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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shihao Gao Caoshuo Li Cunli Mao Xulong Zhang Xiaoyang Qu Taisong Jin Jianzong Wang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Ping An Technology Co. Ltd. Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph’s semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches.

关键词： Acoustic distortion Semantics Contrastive learning Interference Signal processing Acoustics Speech processing

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Integration of Classification and Segmentation for Computer-Aided Diagnosis System of Drowning

Integration of Classification and Segmentation for Computer-...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuwen Zeng Xiaoyong Zhang Kei Ichiji Noriyasu Homma Tohoku University Graduate School of Medicine Tohoku University Sendai Japan Dept. of General Engineering National Institute of Technology Sendai College Sendai Japan Tohoku University Graduate School of Medicine Center for Data-Driven Science and Artificial Intelligence Tohoku University Sendai Japan Tohoku University Graduate School of Medicine Graduate School of Biomedical Engineering Tohoku University Sendai Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The decline in traditional autopsy practices has led to the rise of autopsy imaging as a non-invasive alternative. However, the shortage of forensic pathologists skilled in postmortem image interpretation presents a significant challenge. Our study addresses this gap by advancing the capabilities of computer-aided diagnosis (CAD) systems in forensic diagnosis. This study builds on a previous work which developed a CAD system for drowning diagnosis and identified a critical limitation: the inconsistency between human expertise and the decision basis of the CAD system. To alleviate this issue, we present an end-to-end CAD system based on Y-Net that not only classifies post-mortem images into drowning or non-drowning but also segments regions of interest in alignment with human expertise. Experiment results showed that the model achieved an accuracy of 0.92 for classification and a mean squared error of 0.04 for segmentation, offering a promising performance and medically consistent results in drowning diagnosis.

关键词： Image segmentation Visualization Solid modeling Accuracy Forensics Autopsy Neural networks

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

arXiv

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arXiv 2024年

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

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Application of MOOC and Virtual Simulation Experiment in Experimental Teaching of Mechanical Design

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Forest Chemicals Review 2021年第JULY-AUGUST期2021卷 318-329页

作者： Han, Shaoyong Zhan, Jingchun Wang, Zhen School of Information Engineering and Technology Changzhou Institute of Industry Technology Jiangsu Changzhou China School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Johor Bahru Malaysia School of Data Science and Artificial Intelligence Wenzhou University of Technology Zhejiang Wenzhou China

The mechanical design experiment is a comprehensive experimental course that can cultivate the preliminary engineering practice ability of machinery students. Due to the high requirements for students' basic knowledge and ability of comprehensive application of knowledge, teaching is difficult and the teaching effect is general. This paper uses the MOOC teaching method, a new network-based teaching model, to study the experimental teaching of mechanical design and the construction of virtual simulation experiment teaching system under the new situation. Firstly, analyze the advantages in experimental teaching of mechanical design under virtual simulation experiment. Secondly, it puts forward suggestions for constructing the virtual simulation experiment teaching system of mechanical design, and discusses the advantages of the combination of MOOC and virtual simulation experiment technology, opportunities and reforms under the new situation. Lastly, it can be concluded that by using MOOC and virtual simulation experiments can break through the limitations of traditional teaching and improve teaching. This paper applies the MOOC and virtual simulation experiment to the experimental teaching of mechanical design, can not only improve the teaching of teachers, but also cultivate the practical ability and thinking innovation ability of machinery students. With the advantages of MOOC and virtual simulation experiments, it provides better opportunities and challenges for the teaching of mechanical design. © 2021 Kriedt Enterprises Ltd. All Rights Reserved.

关键词： Teaching

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A Strategic Approach To Tackle Interplanetary Communication Delay: Exploiting artificial intelligence Solutions For Future Space Exploration 73

A Strategic Approach To Tackle Interplanetary Communication ...

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73rd International Astronautical Congress, IAC 2022

作者： Rai, Riddhi Thuluva, Sushmith Kodukula, Ananya Bharadwaj, Vyoma Jain, Alankriti Shadaksharaiah, Anushree Maligehalli Pulicallu, Greeshmanth Kadagadakai, Vishnurat Shukla, Aayush Mitra, Ruhi Nanditha Prabhu, M. Preetham, M.P. Department of Artificial Intelligence & Data Science Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Computer Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Telecommunication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Instrumentation Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Mechanical Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Machine Learning Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Information Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India

