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Frontiers in Education (FIE) Conference

作者： Carlos N. Silla André L. Przybysz Andriano Rivolli Thayna Gimenez Carolina Barroso Jessika Machado Computer Music Technology Laboratory – Graduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paraná (PUCPR) – Curitiba PR Brazil Federal University of Technology of Paraná (UTFPR) – Cornélio Procópio PR Brazil

ISBN: (纸本)9781538611753;9781538611746

In this Innovative Practice paper we describe our experience on using an updated methodology based on the paper “Music Education Meets computer science and Engineering” with a focus on pre-university female students. The main differences from this work are: (1) We taught the girls how to program and how to play the music instruments in parallel, having two classes per week, one for each subject instead of weekly meetings; (2) We started the project by using some of the games and puzzles from *** and *** and then move to the Greenfoot Initial Learning Environment; (3) We asked the students to survey some music games related to their music instrument and design three potential games that they would like to develop using the music instruments; (4) They had to present and pitch the projects they designed to the undergraduate and graduate students of our computer music technology laboratory. Given the time constrains of the project, we had to help them to select the most feasible idea; (5) We applied a questionnaire to capture the perception of the students at the end of the project. It should be noted that all students successfully developed their music-based games using the digital music instruments and the Greenfoot Initial Learning Environment. In total four games were developed: (1) A game that allows drummers to perform ear training where they list to a particular drumming sequence and must replicate the sequence; (2) A game that allows guitar players to practice the execution of musical scales; (3) A game that allows keyboard players to practice their sight reading based on the simultaneous of two or more notes at the same time; (4) A game that allows bass players to practice their sight reading. It should be noted that by participating in the project one of the students acquired the skills and confidence to join the school and church bands.

关键词： Games Instruments Software Music Keyboards programming

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Stream Learning and Anomaly-based Intrusion Detection in the Adversarial Settings

Stream Learning and Anomaly-based Intrusion Detection in the...

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IEEE Symposium on computers and Communications

作者： Eduardo Viegas Altair Santin Vilmar Abreu Luiz S. Oliveira Graduate Program in Computer Science Pontifical Catholic University of Parana Curitiba Parana Brazil Informatics Department Federal University of Parana Curitiba Parana Brazil

Despite existing many anomaly-based intrusion detection studies in the literature, they are not frequently adopted by the industry in production environments (products). Such a usage gap occurs mainly due to the difficulty to maintain the detection rate in acceptable level, given the occurrence of false alarms. In general, the literature does not consider the adversarial settings, when an opponent attempt to evade the detection system, thus possibly rendering the system unreliable over time. In this paper, we propose and evaluate a new approach to reliably perform real time stream learning for anomaly-based intrusion detection. We employ a class-specific stream outlier detector to automatically update the intrusion detection engine over the time, and a rejection mechanism, which makes it possible to obtain indications that an evasion attempt might being happening. Furthermore, the proposal is resilient to causative attacks, providing a secure intrusion detection mechanism even when the attacker can inject misclassified instances in the training dataset. The evaluation tests show that the proposed approach is resilient to exploratory attacks, allowing the system administrator to know when an evasion attempt might be occurring.

关键词： Adversarial Settings Outlier Detection Stream Learning Intrusion Detection

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Exploring quantization error to improve human action classification

Exploring quantization error to improve human action classif...

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International Joint Conference on Neural Networks (IJCNN)

作者： Raquel Almeida Zenilton Kleber Gonçalves do Patrocínio Silvio Jamil F. Guimarães Graduate Program in Informatics - Computer Science Department Pontifical Catholic University of Minas Gerais (PUC Minas)

In human action classification task, a video must be classified into a pre-determined class. To cope with this problem, we propose a mid-level representation, in which information about quantization errors is embedded together with the aggregated data on low level features. The main contributions of this article are twofold: (i) assembly of low-level features (dense trajectories) by a mid-level representation enriched with information about distances between descriptors and codewords; and (ii) a survey of the most common protocols for human action classification methods when applied to three different datasets. Regarding classification protocols, we have experimented the training and testing classification (called split), the leave-one-out cross-validation (LOOCV) and the leave-one-group-out cross-validation (25-fold CV). Experimental results demonstrated that our strategy either has improved the classification rates with respect to the state-of-the-art for KTH dataset, achieving 98%, or it is a competitive one, for UCF-11 with 90%, when compared with methods with no feature learning.

