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IEEE Transactions on Intelligent Vehicles 2024年第11期9卷 1-16页

作者： Hou, Yingzhe Cao, Yue Xiong, Hu Sun, Zhili Mumtaz, Shahid Tian, Daxin School of Cyber Science and Engineering Wuhan University Wuhan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Institute of Communication Systems University of Surrey Guildford U.K Department of Applied Informatics Silesian University of Technology Akademicka Gliwice Poland State Key Laboratory of Vehicle-Infrastructure Integrated ITS the State Key Laboratory of Intelligent Transportation System and the School of Transportation Science and Engineering Beihang University Beijing China

To enhance the privacy of messages in Internet of Vehicles (IoVs), it is critical to preserve the communication security between Road Side Unit (RSU) and numerous vehicles. The primitive of signcryption is introduced to guarantee the confidentiality, integrity and unforgeability of transmitted messages. Nevertheless, the existing signcryption schemes fail to achieve a balance between security and efficiency. In this paper, we construct a Chinese remainder theorem (CRT) empowered certificateless aggregate signcryption scheme with key agreement (CASKA-CRT). As the vehicle joins and leaves dynamically, the proposed scheme can ensure the dynamic security via one modulo division operation. Besides, the certificateless aggregate signcryption and key agreement mechanisms are embedded. The former idea can both address the certificate management and key escrow problems, while the latter technology can create authentication as a premise of secure communication. Based on this construction, the hash-to-group operation and bilinear pairing are avoided, to realize a faster verification with the increased number of messages. Moreover, the security of proposed scheme is proved under the random oracle model. Finally, the performance analysis demonstrates the advantage of CASKA-CRT in terms of security and reliability over related works. IEEE

关键词： Public key cryptography

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Data Trading and Monetization: Challenges and Open Research Directions 7

Data Trading and Monetization: Challenges and Open Research ...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Ramadan, Qusai Boukhers, Zeyd Alshaikh, Muath Lange, Christoph Jurjens, Jan Institute Of Software Engineering University Of Koblenz Germany Fraunhofer Institute For Applied Information Technology Fit Germany Uniklinik Köln Germany College Of Computing And Informatics Saudi Electronic University Saudi Arabia Rwth Aachen University Germany University Of Koblenz Germany Fraunhofer-Institute For Software And Systems Engineering Isst Germany

ISBN: (纸本)9798400709036

Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growing establishment and acceptance of digital data marketplaces, significant challenges hinder efficient data trading. As a result, few companies can derive tangible value from their data, leading to missed opportunities in understanding customers, pricing decisions, and fraud prevention. In this paper, we explore both technical and organizational challenges affecting data monetization. Moreover, we identify areas in need of further research, aiming to expand the boundaries of current knowledge by emphasizing where research is currently limited or lacking. © 2023 Owner/Author.

关键词： Electronic data interchange

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Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

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Frontiers of Computer Science 2024年第2期18卷 217-229页

作者： Yizheng WANG Xin ZHANG Ying JU Qing LIU Quan ZOU Yazhou ZHANG Yijie DING Ying ZHANG Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China Beidahuang Industry Group General Hospital Harbin 150088China School of Informatics Xiamen UniversityXiamen 361005China Department of Anesthesiology Hospital(T.C.M)Affiliated to Southwest Medical UniversityLuzhou 646000China Software Engineering College Zhengzhou University of Light IndustryZhengzhou 450002China

Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been *** developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model *** constructed three kernels in miRNA and disease space and conducted kernel fusion using *** propagation uses matrix factorization and matrix approximation to effectively reduce computation and time *** results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

关键词： human miRNA-disease association multiple kernel learning link propagation miRNA similarity disease similarity

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Understanding Older Adults' (Dis)Engagement with Design Materials 25

Understanding Older Adults' (Dis)Engagement with Design Mate...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Pradhan, Alisha Jelen, Ben Thangaraj, Ramprabu Siek, Katie A. Jette, Shannon Lazar, Amanda Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN United States Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States University of Maryland College ParkMD United States College of Information Studies University of Maryland College ParkMD United States

ISBN: (纸本)9798400713941

Design workshops are a popular approach to include older adults in the technology design process. However, formative design sessions with older adults have had unexpected outcomes such as the non-use of traditional design materials like craft-based prototyping supplies or disengagement from design activities. Analyzing the engagement of 32 older adults across two design workshops, this paper sheds insights on some of these outcomes. Contributing to a growing body of HCI research on understanding older adults' participation in design, we provide an understanding of how design materials can shape older adults' engagement in formative design activities. Our discussion furthers research on understanding who older adults design for and why, argues for a different understanding of creative expression, and offers considerations for choosing design materials. © 2025 Copyright held by the owner/author(s).

