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2024 IEEE International Conference on Modeling, Simulation and Intelligent computing, MoSICom 2024

作者： Buddhawar, Gaurav Dave, Dharin Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Dept. of Computer Science and Engineering Surat India Pune India Flame University School of Computing and Data Sciences Pune India

ISBN: (纸本)9798331533311

Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to frame classification in book flipping videos, leveraging the combined effect of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). In particular, a CNN-LSTM model is employed, where a pre-trained CNN captures spatial features, and LSTMs extend the model's capability to distinguish temporal dependencies critical for page turn detection. Unlike conventional methods, this approach processes entire video sequences, enabling the model to learn intricate Frame of Interest (FoI) detection features indicative of page turns and text visibility. The proposed model offers improved interpretability, effectively classifying frames without compromising transparency. The research opens avenues for automated book digitization, digital library creation, and educational technology applications, enhancing information extraction and reliability from book flipping videos. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Multimodal Generative AI for Education through Co-design Workshops with Students 25

Exploring Multimodal Generative AI for Education through Co-...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Prasad, Prajish Balse, Rishabh Balchandani, Dhwani School of Computing and Data Sciences FLAME University Pune India Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798400713941

Multimodal large language models (MLLMs) are Generative AI models that take different modalities such as text, audio, and video as input and generate appropriate multimodal output. Since such models will be integrated into future educational tools, a human-centered design approach that takes students' perspectives into account is essential while designing such *** paper describes two co-design workshops which were conducted with 79 student groups to examine how they design and prototype future educational tools integrated with MLLMs. Through various activities in the workshops, students discussed relevant educational problems, created journey maps, storyboards and low fidelity prototypes for their applications, and evaluated their applications based on relevant design principles. We found that students' applications used MLLMs for important learning environment design features such as multimodal content creation, personalization, and feedback. Based on these findings, we discuss future research directions for the design of multimodality in generative AI educational applications. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Students

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Designing Healthcare Relational Agents: A Conceptual Framework with User-Centered Design Guidelines 28

Designing Healthcare Relational Agents: A Conceptual Framewo...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Islam, Ashraful Chaudhry, Beenish Moalla Islam, Aminul Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9798350300482

This paper presents a conceptual framework for designing relational agents (RAs) in healthcare contexts, developed through the findings from multiple user studies on RAs about their acceptance, efficacy, and usability. The framework emphasizes a user-centered design (UCD) approach that takes into account the unique needs and preferences of patients, non-patient users, and healthcare professionals (HCPs). Based on the results of these studies, we analyzed and refined the RA designs and proposed a UCD-based conceptual framework for designing effective and user-friendly healthcare RAs. The paper aims to provide an initial resource for researchers, designers, and developers interested in developing RAs for healthcare contexts by thinking of UCD techniques. © 2023 IEEE.

关键词： User centered design

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On RNN-Based k-WTA Models With Time-Dependent Inputs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2034-2036页

作者： Mei Liu Mingsheng Shang the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China

Dear editor,This letter identifies two weaknesses of state-of-the-art k-winnerstake-all(k-WTA)models based on recurrent neural networks(RNNs)when considering time-dependent inputs,i.e.,the lagging error and the infeas... 详细信息

关键词： letter convergence finite

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Intelligent Compound Selection of Anti-cancer Drugs Based on Multi-Objective Optimization

Intelligent Compound Selection of Anti-cancer Drugs Based on...

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2023 International Conference on Intelligent Supercomputing and BioPharma, ISBP 2023

作者： Liu, Xiaoyan Xu, Zhiwei Liu, Guangwen Liu, Limin Inner Mongolia University of Technology College of Data Science and Application Hohhot China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9798350397789

In the compound selection process of anti-cancer drugs, safety properties such as drug activity and pharmacokinetics need to be considered simultaneously. To construct a more complete and precise drug screening mechanism, this paper proposed an intelligent compound selection method for anti-cancer drugs based on multi-objective optimization. The proposed model is executed in the MapReduce environment. Quantitatively analyze the biological activity of the compound, and qualitatively analyze the properties of pharmacokinetics and safety properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) to build a multi-objective optimization model. Guided by Pareto optimization theory, the set of non-inferior solution values was determined, and the compound combination that satisfies the optimization goal was found by genetic optimization. On this basis, a Monte Carlo hypothesis test was used to determine the equipped range of the compounds. Finally, an example of the compound selection of anti-breast cancer drugs is given, and the experimental evaluation proves that the algorithm can screen compounds limitedly, which provides a basis for anti-cancer drug synthesis. © 2023 IEEE.

关键词： Multiobjective optimization

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Machine Learning Approaches for Region-level Prescription Demand Forecasting 9

Machine Learning Approaches for Region-level Prescription De...

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9th IEEE Smart World Congress, SWC 2023

作者： Wang, Xu Chen, Long Wang, Jiangtao Zheng, Dingchang Coventry University Centre for Intelligent Healthcare Coventry United Kingdom Coventry University School of Computing Mathematics and Data Sciences Coventry United Kingdom

ISBN: (纸本)9798350319804

Region-level prescription demand is closely intertwined with the incidence of diseases within a given area. However, conventional forecasting methods primarily rely on historical data, and ignore the spatial correlation in prescription data. In this study, we employ graph structures to capture the interactions among drug demand in different regions. By leveraging two popular graph neural network-based models, our objective is to harness the power of spatial-temporal correlation to enhance the accuracy of predictions. To assess the effectiveness of the graph neural network-based model, we conduct extensive experiments on a comprehensive real world dataset. The results demonstrate that the performance of the graph neural network consistently surpasses that of statistical learning-based methods and traditional deep learning-based methods. © 2023 IEEE.