Despite the extreme measures in any interplanetary mission, a probe is inevitably prone to a handful of unexpected errors. Concerning this, an exceptional challenge in every interplanetary endeavour is the inescapable communication gap (delay) resulting from the various factors. Although the gap is in the range of few minutes to seconds, the actions performed by the probe during critical events creates the necessary to establish a continuous and real time communication with the mission control. Thinking long term on manned missions to Mars and beyond, escalates the concern *** currently tackling such communication issues faced when a satellite communicates with earth is of high *** current paper proposes an alternate strategy to tackle this subject. With recent advancements in the development of artificial intelligence (AI), state-of-the art algorithms,neural networks, and reinforcement based models, it can be so optimized for applications suitable to meet the needs of satellite communication issues. In the light of which, the paper investigates the design of a monitoring satellite. This monitoring satellite is trained with the data and a developed deep learning algorithm/machine learning model is employed by the satellite to make future decisions. Hence using the current knowledge and data we have regarding the communication issues faced while establishing a link with earth, we can train the data models present in the satellite to make the communication with earth more *** the results we gain from this can be later transferred to tackle interplanetary communication *** in the future,we can transfer this knowledge to a a probe that can help tackle interplanetary communication. Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Satellites

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Analysis Of A Zipline Robotic System To Assist Astronauts On Lunar And Martian Terrains 73

Analysis Of A Zipline Robotic System To Assist Astronauts On...

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73rd International Astronautical Congress, IAC 2022

作者： Kadagadakai, Vishnurat Thuluva, Sushmith Kodukula, Ananya Bharadwaj, Vyoma Jain, Alankriti Rao, Bhavana B. Pulicallu, Greeshmanth Rai, Riddhi Shukla, Aayush Mitra, Ruhi Nanditha Prabhu, M. Kamath, Satwik Preetham, M.P. Department of Electronics & Telecommunication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Computer Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Mechanical Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Data Science Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Machine Learning Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Information Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Communication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India

In the realm of drones and probes, this paper proposes a subsequent derivative idea of ziplining automated mobile systems to assist astronauts on Lunar and Martian surfaces. Although the conventional human-like robotic systems alleviate many challenges to astronauts on the moon and the mars, one particular challenge of reaching farther on land is left to the technology of drones. Drones however are more efficient in overviewing the area and are less effective in transporting samples or astronauts to the target. This paper discusses an idea of a ziplining robotic system that can reach farther places from the base camp in a secure and safe way. The system comprises multiple support poles, a subsystem of ziplining and a mobility subsystem to traverse the contents from point to point. This paper describes the overall design of the robotic system, its operations and possible advancements for the future scope. The future applications of such an idea involves the support for constructing a settlement for our Martian outposts. The paper explores these domains of applications and the effectiveness of such a mobility system in various situations. The paper investigates the proposed design on Lunar and Martian surfaces but can be optimised for other planetary bodies as well. © 2022 International Astronautical Federation, IAF. All rights reserved.

关键词： Drones

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Molecular Design for Drug Discovery with the Use of Generative Adversarial Networks

Molecular Design for Drug Discovery with the Use of Generati...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： G. Sumathi P. Sudha Juliet M. Shafiya Banu S. Daisylin Anbu Sujitha Yuvaraj S S. Sujatha Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Electronics and Instrumentation Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Chemistry Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This research study describes how Generative Adversarial Networks (GANs) are used to improve drug discovery molecular design. The goal is to maximize the discovery of potential drug candidates by making use of GANs' ability to produce a wide variety of chemical structures. The goal is to use better molecular design methods to speed up and improve the medication development process. The purpose of using GANs is to make it easier to build new molecular structures with certain features, such more potency and selectivity. This technology has the potential to bypass the drawbacks of conventional drug discovery techniques and speed up the process of exploring chemical space. It demonstrates that GANs have the potential to revolutionize drug development through computational simulations and validation tests. One potential way to find new treatments and solve medical problems that have so far gone unsolved is to include GANs into molecular design processes. Results from Drug Design data Resource, Kaggle datasets shows that the molecular formula with id Activity-Based Learning (ABL) minimum value starts with 0.055 and maximum of 10, from the sample of 10 compounds the minimum affinity value is 0.075 and maximum of 3.42. In another data surveyed the drug discovery values show the minimum of the molecular weight is 356.3 and maximum is 471.5.

关键词： Drugs Technological innovation Design methodology Refining Generative adversarial networks Drug discovery Space exploration Compounds Reliability Chemicals

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Spiking transformer with experts mixture 24

Spiking transformer with experts mixture

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

作者： Zhaokun Zhou Yijie Lu Yanhao Jia Kaiwei Che Jun Niu Liwei Huang Xinyu Shi Yuesheng Zhu Guoqi Li Zhaofei Yu Li Yuan School of Electronic and Computer Engineering Shenzhen Graduate School Peking University and Peng Cheng Laboratory School of Electronic and Computer Engineering Shenzhen Graduate School Peking University College of Computing and Data Science Nanyang Technological University and Deep NeuroCognition Lab I2R and CFAR Agency for Science Technology and Research School of Computer Science Peking University and Peng Cheng Laboratory Institute for Artificial Intelligence Peking University and School of Computer Science Peking University Institute of Automation Chinese Academy of Sciences and Peng Cheng Laboratory

