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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Xuanyu Meng, Jiarui Li, Runyi Xu, Zhipei Zhang, Yongbing Zhang, Jian School of Electronic and Computer Engineering Peking University China Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology Peking University Shenzhen Graduate School China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

3D Gaussian Splatting (3DGS) has already become the emerging research focus in the fields of 3D scene reconstruction and novel view synthesis. Given that training a 3DGS requires a significant amount of time and computational cost, it is crucial to protect the copyright, integrity, and privacy of such 3D assets. Steganography, as a crucial technique for encrypted transmission and copyright protection, has been extensively studied. However, it still lacks profound exploration targeted at 3DGS. Unlike its predecessor NeRF, 3DGS possesses two distinct features: 1) explicit 3D representation;and 2) real-time rendering speeds. These characteristics result in the 3DGS point cloud files being public and transparent, with each Gaussian point having a clear physical significance. Therefore, ensuring the security and fidelity of the original 3D scene while embedding information into the 3DGS point cloud files is an extremely challenging task. To solve the above-mentioned issue, we first propose a steganography framework for 3DGS, dubbed GS-Hider, which can embed 3D scenes and images into original GS point clouds in an invisible manner and accurately extract the hidden messages. Specifically, we design a coupled secured feature attribute to replace the original 3DGS's spherical harmonics coefficients and then use a scene decoder and a message decoder to disentangle the original RGB scene and the hidden message. Extensive experiments demonstrated that the proposed GS-Hider can effectively conceal multimodal messages without compromising rendering quality and possesses exceptional security, robustness, capacity, and flexibility. Our project is available at: https://***/project/gshider/. © 2024 Neural information processing systems foundation. All rights reserved.

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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Novel deep recurrent neural structure with Bayesian distributed backpropagation for biomaterial model involving ferro–copper/blood nanofluids

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European Physical Journal Plus 2024年第1期139卷 1-37页

作者： Khan, Maryam Pervaiz Mukhtar, Roshana Chang, Chuan-Yu Chaudhary, Naveed Ishtiaq Shoaib, Muhammad Raja, Muhamad Asif Zahoor Department of Computer Science and Information Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Yunlin 64002 Taiwan AI center Yuan Ze University Taoyuan 320 Taiwan

The aim of the presented study is to portray the numerical treatment for biomaterial fluid flow models (BFFMs) involving magnetized ferro and copper nanoparticles dispersed in blood between two rotating disks by exploiting the knacks of layered recurrent neural networks with Bayesian distributed backpropagation (LRNN-BDB). Mathematical formulation of BFFMs is governed with PDEs that are renovated into nonlinear ordinary differential system by operating suitable transformations. The synthetic data are acquired numerically by simulated the implicit backward differentiation formulas to measure the impact of porosity permeability, Darcy–Forchheimer, and Joule heating for BFFMs involving both ferro and copper nanomaterial mixed with blood as a base fluid. The supervised computing paradigm of LRNN-BDB is executed on acquired datasets, and outcomes of LRNN-BDB are regularly overlapping with numerical observations for each variant of BFFMs with negligible error. The stupendous knacks of LRNN-BDB procedures are efficaciously depicted through mean squared error-based iterative convergence plots, adaptive controlling metrics of learning, error distribution statistics on histogram illustrations, and regression measures for exhaustive experimentation on BFFMs. © 2024, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.

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Consideration for Creation of Training Samples for Targeted Malware Detection by Machine Learning 6

Consideration for Creation of Training Samples for Targeted ...

