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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Nayan, Nasim Mahmud Islam, Ashraful Islam, Muhammad Usama Ahmed, Eshtiak Hossain, Mohammad Mobarak Alam, Md Zahangir University of Information Technology and Sciences Department of Computer Science and Engineering Bangladesh Independent University Center for Computational and Data Sciences Bangladesh School of Computing and Informatics University of Louisiana at Lafayette United States Independent University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350300482

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention. © 2023 IEEE.

关键词： Machine learning

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Hamiltonian simulation for hyperbolic partial differential equations by scalable quantum circuits

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Physical Review Research 2024年第3期6卷 033246页

作者： Yuki Sato Ruho Kondo Ikko Hamamura Tamiya Onodera Naoki Yamamoto Inc. 1-4-14 Koraku Bunkyo-ku Tokyo 112-0004 Japan Quantum Computing Center IBM Quantum Department of Applied Physics and Physico-Informatics

Solving partial differential equations for extremely large-scale systems within a feasible computation time serves in accelerating engineering developments. Quantum computing algorithms, particularly the Hamiltonian simulations, present a potential and promising approach to achieve this purpose. Actually, there are several oracle-based Hamiltonian simulations with potential quantum speedup, but their detailed implementations and accordingly the detailed computational complexities are all unclear. This paper presents a method that enables us to explicitly implement the quantum circuit for Hamiltonian simulation; the key technique is the explicit gate construction of differential operators contained in the target partial differential equation discretized by the finite difference method. Moreover, we show that the space and time complexities of the constructed circuit are exponentially smaller than those of conventional classical algorithms. We also provide numerical experiments and an experiment on a real device for the wave equation to demonstrate the validity of our proposed method.

关键词： Quantum algorithms Quantum circuits Quantum computation Quantum simulation

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Universal Spam Detection using Transfer Learning of BERT Model

arXiv

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arXiv 2022年

作者： Tida, Vijay Srinivas Hsu, Sonya Center of Advanced Computer Studies School of Computing and Informatics University of Louisiana Lafayette United States Informatics Program School of Computing and Informatics University of Louisiana Lafayette United States

Deep learning transformer models become important by training on text data based on self-attention mechanisms. This manuscript demonstrated a novel universal spam detection model using pre-trained Google's Bidirectional Encoder Representations from Transformers (BERT) base uncased models with four datasets by efficiently classifying ham or spam emails in real-time scenarios. Different methods for Enron, Spamassain, Lingspam, and Spamtext message classification datasets, were used to train models individually in which a single model was obtained with acceptable performance on four datasets. The Universal Spam Detection Model (USDM) was trained with four datasets and leveraged hyperparameters from each model. The combined model was finetuned with the same hyperparameters from these four models separately. When each model using its corresponding dataset, an F1-score is at and above 0.9 in individual models. An overall accuracy reached 97%, with an F1 score of 0.96. Research results and implications were discussed. © 2022, CC BY-NC-ND.

关键词： Classification (of information)

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Revocable Quantum Digital Signatures 19

Revocable Quantum Digital Signatures

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19th Conference on the Theory of Quantum Computation, Communication and Cryptography, TQC 2024

作者： Morimae, Tomoyuki Poremba, Alexander Yamakawa, Takashi Yukawa Institute for Theoretical Physics Kyoto University Japan Computing and Mathematical Sciences Caltech PasadenaCA United States CSAIL Department of Mathematics MIT CambridgeMA United States NTT Social Informatics Laboratories Tokyo Japan NTT Research Center for Theoretical Quantum Information Atsugi Japan

ISBN: (纸本)9783959773287

We study digital signatures with revocation capabilities and show two results. First, we define and construct digital signatures with revocable signing keys from the LWE assumption. In this primitive, the signing key is a quantum state which enables a user to sign many messages and yet, the quantum key is also revocable, i.e., it can be collapsed into a classical certificate which can later be verified. Once the key is successfully revoked, we require that the initial recipient of the key loses the ability to sign. We construct digital signatures with revocable signing keys from a newly introduced primitive which we call two-tier one-shot signatures, which may be of independent interest. This is a variant of one-shot signatures, where the verification of a signature for the message "0" is done publicly, whereas the verification for the message "1" is done in private. We give a construction of two-tier one-shot signatures from the LWE assumption. As a complementary result, we also construct digital signatures with quantum revocation from group actions, where the quantum signing key is simply "returned" and then verified as part of revocation. Second, we define and construct digital signatures with revocable signatures from OWFs. In this primitive, the signer can produce quantum signatures which can later be revoked. Here, the security property requires that, once revocation is successful, the initial recipient of the signature loses the ability to find accepting inputs to the signature verification algorithm. We construct this primitive using a newly introduced two-tier variant of tokenized signatures. For the construction, we show a new lemma which we call the adaptive hardcore bit property for OWFs, which may enable further applications. © Tomoyuki Morimae, Alexander Poremba, and Takashi Yamakawa.

