Добірка наукової літератури з теми "Trust inference"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Trust inference".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Trust inference"
Alahmadi, Dimah, and Xaio-Jun Zeng. "Improving Recommendation Using Trust and Sentiment Inference from OSNs." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 9–17. http://dx.doi.org/10.7763/ijke.2015.v1.2.
Повний текст джерелаOrman, Levent V. "Bayesian Inference in Trust Networks." ACM Transactions on Management Information Systems 4, no. 2 (August 2013): 1–21. http://dx.doi.org/10.1145/2489790.
Повний текст джерелаLi, Lei, and Yan Wang. "Subjective Trust Inference in Composite Services." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1377–84. http://dx.doi.org/10.1609/aaai.v24i1.7504.
Повний текст джерелаLiu, Yu, and Bai Wang. "User Trust Inference in Online Social Networks: A Message Passing Perspective." Applied Sciences 12, no. 10 (May 20, 2022): 5186. http://dx.doi.org/10.3390/app12105186.
Повний текст джерелаNevill, Alan M., A. Mark Williams, Colin Boreham, Eric S. Wallace, Gareth W. Davison, Grant Abt, Andrew M. Lane, and Edward M. Winter. "Can we trust “Magnitude-based inference”?" Journal of Sports Sciences 36, no. 24 (November 4, 2018): 2769–70. http://dx.doi.org/10.1080/02640414.2018.1516004.
Повний текст джерелаWang, Guojun, and Jie Wu. "FlowTrust: trust inference with network flows." Frontiers of Computer Science in China 5, no. 2 (May 9, 2011): 181–94. http://dx.doi.org/10.1007/s11704-011-0323-4.
Повний текст джерелаYao, Yuan, Hanghang Tong, Feng Xu, and Jian Lu. "Pairwise trust inference by subgraph extraction." Social Network Analysis and Mining 3, no. 4 (October 6, 2013): 953–68. http://dx.doi.org/10.1007/s13278-013-0140-x.
Повний текст джерелаDenève, Sophie, and Renaud Jardri. "Circular inference: mistaken belief, misplaced trust." Current Opinion in Behavioral Sciences 11 (October 2016): 40–48. http://dx.doi.org/10.1016/j.cobeha.2016.04.001.
Повний текст джерелаWang, Chenlan, Chongjie Zhang, and X. Jessie Yang. "Automation reliability and trust: A Bayesian inference approach." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 202–6. http://dx.doi.org/10.1177/1541931218621048.
Повний текст джерелаLesani, Mohsen, and Niloufar Montazeri. "FUZZY TRUST AGGREGATION AND PERSONALIZED TRUST INFERENCE IN VIRTUAL SOCIAL NETWORKS." Computational Intelligence 25, no. 2 (May 2009): 51–83. http://dx.doi.org/10.1111/j.1467-8640.2009.00334.x.
Повний текст джерелаДисертації з теми "Trust inference"
Bhuiyan, Touhid. "Trust-based automated recommendation making." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/49168/1/Touhid_Bhuiyan_Thesis.pdf.
Повний текст джерелаVarrier, Rekha Sreekumar. "Never Trust the Teller! Feedback Manipulation and its Impact on Perceptual Inference." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21259.
Повний текст джерелаAccording to the Bayesian brain hypothesis, perception is an inferential process that depends not only on sensory data, but also on our beliefs about likely sensory data and their reliability. Feedback from the environment improves this inferential process. Indeed previous studies have shown that unreliable feedback impairs task performance and increases illusory pattern perception in noise. In this thesis, we explored the hypothesis that the effect of unreliable feedback is a down-weighting of sensory information in perceptual inference. We conducted two studies comprising visual stimuli: Study I comprised two behavioural experiments and Study II comprised a functional magnetic resonance imaging experiment. Based on the hypothesis that sensory data would be down-weighed after unreliable feedback , we predicted that perceptual performance would deteriorate and that perceptual inference would shift towards experimentally induced priors. Further, we investigated whether the sensory data representations in the primary visual cortex (V1) deteriorate as a result of unreliable feedback. Reliable feedback was used as a control condition in all the experiments. Data from both studies demonstrated that performance did decrease following unreliable feedback compared to reliable feedback. Moreover, observers increasingly relied on prior information as the feedback about their percepts became unreliable. At the neural level, low-level stimulus representations deteriorated in V1 with unreliable feedback. To sum up, our results show that inducing beliefs about the reliability of sensory information by manipulating performance feedback can systematically influence perceptual inference and that these changes manifest at the earliest stages of cortical sensory processing.
