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

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Toward Interpretable Machine Learning: Constructing Polynomial Models Based on Feature Interaction Trees 27th

Toward Interpretable Machine Learning: Constructing Polynomi...

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

作者： Jang, Jisoo Kim, Mina Bui, Tien-Cuong Li, Wen-Syan Seoul Natl Univ 1 Gwanak Ro Gwanak Gu Seoul South Korea

ISBN: (纸本)9783031333767;9783031333774

As AI has been applied in many decision-making processes, ranging from loan application approval to predictive policing, the interpretability of machine learning models is increasingly important. Interpretable models and post-hoc explainability are two approaches in eXplainable AI (XAI). We follow the argument that transparent models should be used instead of black-box ones in real-world applications, especially regarding high-stakes decisions. In this paper, we propose PolyFIT to address two major issues in XAI: (1) bridging the gap between black-box and interpretable models and (2) experimentally validating the trade-off relationship between model performance and explainability. PolyFIT is a novel polynomial model construction method assisted by the knowledge of feature interactions in black-box models. PolyFIT uses extracted feature interaction knowledge to build interaction trees, which are then transformed into polynomial models. We evaluate the predictive performance of PolyFIT with baselines using four publicly available data sets, Titanic survival, Adult income, Boston house price, and California house price. Our method outperforms linear models by 5% and 56% in classification and regression tasks on average, respectively. We also conducted usability studies to derive the trade-off relationship between model performance and explainability. The studies validate our hypotheses about the actual relationship between model performance and explainability.

关键词： eXplainable AI transparent models polynomial model explainability evaluation

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Interactive Link Prediction as a Downstream Task for Foundational GUI Understanding Models 1

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46th German Conference on artificial intelligence (KI)

作者： Johns, Christoph Albert Barz, Michael Sonntag, Daniel Aarhus Univ Aarhus Denmark German Res Ctr Artificial Intelligence DFKI Saarbrucken Germany Oldenburg Univ Appl Artificial Intelligence Oldenburg Germany

ISBN: (数字)9783031426087

ISBN: (纸本)9783031426070;9783031426087

AI models that can recognize and understand the semantics of graphical user interfaces (GUIs) enable a variety of use cases ranging from accessibility to automation. Recent efforts in this domain have pursued the development of a set of foundation models: generic GUI understanding models that can be used off-the-shelf to solve a variety of GUI-related tasks, including ones that they were not trained on. In order to develop such foundation models, meaningful downstream tasks and baselines for GUI-related use cases will be required. In this paper, we present interactive link prediction as a downstream task for GUI understanding models and provide baselines as well as testing tools to effectively and efficiently evaluate predictive GUI understanding models. In interactive link prediction, the task is to predict whether tapping on an element on one screen of a mobile application (source element) navigates the user to a second screen (target screen). If this task is solved sufficiently, it can demonstrate an understanding of the relationship between elements and components across screens and enable various applications in GUI design automation and assistance. To encourage and support research on interactive link prediction, this paper contributes (1) a pre-processed large-scale dataset of links in mobile applications (18,830 links from 5,362 applications) derived from the popular RICO dataset, (2) performance baselines from five heuristic-based and two learning-based GUI understanding models, (3) a small-scale dataset of links in mobile GUI prototypes including ratings from an online study with 36 end-users for out-of-sample testing, and (4) a Figma plugin that can leverage link prediction models to automate and assist mobile GUI prototyping.

关键词： Link prediction GUI prototyping Mobile GUIs Online machine learning

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A Novel Explainable Link Forecasting Framework for Temporal Knowledge Graphs Using Time-Relaxed Cyclic and Acyclic Rules 27th

A Novel Explainable Link Forecasting Framework for Temporal ...

