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

作者： Jonas Kulhanek Songyou Peng Zuzana Kukelova Marc Pollefeys Torsten Sattler Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague and Faculty of Electrical Engineering Czech Technical University in Prague and Department of Computer Science ETH Zurich Department of Computer Science ETH Zurich Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

ISBN: (纸本)9798331314385

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework.

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Pending Interest Table (PIT) Performance Analysis in Named Data Networking on Palapa Ring Topology 16

Pending Interest Table (PIT) Performance Analysis in Named D...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Sucipto, Adi Jupriyadi Ahdan, Syaiful Arifin, Hasan Nur Zaki Hamidi, Eki Ahmad Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung35132 Indonesia Uin Sunan Gunung Djati Department of Electrical Engineering Bandung40132 Indonesia

ISBN: (纸本)9781665455947

The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20%. At the amount of interest in 1000 about more than 30%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests. © 2022 IEEE.

关键词： Topology

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Behind the Scenes: An Explainable Artificial Intelligence (XAI) on the Service Classification of the 5G/B5G Network 3

Behind the Scenes: An Explainable Artificial Intelligence (X...

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3rd International Conference on Digital Transformation and Applications, ICDXA 2024

作者： Allias, Noormadinah Kadir, Diyana Ab Abdullahi, Akibu Mahmoud Ismail, Shahrinaz Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia Computer Engineering Technology Universiti Kuala Lumpur- Malaysia Institute of Information Technology Kuala Lumpur Malaysia School of Computing and Informatics Albukhary International University Kedah Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

Fifth generation and beyond (5G/B5G) is part of the next generation of mobile technology, which is expected to offer more capacity and faster speeds than the previous generation Long-Term Evolution (LTE) network. These features enable 5G/B5G to offer users a wide range of services, including smart cities, entertainment and multimedia, healthcare and mission-critical applications that benefit the entire economy and communities. However, due to growing demand, telecom service providers need to provide better Quality of service (QoS) on their networks to meet user expectations. Therefore, it is necessary to implement service classification that enables service providers to select the right network slices for each service to improve network QoS. Previous studies have predicted the classification of 5G/B5G services using traditional machine learning algorithms such as random forest and decision tree, achieving good accuracy. However, prediction based on black-box machine learning models was not transparent enough, so mobile service providers could not understand the reliability and interpretation of the results. Therefore, this article aims to model the system performance of 5G Key Performance Indicators (KPI) and extend the experiment to Explainable Artificial Intelligence (XAI) by using the Explainable AI to look behind the scenes of the chosen black-box models for service classification of 5G/B5G networks, as this is crucial for telecom service providers to make critical investment decisions around customer and employee experience. The results from the experiments show that the random forest has the highest confidence towards class Label 3 with the probability of 35 percent. © 2024 IEEE.

关键词： Decision trees

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, Science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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A comparative study of word embedding techniques for SMS spam detection 14

A comparative study of word embedding techniques for SMS spa...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Joseph, Prashob Yerima, Suleiman Y. School Computer Science and Informatics De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom Cyber Technology Institute De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom

ISBN: (纸本)9781665487719

E-mail and SMS are the most popular communication tools used by businesses, organizations and educational institutions. Every day, people receive hundreds of messages which could be either spam or ham. Spam is any form of unsolicited, unwanted digital communication, usually sent out in bulk. Spam emails and SMS waste resources by unnecessarily flooding network lines and consuming storage space. Therefore, it is important to develop high accuracy spam detection models to effectively block spam messages, so as to optimize resources and protect users. Various word-embedding techniques such as Bag of Words (BOW), N-grams, TF-IDF, Word2Vec and Doc2Vec have been widely applied to NLP problems, however a comparative study of these techniques for SMS spam detection is currently lacking. Hence, in this paper, we provide a comparative analysis of these popular word embedding techniques for SMS spam detection by evaluating their performance on a publicly available ham and spam dataset. We investigate the performance of the word embedding techniques using 5 different machine learning classifiers i.e. Multinomial Naive Bayes (MNB), KNN, SVM, Random Forest and Extra Trees. Based on the dataset employed in the study, N-gram, BOW and TF-IDF with oversampling recorded the highest F1 scores of 0.99 for ham and 0.94 for spam. © 2022 IEEE.

关键词： Embeddings

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A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

A Heterogeneous Network-based Contrastive Learning Approach ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Junwei Bewong, Michael Kwashie, Selasi Zhang, Wen Nofong, Vincent M. Wu, Guangsheng Feng, Zaiwen Huazhong Agricultural University College of Informatics Hubei Wuhan China Charles Sturt University School of Computing Mathematics & Engineering Wagga WaggaNSW Australia Charles Sturt University Artificial Intelligence & Cyber Futures Institute BathurstNSW Australia University of Mines and Technology Faculty of Engineering Tarkwa Ghana Xinyu University School of Mathematics and Computer Science China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://***/Zaiwen/HNCL-DTI. © 2024 IEEE.