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6th International Conference on Information and computer Technologies, ICICT 2023

作者： Uda, Ryuya Kotani, Taeko Tokyo University of Technology Graduate School Graduate School of Bionics Computer and Media Science 1404-1 Katakuramachi Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9798350300956

Targeted malware evolved in recent years and that with encrypted payload appeared. The evolved targeted malware is difficult to be detected by static analysis and dynamic analysis on a virtual or stand-Alone machine. The key for encryption is generated from information of a target computer. Therefore, the key is only known by an attacker and a victim computer. However, any targeted malware must collecting information of victim computer and we think this is a feature of targeted malware. First, we introduce our proposed method which detects targeted malware with encrypted payload by finding features of the collection behavior with machine learning. As a result, all test samples were classified to benign in our evaluation. We considered that features of our simulated samples are different from those of actual samples. Therefore, in this paper, we investigate of the difference and consider how we can create more precise simulated samples. We created map images of matched n-grams of our simulated samples with actual malware samples. We found hints of more precise simulated samples by looking distribution of the matched n-grams. © 2023 IEEE.

关键词： Static analysis

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Thermal coupling analysis and improved dynamic temperature control algorithm for 3D-LSI

Thermal coupling analysis and improved dynamic temperature c...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Wang, Songxiang Usami, Kimiyoshi Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan Shibaura Institute of Technology Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350326413

3D-LSI techniques are proven to be effective in improving performance without miniaturization of transistors. However, 3D-LSI have problems in heat dissipation, especially in 3D-LSI without TSV. An example is the stacked chips where communication among chips is done wirelessly. It is difficult to dissipate the heat generated by layers not connected to the substrate or the cooler. This is because thermal conduction through TSVs becomes impossible. Therefore, thermal management technology is necessary in the TSV-less 3D stacked chips. In this paper, we investigate the effect of multiple heating sources on each other by simulation. Based on the simulation results, we considered the distance between multiple high heat- sources in the proposed method to further reduce the highest temperature in the chip. In addition, we also designed a task migration algorithm when heat source cross-layer migration is allowed. This improved algorithm can select better task migration targets to reduce the generation of hotspots without changing the overall performance of the chip. Simulation verification was conducted using a commercial thermal analysis software. The results show that our approach can reduce the maximum temperature in the chip by 3-7°C when only intra-layer migration is allowed, and by more than 30°C when cross-layer migration of tasks is allowed. © 2023 IEEE.

关键词： Temperature control

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Automatic Multiple Choice Question Generation: A Systematic Literature Review 9

Automatic Multiple Choice Question Generation: A Systematic ...

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9th International Conference on Information technology and Digital Applications, ICITDA 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Nugroho, Kuncahyo Setyo Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark. © 2024 IEEE.

关键词： Semantics

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease 9

Comparative Analysis of CNN Models for Multiclass Detection ...

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9th International Conference on Information technology and Digital Applications, ICITDA 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Isnan, Mahmud Pardamean, Bens Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection © 2024 IEEE.

关键词： Convolutional neural networks

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Climate change and artificial intelligence: assessing the global research landscape

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Discover Artificial Intelligence 2024年第1期4卷 1-12页

作者： Lewis, Joanna I. Toney, Autumn Shi, Xinglan Science Technology and International Affairs Program Edmund A. Walsh School of Foreign Service Georgetown University WashingtonDC United States Center for Security and Emerging Technology Edmund A. Walsh School of Foreign Service Georgetown University WashingtonDC United States Department of Computer Science Georgetown University WashingtonDC United States Communications Culture and Technology Program Georgetown University WashingtonDC United States

Artificial Intelligence (AI) could revolutionize our ability to understand and address climate change. Studies to date have focused on specific AI applications to climate science, technologies, and policy. Yet despite the vast demonstrated potential for AI to change the way in which climate research is conducted, no study has presented a systematic and comprehensive understanding of the way in which AI is intersecting with climate research around the world. Using a novel merged corpus of scholarly literature which contains millions of unique scholarly documents in multiple languages, we review the community of knowledge at the intersection of climate change and AI to understand how AI methods are being applied to climate-related research and which countries are leading in this area. We find that Chinese research institutions lead the world in publishing and funding research at the intersection of climate and AI, followed by the United States. In mapping the specific AI tasks or methods being applied to specific climate research fields, we highlight gaps and identify opportunities to expand the use of AI in climate research. This paper can therefore greatly improve our understanding of both the current use and the potential use of AI for climate research. © The Author(s) 2024.

关键词： AI AI tasks and methods China Climate change Publication analysis

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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