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Cryptanalysis of quantum private comparison with six-particle maximally entangled states

Modern Physics Letters A 2023年第7期38卷

作者： Tsai, Chia-Wei Yang, Chun-Wei Wang, Chun-Hsiang Lin, Jason Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. North District Taichung 404 Taiwan Master Program for Digital Health Innovation College of Humanities and Sciences China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung 406040 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung 40227 Taiwan

The measurement property of the six-particle maximally entangled state was used by Sun [Mod. Phys. Lett. A 37, 2250149 (2022)] to design a quantum private comparison (QPC) protocol. However, this study points out that the entanglement property of the six-particle maximally entangled state is inconsequential as a security measure;that is, participants waste resources to store the entanglement state and perform the Bell measurement without obtaining any additional security advantage. Although a third party (TP) cannot obtain information regarding the participants' secret messages through an attack, an attack renders the six-particle entanglement redundant. Accordingly, this study proposed an improved QPC protocol to prevent third-party attacks. © 2023 World Scientific Publishing Company.

关键词： entanglement state quantum key distribution quantum private comparison Secure multiparty computation

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Improving the web accessibility of a university digital library: A design thinking approach

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AIP Conference Proceedings 2023年第1期2975卷

作者： Ira Puspitasari Tania R. Natalnael Indra K. Raharjana Information System Study Program Faculty of Science and Technology Universitas Airlangga Kampus C Jalan Mulyorejo Surabaya Indonesia Master in Human Resource Development Study Program Postgraduate School Universitas Airlangga Kampus B Jalan Airlangga Surabaya Indonesia

The accelerated reformation of the digital library system has highlighted the significance of accessibility and user experience design in its development and operation. The digital library is expected to serve more diverse and challenging users’ requirements, making accessibility the prominent attribute. Despite its significance, many digital library systems fail to address accessibility accordingly. This study aims to examine accessibility problems in the digital library system in higher education, i.e., Universitas Airlangga’s e-library, and develop a solution based on the design thinking methodology. The questionnaire on the empathize phase gathered 25 respondents’ perceptions about the current library website. The identified major accessibility requirements are unmatched and incomplete library description on the front page, complex and unclear navigation menu, low contrast between the text and background image—making reading more difficult, especially for users with low vision, the absence of the first heading, and unspecified website language. The solutions generated during the Ideation phase corresponded to the list of identified requirements. The prototype implements these solutions by combining them with the Web Content Accessibility Design 2.0 standard. The evaluation results signify the accessibility progress in the redesigned library website prototype. The average user rating increased from 3 on the current library website to 4.63 on the redesigned version, and the task completion rate reached 92.86%. In addition, the accessibility tests using WAVE and AxeDev Tools exhibit major improvements in contrast error and total accessibility issues. Findings from this study are helpful for system developers, user interface/user experience designers, and libraries in formulating an accessible digital library system. In the effort to achieve Sustainable Development Goals (SDGs) targets, the study of accessible digital library systems contributes to SDG number 4, ens

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Secure Handwritten Protocol for Protecting Integrity of Digital Evidences

Secure Handwritten Protocol for Protecting Integrity of Digi...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Chien-Lung Hsu Wei-Qian Lin Sheng-Hsin Tso Graduate Institute of Management Chang Gung University (R.O.C) Taoyuan Taiwan Department of Information Management Chang Gung University (R.O.C) Taoyuan Taiwan Department of Master of Science Degree Program in Innovation for Smart Medicine Chang Gung University (R.O.C) Taoyuan Taiwan Healthy Aging Research Center Chang Gung University (R.O.C) Taoyuan Taiwan Department of Visual Communication Design Ming-Chi University of Technology (R.O.C) Taoyuan Taiwan Adjunct Research Fellow Chang-Gung Memorial Hospital Department of Information Management National Central University (R.O.C) Taoyuan Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

With the advancement of technology, people constantly use IT through the Internet. In this modern living environment, many individuals exploit this technological landscape for illegal gains and criminal activities. Judicial investigation units gather relevant evidence from the crime scene and process it when a crime occurs. When the medium used in criminal activity involves information technology, it is referred to as digital evidence. Unlike traditional evidence, which leaves traces if moved or altered, digital evidence can be copied without any detectable changes, posing challenges to maintaining its integrity and accuracy. This article explores how digital identification can ensure that the content of a report matches the evidence seized at the scene. It also addresses how the validity of digital evidence, as presented by the investigating authority, can be upheld in court. Additionally, it discusses the use of digital evidence and electronic handwritten signatures to enhance evidence preservation measures.

关键词： Biometrics Protocols Accuracy Public key Authentication Signal processing Non-repudiation Internet Security information technology

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Tweets Sentiment Analysis During COVID-19 Pandemic

Tweets Sentiment Analysis During COVID-19 Pandemic

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Maha A. Alanezi Nabil M. Hewahi Big Data Science & Analytics Master Program Collage of Science University of Bahrain Sakhir Kingdom of Bahrain Collage of Information Technology University of Bahrain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3.

