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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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Architecture, Tools, and DSLs for Developing Conversational Agents: An Overview 14

Architecture, Tools, and DSLs for Developing Conversational ...

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14th International Conference on Emerging Ubiquitous systems and Pervasive Networks / 13th International Conference on Current and Future Trends of information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Ouaddi, Charaf Benaddi, Lamya Jakimi, Abdeslam Software Engineering and Information Systems Engineering Team FST Errachidia University of Moulay Ismail Meknes50050 Morocco

Conversational agents (CA) are software programs that can converse with users using natural language. They are now widely used in various domains, such as tourism, healthcare, and others, to perform tasks and provide permanent assistance to users by interacting with them in natural language. The development of such applications is a task that requires expertise in several fields, such as software engineering, machine learning, deep learning, and natural language processing (NLP). However, several platforms and frameworks on the market facilitate the building of CA, such as Dialogflow, Rasa, and others. Recently, several research studies have proposed solutions to reduce the workload of developers and designers by offering their model-driven development approaches using domain-specific languages (DSLs), which facilitate the automation of the development of CA. This work aims to provide an Overview of CA to identify and describe their architecture and the details of its key components. and discuss the tools and technologies for their development. At the same time, discover the research topics that focus on using DSLs for model-driven development to automate and speed up the creation of these agents and discover approaches and technologies employed to implement each of these DSLs. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Towards A software Factory for Developing the Chatbots in Smart Tourism Mobile Applications 14

Towards A Software Factory for Developing the Chatbots in Sm...

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作者： Benaddi, Lamya Ouaddi, Charaf Jakimi, Abdeslam Ouchao, Brahim Software Engineering and Information Systems Engineering Team FST Errachidia University of Moulay Ismail Meknes50050 Morocco

Today, the tourism industry is significantly impacted by mobile applications leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) to enhance tourists' experiences before, during, and after their visits. This technological convergence has given rise to Smart Tourism Destinations (STDs). However, efficiently integrating these functionalities into mobile apps poses a major challenge, leading to the emergence of chatbots. Companies like IBM, Google, Microsoft, and Amazon offer tools such as Watson, Bot Framework, Dialogflow, and Amazon Lex for their development. Nevertheless, creating chatbots remains intricate, demanding expertise in software development and AI, along with associated costs related to NLP service providers. Additionally, employing an appropriate modeling language is crucial for designing a chatbot. At this juncture, the concepts of software factories and Domain-Specific Languages (DSLs) become indispensable. These innovative approaches provide solutions for addressing these complex challenges. software factories automate the development process, expediting chatbot creation while ensuring consistency. Simultaneously, DSLs furnish tools for accurately modeling and articulating the specific requirements of smart tourism, simplifying the development of tailor-made chatbots suited to this domain. This article introduces a model-driven approach for a DSL aimed at offering an abstract, high-level representation of the various aspects of chatbots within the context of an intelligent tourism mobile application. © 2024 Elsevier B.V.. All rights reserved.

关键词： Problem oriented languages

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A Graphical DSL for Accelerating Chatbot Development 7

A Graphical DSL for Accelerating Chatbot Development

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Ouaddi, Charaf Benaddi, Lamya Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Sciences and Technologies Errachidia Morocco

ISBN: (纸本)9798350367027

The development of chatbots utilizing artificial intelligence (AI) techniques represents a significant advancement in Natural Language Processing (NLP). Numerous studies employ deep learning and NLP methodologies to construct sophisticated chatbot systems. Additionally, developers and companies often utilize APIs provided by intent recognition services like Dialogflow and Amazon Lex to easily create chatbots using graphical forms, which enhance chatbot functionality. However, these APIs have limitations, such as potential dependency on specific NLP service providers and associated high costs. To address these limitations, the proposed work addresses critical gaps in chatbot development tools by constructing a Domain-Specific Language (DSL) for chatbot development. This DSL aims to simplify the chatbot development process and reduce costs. Specifically, the paper introduces a graphical DSL designed to accelerate chatbot development. We use code generation templates to easily generate the source code for chatbots of the Rasa framework, simplifying the development process and reducing the time spent on manual coding. © 2024 IEEE.

关键词： Bot (Internet)

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Use Cases and Scenarios engineering in Smart software Application Development Services 7

Use Cases and Scenarios Engineering in Smart Software Applic...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Bouziane, El Mahi Naimi, Lahbib Ouaddi, Charaf Benaddi, Lamya Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Sciences and Technologies Errachidia Morocco

ISBN: (纸本)9798350367027

Unified Modeling Language (UML) is a versatile tool for specifying, visualizing, and documenting software systems through diagrams. In the early stages of development, addressing design issues is critical to improving software quality and reducing costs. Use case modeling and UML scenario engineering are key techniques in requirements elicitation and specification development. This paper presents best practices for applying these techniques, highlighting their role in constructing accurate sequence diagrams and integrating formal specifications with user interface prototyping. Additionally, it explores extending scenarios to manage complex requirements like time and security constraints and offers a framework for visualizing business interactions in B2B environments to improve communication among stakeholders. © 2024 IEEE.

