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Bayesian Co-Clustering Truth Discovery for Mobile Crowd Sensing Systems

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第2期16卷 1045-1057页

作者： Du, Yang Sun, Yu-E Huang, He Huang, Liusheng Xu, Hongli Bao, Yu Guo, Hansong Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Soochow Univ Sch Rail Transportat Suzhou 215006 Peoples R China Anhui Prov Key Lab Network & Informat Secur Wuhu 240002 Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Univ Sci & Technol China Sch Software Engn Suzhou 215123 Peoples R China

With the proliferation of mobile devices, mobile crowd sensing (MCS) has emerged as a new data collection paradigm, which allows the crowd to act as sensors and contribute their observations about entities. Unfortunately, users with varied skills and motivations may provide conflicting information for the same entity. Existing work solves this problem by estimating user reliability and inferring the correct observations (i.e., truths). However, these methods assume that users' expertise degrees are dependent on the truths, but ignore the finer clusters that exist even in the entities with the same truths. To capture users' fine-grained reliability on different entity clusters, we propose a novel Bayesian co-clustering truth discovery model for the task of observation aggregation. This model enables us to produce a more precise estimation while taking into account the entity clusters and the user clusters. Experiments on four real-world datasets reveal that our method outperforms the state-of-the-art approaches in terms of accuracy and F1-score.

关键词： Mobile crowd sensing probabilistic graphical model truth discovery

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CSRNCVA: A model OF CROSS-MEDIA SEMANTIC RETRIEVAL BASED ON NEURAL COMPUTING OF VISUAL AND AUDITORY SENSATIONS

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NEURAL NETWORK WORLD 2018年第4期28卷 305-323页

作者： Liu, Y. Cai, K. Liu, C. Zheng, F. Henan Univ Intelligent Technol & Applicat Engn Res Ctr Henan Key Lab Big Data Anal & Proc Henan Prov Kaifeng 475004 Peoples R China Henan Univ Coll Comp Sci & Informat Engn Kaifeng 475004 Peoples R China Henan Univ Intelligent Technol & Applicat Engn Res Ctr Henan Kaifeng 475004 Peoples R China Henan Univ Key Lab Big Data Anal & Proc Henan Prov Kaifeng 475004 Peoples R China Henan Univ Engn Lab Spatial Informat Henan Prov Kaifeng 475004 Peoples R China

Cross-media semantic retrieval (CSR) and cross-modal semantic mapping are key problems of the multimedia search engine. The cognitive function and neural structure for visual and auditory information process are an important reference for the study of brain-inspired CSR. In this paper, we analyze the hierarchy, the functionality and the structure of visual and auditory in the brain. Considering an idea from deep belief network and hierarchical temporal memory, we presented a brain-inspired intelligent model, called cross-media semantic retrieval based on neural computing of visual and auditory sensation (CSRNCVA). Algorithms based on CSRNCVA were developed. It employs belief propagation algorithms of probabilistic graphical model and hierarchical learning. The experiments show that our model and algorithms can be effectively applied to the CSR. This work provides an important significance for brain-inspired cross-media intelligence framework.

关键词： cross-media cognitive neural computing cross-media semantic retrieval deep belief network hierarchical temporal memory probabilistic graphical model hierarchical reinforcement learning

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modeling concepts and their relationships for corpus-based query auto-completion

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OPEN COMPUTER SCIENCE 2019年第1期9卷 212-225页

作者： Rossiello, Gaetano Caputo, Annalina Basile, Pierpaolo Semeraro, Giovanni Thomas J Watson Res Ctr IBM Res AI Yorktown Hts NY 10598 USA Trinity Coll Dublin ADAPT Ctr Sch Comp Sci & Stat Dublin Ireland Univ Bari Aldo Moro Dept Comp Sci Bari Italy

Query auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web scale scenarios, where usually huge amounts of past user queries can be analyzed to provide reliable suggestions. However, when query logs are not available, e.g. in enterprise or desktop search engines, these methods are not applicable at all. To face these challenging scenarios, we present a novel corpus-based approach which exploits the textual content of an indexed document collection in order to dynamically generate query completions. Our method extracts informative text fragments from the corpus and it combines them using a probabilistic graphical model in order to capture the relationships between the extracted concepts. Using this approach, it is possible to automatically complete partial queries with significant suggestions related to the keywords already entered by the user without requiring the analysis of the past queries. We evaluate our system through a user study on two different real-world document collections. The experiments show that our method is able to provide meaningful completions outperforming the state-of-the art approach.

