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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lou, Yiwei Xu, Dexuan Zhang, Rongchao Zhang, Jiayu Cao, Yongzhi Wang, Hanpin Huang, Yu Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China Peking University School of Computer Science Beijing China Peking University School of Software and Microelectronics Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350386226

Magnetic resonance (MR) image quality assessment plays a crucial role in disease diagnosis and data analysis. Existing methods typically treat the data as images and process them with convolutional networks, thereby ignoring the sequential characteristics of MR data. In this paper, we propose a hybrid spatial-frequency network (HSFNet) for MR image quality assessment, which extracts MR image quality features in both spatial and frequency domains. Specifically, within each data domain, information from local images and global sequences is iteratively integrated by applying a Mamba-based cascading processing module for multiple times. Extensive experiments on both T1-weighting and T2-weighting MR datasets demonstrate the proposed method's effectiveness and generalization ability in comparison with state-of-the-art MR image quality assessment methods. © 2024 IEEE.

关键词： Image quality

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ASLiquid: Non-Intrusive Liquid Counterfeit Identification with Your Earphones 24

ASLiquid: Non-Intrusive Liquid Counterfeit Identification wi...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Li, Zhaohui Luo, Wei Zhang, Yongmin Chen, Jianxi Shu, Yuanchao Zhang, Yaoxue School of Computer Science and Engineering Central South University China College of Control Science and Engineering Zhejiang University China Department of Computer Science and Technology BNRist Tsinghua University China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798400706974

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-intrusive enough. In this work, we propose ASLiquid, the first liquid counterfeit identification system with commercial off-the-shelf earphones. Our core insight is that earphones can effectively induce acoustic resonance in container, and this phenomenon is observed highly associated with the changes in liquid density and solute compositions. Deploying ASLiquid introduces three main challenges: hardware heterogeneity among different earphones, diversity of user operations, and data complexity due to variations in liquid volume and device placement. To address these issues, we first propose to eliminate the existence of hardware noise and frequency response diversity for an earphone-irrelevant solution. Afterwards, we design a user operation adaptation algorithm to extract valuable feature data during each measurement period. To alleviate problems in data complexity, we propose a spectrum projection algorithm that can effectively generate CFR data of unknown liquid volumes and a VAE based anomaly detection model for counterfeit identification. We evaluate our system with six different earphones and under various conditions. Experimental results reveal that ASLiquid can achieve F1 scores of 95%-99.25% for seven frequently occurring liquid counterfeit tasks, even in specialized attacks on liquids with 1% difference in mass fraction and different types of solutions but with the same density. © 2024 Copyright is held by the owner/author(s).

关键词： Earphones

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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A Review of Multi-omics and Machine Learning: Advancements and Applications in Cancer Research 3

A Review of Multi-omics and Machine Learning: Advancements a...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Ayman, Hazem Bassem, Madonna Madkour, Malak Fawzi, Mariam Amr, Nada Yousry, Youmna Elgayar, Ziad Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem. © 2023 IEEE.

关键词： Gaussian distribution

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End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volume 3

End-to-End Multi-View Structure-from-Motion with Hypercorrel...

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3rd IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Chen, Qiao Poullis, Charalambos Concordia University Immersive and Creative Technologies Lab Department of Computer Science and Software Engineering MontrealQC Canada

ISBN: (纸本)9781665472784

Image-based 3D reconstruction is one of the most important tasks in computer Vision with many solutions proposed over the last few decades. The objective is to extract metric information i.e. the geometry of scene objects directly from images. These can then be used in a wide range of applications such as film, games, virtual reality, etc. Recently, deep learning techniques have been proposed to tackle this problem. They rely on training on vast amounts of data to learn to associate features between images through deep convolutional neural networks and have been shown to outperform traditional procedural techniques. In this paper, we improve on the state-of-the-art two-view structure-from-motion(SfM) approach of [11] by incorporating 4D correlation volume for more accurate feature matching and reconstruction. Furthermore, we extend it to the general multi- view case and evaluate it on the complex benchmark dataset DTU [5]. Quantitative evaluations and comparisons with state-of-the-art multi-view 3D reconstruction methods demonstrate its superiority in terms of the accuracy of reconstructions. © 2023 IEEE.

关键词： computer vision

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Discussion on the Influence of Anti-normalization Parameter Selection of BP Neural Network on Mending Seepage Pressure Data of Tailing Dam

Discussion on the Influence of Anti-normalization Parameter ...

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2022 International Joint Conference on Information and Communication engineering, JCICE 2022

作者： Zhu, Hongli Jin, Meiqi Wen, Yumeng Lu, Ling Liaoning University of Science and Technology School of Computer and Software Engineering Department of Software Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Department of Network Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Liaoning Anshan China

ISBN: (数字)9781665460675

ISBN: (纸本)9781665460675

The height of Infiltration line of tailings dam is a very important parameter for the safety of tailings reservoir. Infiltration line is the intersection line between the free water surface and the cross section of the dam body, that the free water surface is formed by the infiltration of water from the earth dam to the downstream. When the buried depth of the pressure sensors at each monitoring point could be obtained and the groundwater seepage pressure of the tailings dam could be measured too, the height of the free water surface can be calculated then. The commonly used pressure sensor is vibrating wire sensor. Among the many seepage pressure monitoring points, because of the lines, power supply or some other reasons, it often occurs that one or some points do not collect groundwater seepage pressure data for a period of time. For the missing data, BP neural network can be used to mend the missed data. Of course, the patched data should be marked to distinguish it from the real collected data. The mending method of missing seepage pressure data using BP neural network model is analyzed. The influence of anti-normalization parameters on mending data is analyzed. In the process of anti-normalization, the maximum and minimum values of the output data of the training data set are not put to use. Instead, a sequences is formed with the existing output data of some range before and after the mission data segment. The maximum and minimum values of the sequence are selected to use as the anti-normalization parameters. This will make the patched data values closer to the actual collected values. © 2022 IEEE.

