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Cryptanalysis and improvement of quantum secure multi-party summation using single photons

Physica Scripta 2024年第3期99卷 035114-035114页

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 section 3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Program for Digital Health Innovation College of Humanities and Sciences China Medical University No. 100 section 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

A previous study [Entropy 25.4 (2023): 590] proposed a quantum secure multi-party summation protocol wherein n participants could obtain the modulo-2 summation result using single photons and single-particle operations. Owing to the absence of an entanglement state, this protocol was more practical than other quantum summation protocols that used entanglement properties. Thus, this study investigated the claim that this proposed protocol prevented the participants’ secret inputs from being disclosed. It was found that the previously proposed protocol was vulnerable to a quantum Trojan horse attack owing to the circular qubit transmission mode. Using a security loophole, this study proposed an attack strategy that allowed an outside attacker to steal any participant’s secret input without being detected by a third party and participants. In addition, a simulation experiment was conducted to demonstrate the feasibility of the proposed attack and evaluate the number of quantum Trojan horse probes that the attacker must inject into regular qubit sequences. Finally, a solution was proposed by modifying the protocol to securely prevent quantum Trojan horse attacks. © 2024 IOP Publishing Ltd.

关键词： Photons

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Effective Tumor Annotation for Automated Diagnosis of Liver Cancer

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IEEE Journal of Translational Engineering in Health and Medicine 2025年 13卷 251-260页

作者： Chuang, Yi-Hsuan Su, Ja-Hwung Lin, Tzu-Chieh Cheng, Hue-Xin Shen, Pin-Hao Ou, Jin-Ping Han, Ding-Hong Liao, Yi-Wen Lee, Yeong-Chyi Cheng, Yu-Fan Hong, Tzung-Pei Li, Katherine Shu-Min Lu, Yi Wang, Chih-Chi Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Liver Transplantation Program Departments of Diagnostic Radiology Niao-Sung District Kaohsiung 833401 Taiwan National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung 811 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung 804 Taiwan National Kaohsiung University of Science and Technology Department of Intelligent Commerce Kaohsiung 824004 Taiwan Cheng Shiu University Department of Information Management Kaohsiung 833301 Taiwan Liver Transplantation Center Department of Surgery Kaohsiung Chang Gung Memorial Hospital Niao-Sung District Kaohsiung 833401 Taiwan

In recent years, visual cancer information retrieval using Artificial Intelligence has been shown to be effective in diagnosis and treatment. Especially for a modern liver-cancer diagnosis system, the automated tumor annotation plays a crucial role. So-called tumor annotation refers to tagging the tumor in Biomedical images by computer vision technologies such as Deep Learning. After annotation, the tumor information such as tumor location, tumor size and tumor characteristics can be output into a clinical report. To this end, this paper proposes an effective approach that includes tumor segmentation, tumor location, tumor measuring, and tumor recognition to achieve high-quality tumor annotation, thereby assisting radiologists in efficiently making accurate diagnosis reports. For tumor segmentation, a Multi-Residual Attention Unet is proposed to alleviate problems of vanishing gradient and information diversity. For tumor location, an effective Multi-SeResUnet is proposed to partition the liver into 8 couinaud segments. Based on the partitioned segments, the tumor is located accurately. For tumor recognition, an effective multi-labeling classifier is used to recognize the tumor characteristics by the visual tumor features. For tumor measuring, a regression model is proposed to measure the tumor size. To reveal the effectiveness of individual methods, each method was evaluated on real datasets. The experimental results reveal that the proposed methods are more promising than the state-of-the-art methods in tumor segmentation, tumor measuring, tumor localization and tumor recognition. Specifically, the average tumor size error and the annotation accuracy are 0.432 cm and 91.6%, respectively, which suggest potential for reducing radiologists’ workload. In summary, this paper proposes an effective tumor annotation for an automated diagnosis support system. © 2013 IEEE.

关键词： Liver cancer diagnosis liver tumor location liver tumor measuring liver tumor recognition liver tumor segmentation

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Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites

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computers in Biology and Medicine 2024年 178卷 108664-108664页

作者： Nguyen, Van-Nui Ho, Trang-Thi Doan, Thu-Dung Le, Nguyen Quoc Khanh University of Information and Communication Technology Thai Nguyen University Thai Nguyen Viet Nam Department of Computer Science and Information Engineering TamKang University New Taipei251301 Taiwan International Degree Program in Animal Vaccine Technology International College National Pingtung University of Science and Technology Pingtung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei110 Taiwan AIBioMed Research Group Taipei Medical University Taipei110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei110 Taiwan

N4-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous investigations have examined the distribution and functional implications of 4mC sites within the Rosaceae genome, focusing on their potential roles in gene expression regulation, environmental adaptation, and evolution. This research aims to improve the accuracy of predicting 4mC sites within the genome of Fragaria vesca, a Rosaceae plant species. Building upon the original 4mc-w2vec method, which combines word embedding processing and a convolutional neural network (CNN), we have incorporated additional feature encoding techniques and leveraged pre-trained natural language processing (NLP) models with different deep learning architectures including different forms of CNN, recurrent neural networks (RNN) and long short-term memory (LSTM). Our assessments have shown that the best model is derived from a CNN model using fastText encoding. This model demonstrates enhanced performance, achieving a sensitivity of 0.909, specificity of 0.77, and accuracy of 0.879 on an independent dataset. Furthermore, our model surpasses previously published works on the same dataset, thus showcasing its superior predictive capabilities. © 2024 Elsevier Ltd

