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DkvSSO: Delegatable Keyed-Verification Credentials for Efficient Anonymous Single Sign-On

IEEE Transactions on Information Forensics and Security 2025年 20卷 4196-4211页

作者： Xue, Wenyi Yang, Yang Huang, Minming Li, Yingjiu Pang, Hwee Hwa Deng, Robert H. Fuzhou University College of Computer and Data Science Fuzhou350116 China Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore University of Oregon Department of Computer and Information Science EugeneOR97403 United States

Anonymous single sign-on (ASSO) is an anonymous multi-service authentication method for end users. However, existing ASSO schemes suffer from heavy ticket requesting and verifying overheads, limiting their applications in large-scale settings. To address this problem, we propose a novel concept called keyed-verification anonymous credentials with disposable delegation (KVAC-DD) in the multi-verifier setting. Next, we extend KVAC-DD to build an efficient ASSO system, dubbed DkvSSO. The construction of DkvSSO can be instantiated in efficient prime-order groups, avoiding costly operations required in previous ASSO systems. We formally prove the security of our proposed constructions. Extensive experiments show that DkvSSO is significantly more efficient than existing ASSO schemes, making it suitable to be deployed in large-scale settings. © 2005-2012 IEEE.

关键词： Anonymity

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Model-based framework for exploiting sensors of IoT devices using a botnet: a case study with android

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Cyber-Physical Systems 2025年第1期11卷 1-46页

作者： Khaliq, Zubair Khan, Dawood Ashraf Baba, Asif Iqbal Ali, Shahbaz Farooq, Sheikh Umar Department of Computer Science University of Kashmir Jammu and Kashmir Srinagar India AI Thndr Dubai United Arab Emirates Department of Computer Science Tuskegee University TuskegeeAL United States Data Engineering hyke.ai Dubai United Arab Emirates

Botnets have become a severe security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic, and the ever-larger number of cybercriminals are incentivising the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make them an excellent platform for malicious activities in IoT devices used by organisations to safeguard the personal and confidential data of their customers, employees, and business partners. The IoT devices have built-in sensors or actuators that can be exploited to monitor or control the physical environment of the entities connected to them, thereby violating the fundamental concept of privacy-by-design of these devices. In this paper, we design and describe a modular botnet framework for IoT. The framework uses an enhanced centralised architecture associated with a novel ‘Domain Fluxing Technique’. The proposed framework will provide insights into how privacy in IoT devices can be incorporated at design time to check the sensors and actuators in these devices against malicious exploitation, consequently preserving privacy. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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Advancements in AI-Driven Cephalometric Analysis for Orthodontic Applications: A Review on Automated Landmark Detection and Angle Assessment 4

Advancements in AI-Driven Cephalometric Analysis for Orthodo...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gurbade, Viraj Vijay Pacharaaney, Utkarsha Puri, Chetan Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

This is an essential tool used in the orthodontic practice for the assessment of craniofacial structures and as a basis in initiating treatment planning. Within the pre-AI/deep learning era, it was achieved through the procedure of manual tracing on 2D radiographs, individual variability, and quite time-consuming to obtain the established norm but which was susceptible. These technologies apply CNNs to properly detect landmarks and reduce the occurrence of human mistake, ensuring reproducibility of measurements across analyses. Automated systems demonstrated promising performance for landmark detection both on 2D and 3D images, particularly within the history of 3D imaging techniques such as cone-beam computed tomography (CBCT), offering a sweeping view of craniofacial anatomy. Studies show that training these AI models based on large datasets enables generalization to many images, thereby allowing landmark placement in reliable ways, with improved diagnostic accuracy;however, there's a challenge in achieving model robustness across different populations and complex 3D data. Future directions include the integration of AI with augmented reality, enabling real-time diagnostics to further depict and adjust clinicians' abilities to visualize treatment plans. With advancements in AI in cephalometry, the model may be hybrid to combine AI automation with manual verification, thus optimising reliability for clinical use, paving the way for more personalized and accurate orthodontic care. © 2025 IEEE.

关键词： computerized tomography

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Launching a data Analytics Major: How Collaboration and Agility Built a Successful program

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American Mathematical Monthly 2025年第1期132卷 77-88页

作者： Forrester, Richard Marshall, Emily Forrester, Jeffrey Richeson, David Schaefer, Jennifer Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Economics Denison University Granville 43023 OH United States Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States

In this article, we share the details of Dickinson College’s journey to establish a data analytics major. Designed to provide students with the technical proficiency required to become a data scientist, Dickinson’s data analytics major also incorporates values of a liberal arts education, including interdisciplinary learning, critical thinking, thoughtful analysis, effective communication, and ethical considerations. We describe how sustained collaboration, a detailed plan to engage the campus community, partnerships with alumni, and support from senior leaders were essential to this achievement. We also discuss the challenges and successes encountered before and after the approval of the major. © 2024 Mathematical Association of America.

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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Fair Machine Learning in Healthcare: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 493-507页

作者： Feng, Qizhang Du, Mengnan Zou, Na Hu, Xia Texas A&M University Department of Computer Science & Engineering College StationTX77843 United States New Jersey Institute of Technology Department of Data Science NewarkNJ07102 United States University of Houston Department of Industrial Engineering HoustonTX77004 United States Rice University Department of Computer Science HoustonTX77251 United States

The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problems is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in ML and healthcare disparities. We adopt a framework based on the principles of distributive justice to categorize fairness concerns into two distinct classes: equal allocation and equal performance. We provide a critical review of the associated fairness metrics from a ML standpoint and examine biases and mitigation strategies across the stages of the ML lifecycle, discussing the relationship between biases and their countermeasures. The article concludes with a discussion on the pressing challenges that remain unaddressed in ensuring fairness in healthcare ML and proposes several new research directions that hold promise for developing ethical and equitable ML applications in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Online forecasting using neighbor-based incremental learning for electricity markets

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Neural Computing and Applications 2025年 1-18页

作者： Melgar-García, L. Gutiérrez-Avilés, D. Rubio-Escudero, C. Troncoso, A. Department of Artificial Intelligence Universidad Politécnica de Madrid Madrid28660 Spain Department of Computer Science University of Seville Avda. Reina Mercedes s/n Seville41012 Spain Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

Electricity market forecasting is very useful for the different actors involved in the energy sector to plan both the supply chain and market operation. Nowadays, energy demand data are data coming from smart meters and have to be processed in real-time for more efficient demand management. In addition, electricity prices data can present changes over time such as new patterns and new trends. Therefore, real-time forecasting algorithms for both demand and prices have to adapt and adjust to online data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand and prices forecasting in real-time. The proposed algorithm generates a prediction model based on the k-nearest neighbors algorithm, which is incrementally updated in an online scenario considering both changes to existing patterns and adding new detected patterns to the model. Both time-frequency and error threshold based model updates have been evaluated. Results using energy demand from 2007 to 2016 and prices data for different time periods from the Spanish electricity market are reported and compared with other benchmark algorithms. © The Author(s) 2025.

关键词： Demand response

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