关键词： Groundwater

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Neural Pathways to Attention Enhancement: EEG Spectral Ratio Analysis of CM-II Meditation's Effect on Student Attention 54

Neural Pathways to Attention Enhancement: EEG Spectral Ratio...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Gopi, Sreekanth Dehbozorgi, Nasrin Kennesaw State University Department of Computer Science KennesawGA United States Kennesaw State University Department of Software Engineering KennesawGA United States

ISBN: (纸本)9798350351507

This research-to-practice paper describes the development of an innovative practice aimed at addressing the global surge in student stress and related mental health challenges, particularly in engineering education. Student well-being is foundational to academic success, with studies indicating that dropout rates for students with stress are more than 50% and mental health problems can range as high as 83% in engineering education. Stress and elevated levels of inattention are associated with decreased academic interest, decision-making, self-efficacy, study skills, and GPA. Research has shown that stress levels can be reduced by meditation techniques and they induce attention. In this research, a three-stage guided meditation called ChakraMarmaKosha Meditation II (CM-II) was developed to improve the attention level of students. It involves past emotional cleansing, reviewing the day, visualizing the future components, and is accompanied by flute music that is composed based on a chosen Raga. In this study, a data analysis pipeline was developed for detailed spectral analysis to measure variations in attention due to meditation. In an in-person EEG experiment, a study group of 15 college students underwent this CM-II meditation process in a lab setting. The EEG spectral analysis underscored an enhancement in attentional focus post-meditation, as evidenced by key shifts in spectral ratios such as the notable decrease in Theta to Beta Ratio (TBR) and the surge in Theta to Gamma Ratio (TGR). The observed results were validated using attention scores from tests on the TestMyBrain tool. This study underscores the potential benefits of the CM-II meditation technique in fostering enhanced attention, reduced stress, and consequently improved academic performance in engineering education. While the current study provides promising results with CM-II meditation in enhancing attention, cognition, and relaxation, it's essential to acknowledge the limitations, such as the confin

关键词： Students

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Integrity Estimation of Twitter Based Event Recognition Using Scrutiny of Analyzed Data 2

Integrity Estimation of Twitter Based Event Recognition Usin...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Siva Rama Lingham, N. Parameswari, M. Karthick, P. Priya, T. Subha Rathi Rajesh, G. Jagannathan, J. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai India School of Engineering and Technology Jeppiaar University Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events. © 2024 IEEE.

关键词： Impact testing

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Learning Analytics Support in Higher-Education: Towards a Multi-Level Shared Learning Analytics Framework 16

Learning Analytics Support in Higher-Education: Towards a Mu...

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16th International Conference on computer Supported Education, CSEDU 2024

作者： Vierhauser, Michael Groher, Iris Sauerwein, Clemens Antensteiner, Tobias Hatmanstorfer, Sebastian University of Innsbruck Department of Computer Science Innsbruck Austria Johannes Kepler University Linz Institute of Business Informatics Software Engineering Linz Austria

ISBN: (纸本)9789897586972

Assurance of Learning and Competency-Based Education are increasingly important in higher education, not only for accreditation or transfer of credit points. Learning Analytics is crucial for making educational goals measurable and actionable, which is beneficial for program managers, course instructors, and students. While universities typically have an established tool landscape where relevant data is managed, information is typically scattered across various systems with different responsibilities and often only limited capabilities for sharing data. This diversity, however, significantly hampers the ability to analyze data, both on the course and curriculum level. To address these shortcomings and to provide program managers, course instructions, and students with valuable insights, we devised an initial concept for a Multi-Level Shared Learning Analytics Framework to provide consistent definition and measurement of learning objectives, as well as tailored information, visualization, and analysis for different stakeholders. In this paper, we present the results of initial interviews with stakeholders, devising core features. In addition, we assess potential risks and concerns that may arise from the implementation of such a framework and data analytics system. As a result, we identified six essential features and six main risks to guide further requirements elicitation and development of our proposed framework. Copyright © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Students

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Matching Gains with Pays: Effective and Fair Learning in Multi-Agent Public Goods Dilemmas 27

Matching Gains with Pays: Effective and Fair Learning in Mul...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Chen, Yitian Liu, Xuan Zhang, Shigeng Chen, Xinning Guo, Song School of Computer Science and Engineering Central South University China College of Computer Science and Electronic Engineering Hunan University China The Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" China Department of Computer Science and Engineering Hong Kong University of Science and Technology China

ISBN: (纸本)9781643685489

The training of multi-agent reinforcement learning (MARL) tasks with the public goods dilemma (PGD) is difficult because the selfish actions of individual agents for high personal rewards may reduce the collective utility of the whole group. Existing solutions to this problem, e.g., reward gifting or intrinsic rewards, although inducing cooperation among agents in small groups, cannot guarantee fairness among agents’ policies and fail to achieve optimal group utility in large-scale systems. In this paper, we propose F4PGD, an effective method to train large-scale MARL tasks with PGD in a decentralized manner, which is inspired by Adam’s equity theory that the match between a person’s payoff and his contribution is the key incentive for people to contribute to the common good. In F4PGD, a mechanism is designed to match an agent’s reward with its contribution, which suppresses agents from taking a free ride and meanwhile encourages well-learned agents to contribute to public goods. Experimental results show that F4PGD effectively learns optimal policies for the whole group and guarantees fairness among agents in several typical MARL tasks with PGD. © 2024 The Authors.

关键词： Adversarial machine learning

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