关键词： Embeddings

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Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification

Enhancing Protein Sequence Classification with a Fuzzy Neura...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Nguyen Quoc Khanh Le Van-Nui Nguyen Thi-Tuyen Nguyen Thi-Xuan Tran Trang-Thi Ho Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan University of Information and Communication Technology Thai Nguyen University Thai Nguyen Vietnam University of Economics and Business Administration Thai Nguyen University Thai Nguyen Vietnam Department of Computer Science and Information Engineering TamKang University New Taipei Taiwan

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

In bioinformatics, classifying protein sequences into anticancer peptides (ACPs) and non-ACPs is crucial yet challenging due to the inherent uncertainties of biological data. This study introduces a novel fuzzy neural network (FNN) model that integrates fuzzy logic within neural network architectures, enhancing the handling of ambiguity and improving classification accuracy. Our model, tested against several conventional machine learning models and recent studies, demonstrated superior specificity (83.28%) and overall accuracy (79.14%), marking a significant advancement in the identification of therapeutically relevant peptides. The integration of fuzzy logic not only optimized the performance but also increased the interpretability of the results, making it a valuable tool for complex bioinformatic analyses. These findings underscore the potential of fuzzy systems to refine predictive capabilities in computational biology, aligning perfectly with the themes of enhancing fuzzy theory applications in practical and impactful ways.

关键词： Fuzzy logic Uncertainty Accuracy Biological system modeling Peptides Machine learning Fuzzy neural networks

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Double-SP Method to Enhance Privacy and Security of Smart City Applications

Double-SP Method to Enhance Privacy and Security of Smart Ci...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Nour Mahmoud Bahbouh Abdulelah Alhussain Hassan Hakami Yaser Mohammed Aljohani Adnan Ahmed Abi Sen Ahmad Alkhodre Aeshah Alsughayyir Department of Information and Communication Sciences Granada University Granada Spain Saudi Electronic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Computing and Information Technology Islamic University Al-Madinah Saudi Arabia Faculty of Computer and Information Systems Islamic University Al-Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Al-Madinah Saudi Arabia

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks.

关键词： Resistance Privacy Data privacy Machine learning algorithms Smart cities Knowledge based systems Quality of service

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Legal Protection of Copyright on Copyrighted Content Downloaded Through the Internet 2020

Legal Protection of Copyright on Copyrighted Content Downloa...

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5th International Conference on Intelligent information technology, ICIIT 2020

作者： Indrawan, Arvin Stevens, Garry Brianto, Gratianus Martin Gaol, Ford Lumban Oktavia, Tanty Mobile Application and Technology-School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Program-School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450376594

The development of the internet made new problems within the scope of copyright. The internet is used to download copyrighted content and use it without permission from the creators. The legal protection of copyright the creators of content downloaded through the internet done in a way that the administration, through the instrument of criminal law and civil lawsuit. Legal efforts to protect the copyright of songwriters downloaded via the internet are carried out in several ways, namely the Ministry of information and technology to play an active role in blocking sites that provide free song download facilities and prepare human resources in the field of investigation such as from the National Police The Republic of Indonesia as well as Civil Servants and other Investigators such as prosecutors and judges who have the ability and knowledge in the field of IPR as well as the role of the Copyright Council to provide knowledge of the importance of IPR. © 2020 ACM.

关键词： Copyrights

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

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International Conference of computer science and information technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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Gamification of Donation App: What Affect Donators’ Decision?

Gamification of Donation App: What Affect Donators’ Decisio...

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International Conference on information technology (INCIT)

作者： Thidawan Chaichana Peetikorn Preechachot Surapong Uttama Computer Science and Innovation Program School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

This paper proposes a donation mobile app gamified to monitor players’ donation decision. Three main scenarios were simulated to observe which factors affect the donation. First, five categories of donation project were available. Second, an expiring donation project was set and was alerted to players. Last, auctions were added to some donation projects to draw more players’ attention. Testing with twelve young players revealed that the most influenced donation factor is the project’s type. Health-related project was the most attentive. Also, the expiring donation project triggered the players’ interest. Unfortunately, auctions rarely increased the amount of donation which may be due to the uninteresting bidding products.

关键词： Mobile applications information technology Monitoring Testing

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Behavioral Office Syndrome with IoT Devices

Behavioral Office Syndrome with IoT Devices

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International Conference on information technology (INCIT)

作者： Pun Kongsomboon Surapong Uttama Computer Science and Innovation Program School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

This paper presents a collection of computer use behavior prone to office syndrome by using pressure sensors in conjunction with IoT devices. IoT device was attached with a chair, and sensors were installed on the seat and the backrest. Another IoT set was linked with sensors installed under the mouse pad. The collected data of seven testers instructed to use computer and move freely revealed different behaviors. Some were sitting constantly with or without using backrest or touching mouse. Also a unique character like standing while using computer was found. To link with office syndrome estimation, we proposed a set of rules including thresholds for pressure values, time criteria and risk scoring. Using these criteria, the testers were found to have low and moderate risks of office syndrome.

关键词： Pressure sensors Estimation Prototypes Medical services Mice Behavioral sciences Internet of Things

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