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A Differential Privacy-based Protecting data Preprocessing Method for Big data mining 18

A Differential Privacy-based Protecting Data Preprocessing M...

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18th IEEE international conference on Trust, Security and Privacy in computing and Communications (IEEE TrustCom) / 13th IEEE international conference on Big data Science and Engineering (IEEE BigdataSE)

作者： Mo, Ran Liu, Jianfeng Yu, Wentao Jiang, Fu Gu, Xin Zhao, Xiaoshuai Liu, Weirong Peng, Jun Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Cent South Univ Sch Automat Changsha Peoples R China Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha Peoples R China Hunan Engn Lab Rail Vehicles Braking Technol Changsha Peoples R China

ISBN: (纸本)9781728127767

Analyzing clustering results may lead to the privacy disclosure issue in big data mining. In this paper, we put forward a differential privacy-based protecting data preprocessing method for distance-based clustering. Firstly, the data distortion technique differential privacy is used to prevent the distances in distance-based clustering from disclosing the relationships. Differential privacy may affect the clustering results while protecting privacy. Then an adaptive privacy budget parameter adjustment mechanism is applied for keeping the balance between the privacy protection and the clustering results. By solving the maximum and minimum problems, the differential privacy budget parameter can be obtained for different clustering algorithms. Finally, we conduct extensive experiments to evaluate the performance of our proposed method. The results demonstrate that our method can provide privacy protection with precise clustering results.

关键词： Differential privacy data mining distance based clustering adaptive mechanism

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Fuzzy Decision Tree for Breast Cancer Prediction

Fuzzy Decision Tree for Breast Cancer Prediction

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1st international conference on advanced Information Science and System (AISS)

作者： Domingo, Mylene J. Gerardo, Bobby D. Medina, Ruji P. Technol Inst Philippines Grad Programs Quezon City Philippines West Visayas State Univ Lapaz Inst Informat & Communicat Technol Iloilo Philippines

ISBN: (纸本)9781450372916

Medical errors are considered as the leading cause of death and injury. Breast cancer becomes one of the leading causes of death among women, not only in the Philippines but worldwide. In this paper, data mining was used to predict the stage of breast cancer using a hybrid of fuzzy logic and decision tree. This aims to help experts to make decisions rather than replacing them. The result will only give an expert a recommendation, but the final decision is still on the hands of the experts. Feature selection was used to determine the best attribute in the dataset from Surveillance Epidemiology and End Results ( SEER). The data set consists of incidence from 1975 to 2016, but the study limits the analysis from 2010 to 2016. Different cleaning and preprocessing of data are conducted. After thorough preprocessing of data, six (6) attributes are selected, and one (1) target class. Performance comparison shows that the fuzzy decision tree achieved a higher accuracy of 99.96%, sensitivity of 99.26% and specificity of 99.98% than the decision tree classification technique. The simulation result shows a correctly classified instance of 165,124, which is equivalent to 99.97% and only 351 incorrect classified instances or 0.21%. Thus, a fuzzy decision tree is more robust than the traditional decision tree classifier for predicting the stage of breast cancer.

关键词： Medical Errors Breast Cancer Stage of Breast Cancer Prediction data mining Fuzzy Decision Tree Decision Tree Fuzzy Logic Classification

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Reddit sentiment analysis to improve election predictions 4

Reddit sentiment analysis to improve election predictions

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4th international conference on Big data Analytics, data mining and Computational Intelligence 2019, BigDaCI 2019 and the 8th international conference on Theory and Practice in Modern computing 2019, TPMC 2019

作者： Hwang, Bryant Korea International School Korea Republic of

ISBN: (纸本)9789898533920

Despite high rates of past success, the majority of polling agencies were unable to predict the outcome of the 2016 United States presidential election between Hillary Clinton and Donald Trump. Further research has hypothesized that these agencies underestimated the amount of support for Trump and overestimated the representation of recent college graduates. Sentiment analysis of posts on popular social media sites presents promising possibilities for better gauging public opinion. In our research, we analyze positive and negative sentiment in Reddit posts from key dates during the 2016 election that mention either candidate. The results of our study match the results of a poll conducted by the New York Times, which showed that public opinion for Trump tended to stay consistent while Clintons positive sentiment decreased over time. Our study also shows that public opinion expressed on Reddit changed drastically in accordance to major events, like debates and email controversies. Alone, our work in sentiment analysis does not produce statistically significant enough results to predict the final outcome of an election, but having the ability to gauge public opinion through social networks is a powerful way that polling agencies and researchers can strengthen their predictions in real time. © Multi conference on Computer Science and Information Systems, MCCSIS 2019. All rights reserved.

