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1
Lee, Hyo-Cheol, and Seok-Won Lee. "Provenance-Based Trust-Aware Requirements Engineering Framework for Self-Adaptive Systems." Sensors 23, no. 10 (May 10, 2023): 4622. http://dx.doi.org/10.3390/s23104622.
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With the development of artificial intelligence technology, systems that can actively adapt to their surroundings and cooperate with other systems have become increasingly important. One of the most important factors to consider during the process of cooperation among systems is trust. Trust is a social concept that assumes that cooperation with an object will produce positive results in the direction we intend. Our objectives are to propose a method for defining trust during the requirements engineering phase in the process of developing self-adaptive systems and to define the trust evidence models required to evaluate the defined trust at runtime. To achieve this objective, we propose in this study a provenance-based trust-aware requirement engineering framework for self-adaptive systems. The framework helps system engineers derive the user’s requirements as a trust-aware goal model through analysis of the trust concept in the requirements engineering process. We also propose a provenance-based trust evidence model to evaluate trust and provide a method for defining this model for the target domain. Through the proposed framework, a system engineer can treat trust as a factor emerging from the requirements engineering phase for the self-adaptive system and understand the factors affecting trust using the standardized format.
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Tikhvinskiy, Valery O., Grigory Bochechka, and Andrey Gryazev. "QoS Requirements as Factor of Trust to 5G Network." Journal of Telecommunications and Information Technology, no. 1 (March 30, 2016): 3–8. http://dx.doi.org/10.26636/jtit.2016.1.697.
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Trust to modern telecommunications networks plays an important role as a driver of technological and market success of any technology or telecommunication services. Most of the technological approaches to this problem are focused only on network security and do not include such a factor as the quality of service (QoS), which also plays an important role in the formation of trust both from the consumers and the regulator. The future 5G mobile technology will be the engine of development of telecommunications until 2020 and the formation of trust to the 5G networks is one of the main tasks for developers. The authors present the view on the trust to 5G networks in the plane of QoS requirements formation and QoS management. QoS requirements to 5 G networks were determined on the basis of three main business models of services: xMBB, M-MTC and U-MTC and the need to ensure user trust to networks. Infrastructure requirements for QoS control and spectrum management network entities which are based on Network Function Virtualization (NFV) principles have been formed.
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Hussein, Aya, Sondoss Elsawah, and Hussein Abbass. "A System Dynamics Model for Human Trust in Automation under Speed and Accuracy Requirements." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 822–26. http://dx.doi.org/10.1177/1071181319631167.
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Research shows that human trust in automation is a key predictor of human reliance on the automation. Several models have been proposed to capture the interplay between trust and reliance and their combined impacts on task performance. Whereas some models assume that trust is affected by automation reliability, others assume that trust is affected by automation speed. In fact, both speed and reliability can be crucial for mission performance, therefore, these models do not represent the interrelationships among automation speed, automation reliability, human decision making, and subsequent effects on mission performance. To address this gap, we propose a system dynamics model which incorporates both the speed and reliability of automation and their combined effects on trust. Our model explicitly represents the speed-accuracy compromise adopted by the subjects to weigh the perceived relative importance of these aspects while evaluating the reliance decision. The model is calibrated and evaluated using data collected from a human experiment in which 33 subjects interacted with an automated aid for swarm supervision in a foraging mission. The simulation results show that the model can closely replicate and predict the experimental data in terms of the reliance rate and the number of targets collected. Model limitations and further efforts for model extension are discussed.
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Wang, Jingpei, and Jie Liu. "The Comparison of Distributed P2P Trust Models Based on Quantitative Parameters in the File Downloading Scenarios." Journal of Electrical and Computer Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4361719.
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Varied P2P trust models have been proposed recently; it is necessary to develop an effective method to evaluate these trust models to resolve the commonalities (guiding the newly generated trust models in theory) and individuality (assisting a decision maker in choosing an optimal trust model to implement in specific context) issues. A new method for analyzing and comparing P2P trust models based on hierarchical parameters quantization in the file downloading scenarios is proposed in this paper. Several parameters are extracted from the functional attributes and quality feature of trust relationship, as well as requirements from the specific network context and the evaluators. Several distributed P2P trust models are analyzed quantitatively with extracted parameters modeled into a hierarchical model. The fuzzy inferring method is applied to the hierarchical modeling of parameters to fuse the evaluated values of the candidate trust models, and then the relative optimal one is selected based on the sorted overall quantitative values. Finally, analyses and simulation are performed. The results show that the proposed method is reasonable and effective compared with the previous algorithms.
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Ayed, Samiha, Lamia Chaari, and Amina Fares. "A Survey on Trust Management for WBAN: Investigations and Future Directions." Sensors 20, no. 21 (October 23, 2020): 6041. http://dx.doi.org/10.3390/s20216041.
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The rapid uptake of the Wireless Body Area Networks (WBAN) and their services poses unprecedented security requirements. WBAN are evolving to support these requirements. Fulfilling these tasks is challenging as their mobile context is increasingly complex, heterogeneous, and evolving. One potential solution to meet the WBAN security requirements is trust management that helps to reach a more secure and adaptable WBAN environment. Accordingly, this article aims to serve as a brief survey of trust management approaches within intra-WBAN and inter-WBAN. For that, we first summarize trust management concepts, discuss WBAN challenges and classify the attacks on WBAN trust management models. Subsequently, we detail and compare the existing trust based approaches in a WBAN context. We pinpoint their limitations and provide a new classification of these different approaches. We also propose a set of best practices that may help the reader to build a robust and an efficient trust management framework. We complete this survey by highlighting the open future directions and perspectives for research.
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Sun, Po-Ling, and Cheng-Yuan Ku. "Review of threats on trust and reputation models." Industrial Management & Data Systems 114, no. 3 (April 8, 2014): 472–83. http://dx.doi.org/10.1108/imds-11-2013-0470.
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Purpose – As the number of available services increases on the web, it becomes greatly vital in service-oriented computing to discover a trustworthy service for a service composition so as to best-fit business requirements. However, some dishonest service providers may advertise more than what they can offer and cause a great loss to users. In the last few years, trust and reputation management over web service selection mechanism becomes an emerging way of dealing with security deficiencies which are inherent in web services environment. The purpose of this paper is to review the security threats carefully and expect that the results serve as a reference guide for designing the robust trust and reputation management mechanisms. Design/methodology/approach – Based on the published literature, the paper reviews and categorizes the most critical and important security threats that apply to trust and reputation models. Findings – A detailed review and a dedicated taxonomy table are derived. As emphasized earlier, they could serve as the most important guideline for design of trust and reputation mechanisms. Originality/value – The papers work contributes to: first, understand in details what kind of security threats may cause damage to trust and reputation management mechanisms for web services selection; and second, categorize these complex security threats and then assist in planning the defense mechanisms.
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Soni, Sonam. "Reliable Trust Computation model in Vehicular ad-hoc Network." American Journal of Advanced Computing 1, no. 3 (July 1, 2020): 1–5. http://dx.doi.org/10.15864/ajac.1304.
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This paper concerns about the various implemented work has been studied and analyzed to form a new survey on trust model to VANET. In this paper it is observed that there are lot of new techniques are possible to form a new trust model in VANET to provide better security with trust concern over the entire environment of trust management in VANET. This work concerns of entire trust calculation work which has been done yet over it. Here summarizing the various trust models, various security requirements, issues over it.
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Asiri, Ahmad Yahya, and Sultan S. Alshamrani. "Performance Evaluation of a B2C Model Based on Trust Requirements and Factors." Scientific Programming 2021 (May 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/9935849.
