Relevant bibliographies by topics / Attribute inference

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Attribute inference'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Attribute inference"

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Al Zamal, Faiyaz, Wendy Liu, and Derek Ruths. "Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (2021): 387–90. http://dx.doi.org/10.1609/icwsm.v6i1.14340.

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In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, diffe
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Zhang, Xiao Lei, and Yi Tang. "Protecting Encrypted Data against Inference Attacks in Outsourced Databases." Applied Mechanics and Materials 571-572 (June 2014): 621–25. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.621.

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Ensuring data privacy and improving query performance are two closely linked challenges for outsourced databases. Using mixed encryption methods to data attributes can reach an explicit trade-off between these two challenges. However, encryption cannot always conceal relations between attributes values. When the data tuples are accessed selectively, inferences based on comparing encrypted values could be launched and sensitive values may be disclosed. In this paper, we explore the attribute based inferences in mixed encrypted databases. We develop a method to construct private indexes on encry
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Drew, Paul. "Inferences and Indirectness in Interaction." Open Linguistics 4, no. 1 (2018): 241–59. http://dx.doi.org/10.1515/opli-2018-0013.

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Abstract I explore some of the interconnections between inferences that participants make about one another’s (verbal) conduct, the implications they attribute to prior turns at talk, and the indirectness with which recipients may respond to enquiries - in short, the interconnections between inference, implication and indirectness. These are explored in the context of naturally occurring conversations (UK and US), from the methodological perspective of Conversation Analysis. Because inference has come to be associated closely with Grice’s concept of implicature, I begin by setting out my reaso
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Jiang, Yutong, Yuhan Gao, Yaoqi Sun, Shuai Wang, and Chenggang Yan. "Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference." Electronics 13, no. 5 (2024): 903. http://dx.doi.org/10.3390/electronics13050903.

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In the field of collaborative filtering, attribute information is often integrated to improve recommendations. However, challenges remain unaddressed. Firstly, existing data modeling methods often fall short of appropriately handling attribute information. Secondly, attribute data are often sparse and can potentially impact recommendation performance due to the challenge of incomplete correspondence between the attribute information and the recommendations. To tackle these challenges, we propose a hypergraph collaborative filtering with attribute inference (HCFA) framework, which segregates at
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Yang, Ziqi, Lijin Wang, Da Yang, et al. "Purifier: Defending Data Inference Attacks via Transforming Confidence Scores." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10871–79. http://dx.doi.org/10.1609/aaai.v37i9.26289.

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Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where the attacker could infer useful information such as the membership, the reconstruction or the sensitive attributes of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a method, namely PURIFIER, to defend against membership inference attacks. It transforms the confidence score vectors predicted by the target classifier and makes purified confidence scores indisting
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Middelkoop, Arie, Atze Dijkstra, and S. Doaitse Swierstra. "Iterative type inference with attribute grammars." ACM SIGPLAN Notices 46, no. 2 (2011): 43–52. http://dx.doi.org/10.1145/1942788.1868302.

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Chen, Xin, Yu Wang, Eugene Agichtein, and Fusheng Wang. "A Comparative Study of Demographic Attribute Inference in Twitter." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (2021): 590–93. http://dx.doi.org/10.1609/icwsm.v9i1.14656.

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Social media platforms have become a major gateway to receive and analyze public opinions. Understandingusers can provide invaluable context information of their social media posts and significantly improve traditional opinion analysis models. Demographic attributes,such as ethnicity, gender, age, among others,have been extensively applied to characterize social mediausers. While studies have shown that user groups formed by demographic attributes can have coherent opinions towards political issues, these attributes are often not explicitly coded by users through their profiles.Previous work h
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ter Meulen, Alice G. B. "Agency, argument structure, and causal inference." Behavioral and Brain Sciences 31, no. 6 (2008): 728–29. http://dx.doi.org/10.1017/s0140525x08006055.

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AbstractLogically, weighting is transitive, but similarity is not, so clustering cannot be either. Entailments must help a child to review attribute lists more efficiently. Children's understanding of exceptions to generic claims precedes their ability to articulate explanations. So agency, as enabling constraint, may show coherent covariation with attributes, as mere extensional, observable effect of intensional entailments.
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Gong, Neil Zhenqiang, Ameet Talwalkar, Lester Mackey, et al. "Joint Link Prediction and Attribute Inference Using a Social-Attribute Network." ACM Transactions on Intelligent Systems and Technology 5, no. 2 (2014): 1–20. http://dx.doi.org/10.1145/2594455.

