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27th International Conference on Knowledge Based and intelligent Information and Engineering Sytems, KES 2023

作者： Shekhovtsov, Andrii Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

In every decision-making problem which involves two or more criteria, there is to identify the relative importance of those criteria in order to make a proper decision. Very often, a decision-makers employee, for this purpose, subjective weighting methods, such as Analytic Hierarchy Process (AHP) or RANking COMparison (RANCOM). However, there is no simple way to compare the quality of the weights identification using different methods. To address this issue, this paper proposes a simple but efficient Weights Similarity Coefficient, which allows us to evaluate the relative performance of different subjective weighting methods. Furthermore, we propose a framework that utilizes the proposed coefficient in order to provide more complete comparison results. To demonstrate the applicability and effectiveness of the proposed framework, a case study is performed to compare two popular weighting methods: AHP and RANCOM. The results of the comparison prove that proposed coefficient with the combination of the proposed framework is an efficient instrument for weighing methods comparison. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： AHP Criteria weights Manhattan distance MCDA RANCOM SPOTIS

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GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

arXiv

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arXiv 2024年

作者： Zheng, Linfang Tse, Tze Ho Elden Wang, Chen Sun, Yinghan Chen, Hua Leonardis, Aleš Zhang, Wei Chang, Hyung Jin Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology China Department of Computer Science The University of Hong Kong Hong Kong School of Computer Science University of Birmingham United Kingdom

Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress towards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrepancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach integrates an HS-layer and learnable affine transformations, which aims to enhance the extraction and alignment of geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges diverse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive experiments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics. © 2024, CC BY-NC-SA.

关键词： Metadata

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Causal Modeling with Stationary Diffusions

arXiv

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arXiv 2023年

作者： Lorch, Lars Krause, Andreas Schölkopf, Bernhard Department of Computer Science ETH Zürich Switzerland MPI for Intelligent Systems Tübingen Germany

We develop a novel approach towards causal inference. Rather than structural equations over a causal graph, we learn stochastic differential equations (SDEs) whose stationary densities model a system’s behavior under interventions. These stationary diffusion models do not require the formalism of causal graphs, let alone the common assumption of acyclicity. We show that in several cases, they generalize to unseen interventions on their variables, often better than classical approaches. Our inference method is based on a new theoretical result that expresses a stationarity condition on the diffusion’s generator in a reproducing kernel Hilbert space. The resulting kernel deviation from stationarity (KDS) is an objective function of independent interest. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Stochastic Triangular Fuzzy Number (S-TFN) Normalization: A New Approach for Nonmonotonic Normalization 27

Stochastic Triangular Fuzzy Number (S-TFN) Normalization: A ...

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27th International Conference on Knowledge Based and intelligent Information and Engineering Sytems, KES 2023

作者： Kizielewicz, Bartlomiej Dobryakova, Larisa Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Data normalization is essential in many fields, such as speech recognition, deep learning, machine learning, and optimization. Many researchers focus on developing various normalization techniques, such as min-max, score-based, maximum, vector, or sum, on processing data efficiently. However, classical normalization methods may need to be improved in Multi-Criteria Decision-Analysis (MCDA), where criteria are often non-monotonic. This paper proposes a new approach for identifying and normalizing non-monotonic criteria in MCDA. The method combines a stochastic optimization technique - a genetic algorithm - with normalization based on triangular fuzzy numbers. This paper compares the proposed approach with classical normalization techniques, such as min-max, maximum, vector, or non-linear, using the classical MCDA method - the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS). The study results showed that the proposed approach to normalizing non-monotonic criteria yields a better match between decision preferences and the actual reference model than traditional normalization methods. Optimization using the genetic algorithm helps identify the means of triangular fuzzy numbers, which generate more accurate results. Analysis of the R2 coefficient confirms a very good fit of the proposed model. Therefore, the new non-linear normalization approach in MCDA presented in this work has great potential in adjusting decision preferences to non-monotonic criteria. Future research should continue on the accuracy of this approach in identifying non-linear decision models and apply it to other MCDA methods and real-world decision problems. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decision analysis Fuzzy sets MCDA Normalization TOPSIS

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Influence of Feature Extraction Methods using Neighborhood Component Analysis (NCA) to Improve Classification Performance for Hepatitis C Disease

Influence of Feature Extraction Methods using Neighborhood C...