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This paper evaluates the performance of a newly proposed B2C e-commerce model based on trust factors and requirements in the context of Saudi Arabia. Two categories of trust factors, namely, governmental and nongovernmental types, are identified to create the model for determining the feasibility of an efficient online business strategy in the Kingdom. Data are collected over a duration of 10 weeks based on the designed questionnaire, carefully analyzed, and interpreted. The standpoint of the end user is analyzed to determine the influence of the proposed trust requirements and factors on B2C e-commerce in Saudi Arabia. The reliability of the questionnaires for each requirement with their factors is quantitatively analyzed using Cronbach’s alpha. The questionnaire consists of three parts, namely, demographic component, questions related to the identified requirements, and additional notes as an open question. Questions are designed as per the requirements and the factors of trust models to demonstrate their possible relationship. Furthermore, the questionnaires’ content validity is judged by expert lecturers with relevant specialization before distributing them, which are well organized together with easy understandability. They are randomly distributed among 222 academic and administrative staff (female and male) in addition to university students from the Faculty of Computer Science and Information System in Saudi Arabia. This random selection performed on total 222 responders ensures the statistical accuracy of the sampling. Adaptable government approaches, enactment, rules, insurance of buyer rights, and banking network situation with less web expenses are demonstrated to be significant to e-commerce expansion in the Kingdom. Implementation of the proposed model is believed to augment consumer self-confidence and reliance together with e-commerce growth in Saudi Arabia.
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Kulaklıoğlu, Duru. "Explainable AI: Enhancing Interpretability of Machine Learning Models." Human Computer Interaction 8, no. 1 (December 6, 2024): 91. https://doi.org/10.62802/z3pde490.
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Explainable Artificial Intelligence (XAI) is emerging as a critical field to address the “black box” nature of many machine learning (ML) models. While these models achieve high predictive accuracy, their opacity undermines trust, adoption, and ethical compliance in critical domains such as healthcare, finance, and autonomous systems. This research explores methodologies and frameworks to enhance the interpretability of ML models, focusing on techniques like feature attribution, surrogate models, and counterfactual explanations. By balancing model complexity and transparency, this study highlights strategies to bridge the gap between performance and explainability. The integration of XAI into ML workflows not only fosters trust but also aligns with regulatory requirements, enabling actionable insights for stakeholders. The findings reveal a roadmap to design inherently interpretable models and tools for post-hoc analysis, offering a sustainable approach to democratize AI.
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Kapse, Smita, Latesh Malik, and Sanjay Kumar. "NTHCMB: DESIGN OF AN EFFICIENT NOVEL TRUST-BASED HYBRID CONSENSUS MODEL FOR SECURING BLOCKCHAIN DEPLOYMENTS." ASEAN Engineering Journal 14, no. 2 (May 31, 2024): 83–90. http://dx.doi.org/10.11113/aej.v14.20763.
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Blockchain deployments require efficient consensus models in order to be scaled for larger networks. Existing consensus models either use stake-levels, trust-levels, authority-levels, etc. or their combinations in order to reduce mining delay while maintaining higher security levels. But these models either have higher energy requirements, lower security, or have linear/exponential relationship between mining delay and length of the chains. Due to these restrictions, the applicability of these models is affected when deployed under real-time network scenarios. To overcome these issues, this text proposes design of an efficient novel trust-based hybrid consensus model for securing blockchain deployments. The proposed model initially uses a hybrid consensus model that fuses Proof-of-Work (PoW), Proof-of-Stake (PoS) with Proof-of-Temporal-Trust (PoTT) for improving security while maintaining higher Quality of Service (QoS) levels. The PoTT Model fuses together temporal mining delay, temporal mining energy, throughput and block mining efficiency in order to generate miner-level trusts. These trust values are fused with Work efficiency and Stake levels and used for selection of miners. The selected miners are used for serving block addition requests, which assists in improving mining speed by 3.2%, reducing energy consumption 4.5%, and improving throughput by 8.5%, while improving block mining efficiency by 2.9% when compared with existing mining optimization models. This performance was validated under Sybil, Finney, Man-in-the-Middle, and Spoofing attacks. Performance of the model was observed to be consistent even under attacks, thereby making it useful for real-time network scenarios.
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Gupta, Punit, and Deepika Agrawal. "Trusted Cloud Platform for Cloud Infrastructure." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 8 (August 30, 2013): 1884–91. http://dx.doi.org/10.24297/ijct.v10i8.1473.
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Reliability and trust Models are used to enhance secure , reliable scheduling , load balancing and QoS in cloud and Distributed environment. Trust models that are being used in Distributed and Grid environment, does not qualify cloud computing environment requirements. Since the parameters that have being taken into consideration in these trust models, does not fit in the cloud Infrastructure As A Service, a suitable trust model is proposed based on the existing model that is suitable for trust value management for the cloud IaaS parameters. Based on the above achieved trust values, trust based scheduling and load balancing is done for better allocation of resources and enhancing the QOS of services been provided to the users. In this paper, an trust based cloud computing framework is proposed using trust model ,trust based scheduling and load balancing algorithms. Here we describe the design and development of trusted Cloud service model for cloud Infrastructure as a service (IaaS) known as VimCloud .VimCloud an open source cloud computing framework that implements the tusted Cloud Service Model and trust based scheduling and load balancing algorithm . However one of the major issues in cloud IaaS is to ensure reliability and security or used data and computation. Trusted cloud service model ensures that user virual machine executes only on trusted cloud node, whose integrity and reliability is known in term of trust value . VimCloud shown practical in term of performace which is better then existing models.
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Sarkar, Sirshak, Gaurav Choudhary, Shishir Kumar Shandilya, Azath Hussain, and Hwankuk Kim. "Security of Zero Trust Networks in Cloud Computing: A Comparative Review." Sustainability 14, no. 18 (September 7, 2022): 11213. http://dx.doi.org/10.3390/su141811213.
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Recently, networks have shifted from traditional in-house servers to third-party-managed cloud platforms due to its cost-effectiveness and increased accessibility toward its management. However, the network remains reactive, with less accountability and oversight of its overall security. Several emerging technologies have restructured our approach to the security of cloud networks; one such approach is the zero-trust network architecture (ZTNA), where no entity is implicitly trusted in the network, regardless of its origin or scope of access. The network rewards trusted behaviour and proactively predicts threats based on its users’ behaviour. The zero-trust network architecture is still at a nascent stage, and there are many frameworks and models to follow. The primary focus of this survey is to compare the novel requirement-specific features used by state-of-the-art research models for zero-trust cloud networks. In this manner, the features are categorized across nine parameters into three main types: zero-trust-based cloud network models, frameworks and proofs-of-concept. ZTNA, when wholly realized, enables network administrators to tackle critical issues such as how to inhibit internal and external cyber threats, enhance the visibility of the network, automate the calculation of trust for network entities and orchestrate security for users. The paper further focuses on domain-specific issues plaguing modern cloud computing networks, which leverage choosing and implementing features necessary for future networks and incorporate intelligent security orchestration, automation and response. The paper also discusses challenges associated with cloud platforms and requirements for migrating to zero-trust architecture. Finally, possible future research directions are discussed, wherein new technologies can be incorporated into the ZTA to build robust trust-based enterprise networks deployed in the cloud.
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Mohammad, Abdulghafour. "Distributed Authentication and Authorization Models in Cloud Computing Systems: A Literature Review." Journal of Cybersecurity and Privacy 2, no. 1 (March 4, 2022): 107–23. http://dx.doi.org/10.3390/jcp2010008.
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As the functionality and services provided by cloud computing increase, control access to these services becomes more complex, and more security breaches are generated. This is mainly based on the emergence of new requirements and constraints in the open, dynamic, heterogeneous, and distributed cloud environment. Despite the importance of identifying these requirements for designing and evaluating access control models, the available studies do not provide a rigorous review of these requirements and the mechanisms that fulfill them. The purpose of this study was to conduct a literature review of the published articles that have dealt with cloud access control requirements and techniques. This paper allowed us to answer the following two research questions: What cloud access control security requirements have been presented in the published literature? What access control mechanisms are proposed to fulfill them? This review yielded 21 requirements and nine mechanisms, reported by 20 manuscripts. The identified requirements in this review will help researchers, academics and practitioners assess the effectiveness of cloud access control models and identify gaps that are not addressed in the proposed solutions. In addition, this review showed the current cloud access control mechanisms used to meet these requirements such as access control based on trust, risk, multi-tenant, and attribute encryption.
