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Text Prompt Translation for Zero Shot Prediction under Lexical Constraint in Aviation Domain 6

Text Prompt Translation for Zero Shot Prediction under Lexic...

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6th international conference on Software Engineering and Computer Science, CSECS 2023

作者： Chen, Fan Fang, Yongming Man, Xuedong Zhao, Yunbo Fang, Tianzhu Lin, Luxin Tan, Meiyun Huang, Siying Xiamen Institute of Data Intelligence Xiamen China Xiamen Airlines Xiamen China

ISBN: (纸本)9798350306378

In real-world translation scenarios, particularly those involving safety in the aviation domain, it is expected that neural machine translation (NMT) can translate with idiomatic expressions and constraints provided by aviation experts. Generally, this topic problem can be addressed by fine-tuning NMT model with parallel corpus. However, parallel corpus is usually lacking and with noise. Conventional pre-training & fine-tuning paradigm may not perform well in this situation. Recently, a series prompt-tuning works have been proposed to solve the few-shot and zero-shot problems existing in downstream task. Inspired by those, we designed a prompt-based methods for NMT. With the help of injected related domain knowledge, the NMT models can strictly follow to word-level constraints. Specifically, in this work, we proposed two well-designed modules that can satisfy translation constraints in the aviation domain, including the preservation of words and observation of terminology. We extensively conduct experiments on a real-world large corpus of aviation maintenance text in English and Chinese and evaluated our method with four metrics, including missing or redundant information, incorrect formats, bad word or sentence patterns, and unconventional expressions. the experiment results show that our prompt methods were able to make the translation satisfy domain-specific lexical constraints in low-resource settings, using a pre-trained Transformer as the backbone model across all tasks. © 2023 IEEE.

关键词： Zero-shot learning

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An Application of Co-plot Analysis: A Multidimensional Scaling data Visualization 6

An Application of Co-plot Analysis: A Multidimensional Scali...

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6th international conference on machine learning and Natural Language Processing, MLNLP 2023

作者： Poosapati, Priyanka Shi, Yao Gebauer, Judith Song, Yang Department of Computer Science University of North Carolina Wilmington United States Congdon School of Supply Chain Business Analytics Information Systems University of North Carolina Wilmington United States

ISBN: (纸本)9798400709241

Exploratory graphs are a crucial element of statistical analysis and models that help us identify and interpret data patterns. As one example of exploratory graphs, multidimensional scaling (MDS) allows the visualization of large and complex datasets in a reduced dimensional space, while preserving pairwise differences and similarities between data points. Co-plot analysis extends the capabilities of traditional MDS and enables a multifaceted view of relationships between variables from different datasets. To develop a co-plot graph, the analyst starts out by standardizing the dataset and by generating a special coefficient (MADCC-median absolute deviation correlation coefficient) that subjects the dataset to MDS-embedding and helps to determine how the dataset fits the surface. In the current paper, we demonstrate how co-plot analysis can be applied to describe and compare the behavioral patterns of ecological communities and species across multiple sites and environmental contexts. We demonstrate the use of non-metric multidimensional scaling (NMDS) and robust multidimensional scaling (RMDS) techniques to visualize dissimilarities and distances of the observations in a reduced dimensional space. We also explain the use of Shepard graphs as a second technique to visualize categorical variables in a reduced-dimensional space, with points closer together indicating higher similarity. Shepard graphs also allow us to assess the goodness of fit, which is indicated by the strength of a positive linear relationship. For our application, the co-plots graphs are useful to visualize a complete set of co-relations between the species of different communities. It also allows us to plot how every factor affects the number of species against the mean and against the median and to determine the best approach: A co-plot graph against the mean may be influenced by the shape of the distribution, especially if the data is skewed, whereas a co-plot graph against the median is typicall

关键词： data visualization

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Calibration techniques for node classification using graph neural networks on medical image data 6

Calibration techniques for node classification using graph n...

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6th international conference on Medical Imaging with Deep learning (MIDL)

作者： Vosl, I. N. Bhatl, I. R. Velt, B. K. Ruigrok, Y. M. Kuijf, H. J. . Univ Med Ctr Utrecht Image Sci Inst Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiol Utrecht Netherlands Univ Med Ctr Utrecht Dept Neurol & Neurosurg Utrecht Netherlands

Miscalibration of deep neural networks (DNNs) can lead to unreliable predictions and hinder their use in clinical decision-making. this miscalibration is often caused by overconfident probability estimates. Calibration techniques such as model ensembles, regularization terms, and post-hoc scaling of the predictions can be employed to improve the calibration performance of DNNs. In contrast to DNNs, graph neural networks (GNNs) tend to exhibit underconfidence. In this study, we investigate the efficacy of calibration techniques developed for DNNs when applied to GNNs trained on medical image data, and compare the calibration performance of binary and multiclass node classification on a benchmark dataset and a medical image dataset. We find that post-hoc methods using Platt scaling or Temperature scaling, or methods that add a regularization term to the loss function during training are most effective to improve calibration. Our results further indicate that these calibration techniques are more effective for multiclass classification tasks compared to binary classification tasks.

