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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Ping, Tan Ping Verspoor, Karin Miller, Timothy Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Sarawk Malaysia School of Computing Technologies Royal Melbourne Institute of Technology Melbourne Australia School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9798350397000

Despite advancement in collaborative writing tools, the track changes capability in modern editors remains limited to highlighting syntactic changes, with authors still required to manually read through each of the revisions. We envision a collaborative authoring system where an author could accept all minor edits first and then focus on the substantial changes. To support this, we define the task of significant revision identification as the task of identifying the revisions between two versions of a text according to one of four categories, i.e. formal, meaning preserving, micro-and macro-structure. Micro-structure change corresponds to minor meaning change while macro-structure change corresponds to major meaning change. Our main contribution is to define a computational approach to this task, by framing the task as bi-directional entailment between the original and revised sentences. An existing recognition of textual entailment (RTE) system is applied to evaluate whether the revised texts entails. We evaluate the approach through a novel corpus consisting of multiple versions of drafts of academic papers written by multiple authors, which were annotated with the four revision types by both authors and non-authors of the papers. The proposed bi-directional textual entailment approach performs better than baseline edit distance approaches, which is similar to the current track changes capability built into most word processors. © 2022 IEEE.

关键词： Text processing

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Remaining Useful Life Prediction of Degraded Systems Based on Multiple Performance Indicators Constructed by Multiple Features

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IEEE Sensors Journal 2025年第11期25卷 19613-19631页

作者： Wu, Bin Shi, Hui Zeng, Jianchao Zhang, Xiaohong Wang, Zhijian Wang, Zuolu Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China North University of China institute for Big Data and Visual Computing Taiyuan030051 China University of Huddersfield Centre for Efficiency and Performance Engineering HuddersfieldHD1 3DH United Kingdom

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method is proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation. © 2001-2012 IEEE.

关键词： Prediction models

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What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration 38

What Factors Affect Multi-Modal In-Context Learning? An In-D...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Qin, Libo Chen, Qiguang Fei, Hao Chen, Zhi Li, Min Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval China Harbin Institute of Technology China Tsinghua University China ByteDance China

Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved notable success, which is capable of achieving superior performance across various tasks without requiring additional parameter tuning. However, the underlying rules for the effectiveness of MM-ICL remain under-explored. To fill this gap, this work aims to investigate the research question: "What factors affect the performance of MM-ICL?" To this end, we investigate extensive experiments on the three core steps of MM-ICL including demonstration retrieval, demonstration ordering, and prompt construction using 6 vision large language models and 20 strategies. Our findings highlight (1) the necessity of a multi-modal retriever for demonstration retrieval, (2) the importance of intra-demonstration ordering over inter-demonstration ordering, and (3) the enhancement of task comprehension through introductory instructions in prompts. We hope this study can serve as a foundational guide for optimizing MM-ICL strategies in future research. © 2024 Neural information processing systems foundation. All rights reserved.

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Missing Data Recovery for Downstream Learning in Low-Cost Environmental Sensor Networks 9

Missing Data Recovery for Downstream Learning in Low-Cost En...

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9th International Conference on information technology Trends, ITT 2023

作者： Agbo, Benjamin Al-Aqrabi, Hussain Alsboui, Tariq Hussain, Muhammad Hill, Richard Keele University School of Computing and Mathematics Keele United Kingdom Higher Colleges of Technology Faculty of Computer Information Science Sharjah United Arab Emirates University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom

ISBN: (纸本)9798350327502

The development of low-cost sensor networks (LCSNs) has facilitated the generation of vast amounts of environmental monitoring data in air quality monitoring stations. State-of-the-art research has focused on developing robust models to detect and mitigate events which may impact societies. Making adequate predictions is important for meeting the sustainability needs of future generations. Therefore, quality data is required to produce accurate results for downstream analytics and visualisations. When dealing with large amounts of data, it is however common to observe numerous amounts of missing data. This is a challenge for data miners because various methods for data analysis only work well on complete datasets. A traditional approach to handling missing data is to discard instances of missing values and only use complete cases for analysis. However, research has shown that this approach is not practical especially when large amounts of data are missing. This led to an increased need to develop strategies for replacing missing values with plausible values through imputation. This study presents an imputation strategy called k-BFMVI for recovering missing values before training downstream regression models for low cost sensor calibration. Experiments simulated missingness from 10% to 40% using MCAR and MAR mechanisms and the performance of the proposed technique was measured against state-of-the-art techniques. Overall, the proposed algorithm recorded the best imputation accuracy as opposed to existing techniques and showed significant improvements on downstream learning. © 2023 IEEE.