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Wang, Jiuru, Zhiyuan Wang, Jingcheng Song, and Hongyuan Cheng. "Attribute and User Trust Score-Based Zero Trust Access Control Model in IoV." Electronics 12, no. 23 (November 29, 2023): 4825. http://dx.doi.org/10.3390/electronics12234825.
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The Internet of Vehicles (IoV) is an innovative area of interest in modern mobility that is rapidly evolving while facing complex challenges. Traditional IoV networks are susceptible to intrusion threats, which can lead to data leakage and seizure of vehicle control by attackers, thereby endangering vehicle users’ privacy and personal safety. An Attribute and User Trust Score-based Zero Trust Access Control Model (AU-ZTAC) is proposed, combining the zero-trust and attribute-based access control models to meet network protection requirements while achieving fine-grained dynamic access control and incorporating trust evaluation in the access control process to better reflect users’ intent. Experimental results demonstrate the effectiveness and feasibility of trust assessment through the proposed model. A comparison with the classical schemes illustrates that AU-ZTAC allows for more flexible and fine-grained access control in complex access control environments while improving IoV security.
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Senan Mahmod Attar Bashi, Zainab, and Shayma Senan. "A Comprehensive Review of Zero Trust Network Architecture (ZTNA) and Deployment Frameworks." International Journal on Perceptive and Cognitive Computing 11, no. 1 (January 30, 2025): 148–53. https://doi.org/10.31436/ijpcc.v11i1.494.
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The zero trust (ZT) approach has initiated significant advancements in network security, addressing the limitations of traditional security models. Traditional network security approaches have faced challenges adapting to modern trends such as bring your own device (BYOD) and cloud computing, resulting in increased complexity in meeting new security requirements. The zero trust security model operates on the principle that no entity within the network, whether internal or external, is inherently trusted. Therefore, all users and devices must undergo strict authentication and authorization processes prior to accessing organizational resources. This review paper provides a comprehensive analysis of zero trust network architecture (ZTNA) and outlines a general deployment framework model, highlighting the critical role of zero trust in modern network security.
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Baslyman, Malak, Daniel Amyot, and John Mylopoulos. "Reasoning about Confidence in Goal Satisfaction." Algorithms 15, no. 10 (September 23, 2022): 343. http://dx.doi.org/10.3390/a15100343.
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Goal models are commonly used requirements engineering artefacts that capture stakeholder requirements and their inter-relationships in a way that supports reasoning about their satisfaction, trade-off analysis, and decision making. However, when there is uncertainty in the data used as evidence to evaluate goal models, it is crucial to understand the confidence or trust level in such evaluations, as uncertainty may increase the risk of making premature or incorrect decisions. Different approaches have been proposed to tackle goal model uncertainty issues and risks. However, none of them considers simple quality measures of collected data as a starting point. In this paper, we propose a Data Quality Tagging and Propagation Mechanism to compute the confidence level of a goal’s satisfaction level based on the quality of input data sources. The paper uses the Goal-oriented Requirement Language (GRL), part of the User Requirements Notation (URN) standard, in examples, with an implementation of the proposed mechanism and a case study conducted in order to demonstrate and assess the approach. The availability of computed confidence levels as an additional piece of information enables decision makers to (i) modulate the satisfaction information returned by goal models and (ii) make better-informed decisions, including looking for higher-quality data when needed.
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Brazhuk, A. I., and E. V. Olizarovich. "Towards the computer systems design based on Zero Trust Architecture." Informatics 21, no. 4 (December 30, 2024): 85–98. https://doi.org/10.37661/1816-0301-2024-21-4-85-98.
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The work is devoted to theoretical and practical aspects of computer systems design based on the zero trust concept. Based on the system thinking of analyzing existing zero trust systems and theoretical models used in their design, the work describes key problems of implementing zero trust systems. Also, based on design and security patterns, the principles representation of the zero trust concept and the abstract access control model (pattern) of the Zero Trust Architecture are considered.The principles representation can be used to mine abstract design and security patterns, and the access control pattern – to create derivative patterns and architectures of computer systems based on zero trust. An advance of the access control pattern is the capacity to more fully formulate functional requirements and represent the architectures of the designed systems due to the description of access control levels (network path, session, transaction).
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Krul, Evan, Hye-young Paik, Sushmita Ruj, and Salil S. Kanhere. "SoK: Trusting Self-Sovereign Identity." Proceedings on Privacy Enhancing Technologies 2024, no. 3 (July 2024): 297–313. http://dx.doi.org/10.56553/popets-2024-0079.
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Digital identity is evolving from centralized systems to a decentralized approach known as Self-Sovereign Identity (SSI). SSI empowers individuals to control their digital identities, eliminating reliance on third-party data custodians and reducing the risk of data breaches. However, the concept of trust in SSI remains complex and fragmented. This paper systematically analyzes trust in SSI in light of its components and threats posed by various actors in the system. As a result, we derive three distinct trust models that capture the threats and mitigations identified across SSI literature and implementations. Our work provides a foundational framework for future SSI research and development, including a comprehensive catalogue of SSI components and design requirements for trust, shortcomings in existing SSI systems and areas for further exploration.
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Farayola, Michael Mayowa, Irina Tal, Regina Connolly, Takfarinas Saber, and Malika Bendechache. "Ethics and Trustworthiness of AI for Predicting the Risk of Recidivism: A Systematic Literature Review." Information 14, no. 8 (July 27, 2023): 426. http://dx.doi.org/10.3390/info14080426.
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Artificial Intelligence (AI) can be very beneficial in the criminal justice system for predicting the risk of recidivism. AI provides unrivalled high computing power, speed, and accuracy; all harnessed to strengthen the efficiency in predicting convicted individuals who may be on the verge of recommitting a crime. The application of AI models for predicting recidivism has brought positive effects by minimizing the possible re-occurrence of crime. However, the question remains of whether criminal justice system stakeholders can trust AI systems regarding fairness, transparency, privacy and data protection, consistency, societal well-being, and accountability when predicting convicted individuals’ possible risk of recidivism. These are all requirements for a trustworthy AI. This paper conducted a systematic literature review examining trust and the different requirements for trustworthy AI applied to predicting the risks of recidivism. Based on this review, we identified current challenges and future directions regarding applying AI models to predict the risk of recidivism. In addition, this paper provides a comprehensive framework of trustworthy AI for predicting the risk of recidivism.
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Arora, Shakti, and Surjeet Dalal. "Trust Evaluation Factors in Cloud Computing with Open Stack." Journal of Computational and Theoretical Nanoscience 16, no. 12 (December 1, 2019): 5073–77. http://dx.doi.org/10.1166/jctn.2019.8566.
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Cloud computing can be used with three deploying models SAAS, IAAS, and PAAS. IAAS is the most adopted model used for research and innovations. On daily basis Tera bytes of data are being uploaded on the cloud. The Public cloud, Private cloud and hybrid clouds services are used by the people for hosting their applications and computations. One of the major obstacle used in selection of cloud service providers is the lack of trust on CSPs. CSPs are providing different levels of security according to the requirements of users. Proposed paper demonstrates the Trust evaluation, Trust calculation models, based on different security parameters like Turnaround time, Integrity, and reliability of data on cloud. How to select a cloud provider’s service, based on the derived parameters determines the trust value of service provider. A new dynamic trust calculation approach is proposed in this paper. Integrity, Turnaround time (TE) and reliability parameters are considered for trust evaluation. Comparative analysis of different cloud providers with real time calculations of different cloud machines are done and made a comparative analysis of the parameter, approximately 63% efficiency level is achieved while compiling the trust factor with standard cloud nodes. Open stack cloud software is used to create a cloud environment of the local machine which helps in compilation of the data at real time. Fog computing is used to calculate the results.