关键词： Contrastive Learning

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Subject-independent multi-channel voting for EEG-based emotion recognition using wavelet scattering deep network and advanced signal metrics

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Pattern Analysis and Applications 2025年第3期28卷 1-29页

作者： Elrefaiy, Ahmed Tawfik, Nahed Zayed, Nourhan Elhenawy, Ibrahim Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig Egypt Field of Artificial Intelligence & Computer Engineering Technology El Sewedy University of Technology Cairo Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt Mechanical Engineering Department The British University in Egypt Cairo Egypt

Electroencephalography (EEG) signals, reflecting human brain activity, hold potential beyond medical diagnosis, particularly in emotion recognition. Despite the development of machine learning models utilizing EEG data for this purpose, achieving good enough accuracy remains a challenge due to signals complexity and non-stationary nature, especially in extracting effective features that encapsulate temporal and frequency information. This paper introduces a novel hand-crafted feature extraction technique that avoids conventional signal segmentation and analyzes the entire length of EEG signals. This method builds a convolutional network utilizing Wavelet Scattering Transform (WST) blocks, followed by deriving a comprehensive 17-feature set from the raw EEG data and WST scattering coefficients. This integrative set takes advantage of the WST’s ability to produce a signal representation that is stable against noise, invariant to time shifts, and captures both temporal and frequency components while also leveraging the intrinsic properties of the raw data, offering an alternative to the computational deep models. The integration of Linear Discriminant Analysis for dimensionality reduction and the K-Nearest Neighbors algorithm for classification, further refined by a majority voting mechanism across all channels, results in a robust classification framework. The proposed method is evaluated across GAMEEMO and DEAP datasets with two and four emotional classes, using Leave-One-Subject-Out validation, achieving classification accuracy exceeding 97%. The findings support the effectiveness of this approach in EEG-based emotion recognition. Furthermore, an ablation study on the two datasets is implemented to assess each component’s impact, revealing insights into the model’s effectiveness and improvement areas.

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On a Piecewise Linear Function Approximation for Quantum Computation

On a Piecewise Linear Function Approximation for Quantum Com...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Hideaki Okazaki Dept. of Informatics Faculty of Informatics Computer Science Course (or Field) Graduate school of Electrical and Information Engineering Shonan Institute of Technology Fujisawa Kanagawa Japan

In quantum computing, although correspondences between quantum circuits and polynomials have been explored, a correspondence between a quantum circuit and a low-degree polynomial, even a linear function has not fully clarified. In this paper, a quantum circuit approximating a linear function for building piecewise linear functions, is proposed. First, for qubit operations, a dimensionless form in $[0,1]$ of a linear function is provided. Secondly, a theorem of a parameterized quantum circuit approximating linear functions are provided. Thirdly, by using Yao: an open source Julia package, the quantum circuit simulation results to approximate the linear functions are presented. Finally, the principal results are summarized.

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Digital Twins and Intelligent IoT for a Better World (Panel Discussion Report-I of IEEE AIoTSys2023)

Digital Twins and Intelligent IoT for a Better World (Panel ...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Huang, Runhe Zhang, Daqing Yu, Zhiwen Nugent, Chris Jia, Weijia Yang, Kun Zhang, Yan Sekimoto, Yoshihide Guo, Bin Faculty of Computer and Information Sciences Hosei University Tokyo Japan Peking University Ip Paris France Harbin Engineering University Northwestern Polytechnical University China School of Computing Ulster University Ireland BNU-UIC Joint Ai Research Institute Beijing Normal University China School of Computer Science and Electronic Engineering University of Essex United Kingdom Department of Informatics University of Oslo Norway Institute of Industrial Science University of Tokyo Japan School of Computer Science Northwestern Polytechnical University China

ISBN: (纸本)9798350312270

This position paper provides a summary report of the panel discussion on "Digital Twins and Intelligent IoT for a Better World"at the International Conference on Artificial Intelligence of Things and Systems (AIoTSys'23). The panel featured seven distinguished invited panelists, including four IEEE Fellows from Peking University, Beijing Normal University, University of Essex, and the University of Oslo. Additionally, there was one vice president from Harbin engineering University, one school head from Ulster University, and one center vice director from the University of Tokyo. The panel covered seven topics: (1) The cost-effectiveness of IoTenabled digital twin design, (2) Ubiquitous crowd computing systems, (3) The vision for the fusion of the Intelligent IoT with the Digital Twin, (4) Intelligent networking in relation to Chinese culture and education, (5) Data-driven digital twin wireless networks, (6) Digital twins edge networking, and (7) The automatic generation of urban digital twins from local government s open data. The panel generated lively and comprehensive discussions, and this report represents the enriched outcome of our panelists insights, visions, and the interactive discussions with the audience. © 2023 IEEE.

关键词： Internet of things

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Architecture Open Workspace Concept for Prescriptive Model of Digital Twin 9

Architecture Open Workspace Concept for Prescriptive Model o...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Supangkat, Suhono Harso Supangkat, Suhono Harso Leksono, Edi Ramelan, Agus Latifah, Ayu School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Engineering Physics Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia Electrical Engineering Departement Universitas Sebelas Maret Solo Indonesia Department of Computer Science Institut Teknologi Garut Garut Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

The workspace is a portion of a structure that can currently apply a smart building system adaption for more ideal design outcomes. Millions of hours are reportedly wasted each year as a result of the harmful effects that poor environmental quality has on workers' productivity and health, according to the findings of several studies. Therefore, a centralized approach is required to be able to give quick and accurate information. This may be accomplished via digital twin technology, which will result in a workspace design that is more ideal in the future. The major goal of this research is to create a digital twin prescriptive model in a physical workspace environment that can create a strategic space design plan and be flexible enough to react to changes in the physical environment, which will improve user comfort and productivity. A life cycle model was employed as the study methodology, which begins with the identification of the model's requirements and ends with its maintenance. This model is employed to offer suggestions for preventative and remedial activities on actual things. © 2022 IEEE.

关键词： Productivity

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