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arXiv 2024年

作者： Zhao, Feng Wu, Yizhou Hu, Mingzhe Chang, Chih-Wei Liu, Ruirui Qiu, Richard Yang, Xiaofeng Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30322 United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Radiation Oncology University of Nebraska Medical Center OmahaNE United States

Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human anatomy and physiological functions. A key component in constructing these digital twins is the integration of high-resolution imaging data—such as MRI, CT, PET, and ultrasound—with sophisticated computational models. Advances in medical imaging significantly enhance real-time simulation, predictive modeling, and early disease diagnosis, individualized treatment planning, ultimately boosting precision and personalized care. Although challenges persist, such as the complexity of anatomical modeling, integrating various imaging modalities, and high computational demands, recent progress in imaging and machine learning has greatly improved the precision and clinical applicability of digital twins. This review investigates the role of medical imaging in developing digital twins across organ systems. Key findings demonstrate that improvements in medical imaging have enhanced the diagnostic and therapeutic potential of digital twins beyond traditional methods, particularly in imaging accuracy, treatment effectiveness, and patient outcomes. The review also examines the technical barriers that currently limit further development of digital twin technology, despite advances in medical imaging, and outlines future research avenues aimed at overcoming these challenges to unlock the full potential of this technology in precision medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis

arXiv

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arXiv 2023年

作者： Ahmed, Aram M. Rashid, Tarik A. Hassan, Bryar A. Majidpour, Jaffer Noori, Kaniaw A. Rahman, Chnoor Maheadeen Abdalla, Mohmad Hussein Qader, Shko M. Tayfor, Noor Mohammed, Naufel B. Department of Information Technology College of Science and Technology University of Human Development Sulaimani Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Department of Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani46023 Iraq Department of Computer Science University of Raparin Sulaimani Iraq Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq

Agents of any metaheuristic algorithms are moving in two modes, namely exploration and exploitation. Obtaining robust results in any algorithm is strongly dependent on how to balance between these two modes. Whale optimization algorithm as a robust and well recognized metaheuristic algorithm in the literature, has proposed a novel scheme to achieve this balance. It has also shown superior results on a wide range of applications. Moreover, in the previous chapter, an equitable and fair performance evaluation of the algorithm was provided. However, to this point, only comparison of the final results is considered, which does not explain how these results are obtained. Therefore, this chapter attempts to empirically analyze the WOA algorithm in terms of the local and global search capabilities i.e. the ratio of exploration and exploitation phases. To achieve this objective, the dimension-wise diversity measurement is employed, which, at various stages of the optimization process, statistically evaluates the population's convergence and diversity. © 2023, CC BY.

关键词： Optimization

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Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

arXiv

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arXiv 2024年

作者： Ye, Mang Shen, Wei Du, Bo Snezhko, Eduard Kovalev, Vassili Yuen, Pong C. The National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China United Institute of Informatics Problems of Belarus National Academy of Sciences National Academy of Sciences of Belarus Minsk Belarus Department of Computer Science Hong Kong Baptist University Hong Kong

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data. Recent research has shown promising results addressing various challenges in VFL, highlighting its potential for practical applications in cross-domain collaboration. However, the corresponding research is scattered and lacks organization. To advance VFL research, this survey offers a systematic overview of recent developments. First, we provide a history and background introduction, along with a summary of the general training protocol of VFL. We then revisit the taxonomy in recent reviews and analyze limitations in-depth. For a comprehensive and structured discussion, we synthesize recent research from three fundamental perspectives: effectiveness, security, and applicability. Finally, we discuss several critical future research directions in VFL, which will facilitate the developments in this field. We provide a collection of research lists and periodically update them at https://***/shentt67/VFL_Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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Code Prediction Based on Graph Embedding Model 16th

Code Prediction Based on Graph Embedding Model

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Yang, Kang Yu, Huiqun Fan, Guisheng Yang, Xingguang Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing China Shanghai China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai200235 China

ISBN: (纸本)9783030675394

Code prediction aims to accelerate the efficiency of programmer development. However, its prediction accuracy is still a great challenge. To facilitate the interpretability of the code prediction model and improve the accuracy of prediction. In this paper, the source code’s Abstract Syntax Tree (AST) is used to extract relevant structural paths between nodes and convert them into training graphs. The embedded model can convert the feature of the node sequence in the training graph into a vector that is convenient for quantization. We calculate the similarity between the candidate value and the parent node vector of the predicted path to obtain the predicted value. Experiments show that by using prediction data to increase the weight of related nodes in the graph, the model can extract more useful structural features, especially in Value prediction tasks. Adjusting the parameters embedded in the graph can improve the accuracy of the model. © 2021, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Trees (mathematics)