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Kang, Hongzhaoning, Gang Liu, Quan Wang, Lei Meng, and Jing Liu. "Theory and Application of Zero Trust Security: A Brief Survey." Entropy 25, no. 12 (November 28, 2023): 1595. http://dx.doi.org/10.3390/e25121595.
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As cross-border access becomes more frequent, traditional perimeter-based network security models can no longer cope with evolving security requirements. Zero trust is a novel paradigm for cybersecurity based on the core concept of “never trust, always verify”. It attempts to protect against security risks related to internal threats by eliminating the demarcations between the internal and external network of traditional network perimeters. Nevertheless, research on the theory and application of zero trust is still in its infancy, and more extensive research is necessary to facilitate a deeper understanding of the paradigm in academia and the industry. In this paper, trust in cybersecurity is discussed, following which the origin, concepts, and principles related to zero trust are elaborated on. The characteristics, strengths, and weaknesses of the existing research are analysed in the context of zero trust achievements and their technical applications in Cloud and IoT environments. Finally, to support the development and application of zero trust in the future, the concept and its current challenges are analysed.
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Aluvalu, Rajanikanth, Krishna Keerthi Chennam, M. A.Jabbar, and Shaik Sarfaraz Ahamed. "Risk aware Access Control model for Trust Based collaborative organizations in cloud." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 49. http://dx.doi.org/10.14419/ijet.v7i4.6.20235.
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Secure interactions between collaborative organizations having their applications and data stored in “Cloud Computing” are a critical issue. Access control is the biggest challenge and trust is regarded as an essential secured relationship within a distributed system. Basic access control models, like Discretionary Access Control, Mandatory Access Control, and Role Based Access Control, cannot satisfy requirements in such environment, and need some improvements. During the collaboration, the attitude of the user may change. Therefore, in this context, adding trust management to an access control model is mandatory. To achieve this goal, in this paper, a new trust model to control access in the cloud is proposed. The aim is to monitor in real-time security for collaborative organizations, having decided to migrate to the cloud.
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Djamaluddin, Nursyamsi, Ayub I. Anwar, Burhanuddin Pasiga, Fuad H. Akbar, Rasmidar Samad, Hasanuddin H, and Yu Ri-Kim. "The intersection of trust and satisfaction in Dentist-Patient interactions in Indonesia." Journal of Dentomaxillofacial Science 9, no. 1 (April 1, 2024): 22–27. http://dx.doi.org/10.15562/jdmfs.v9i1.1689.
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Objectives: This cross-sectional study aimed to assess the similarities of two constructs of the doctor-patient relationship in terms of trust and satisfaction among patients receiving dental care in Makassar, Indonesia. Materials and Methods: The study involved 322 participants aged 18 years or older who had received dental treatment. The snowball sampling technique was used to recruit participants through online questionnaires distributed on various social media platforms. The questionnaires included demographic information and The Dentist Trust Scale (DTS) and Dental Care Satisfaction Scale (DCS) to measure trust and satisfaction with dental care. Data analysis involved Exploratory Factor Analysis and Confirmatory Factor Analysis using SPSS and AMOS software. Results: Trust and satisfaction are interrelated, although the relationship was insignificant (0.36). Exploratory factor analysis (EFA) identified three factors in these instruments. Four models were created and evaluated using model fit analysis. All default models display a GFI value that does not correspond to the universal threshold value. Model 1 had the greatest GFI value; hence, adjustments were performed to create models 1(a) and 1(b). The updated model did not produce a more appropriate value than the default model. Model 1 was picked as the model that best matched the requirements. Conclusion: The study findings highlight the importance of trust and satisfaction in dental care experiences among patients in Makassar City. The identified factors provide insights into different aspects of the doctor-patient relationship and can be used to improve the quality of care provided.
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Alrahhal, Hosam, Razan Jamous, Rabie Ramadan, Abdulaziz M. Alayba, and Kusum Yadav. "Utilising Acknowledge for the Trust in Wireless Sensor Networks." Applied Sciences 12, no. 4 (February 16, 2022): 2045. http://dx.doi.org/10.3390/app12042045.
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Wireless Sensor Networks (WSNs) are emerging networks that are being utilized in a variety of applications, such as remote sensing images, military, healthcare, and traffic monitoring. Those critical applications require different levels of security; however, due to the limitation of the sensor networks, security is a challenge where traditional algorithms cannot be used. In addition, sensor networks are considered as the core of the Internet of Things (IoT) and smart cities, where security became one of the most significant problems with IoT and smart cities applications. Therefore, this paper proposes a novel and light trust algorithm to satisfy the security requirements of WSNs. It considers sensor nodes’ limitations and cross-layer information for efficient secure routing in WSNs. It proposes a Tow-ACKs Trust (TAT) Routing protocol for secure routing in WSNs. TAT computes the trust values based on direct and indirect observation of the nodes. TAT uses the first-hand and second-hand information from the Data Link and the Transmission Control Protocol layers to modify the trust’s value. The suggested TATs’ protocols performance is compared to BTRM and Peertrust models in terms of malicious detection ratio, accuracy, average path length, and average energy consumption. The proposed algorithm is compared to BTRM and Peertrust models, the most recent algorithms that proved their efficiency in WSNs. The simulation results indicate that TAT is scalable and provides excellent performance over both BTRM and Peertrust models, even when the number of malicious nodes is high.
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Qi, Ping, Fucheng Wang, and Shu Hong. "A Novel Trust Model Based on Node Recovery Technique for WSN." Security and Communication Networks 2019 (September 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/2545129.
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With the rapid development of sensor technology and wireless network technology, wireless sensor network (WSN) has been widely applied in many resource-constrained environments and application scenarios. As there are a large number of sensor nodes in WSN, node failures are inevitable and have a significant impact on task execution. In this paper, considering the vulnerability, unreliability, and dynamic characteristics of sensor nodes, node failures are classified into two categories including unrecoverable failures and recoverable failures. Then, the traditional description of the interaction results is extended to the trinomial distribution. According to the Bayesian cognitive model, the global trust degree is aggregated by both direct and indirect interaction records, and a novel trust model based on node recovery technique for WSNs is proposed to reduce the probability of failure for task execution. Simulation results show that compared with existing trust models, our proposed TMBNRT (trust model based on node recovery technique) algorithm can effectively meet the security and the reliability requirements of WSN.
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Xin, Ruyue, Jingye Wang, Peng Chen, and Zhiming Zhao. "Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review." ACM Computing Surveys 57, no. 5 (January 9, 2025): 1–37. https://doi.org/10.1145/3701740.
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Performance diagnosis systems are defined as detecting abnormal performance phenomena and play a crucial role in cloud applications. An effective performance diagnosis system is often developed based on artificial intelligence (AI) approaches, which can be summarized into a general framework from data to models. However, the AI-based framework has potential hazards that could degrade the user experience and trust. For example, a lack of data privacy may compromise the security of AI models, and low robustness can be hard to apply in complex cloud environments. Therefore, defining the requirements for building a trustworthy AI-based performance diagnosis system has become essential. This article systematically reviews trustworthiness requirements in AI-based performance diagnosis systems. We first introduce trustworthiness requirements and extract six key requirements from a technical perspective, including data privacy, fairness, robustness, explainability, efficiency, and human intervention. We then unify these requirements into a general performance diagnosis framework, ranging from data collection to model development. Next, we comprehensively provide related works for each component and concrete actions to improve trustworthiness in the framework. Finally, we identify possible research directions and challenges for the future development of trustworthy AI-based performance diagnosis systems.
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Schnabel, Georg, Roberto Capote, and Daniel L. Aldama. "Why trust nuclear data evaluations with Bayesian networks." EPJ Web of Conferences 284 (2023): 12004. http://dx.doi.org/10.1051/epjconf/202328412004.