关键词： Feature extraction Trajectory Quantization (signal) Training Protocols Histograms Visualization

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Using simplified chords sequences to classify songs genres

Using simplified chords sequences to classify songs genres

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Rodolfo Miranda Pereira Carlos N. Silla Informatics Department State University of Maringa (UEM) Maringa PR Brazil Computer Music Technology Laboratory — Graduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil

ISBN: (纸本)9781509060689

Music Genre Recognition (MGR) is one of the most important forms of music organization and has been an important research subject. Most MGR methods consider the audio content itself instead of the songs meta-data, such as its chords. However, the audio is not always available due to copyright issues, which makes the use of meta-data for MGR an important task. The main objective of this paper is to propose a new method for MGR using simplified chords sequences. We also propose a new public dataset with 8,994 songs containing audio and chords features from six different genres in order to evaluate our method. The experimental results reached an accuracy rate of 56.13% using only the chords sequence feature with the Random Forest classifier, and 78.40% combining audio and chords features with the SVM classifier.

关键词： Feature extraction Support vector machines Frequency modulation Databases Rhythm Histograms Radio frequency

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An Investigation of the Hoeffding Adaptive Tree for the Problem of Network Intrusion Detection

An Investigation of the Hoeffding Adaptive Tree for the Prob...

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International Joint Conference on Neural Networks

作者： Diego Guarnieri Corrêa Fabrício Enembreck Carlos N. Silla Graduate Program in Informatics (PPGI) - Federal University of Technology of Paraná (UTFPR) Cornélio Procópio PR Brazil 86300-000 Graduate Program in Computer Science (PPGIa) - Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil 80215-901

ISBN: (纸本)9781509061839

Intrusion detection in computer networks is a important topic in information security. Due to numerous cases of security breaches that caused economic and social losses in recent years, this topic has been the subject of several studies in order to mitigate problems related to network intrusion and computer attacks. Information security systems have been using different techniques for network intrusion detection. However, with the development of communication mechanisms and consequently with the increase in data traffic, some techniques used for intrusion detection lost their information processing capability. The emergence of new forms of attacks on computer systems also contribute to the depreciation of some of the existing tools. In this scenario, new techniques capable of processing large amounts of information and that perform proactive discovery of new attack vectors are necessary. This paper presents a study on the use of a data stream mining technique known as Hoeffding Adaptive Tree to create a predictive model for network intrusion detection. The experiments performed in this work show the effectiveness of this technique when applied to a database of computer network attacks.

关键词： Intrusion detection Data mining Tools computers Databases Prediction algorithms Adaptation models

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Trust in government moderates the association between fear of COVID-19 as well as empathic concern and preventive behaviour

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Communications Psychology 2023年第1期1卷 1-16页