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Fang, Quan, Jitao Sang, Changsheng Xu, and M. Shamim Hossain. "Relational User Attribute Inference in Social Media." IEEE Transactions on Multimedia 17, no. 7 (2015): 1031–44. http://dx.doi.org/10.1109/tmm.2015.2430819.

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Dissertations / Theses on the topic "Attribute inference"

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Bull, Bruce James. "Type inference with overloading using an attribute grammar." Thesis, Monterey, California. Naval Postgraduate School, 1994. http://hdl.handle.net/10945/30880.

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Interactive programming environment for language offer many advantages over traditional batch-oriented ones, such as immediate static analysis. One form of analysis is type checking, yet type checking in this setting for languages with common features like overloading has received little attention. We implement an interactive type checker for the polymorphic type system of ML with overloading. The implementation was produced automatically from an attribute grammar using the Synthesizer Generator, an attribute evaluator generator. Type inference then is accomplished via attribute evaluatio
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Ding, Shichang [Verfasser]. "User Attribute Inference via Mining User-Generated Data / Shichang Ding." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2020. http://d-nb.info/1222738309/34.

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Nagashima, Kazunobu. "Inference system for selection of an appropriate multiple attribute decision making method." Thesis, Kansas State University, 1986. http://hdl.handle.net/2097/9942.

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Kim, Hyun Seok (John). "Diagnosing examinees' attributes-mastery using the Bayesian inference for binomial proportion: a new method for cognitive diagnostic assessment." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41144.

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Purpose of this study was to propose a simple and effective method for cognitive diagnosis assessment (CDA) without heavy computational demand using Bayesian inference for binomial proportion (BIBP). In real data studies, BIBP was applied to a test data using two different item designs: four and ten attributes. Also, the BIBP method was compared with DINA and LCDM in the diagnosis result using the same four-attribute data set. There were slight differences in the attribute mastery probability estimate among the three model (DINA, LCDM, BIBP), which could result in different attribute mastery p
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Collins, Andrew. "Attribute nonattendance in discrete choice models: measurement of bias, and a model for the inference of both nonattendance and taste heterogeneity." Thesis, The University of Sydney, 2012. http://hdl.handle.net/2123/8966.

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An extensive literature has recognised that when discrete choices are made, only a subset of the attributes of the choice alternatives may be considered or attended to by each decision maker. The wider literature suggests that attribute nonattendance (ANA) is important, and that failure to recognise ANA contributes to biased model outputs such as willingness to pay measures, masked sensitivities, implausibly signed random parameters coefficients, and exaggerated taste heterogeneity. It may also be of intrinsic interest to the analyst, and reveal problems with stated choice experimental designs
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Fung, Ying-Kit (Richard). "An intelligent hybrid model for customer requirements interpretation and product design targets determination." Thesis, Loughborough University, 1997. https://dspace.lboro.ac.uk/2134/7419.

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The transition of emphasis in business competition from a technology-led age to a market-oriented era has led to a rapid shift from the conventional "economy of scale" towards the "economy of scope" in contemporary manufacturing. Hence, it is necessary and essential to be able to respond to the dynamic market and customer requirements systematically and consistently. The central theme of this research is to rationalise and improve the conventional means of analysing and interpreting the linguistic and often imprecise customer requirements in order to identify the essential product features and
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Petrillo, Jennifer. "Development and validation of the EXACT-U* to report utilities for COPD exacerbations : a comparison of statistical inference and multi-attribute utility theory method." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549744.

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Abid, Younes. "Analyse automatisée des risques sur la vie privée dans les réseaux sociaux." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0088/document.

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Cette thèse vise à comprendre le risque de fuite d’informations personnelles sur un réseau social. Nous étudions les violations potentielles de la vie privée, concevons des attaques, prouvons leur faisabilité et analysons leur précision. Cette approche nous aide à identifier l’origine des menaces et constitue un premier pas vers la conception de contre-mesures efficaces. Nous avons d’abord introduit une mesure de sensibilité des sujets à travers une enquête par questionnaire. Puis, nous avons conçu des attaques de divulgation (avec certitude) des liens d’amitié et des liens d’appartenance aux
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Alipour, Pijani Bizhan. "Attaques par inférence d'attributs sur les publications des réseaux sociaux." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0009.

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Les réseaux sociaux contiennent de nombreuses informations personnelles telles que le genre, l'âge ou le statut d'une relation. Leur popularité et leur importance en font des cibles privilégiés pour des activités malveillantes menaçant la vie privée des utilisateurs. Les paramètres de sécurité disponibles sur les réseaux sociaux n'empêchent pas les attaques par inférence d'attribut, qui consistent pour l'attaquant à obtenir des données privées (comme le genre) à partir d'informations publiques. La divulgation d'une information personnelle peut avoir des conséquences négatives comme devenir la
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Farrar, Rebekah L. "Bayesian Inference via Low-Dimensional Manifolds for Attributed Data Points." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1367415154.