关键词： query auto-completion information retrieval information extraction probabilistic graphical model

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Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization

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KUNSTLICHE INTELLIGENZ 2015年第4期29卷 363-368页

作者： Kersting, Kristian Natarajan, Sriraam TU Dortmund Univ Dortmund Germany Indiana Univ Bloomington IN USA

Statistical Relational AI-the science and engineering of making intelligent machines acting in noisy worlds composed of objects and relations among the objects-is currently motivating a lot of new AI research and has tremendous theoretical and practical implications. Theoretically, combining logic and probability in a unified representation and building general-purpose reasoning tools for it has been the dream of AI, dating back to the late 1980s. Practically, successful statistical relational AI tools enable new applications in several large, complex realworld domains including those involving big data, natural text, social networks, the web, medicine and robotics, among others. Such domains are often characterized by rich relational structure and large amounts of uncertainty. Logic helps to faithfully model the former while probability helps to effectively manage the latter. Our intention here is to give a brief (and necessarily incomplete) overview and invitation to the emerging field of Statistical Relational AI from the perspective of acting optimally and learning to act.

关键词： Inductive Logic Programming Logical Query Markov Decision Process probabilistic graphical model Situation Calculus

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A Bayesian Approach to Constructing probabilistic models from Knowledge Graphs

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2024年第1期18卷 25-49页

作者： Freedman, Hayden Metzger, Jacob Abolhassani, Neda Tudor, Ana Tomlinson, Bill Paul, Sanjoy Univ Calif Irvine Dept Informat Donald Bren Hall 5019 Irvine CA 92651 USA Accenture Labs Syst & Platforms 415 Mission St Floor 35 San Francisco CA 94105 USA Univ Calif Irvine Dept Informat Donald Bren Hall 5019 Irvine CA 92651 USA Accenture Labs Syst & Platforms 415 Mission St Floor 35 San Francisco CA 94105 USA

Making predictions bounded by uncertainty is a crucial component of modern decision support systems. Knowledge graphs provide a structured, semantic-oriented approach to data integration, storage and retrieval;however, they represent data in an absolute way and do not natively support reasoning under uncertain future conditions. In this paper, we present a novel technique for building a probabilistic model from a knowledge graph using a Bayesian network, which is a step towards enabling probabilistic reasoning under uncertainty within a knowledge graph. Our approach supports mixing of continuously and discretely-distributed variables, which is necessary for jointly processing real-world data, but has not typically been supported in prior work. As an interface to the probabilistic model, we propose an extension of the SPARQL query language called Orion DSL, which is currently a working prototype. We also define a custom probabilistic ontology in order to store outputs of the model directly in the knowledge graph alongside the original data, which we refer to as a probabilistic Knowledge Graph (PKG). The evaluation shows that: (1) the dependencies and distributions of data in a synthetically-generated knowledge graph were accurately captured by the Bayesian model, and (2) an SPARQL query against the PKG to retrieve computed probability distributions was orders of magnitude more performant than a similarly-intentioned query against the base knowledge graph. We anticipate that the models generated by this system will have applications in the areas of predicting missing data, approximate query processing, and utility-based optimization. Future work will involve more detailed explorations of each of these topics as we work towards a PKG-based decision support system.

关键词： probabilistic knowledge graph probabilistic graphical model Bayesian network ontology

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Generating realistic assemblages with a joint species distribution model

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METHODS IN ECOLOGY AND EVOLUTION 2015年第4期6卷 465-473页

作者： Harris, David J. Ctr Populat Biol Davis CA 95616 USA

Species distribution models (SDMs) represent important analytical and predictive tools for ecologists. Until now, these models have either assumed (i) that species' occurrence probabilities are uncorrelated or (ii) that species respond linearly to preselected environmental variables. These two assumptions currently prevent ecologists from modelling assemblages with realistic co-occurrence and species richness properties. This paper introduces a stochastic feedforward neural network, called mistnet', which makes neither assumption. Thus, unlike most SDMs, mistnet can account for non-independent co-occurrence patterns driven by unobserved environmental heterogeneity. And unlike several recently proposed joint SDMs, the model can also learn nonlinear functions relating species' occurrence probabilities to environmental predictors. Mistnet makes more accurate predictions about the North American bird communities found along Breeding Bird Survey transects than several alternative methods tested. In particular, typical assemblages held out of sample for validation were each tens of thousands of times more likely under the mistnet model than under independent combinations of single-species predictions. Apart from improved accuracy, mistnet shows two other important benefits for ecological research and management. First: by analysing co-occurrence data, mistnet can identify unmeasured - and perhaps unanticipated - environmental variables that drive species turnover. For example, the model identified a strong grassland/forest gradient, even though only temperature and precipitation were given as model inputs. Second: mistnet is able to take advantage of outside information to guide its predictions towards more realistic assemblages. For example, mistnet automatically adjusts its expectations to include more forest-associated species in response to a stray observation of a forest-dwelling warbler.