作者： Arzu Karakulak Beyza Tepe Radosveta Dimitrova Mohamed Abdelrahman Azzam Amin Plamen Akaliyski Rana Alaseel Batoul Bakkar Wassim Gharz Edine Fatema Mohsen Yousuf Abdulqader Alkamali Danny A. Lizarzaburu Aguinaga Andrii Andres John Jamir Benzon R. Aruta Marios Assiotis Olga Solomontos-Kountouri Hrant Avanesyan Maria Muradyan Norzihan Ayub Maria Bacikova-Sleskova Raushan Baikanova Aidos Bolatov Telman Seisembekov Sunčica Bartoluci Ksenija Bosnar Anna Janovská Darko Katović Franjo Prot David Benitez Rita Rivera Ivanna Bodnar Iuliia Pavlova Judyta Borchet Maria Kazmierczak Aleksandra Lewandowska-Walter Justyna Michalek-Kwiecien Katarzyna Skrzypińska Yunier Broche-Pérez Carmen Buzea Rosalinda Cassibba Pasquale Musso Fabiola Silletti Marta Martín Carbonell Bin-Bin Chen Gordana Ristevska Dimitrovska Dương Công Doanh Alejandra del Carmen Dominguez Espinosa Nelli Ferenczi Regina Fernández-Morales Jorge Gaete Yiqun Gan Suely Giolo Rubia Carla Formighieri Giordani Camila Muhl José Rasia Maria-Therese Friehs Shahar Gindi Biljana Gjoneska Juan Carlos Godoy Lorena Cecilia López Steinmetz Maria del Pilar Grazioso Marinés Mejía Camellia Hancheva Given Hapunda Shogo Hihara Russell Sarwar Kabir Tinka Delakorda Kawashima Kazumi Sugimura Brett Walter Mohd Saiful Husain Md Saiful Islam Sadia Sultana Nino Javakhishvili Ana Makashvili Veljko Jovanović Nor Ba’yah Abdul Kadir Johannes Karl Zhumaly Kauyzbay Richa Khanna Meetu Khosla Martina Klicperová-Baker Iva Poláčková Šolcová Ana Kozina Steven Eric Krauss Rodrigo Landabur Katharina Lefringhausen Yun-Hsia Liang Sadia Malik Denisse Manrique-Millones Stefanos Mastrotheodoros Breeda McGrath Enkeleint A. Mechili Samson Mhizha Diana Miconi Rodrigo Moreta-Herrera Andrej Naterer Arash Nemat Felix Neto Joana Neto Luz Marina Alonso Palacio Hassan Okati-Aliabad Carlos Iván Orellana Ligia María Orellana Sushanta Kumar Mishra Joonha Park Eddy Peralta Petro Petrytsa Saša Pišot Benedicta Prihatin Dwi Riyanti Laura Francisca Sudarnoto Angela Oktavia Suryani Adil Samekin Danielius Serapinas Rasa Pilkaus Istanbul Policy Center Sabanci University Istanbul Turkey Department of Psychology MEF University Istanbul Turkey Department of Psychology Stockholm University Stockholm Sweden Social Psychology Department Doha Institute for Graduate Studies Doha Qatar Mokhtass for Consultations and Research Doha Qatar Department of Sociology and Social Policy Lingnan University Hong Kong SAR China Faculty of Medicine Syrian Private University Damascus Syrian Arab Republic Ipswich Hospital Ipswich UK United Private School Athaiba Oman Universidad César Vallejo Trujillo Peru Department of Physical Education Lviv Polytechnic National University Lviv Ukraine Department of Psychology De La Salle University Manila Philippines Theological School Church of Cyprus Nicosia Cyprus General Psychology Chair Yerevan State University Yerevan Armenia Faculty of Psychology and Education University of Malaysia Sabah Sabah Malaysia Department of Educational Psychology and Health Psychology Pavol Jozef Šafárik University Košice Slovakia Astana Medical University Astana Kazakhstan University of Zagreb Zagreb Croatia Clinical Psychology Department Albizu University Miami FL USA Counseling & Psychological Services Duke University Durham NC USA Lviv State University of Physical Culture Lviv Ukraine Institute of Psychology University of Gdansk Gdansk Poland Polish Society for the Psychology of Religion University of Gdańsk Gdańsk Poland Department of Psychology Universidad Central Marta Abreu de Las Villas Santa Clara Cuba Department of Social Sciences and Communication Transilvania University of Brasov Brasov Romania Department of Education Psychology Communication University of Bari Bari Italy Faculty of Psychology Cooperative University of Colombia Santa Marta Colombia Psychology Department Fudan University Shanghai China Higher Medical School University St. Kliment Ohridski Bitola North Macedonia PHI Psihomedika Bitola North Macedonia National Economics University

With the COVID-19 pandemic, behavioural scientists aimed to illuminate reasons why people comply with (or not) large-scale cooperative activities. Here we investigated the motives that underlie support for COVID-19 preventive behaviours in a sample of 12,758 individuals from 34 countries. We hypothesized that the associations of empathic prosocial concern and fear of disease with support towards preventive COVID-19 behaviours would be moderated by trust in the government. Results suggest that the association between fear of disease and support for COVID-19 preventive behaviours was strongest when trust in the government was weak (both at individual- and country-level). Conversely, the association with empathic prosocial concern was strongest when trust in the government was high, but this moderation was only found at individual-level scores of governmental trust. We discuss how motivations may be shaped by socio-cultural context, and outline how findings may contribute to a better understanding of collective action during global crises.