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Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery

arXiv

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arXiv 2024年

作者： Chang, Ching-Chun Gao, Kai Xu, Shuying Kordoni, Anastasia Leckie, Christopher Echizen, Isao National Institute of Informatics Chiyoda Tokyo Japan The Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan The Department of Psychology Lancaster University Lancaster United Kingdom The Department of Computing and Information Systems University of Melbourne MelbourneVIC Australia The Information and Society Research Division National Institute of Informatics The Department of Informatics Graduate University for Advanced Studies The Department of Information and Communication Engineering University of Tokyo Chiyoda Tokyo Japan

Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject's subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoors threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine's behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern being the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability. © 2024, CC BY.

关键词： Reverse engineering

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Implementation of New Reliable Network Model to Analyze Network Reliability by Repairing Route Link Failure with Help of Duplicate Nodes

Implementation of New Reliable Network Model to Analyze Netw...

引用

Computational Intelligence, Networks and Security (ICCINS), International Conference on

作者： M.Veeresh Babu Pushpneel Verma M. Giri Zabi Ur Rahaman K Department of Computer Science Bhagwant University Ajmer Rajasthan India Department of Computer Science Bagawanth University Ajmir Rajasthan India Department CSE Siddharth Institute of Engineering and Technology Puttur Andhra Pradesh India Academic Dean Paradise International School K.R. Nagara

In general wireless networks facing list of problems, these networks are deployed remotely with bad environment situations that leads affect on sensor device performance, it is possible to occur congestion due to limited bandwidth, limited processing capability there is a chance of data overflow, and no proper security mechanisms available. In reactive routing, to transfer data number of routes between sender to receiver are discovered, first shortest path is selected to transfer data, this route is available as long as no mobility of nodes, if route is expired due to network problems, and source must choose next optimal route. The route failure occurred due to movement of only one node and may waste bandwidth of wireless networks. It is a one of the major problem when mobility of nodes in the network are high and which may raise more number of route error messages. To solve route link failure problem, in this research paper we proposed new reliable network model, original network is surrounded with extra duplicate nodes, when a route link failure occur then use extra node to repair route, and no need to choose alternative path. Network reliability of proposed method is high when compared with other conventional routing protocols.

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Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics

Deep Attention-Based Alignment Network for Melody Generation...

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IEEE International Symposium on Multimedia

作者： M Gurunath Reddy Zhe Zhang Yi Yu Florian Harscoet Simon Canales Suhua Tang Digital Content and Media Sciences Research Division National Institute of Informatics SOKENDAI Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications

ISBN: (纸本)9781665471732

We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans. Most importantly, a deep neural lyrics-to-melody net is trained in an encoder-decoder way to predict possible pairs of lyrics-melody when given incomplete lyrics (few keywords). The attention mechanism is exploited to align the predicted lyrics with the melody during the lyrics-to-melody generation. The qualitative and quantitative evaluation metrics reveal that the proposed method is indeed capable of generating proper lyrics and corresponding melody for composing new songs given a piece of incomplete seed lyrics.

关键词： Measurement

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Early Detection of Interproximal Caries using YOLOv8 Convolutional Neural Network on Bitewing Radiographs

Early Detection of Interproximal Caries using YOLOv8 Convolu...

引用

Data Intelligence and Cognitive informatics (ICDICI), International Conference on

