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Secure data sharing for mobile cloud computing using RSA

IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： M Arumugam S Deepa G Arun P Sathishkumar K Jeevanantham Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Information Technology MP Nachimuthu M Jaganathan Engineering College Chennimalai India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai Erode India

Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has created scalable distributed computing. This technology provides consumers a number of points of interest, similar to storage limits, reliability, scalability and access to real-time information. As a result, it is expanding steadily and is undoubtedly organised into a daily day-to-day life. The Cloud servers can be used for the preparation and storage of ***, in the current conditions, the secrecy of photos and information is generally important. In this paper, we concentrate mainly on the safe re-appropriation of photographs.

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Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns 1

Implementation of Apriori Algorithm for Analysis of Consumer...

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1st International Conference on computer science and Applied Mathematic, ICCSAM 2018

作者： Panjaitan, Suprianto Sulindawaty Amin, Muhammad Lindawati, Sri Watrianthos, Ronal Sihotang, Hengki Tamando Sinaga, Bosker Teknik Informatika STMIK Pelita Nusantara Medan Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Program Doctoral Vocational Technology Education Postgraduate Engineering Faculty Padang State University Padang West Sumatera Indonesia Manajemen Informatika AMIK Logika Sumatera Utara Indonesia Manajemen Informatika AMIK Labuhan Batu Sumatera Utara Indonesia

Consumer purchasing patterns are a form of purchases made by consumers, whether someone or a lot of people to get the desired item by making a purchase transaction. One characteristic of the purchase pattern is the existence of acquiring something through exchanging money. This study aims to create an application that is used in determining consumer purchasing patterns by applying a priori algorithms and using Visual Basic 2010 as a tool for determining consumer purchase patterns. This application uses a priori algorithm calculation method where the sample consumer purchase data will be sorted and calculated by providing the value of the minium support and configuration parameters and based on the results of confidence the largest number of conclusions such as: can be used as information for determining sales, the application of a priori algorithms can provide information pattern combination item set from consumer purchase data that is with support above 15% and confidence above 50% on item set. © Published under licence by IOP Publishing Ltd.

关键词： Condensed matter physics

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E-Translator Kawi to Bahasa 2

E-Translator Kawi to Bahasa

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2nd International Joint Conference on Advanced engineering and Technology, IJCAET 2017 and International Symposium on Advanced Mechanical and Power engineering, ISAMPE 2017

作者： Sudana, Oka Putra, Darma Sudarma, M. Hartati, Rukmi S. Pradnya Prastika, Ryan Wirdiani, Ayu Study Program of Doctoral Engineering Science Faculty of Engineering Udayana University Kabupaten Badung-Bali80361 Indonesia Information Technology Departement Faculty of Engineering Udayana University Kabupaten Badung-Bali80361 Indonesia Electrical and Computer EngineeringDepartement Faculty of Engineering Udayana University Jalan Raya Bukit Jimbaran Kabupaten Badung-Bali80361 Indonesia

Kawi Language is a kind of language which has developed in Java during the Hindu-Buddhist era in the ancient Indonesian archipelago. As the time passed, Kawi began to come to extinction. To overcome this, preservation of Kawi language can be done by enhancing the understanding of culture through the use of technology. Technology that can be used for cultural preservation is one of NLP based applications such as Kawi Language to Bahasa Translator Application which is developed in this research. The translator application is an application that translates from one language to another. The result of translation of the translator application is a language that is easy to understand by the people or commonly used language. This translator application is developed to processes Kawi Language text into bahasa based on search method which each word inserted. The stemming method used is Bobby Nazief & Mima Adriani Algorithm using Kawi Language rule. The translated Kawi Language text can be a word, sentence or paragraph with the result of the Text in Bahasa as well as words, sentences or paragraphs. © The Authors, published by EDP sciences, 2018.

关键词： Translation (languages)

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Underrepresented Groups Threats to Belonging in Computing

Underrepresented Groups Threats to Belonging in Computing

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Research in Equity and Sustained Participation in engineering, Computing, and Technology (RESPECT)

作者： Marlon Mejias Ketly Jean-Pierre Gloria Washington Legand Burge Dept. of Software & Information Systems University. of North Carolina at Charlotte Charlotte NC USA Post-Doctoral Fellow - Process Data Educcational Testing Services Princeton NJ USA Dept. of Electrical Engineering & Computer Science Howard Unversity Washingington D.C. USA

Increasing the number of students exposed to computer science (CS) may not increase throughput in the pipeline if barriers to retention are not addressed. One major barrier is student's perception of belonging. To begin creating theoretical grounds for a common discourse to successfully matriculating underrepresented students, and retaining them in the field, threats to their belonging (barriers to retention) and how a survey of literature suggest they impact CS are presented.