关键词： Design for manufacturability

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Intelligent water quality monitoring using WSN and Machine Learning Approaches 23

Intelligent water quality monitoring using WSN and Machine L...

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7th International Conference on Information System and Data Mining, ICISDM 2023

作者： Belachew, Ermiyas Birihanu Bekele, Mebiratu Beyene Birawo, Birtukan Adamu Elte Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Hungary Department of Software Engineering Wolkite University Ethiopia Bahir Dar Institute of Technology Ict 4D Research Centre Bahir Dar University Ethiopia Applied Informatics Silesian University of Technology Poland

ISBN: (纸本)9798400700637

In recent years, wireless sensor networks (WSN) have received considerable attention in environmental, industrial monitoring, and control applications. It is used to monitor, collect, maintain, and analyse environmental data from a remote place with the help of remote sensing and monitoring technologies. Identification of suitable algorithms and protocol WSNs for water quality monitoring is a challenge due to the limitation of energy, computation capability, and storage resources of the sensor nodes. In this paper, we designed a model for a WSN-based water quality monitoring system that employs a machine learning technique to analyse water data and facilitates decision-making. The suggested model can gather water quality parameters from a water body and send them to a distant data centre, where they will be processed using a machine learning (ML) algorithm. The experiment shown that by adopting WSN and ML approach, we can obtain state-of-the-art 0.0723 RMSE and 0.0052 MAE, indicating that the proposed methodology is appropriate for usage in intelligence water quality monitoring. © 2023 Owner/Author.

关键词： Water quality

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An Assessment of Robustness for Adversarial Attacks and Physical Distortions on Image Classification using Explainable AI

An Assessment of Robustness for Adversarial Attacks and Phys...

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2021 Workshop on AI and Cybersecurity, AI-Cybersec 2021

作者： Mahima, K.T.Y. Ayoob, Mohamed Poravi, Guhanathan Department of Software Engineering Informatics Institute of Technology Colombo Sri Lanka

Introducing defence mechanisms to overcome the vulnerability of adversarial attacks is a highly focused research area. However recent research highlights that introducing defence approaches for man-made adversarial attacks is not sufficient, because the deep learning models are vulnerable to the perturbations outside the scope of the training set and the physical world itself acts as an adversarial sample generator. Given this caveat, there is a necessity to introduce general defence approaches for both man-made and physical world adversarial samples. Prior to that, a brief explanation of how the model's decision-making process happens in the inference phase under the various adversarial perturbations is required. However, the deep learning models act as black boxes in the inference phase where the decision-making is not interpretable. As a result, research on model interpretability and explainability has been carried out in the domain which is collectively known as Explainable AI. Using a set of Explainable AI techniques, this study is investigating the deep learning networks' robustness;i.e., the decision-making process in neural networks and important pixel attributes for the predictions that are captured when the deep learning model inference gets adversarial inputs. These adversarial inputs are perturbed by adversarial attacks or the physical world adversaries using the deep learning network trained on the CIFAR10 dataset. The study reveals, that when the inference gets adversarial samples, the necessary pixel attributes for the prediction captured by the network vary everywhere in the image. However, when the network is re-trained using adversarial training or data transformation-based augmentation, it will be able to capture pixel attributes within the particular object or reduce the capture of negative pixel attributes. Based on the deductions gained from the findings, this paper states some potential research approaches to introduce a general adversarial de

关键词： Pixels

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Discussion Manipulation, Language and Domain Dependent Models: An Overview

Discussion Manipulation, Language and Domain Dependent Model...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Filip Chalás Igor Stupavský Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

The amount of antisocial online behavior (AOB) in the political field has been on the rise in recent years. In the research, we are dealing with the reason for confusing AOB with other forms of antisocial behavior. We specifically dealt with NLP in the political field in the Slovak language, with a specific number of prefixes and suffixes. In this paper, we focus on the area of detection of manipulation of political discussions. The data source consisted of previously collected data in the period from April 2018 with an update until November 19, 2022 realized within this research from the site ***, which we supplemented with new claims. We trained several classification models on preprocessed data and evaluated them. For multi-class classification, the best results were achieved using logistic regression and a support vector machine trained on the resampled dataset—both achieving an accuracy of 0.56 and a macro F1 score of 0.39. In the case of binary classification, the best results were achieved by logistic regression—accuracy 0.7 and macro F1 score 0.56. These models could help detect manipulation in online political discussions.