关键词： Calibration graph neural networks node classification medical imaging

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A Study on Role of Artificial Intelligence in Education 6

A Study on Role of Artificial Intelligence in Education

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6th international conference on Computing, Electronics and Communications Engineering, iCCECE 2023

作者： Mon, Bisni Fahad Wasfi, Asma Hayajneh, Mohammad Slim, Ahmad Al Ain United Arab Emirates United Arab Emirates University Department of Electrical Engineering Al Ain United Arab Emirates University of New Mexico Department of Electrical and Computer Engineering NM United States

ISBN: (纸本)9798350340921

Artificial intelligence (AI) has revolutionized the field of education, leveraging advancements in computing and information processing technologies. the application of AI in education (AIED) has brought forth a multitude of possibilities, opportunities, and challenges, profoundly impacting educational practices across various domains. Within the realm of learning, instruction, and administration, AI has gained considerable acceptance and utilization in educational institutions. Educators have leveraged the AI tools to enhance the quality of instructional methods and streamline administrative tasks. With AI-powered systems, tasks such as evaluating and grading learner assignments can be accomplished more efficiently and expeditiously. Moreover, the use of cognitive computing and customization has facilitated the individualization and tailoring of syllabus and materials to meet the unique needs of learners. this personalized approach has fostered increased absorption, engagement, and an enriched learning environment for students overall. the impact of AI in education extends beyond traditional boundaries, permeating multiple areas of educational practice. From personalized learning experiences to efficient administrative processes, AI has revolutionized how knowledge is disseminated and acquired. As AI continues to advance, it holds immense potential for further innovation and transformation within the education sector, propelling us into a new era of enhanced educational outcomes and opportunities for students worldwide. this paper provides the information about intelligent education tools, examines the transformative impact of AI in diverse areas of education, and addresses the challenges associated with its implementation. After examining the existing literature on impact of AI in different areas of education, future research directions are also recommended in this paper. © 2023 IEEE.

关键词： machine learning

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A Robust Mean Teacher Framework for Semi-Supervised Cell Detection in Histopathology Images 6

A Robust Mean Teacher Framework for Semi-Supervised Cell Det...

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6th international conference on Medical Imaging with Deep learning (MIDL)

作者： Wen, Ziqi Ye, Chuyang Beijing Inst Technol Sch Integrated Circuits & Elect Beijing Peoples R China

Cell detection in histopathology images facilitates clinical diagnosis, and deep learning methods have been applied to the detection problem with substantially improved performance. However, cell detection methods based on deep learning usually require a large number of annotated training samples, which are costly and time-consuming to obtain, and it is desirable to develop methods where detection networks can be adequately trained with only a few annotated training samples. Since unlabeled data is much less expensive to obtain, it is possible to address this problem with semi-supervised learning, where abundant unlabeled data is combined with the limited annotated training samples for network training. In this work, we propose a semi-supervised object detection method for cell detection in histopathology images, which is based on and improves the mean teacher framework. In standard mean teacher, the detection results on unlabeled data given by the teacher model can be noisy, which may negatively impact the learning of the student model. To address this problem, we propose to suppress the noise in the detection results of the teacher model by mixing the unlabeled training images with labeled training images of which the ground truth detection results are available. In addition, we propose to further incorporate a loss term that is robust to noise when the the student model learns from the teacher model. To evaluate the proposed method, experiments were performed on a publicly available dataset for multi-class cell detection, and the experimental results show that our method improves the performance of cell detection in histopathology images in the semi-supervised setting.

