effectiveness of the proposed method is demonstrated with two different dynamic models using the various number of obstacles. The results show that the proposed method is able to find the optimal path in a timely manner, while also being able to handle a large number of obstacles in the environment and the constraints on the motion of the object. © 2024, CC BY.

关键词： Motion planning

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Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for LatentSkill Discovering

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Intelligent Automation & Soft Computing 2023年第8期37卷 1311-1324页

作者： Jing Geng Huali Yang Shengze Hu National Engineering Research Center of Educational Big Data Central China Normal UniversityWuhan430079China Faculty of Artificial Intelligence in Education Central China Normal UniversityWuhan430079China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan430079China

Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at home and abroad and has made a series of important research *** this end,we propose a noise-filtering enhanced deep cognitive diagno-sis method to improve the fitting ability of traditional models and obtain students’skill mastery status by mining the interaction between students and problems nonlinearly through neural ***,modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of the proposed model;second,the neural network is used to predict students’learning performance,diagnose students’skill mastery and provide immediate feedback;finally,by comparing the proposed model with several baseline models,extensive experimental results on real data sets demonstrate that the proposed Finally,by comparing the proposed model with several baseline models,the extensive experimental results on the actual data set demon-strate that the proposed model not only improves the accuracy of predicting students’learning performance but also enhances the interpretability of the neurocognitive diagnostic model.

关键词： Cognitive diagnosis nonlinear interaction interpretability intelligent education system skill diagnosis

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Multi-trial Vector-based Whale Optimization Algorithm

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Journal of Bionic Engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm

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Evolution of the Participation of Women in University Computer science Studies in Spain and in Europe 53

Evolution of the Participation of Women in University Comput...

引用

53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Nebot, Àngela Mugica, Francisco Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politécnica de Catalunya Barcelona Spain

ISBN: (纸本)9798350336429

This paper presents a comprehensive examination of the current state of underrepresentation of women in computer science (CS) degrees in Spain. It offers an overview of the situation across autonomous communities in Spain, highlighting the evolving trends within the country, regional variations, and the distinctions between public and private universities. The research employs descriptive statistical methods to analyze data obtained from the Spanish Ministry of Universities, Eurostat, as well as recent literature reviews. Furthermore, the study contextualizes the evolution of female enrollments in Spain by comparing it with data from other European countries. The primary objective is to gain a thorough understanding of the broader Spanish landscape and suggest future research directions aimed at reducing the gender gap in CS studies. © 2023 IEEE.

关键词： computer science degree descriptive statistical methods gender imbalance

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A NOVEL RULE-BASED OVERSAMPLING APPROACH FOR IMBALANCED data CLASSIFICATION 37

A NOVEL RULE-BASED OVERSAMPLING APPROACH FOR IMBALANCED DATA...

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37th Annual European Simulation and Modelling Conference, ESM 2023

作者： Zhang, Xiao Paz, Iván Nebot, Àngela Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789492859280

When confronted with imbalanced datasets, traditional classifiers frequently struggle to correctly categorize samples from the minority class, adversely impacting the overall predictive performance of machine learning models. Current oversampling techniques generally focus on data interpolation through neighbor selection, often neglecting to uncover underlying data structures and relationships. This study introduces a novel application for RuLer, an algorithm originally developed for identifying sound patterns in the artistic domain of live coding. When adapted for data oversampling (as Ad-RuLer), the algorithm shows significant promise in addressing the challenges associated with imbalanced class distribution. We undertake a thorough comparative evaluation of Ad-RuLer against established oversampling algorithms such as SMOTE, ADASYN, Tomek-links, Borderline-SMOTE, and KmeansSMOTE. The evaluation employs various classifiers including logistic regression, random forest, and XGBoost, and is conducted over six real-world biomedical datasets with varying degrees of imbalance. © 2023 ESM. All Rights Reserved.

关键词： Classification data synthesis Imbalanced data Oversampling Rule-based approach

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