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A Pattern Language of Multi-Organizational Collaboration in Public Sector in Developing Countries 22

A Pattern Language of Multi-Organizational Collaboration in ...

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Proceedings of the 29th Conference on Pattern Languages of Programs

作者： Haji Gul Wahaj Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

ISBN: (纸本)9781941652183

Researchers have extensively discussed the importance of collaborative efforts between public organizations, especially in developing countries. For outsourcing projects to be delivered successfully, collaboration is seen as a vital business trait. Due to low economy in often developing countries, several public organizations may not have access to the required resources. Therefore, sharing the resources, expertise, and lessons learned could be a good practice for governments, particularly in developing nations. Several public organizations are assigned individual tasks and responsibilities in different sectors. Collaboration with other public organizations might be facilitated by sharing infrastructure, public data, consultations, computer networks, lessons learned, and building capacity of stakeholders. Despite lack of resources, public organizations distributed across the country faced other obstacles such as inadequate infrastructure for collaboration. This study discusses a comprehensive collaboration between public organizations to share existing resources with other public organizations. During this study, we attempt to document some newly observed organizational patterns. We interlink them into a language that can assist both researchers and practitioners in understanding how to convince public organizations to collaborate.

关键词： collaboration developing countries organizational patterns patterns public sector software development software engineering

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Machine Learning Based Delivery Date Prediction for Child Birth 5

Machine Learning Based Delivery Date Prediction for Child Bi...

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5th International Conference on Information and Communication technology for Development for Africa, ICT4DA 2023

作者： Mulugeta, Tsedale Melese, Micheal Assefa, Yibeltal Gedif, Birku Wollega Institute of Technology Wollega University Information Science Ethiopia College Natural and Computation Science Addis Abeba University Information Science Ethiopia Kombolcha Institute of Technology Wollo University Software Engineering Ethiopia College of Informatics University of Gonder Information Science Ethiopia

ISBN: (纸本)9798350328486

Health systems bear a crucial responsibility for the well-being of individuals across their entire lifespan. Accurate prediction of the delivery date for childbirth is of paramount importance for both the expectant mother and her infant. It plays a vital role in ensuring the safety and health of both parties. A miscalculated decision in this regard not only increases the risks for the mother's life but also poses potential harm to the newborn. To address this issue and mitigate associated risks, we have developed a prediction model based on machine learning techniques. In this research study, we utilized the Ethiopian Demographic and Health Survey (EDHS) dataset to predict the delivery date for childbirth and create our model. We follow experimental research design methodology as a result we employed an experiment regression-based machine learning algorithms, including Linear Regression, Multilayer Perceptron, and Random Forest, to develop the predictive model. Once the model was constructed, we evaluated its performance using well established evaluation metrics, such as mean absolute error, mean squared error, and ${\boldsymbol R}^{2}$ score. Upon careful examination of the evaluation results, we determined that the ${\boldsymbol R}^{2}$ score served as a reliable metric to assess the performance of our models. The obtained ${\boldsymbol R}^{2}$ scores were 0.35 for Linear Regression,-25.1 for Multilayer Perceptron, and 0.67 for Random Forest. Based on these scores, it became evident that the Random Forest algorithm exhibited superior performance compared to the other algorithms. © 2023 IEEE.

关键词： Forecasting

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Building Customer Capacity Through Organizational Patterns Improves the Development Team's Understanding 22

Building Customer Capacity Through Organizational Patterns I...

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Proceedings of the 29th Conference on Pattern Languages of Programs

作者： Mohammad Ismail Khattab Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

ISBN: (纸本)9781941652183

The development team needs to work together with a customer who has a basic understanding of information technology. Customers need to be aware of the business processes and the importance of technology. A customer and the development team need to communicate and understand each other in the software project's development. software projects can be successfully built when the customer has sufficient capacity. The process of building an organization's strength and sustainability is called capacity-building. software projects are more effective and quality when capacity development takes place at the organizational level. Capacity directly affects work performance and business outcomes. Due to low customer capacity, we have found in practice and in the literature that False Requirements, No Basic technology Knowledge, No Domain Knowledge, Mismanaged Timeline, No Trust in software Project, and Unproductive Discussion are the main customer capacity challenges to the software project development team. We documented our experiences and five newly discovered patterns in government organizations to solve the mentioned challenges. We present this documented experience in the form of patterns in this paper to improve the customer capacity Before work begins on a software project or during the software development process. Some organizational patterns that reduce the mentioned challenges are also used along with newly discovered patterns. We have illustrated the sequences of the patterns and explained how we could make a pattern language from these patterns.

关键词： customer customer capacity building development team organizational patterns patterns software projects

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