Kramdi, Seifeddine. "A modal approach to model computational trust." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30146/document.
Повний текст джерелаThe concept of trust is a socio-cognitive concept that plays an important role in representing interactions within concurrent systems. When the complexity of a computational system and its unpredictability makes standard security solutions (commonly called hard security solutions) inapplicable, computational trust is one of the most useful concepts to design protocols of interaction. In this work, our main objective is to present a prospective survey of the field of study of computational trust. We will also present two trust models, based on logical formalisms, and show how they can be studied and used. While trying to stay general in our study, we use service-oriented architecture paradigm as a context of study when examples are needed. Our work is subdivided into three chapters. The first chapter presents a general view of the computational trust studies. Our approach is to present trust studies in three main steps. Introducing trust theories as first attempts to grasp notions linked to the concept of trust, fields of application, that explicit the uses that are traditionally associated to computational trust, and finally trust models, as an instantiation of a trust theory, w.r.t. some formal framework. Our survey ends with a set of issues that we deem important to deal with in priority in order to help the advancement of the field. The next two chapters present two models of trust. Our first model is an instantiation of Castelfranchi & Falcone's socio-cognitive trust theory. Our model is implemented using a Dynamic Epistemic Logic that we propose. The main originality of our solution is the fact that our trust definition extends the original model to complex action (programs, composed services, etc.) and the use of authored assignment as a special kind of atomic actions. The use of our model is then illustrated in a case study related to service-oriented architecture. Our second model extends our socio-cognitive definition to an abductive framework that allows us to associate trust to explanations. Our framework is an adaptation of Bochman's production relations to the epistemic case. Since Bochman approach was initially proposed to study causality, our definition of trust in this second model presents trust as a special case of causal reasoning, applied to a social context. We end our manuscript with a conclusion that presents how we would like to extend our work
Ayday, Erman. "Iterative algorithms for trust and reputation management and recommender systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45868.
Повний текст джерелаAlhaqbani, Bandar Saleh. "Privacy and trust management for electronic health records." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37635/1/Bandar_Alhaqbani_Thesis.pdf.
Повний текст джерелаVarrier, Rekha Sreekumar [Verfasser], Martin [Gutachter] Rolfs, Florian [Gutachter] Schlagenhauf, and Philipp [Gutachter] Sterzer. "Never Trust the Teller! Feedback Manipulation and its Impact on Perceptual Inference / Rekha Sreekumar Varrier ; Gutachter: Martin Rolfs, Florian Schlagenhauf, Philipp Sterzer." Berlin : Humboldt-Universität zu Berlin, 2020. http://d-nb.info/1206587792/34.
Повний текст джерелаBen-Mosbah, Azza. "Privacy-preserving spectrum sharing." Thesis, Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0008/document.
Повний текст джерелаRadio frequencies, as currently allocated, are statically managed. Spectrum sharing between commercial users and incumbent users in the Federal bands has been considered by regulators, industry, and academia as a great way to enhance productivity and effectiveness in spectrum use. However, allowing secondary users to share frequency bands with sensitive government incumbent users creates new privacy threats in the form of inference attacks. Therefore, the aim of this thesis is to enhance the privacy of the incumbent while allowing secondary access to the spectrum. First, we present a brief description of different sharing regulations and privacy requirements in Federal bands. We also survey the privacy-preserving techniques (i.e., obfuscation) proposed in data mining and publishing to thwart inference attacks. Next, we propose and implement our approach to protect the operational frequency and location of the incumbent operations from inferences. We follow with research on frequency protection using inherent and explicit obfuscation to preserve the incumbent's privacy. Then, we address location protection using trust as the main countermeasure to identify and mitigate an inference risk. Finally, we present a risk-based framework that integrates our work and accommodates other privacy-preserving approaches. This work is supported with models, simulations and results that showcase our work and quantify the importance of evaluating privacy-preserving techniques and analyzing the trade-off between privacy protection and spectrum efficiency
Hao, F., Geyong Min, M. Lin, C. Luo, and L. T. Yang. "MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks." 2014. http://hdl.handle.net/10454/10644.
Повний текст джерелаMobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.
Tseng, Shihhao, and 曾世豪. "Trust Inference and Social Network for Blog Recommendation ─ Physician Search of Virtual Community." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/78413194091907245283.
