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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Declarative Reasoning on Explanations Using Constraint Logic Programming 18th

Declarative Reasoning on Explanations Using Constraint Logic...

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18th European Conference on Logics in artificial intelligence (JELIA)

作者： State, Laura Ruggieri, Salvatore Turini, Franco Univ Pisa Pisa Italy Scuola Normale Super Pisa Pisa Italy

ISBN: (纸本)9783031436185;9783031436192

Explaining opaque Machine Learning (ML) models is an increasingly relevant problem. Current explanation in AI (XAI) methods suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of abstraction and interactivity with the user. We propose reasonx, an explanation method based on Constraint Logic Programming (CLP). reasonx can provide declarative, interactive explanations for decision trees, which can be the ML models under analysis or global/local surrogate models of any black-box model. Users can express background or common sense knowledge using linear constraints and MILP optimization over features of factual and contrastive instances, and interact with the answer constraints at different levels of abstraction through constraint projection. We present here the architecture of reasonx, which consists of a Python layer, closer to the user, and a CLP layer. reasonx's core execution engine is a Prolog meta-program with declarative semantics in terms of logic theories.

关键词： Constraint programming

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Exploring Mental Models for Explainable artificial intelligence: Engaging Cross-disciplinary Teams Using a Design Thinking Approach 4th

Exploring Mental Models for Explainable Artificial Intellige...

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4th International Conference on artificial intelligence in HCI (AI-HCI) Held as Part of the 25th International Conference on Human-Computer Interaction (HCII)

作者： Sheridan, Helen Murphy, Emma O'Sullivan, Dympna TU Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9783031358906;9783031358913

Exploring end-users' understanding of artificial intelligence (AI) systems' behaviours and outputs is crucial in developing accessible Explainable artificial intelligence (XAI) solutions. Investigating mental models of AI systems is core in understanding and explaining the often opaque, complex, and unpredictable nature of AI. Researchers engage surveys, interviews, and observations for software systems, yielding useful evaluations. However, an evaluation gulf still exists, primarily around comprehending end-users' understanding of AI systems. It has been argued that by exploring theories related to human decision-making examining the fields of psychology, philosophy, and human computer interaction (HCI) in a more people-centric rather than product or technology-centric approach can result in the creation of initial XAI solutions with great potential. Our work presents the results of a design thinking workshop with 14 cross-collaborative participants with backgrounds in philosophy, psychology, computer science, AI systems development and HCI. Participants undertook design thinking activities to ideate how AI system behaviours may be explained to end-users to bridge the explanation gulf of AI systems. We reflect on design thinking as a methodology for exploring end-users' perceptions and mental models of AI systems with a view to creating effective, useful, and accessible XAI.

关键词： artificial intelligence Explainable artificial intelligence Human Computer Interaction Design Thinking

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Inverse Problem of Censored Markov Chain: Estimating Markov Chain Parameters from Censored Transition Data 27th

Inverse Problem of Censored Markov Chain: Estimating Markov ...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Kohjima, Masahiro Kurashima, Takeshi Toda, Hiroyuki NTT Corp NTT Human Informat Labs Yokohama Kanagawa Japan

ISBN: (纸本)9783031333767;9783031333774

Due to the difficulty of collecting comprehensive data sets (factors include limited GPS coverage and the existence of competitors), most transition data collected from, for example, people and automobiles, have been censored, and so record only the visits of known observable states (locations). In this paper, we tackle the problem of estimating Markov chain parameters from censored transition data. Our parameter estimation method utilizes the theory of the censored Markov chain, the Markov chain that has unobservable states. Our problem formulation can be seen as the inverse of existing studies that construct censored Markov chains from (original) Markov chains and unobservable states. We confirm the effectiveness of the proposal by experiments on synthetic and real data sets.

关键词： probabilistic models markov chain censored transition data inverse problem

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Dealing with Data Scarcity in Rare Diseases: Dynamic Bayesian Networks and Transfer Learning to Develop Prognostic Models of Amyotrophic Lateral Sclerosis 21st

Dealing with Data Scarcity in Rare Diseases: Dynamic Bayesia...

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21st International Conference on artificial intelligence in Medicine (AIME)

作者： Longato, Enrico Tavazzi, Erica Chio, Adriano Mora, Gabriele Sparacino, Giovanni Di Camillo, Barbara Univ Padua Dept Informat Engn Padua Italy Univ Torino Neurosci Dept Rita Levi Montalcini Turin Italy Univ Padua Dept Comparat Biomed & Food Sci Padua Italy