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

作者： Rage, Uday Kiran Maharana, Abinash Polepalli, Krishna Reddy Univ Aizu Aizu Wakamatsu Fukushima Japan Int Inst Informat Technol Hyderabad Telangana India

ISBN: (纸本)9783031333736;9783031333743

Link forecasting in a temporal Knowledge Graph (tKG) involves predicting a future event from a given set of past events. Most previous studies suffered from reduced performance as they disregarded acyclic rules and enforced a tight constraint that all past events must exist in a strict temporal order. This paper proposes a novel explainable rule-based link forecasting framework by introducing two new concepts, namely 'relaxed temporal cyclic and acyclic random walks' and 'link-star rules'. The former concept involves generating rules by performing cyclic and acyclic random walks on a tKG by taking into account the real-world phenomenon that the order of any two events may be ignored if their occurrence time gap is within a threshold value. Link-star rules are a special class of acyclic rules generated based on the natural phenomenon that history repeats itself after a particular time. Link-star rules eliminate the problem of combinatorial rule explosion, thereby making our framework practicable. Experimental results demonstrate that our framework outperforms the state-of-the-art by a substantial margin. The evaluation measures hits@1 and mean reciprocal rank were improved by 45% and 23%, respectively.

关键词： Knowledge Graphs Graph Analytics Forecasting

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Buy One Get 14 Free: Evaluating Local Reductions for Modal Logic 29th

Buy One Get 14 Free: Evaluating Local Reductions for Modal L...

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29th International Conference on Automated Deduction (CADE)

作者： Nalon, Claudia Hustadt, Ullrich Papacchini, Fabio Dixon, Clare Univ Brasilia Dept Comp Sci Brasilia DF Brazil Univ Liverpool Dept Comp Sci Liverpool Merseyside England Lancaster Univ Leipzig Sch Comp & Commun Leipzig Germany Univ Manchester Dept Comp Sci Manchester Lancs England

ISBN: (纸本)9783031384998;9783031384981

We are interested in widening the reasoning support for propositional modal logics in the so-called modal cube. The modal cube consists of extensions of the basic modal logic K with an arbitrary combination of the modal axioms B, D, T, 4 and 5. We revisit recently developed local reductions from all logics in the modal cube to a normal form comprising sets of clausal formulae with associated modal levels. We extend these reductions further to the basic modal logic K, called definitional reductions. This enables any prover for K to be used to solve the satisfiability problem for all logics in the modal cube. We also present alternative, axiomatic, reductions based on ideas originally proposed by Kracht, providing new theoretical results and improved bounds on the size of the reductions. We compare both sets of reductions combined with state-of-the-art provers for K on a large set of parametric benchmarks for all logics in the modal cube. The results show that the provers perform better with reductions based on the clausal normal form than the axiomatic reductions.

关键词： Geometry

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Aggregation Functions and Extent Structure Preservation in Formal Concept Analysis 1

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28th International Conference on Graph-Based Representation and Reasoning (ICCS)

作者： Bejines, Carlos Lopez-Rodriguez, Domingo Ojeda-Hernandez, Manuel Univ Malaga Andalucia Tech Malaga Spain

ISBN: (数字)9783031409608

ISBN: (纸本)9783031409592;9783031409608

Formal Concept Analysis (FCA) is a mathematical framework for analysing data tables that capture the relationship between objects and attributes. The concept lattice derived from such a table is a representation of the implicit knowledge about this relationship, where each concept corresponds to a bicluster of objects and attributes. FCA has been widely used for knowledge acquisition and representation, conceptual data analysis, information retrieval and other applications. In this paper, we use an extension of the classical FCA to deal with fuzzy formal contexts, where the relationship between objects and attributes is modelled by truth values indicating the degree to which an object possesses a property or attribute. Fuzzy Formal Concept Analysis (FFCA) allows us to capture vague or imprecise information and handle uncertainty or ambiguity in data analysis. Our purpose is to use aggregation functions in order to manipulate and explore fuzzy formal concepts in different ways depending on the desired properties or criteria. In this work, we will focus on the structure of the extents of the concept lattice. We define the aggregation of fuzzy extents point-wise and study how it affects its structure. We characterise the aggregation functions that preserve the fuzzy extent structure and show that they depend on the number of objects in the context. Our results contribute to a better understanding of how aggregation functions can be used to manipulate and explore fuzzy formal concepts.

