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25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Naoi, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Graph similarity search (GSS) models chemical compounds as a graph database. GSS is an essential tool for drug discovery because they can find similar graphs (compounds) for a query. Existing GSS methods have two critical limitations. First, handling large databases is time consuming. Second, finding compounds with the structure-activity relationship (SAR), which is vital in drug discovery, remains difficult. Herein a novel graph-based method for chemical compound searches is proposed to overcome these limitations. Since compounds with SAR share similar substructures, the proposed method extracts correlated subgraphs included in a query and explores similar compounds. In practical drug discovery task, our method achieves faster searches and improved accuracy compared to existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph databases

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Educational Digital Twin: Tackling Complexity in Educational Big data

Educational Digital Twin: Tackling Complexity in Educational...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Huang, Luwen Willcox, Karen E. University of Texas Department of Computer Science Austin United States University of Texas Oden Institute for Computational Engineering & Sciences Austin United States

ISBN: (纸本)9798350362480

"Everything is bigger in Texas"and this includes the big data challenges of the state's educational system. But just as big is the opportunity for digital twin technologies to improve decision-making in education, with potential to improve student outcomes. This paper formulates an Educational Digital Twin, a novel approach to understanding, modeling, and analyzing educational data to address the complexities inherent in student pathways. By "student pathways", we refer to the joint facets of student behavior - evolving dynamically over time and encompassing not only course enrollments but also behavioral and academic factors. Unlike traditional approaches that rely on static analyses of historical data, the Educational Digital Twin applies the digital twin paradigm to model these pathways as a living construct that dynamically evolves alongside the physical world. At the heart of the Educational Digital Twin is a knowledge graph that organizes data into a semantic structure. By employing graph-theoretic methods, we manipulate this structure to derive multi-granular insights that inform decision-making. Our evaluation demonstrates how the Educational Digital Twin not only facilitates the dynamic integration of new data but also scales efficiently to handle complex datasets covering millions of students across hundreds of demographic and academic dimensions. © 2024 IEEE.

关键词： Complex networks Digital twin Educational big data Graph modeling Network visualization

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Using Video Games as a Tool in Math Education

Using Video Games as a Tool in Math Education

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Omirgaliyev, Ruslan Mukhidenov, Diyar Sydyk, Ayazhan Astana It University Department of Computer Engineering Astana Kazakhstan Astana It University Department of Computer Engineering Astana201437 Kazakhstan Astana It University Department of Computational and Data Science Astana201078 Kazakhstan

ISBN: (纸本)9798350335040

Today, you can see how children are increasingly spending their free time playing desktop or mobile video games. The question then becomes whether a simple hobby can be turned into a rewarding learning pastime? In the introduction of the article, the current situation and the problem that video games can create and the direction in which it can be solved using video games will be considered. The literature review considers some interesting aspects and conclusions of earlier similar studies. The methodology will present plans for quantitative and qualitative surveys of the population and a proposal for the development of a gaming product for educational purposes. The results will show what the situation is and what people's opinion about the use of video games in education. All the data obtained will help in the development of an educational project. Summing up, we will talk about the meaning and significance of the project and similar ideas in the modern world. © 2023 IEEE.

关键词： Human computer interaction

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Digital Twin: Graph Formulations for Managing Complexity and Uncertainty 10

Digital Twin: Graph Formulations for Managing Complexity and...

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10th IEEE Smart World Congress, SWC 2024

作者： Huang, Luwen Kapteyn, Michael Willcox, Karen E. University of Texas at Austin Department of Computer Science United States University of Texas at Austin Oden Institute for Computational Engineering and Sciences United States

ISBN: (纸本)9798331520861

This paper articulates the importance of graphical models and graph-based methods as fundamental enablers of Digital Twins. Graph-based representations are well known to be suited for describing complex systems where the connections between entities are as important as the entities themselves. The interconnections within and across data, models, and decisions are central to a Digital Twin's value. Not only does a graph emphasize the scalable representation of such interrelationships, it also provides a natural mathematical setting for addressing uncertainty and complexity - arguably the two biggest barriers to scalable deployment and adoption of Digital Twins. We discuss how recent advances in theory and algorithms for large-scale knowledge graphs and graphical models can be combined in a multi-layered formulation to provide a powerful foundation for achieving scalable Digital Twins for complex systems. © 2024 IEEE.

关键词： Knowledge graph

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Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs 38

Bandit-Feedback Online Multiclass Classification: Variants a...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Filmus, Yuval Hanneke, Steve Mehalel, Idan Moran, Shay Faculty of Computer Science Faculty of Mathematics Technion Israel Department of Computer Science Purdue University United States Faculty of Computer Science Technion Israel Faculty of Mathematics Faculty of Computer Science Faculty of Data and Decision Sciences Technion Israel Google research Israel

Consider the domain of multiclass classification within the adversarial online setting. What is the price of relying on bandit feedback as opposed to full information? To what extent can an adaptive adversary amplify the loss compared to an oblivious one? To what extent can a randomized learner reduce the loss compared to a deterministic one? We study these questions in the mistake bound model and provide nearly tight answers. We demonstrate that the optimal mistake bound under bandit feedback is at most O(k) times higher than the optimal mistake bound in the full information case, where k represents the number of labels. This bound is tight and provides an answer to an open question previously posed and studied by Daniely and Helbertal ['13] and by Long ['17,'20], who focused on deterministic learners. Moreover, we present nearly optimal bounds of Θ˜(k) on the gap between randomized and deterministic learners, as well as between adaptive and oblivious adversaries in the bandit feedback setting. This stands in contrast to the full information scenario, where adaptive and oblivious adversaries are equivalent, and the gap in mistake bounds between randomized and deterministic learners is a constant multiplicative factor of 2. In addition, our results imply that in some cases the optimal randomized mistake bound is approximately the square-root of its deterministic parallel. Previous results show that this is essentially the smallest it can get. Some of our results are proved via a reduction to prediction with expert advice under bandit feedback, a problem interesting on its own right. For this problem, we provide a randomized algorithm which is nearly optimal in some scenarios. © 2024 Neural information processing systems foundation. All rights reserved.