ISBN: (纸本)9783031343438;9783031343445

The extremely low prevalence of rare diseases exacerbates many of the typical challenges to prognostic model development, resulting, at the same time, in low data availability and difficulties in procuring additional data due to, e.g., privacy concerns over the risk of patient reidentification. Yet, developing prognostic models with possibly limited in-house data is often of interest for many applications (e.g., prototyping, hypothesis confirmation, exploratory analyses). Several options exist beyond simply training a model with the available data: data from a larger database might be acquired;or, lacking that, to sidestep limitations to data sharing, one might resort to simulators based, e.g., on dynamic Bayesian networks (DBNs). Additionally, transfer learning techniques might be applied to integrate external and in-house data sources. Here, we compare the effectiveness of these strategies in developing a predictive model of 3-year mortality in amyotrophic lateral sclerosis (ALS, a rare neurodegenerative disease with <0.01% prevalence) using the in-house dataset of a single ALS clinic in Milan, Italy (N= 116). We test several combinations of direct and transfer-learning-mediated development based on additional real data from the Italian PARALS register (N= 568). We also train two DBNs, one for each dataset, and use them to simulate large numbers of virtual subjects whose variables are linked by the same probabilistic relationships as in the real data. We show that, compared to a baseline model developed on the smaller dataset (AUROC= 0.633), the largest performance increase was obtained using data simulated using a DBN trained on the larger PARALS register (AUROC= 0.734).

关键词： Amyotrophic Lateral Sclerosis Dynamic Bayesian Networks Transfer Learning

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What Boosts Fake News Dissemination on Social Media? A Causal Inference View 1

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Li, Yichuan Lee, Kyumin Kordzadeh, Nima Guo, Ruocheng Worcester Polytech Inst Worcester MA 01605 USA Bytedance Res London England

ISBN: (数字)9783031333835

ISBN: (纸本)9783031333828;9783031333835

There has been an upward trend of fake news propagation on social media. To solve the fake news propagation problem, it is crucial to understand which media posts (e.g., tweets) cause fake news to disseminate widely, and further what lexicons inside a tweet play essential roles for the propagation. However, only modeling the correlation between social media posts and dissemination will find a spurious relationship between them, provide imprecise dissemination prediction, and incorrect important lexicons identification because it did not eliminate the effect of the confounder variable. Additionally, existing causal inference models cannot handle numerical and textual covariates simultaneously. Thus, we propose a novel causal inference model that combines the textual and numerical covariates through soft-prompt learning, and removes irrelevant information from the covariates by conditional treatment generation toward learning effective confounder representation. Then, the model identifies critical lexicons through a post-hoc explanation method. Our model achieves the best performance against baseline methods on two fake news benchmark datasets in terms of dissemination prediction and important lexicon identification related to the dissemination. The code is available at https://***/bigheiniu/CausalFakeNews.

关键词： Causal inference on text Fake news propagation

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Some Properties of the Left Recursive Form of the Convex Combination Linguistic Aggregator 15th

Some Properties of the Left Recursive Form of the Convex Com...

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15th International Conference on Flexible Query Answering Systems (FQAS)

作者： Huitzil, Ignacio Bobillo, Fernando Univ Milano Bicocca Lab IKR3 Milan Italy Univ Zaragoza Zaragoza Spain Aragon Inst Engn Res I3A Zaragoza Spain

ISBN: (纸本)9783031429347;9783031429354

The Left Recursive Form of the Convex Combination (CONV-LRF) is a linguistic aggregation operator which has been recently proposed in the framework of fuzzy ontology learning. The objective of this paper is to study some properties of the operator, namely commutativity, smoothness, internality, monotonicity, and orness. Our results show that, contrary to what preliminary experiments suggested, CONV-LRF has a similar orness that the Right Recursive Form (CONV-RRF).

关键词： Fuzzy Logic Linguistic Aggregation Fuzzy Ontology Building

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Efficient Simulation of Pollutant Dispersion Using Machine Learning 18th

Efficient Simulation of Pollutant Dispersion Using Machine L...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Carvalho, Guido F. M. G. Correa, Douglas F. Pelta, David A. Knupp, Diego C. Silva Neto, Antonio J. Univ Estado Rio de Janeiro Nova Friburgo Brazil Univ Granada Dept Comp Sci & AI Granada Spain

ISBN: (纸本)9783031407246;9783031407253

An efficient and accurate system capable of predicting contaminant source location is important for environmental monitoring and security systems. However, one of the main challenges in developing such a system is the high computational cost associated with modeling the physical aspects of atmospheric dispersion. The present work addresses this issue by proposing a faster solution that uses a Multi Layer Perceptron (MLP) Neural Network to predict sensor readings based on the source location coordinates. The MLP is trained using synthetic data generated by solving the advection diffusion equation numerically, and collected at predetermined sensor locations and time. The proposed method generates accurate predictions in a fraction of the time required for the conventional method. This approach can help in detecting and identifying contaminants quickly and efficiently, and it has potential applications in environmental monitoring and security systems, making it an important tool for protecting public health and safety.