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Books on the topic "Attribute inference"

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Goodman, Steven N., and Jonathan M. Samet. Causal Inference in Cancer Epidemiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0007.

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Judgments about causality are central to the development of interventions intended to reduce exposure to risk factors that cause cancer. Because causation is not directly observable in medicine, scientists and philosophers have had to develop sets of constructs and heuristics that define “cause” operationally. The criteria in this framework, often attributed to the British medical statistician Sir Austin Bradford Hill or to the 1964 Report of the US Surgeon General on tobacco, include consistency, strength of association, specificity, temporality, coherence/plausibility/analogy, biological gra
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Hunter, David. On Believing. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192859549.001.0001.

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This book develops original accounts of the logical, psychological, and normative aspects of belief, grounded in ontological views that put the believer at the heart of the story. Hunter argues that to believe something is to be in position to do, think, and feel things in light of a possibility whose obtaining would make one right. The logical aspect is that being right depends only on whether that possibility obtains. The psychological one concerns how that possibility can rationalize what one does, thinks, and feels. But, Hunter argues, beliefs are not causes, capacities, or dispositions. R
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Book chapters on the topic "Attribute inference"

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Moustapha Askia, Khalid, and Marie-Jean Meurs. "Personalized Student Attribute Inference." In Advances in Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_56.

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Li, Fangyi, and Qiang Shen. "Attribute-Weighted Fuzzy Interpolative Reasoning." In Fuzzy Rule-Based Inference. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0491-0_6.

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Li, Fangyi, and Qiang Shen. "Attribute-Weighted Fuzzy Rule-Based Inference." In Fuzzy Rule-Based Inference. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0491-0_5.

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Li, Fangyi, and Qiang Shen. "Attribute Weighting and Weighted Fuzzy Rule Bases." In Fuzzy Rule-Based Inference. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0491-0_4.

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Kwatra, Saloni, and Vicenç Torra. "Empirical Evaluation of Synthetic Data Created by Generative Models via Attribute Inference Attack." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57978-3_18.

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AbstractThe disclosure risk of synthetic/artificial data is still being determined. Studies show that synthetic data generation techniques generate similar data to the original data and sometimes even the exact original data. Therefore, publishing synthetic datasets can endanger the privacy of users. In our work, we study the synthetic data generated from different synthetic data generation techniques, including the most recent diffusion models. We perform a disclosure risk assessment of synthetic datasets via an attribute inference attack, in which an attacker has access to a subset of publicly available features and at least one synthesized dataset, and the aim is to infer the sensitive features unknown to the attacker. We also compute the predictive accuracy and F1 score of the random forest classifier trained on several synthetic datasets. For sensitive categorical features, we show that Attribute Inference Attack is not highly feasible or successful. In contrast, for continuous attributes, we can have an approximate inference. This holds true for the synthetic datasets derived from Diffusion models, GANs, and DPGANs, which shows that we can only have approximated Attribute Inference, not the exact Attribute Inference.
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Idan, Lihi. "Temporal-Attribute Inference Using Dynamic Bayesian Networks." In Computational Science – ICCS 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08754-7_67.

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Starkie, Bradford. "Inferring Attribute Grammars with Structured Data for Natural Language Processing." In Grammatical Inference: Algorithms and Applications. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45790-9_19.

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Uesaka, Yuri, Shun Saso, and Takeshi Akisawa. "How Can We Statistically Analyze the Achievement of Diagrammatic Competency from High School Regular Tests?" In Diagrammatic Representation and Inference. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86062-2_57.

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AbstractOwing to the recent global changes in education goals, students nowadays need to achieve ‘key competencies’ in school. ‘Diagrammatic competency’ is an essential part of such competencies. To cultivate diagrammatic competency, it is necessary to evaluate teachers and students and provide feedback on the students’ degree of achieving diagrammatic competency. Regular school tests can provide useful opportunities for assessing such achievement. However, in such tests, Japanese high schools mainly focus on evaluating the understanding of learning contents rather than the development of competencies (such as diagrammatic competency). The current study was a collaboration between educational psychologists and a high school mathematics teacher. Together they modified a regular school test to incorporate tasks that require diagrammatic competency to solve them, thus enabling the assessment of such achievement. The study was conducted in an actual high school. The students’ performance was analyzed using cognitive diagnostic models [1], which statistically estimate how well students have mastered the elements of cognitive abilities and skills required to solve problems, generating ‘attribute mastery probabilities’. The attribute mastery probabilities obtained demonstrated that students’ achievement of diagrammatic competency was insufficient, indicating a need for cultivating such competency in subject learning instruction provided in schools.
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Medvet, Eric, Alberto Bartoli, Barbara Carminati, and Elena Ferrari. "Evolutionary Inference of Attribute-Based Access Control Policies." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15934-8_24.