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Unveiling long COVID symptomatology, co-occurrence trends, and symptom distress post SARS-CoV-2 infection

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Journal of infection and public health 2024年第7期17卷 102464页

作者： Shikha Kukreti Chun-Yin Yeh Yi-Jhen Chen Meng-Ting Lu Ming-Chi Li Yi-Yin Lai Chung-Yi Li Nai-Ying Ko Department of Nursing College of Medicine National Cheng Kung University Tainan Taiwan Department of Public Health College of Medicine National Cheng Kung University Taiwan. Department of Computer Science and Information Engineering National Cheng Kung University Taiwan. Department of Internal Medicine National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan. Centre of Infection Control College of Medicine National Cheng Kung University and Hospital Tainan Taiwan. Department of Public Health College of Medicine National Cheng Kung University Taiwan Department of Public Health College of Public Health China Medical University Taichung Department of Healthcare Administration College of Medical and Health Science Asia University Taichung Taiwan. International Doctoral Program in Nursing Department of Nursing College of Medicine National Cheng Kung University Tainan Taiwan. Electronic address: nyko@mail.ncku.edu.tw.

BACKGROUND:Long COVID, an emerging public health issue, is characterized by persistent symptoms following SARS-CoV-2 infection. This study aims to explore the relationship between post-COVID-19 symptomatology and patient distress employing Latent Class Analysis to uncover symptom co-occurrence patterns and their association with distress. METHODS:A cross-sectional study was conducted using an online survey among 240 participants from a university and affiliated hospital of southern Taiwan. The survey quantified distress due to persistent symptoms and assessed the prevalence of Long COVID, symptom co-occurrence, and latent symptom classes. Latent Class Analysis (LCA) identified distinct symptom patterns, and multiple regression models evaluated associations between symptom patterns, distress, and demographic factors. RESULTS:The study found that 80 % of participants experienced Long COVID, with symptoms persisting for over three months. Individuals with multiple COVID-19 infections showed a significant increase in general (β = 1.79), cardiovascular (β = 0.61), and neuropsychological symptoms (β = 2.18), and higher total distress scores (β = 6.35). Three distinct symptomatology classes were identified: "Diverse", "Mild", and "Severe" symptomatology. The "Mild Symptomatology" class was associated with lower distress (-10.61), while the "Severe Symptomatology" class showed a significantly higher distress due to symptoms (13.32). CONCLUSION:The study highlights the significant impact of Long COVID on individuals, with distinct patterns of symptomatology and associated distress. It emphasizes the cumulative effect of multiple COVID-19 infections on symptom severity and the importance of tailored care strategies.

关键词： Co-occurrence Distress Latent class Long COVID

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Author Correction: An interpretable machine learning system for colorectal cancer diagnosis from pathology slides

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NPJ precision oncology 2024年第1期8卷 83页

作者： Pedro C Neto Diana Montezuma Sara P Oliveira Domingos Oliveira João Fraga Ana Monteiro João Monteiro Liliana Ribeiro Sofia Gonçalves Stefan Reinhard Inti Zlobec Isabel M Pinto Jaime S Cardoso Institute for Systems and Computer Engineering Technology and Science (INESC TEC) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. pedro.d.carneiro@inesctec.pt. Faculty of Engineering University of Porto (FEUP) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. pedro.d.carneiro@inesctec.pt. IMP Diagnostics Praça do Bom Sucesso 61 sala 808 Porto 4150-146 Porto Portugal. diana.felizardo@***. Cancer Biology and Epigenetics Group Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network) Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center (Porto.CCC) R. Dr. António Bernardino de Almeida 865 Porto 4200-072 Porto Portugal. diana.felizardo@***. Doctoral Programme in Medical Sciences School of Medicine and Biomedical Sciences - University of Porto (ICBAS-UP) R. Jorge de Viterbo Ferreira 228 Porto 4050-313 Porto Portugal. diana.felizardo@***. Institute for Systems and Computer Engineering Technology and Science (INESC TEC) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. s.oliveira@nki.nl. Faculty of Engineering University of Porto (FEUP) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. s.oliveira@nki.nl. IMP Diagnostics Praça do Bom Sucesso 61 sala 808 Porto 4150-146 Porto Portugal. Department of Pathology IPO-Porto R. Dr. António Bernardino de Almeida 865 Porto 4200-072 Porto Portugal. Institute of Pathology University of Bern Uni Bern Murtenstrasse 31 Bern 3008 Bern Switzerland. Institute for Systems and Computer Engineering Technology and Science (INESC TEC) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. Faculty of Engineering University of Porto (FEUP) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal.