引用

International Conference on Informatics and Computing (ICIC)

作者： Christiella Abinosy Setiawan Maria Susan Anggreainy Nora Fitriawati Raymasterio Vera Lucky Renatha Dewi Sabrina Steven Matthew Vierdaria Wijayanti Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Research in the development of Hepatitis C disease prediction is increasingly developing, especially using machine learning models which is able to make predictions quickly and accurately. In this study, a comparison of several classification methods was carried out by also applying feature reduction, NCA. In this study, a comparison of performance was carried out if the data was entered into the NCA feature extraction method with KNearest Neighborhood (KNN) and Support Vector Machine (SVM) and a comparison of performance if the data did not use the NCA feature extraction method. The performance comparison metrics used in the study were accuracy, sensitivity, specification, Matthews Correlation Coefficient (MCC), and Kappa value. The highest accuracy (99.36%), sensitivity (91.94%), specification (99.67%), MCC $(0.895)$ and the best Kappa value $(0.889)$ were obtained in the KNN-NCA prediction method.

关键词： Support vector machines Accuracy Sensitivity Liver diseases Computational modeling Nearest neighbor methods Predictive models Feature extraction Diseases Testing

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Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features

arXiv

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arXiv 2024年

作者： Zelch, Christoph Peters, Jan von Stryk, Oskar Simulation Systems Optimization and Robotics Group Department of Computer Science TU Darmstadt Hochschulstr. 10 Darmstadt64289 Germany Intelligent Autonomous Systems Group Department of Computer Science TU Darmstadt Hochschulstr. 10 Darmstadt64289 Germany

Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it allows automated analysis of recorded motion data. Many clustering algorithms for trajectories build upon distance metrics that are based on pointwise Euclidean distances. However, our work indicates that focusing on salient characteristics is often sufficient. We present a novel distance measure for motion plans consisting of state and control trajectories that is based on a compressed representation built from their main features. This approach allows a flexible choice of feature classes relevant to the respective task. The distance measure is used in agglomerative hierarchical clustering. We compare our method with the widely used dynamic time warping algorithm on test sets of motion plans for the Furuta pendulum and the Manutec robot arm and on real-world data from a human motion dataset. The proposed method demonstrates slight advantages in clustering and strong advantages in runtime, especially for long trajectories. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Domain Model Discovery from Textbooks for computer programming intelligent Tutors 34th

Domain Model Discovery from Textbooks for Computer Programmi...

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34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021

作者： Banjade, Rabin Oli, Priti Tamang, Lasang Jimba Chapagain, Jeevan Rus, Vasile Department of Computer Science Institute of Intelligent Systems University of Memphis MemphisTN United States

We present a novel approach to intro-to-programming domain model discovery from textbooks using an overgeneration and ranking strategy. We first extract candidate key phrases from each chapter in a computer science textbook focusing on intro-to-programming and then rank those concepts according to a number of metrics such as the standard tf-idf weight used in information retrieval and metrics produced by other text ranking algorithms. Specifically, we conduct our work in the context of developing an intelligent tutoring system for source code comprehension for which a specification of the key programming concepts is needed-the system monitors students’ performance on those concepts and scaffolds their learning process until they show mastery of the concepts. Our experiments with programming concept instruction from Java textbooks indicate that the statistical methods such as KP Miner method are quite competitive compared to other more sophisticated methods. Automated discovery of domain models will lead to more scalable intelligent Tutoring systems (ITSs) across topics and domains, which is a major challenge that needs to be addressed if ITSs are to be widely used by millions of learners across many domains. © 2021by the authors. All rights reserved.