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Ideno, Takashi, Masahiro Morii, Kazuhisa Takemura, and Mitsuhiro Okada. "On Effects of Changing Multi-attribute Table Design on Decision Making: An Eye-Tracking Study." In Diagrammatic Representation and Inference. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54249-8_29.

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Conference papers on the topic "Attribute inference"

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Li, Junliang, Yajun Yang, Qinghua Hu, Xin Wang, and Hong Gao. "Hierarchical Label Inference Incorporating Attribute Semantics in Attributed Networks." In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 2023. http://dx.doi.org/10.1109/icdm58522.2023.00129.

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Li, Feng, Xuehui Du, Aodi Liu, and Liu Zhang. "Image Security Attribute Inference Method Based on Class Attribute Portrait." In 2023 3rd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT). IEEE, 2023. http://dx.doi.org/10.1109/icfeict59519.2023.00011.

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Middelkoop, Arie, Atze Dijkstra, and S. Doaitse Swierstra. "Iterative type inference with attribute grammars." In the ninth international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1868294.1868302.

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Ishigure, Hayato, Atsuko Mutoh, Tohgoroh Matsui, and Nobuhiro Inuzuka. "Concept lattice reduction using attribute inference." In 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE). IEEE, 2015. http://dx.doi.org/10.1109/gcce.2015.7398625.

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Wu, Le, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, and Meng Wang. "Joint Item Recommendation and Attribute Inference." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2020. http://dx.doi.org/10.1145/3397271.3401144.

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Jayaraman, Bargav, and David Evans. "Are Attribute Inference Attacks Just Imputation?" In CCS '22: 2022 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2022. http://dx.doi.org/10.1145/3548606.3560663.

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Yang, Yitong, Qixiao Lin, Jian Mao, and Lipei Liu. "An improved social attribute inference scheme based on multi-attribute correlation." In 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI). IEEE, 2021. http://dx.doi.org/10.1109/swc50871.2021.00057.

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Chen, Hui, Guiguang Ding, Zijia Lin, Sicheng Zhao, and Jungong Han. "Show, Observe and Tell: Attribute-driven Attention Model for Image Captioning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/84.

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Despite the fact that attribute-based approaches and attention-based approaches have been proven to be effective in image captioning, most attribute-based approaches simply predict attributes independently without taking the co-occurrence dependencies among attributes into account. Besides, most attention-based captioning models directly leverage the feature map extracted from CNN, in which many features may be redundant in relation to the image content. In this paper, we focus on training a good attribute-inference model via the recurrent neural network (RNN) for image captioning, where the c
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Dawkins, Hillary. "Marked Attribute Bias in Natural Language Inference." In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-acl.369.

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Ryu, Eunsu, Yao Rong, Jie Li, and Ashwin Machanavajjhala. "curso: protect yourself from curse of attribute inference." In the ACM SIGMOD Workshop. ACM Press, 2013. http://dx.doi.org/10.1145/2484702.2484706.

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Reports on the topic "Attribute inference"

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Araujo, María Caridad, Marta Rubio-Codina, and Norbert Schady. 70 to 700 to 70,000: Lessons from the Jamaica Experiment. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003210.

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This document compares three versions of the same home visiting model, the well-known Jamaica model, which was gradually scaled-up from an efficacy trial (proof of concept) in Jamaica, to a pilot in Colombia, to an at-scale program in Peru. It first describes the design, implementation and impacts of these three programs. Then, it analyzes the threats to scalability in each of these experiences and discusses how they could have affected program outcomes, with a focus on three of the elements of the economic model of scaling in Al-Ubaydli, et al. (Forthcoming): appropriate statistical inference
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Stewart, Jonathan, Grace Parker, Joseph Harmon, et al. Expert Panel Recommendations for Ergodic Site Amplification in Central and Eastern North America. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2017. http://dx.doi.org/10.55461/tzsy8988.

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The U.S. Geological Survey (USGS) national seismic hazard maps have historically been produced for a reference site condition of VS30 = 760 m/sec (where VS30 is time averaged shear wave velocity in the upper 30 m of the site). The resulting ground motions are modified for five site classes (A-E) using site amplification factors for peak acceleration and ranges of short- and long-oscillator periods. As a result of Project 17 recommendations, this practice is being revised: (1) maps will be produced for a range of site conditions (as represented by VS30 ) instead of a single reference condition;
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