关键词： Forecasting

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Mass Composition from 3 EeV to 100 EeV using the Depth of the Maximum of Air-Shower Profiles Estimated with Deep Learning using Surface Detector data of the Pierre Auger Observatory 38

Mass Composition from 3 EeV to 100 EeV using the Depth of th...

引用

38th international Cosmic Ray conference, ICRC 2023

作者： Glombitza, Jonas Abdul Halim, A. Abreu, P. Aglietta, M. Allekotte, I. Almeida Cheminant, K. Almela, A. Aloisio, R. Alvarez-Muñiz, J. Ammerman Yebra, J. Anastasi, G.A. Anchordoqui, L. Andrada, B. Andringa, S. Aramo, C. Araújo Ferreira, P.R. Arnone, E. Arteaga Velázquez, J.C. Asorey, H. Assis, P. Avila, G. Avocone, E. Badescu, A.M. Bakalova, A. Balaceanu, A. Barbato, F. Bartz Mocellin, A. Bellido, J.A. Berat, C. Bertaina, M.E. Bhatta, G. Bianciotto, M. Biermann, P.L. Binet, V. Bismark, K. Bister, T. Biteau, J. Blazek, J. Bleve, C. Blümer, J. Boháčová, M. Boncioli, D. Bonifazi, C. Bonneau Arbeletche, L. Borodai, N. Brack, J. Brichetto Orchera, P.G. Briechle, F.L. Bueno, A. Buitink, S. Buscemi, M. Büsken, M. Bwembya, A. Caballero-Mora, K.S. Cabana-Freire, S. Caccianiga, L. Caracas, I. Caruso, R. Castellina, A. Catalani, F. Cataldi, G. Cazon, L. Cerda, M. Cermenati, A. Chinellato, J.A. Chudoba, J. Chytka, L. Clay, R.W. Cobos Cerutti, A.C. Colalillo, R. Coleman, A. Coluccia, M.R. Conceição, R. Condorelli, A. Consolati, G. Conte, M. Convenga, F. Correia dos Santos, D. Costa, P.J. Covault, C.E. Cristinziani, M. Cruz Sanchez, C.S. Dasso, S. Daumiller, K. Dawson, B.R. de Almeida, R.M. de Jesús, J. de Jong, S.J. de Mello Neto, J.R.T. De Mitri, I. de Oliveira, J. de Oliveira Franco, D. de Palma, F. de Souza, V. De Vito, E. Del Popolo, A. Deligny, O. Denner, N. Deval, L. di Matteo, A. Dobre, M. Dobrigkeit, C. D’Olivo, J.C. Domingues Mendes, L.M. dos Anjos, R.C. Ebr, J. Ellwanger, F. Emam, M. Engel, R. Epicoco, I. Erdmann, M. Etchegoyen, A. Evoli, C. Falcke, H. Farmer, J. Farrar, G. Fauth, A.C. Fazzini, N. Feldbusch, F. Fenu, F. Fernandes, A. Fick, B. Figueira, J.M. Filipčič, A. Fitoussi, T. Flaggs, B. Fodran, T. Fujii, T. Fuster, A. Galea, C. Galelli, C. García, B. Gaudu, C. Gemmeke, H. Gesualdi, F. Gherghel-Lascu, A. Ghia, P.L. Giaccari, U. Giammarchi, M. Glombitza, J. Gobbi, F. Gollan, F. Golup, G. Gómez Berisso, M. Gómez Vitale, P.F. Erlangen Centre for Astroparticle Physics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany III. Physikalisches Institut A RWTH Aachen University Aachen Germany Observatorio Pierre Auger Av. San Martín Norte 304 Malargüe5613 Argentina San Carlos de Bariloche Argentina Departamento de Física Departamento de Ciencias de la Atmósfera y los Océanos FCEyN Universidad de Buenos Aires and CONICET Buenos Aires Argentina IFLP Universidad Nacional de La Plata and CONICET La Plata Argentina Buenos Aires Argentina CONICET/U.N.R. and Facultad de Ciencias Bioquímicas y Farmacéuticas U.N.R. Rosario Argentina Instituto de Tecnologías en Detección y Astropartículas CNEA CONICET UNSAM Universidad Tecnológica Nacional Facultad Regional Mendoza CONICET CNEA Mendoza Argentina Instituto de Tecnologías en Detección y Astropartículas CNEA CONICET UNSAM Buenos Aires Argentina International Center of Advanced Studies Instituto de Ciencias Físicas ECyT-UNSAM CONICET Campus Miguelete – San Martín Buenos Aires Argentina Argentina Observatorio Pierre Auger and Comisión Nacional de Energía Atómica Malargüe Argentina Universidad Tecnológica Nacional Facultad Regional Buenos Aires Buenos Aires Argentina University of Adelaide AdelaideSA Australia Brussels Belgium Vrije Universiteit Brussels Brussels Belgium Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Petropolis Brazil Brazil Universidade de São Paulo Escola de Engenharia de Lorena SP Lorena Brazil Universidade de São Paulo Instituto de Física de São Carlos SP São Carlos Brazil Universidade de São Paulo Instituto de Física SP São Paulo Brazil Universidade Estadual de Campinas IFGW SP Campinas Brazil Universidade Estadual de Feira de Santana Feira de Santana Brazil Universidade Federal de Campina Grande Centro de Ciencias e Tecnologia Campina Grande Brazil Universidade Federal do ABC SP Santo André Brazil Universidade Federal do Paraná Setor Palotina Palotina Brazil Uni