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Bayesian networks were recently suggested as a framework for nuclear data evaluation. Their theory was to some extent described in a recent preprint and some example evaluations were presented. However, due to their newness in the context of nuclear data evaluation and consequently the lack of experience with them within the community makes it dicult to develop trust in the underlying methodology and consequently also the results produced by it. In this contribution, we aim to make a case why evaluators can trust this methodology in principle but will also elaborate on the fact that Bayesian networks are not a silver bullet for evaluation work. On the contrary, evaluators must assess and quantify essential assumptions about nuclear models and experiments with the same dilligence that is already necessary for the application of the wellestablished Generalized Least Squares (GLS) method. We also explain that the increased ease and flexibility to introduce assumptions regarding nuclear models, experiments and their relationships can help an evaluator to rigorously account for assumptions that are very often neglected in evaluations with the GLS method, such as the non-negativity of cross sections, relative experimental normalization uncertainties and the non-linearity in ratios of cross sections. We believe that adopting the Bayesian network paradigm can help both humans to produce evaluations with clearly traceable assumptions and machines to deal with nuclear data more eciently in terms of execution speed and storage size requirements.
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Trudy-Ann Campbell, Samson Eromonsei, and Olusegun Afolabi. "Efficient compliance with GDPR through automating privacy policy captions in web and mobile application." World Journal of Advanced Engineering Technology and Sciences 12, no. 2 (July 30, 2024): 446–67. http://dx.doi.org/10.30574/wjaets.2024.12.2.0317.
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Ensuring compliance with the General Data Protection Regulation (GDPR) presents significant challenges for organizations, especially those developing web and mobile applications. This study investigates the use of automation to enhance GDPR compliance by generating privacy policy captions through static code analysis and deep learning models. Privacy policy captions offer concise, user-friendly summaries of data processing practices, improving transparency and user trust. The research combines qualitative and quantitative methodologies, including static code analysis of application source codes and the application of neural machine translation models to generate privacy policy captions. Findings indicate that automation can effectively produce accurate, consistent, and comprehensible privacy policy captions that align with GDPR requirements. However, limitations such as tool capabilities, dataset diversity, and user testing scale highlight areas for future research. This study provides practical guidelines for implementing automated privacy policy captions, emphasizing the importance of continuous monitoring and updates to maintain compliance. By leveraging automation, organizations can enhance their data protection practices, build user trust, and achieve efficient GDPR compliance.
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Stanešić, Josip, Zlatan Morić, Damir Regvart, and Ivan Bencarić. "Digital signatures and their legal significance." Edelweiss Applied Science and Technology 9, no. 1 (January 9, 2025): 431–40. https://doi.org/10.55214/25768484.v9i1.4154.
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This paper examines the crucial function of digital signatures in guaranteeing electronic communications' validity, integrity, and non-repudiation. It investigates digital signatures' technological advancement and practical uses in diverse sectors by thoroughly analyzing foundational technologies, including Public Key Infrastructure (PKI) and cryptographic hash functions. It also considers emerging innovations such as blockchain-based trust models and quantum-resistant algorithms. Significant difficulties, such as cryptographic flaws and regulatory harmonization, are also addressed. The results indicate the imperative for ongoing improvements in cryptographic techniques and incorporating decentralized trust mechanisms to bolster system resilience, as digital signatures are indispensable for safe digital transactions. The results underscore the necessity of implementing innovative cryptographic solutions and aligning international rules to address the requirements of advancing digital ecosystems.
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Kolomvatsos, Kostas, Maria Kalouda, Panagiota Papadopoulou, and Stathes Hadjiefthymiades. "Fuzzy Trust Modelling for Pervasive Computing Applications." Journal of Data Intelligence 2, no. 2 (June 2021): 101–15. http://dx.doi.org/10.26421/jdi2.2-1.
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Pervasive computing applications involve the interaction between autonomous entities for performing complex tasks and producing knowledge. Autonomous entities can interact to exchange data and knowledge to fulfil applications requirements. Intelligent Agents (IAs) ‘activated’ in various devices offer a lot of advantages when representing such entities due to their autonomous nature that enables them to perform the desired tasks in a distributed way. However, in such open and dynamic environments, IAs should be based on an efficient mechanism for trusting unknown entities when exchanging data. The trust level of an entity should be automatically calculated based on an efficient methodology. Each entity is uncertain for the characteristics and the intentions of the others. Fuzzy Logic (FL) seems to be the appropriate tool for handling such kind of uncertainty. In this paper, we present a model for trust calculation under the principles of FL. Our scheme takes into consideration the social dimension of trust as well as personal experiences of entities before they decide interactions with an IA. The proposed model is a two-level system involving three FL sub-systems to calculate (a) the social trust (based on experiences retrieved by the community), (b) the individual trust (based on personal experiences) and (c) the final trust. We present our results by evaluating the proposed system compared to other models and reveal its significance.
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Gelei, Andrea, and Imre Dobos. "Evolving trust in business relationships – A behavioural experiment." Acta Oeconomica 72, no. 2 (June 23, 2022): 231–47. http://dx.doi.org/10.1556/032.2022.00013.
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Abstract Using situation-specific and dyadic data, we analyse how trust in inter-organisational relationships evolve over time. Based on a multidisciplinary approach, we define four trust-related concepts, which include both behavioural and perceptual aspects of this multifaceted phenomenon. We also develop the hypothesis that the behavioural consistency of the trustee affects the level of his/her trustworthiness as perceived by the trustor. To test this hypothesis, the paper specifies a finite Dynamic Trust Game that, in a unique way, models longer-term relationships characterised by interdependent actions between partners. In contrast to the simple Repeated Games modelling discrete exchange episodes, this game corresponds to the requirements of the interaction approach of the relationship management, since the iterations of the game are interrelated and embedded in previous ones. Timely development of the behavioural variables in the game reflects an inverse U-shape with an increasing willingness to cooperate until round 8, with a maximum cooperation level of 80% on average. Behaviour seems to affect the perceived level of trustworthiness. However, we need additional experimental data on inconsistent behaviours to get a clear understanding of this effect.
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Ali, Ali Mohammed Omar. "Explainability in AI: Interpretable Models for Data Science." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (February 28, 2025): 766–71. https://doi.org/10.22214/ijraset.2025.66968.
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As artificial intelligence (AI) continues to drive advancements across various domains, the need for explainability in AI models has become increasingly critical. Many state-of-the-art machine learning models, particularly deep learning architectures, operate as "black boxes," making their decision-making processes difficult to interpret. Explainable AI (XAI) aims to enhance model transparency, ensuring that AI-driven decisions are understandable, trustworthy, and aligned with ethical and regulatory standards. This paper explores different approaches to AI interpretability, including intrinsically interpretable models such as decision trees and logistic regression, as well as post-hoc methods like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). Additionally, we discuss the challenges of explainability, including the trade-off between accuracy and interpretability, scalability issues, and domain-specific requirements. The paper also highlights real-world applications of XAI in healthcare, finance, and autonomous systems. Finally, we examine future research directions, emphasizing hybrid models, causal explainability, and human-AI collaboration. By fostering more interpretable AI systems, we can enhance trust, fairness, and accountability in data science applications.
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Gharbi, Atef, and Saleh M. Altowaijri. "A Multi-Robot-Based Architecture and a Trust Model for Intelligent Fault Management and Control Systems." Electronics 12, no. 17 (August 31, 2023): 3679. http://dx.doi.org/10.3390/electronics12173679.
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One of the most important challenges in robotics is the development of a Multi-Robot-based control system in which the robot can make intelligent decisions in a changing environment. This paper proposes a robot-based control approach for dynamically managing robots in such a widely distributed production system. A Multi-Robot-based control system architecture is presented, and its main features are described. Such architecture facilitates the reconfiguration (either self-reconfiguration ensured by the robot itself or distributed reconfiguration executed by the Multi-Robot-based system). The distributed reconfiguration is facilitated through building a trust model that is based on learning from past interactions between intelligent robots. The Multi-Robot-based control system architecture also addresses other specific requirements for production systems, including fault flexibility. Any out-of-control fault occurring in a production system results in the loss of production time, resources, and money. In these cases, robot trust is critical for successful job completion, especially when the work can only be accomplished by sharing knowledge and resources among robots. This work introduces research on the construction of trust estimation models that experimentally calculate and evaluate the trustworthiness of robots in a Multi-Robot system where the robot can choose to cooperate and collaborate exclusively with other trustworthy robots. We compare our proposed trust model with other models described in the literature in terms of performance based on four criteria, which are time steps analysis, RMSD evaluation, interaction analysis, and variation of total feedback. The contribution of the paper can be summarized as follows: (i) the Multi-Robot-based Control Architecture; (ii) how the control robot handles faults; and (iii) the trust model.