关键词： Sensor networks

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Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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Exploring Representations Learned via Self-Supervised Transfer Learning for Medical Image Classification 5th

Exploring Representations Learned via Self-Supervised Transf...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Devadas, Raghavendra M. Alam, Intekhab Raghavendra, R. Nautiyal, Kapish School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electronic and Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

This paper gives a singular self-supervised transfer new technique for clinical image segmentation. The proposed method entails a deep state-of-the-art structure capable of learning strong feature representations by leveraging unlabelled records from a supply domain thru self-supervised techniques and then shifting those representations to a target domain. Experiments were conducted on medical photo segmentation datasets: manual mind tumor segmentation and chest X-ray segmentation. The proposed technique has improved the version's overall performance compared to the present baseline processes. It significantly improved over the self-supervised strategies, especially in the brain tumor segmentation dataset. The paper highlights capacity destiny guidelines for research in self-supervised transfer brand new for clinical picture segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Design of a Secure Machine Learning-Based Malware Detection and Analysis Scheme

Design of a Secure Machine Learning-Based Malware Detection ...

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2023 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2023

作者： Kumar, Pankaj Obaidat, Mohammad S. Pandey, Piyush Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computing Srm University Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Algorithmic Research International Institute of Information Technology Center for Security Theory Hyderabad India

ISBN: (纸本)9798350302561

Today's world is rapidly growing towards the advanced concept of technology. technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score. © 2023 IEEE.

关键词： Machine learning

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Skin Cancer Classification using A Hybrid Convolutional Neural Network with SVM Classifier

Skin Cancer Classification using A Hybrid Convolutional Neur...

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2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Chatrapathy, K. Prasad, M Lakshmi Kiran, Ajmeera Shaker Reddy, Pundru Chandra Babu, G Charles Partheeban, N. Reva University School of Computing & Information Technology Karnataka Bengaluru India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Mlr Institute of Technology Computer Science and Engineering Telangana Dundigal Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Gokaraju Rangaraju Institute of Engineering and Technology Computing Science and Engineering Telangana Hyderabad India Galgotias University School of Computing Science and Engineering Utter Pradesh Greater Noida India

ISBN: (纸本)9798350308167

Digital dermoscopy is commonly employed by dermatologists for the diagnosis of malignancy. Clinicians' recognition of tumor is not only highly subjective, but also highly dependent on their level of experience. The inherent inter-operator unevenness in the private processing of dermoscopy pictures calls for fully automated computer-aided diagnosis(CAD) methods. The fine-grained differences in the manifestation of these abrasions on the skin-surface make automated skin lesion classification difficult. In several different types of fine-grained object classifications, deep convolutional-neural-network(CNN) has demonstrated excellent reparability. In order to categorize dermoscopy pictures into benign or malignancy lesions, this research introduces two innovative hybrid CNN strategies that use a support-vector-machine (SVM) at the output layer. In order to classify data, the characteristics mined by the first &second CNN strategies are combined and supplied to the SVM. The suggested model's efficacy is measured against a reference set of labels provided from a dermatology specialist. In a study using CAD for skin disease categorization, the generated CNN-segmented image folder distinguished atopic dermatitis from nasty diseases and obstacles like mycosis fungoides, impetigo, and herpesvirus contagion with a sensitivity and specificity of around 98%. In comparison to the traditional CNN strategies, the proposed models performed better on the ISBI 2016 public dataset. Both of the proposed models, with an accuracy of 88.02% and 87.43%, outperformed the standard CNN models. Reduced reliance on physician annotation and enhanced CAD performance are both benefits of our CNN segmentation algorithm, which automatically removes lesions and segments images of the skin area across all image fields. © 2023 IEEE.

关键词： CAD Convolutional-neural-network deep learning dermoscopy Skin cancer SVM

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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

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Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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Novel looped functionals in designing output feedback controllers for aperiodic sampled-data control systems

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IEEE Transactions on Automation science and Engineering 2025年 22卷 16397-16402页

作者： Wang, Wei Liang, Jin-Ming Zeng, Hong-Bing Zhang, Xian-Ming Hunan University of Technology School of Electrical and Information Engineering Hunan412007 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

This paper addresses the problem of stabilizing aperiodic sampled-data systems using output feedback control. First, a novel two-sided looped functional is constructed, providing a sufficient condition to ensure the asymptotic stability of the resulting closed-loop system. This condition is then adapted for control design. Using a cone complementary linearization algorithm with a stringent iteration criterion, a practical approach is developed to compute the control gain. Finally, three examples, including an inverted pendulum system and a one-area load-frequency control system, demonstrate the effectiveness and superiority of the proposed method over others in the literature. © 2004-2012 IEEE.

关键词： Sampled data control systems

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