关键词： Cell detection Histopathology image analysis Semi-supervised learning

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the Empirical Analysis of machine learning-Based Techniques for Improving Cyber Security 6

The Empirical Analysis of Machine Learning-Based Techniques ...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Chanti, Yerrolla Chalapathi Rao, M. Reddy, Ch. Srinivasa Wani, Mohd. Yaseen Samdani, Abdullah Tiwari, Mohit School of CS & AI SR University Telangana Warangal India Malla Reddy University Department of CSE Telangana Hyderabad India Department of IT Duvvada Visakhapatnam India School of Law Kashmir University Jammu & Kashmir Srinagar India School of Law University of Petroleum & Energy Studies Uttarakhand Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Paschim Vihar Delhi India

ISBN: (纸本)9798350304480

the absence of training statistics is still the major obstacle among the numerous that tend to cause cyberattack identification difficult to identify. Millions of consumers are still at risk due to an absence of knowledge about website activities and characteristics that could lead to an attack, even when businesses and companies utilize well-known network surveillance technologies. In reality, the majority of attacks happen not since attackers employ sophisticated programming and evasive strategies, yet rather since victims lack the fundamental skills needed to recognize and prevent the *** of these difficulties, machine learning (ML) is demonstrated to fundamentally alter our awareness of the context of cyberattacks. For this research, ML techniques were applied to data from phishing websites to improve cybersecurity by contrasting five techniques and offering knowledge that the broad accessible may employ to prevent common phishing traps. the research outcomes imply that the neural network (NN) technique is the most effective one. Since NNs are built on multi-layer perceptrons, the foundation of intelligence, phishing identification will eventually be automated and transformed into an artificial intelligence endeavor. © 2023 IEEE.

关键词： machine learning

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Optimal Deep Transfer learning-Based Road Extraction for Intelligent Transportation Systems Using High-Resolution Remote Sensing Imagery 6

Optimal Deep Transfer Learning-Based Road Extraction for Int...

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6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Al Sadi, Hafidh I. Sabah, Hawraa Ali Kurdi, Waleed Hadi Madhloom Najm, Nahlah M.A.M. Alazzai, Waleed Khalid Alzubaidi, Laith H. College of MLT Ahl Al Bayt University karbala Iraq National University of Science and Technology Dhi Qar Iraq Altoosi University College Najaf Iraq Al Rafidain University College Computer Technology Engineering Department Bagdad Iraq Medical Technical College Al-Farahidi University Baghdad Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350303339

Remote sensing (both satellite and terrestrial) is deemed a suitable technique for collecting data on a large scale effectively and accurately that satisfies the demand for intelligent transportation systems (ITS). In recent times, the research works relevant to the use of remote sensing (RS) technologies towards their execution in transportation systems had raised immensely. Road network extraction will be one of the important projects for intelligent transportation systems, real-time updating road networks, and disaster emergency responses. Road extraction related to high-resolution remote sensing images (RSI) was becoming a hot research topic. At present, many studies are relevant to conventional machine learning methods that can be complex and computational due to impervious surfaces namely buildings and roads that are visible in the images. With this motivation, this study progresses a salp swarm optimization with Deep Transfer learning based Road Extraction (SSODTL-RE) technique for ITS using high-resolution RSIs. the presented SSODTL-RE technique aims to extract the rod utilizing high-resolution RSIs. Further, to generate a collection of deep features from the RS images, the SSODTL-RE approach employs a capsule network (CapsNet). Moreover, the SSODTL-RE technique applies an adaptive neuro-fuzzy inference system (ANFIS) model for the integration of broken roads and improvises efficiency because of road connectivity. To improve the efficacy of the CapsNet model, the SSO is applied for the process of hyperparameter tuning. the stimulation analysis of the SSODTL-RE system was tested on two benchmark datasets. the experimental results stated the betterment of the SSODTL-RE technique over other DL approaches. © 2023 IEEE.

关键词： Roads and streets

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Double-Edged Agriculture 4.0: Hodiernal Expedient Technologies and Cyber-Security Challenges 6

Double-Edged Agriculture 4.0: Hodiernal Expedient Technologi...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arya, Swati Tripathi, Shubham Srivastava, Archana Aggarwal, Shruti Soni, Nupur Ansar, Syed Anas School of Computer Application Babu Banarasi das University Lucknow India Thapar Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350304480