Повний текст джерела輔仁大學
資訊管理學系
99
In recent years, social network sites ( SNS ) had developed rapidly. SNSs also can be viewed as an important information platform for knowledge establishing, knowledge sharing, and knowledge dissermation. However, some SNS platforms provide the query users to search for health information from the blogs on which the collectes the other users’ medical treatment’s experiences., Based on many blogs’ interactive relationships among the users, this research proposes a collaborative information and knowledge sharing. The practical experiences from the other users’ sharing can be as a reference for physicians choice, but the blogs’ contents are not easy to make sure their credibility. Therefore, this research develops a social network servcie aligning with trust mechanism for systematic collaborative decision-making model to achieve the goal of service innovation. This research proposes a new service model, service system development, and some experimental analysis and verification. According to the properties of SSME, the research motivation of physician search through virtual community can be a Web 2.0 applicaiton. Thus, a new service design of Web 2.0 SNS application is to develop systematic physician recommendation service system using SNA approach. When the query user search for the blogs, the analyized and recommended blogs can be determined by social computing and TidalTrust algorithm. However, the trackback is a interactive behavior hat can be explored the social relationships among the bloggers. The average shortest path length, clustering coefficient, and network centrality, the high level of citations and linkage relationships can be measurable to provide the search and recommendation results. In addition, the trust estimation can further facilitate the inference of reliable blogs’ recommendaitons. An integration of SNA and trust network can be used to design the service system that can provide the collaborative recommendation to enhance the decision making of query users. In ther experiments, the effects of the proposed service system using blogs’ search and recommendation.can be verified.
Tsai, Cheng Han, and 蔡承翰. "A study of semantic web-based specialist recommendation & trust inference mechanism-a case of EMBA database." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/01081799265085803740.
Повний текст джерела國立政治大學
資訊管理研究所
98
"Human Resource" is one of the most important assets of company, especially in knowledge-intensive industries. As network technologies developed, commercial job site has also become another kind of recruitment channel. But through this kind of channel, companies don’t have better chance to know new employee than traditional way. Therefore this study filters new employees by a Recommendation & Trust Inference mechanism. Hope that commercial job site would continue to keep the advantages of high efficiency in recruitment, and enhance its filtering capability at the same time. First, this study surveys literatures in recruitment channels. And it proposes a Recommendation & Trust Inference mechanism using a national university EMBA program member data as an example. The Recommendation mechanism recommend candidates having the same specialty by comparing their similarity of education and work experience. Furthermore, recruitment unit could use Trust Inference mechanism to get suitable candidates. Third, we conduct experiments to find the key parameters for the prototype system. Make the system able to work better and meet users’ needs. The prototype system combines the benefit of commercial job site which can quickly recruit a large number of employees and the feature providing more appropriate candidates for the company recommended by staff. Simultaneously by taking use of the FOAF format, we can unify the data format in online social network. The way mentioned above will effectively reduce the system set-up time.
Книги з теми "Trust inference"
Kawachi, Ichiro. Trust and Population Health. Edited by Eric M. Uslaner. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190274801.013.35.
Повний текст джерелаDinesen, Peter Thisted, and René Bekkers. The Foundations of Individuals’ Generalized Social Trust. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190630782.003.0005.
Повний текст джерелаIchino, Anna, and Greg Currie. Truth and Trust in Fiction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198805403.003.0004.
Повний текст джерелаЧастини книг з теми "Trust inference"
Seyedi, Amir, Rachid Saadi, and Valérie Issarny. "Proximity-Based Trust Inference for Mobile Social Networking." In Trust Management V, 253–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22200-9_20.
Повний текст джерелаCai, Liang, and Hao Chen. "On the Practicality of Motion Based Keystroke Inference Attack." In Trust and Trustworthy Computing, 273–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30921-2_16.
Повний текст джерелаBuckley, Oliver, Duncan Hodges, Melissa Hadgkiss, and Sarah Morris. "Keystroke Inference Using Smartphone Kinematics." In Human Aspects of Information Security, Privacy and Trust, 226–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58460-7_15.
Повний текст джерелаAlipour, Bizhan, Abdessamad Imine, and Michaël Rusinowitch. "Gender Inference for Facebook Picture Owners." In Trust, Privacy and Security in Digital Business, 145–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27813-7_10.
Повний текст джерелаZiegler, Cai-Nicolas, and Jennifer Golbeck. "Models for Trust Inference in Social Networks." In Intelligent Systems Reference Library, 53–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15916-4_3.