We present a new analysis for estimating the depth of the maximum of air-shower profiles, Xmax, to investigate the evolution of the ultra-high-energy cosmic ray mass composition from 3 to 100 EeV. We use a recently developed deep-learning-based technique for the reconstruction of Xmax from the data of the surface detector of the Pierre Auger Observatory. To avoid systematic uncertainties arising from hadronic interaction models in the simulation of surface detector data, we calibrate the new reconstruction technique with observations of the fluorescence detector. Using the novel analysis, we have a 10-fold increase of statistics at E > 5 EeV with respect to fluorescence detector data. We are able, for the first time, to study the evolution of the mean and standard deviation of the Xmax distributions up to 100 EeV. We find an excellent agreement with fluorescence observations and confirm the increase of the mean logarithmic mass hln(A)i and a decrease of the Xmax fluctuations with energy. The Xmax measurement at the highest — so far inaccessible — energies is consistent with a pure mass composition and a mean logarithmic mass of around ∼ 3 (estimated using the Sibyll 2.3d and the EPOS-LHC hadronic interaction models). Furthermore, with the increase in statistics, we find indications for a structure beyond a constant elongation rate in the evolution of Xmax © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Improved Impossible Polytopic Attacks on Round-reduced des 4

Improved Impossible Polytopic Attacks on Round-reduced des

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2019 4th international Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2019

作者： Wang, Jing Wu, Qianqiong Fu, Chaohui Zhou, Guomiao Duan, Ming State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450000 China College of Electrical Engineering and Automation Luoyang Institute of Science and Technology Luoyang Henan471000 China

In Eurocrypt 2016, Tyge Tiessen introduced the d-difference which considering the differences between multiple plaintexts, and proposed the impossible polytopic attack on block cipher which effectively reducing the data complexity. In this paper, we improve the impossible polytopic attacks of round-reduced DES by some ideas like truncated differentials. Given the input 3-difference of each S-box in the third round, the number of the output 3-difference is actually smaller than the theoretical upper bound, which helps us reduce the memory complexity of the attack on 5-round DES from 212 bytes to 26.9 bytes and increase the success rate of the attack. Using the idea of truncated differentials, the time complexity of the attack on 6-round DES is reduced from 232.2 encryptions to 225.8 encryptions by selecting the output 3-differences of 6 S-boxes for key recovery. We also improve the attack on 7-round DES by using more plaintexts based on our improved attack on 6-round DES. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Gears

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Providing Malaria Analytics as a Service 9

Providing Malaria Analytics as a Service

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9th international conference on Cloud computing and Services Science (CLOSER)

作者： Barreto, Marcos Bertoldo, Juracy Sironi, Alberto Sampaio, Vanderson Fed Univ Bahia UFBA Comp Sci Dept AtylmoLab Salvador BA Brazil Amazonas State Fdn Hlth Surveillance FVS AN Manaus Amazonas Brazil State Univ Amazonas UEA Manaus Amazonas Brazil

ISBN: (纸本)9789897583650

Malaria is still a worrying disease worldwide, being responsible for around 219 million cases reported in 2017 and around 435,000 deaths a year. The consensus among researchers, governmental bodies and health professionals is that many countries have relapsed their investments and surveillance actions after a few years of apparent disease reduction. Brazil is within such countries and, consequently, is presenting a constant increase in the number of reported cases since 2016 (more than 20% a year). Given this context, the National Malaria Control Program (NMCP) promotes several actions to redirect the country towards the malaria elimination path. Among such actions, the improvement of the surveillance ecosystem is considered crucial to allow efficacy of control actions, including vector control as well as early diagnosis and prompt treatment. In this paper, we present our efforts in designing a visual mining tool allowing descriptive and predictive analytics over an integrated database comprising malaria surveillance data, climate and vector control data. This tool has been used as a "data service" by NMCP and partner researchers for validation purposes. So far, our results have demonstrated that surveillance and combat actions can be highly improved by using this tool.

关键词： data Analytics data Linkage Visual mining data as a Service

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3rd international conference on Future data and Security Engineering, FDSE 2016 and the 10th international conference on advanced computing and Applications, ACOMP 2016

3rd International Conference on Future Data and Security Eng...