关键词： Textbooks

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Scalable safe policy improvement for factored multi-agent MDPs 24

Scalable safe policy improvement for factored multi-agent MD...

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Proceedings of the 41st International Conference on Machine Learning

作者： Federico Bianchi Edoardo Zorzi Alberto Castellini Thiago D. Simão Matthijs T. J. Spaan Alessandro Farinelli Department of Computer Science University of Verona Verona Italy Department of Software Science Eindhoven University of Technology Eindhoven Netherlands Department of Intelligent Systems Delft University of Technology Delft Netherlands

In this work, we focus on safe policy improvement in multi-agent domains where current state-of-the-art methods cannot be effectively applied because of large state and action spaces. We consider recent results using Monte Carlo Tree Search for Safe Policy Improvement with Baseline Bootstrapping and propose a novel algorithm that scales this approach to multi-agent domains, exploiting the factorization of the transition model and value function. Given a centralized behavior policy and a dataset of trajectories, our algorithm generates an improved policy by selecting joint actions using a novel extension of Max-Plus (or Variable Elimination) that constrains local actions to guarantee safety criteria. An empirical evaluation on multi-agent SysAdmin and multi-UAV Delivery shows that the approach scales to very large domains where state-of-the-art methods cannot work.

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Disentangling interpretable factors with supervised independent subspace principal component analysis 24

Disentangling interpretable factors with supervised independ...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Jiayu Su David A. Knowles Raul Rabadan Program for Mathematical Genomics and Department of Systems Biology Columbia University and New York Genome Center Department of Systems Biology and Department of Computer Science Columbia University and New York Genome Center Program for Mathematical Genomics and Department of Systems Biology and Department of Biomedical Informatics Columbia University

ISBN: (纸本)9798331314385

The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more than one space, while more expressive deep learning approaches lack interpretability. Here, we introduce Supervised Independent Subspace Principal Component Analysis (sisPCA), a PCA extension designed for multi-subspace learning. Leveraging the Hilbert-Schmidt Independence Criterion (HSIC), sisPCA incorporates supervision and simultaneously ensures subspace disentanglement. We demonstrate sisPCA's connections with autoencoders and regularized linear regression and showcase its ability to identify and separate hidden data structures through extensive applications, including breast cancer diagnosis from image features, learning aging-associated DNA methylation changes, and single-cell analysis of malaria infection. Our results reveal distinct functional pathways associated with malaria colonization, underscoring the essentiality of explainable representation in high-dimensional data analysis.

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Sketch-to-3D: Transforming Hand-Sketched Floorplans into 3D Layouts 25

Sketch-to-3D: Transforming Hand-Sketched Floorplans into 3D ...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Usman, Muhammad Almulhim, Abdullah Alaseri, Mohammad Alzahran, Mona Luqman, Hamzah Anwar, Saeed Kfupm Department of Information & Computer Science Dhahran Saudi Arabia Kfupm SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran Saudi Arabia Kfupm Center for Intelligent Secure Systems Dhahran Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia The Australian National University Australia University of Canberra Australia

ISBN: (纸本)9798350379037

The conventional approach for analyzing built environments' spatial relations and structural components utilizes 2D floorplans as the primary representation method. However, these 2D blueprints can be challenging and potentially misleading for end-users who lack expertise in architecture or design, as they rely on low-dimensional abstractions to convey complex, high-dimensional information. This paper introduces an automated pipeline that transforms hand-sketched 2D floorplans into immersive 3D layouts, addressing this issue through a supervised learning approach. Initially, a dataset of diverse hand-sketched floorplans is compiled, each labeled with semantics for the object and structural element. Next, supervised deep learning models were developed for layout and object detection using this dataset. The performance of the supervised models was evaluated on a test dataset. The 2D digital designs were then converted to 3D immersive layouts through automated mapping. The entire pipeline is an interactive application that allows novice users to capture a picture of their hand-sketched floorplan and view their design ideas come to life as immersive 3D layouts populated with virtual crowds. © 2024 IEEE.

关键词： Supervised learning

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