关键词： birds breeding bird survey neural network probabilistic graphical model species assemblages species distribution model

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Moments of the Count of a Regular Expression in a Heterogeneous Random Sequence

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2019年第3期21卷 875-887页

作者： Nuel, G. Sorbonne Univ Natl Inst Math Sci INSMI Probabil & Stat LPSM CNRS 8001LPMACNRS 7599 F-75005 Paris France

We focus here on the distribution of the random count N of a regular expression in a multi-state random sequence generated by a heterogenous Markov source. We first briefly recall how classical Markov chain embedding techniques allow reducing the problem to the count of specific transitions in a (heterogenous) order 1 Markov chain over a deterministic finite automaton state space. From this result we derive the expression of both the mgf/pgf of N as well as the factorial and non-factorial moments of N. We then introduce the notion of evidence-based constraints in this context. Following the classical forward/backward algorithm in hidden Markov models, we provide explicit recursions allowing to compute the mgf/pgf of N under the evidence constraint. All the results presented are illustrated with a toy example.

关键词： Probability generating function Moment generating function probabilistic graphical model Bayesian network Sum-product algorithm Forward backward algorithms

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Bayesian accounts of covert selective attention: A tutorial review

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ATTENTION PERCEPTION & PSYCHOPHYSICS 2015年第4期77卷 1013-1032页

作者： Vincent, Benjamin T. Univ Dundee Sch Psychol Dundee Scotland

Decision making and optimal observer models offer an important theoretical approach to the study of covert selective attention. While their probabilistic formulation allows quantitative comparison to human performance, the models can be complex and their insights are not always immediately apparent. Part 1 establishes the theoretical appeal of the Bayesian approach, and introduces the way in which probabilistic approaches can be applied to covert search paradigms. Part 2 presents novel formulations of Bayesian models of 4 important covert attention paradigms, illustrating optimal observer predictions over a range of experimental manipulations. graphical model notation is used to present models in an accessible way and Supplementary Code is provided to help bridge the gap between model theory and practical implementation. Part 3 reviews a large body of empirical and modelling evidence showing that many experimental phenomena in the domain of covert selective attention are a set of by-products. These effects emerge as the result of observers conducting Bayesian inference with noisy sensory observations, prior expectations, and knowledge of the generative structure of the stimulus environment.

关键词： Covert attention Signal detection theory Bayesian Optimal observer probabilistic graphical model

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Predictive coding and thought

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SYNTHESE 2020年第4期197卷 1749-1775页

作者： Williams, Daniel Univ Cambridge Fac Philosophy Wychfield Site Trinity Hall Cambridge CB3 0DZ England Monash Univ Cognit & Philosophy Lab Clayton Vic Australia

Predictive processing has recently been advanced as a global cognitive architecture for the brain. I argue that its commitments concerning the nature and format of cognitive representation are inadequate to account for two basic characteristics of conceptual thought: first, its generality-the fact that we can think and flexibly reason about phenomena at any level of spatial and temporal scale and abstraction;second, its rich compositionality-the specific way in which concepts productively combine to yield our thoughts. I consider two strategies for avoiding these objections and I argue that both confront formidable challenges.

关键词： Predictive processing Predictive coding Free-energy principle probabilistic graphical model Hierarchical probabilistic generative model Concepts Thought Higher cognition Bayesian brain

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Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2016年第515期111卷 1004-1019页

作者： Han, Sung Won Chen, Gong Cheon, Myun-Seok Zhong, Hua Korea Univ Sch Ind Management Engn Seoul South Korea Roche Innovat Ctr New York Pharmaceut Sci Pharma Early Res & Dev New York NY USA Georgia Inst Technol Atlanta GA 30332 USA NYU Dept Populat Hlth 550 1St Ave New York NY 10016 USA

graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed, graphical models, where all the edges are directed edges and contain. no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the acyclic constraints, and the presence of equivalence class from observational data. To overcome these challenges, we propose a two stage adaptive Lasso approach, called NS-DIST, which performs neighborhood selection (NS) in stage 1, and then estimates DAGs by the discrete improving search with Tabu (DIST) algorithm within the selected neighborhood. Simulation studies are presented to demonstrate the effectiveness of the method and its computational efficiency. Two real data examples are used to demonstrate the practical usage of our method for gene regulatory network inference. Supplementary materials for this article are available online.

关键词： Directed acyclic graphs Lasso estimation Neighborhood selection probabilistic graphical model Structure equation model

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