关键词： Requirements engineering

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Code Generation Templates for Accelerating Chatbot Development in Smart Tourism 7

Code Generation Templates for Accelerating Chatbot Developme...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Benaddi, Lamya Ouaddi, Charaf Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Sciences and Technologies Errachidia Morocco

ISBN: (纸本)9798350367027

The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has positioned chatbots as essential tools across various industries, particularly in the tourism sector. These agents are computer programs designed to interact with users naturally. They can access services at any time, such as booking hotels, getting information about local attractions, or managing their travel itineraries. These chatbots enhance the customer experience by providing quick responses 24/7. However, developing chatbots tailored to specific tourism needs can be a time-consuming and complex process due to the use of a wide range of platforms like Dialogflow and *** providing NLP services, which can often be locked and require high costs for access. This paper proposes a solution using code generation templates and domain-specific language (DSL) that accelerates the development of chatbots in smart tourism. With this approach, both developers and non-Technical users can easily create chatbots, significantly reducing the need for manual coding efforts. © 2024 IEEE.

关键词： Chatbots

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Towards Label-Efficient Deep Learning-based Aging-related Bug Prediction with Spiking Convolutional Neural Networks

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Tian, Yunzhe Li, Yike Chen, Kang Zhang, Zhenguo Tong, Endong Liu, Jiqiang Qin, Fangyun Zheng, Zheng Niu, Wenjia Hebei Boshilin Technology Development Co. Ltd. Shijiazhuang050051 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tangshan Research Institute of Beijing Jiaotong University Tangshan063000 China Capital Normal University College of Information Engineering Beijing100048 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210093 China Beihang University Laboratory of Dependable Intelligent Systems School of Automation Science and Electrical Engineering Beijing100191 China

Recent advances in Deep Learning (DL) have enhanced Aging-Related Bug (ARB) prediction for mitigating software aging. However, DL-based ARB prediction models face a dual challenge: overcoming overfitting to enhance generalization and managing the high labeling costs associated with extensive data requirements. To address the first issue, we utilize the sparse and binary nature of spiking communication in Spiking Neural Networks (SNNs), which inherently provides brain-inspired regularization to effectively alleviate overfitting. Therefore, we propose a Spiking Convolutional Neural Network (SCNN)-based ARB prediction model along with a training framework that handles the model's spatial-temporal dynamics and non-differentiable nature. To reduce labeling costs, we introduce a Bio-inspired and Diversity-aware Active Learning framework (BiDAL), which prioritizes highly informative and diverse samples, enabling more efficient usage of the limited labeling budget. This framework incorporates bio-inspired uncertainty to enhance informativeness measurement along with using a diversity-aware selection strategy based on clustering to prevent redundant labeling. Experiments on three ARB datasets show that ARB-SCNN effectively reduces overfitting, improving generalization performance by 6.65% over other DL-based classifiers. Additionally, BiDAL boosts label efficiency for ARB-SCNN training, outperforming four state-of-the-art active learning methods by 4.77% within limited labeling budgets. © 2013 IEEE.

关键词： Active learning

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Diff-Reg v2: Diffusion-Based Matching Matrix Estimation for Image Matching and 3D Registration

arXiv

引用

arXiv 2025年

作者： Wu, Qianliang Jiang, Haobo Ding, Yaqing Luo, Lei Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China School of Computer Science and Engineering Nanjing University of Science and Technology China Nanyang Technological University Singapore Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

Establishing reliable correspondences is crucial for all registration tasks, including 2D image registration, 3D point cloud registration, and 2D-3D image-to-point cloud registration. However, these tasks are often complicated by challenges such as scale inconsistencies, symmetry, and large deformations, which can lead to ambiguous matches. Previous feature-based and correspondence-based methods typically rely on geometric or semantic features to generate or polish initial potential correspondences. Some methods typically leverage specific geometric priors, such as topological preservation, to devise diverse and innovative strategies tailored to a given enhancement goal, which cannot be exhaustively enumerated. Additionally, many previous approaches rely on a single-step prediction head, which can struggle with local minima in complex matching scenarios. To address these challenges, we introduce an innovative paradigm that leverages a diffusion model in matrix space for robust matching matrix estimation. Our model treats correspondence estimation as a denoising diffusion process in the matching matrix space, gradually refining the intermediate matching matrix to the optimal one. Specifically, we apply the diffusion model in the doubly stochastic matrix space for 3D-3D and 2D-3D registration tasks. In the 2D image registration task, we deploy the diffusion model in a matrix sub-space, where dual-softmax projection regularization is applied. For all three registration tasks, we provide adaptive matching matrix embedding implementations tailored to the specific characteristics of each task while maintaining a consistent"match-to-warp" encoding pattern. Furthermore, we adopt a lightweight design for the denoising module. In inference, once points or image features are extracted and fixed, this module performs multi-step denoising predictions through reverse sampling. Evaluations on both 2D and 3D registration tasks demonstrate the effectiveness of our approach. Copyrigh

关键词： Stochastic systems

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UNDECIDABILITY OF SAFETY FOR THE SCHEMATIC PROTECTION MODEL WITH CYCLIC CREATES

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 1992年第1期44卷 141-159页

作者： SANDHU, RS Department of Information and Software Systems Engineering George Mason University Fairfax Virginia 22030-4444 USA

In the schematic protection model subjects are classified into protection types. Creation is authorized by a can-create binary relation on types. It is shown that with arbitrary cycles in can-create safety is undecidable. Whereas it has been previously shown safety is decidable for acyclic can-create. It is also shown that safety remains undedicable even if all creates are attenuating in that tickets (capabilities) given to a subject on its creation are attenuated copies of tickets available to its parent. This contrasts with decidable safety for attenuating cycles of length one. It appears safety is decidable for the practically useful cases while undecidability results from undue laxity in authorizing creation.

关键词： Computer systems, Digital

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