关键词： Quantum cryptography

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DELIFFAS: deformable light fields for fast avatar synthesis 23

DELIFFAS: deformable light fields for fast avatar synthesis

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Youngjoong Kwon Lingjie Liu Henry Fuchs Marc Habermann Christian Theobalt University of North Carolina at Chapel Hill University of Pennsylvania and Max Planck Institute for Informatics Saarland Informatics Campus Max Planck Institute for Informatics Saarland Informatics Campus and Saarbrücken Research Center for Visual Computing Interaction and AI

Generating controllable and photorealistic digital human avatars is a long-standing and important problem in Vision and Graphics. Recent methods have shown great progress in terms of either photorealism or inference speed while the combination of the two desired properties still remains unsolved. To this end, we propose a novel method, called DELIFFAS, which parameterizes the appearance of the human as a surface light field that is attached to a controllable and deforming human mesh model. At the core, we represent the light field around the human with a deformable two-surface parameterization, which enables fast and accurate inference of the human appearance. This allows perceptual supervision on the full image compared to previous approaches that could only supervise individual pixels or small patches due to their slow runtime. Our carefully designed human representation and supervision strategy leads to state-of-the-art synthesis results and inference time.

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Broadening Student Engagement To Build the Next Generation of Cyberinfrastructure Professionals 23

Broadening Student Engagement To Build the Next Generation o...

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2023 Practice and Experience in Advanced Research computing, PEARC 2023

作者： Murillo, Angela Brower, Don Hossain, Sarowar Kee, Kerk Mandal, Anirban Nabrzyski, Jarek Scott, Erik Virdone, Nicole Ewing, Rodney Deelman, Ewa Indiana University-Indianapolis Luddy School of Informatics Computing and Engineering United States University of Notre Dame Center for Research Computing United States Texas Tech University United States University of North Carolina RENCI United States University of Southern California Information Science Institute United States University of Southern California Information Science Institutes United States

ISBN: (纸本)9781450399852

The CI Compass Fellowship Program (CICF) was developed to broaden undergraduate student participation in cyberinfrastructure (CI) research, development, and operations. CICF is a distinctive program for undergraduate students pursuing studies in computer science, information science, data science, and other related fields. During year one of the program, CICF had six students participate from two institutions. During year 2 of the program, CICF had fourteen students participate from nine institutions. This poster provides details of the CICF program development and summarizes the impact of the first two years. © 2023 Owner/Author.

关键词： Students

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Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory 2

Electricity Anomaly Point Detection using Unsupervised Techn...

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2nd International Conference on Artificial Intelligence and Data Sciences, AiDAS 2021

作者： Salleh, Nur Shakirah Md Saripuddin, Mulyana Suliman, Azizah Jorgensen, Bo Norregaard College of Computing and Informatics Universiti Tenaga Nasional Kajang Malaysia Center for Energy Informatics Mærsk Mc-Kinney Moller Institutte Odense Denmark

ISBN: (纸本)9781665417266

Electricity theft caused a major loss for electricity power provider. The anomaly detection helps to predict the abnormal load usage of a consumer. Usually, the classification method used in anomaly detection. This research paper proposed to identify the potential anomaly points by using threshold and outliers. The prediction in time-series applied Long Short-Term Memory (LSTM) algorithm. The historical electricity load dataset of a single industrial consumer was used to generate the prediction of electricity load. There were five optimizers used to produce the model: Adam, Adadelta, Adagrad, RMSProp, and Stochastic gradient descent (SGD). The prediction model was evaluated using mean squared error (MSE) and mean absolute error (MAE). The best model among all five models was generated by Adadelta optimizer with the error rate value of 0.091982 for MSE and 0.018433 for MAE. The prediction values were generated by this model. The anomaly point was detected by using threshold and outliers. The threshold value was 0.218983. One week in August 2019 was chosen to detect any anomaly load occurrences. There were 24 outliers were found within the selected week. The study shall expand on the electricity usage trend during COVID-19 pandemic period. © 2021 IEEE.