关键词： Human behaviour Infectious diseases

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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Providing security and privacy in smart house through mobile cloud computing

Providing security and privacy in smart house through mobile...

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IEEE Latin-American Conference on Communications, LATINCOM

作者： Rafael Ribeiro Altair Santin Vilmar Abreu Joao Marynowski Eduardo Viegas Graduate Program in Computer Science Pontifical Catholic University of Parana Curitiba Parana Brazil Analysis and Development Systems Federal University of Parana Curitiba Parana Brazil

ISBN: (纸本)9781509051380

Smart house allows residents to access appliances and smart grid data to reconfigure its power consumption. This paper aims to provide security and privacy by design in a smart house, isolating appliances from direct Internet access, while allowing external entities to reach power consumption features. Data acquisition, processing, and consumer profiles updates are only possible inside a smart house, using a Mobile Cloud Computing (MCC). MCC enables to reconfigure consumption profile of smart houses, for instance, to avoid a potential blackout. In addition, the proposal improves security and privacy for smart houses, preventing an attacker to remotely control appliances or violating the privacy of residents by monitoring their energy consumption habits. Prototype tests showed the feasibility of our proposal, isolating the appliances and MCC mobiles, besides collecting, processing, and storing 1200 appliances attributes in 15.25 seconds while using only three mobile devices in an MCC.

关键词： Home appliances Security Privacy Mobile handsets Internet Energy consumption Mobile communication

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Teaching genetic algorithm-based parameter optimization using Pacman

Teaching genetic algorithm-based parameter optimization usin...

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Frontiers in Education (FIE) Conference

作者： Carlos N. Silla Graduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil

ISBN: (纸本)9781509017911

Previous artificial intelligence education research (DeNero and Klein, 2010) has used the classic video game Pacman to teach introductory artificial intelligence concepts. One of the advantages of the work proposed in (DeNero and Klein, 2010) is that the same framework, in this case using the video game Pacman, can be used for different student assignments covering different artificial intelligence algorithms and methods. The issue of how to use the same practical framework is an important one, because if students have to learn a different framework for every assignment they will often feel discouraged. For this reason, the main contribution of this paper is to present a practical assignment to teach students about genetic algorithm-based parameter optimization using the Pacman framework.

关键词： Artificial intelligence Sociology Statistics Optimization Manuals

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An Evaluation of Different Evolutionary Approaches Applied in the Process of Automatic Transcription of Music Scores into Tablatures

An Evaluation of Different Evolutionary Approaches Applied i...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： João Victor Ramos André Stylianos Ramos Carlos N. Silla Danilo Sipoli Sanches Federal University of Technology of Paraná (UTFPR) Cornélio Procópio PR Brazil Computer Music Technology Laboratory - Graduate Program in Computer Science (PPGIa) Pontifical Catholic University of Parana (PUCPR) Curitiba PR Brazil

The problem of converting a music in standard music notation (music sheet) to the alternative notation of guitar tablature is known as transcription. The process of transcription consists of indicating where each note from the original music sheet needs to be played in the guitar, i.e. which string and fret of the guitar that needs to be played to produce a particular note. However, considering that each note can be played in different positions of the guitar fretboard, this is not a straightforward process, and can be classified as a combinatorial optimization problem. For this reason, we have employed a comparative study of different algorithms: A-star, genetic algorithms (GA), genetic algorithms based on subpopulations (GA-SP), ant colony optimization (ACO) and differential evolution (DE). It was also included heuristics based on local search 2-opt and 3-opt in the approaches GA, GA-SP and DE. The experimental results with a dataset of 87 musics indicated that the approaches ACO, GA-SP with 2-opt and GA with 2-opt reached the best performance. Also, the results obtained with each approach were statistically compared using ANOVA test with post hoc Tukey.

关键词： Genetic algorithms Music Optimization Standards Analysis of variance Conferences Artificial intelligence

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