关键词： Aggregation Function Formal Concept Analysis Fuzzy Sets

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kNN-Embed: Locally Smoothed Embedding Mixtures for Multi-interest Candidate Retrieval 27th

kNN-Embed: Locally Smoothed Embedding Mixtures for Multi-int...

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

作者： El-Kishky, Ahmed Markovich, Thomas Leung, Kenny Portman, Frank Haghighi, Aria Xiao, Ying Twitter Cortex San Francisco CA 94103 USA

ISBN: (纸本)9783031333798;9783031333804

Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in later stages of recommender systems using a more complex ranking model. As the top of the recommendation funnel, it is important to retrieve a high-recall candidate set to feed into downstream ranking models. A common approach is to leverage approximate nearest neighbor (ANN) search from a single dense query embedding;however, this approach this can yield a low-diversity result set with many near duplicates. As users often have multiple interests, candidate retrieval should ideally return a diverse set of candidates reflective of the user's multiple interests. To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval. kNN-Embed represents each user as a smoothed mixture over learned item clusters that represent distinct "interests" of the user. By querying each of a user's mixture component in proportion to their mixture weights, we retrieve a high-diversity set of candidates reflecting elements from each of a user's interests. We experimentally compare kNN-Embed to standard ANN candidate retrieval, and show significant improvements in overall recall and improved diversity across three datasets. Accompanying this work, we open source a large Twitter follow-graph dataset (https://***/datasets/Twitter/TwitterFollowGraph), to spur further research in graph-mining and representation learning for recommender systems.

关键词： candidate retrieval embedding nearest neighbor, diversity

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Removing Ambiguity in Natural Language for Generating Self-Join Queries 22nd

Removing Ambiguity in Natural Language for Generating Self-J...

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22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Sawant, Pradnya Sonawane, Kavita St Francis Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9783031425073;9783031425080

Databases systems are used almost everywhere in modern life and a prior knowledge of query languages like SQL is required to interact with these systems. Generally, relational databases are very useful for storing a significant amount of the world's data. But the user must know a query language to access this data efficiently. Nowadays, Natural Language Processing is used in almost every field of human-to-machine interaction. Mapping natural language into the form of structured query Language (SQL) has many benefits. An interface that uses natural language processing allows the user to interact with the relational database as if the communication is happening between human beings. There are different models available for mapping the natural language to SQL addressing various types of SQL queries. But some of the SQL queries like self join where the table needs to join with itself for generating the results, can not be handled by these existing state-of-theart models as they require some common sense knowledge. And because of these ambiguities in natural language, converting natural language to SQL becomes a complex task. In this paper, a novel approach using word sense disambiguation, for understanding the context of the natural language question addressing self-join queries is being proposed. This proposed approach can be integrated with existing text to SQL models to understand the natural language questions which require common sense knowledge.

关键词： Word Sense Disambiguation Self-Join Synset Glosses

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Utilization of Vision Transformer for Classification and Ranking of Video Distortions 10th

Utilization of Vision Transformer for Classification and Ran...

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10th IAPR TC3 workshop on artificial Neural Networks for Pattern Recognition (ANNPR)

作者： AlDahoul, Nouar Karim, Hezerul Abdul Tan, Myles Joshua Toledo Multimedia Univ Fac Engn Cyberjaya Malaysia Univ St La Salle Dept Nat Sci Bacolod Philippines Univ St La Salle Dept Chem Engn Bacolod Philippines Univ St La Salle Dept Elect Engn Bacolod Philippines