关键词： COVID-19 Social networking (online) Pandemics Sentiment analysis Blogs Elbow Viruses (medical)

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Prediction of Stock Price and Direction Using Neural Networks: Datasets Hybrid Modeling Approach

Prediction of Stock Price and Direction Using Neural Network...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Mohamed Al Aradi Nabil Hewahi Big Data Science & Analytics Master Program Collage of Science Univestity of Bahain Sakhir Kingdom of Bahrain Collage of Information Technology Univestity of Bahain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Stock prices prediction is one of the most daunting tasks to achieve for day traders, investors, and data scientists. They are complex functions of a wide array of contributing factors that affects the movement dynamics. Politics, social outlook, the company's sales performance, and socio-economic factors all have an influence of how the stock is perceived globally and thus affecting the price movement based on the supply/demand balance. In this work a Framework is proposed to predict the price and movement direction by using multiple datasets that includes news sentiment, social sentiment, company earnings announcement, and technical indicators. A case study was applied on the Apple Inc. stock using Long short-term Memory Neural Networks (LSTM) and Deep Neural Networks (DNN). Results showed superiority of the LSTM model in both predicting stock value [ 75.4 MSE, +-2.52 PE] and the direction with [70.1% classification accuracy] over the DNN model which suffered from prediction lag and dependency on trend indicators achieving [53.1%] in predicting the direction of the stock.

关键词： Predictive models Machine learning Companies Neural networks Market research Data models Recurrent neural networks

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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The Effect of Several Kernel Functions to Financial Transaction Anomaly Detection Performance using One-Class SVM

The Effect of Several Kernel Functions to Financial Transact...

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2019 International Congress on Applied information technology, AIT 2019

作者： Heryadi, Yaya Dandalina, D. Bina Nusantara University Computer Science Department Jakarta11530 Indonesia Master of Information and Technology Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728158624

Anomalies in data become ubiquitous and often unavoidable. Despite it might be caused by various errors during the data collection or transportation, some anomalies potentially give important information such as indication of a new underlying process. The aim of anomaly detection task is to determine all such instances in the input dataset in a data-driven fashion. In the past decade, the proliferation of anomaly data in many domains have raised research interest resulted in a plethora of anomaly detection methods A vast number of previous study reports suggested that there is no single model will achieve the best performance for every dataset. This paper presents empiric results on the effect of several kernel functions to performance of One-Class Support Vector Machine (OC-SVM) as an anomaly detector model. The proposed model is tested using financial transactions of microfinance service dataset from an Indonesian Bank. The empiric results showed that OC-SVM model with Sigmoid and RBF kernels achieve the best statistically significant value of training, validation, and testing accuracies than the OC-SVM model with no-kernel, linear kernel and polynomial (degree 3,4,5,6) kernels. © 2019 IEEE.

关键词： Anomaly detection

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Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Comparing Naïve Bayes, Decision Tree and Logistic Regressio...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Maha A. Alanezi Mawra T. Homeed Zahra S. Mohamed Ahmed M. Zeki Big Data Science & Analytics Master Program University of Bahrain Sakhir Kingdom of Bahrain Collage of Information Technology University of Bahrain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for Data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others.

关键词： Support vector machines Business Bayes methods Hidden Markov models Logistics Data models Credit cards

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Design and Performance Evaluation of a P4 based Load Balancer 8

Design and Performance Evaluation of a P4 based Load Balance...

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8th International Conference on Innovation, Communication and Engineering, ICICE 2019

作者： Hsu, Shih-Jung Ke, Chih-Heng Chen, Yeong-Sheng Hung, Cheng-Feng Lo, Yu-Wen Master Degree Program of Computational Intelligence and Applications National Quemoy University Taiwan National Taipei University of Education Department of Computer Science Taiwan Master of Information Technology and Application National Quemoy University Taiwan National Quemoy University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781728158396

Being increasingly dense and mature in layout, Internet has become an infrastructure for a state. The traditional single server can no longer bear the network traffic in rapid growth. For example, once the booking service for concert tickets is made available to the public, we are always prompted when clicking into the booking system that the server is busy. And when the system is finally restored normal, the tickets have been sold out. The load issue puzzling the ticket system also occurs in the transportation section every time when a long holiday is coming. Even though the system is updated on a yearly basis, net- work congestion still happens frequently. Therefore, there is still great space for the development of the ways and mechanisms of load balance in traditional multi-servers. Evaluation of the mech- anism and efficacy of load balance through P4Switch is proposed in this paper in the hope to achieve more efficient server load balance. © 2019 IEEE.

关键词： loadbalance openflow p4switch sdn

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Applying mobility business intelligence concept in analyzing oil palm plantation productivity

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AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

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