作者： Soundar Ida Mahizha Joseph Annrose Jeyebalaji Mano Christaine Angelo Israel Domilin Shyni Department of Information Technology St. Xavier’s Catholic College of Engineering Nagercoil India Faculty of Dentistry Sri Ramachandra Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering DMI College of Engineering India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Early childhood caries is a common oral health issue in both developed and developing countries. Identifying caries lesions early can help in implementing minimally invasive treatment plans, preserving the remaining tooth structure. Interproximal lesions are typically found between the adjacent surfaces of two teeth. The first step is visual inspection, followed by examination using dental probes to detect the dental caries. Usually, radiographic examination is done for early detection of dental caries. This process is time-consuming. In recent years, AI has been used to detect early caries from X-ray images. In dentistry, proximal caries is difficult to identify from periapical and panoramic radiographs, so Bitewing Radiographs (BWR) are used to detect the interproximal caries. Since the X-ray source is parallel to the sensor, Bitewing Radiographs has least distortion and help in the detection of early proximal caries. Several studies have shown that Bitewing Radiographs have high sensitivity in detecting interproximal caries occurring in existing restorations compared to visual clinical detection. The main objective of this study is to detect dental caries and classify them into various classes enamel, dentin and restorations. In this study, a dataset was collected from Mendeley data and Kaggle. The caries classification utilized a Deep Convolutional Neural Network. A YOLOv8 algorithm was developed based on the severity of the lesions observed in bitewing digital radiographs, enabling convenient and reliable detection of these lesions. The performance of these classifications is evaluated. This study aimed to evaluate the performance of the deep neural network for detecting interproximal caries lesions on Bitewing Radiographs (BWR). Accurate detection and diagnosis of dental caries are crucial for reducing the cost of oral health management and improving the likelihood of preserving natural teeth, leading to better patient outcomes.

关键词： Visualization Sensitivity Teeth Dentistry Image restoration Lesions Convolutional neural networks Diagnostic radiography X-ray imaging Surface treatment

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Authentication and Authorization in Modern Web Apps for Data Security Using Nodejs and Role of Dark Web 4th

Authentication and Authorization in Modern Web Apps for Data...

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4th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Verma, Chaman Raboaca, Maria Simona Enescu, Florentina Magda School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Computer Science and Engg Galgotias University Greater Noida India Faculty of Informatics University of Eötvös Loránd Budapest1053 Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea240050 Romania Department of Electronics Communications and Computers University of Pitesti Pitesti Romania

Authentication and Authorization are the base of security for all the Technologies present in this world today. Starting from your smartphone where a user authenticates himself before he could access the data inside to Entering into the White House, you must authenticate yourself, and based on that you are authorized. In this digital world where every Business, MNC, Government Body, Companies, Users, etc. needs a website to inform the world about their presence on the internet, provide services online and become a "Brand", the risk of leaking user's sensitive information increases. It could be dangerous to the users of the hacked website because their sensitive information like a credit card, bank account details, etc. could be sold in the black market of the "dark web". The role of the dark web is described in the paper and how the data is sold there and what becomes of it. The paper helps to understand how a secure website is developed that promises the user to keep the sensitive information safe, increases the bond of trust between a client and server which results in a long-term relationship. The aim behind developing an authentication system is to keep users' sensitive information safe so that hackers cannot steal and sell the information on the dark web's back market. To perform this, the developer needs to understand how to implement authentication. NodeJS, with the help of its framework expressJS and some other packages, is used to develop the authentication and authorization system of the website by the research. Previous papers on this field covered the authentication topic in general. This paper overcame that by going deeper into the field and being server-side language specific. The common types of authentication methods used in different types of websites are discussed in detail and the best methods are purposed for the developer to be implemented for a more secure website. This research put light on Artificial Intelligence and blockchain as the future

关键词： Websites

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EFMLP: A Novel Model for Web Service QoS Prediction 16th

EFMLP: A Novel Model for Web Service QoS Prediction

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Ye, Kailing Yu, Huiqun Fan, Guisheng Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

ISBN: (纸本)9783030675394

With the emergence of service-oriented architecture, quality of service (QoS) has become a crucial factor in describing the non-functional characteristics of Web services. In the real world, the user only requests limited Web services, the QoS record of Web services is sparsity. In this paper, we propose an approach named factorization machine and multi-layer perceptron model based on embedding technology (EFMLP) to solve the problem of sparsity and high dimension. First, the input data will be sent to embedding layer to reduce the data dimension. Then, the embedded feature vector will send to the factorization machine. After that, the first-order and second-order weights of the factorization machine are used as the initial weights of the first layer of the multi-layer perceptron. And the multi-layer perceptron is trained to adjust the weights. Finally, 1,974,675 pieces of data from an open dataset is used as experiment data to validate the model, and the result shows that our EFMLP model can predict QoS value accurately on the client side. © 2021, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Forecasting

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