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Segmentation of Libraries, CMS, and PHP Frameworks Based on Code Characteristics: Implementation of Clustering Using K-Means

Segmentation of Libraries, CMS, and PHP Frameworks Based on ...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Parulian, Onesinus Saut Aji, Indra Budi Patria, Omega Joel Nugroho, Ariyanto Adi Susanto, Nanang Mariyanto, Totox Salsabila, Nurul Jannah Haris, Muhammad Manik, Lindung Parningotan Universitas Nusa Mandiri Jakarta Computer Science Department Indonesia Universitas Nusa Mandiri Jakarta Research Center for Data and Information Sciences Badan Riset dan Inovasi Nasional Computer Science Department Indonesia

ISBN: (纸本)9798350376210

Rapid development of software significantly facilitates developers in all phases of software development. Developers can easily leverage frameworks, content management systems (CMS), and libraries available in each programming language. With the large and rapid addition of frameworks, CMS, and libraries, developers must be able to choose precisely which ones to use based on the requirements of their software development projects. The utilization of machine learning for segmentation or clustering enables developers to identify groups of frameworks, CMS, and libraries by leveraging features obtained from static analysis, such as the size of codebases, complexities, structures, and other code characteristics. One algorithm that can be used for segmentation is K-means clustering. However, the availability of datasets is crucial as it can offer information about the code characteristics of each software. The use of machine learning on datasets can be an ongoing research area in software development related to utilizing code characteristics in software analysis. This study provides a novel dataset for the domains of software development and machine learning. The dataset we created contains 30 instances and 68 features that can be utilized for related or future research. This research utilizes the Silhouette score to determine the optimal k value. © 2024 IEEE.

关键词： Software design

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Knowledge Graphs Can be Learned with Just Intersection Features 41

Knowledge Graphs Can be Learned with Just Intersection Featu...

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41st International Conference on Machine Learning, ICML 2024

作者： Le, Duy Zhong, Shaochen Liu, Zirui Xu, Shuai Chaudhary, Vipin Zhou, Kaixiong Xu, Zhaozhuo Department of Computer and Data Sciences Case Western Reserve University United States Department of Computer Science Rice University United States Department of Electrical and Computer Engineering North Carolina State University United States Department of Computer Science Stevens Institute of Technology United States

Knowledge Graphs (KGs) are potent frameworks for knowledge representation and reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted relationships and facts awaiting discovery. Deep learning methodologies have proven effective in enhancing KG completion by framing it as a link prediction task, where the goal is to discern the validity of a triple comprising a head, relation, and tail. The significance of structural information in assessing the validity of a triple within a KG is well-established. However, quantifying this structural information poses a challenge. We need to pinpoint the metric that encapsulates the structural information of a triple and smoothly incorporate this metric into the link prediction learning process. In this study, we recognize the critical importance of the intersection among the k-hop neighborhoods of the head, relation, and tail when determining the validity of a triple. To address this, we introduce a novel randomized algorithm designed to efficiently generate intersection features for candidate triples. Our experimental results demonstrate that a straightforward fully-connected network leveraging these intersection features can surpass the performance of established KG embedding models and even outperform graph neural network baselines. Additionally, we highlight the substantial training time efficiency gains achieved by our network trained on intersection features. Copyright 2024 by the author(s)

关键词： Knowledge graph

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Zero-shot causal learning 37

Zero-shot causal learning

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Nilforoshan, Hamed Moor, Michael Roohani, Yusuf Chen, Yining Šurina, Anja Yasunaga, Michihiro Oblak, Sara Leskovec, Jure Department of Computer Science Stanford University United States Department of Biomedical Data Science Stanford University United States School of Computer and Communication Sciences EPFL Switzerland Department of Computer Science University of Ljubljana Slovenia

ISBN: (纸本)9781713899921

Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it. However, in many settings it is important to predict the effects of novel interventions (e.g., a newly invented drug), which these methods do not address. Here, we consider zero-shot causal learning: predicting the personalized effects of a novel intervention. We propose CaML, a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. CaML trains a single meta-model across thousands of tasks, each constructed by sampling an intervention, its recipients, and its nonrecipients. By leveraging both intervention information (e.g., a drug's attributes) and individual features (e.g., a patient's history), CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training. Experimental results on real world datasets in large-scale medical claims and cell-line perturbations demonstrate the effectiveness of our approach. Most strikingly, CaML's zero-shot predictions outperform even strong baselines trained directly on data from the test interventions. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Forecasting

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Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning 53

Kidney Tumor Recognition from Abdominal CT Images using Tran...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Wasi, Sefatul Alam, Saadia Binte Rahman, Rashedur Amin, M Ashraful Kobashi, Syoji Independent University Center for Computational & Data Sciences Department of Computer Science and Engineering Dhaka Bangladesh1299 Bangladesh University of Hyogo Graduate School of Engineering 2167 Shosha Hyogo Himeji Japan

ISBN: (纸本)9781665464161

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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