Повний текст джерелаYao, Yuan, Hanghang Tong, Feng Xu, and Jian Lu. "Subgraph Extraction for Trust Inference in Social Networks." In Encyclopedia of Social Network Analysis and Mining, 1–15. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_357-1.
Повний текст джерелаYao, Yuan, Hanghang Tong, Feng Xu, and Jian Lu. "Subgraph Extraction for Trust Inference in Social Networks." In Encyclopedia of Social Network Analysis and Mining, 2084–98. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_357.
Повний текст джерелаYao, Yuan, Hanghang Tong, Feng Xu, and Jian Lu. "Subgraph Extraction for Trust Inference in Social Networks." In Encyclopedia of Social Network Analysis and Mining, 3011–25. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_357.
Повний текст джерелаZhang, Huqiu, and Aad van Moorsel. "Evaluation of P2P Algorithms for Probabilistic Trust Inference in a Web of Trust." In Computer Performance Engineering, 242–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87412-6_18.
Повний текст джерелаKim, Tiffany Hyun-Jin, Virgil Gligor, Jorge Guajardo, Jason Hong, and Adrian Perrig. "Soulmate or Acquaintance? Visualizing Tie Strength for Trust Inference." In Financial Cryptography and Data Security, 112–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41320-9_8.
Повний текст джерелаТези доповідей конференцій з теми "Trust inference"
Liu, Guanfeng, Yan Wang, and Mehmet Orgun. "Trust Inference in Complex Trust-Oriented Social Networks." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.248.
Повний текст джерелаShi, Leyi, Yao Wang, and Xin Liu. "An ACO-Based Trust Inference Algorithm." In 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA). IEEE, 2014. http://dx.doi.org/10.1109/bwcca.2014.70.
Повний текст джерелаPapaoikonomou, Athanasios, Magdalini Kardara, and Theodora Varvarigou. "Trust Inference in Online Social Networks." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2809418.
Повний текст джерелаHe, Xu, Bin Liu, and Kejia Chen. "ITrace: An implicit trust inference method for trust-aware collaborative filtering." In ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS II: Proceedings of the 2nd International Conference on Advances in Materials, Machinery, Electronics (AMME 2018). Author(s), 2018. http://dx.doi.org/10.1063/1.5033766.
Повний текст джерелаMolina-Markham, Andres, and Joseph J. Rushanan. "Positioning, Navigation, and Timing Trust Inference Engine." In 2020 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2020. http://dx.doi.org/10.33012/2020.17195.
Повний текст джерелаHamdi, Sana, Amel Bouzeghoub, Alda Lopes Gancarski, and Sadok Ben Yahia. "Trust Inference Computation for Online Social Networks." In 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2013. http://dx.doi.org/10.1109/trustcom.2013.240.
Повний текст джерелаGupta, Shashank, Pulkit Parikh, Manish Gupta, and Vasudeva Varma. "Simultaneous Inference of User Representations and Trust." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3110093.
Повний текст джерелаChen, Hongwei, Zhiwei Ye, Wei Liu, and Chunzhi Wang. "Fuzzy Inference Trust in P2P Network Environment." In 2009 International Workshop on Intelligent Systems and Applications. IEEE, 2009. http://dx.doi.org/10.1109/iwisa.2009.5072876.
Повний текст джерелаO'Doherty, Daire, Salim Jouili, and Peter Van Roy. "Towards trust inference from bipartite social networks." In the 2nd ACM SIGMOD Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2304536.2304539.
Повний текст джерелаHintersdorf, Dominik, Lukas Struppek, and Kristian Kersting. "To Trust or Not To Trust Prediction Scores for Membership Inference Attacks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/422.
Повний текст джерелаЗвіти організацій з теми "Trust inference"
Ding, Li, Pranam Kolari, Shashidhara Ganjugunte, Tim Finin, and Anupam Joshi. Modeling and Evaluating Trust Network Inference. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439712.
Повний текст джерелаKeefer, Philip, Sergio Perilla, and Razvan Vlaicu. Research Insights: Public Sector Employee Behavior and Attitudes during a Pandemic. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003388.
Повний текст джерелаVlaicu, Razvan. Trust, Collaboration, and Policy Attitudes in the Public Sector. Inter-American Development Bank, May 2021. http://dx.doi.org/10.18235/0003280.
Повний текст джерела