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Geitz, Sarah, Brian Moon, Anita D'Amico, Robert R. Hoffman, and Rob Page. "Beyond Requirements: Improving Software Tools for Intelligence Analysts." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 10 (September 2005): 932–35. http://dx.doi.org/10.1177/154193120504901013.
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The goal of this panel is to discuss critical human factors concerns in the development of software for intelligence analysts. The panel presentations are designed to provide a high level overview of the software development process, the intelligence analysis process, and the challenges encountered in both obtaining user feedback. Presentations will examine a variety of issues including the analysis of imagery, text, information assurance, data fusion, visualization models and in establishing situational awareness, as well as empowering analysts with open source software. The panel discussion will focus on extracting generic processes that can be applied to obtaining more accurate software metrics, requirements and solutions from a world where certain topics cannot be discussed. Methods and metaphors for better describing to individuals working outside of the classified world the context a within which a tool will be used, may be touched upon, as well as identifying ways of overcoming both internal and external politics. Human factors concerns may also be addressed, such as evaluating how trust affects the feedback received from individual analysts and communication and interaction within and between groups of analysts. Identifying and overcoming potential perceptual problems in the software development process will also be discussed. The anticipated outcome of the panel will be to target individual processes, techniques and technologies that can be applied to obtaining requirements to support cognitive processes, which can in turn be applied developing software tools that better fit the needs intelligence analysts.
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Shah, Syed Alamdar Ali, Raditya Sukmana, and Bayu Arie Fianto. "Duration model for maturity gap risk management in Islamic banks." Journal of Modelling in Management 15, no. 3 (February 12, 2020): 1167–86. http://dx.doi.org/10.1108/jm2-08-2019-0184.
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Purpose The purpose of this paper is to propose models of duration for maturity gap risk management in Islamic banks. Design/methodology/approach A thorough review of literature on duration modeling, duration measurement in Islamic banks and Shariah compliance has been conducted to set parameters to develop Shariah-compliant maturity gap risk management mechanism. Findings Models based on durations of earning assets and return bearing liabilities using various rates of return earned and paid, benchmark rates and industry standards commonly used by Islamic and conventional banks. Practical implications Increased Shariah compliance has threefold impact. Firstly, it will increase trust of customers. Secondly, it will help improve profitability by reducing non-Shariah compliance penalties from the regulators. And finally, it will enhance market capitalization and returns stability to investors because of enhanced customer base, increased level of trust and increased profitability. Originality/value This research proposes Shariah-compliant maturity gap risk management models based on the concept of duration according to recommendations of Bank for International Settlements. As there is no such maturity gap risk management mechanism that meets the requirements of Shariah using benchmarks that are common between Islamic and conventional banks; therefore, this research presents risk management solutions that can be applied simultaneously in the entire banking sector.
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Zelensky, A. A., T. K. Abdullin, M. M. Zhdanova, V. V. Voronin, and A. A. Gribkov. "Challenge of the performance management of trust control systems with deep learning." Advanced Engineering Research 22, no. 1 (March 30, 2022): 57–66. http://dx.doi.org/10.23947/2687-1653-2022-22-1-57-66.
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Introduction. The significance of machine learning under the conditions of digital transformation of industry, and methods of implementing deep learning to provide the performance of trust management systems are considered. The necessity of using convolutional artificial neural networks for deep machine learning is determined. Various technologies and architectures for the implementation of artificial neural networks are briefly considered; a comparative analysis of their performance is carried out. The work objective is to study the need to develop new approaches to the architecture of computing machines for solving problems of deep machine learning in the trust management system implementation.Materials and Methods. In the context of digital transformation, the use of artificial intelligence reaches a new level. The technical implementation of artificial neural systems with deep machine learning is based on the use of one of three basic technologies: high performance computing (HPC) with parallel data processing, neuromorphic computing (NC), and quantum computing (QC).Results. Implementation models for deep machine learning, basic technologies and architecture of computing machines, as well as requirements for trust assurance in control systems using deep machine learning are analyzed. The problem of shortage of computation power for solving such problems is identified. None of the currently existing technologies can solve the full range of learning and impedance problems. The current level of technology does not provide information security and reliability of neural networks. The practical implementation of trust management systems with deep machine learning based on existing technologies for a significant part of the tasks does not provide a sufficient level of performance.Discussion and Conclusions. The study made it possible to identify the challenge of the computation power shortage for solving problems of deep machine learning. Through the analysis of the requirements for trust management systems, the external challenges of their implementation on the basis of existing technologies, and the need to develop new approaches to the computer architecture are determined.
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Hellani, Houssein, Layth Sliman, Abed Ellatif Samhat, and Ernesto Exposito. "Overview on the Blockchain-Based Supply Chain Systematics and Their Scalability Tools." Emerging Science Journal 4 (August 23, 2021): 45–69. http://dx.doi.org/10.28991/esj-2021-sp1-04.
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Modern IT technologies shaped the shift in economic models with many advantages on cost, optimization, and time to market. This economic shift has increased the need for transparency and traceability in supply chain platforms to achieve trust among partners. Distributed ledger technology (DLT) is proposed to enable supply chains systems with trust requirements. In this paper, we investigate the existing DLT-based supply chain projects to show their technical part and limitations and extract the tools and techniques used to avoid the DLT scalability issue. We then set the requirements for a typical DLT-based supply chain in this context. The analyses are based on the scalability metrics such as computing, data storage, and transaction fees that fit the typical supply chain system. This paper highlights the effects of Blockchain techniques on scalability and their incorporation in supply chains systems. It also presents other existing solutions that can be applied to the supply chain. The investigation shows the necessity of having such tools in supply chains and developing them to achieve an efficient and scalable system. The paper calls for further scalability enhancements throughout introducing new tools and/or reutilize the current ones. Doi: 10.28991/esj-2021-SP1-04 Full Text: PDF
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Araddhana Arvind Deshmukh,. "Explainable AI for Adversarial Machine Learning: Enhancing Transparency and Trust in Cyber Security." Journal of Electrical Systems 20, no. 1s (March 28, 2024): 11–27. http://dx.doi.org/10.52783/jes.749.
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Explainable artificial intelligence (XAI) is essential for improving machine learning models' interpretability, transparency, and reliability—especially in challenging and important fields like cybersecurity. These abstract addresses approaches, structures, and evaluation criteria for putting XAI techniques into practice and comparing them, as well as offering a thorough understanding of all the important components of XAI in the context of adversarial machine learning. Model-agnosticism, global/local explanation, adversarial assault resistance, interpretability, computing efficiency, and scalability are all covered in the discussion. Notably, the suggested SHIME approach shows excellent performance in a number of dimensions, making it a promising solution. The need of carefully weighing XAI solutions based on particular application requirements is emphasized in the abstract's conclusion, opening the door for future developments in the field to handle changing difficulties at the nexus of cybersecurity and artificial intelligence.
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Barugahara, Florence. "Financial Inclusion in Zimbabwe: Determinants, Challenges, and Opportunities." International Journal of Financial Research 12, no. 3 (February 4, 2021): 261. http://dx.doi.org/10.5430/ijfr.v12n3p261.