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ENhancing remote sensing image retrieval with triplet deep metric learning network

arXiv

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arXiv 2019年

作者： Cao, Rui Zhang, Qian Zhu, Jiasong Li, Qing Li, Qingquan Liu, Bozhi Qiu, Guoping International Doctoral Innovation Centre School of Computer Science University of Nottingham Ningbo China Ningbo315100 China Shenzhen Key Laboratory of Spatial Smart Sensing and Services Key Lab. for Geo-Environ. Monitoring of Coast. Zone of the Natl. Admin. of Surv. Mapping and Geoinformation Shenzhen University Shenzhen518060 China College of Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two publicly available remote sensing image retrieval datasets and show that our method significantly outperforms state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Content based retrieval

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Manifold-adaptive dimension estimation revisited

arXiv

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arXiv 2020年

作者： Benkő, Zsigmond Stippinger, Marcell Rehus, Roberta Bencze, Attila Fabó, Dániel Hajnal, Boglárka Erőss, Loránd Telcs, András Somogyvári, Zoltán Department of Computational Sciences Wigner Research Centre for Physics H-1121 Hungary János Szentágothai Doctoral School of Neurosciences Semmelweis University H-1085 Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BudapestH-1111 Hungary Epilepsy Center Department of Neurology National Institute of Clinical Neurosciences BudapestH-1145 Hungary Department of Functional Neurosurgery National Institute of Clinical Neurosciences BudapestH-1145 Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University BudapestH-1083 Hungary Department of Quantitative Methods University of Pannonia Faculty of Business and Economics VeszprémH-8200 Hungary Neuromicrosystems ltd. BudapestH-1113 Hungary

Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator, making it one of the best nearest neighbor-based dimension estimators available. We compute the probability density function of local FSA estimates, if the local manifold density is uniform. Based on the probability density function, we propose to use the median of local estimates as a basic global measure of intrinsic dimensionality, and we demonstrate the advantages of this asymptotically unbiased estimator over the previously proposed statistics: the mode and the mean. Additionally, from the probability density function, we derive the maximum likelihood formula for global intrinsic dimensionality, if i.i.d. holds. We tackle edge and finite-sample effects with an exponential correction formula, calibrated on hypercube datasets. We compare the performance of the corrected-median-FSA estimator with kNN estimators: maximum likelihood (ML, Levina-Bickel) and two implementations of DANCo (R and matlab). We show that corrected-median-FSA estimator beats the ML estimator and it is on equal footing with DANCo for standard synthetic benchmarks according to mean percentage error and error rate metrics. With the median-FSA algorithm, we reveal diverse changes in the neural dynamics while resting state and during epileptic seizures. We identify brain areas with lower-dimensional dynamics that are possible causal sources and candidates for being seizure onset zones. Copyright © 2020, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Quality classified image analysis with application to face detection and recognition

arXiv

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arXiv 2018年

作者： Yang, Fei Zhang, Qian Wang, Miaohui Qiu, Guoping International Doctoral Innovation Centre University of Nottingham Ningbo China School of Computer Science University of Nottingham Ningbo China College of Information Engineering Shenzhen University Shenzhen China

Motion blur, out of focus, insufficient spatial resolution, lossy compression and many other factors can all cause an image to have poor quality. However, image quality is a largely ignored issue in traditional pattern recognition literature. In this paper, we use face detection and recognition as case studies to show that image quality is an essential factor which will affect the performances of traditional algorithms. We demonstrated that it is not the image quality itself that is the most important, but rather the quality of the images in the training set should have similar quality as those in the testing set. To handle real-world application scenarios where images with different kinds and severities of degradation can be presented to the system, we have developed a quality classified image analysis framework to deal with images of mixed qualities adaptively. We use deep neural networks first to classify images based on their quality classes and then design a separate face detector and recognizer for images in each quality class. We will present experimental results to show that our quality classified framework can accurately classify images based on the type and severity of image degradations and can significantly boost the performances of state-of-the-art face detector and recognizer in dealing with image datasets containing mixed quality images. Copyright © 2018, The Authors. All rights reserved.

关键词： Image quality

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Supply Chain Framework at the Rubber Company with a Profit Sharing approach in ensuring Material Availability

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IOP Conference Series: Materials science and engineering 2019年第1期505卷

作者： Meilita Tryana Sembiring Irwan Budiman Anita Christine Sembiring Evta Indra Delima Sitanggang Dini Wahyuni Harmein Nasution Andree Chen Magister of Industrial Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia Industrial Engineering Department Faculty of Technology and Computer Sciences Universitas Prima Indonesia Indonesia Information System Department Faculty of Technology and Computer Sciences Universitas Prima Indonesia Indonesia Doctoral Programme of Industrial Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia

This research was conducted in rubber factory in Indonesia. Today, businesses need a more efficient supply chain that is able to resolve complex challenges in the present. This research is aimed at increasing the company's competitive advantage by designing supply chain framework with profit sharing system to ensure material availability. This research is conducted by using data reduction method in determining quantitative data for input Stream Value Mapping and Supply Chain Management. Determination of rubber material purchase price is done by using the average trend of rubber price so that the purchase of rubber material is done flat. In order to create trusts in the supply chain, profit-sharing systems are used so that suppliers will supply the right, consistent quantity, quality, and timing of delivery. The results obtained supply chain frameworks are better able to ensure process efficiency and material availability. If applied, this framework will save up to 29.17% processing time.

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