ISBN: (纸本)9798331314385

Spiking Neural Networks (SNNs) provide a sparse spike-driven mechanism which is believed to be critical for energy-efficient deep learning. Mixture-of-Experts (MoE), on the other side, aligns with the brain mechanism of distributed and sparse processing, resulting in an efficient way of enhancing model capacity and conditional computation. In this work, we consider how to incorporate SNNs' spike-driven and MoE's conditional computation into a unified framework. However, MoE uses softmax to get the dense conditional weights for each expert and TopK to hard-sparsify the network, which does not fit the properties of SNNs. To address this issue, we reformulate MoE in SNNs and introduce the Spiking Experts Mixture Mechanism (SEMM) from the perspective of sparse spiking activation. Both the experts and the router output spiking sequences, and their element-wise operation makes SEMM computation spike-driven and dynamic sparse-conditional. By developing SEMM into Spiking Transformer, the Experts Mixture Spiking Attention (EMSA) and the Experts Mixture Spiking Perceptron (EMSP) are proposed, which performs routing allocation for head-wise and channel-wise spiking experts, respectively. Experiments show that SEMM realizes sparse conditional computation and obtains a stable improvement on neuromorphic and static datasets with approximate computational overhead based on the Spiking Transformer baselines.

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An Innovative Design Of A Reusable Constellation Of CubeSats For Space Debris Removal 73

An Innovative Design Of A Reusable Constellation Of CubeSats...

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73rd International Astronautical Congress, IAC 2022

作者： Shukla, Aayush Thuluva, Sushmith Kodukula, Ananya Bharadwaj, Vyoma Jain, Alankriti Shadaksharaiah, Anushree Maligehalli Pulicallu, Greeshmanth Kadagadakai, Vishnurat Rai, Riddhi Mitra, Ruhi Prabhu, M. Nanditha Department of Artificial Intelligence & Machine Learning Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Computer Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Telecommunication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Instrumentation Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Mechanical Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Data Science Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Information Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India

Space debris is one significant setback in our attempt to explore space and enhance satellite technology. Eradicating this upshot is eminent to our future missions and a safe environment for our planet. The scale of the space junk in the low earth orbit is around few hundreds of thousands and more, sizing from 10s of centimeters in diameter and lower. Although it is inevitable for the space debris to reenter the atmosphere naturally, it is a matter of years and hence the rate is too slow. With advances in the CubeSat technology, a variety of designs are possible for CubeSat systems to function as per the mission requirements. The current paper hence nominates an idea to remove some of the inoperative satellites and it's corresponding components with the help of a constellation of CubeSats that are equipped with distributed actuators for propulsion. The CubeSats are so designed to function as a propulsive device alone with minimal subsystem requirements. The constellation is contained in a cuboidal station with CubeSats docked on all the lateral sides. Once a target body is locked and intended to be removed, the constellation releases one or more CubeSats en route to the target body. Once at site, the CubeSats attach themselves to the target and propel the body to reenter the atmosphere. This constellation station is so designed to be compatible for reusability and the maintenance of such a design is discussed in the paper. The entire approach is thoroughly analyzed investigating multiple designs for the CubeSats. An additional benefit of the proposed idea is to ensure an immediate solution for deorbiting any critically unstable debris in the future in an event of emergency. Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Space debris

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A Novel Deep Learning based Stress Analysis and Detection Scheme using Characteristic data

A Novel Deep Learning based Stress Analysis and Detection Sc...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： R. Geetha N V Krishnamoorthy K. H. Shakthi Murugan C. Gnanaprakasam M. Swarna Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Tamilnadu India Department of Electronics and Communication Engineering Narayana Engineering College Gudur AndhraPradesh India Department of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Chennai Tamilnadu India Department of Electronics and Communication Engineering Dr. M.G.R. Educational And Research Institute University Chennai Tamilnadu India

One of the most time-consuming and complex study subjects in the present day is trying to anticipate the amount of stress experienced by working professions. Although short-term stress might help us perform under pressure, chronic or severe stress compromises our well-being and causes disruptions in our daily life. Many stress-related health issues are avoidable if identified and treated early. A person's stress level may be determined by monitoring changes in a variety of biosignals, including temperature, electromagnetic, resistive, acoustical, visual and so on. There are a plethora of studies that may help you figure out what causes your stress and what kinds of things help you deal with it. Previous studies had a number of drawbacks, including more design complexity, more mistakes, worse efficiency, and a higher rate of misclassification. This research aims to apply an enhanced deep learning technique called Learning based Novel Stress Analyzer (LNSA) to forecast the level of stress of industry professionals in an effort to address these problems. Included in the methods proposed by this study are information preparation, extraction of features, classification and LNSA-based identification. Professionals' logical characteristic data is gathered via sensors and used as a dataset for processing the complete system. The sensors known as 3-Axis Accelerometer, Electrocardiogram, Pulse Sensor, Body Temperature and Respiration Sensor collect the defining data. Specifically, it eliminates redundant characteristics and completes incomplete data in the relevant dataset through a process called pre-processing. This paper provides the final classification findings, in which it show high accuracy in results and the technique of LNSA suggested in this paper accurately estimate the level of stress in a given situation.

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