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Financial inclusion is a highly topical issue for policymakers since inclusive finance is viewed as a channel of social and economic development. Therefore, this paper seeks to ascertain and examine the determinants, challenges, and opportunities for financial inclusion in Zimbabwe. The research is done by examining existing literature and estimating Logit and Probit models. This paper finds that, the major determinants of financial inclusion in Zimbabwe are; gender, age, education, income levels, employment status, the cost of financial services, account opening requirements, and level of trust in the financial system. Challenges to financial inclusion in Zimbabwe include; financial illiteracy, lack of formal identification documents, lack of trust in the financial system, fragile economy, rural poor and gender inequality, and high transaction costs of financial services. However, mobile money services such as Eco-cash, Tel-cash, and One-money have proved an opportunity for inclusive finance in Zimbabwe. Furthermore, the establishment of the women’s Bank of Zimbabwe is one of the strategies to enhance inclusive finance for women in Zimbabwe. The simplified KYC requirements for low-income groups and the financial inclusion strategy commissioned by the Reserve Bank of Zimbabwe are hoped to promote financial inclusion. This paper recommended that to make finance inclusive, the government should develop policies that target marginalized groups such as the elderly, rural population, low-income earners, females, and the unemployed. The government should also develop a strong consumer protection regulatory framework, promote financial literacy, reduce the transaction cost of financial services and encourage the use of accounts with simplified KYC requirements to ease documentation needs.
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Wu, Kewei, Yiran Wang, Xiaogang He, Jinyu Yan, Yang Guo, Zhuqing Jiang, Xing Zhang, et al. "Semi-Open Set Object Detection Algorithm Leveraged by Multi-Modal Large Language Models." Big Data and Cognitive Computing 8, no. 12 (November 29, 2024): 175. https://doi.org/10.3390/bdcc8120175.
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Currently, closed-set object detection models represented by YOLO are widely deployed in the industrial field. However, such closed-set models lack sufficient tuning ability for easily confused objects in complex detection scenarios. Open-set object detection models such as GroundingDINO expand the detection range to a certain extent, but they still have a gap in detection accuracy compared with closed-set detection models and cannot meet the requirements for high-precision detection in practical applications. In addition, existing detection technologies are also insufficient in interpretability, making it difficult to clearly show users the basis and process of judgment of detection results, causing users to have doubts about the trust and application of detection results. Based on the above deficiencies, we propose a new object detection algorithm based on multi-modal large language models that significantly improves the detection effect of closed-set object detection models for more difficult boundary tasks while ensuring detection accuracy, thereby achieving a semi-open set object detection algorithm. It has significant improvements in accuracy and interpretability under the verification of seven common traffic and safety production scenarios.
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Whaiduzzaman, Md, Mohammad Nazmul Haque, Md Rejaul Karim Chowdhury, and Abdullah Gani. "A Study on Strategic Provisioning of Cloud Computing Services." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/894362.
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Cloud computing is currently emerging as an ever-changing, growing paradigm that models “everything-as-a-service.” Virtualised physical resources, infrastructure, and applications are supplied by service provisioning in the cloud. The evolution in the adoption of cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, the increasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the best services. By employing successful service provisioning, the essential services required by customers, such as agility and availability, pricing, security and trust, and user metrics can be guaranteed by service provisioning. Hence, continuous service provisioning that satisfies the user requirements is a mandatory feature for the cloud user and vitally important in cloud computing service offerings. Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements, necessary metrics, and pricing mechanisms. We synthesize and summarize different provision techniques, approaches, and models through a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematic review. Finally, future research directions and open research issues are identified.
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Noardo, Francesca, Rob A. Atkinson, Alejandro Villar, Piotr Zaborowski, and Ingo Simonis. "Profiling Standards to Improve Practical Interoperability." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-2024 (October 21, 2024): 359–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-2024-359-2024.
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Abstract. Standard data models are key to enable a set of data integration functionalities, often characterised using the Findability, Accessibility, Interoperabilty and Reusability (FAIR) principles. However, standardisation is a process of trying to meet many requirements, and standard data models are inherently either very abstract or very comprehensive in the details. This results in several ambiguity pitfalls, inconsistent implementation of standard data models, which in turn hinders trust in the interoperability potential of standardised data, and complicates any integration processes. In practice profiling such standards is useful to overcome such issues to create more useful forms of standardised data for specific applications. However defining custom profiles typically requires a great deal of technical expertise in the underlying expression language of the standard. Maintaining access to this level of expertise is a challenge as profiles become outdated through the time and lose connection with the maintenance of the parent standard from which they originate. Therefore, in this paper, a scalable methodology is proposed, built on the OGC Building Blocks Model approach, that uses semantic modelling to support an easier composition of geospatial data models profiles which directly derive from available standards without losing the relevant dependencies that inform stakeholders which components are interoperable with other standards. The approach is tested within a digital building permit project (CHEK), in which data requirements derive from the semantics of city regulations and common geospatial standards (i.e., CityGML and INSPIRE) are used as reference.
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Rahman, Ashiqur Md, and Rashedur M. Rahman. "CAPM Indexed Hybrid E-Negotiation for Resource Allocation in Grid Computing." International Journal of Grid and High Performance Computing 5, no. 2 (April 2013): 72–91. http://dx.doi.org/10.4018/jghpc.2013040105.
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Computational Grids are a promising platform for executing large-scale resource intensive applications. This paper identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies distributed resource management challenges and requirements of economy-based Grid systems, and proposes an economy based negotiation system protocol for cooperative and competitive trading of resources. Dynamic pricing for services and good level of Pareto optimality make auctions more attractive for resource allocation over other economic models. In a complex Grid environment, the communication demand can become a bottleneck; that is, a number of messages need to be exchanged for matching suitable service providers and consumers. The Fuzzy Trust integrated hybrid Capital Asset Pricing Model (CAPM) shows the higher user centric satisfaction and provides the equilibrium relationship between the expected return and risk on investments. This paper also presents an analysis on the communication requirements and the necessity of the CAPMAuction in Grid environment.
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Brazil, Debbie, Charlotte Moss, and Karen Blinko. "Acute hospital preoperative assessment redesign: streamlining the patient pathway and reducing on-the-day surgery cancellations." BMJ Open Quality 10, no. 3 (September 2021): e001338. http://dx.doi.org/10.1136/bmjoq-2021-001338.
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IntroductionAvoidable surgery cancellations in an acute trust were often attributed to inadequate preoperative assessment. These assessments, undertaken shortly before surgery, were delivered across eight different locations, 60% by a central nursing team and the remainder by other healthcare professionals. There was inconsistency in what and who were assessed, and inadequate time to optimise patients. There was difficulty finding capacity for urgent patient assessment, plus a lack of a pool of ‘ready-assessed’ patients to fill last-minute operating list gaps.MethodsA diagnostic phase using data analysis, root cause analyses and clinic observations identified multiple systemic issues confirming the need for system change.InterventionsOther trusts operating different models were visited and their processes were adapted to create a preassessment model relevant to our trust context. Key features included early preassessment, triage and streaming, in-clinic support from a prescribing pharmacist and consultant anaesthetist, a standardised outcome form documenting specific patient requirements needing action when a surgery date was agreed, surgery dating only on confirmation of patient optimisation, an administrative office (hub) with a tracking database to coordinate follow-up tasks and a patient hotline. A key enabler was a single, bespoke location. Where possible, testing took place in advance of the go-live. However, due to the transformational nature of the new model, some changes could only be tested and refined at scale in the new, single location.ResultsTwo months post implementation, a preliminary audit was positive, but clinic observations indicated that patient clinic flow was suboptimal. Further structural and process modifications were made. Ten months post implementation, a further root cause audit showed a near-elimination of on-the-day surgery cancellations for patients assessed in the redesigned service.ConclusionThe bundle of 17 interlinked interventions proved highly effective in delivering sustained improvements, which could be adopted by other trusts.
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Yuan, Ziyao, Houjun Fang, Binqiang Han, and Zejiong Zhou. "A Comparative Study on Poverty Reduction Models of Rural Collective Economic Organizations from the Perspective of Relative Poverty." International Journal of Education and Humanities 5, no. 3 (November 16, 2022): 177–84. http://dx.doi.org/10.54097/ijeh.v5i3.2798.