ISBN: (纸本)9783031206498;9783031206504

The perceptual quality of video surveillance footage has impacts on several tasks involved in the surveillance process, such as the detection of anomalous objects. The videos captured by a camera are prone to various distortions such as noise, smoke, haze, low or uneven illumination, blur, rain, and compression, which affect visual quality. Automatic identification of distortions is important when enhancing video quality. Video quality assessment involves two stages: (1) classification of distortions affecting the video frames and (2) ranking of these distortions. A novel video dataset was utilized for training, validating, and testing. Working with this dataset was challenging because it included nine categories of distortions and four levels of severity. The greatest challenge was the availability of multiple types of distortions in the same video. The work presented in this paper addresses the problem of multi-label distortion classification and ranking. A vision transformer was used for feature learning. The experiment showed that the proposed solution performed well in terms of F1 score of single distortion (77.9%) and F1 score of single and multiple distortions (69.9%). Moreover, the average accuracy of level classification was 62% with an average F1 score of 61%.

关键词： Distortion classification and ranking Multi-label classification Video quality assessment Vision transformer

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From Fragmented Data to Collective intelligence: A Data Fabric Approach for University Knowledge Management 15th

From Fragmented Data to Collective Intelligence: A Data Fabr...

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15th International Conference on Computational Collective intelligence (ICCCI)

作者： Nguyen, Lan T. K. Nguyen, Hoa N. Nguyen, Son H. VNU Univ Social Sci & Human Hanoi Vietnam VNU Univ Engn & Technol Hanoi Vietnam VNU Digital Knowledge Hanoi Vietnam

ISBN: (纸本)9783031414558;9783031414565

Liberal education-especially in universities-requires knowledge management. It soon becomes a trend, improving corporate performance and employee retention. In nine institutions and hundreds of departments and majors, Vietnam National University in Hanoi has around 2,500 researchers. Thus, knowledge management manages massive amounts of fragmented data and knowledge from many departments, disciplines, multidisciplines, geographies, and unevenly distributed systems. We propose a data fabric strategy for managing university knowledge by integrating and harmonizing fragmented data across systems and sources. Data-driven decision making promotes collective intelligence by creating a unified and complete picture of institutional knowledge. The data fabric technique powers vDFKM, a knowledge management platform that goes beyond metadata and the knowledge graph paradigm in integrating, exploiting, and reusing knowledge. The paradigm includes KM-based Applications, KM Coordinator, and Runtime and Data Infrastructure. A VNU Digital Knowledge Hub (VNU-DKH) prototype promotes the vDFKM technique. Initial experimental findings show that VNU-DKH can model knowledge representation of learning resources more efficiently and improve knowledge integration across universities.

关键词： Data Fabric Knowledge Management Knowledge Integration Knowledge Graph Active Metadata

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Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Lojasiewicz Condition

Fast Convergence of Random Reshuffling Under Over-Parameteri...

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5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Fan, Chen Thrampoulidis, Christos Schmidt, Mark Univ British Columbia Dept Comp Sci Vancouver BC Canada Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Canada CIFAR AI Chair Amii Montreal PQ Canada

ISBN: (纸本)9783031434204;9783031434211

Modern machine learning models are often over-parameterized and as a result they can interpolate the training data. Under such a scenario, we study the convergence properties of a sampling-without-replacement variant of stochastic gradient descent (SGD) known as random reshuffling (RR). Unlike SGD that samples data with replacement at every iteration, RR chooses a random permutation of data at the beginning of each epoch and each iteration chooses the next sample from the permutation. For under-parameterized models, it has been shown RR can converge faster than SGD under certain assumptions. However, previous works do not show that RR outperforms SGD in over-parameterized settings except in some highly-restrictive scenarios. For the class of Polyak-Lojasiewicz (PL) functions, we show that RR can outperform SGD in over-parameterized settings when either one of the following holds: (i) the number of samples (n) is less than the product of the condition number (kappa) and the parameter (alpha) of a weak growth condition (WGC), or (ii) n is less than the parameter (rho) of a strong growth condition (SGC).

关键词： Parameterization

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