关键词： Long short-term memory

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Improving Multilingual Sign Language Translation with Automatically Clustered Language Family Information 31

Improving Multilingual Sign Language Translation with Automa...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Ruiquan Hu, Cong Yu, Pei Chen, Yidong Department of Artificial Intelligence School of Informatics Xiamen University 361005 China Ministry of Culture and Tourism 361005 China National Language Resources Monitoring and Research Center for Education and Teaching Media Xiamen University 361005 China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China

ISBN: (纸本)9798891761964

Sign Language Translation (SLT) bridges the communication gap between deaf and hearing individuals by converting sign language videos into spoken language texts. While most SLT research has focused on bilingual translation models, the recent surge in interest has led to the exploration of Multilingual Sign Language Translation (MSLT). However, MSLT presents unique challenges due to the diversity of sign languages across nations. This diversity can lead to cross-linguistic conflicts and hinder translation accuracy. To use the similarity of actions and semantics between sign languages to alleviate conflict, we propose a novel approach that leverages sign language families to improve MSLT performance. Sign languages were clustered into families automatically based on their Language distribution in the MSLT network. We compare the results of our proposed family clustering method with the analysis conducted by sign language linguists and then train dedicated translation models for each family in the many-to-one translation scenario. Our experiments on the SP-10 dataset demonstrate that our approach can achieve a balance between translation accuracy and computational cost by regulating the number of language families. The source codes and models are available at FamilyCluST. © 2025 Association for Computational Linguistics.

关键词： Semantics

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LP-Gaussians: Learnable Parametric Gaussian Splatting for Efficient Dynamic Reconstruction of Single-View Scenes

LP-Gaussians: Learnable Parametric Gaussian Splatting for Ef...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Shaoqi Xie, Weixing Peng, Youhong Li, Jinwen Yao, Jiawei Yao, Junfeng Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Taiwan

ISBN: (纸本)9798350368741

With the popularity of short video platforms, the number of single-view videos has increased significantly. Existing NeRF-based methods can reconstruct dynamic scenes in a single-view setting, but slow rendering speed and low rendering quality limit their practical applications. To address these challenges, we propose a fast single-view scenes reconstruction framework based on 3D Gaussian Splatting. Our method uses point clouds obtained with depth priors as the Gaussian initialization and introduces learnable parametric functions to model the time-dependent deformation of Gaussians. The explicit deformation modeling for Gaussians significantly reduces training and rendering time. Furthermore, to improve the rendering quality of challenging areas, we adopt an adaptive sampling strategy to densify Gaussians. For occlusion problems from single-view videos, we design a smooth loss function to restore the color of the occluded areas. Experimental results demonstrate that our method significantly reduces training time, enhances rendering quality, and accelerates rendering speed. Project page: https://***/LPGaussians. © 2025 IEEE.

关键词： 3D Gaussian Splatting Dynamic Scene Reconstruction Single-view Reconstruction

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Is COVID-19 Being Used to Spread Malware

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SN Computer Science 2023年第4期4卷 398页

作者： Ahmed, Ruqayah N. Javed, Amir Bedewi, Wafi Computer Center University of Samarra Salah Aldin Samarra 34010 Iraq School of Computer Science and Informatics Cardiff University Cathays Cardiff CF24 4AG United Kingdom Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

With the rising number of people using social networks after the pandemic of COVID-19, cybercriminals took the advantage of (i) the increased base of possible victims and (ii) the use of a trending topic as the pandemic COVID-19 to lure victims and attract their attention and put malicious content to infect the most possible number of people. Twitter platform forces an auto-shortening to any included URL within a 140-character message called “tweet” and this makes it easier for the attackers to include malicious URLs within Tweets. Here comes the need to adopt new approaches to resolve the problem or at least identify it to better understand it to find a suitable solution. One of the proven effective approaches is the adaption of machine learning (ML) concepts and applying different algorithms to detect, identify, and even block the propagation of malware. Hence, this study’s main objectives were to collect tweets from Twitter that are related to the topic of COVID-19 and extract features from these tweets and import them as independent variables for the machine learning models to be developed later, so they would identify imported tweets as to be malicious or not. © 2023, The Author(s).

关键词： AI COVID-19 Cyber security Machine learning Malicious Malware Pandemic Tweet Twitter

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