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Rural collective economic organization is an important guarantee to develop rural collective economy and increase farmers' income. In order to further explore its future development ideas and improve the development mechanism, this paper conducts a comparative study on the two poverty reduction models of Toupu Town and Liyuan Village. From the overall perspective, they reflect the realistic requirements of returning public services to the rural grass-roots level in the perspective of relative poverty, as well as the positive efforts made by local Party committees and governments to achieve rural revitalization. From the perspective of system design, Toupu Town and Liyuan Village show different development ideas and specific designs. The "political economic integration" route of Liyuan Village is reasonable and effective, while the "political economic separation" route of Toupu Town presents a crisis of trust. We put forward specific ideas on how to improve and amend the poverty reduction model of Liyuan and Toupu.
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Englund, Cristofer, Eren Erdal Aksoy, Fernando Alonso-Fernandez, Martin Daniel Cooney, Sepideh Pashami, and Björn Åstrand. "AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control." Smart Cities 4, no. 2 (May 18, 2021): 783–802. http://dx.doi.org/10.3390/smartcities4020040.
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Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.
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Drin, Svitlana, and Fedir Serdiuk. "Expected credit loss modeling." Mohyla Mathematical Journal 6 (April 18, 2024): 14–19. http://dx.doi.org/10.18523/2617-70806202314-19.
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This article proposes a method for modeling the probability of default, describes the statistical evaluation of the model, and presents a model of the software implementation algorithm. The algorithm automatically selects from the group of regression models where the models are both linear regression and various modifications of semi-logarithmic models and lag models for macro factors Xi,t,Xi,t-1, ...,Xi,t-TStatistical analysis is carried out using the coefficient of determination R-squared, p-value, VIF (variance inflation factor).The relevance of this topic is determined by the need for banking organizations to comply with international standards, such as International Financial Reporting Standards (IFRS 9) and the Agreement on Banking Supervision and Capital (Basel 3). These standards define credit risk assessment requirements and capital requirements. Adherence to these standards is important not only for ensuring the stability and reliability of the financial system, but also for maintaining the trust of clients and investors. Compliance with international standards also makes banks competitive in the global market and promotes investment inflows and the development of the financial sector.IFRS 9 can be presented in various mathematical models. The article proposes an approach to choosing the appropriate model for forecasting the probability of default. The described model selection method allows banks to choose the optimal default forecast assessment model within the framework of the given standard. This contributes to a more accurate and reliable assessment of credit risk, in accordance with regulatory requirements, which will provide banks with the means for better forecasting and management of financial resources, as well as risk reduction.The model selection methodology saves a significant amount of time and resources, since the search for the optimal model occurs automatically. This allows us to react more quickly to changes in the economic environment, improve decision-making strategies and manage credit risks, which is of great importance for financial institutions in a competitive environment.There is currently a war going on in Ukraine, and forecasting using current methods becomes a difficult task due to unpredictable stressful situations for the economy. In such conditions, standard models may not be sufficiently adapted to account for increased risk and volatility. The proposed approach allows finding more conservative forecasting models that can be useful in unstable periods and war.
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Rehman, Abdul, Mohd Fadzil Hassan, Kwang Hooi Yew, Irving Paputungan, and Duc Chung Tran. "State-of-the-art IoV trust management a meta-synthesis systematic literature review (SLR)." PeerJ Computer Science 6 (December 14, 2020): e334. http://dx.doi.org/10.7717/peerj-cs.334.
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In the near future, the Internet of Vehicles (IoV) is foreseen to become an inviolable part of smart cities. The integration of vehicular ad hoc networks (VANETs) into the IoV is being driven by the advent of the Internet of Things (IoT) and high-speed communication. However, both the technological and non-technical elements of IoV need to be standardized prior to deployment on the road. This study focuses on trust management (TM) in the IoV/VANETs/ITS (intelligent transport system). Trust has always been important in vehicular networks to ensure safety. A variety of techniques for TM and evaluation have been proposed over the years, yet few comprehensive studies that lay the foundation for the development of a “standard” for TM in IoV have been reported. The motivation behind this study is to examine all the TM models available for vehicular networks to bring together all the techniques from previous studies in this review. The study was carried out using a systematic method in which 31 papers out of 256 research publications were screened. An in-depth analysis of all the TM models was conducted and the strengths and weaknesses of each are highlighted. Considering that solutions based on AI are necessary to meet the requirements of a smart city, our second objective is to analyze the implications of incorporating an AI method based on “context awareness” in a vehicular network. It is evident from mobile ad hoc networks (MANETs) that there is potential for context awareness in ad hoc networks. The findings are expected to contribute significantly to the future formulation of IoVITS standards. In addition, gray areas and open questions for new research dimensions are highlighted.
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Zhang, Peng, and Maged N. Kamel Boulos. "Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges." Future Internet 15, no. 9 (August 24, 2023): 286. http://dx.doi.org/10.3390/fi15090286.
Full textAbstract:
Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI’s ChatGPT, that can be prompted to generate various types of content. In this narrative review, we present a selection of representative examples of generative AI applications in medicine and healthcare. We then briefly discuss some associated issues, such as trust, veracity, clinical safety and reliability, privacy, copyrights, ownership, and opportunities, e.g., AI-driven conversational user interfaces for friendlier human-computer interaction. We conclude that generative AI will play an increasingly important role in medicine and healthcare as it further evolves and gets better tailored to the unique settings and requirements of the medical domain and as the laws, policies and regulatory frameworks surrounding its use start taking shape.
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Smruti P. Patil. "Design of an Efficient QOS Aware Trust-Based Security Model with Bioinspired Sidechains for Healthcare Deployments." Communications on Applied Nonlinear Analysis 32, no. 3 (October 11, 2024): 181–96. http://dx.doi.org/10.52783/cana.v32.1938.
Full textAbstract:
Increased adoption of blockchain technology in healthcare systems has paved the way for transparent and secure data management. However, the inherent difficulties of scalability, performance, and security in blockchain networks necessitate the development of efficient models to ensure Quality of Service (QoS) and reliability. In this paper, we propose a novel QoS-aware trust-based security model for healthcare blockchain deployments that addresses these challenges by taking temporal energy consumption, temporal delay, temporal throughput, and temporal Packet Delivery Ratio (PDR) levels into account when selecting miner nodes. For efficient miner node selection, our model employs trust-based analysis. While for the formation of sidechains an efficient Grey Wolf Optimizer (GWO) Model is used, which is a metaheuristic technique inspired by hunting behaviour of Wolves in real-time scenarios. Effectively balancing the trade-off between exploration and exploitation, the GWO algorithm enables the selection of optimal miner nodes that meet the desired QoS requirements. By incorporating temporal metrics, our model adapts dynamically to changing network conditions, ensuring optimal resource utilization and enhanced network performance levels. To assess the efficacy of our proposed model, we ran extensive simulations and compared its performance to that of existing sidechaining models. The outcomes demonstrate significant enhancements in multiple aspects. In comparison to state-of-the-art sidechaining models, our model achieves an impressive 8.5% reduction in delay, 3.9% reduction in energy consumption, 4.5% increase in throughput, and 2.5% improvement in PDR levels. These enhancements demonstrate the efficacy and efficiency of our healthcare blockchain deployment models. The proposed model has applications in a variety of real-time healthcare scenarios. It can be used in electronic health record (EHR) systems where data integrity, confidentiality, and accessibility are crucial. By ensuring QoS-aware miner node selection, our model contributes to dependable and efficient data management, allowing for streamlined access to patient records while maintaining the necessary security standards. Moreover, the trust-based approach of our model improves the overall security of healthcare blockchain deployments. Our model reduces the risks associated with malicious or compromised miner nodes by incorporating trustworthiness metrics such as reputation and behavior analysis. This is especially important in the healthcare industry, where the sensitive nature of patient data necessitates stringent security measures.