Digitalization and intelligent systems have engulfed all dimensions and vicinity of existence. the intense fusion of hodiernal information technology and archaic agriculture has ushered in the epoch of agriculture 4.0, invariably reputed as smart agriculture. Variegated operational or forthcoming technologies reckon in agriculture 4.0, e.g., algae feedstock, automation, hydroponics, nanotechnology, genome editing, cognitive computing, blockchain, and machine learning, may have far-reaching implications for future agriculture and food systems and radical rudimentary potential. Although the profusion benefits of this Fourth Revolution abound, many unresolved issues and stumbling blocks must be conquered to make this paradigm shift feasible. Future solutions will coerce data availability and accuracy as vital factors to assist farmers, also security will become indispensable for constructing vigorous and efficacious systems. Security deals with difficulties like compatibility, scarce funds, and gargantuan data as smart agriculture incorporate a huge gamut and volume of resources. In this frame of reference, the authors opine that the inclusion and exclusion impact of Agriculture 4.0 technologies require ancillary scrutiny. this paper articulates the core technologies of agriculture 4.0 with an explicit focus on its applications in veritable contexts whilst delving into security issues (chiefly cyber-security). this research is envisaged to have a pragmatic influence on agriculture 4.0, emphasizing paradigms and security measures to aid farm stakeholders to navigate the firm's transition to digitization. © 2023 IEEE.

关键词： Agriculture 4.0 Cloud Computing Cyber Security data analytics Internet of things Sensor

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Improved Deep learning with Metaheuristics Driven COVID-19 Diagnosis on Chest X-Ray Images 6

Improved Deep Learning with Metaheuristics Driven COVID-19 D...

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6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Alkhafaij, Mahdi Abdulkhudur Oleiwi, A. Sahib Ali, Rabei Raad Ali, Eyhab Al-Tahee, Mustafa Almusawi, Muntather College of MLT Ahl Al Bayt University Karbala Iraq Altoosi University College Najaf Iraq National University of Science and Technology Dhi Qar Iraq Al-Zahraa University for Women Karbala Iraq Medical Technical College Al-Farahidi University Baghdad Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350303339

Recently, Artificial Intelligence (AI) has undergone significant changes in part of medical image processing. Earlier diagnosis using chest X-ray (CXR) images proved that a key solution in fighting COVID19. Several computer-aided diagnostic (CAD) techniques have been used for assisting radiologists and provide a secondary recommendation for the same. CXR has become the first screening method to play a considerable role in the analysis of COVID19 contamination. Formerly, various traditional machine learning (ML) and deep learning (DL) systems were utilized for automatic diagnosis of chest radiography X-ray images. One method widely applied with DL is transfer learning (TL) which re-uses the data learned from trained modules like resolving one problem and applying it to the same problem. this study presents an Improved Deep learning with Metaheuristics based disease recognition and classification (IDLM-D2C) for COVID19 on CXR images. the presented IDLM-D2C approach concentrates on the precise detection and classification of COVID19 on CXR images. Initially, the IDLM-D2C algorithm implements the Weiner filter (WF) as a noise elimination step. In addition, the IDLM-D2C method exploits seagull optimization algorithm (SGO) with an improved Faster SqueezeNet model for feature extraction. For COVID19 detection, deep learning modified neural network (DLMNN) model was used with chimp optimization algorithm as hyperparameter optimizer. the experimental validation of the IDLM-D2C method is tested on benchmark medical image dataset. the comparison analysis highlighted the improvements of the IDLM-D2C method over other approaches. © 2023 IEEE.

关键词： COVID-19

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State of Health Estimation of Lithium-Ion Batteries from Charging data: A machine learning Method 6th

State of Health Estimation of Lithium-Ion Batteries from Cha...

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conference of the-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th international conference on Maintenance Engineering (IncoME)

作者： Wang, Zuolu Feng, Guojin Zhen, Dong Gu, Fengshou Ball, Andrew D. Univ Huddersfield Ctr Efficiency & Performance Engn Huddersfield HD1 3DH W Yorkshire England Hebei Univ Technol Sch Mech Engn Tianjin Key Lab Power Transmiss & Safety Technol Tianjin 300401 Peoples R China

ISBN: (纸本)9783030990756;9783030990749

Accurate state of health (SOH) estimation of the lithium-ion battery plays an important role in ensuring the reliability and safety of the battery management system (BMS). the data-driven method based on the selection of degradation features can be effectively applied to SOH estimation. In practice, lithium batteries often work in complex discharge conditions, but they are charged under constant current (CC) conditions. therefore, the suitable degradation features of the battery are extracted in this work for accurate SOH estimation. First, the degradation features are summarized and extracted from the CC charging data. Second, the Pearson correlation coefficient is utilized to quantify the relationship between the extracted degradation features and the battery SOH, thus determining the most influential degradation feature. Finally, the long short term memory (LSTM) is used for model training and SOH estimation based on the selected feature. the results show that LSTM model can give reliable and accurate SOH estimation with R-2 of 1 and lower mean absolute error (MAE) and maximum error (MAX).

关键词： Lithium-ion battery State of health Feature extraction Long short term memory

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