关键词： Pollution Dispersion Advection-Diffusion Equation Finite Difference Method Multi Layer Perceptron Neural Network Environmental Monitoring

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Decentralized Web-Based Clinical Decision Support Using Semantic GLEAN Workflows 21st

Decentralized Web-Based Clinical Decision Support Using Sema...

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21st International Conference on artificial intelligence in Medicine (AIME)

作者： Van Woensel, William Abidi, Samina Abidi, Syed Sibte Raza Univ Ottawa Telfer Sch Management Ottawa ON Canada Dalhousie Univ Med Informat Fac Med Halifax NS Canada Dalhousie Univ NICHE Res Grp Halifax NS Canada

ISBN: (纸本)9783031343438;9783031343445

Decentralized clinical decision support (CDS), using the Web browser as a local application platform, fully decouples the CDS from vendor-specific EMR, removes reliance on server infrastructure, and does not require custom software. Using GLEAN, a clinical workflow can be loaded within aWeb browser to provide decentralized and specialized CDS at a point-of-care. To that end, GLEAN workflows include all knowledge needed for their local execution;the standards-based and secure data sharing with EMR, if needed;and detection of multimorbidity conflicts. This specialized CDS will execute all decision logic locally in the Web browser;using SMART-on-FHIR, locally entered data can be securely submitted to a FHIR-compliant EMR, and remote data can be retrieved. In such a decentralized setting, clinicians can securely collaborate on multimorbidity patients: (1) by sharing workflow traces, i.e., progression of their local workflows, other clinicians can keep appraised of their decision making;and (2) by leveraging medical online knowledge sources, conflicts (e.g., drug-drug, drug-interaction) between multimorbidity decisions can be detected and resolved.

关键词： Clinical Decision Support Multimorbidity Semantic Web Notation3

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F3: Fair and Federated Face Attribute Classification with Heterogeneous Data 27th

F3: Fair and Federated Face Attribute Classification with He...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Kanaparthy, Samhita Padala, Manisha Damle, Sankarshan Sarvadevabhatla, Ravi Kiran Gujar, Sujit Int Inst Informat Technol IIIT Hyderabad India

ISBN: (纸本)9783031333736;9783031333743

Fairness across different demographic groups is an essential criterion for face-related tasks, Face Attribute Classification (FAC) being a prominent example. Simultaneously, federated Learning (FL) is gaining traction as a scalable paradigm for distributed training. In FL, client models trained on private datasets get aggregated by a central aggregator. Existing FL approaches require data homogeneity to ensure fairness. However, this assumption is restrictive in real-world settings. E.g., geographically distant or closely associated clients may have heterogeneous data. In this paper, we observe that existing techniques for ensuring fairness are not viable for FL with data heterogeneity. We introduce F3, an FL framework for fair FAC under data heterogeneity. We propose two methodologies in F3, (i) Heuristic-based and (ii) Gradient-based, to improve fairness across demographic groups without requiring data homogeneity assumption. We demonstrate the efficacy of our approaches through empirically observed fairness measures and accuracy guarantees on popular face datasets. Using Mahalanobis distance, we show that F3 obtains a practical balance between accuracy and fairness for FAC. The code is available at: ***/magnetar-iiith/F3.

关键词： Fairness Federated Learning Data Heterogeneity

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An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance 27th

An Extended Variational Mode Decomposition Algorithm Develop...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Rudd, David Hason Huo, Huan Xu, Guandong Univ Technol Sydney 15 Broadway Ultimo Australia Data Sci Inst 15 Broadway Ultimo Australia

ISBN: (纸本)9783031333798;9783031333804

Emotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis. Valuable information underlying the emotions are significant for human-computer interactions enabling intelligent machines to interact with sensitivity in the real world. Previous ER studies through speech signal processing have focused exclusively on associations between different signal mode decomposition methods and hidden informative features. However, improper decomposition parameter selections lead to informative signal component losses due to mode duplicating and mixing. In contrast, the current study proposes VGG-optiVMD, an empowered variational mode decomposition algorithm, to distinguish meaningful speech features and automatically select the number of decomposed modes and optimum balancing parameter for the data fidelity constraint by assessing their effects on the VGG16 flattening output layer. Various feature vectors were employed to train the VGG16 network on different databases and assess VGG-optiVMD reproducibility and reliability. One, two, and three-dimensional feature vectors were constructed by concatenating Mel-frequency cepstral coefficients, Chromagram, Mel spectrograms, Tonnetz diagrams, and spectral centroids. Results confirmed a synergistic relationship between the fine-tuning of the signal sample rate and decomposition parameters with classification accuracy, achieving state-of-the-art 96.09% accuracy in predicting seven emotions on the Berlin EMO-DB database.

关键词： Speech emotion recognition (SER) Variational mode decomposition (VMD) Sound signal processing Convolutional neural network (CNN) Acoustic features

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