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3rd international conference on Future data and Security Engineering, FDSE 2016 and the 10th international conference on advanced computing and Applications, ACOMP 2016

ISBN: (纸本)9783662562659

The proceedings contain 8 papers. The special focus in this conference is on Future data and Security Engineering. The topics include: Towards a fine-grained privacy-enabled attribute-based access control mechanism;One-class collective anomaly detection based on LSTM-RNNs;multihop wireless access networks for flood mitigation crowd-sourcing systems;assessment of aviation security risk management for airline turnaround processes;scalable automated analysis of access control and privacy policies;partitioning-insensitive watermarking approach for distributed relational databases.

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Discovering Relationship Patterns Among Associated Temporal Event Sequences 24th

Discovering Relationship Patterns Among Associated Temporal ...

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24th international conference on database Systems for advanced Applications (DASFAA)

作者： Han, Chao Duan, Lei Lin, Zhangxi Qin, Ruiqi Zhang, Peng Nummenmaa, Jyrki Sichuan Univ Sch Comp Sci Chengdu Sichuan Peoples R China Sichuan Univ West China Sch Publ Hlth Chengdu Sichuan Peoples R China Texas Tech Univ Lubbock TX 79409 USA Tampere Univ Tampere Finland

ISBN: (纸本)9783030185763;9783030185756

Sequential data mining is prevalent in many real world applications, such as gene sequence analysis, consumer shopping log analysis, social networking analysis, and banking transaction analysis. Contrast sequence data mining is useful in describing the differences between two sets (classes) of sequences. However, in prior studies, little work has been done in how to mine the patterns from sequences formed by associated temporal events, where there exist relationships in chronological order between any two events in a sequence. To fill this gap, we consider the problem of mining associated temporal relationship pattern (ATRP) and propose a method, called ATTEND (AssociaTed Temporal rElationship patterN Discovery), to discover ATRPs with top contrast measure from two sets of associative temporal event sequences. Moreover, we design several heuristic strategies to improve the efficiency of ATTEND. Experiments on both real and synthetic data demonstrate that ATTEND is effective and efficient.

关键词： Contrast sequence data mining Relationship pattern Temporal event sequence

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Determining Spatial Co-Location Graph Influencing Factors using Mobile Applications data 3

Determining Spatial Co-Location Graph Influencing Factors us...

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3rd international conference on Intelligent computing in data Sciences (ICDS)

作者： Radzi, Nur Fadzilah Mohamad Hoong, Poo Kuan Chua, Hiang-Kwang Norazman, Noor Fatin Atiqah Axiata Digital Advertising Data Sci & Engn Kuala Lumpur Malaysia

ISBN: (纸本)9781728100036

With the recent explosion of smart phone users and the usage of mobile applications, it is undeniable that mobile applications are rich data sources for data mining. In the recent studies conducted by Statista, for 2016, the number of smart phone users is forecast to reach 2.1 billion while the number of mobile phone users in the world is expected to pass the five billion mark by 2019. The ownership of smart phones also leads to the rise of mobile application usage where most of the mobile app usages help users to manage life activities, increase productivity, and constantly stay connected while on-the-go. With permission granted, some mobile applications provide devices location with reasonable high accuracy. Spatial information derived from these mobile applications allow better understanding of human lifestyle behaviors as well as determine the social influence patterns. In this paper, we investigate factors that contribute to spatial co-location graph data mining patterns for the period of three months data (October to December 2018) for three South-East Asia countries, namely Malaysia, Singapore and Thailand. Devices that are co-located are defined by determining two or more devices are appeared in close proximity within a predetermined range of distance at the same time frame. Based on these co-located devices, spatial co-location graphs were constructed to analyze factors that influence graph.

关键词： social influence co-location social advertising social network

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BDNE: A Method of Bi-Directional Distance Network Embedding

BDNE: A Method of Bi-Directional Distance Network Embedding

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international conference on Cyber-Enabled Distributed computing and Knowledge Discovery (CyberC)

作者： Zhu, Dongjie Sun, Yundong Cao, Ning Qiao, Xueming Xu, Ming Li, Jinlin Yang, Junzhou Harbin Inst Technol Sch Comp Sci & Technol Weihai Peoples R China State Grid Shandong Elect Power WeiHai Power Supply Co Weihai Peoples R China

ISBN: (纸本)9781728125428

In order to capture the directed relationship between nodes more accurately, this paper proposed a novel network embedding model called BDNE. The model adds the bi-directional distance while preserving the co-occurrence frequency of the nodes within a context window, which is of great significance in some applications of social networks, such as public opinion monitoring and control, and group discovery. Experimental results show that the embedding results of our model as features in the node classification task are better than DeepWalk, Node2Vector and Line on real data sets of different types and sizes.

关键词： network embedding representation learning data mining

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