关键词： graph neural network Region-level prescription demand spatial temporal correlation

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Hash Table Notional Machines: A Comparison of 2D and 3D Representations 2024

Hash Table Notional Machines: A Comparison of 2D and 3D Repr...

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1st ACM Virtual Global computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Lewis, Colleen M. Miller, Craig S. Jeuring, Johan Pearce, Janice L. Petersen, Andrew Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL United States School of Computing DePaul University ChicagoIL United States Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science Berea College BereaKY United States Department of Mathematical and Computational Sciences University of Toronto Mississauga MississaugaON Canada

ISBN: (纸本)9798400705984

Background: Notional machines appear to be an essential aspect of computing education, but there are few papers that identify strengths and weaknesses of particular notional machines. Purpose: This article fills a gap in the notional machine literature by using a randomized controlled trial to compare the effectiveness of different notional machine representations. Methods: Our study used notional machines for two hash table algorithms: chaining and open addressing. Students were randomly assigned a video sequence using either 2D or 3D representations. Findings: We found minimal effect of 2D vs 3D representational form on students' learning and perceptions of helpfulness. Implications: Our paper provides an example of how educational research can inform the design and evaluation of notional machines. © 2024 Owner/Author.

关键词： Students

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Preventing Fraud in E-tickets Validation Using The 2FA Approach 5

Preventing Fraud in E-tickets Validation Using The 2FA Appro...

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5th International Conference on Computer and Applications, ICCA 2023

作者： Alnuaimi, Lolwa Hassan Jaam, Jihad Al John Moores University School of Computing and Data Sciences Dept. of Computer Science Oryx Universal College in Partnership with Liverpool Doha Qatar

ISBN: (纸本)9798350303254

Electronic ticketing (e-ticket) systems employing QR codes or barcodes are essential in the digital era but come with cybersecurity risks like fraud, counterfeiting, and ticket scalping. This study explores the cybersecurity implications of large-scale event e-ticketing, with a focus on comprehending these technologies and improving security. It introduces the Ticket_Events application, which combines online registration and the secure generation of QR-coded electronic tickets. The app mandates QR code scanning at the event venue and encourages the use of location services for enhanced verification, preventing ticket swapping and potential fraud. The primary objective is to create a dependable e-ticketing application centered on ownership verification. In conclusion, the study delves into the intricacies of e-ticketing and barcode scanning, pinpoints challenges, and proposes solutions to fortify the security of digital ticket delivery. © 2023 IEEE.

关键词： Bar codes

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Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced data: Improving Machine Failure Prediction

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Computers, Materials & Continua 2023年第6期75卷 4821-4841页

作者： Yap Bee Wah Azlan Ismail Nur Niswah Naslina Azid Jafreezal Jaafar Izzatdin Abdul Aziz Mohd Hilmi Hasan Jasni Mohamad Zain Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA(UiTM)Shah Alam40450SelangorMalaysia School of Computing Sciences College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)40450Shah AlamSelangorMalaysia Mathematical Sciences Studies College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)Kelantan BranchMachang CampusBukit Ilmu18500MachangKelantan Darul NaimMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer and Information Sciences(DCIS)Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610PerakMalaysia UNITAR International University Jalan SS6/3SS6Petaling Jaya47301SelangorMalaysia

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure *** common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)*** paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas *** original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level *** accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to *** performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and *** showed that the sensitivity of all classifiers increases as the imbalance rate *** with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

关键词： Machine failure machine learning imbalanced data SMOTE classification

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Augmentation-Aware Self-Supervision for data-Efficient GAN Training 37

Augmentation-Aware Self-Supervision for Data-Efficient GAN T...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Hou, Liang Cao, Qi Yuan, Yige Zhao, Songtao Ma, Chongyang Pan, Siyuan Wan, Pengfei Wang, Zhongyuan Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Kuaishou Technology China

ISBN: (纸本)9781713899921

Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation demonstrates improved data efficiency of training GANs. However, the augmentation implicitly introduces undesired invariance to augmentation for the discriminator since it ignores the change of semantics in the label space caused by data transformation, which may limit the representation learning ability of the discriminator and ultimately affect the generative modeling performance of the generator. To mitigate the negative impact of invariance while inheriting the benefits of data augmentation, we propose a novel augmentation-aware self-supervised discriminator that predicts the augmentation parameter of the augmented data. Particularly, the prediction targets of real data and generated data are required to be distinguished since they are different during training. We further encourage the generator to adversarially learn from the self-supervised discriminator by generating augmentation-predictable real and not fake data. This formulation connects the learning objective of the generator and the arithmetic - harmonic mean divergence under certain assumptions. We compare our method with state-of-the-art (SOTA) methods using the class-conditional BigGAN and unconditional StyleGAN2 architectures on data-limited CIFAR-10, CIFAR-100, FFHQ, LSUN-Cat, and five low-shot datasets. Experimental results demonstrate significant improvements of our method over SOTA methods in training data-efficient GANs. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Generative adversarial networks

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